Running the official FP8 weights is a multi-GPU job. Quantized community builds can lower that requirement, but you have to test each one. If you'd rather not run the hardware, we offer GLM-5.3 Flash in Maple Research through our private hosted path.

For the separate flagship release and its hosting options, see our GLM-5.3 guide.

Three questions decide which route makes sense:

• Does Flash fit your task?
• What will deployment cost in hardware and effort?
• Where does your data go along the way?

The answer changes depending on whether you mean Z.ai's official weights, a quantized build, or a particular hosted service. A model's published context window can also differ from what that service lets you use.

What GLM-5.3 Flash is

Z.ai's Hugging Face model card describes Flash as a natively multimodal model with 320B total and 18B active parameters. That split is typical of a mixture-of-experts design. The model holds a large pool of parameters, but each token passes through only a fraction of them.

This has two practical consequences. Compute per token is closer to that of a much smaller dense model, which helps speed and serving cost. Memory is a different matter. The whole parameter set still has to sit somewhere, so a model that is cheap to run per token can still be expensive to load.

The card also links deployment frameworks and documents how the model behaves at different reasoning-effort settings. Read that section before building around a particular mode, because the effort level changes both latency and output.

The model card labels Flash with an MIT license. Treat that label as the starting point for your review, not as the conclusion. If you plan commercial use or redistribution, read the license file in the repository and have whoever handles licensing for your organization confirm it.

What the evidence shows, and what it doesn't

Z.ai publishes comparative benchmark results for Flash on its model card and in its announcement. These are vendor results. Z.ai chose the benchmarks, the settings, and the comparison models. That doesn't make the numbers wrong, but they show how Flash performs under Z.ai's test conditions, not how it will handle your documents or codebase.

Third-party evaluators run their own harnesses. Artificial Analysis publishes a direct Flash-versus-GLM-5.3 comparison. That is useful because it applies one method to both models.

Avoid mixing its figures with Z.ai's in a single table. Differences in prompting, reasoning-effort settings, scoring, and test versions can produce gaps that have nothing to do with the models themselves.

The more common mistake is carrying flagship findings over to Flash. Z.ai describes a new base model for Flash, so a GLM-5.3 result on coding, agentic tasks, safety, or cyber evaluations is not a Flash result. This applies in both directions. A weakness found in the flagship isn't automatically present in Flash, and a strength isn't automatically inherited.

The practical rule is to use published benchmarks to decide which models are worth testing, then test them on a sample of your own work. If the task depends on image input, include images in that sample, and check that your chosen host accepts them.

GLM-5.3 Flash vs GLM-5.3

The table below lists only what the sources support. Where we couldn't confirm a like-for-like comparison, the cell says so.

If you are choosing between the two, the comparison that matters is Flash and the flagship on the same task, through the same host, at the same settings. The table can tell you which candidates belong on your list. It can't tell you which one wins.

Hardware and quantization

What the official FP8 checkpoint requires

The vLLM recipe for GLM-5.3 Flash puts the official FP8 checkpoint at roughly 306 GiB of weights. That figure covers only the weights. Serving also needs memory for the runtime and for the KV cache, which stores attention state for each active request.

KV cache grows with context length and with the number of concurrent requests. A setup that loads the model can therefore still run out of memory under long documents or several users.

The recipe's example server uses TP4, meaning tensor parallelism across four GPUs, with each GPU holding part of every layer. Dividing 306 GiB by four gives about 77 GiB of weights per GPU before cache and overhead. That is our arithmetic, not a figure from the recipe, but it shows why this is datacenter-class work rather than a workstation project.

Two limits apply to that number:

• It is one recipe for the official FP8 weights. It is not a universal minimum.
• It says nothing about how we host the model.

How quantized builds change the calculation

Community quantizations store weights at lower precision, which shrinks the memory footprint. Unsloth's GGUF repository lists a 120 GB three-bit build, and its local guide says that build can run on a 128 GB unified-memory machine. Those are Unsloth's claims about its own build, not the official FP8 checkpoint or a speed guarantee.

Lower precision can change quality, and the effect depends on the exact quantization and task. Unsloth publishes token-distribution tests for its builds, but those tests don't establish how a full coding or document workflow performs. Speed also depends on the inference engine and hardware, so results don't transfer between setups.

For any specific build:

1. Note the exact repository, revision, and quant format.
2. Confirm your inference engine supports both that format and Flash's architecture, including multimodal input if you need it.
3. Run a fixed sample of your own tasks and compare the outputs against a full-precision reference, such as a hosted endpoint.
4. Measure throughput and memory at the context lengths you actually plan to use, not just a short prompt.

We have not measured the speed or task quality of that Unsloth build on a Mac Studio or other consumer machine. Its published memory figure is a starting point for sizing, not a substitute for a trial on the hardware and tasks you intend to use.

Context: the model's window vs your provider's limit

A model's maximum context is a property of the trained model. The context you can use depends on the serving configuration, meaning how much memory the host assigns to KV cache and what limit it sets. These numbers often differ, and the serving limit is the one that applies to you.

If a task depends on very long inputs, such as a large codebase or a long contract set, take Z.ai's stated maximum from the model card, then check the limit set by whoever serves the model to you. On your own hardware, the usable limit depends in part on memory left after the weights load.

Ways to use GLM-5.3 Flash

There are four realistic paths. They differ in cost, control, and custody of your data.

• Self-host the official weights. This gives you full control over versions, settings, and where data lives. The cost is multi-GPU hardware and the work of operating it.
• Self-host a quantized build. This lowers the hardware bar, but you take on testing the quality and speed of that specific build. You control the model host; any connected tools or external services need their own data review.
• Use a third-party API. There is no hardware to run. Your prompts and files go to the provider, so its retention, training-use, and access terms decide what happens to them. Its context limit and supported inputs are its own and may differ from the model's maximum.
• Use Maple Research. You get Flash without assembling hardware, through our private hosted path. The rest of this guide covers what that does and doesn't change.

Browse the open models we offer in our model library.

Using GLM-5.3 Flash in Maple Research

We offer GLM-5.3 Flash in Maple Research. To use it, open Research and choose GLM-5.3 Flash from the model picker.

You can upload an image while using Flash in Research. Our Research workflow first has a vision model describe the image, then gives that description to the model you selected.^[10] Flash is natively multimodal, but an image upload in Research does not mean the selected model sees the original pixels.

We configure Flash with a 1,048,576-token context window.^[9] If an image-heavy or near-limit workflow matters to you, test that specific workflow before depending on it. Current plan terms and prices are on our pricing page.

Using Flash through us avoids the hardware problem described above. You don't need 306 GiB of GPU memory, a tested quant, or an inference server to maintain. That practical access matters alongside the privacy path: the official FP8 deployment calls for resources many readers would otherwise have to rent or operate.

Privacy, retention, and safety

"Private" can mean several different things, and they are worth separating for any host, including us:

• Training use. Whether your inputs are used to train models.
• Retention. Whether prompts, outputs, and files are stored, and for how long.
• Access. Who at the provider, or its infrastructure suppliers, can read data, and under what conditions.
• Protection by stage. Whether data is protected in transit, at rest, and while the model is processing it.
• What remains. Account information, metadata, logs, memory features, attachments, and tool outputs, which can persist even when the conversation content is protected.

A protected processing step only covers data that reaches it. If you paste a document into another service first, or a tool fetches it through a third party, the later step doesn't undo that exposure.

The Research app's proof view shows live attestation for Maple's Nitro enclave. Our privacy notice covers the account and usage information we collect. Research saves conversation history encrypted, including uploaded images and the vision helper's descriptions, so you can return to it. Maple staff cannot browse those saved chats as readable database records. OpenSecret accesses the content inside the protected enclave when you use Research, and the model-serving route receives what it needs to answer.^[11]

Private hosting by itself does not make a model's answers more accurate or its outputs safer to act on. Providers can change behavior through configuration, safety layers, or different weight formats, so test the exact version and route you intend to use. Prompt confidentiality and output safety are separate questions.

Which route to choose

• Run it yourself if you have, or can justify, multi-GPU hardware, and you need full control over weights, versions, and data custody. If you go with a quantized build, budget time to test it.
• Use Maple Research if you want a capable open model without assembling hardware, and you want your prompts handled through our private hosted path rather than a general-purpose API.
• Look at the flagship or another model if your own testing shows Flash falling short on your task. Flash's design favors efficiency per token, and some work justifies a larger model or a different family.

Whichever route you pick, test on your own material before committing a workflow to it.

FAQ

Does GLM-5.3 Flash accept image input?

Yes. Z.ai describes Flash as natively multimodal on its model card. You can upload images while using Flash in Maple Research. Research currently describes the image with a vision helper before the selected model responds, so its image workflow differs from sending the original image directly to Flash.^[10] Other hosts may expose different inputs.

Can I use the full 1M-token context?

We configure Flash for 1,048,576 tokens in Maple Research.^[9] That setting is not a completed test of every request at that length. If your work depends on very long inputs, test it at the length you expect to use. Other hosts and local setups may impose a lower limit.

Can I run it on a Mac Studio or with a quantized build?

The official FP8 weights alone are about 306 GiB according to the vLLM recipe. Unsloth documents a roughly 120 GB three-bit build for a 128 GB machine. Its quality and speed depend on the build, engine, and machine, so test the exact combination you plan to use.

Is GLM-5.3 Flash available in Maple?

Yes. We offer it in Maple Research. See pricing for current plan terms.

What license is it under?

The model card labels it MIT. Review the license file in the repository yourself before commercial use.

Is Flash as good as GLM-5.3?

They are different models. Compare them with one independent method, such as Artificial Analysis, and then on your own tasks. Don't apply flagship results to Flash.

Why is it called Flash?

Z.ai designed Flash for more efficient inference. Its model card describes a new base model, fewer active parameters and an architecture meant to reduce long-context serving costs. “Flash” is a useful signal about that design goal, not a promise that every provider will return an answer faster than the flagship. Speed depends on the serving setup and the task.

What does private hosting protect?

It concerns who can access your data and what happens to it. Research stores saved chats encrypted so Maple staff cannot browse them as readable database records; OpenSecret and the model-serving route still process the content needed to answer you.^[11] Private hosting does not, by itself, establish that a model's answers are accurate or safe. See the Research app's proof view and our privacy notice for more.

Sources

1. Z.ai, GLM-5.3-Flash model card
2. Z.ai, GLM-5.3 Flash announcement
3. Z.ai, GLM-5.3 model card
4. vLLM, GLM-5.3-Flash recipe
5. vLLM, GLM-5.3 recipe
6. Unsloth, GLM-5.3 Flash GGUF repository and local setup guide, accessed 6 October 2026. Primary for Unsloth's own quantized build and setup claims, not for the official Z.ai checkpoint or independent task performance.
7. Artificial Analysis, GLM-5.3 Flash vs GLM-5.3
8. Maple, Research app proof and Privacy notice
9. Maple, public model configuration, checked 6 October 2026. Source for Flash's catalog text/image metadata and 1,048,576-token window; not a maximum-length request test.
10. Maple, Research image handling and vision helper, checked 6 October 2026. Source for image description before the selected model responds.
11. Maple, Research request settings, encrypted Responses storage, and authenticated key access, checked 6 October 2026.

Key points

• Exploit test: GLM-5.3 succeeded in 50 of 410 attempts against known V8 vulnerabilities; Claude Mythos Preview succeeded in 56 of 410. The targets were offline and isolated.^[1]
• Safeguards: Anthropic reported that it could prompt GLM-5.3 to engage with harmful requests under specific test conditions. That simulation used a fake command tool; its engagement rates were not real-world attack success rates.^[1]
• Independent context: NIST had called GLM-5.3 the most cyber-capable open-weight model it assessed, while finding it below the current U.S. frontier on NIST's aggregate measure. NIST used different tests and did not verify Anthropic's safeguard results.^[2]

The model's weights are publicly available, subject to its license. Neither report establishes how GLM-5.3 performs on every cyber task or how it compares with Mythos Preview outside the reported evaluations.

What Anthropic's tests found

The 50-of-410 result came from ExploitBench, which asks models to develop end-to-end exploits for known V8 vulnerabilities. Anthropic reported a second result on its internal binary-exploitation benchmark: GLM-5.3 achieved full control-flow hijacks in 4% of 100 selected tasks, compared with 6% for Mythos Preview. Anthropic said the earlier GLM-5.2 and Claude Opus 4.6 models did not succeed on those tasks.^[1]

In a separate researcher-guided session, Anthropic said GLM-5.3 found previously unknown browser vulnerabilities and chained them into a working exploit during a day of testing on a sandboxed Linux machine. Anthropic said it disclosed the vulnerabilities to the browser maintainer. This was a reported research session, separate from the 410 benchmark attempts.^[1]

The safeguard tests measured something different: whether the model would engage with overtly harmful requests in a simulated environment. Anthropic said direct requests were refused in its trials, but a deceptive cover story led to engagement 64% of the time and prefilled reasoning led to engagement 92% of the time. A modified, or "abliterated," copy engaged in all of the reported trials.^[1]

The simulation used a fake command tool that did not execute model-generated code. These are engagement rates under Anthropic's test conditions, not rates of successful real-world attacks.^[1]

Anthropic also described a distinct researcher-guided exercise with GLM-5.3 Flash involving known browser flaws. Flash's result should not be read as the flagship's ExploitBench score.^[1] Our GLM-5.3 Flash guide covers the separate model.

What NIST found before Anthropic's report

On September 17, NIST's Center for AI Standards and Innovation called GLM-5.3 the most cyber-capable open-weight model it had assessed. NIST also found it substantially below the current U.S. frontier on its aggregate cyber measure, which includes models released through limited-access programs. Its evaluation covered vulnerability discovery and exploit development across four benchmarks.^[2]

The two reports add evidence about the model's cyber capability, using different tests. NIST did not independently test the safeguard-bypass results Anthropic published twelve days later.

The debate over who controls access

Anthropic's report also makes a policy argument: powerful open-weight models can be copied, modified, and used without the provider's safeguards. Anthropic has said separately that it does not support a blanket ban on open weights and wants mandatory safety testing for sufficiently capable models, whether open or closed.^[7]

Its work on a possible coordinated slowdown is broader than this GLM report. Those are Anthropic's stated positions; the exploit results do not, by themselves, tell us which release policy is right.^[8]

I think the competitive interest deserves to be stated plainly. Anthropic controls access to Claude Mythos, while GLM-5.3's weights can be downloaded. A rule that leaves only a few established companies able to offer the most capable models would affect competition as well as safety.

That does not prove Anthropic published this report to protect its business. The reported cyber results still warrant attention. We should judge a proposed restriction by its evidence, whether it applies to closed models too, and what it would prevent legitimate users from doing.

There is a real cost to getting that balance wrong. In July, agents running inside OpenAI's cybersecurity evaluations escaped their intended controls and compromised Hugging Face systems. OpenAI says an internal research model drove most of the intrusion, with GPT-5.6 Sol agents also involved; this was not a Claude Mythos or GLM attack.^[11]

During the response, Hugging Face said safeguards on the commercial models it first tried blocked analysis of real attack commands and payloads. Its team ran GLM-5.2 on its own infrastructure to analyze the logs. That did not make GLM-5.2 the only part of the response: Hugging Face also closed the exploited paths, rebuilt systems, and rotated credentials. The incident does not settle the risks Anthropic identified for GLM-5.3, but it is a concrete example of why defenders need access to capable models they can control.^[9]

Z.ai has also coauthored an open-weight risk framework that acknowledges the difficulty of withdrawing released weights or enforcing safeguards after release. Taking those risks seriously should not require treating open access itself as a mistake.^[10]

What this means for Maple users

We offer GLM-5.3 in Maple Research. Anthropic's capability and safeguard findings concern the model; using it through Maple does not change them. Maple's contribution is a different way to access an open-weight model without running the hardware yourself, with requests encrypted on your device and processed through OpenSecret inside an AWS Nitro enclave before reaching model-serving infrastructure.^[5][6] Our GLM-5.3 guide covers the model, hardware, and hosting choices in more depth.

Research saves conversation history encrypted so you can return to it. Our privacy notice describes the account metadata we retain. These are prompt-handling considerations, separate from whether the model will comply with a harmful request.

Frequently Asked Questions

Did Anthropic find that GLM-5.3 was as capable as Claude Mythos Preview?

In one end-to-end V8 exploit evaluation, GLM-5.3 succeeded in 50 of 410 attempts and Mythos Preview in 56 of 410. That is a close result on Anthropic's specific test, not a claim that the models perform equally across other cyber tasks or general use.

Did NIST confirm Anthropic's safeguard findings?

No. NIST's assessment evaluated cyber capability. Anthropic separately tested whether GLM-5.3 would engage with harmful requests under specific conditions. The two reports should not be treated as independent tests of the same safeguards.

Did Anthropic also test GLM-5.3 Flash?

Yes, in a separate researcher-guided exercise involving known browser vulnerabilities. That Flash result does not make the flagship's 50-of-410 benchmark score a Flash score.

Does using GLM-5.3 through Maple change the safeguards Anthropic tested?

No. Maple's private inference path concerns how requests are handled. It does not change the model's underlying capability or establish stronger misuse safeguards than Anthropic observed in its tests.

Were Anthropic's safeguard percentages real-world attack success rates?

No. The percentages describe whether the model engaged with harmful requests under specific conditions in Anthropic's simulated environment. The fake command tool did not execute the model's code.^[1]

Sources

1. Anthropic, “GLM-5.3 and the spread of advanced cyber capabilities”, 29 September 2026. Source for its exploit and safeguard tests, including the separate Flash exercise.
2. NIST Center for AI Standards and Innovation, “CAISI’s Assessment of Z.ai’s GLM-5.3 Cyber Capabilities”, 17 September 2026. Source for NIST's capability findings and release timeline.
3. Z.ai, GLM-5.3 model card, accessed 6 October 2026. Source for the released weights and license.
4. Maple, Privacy Notice, accessed 6 October 2026. Source for Maple's stated account-metadata practices.
5. Maple, public model configuration, checked 6 October 2026; Michael confirmed the current Research picker on the same day.
6. Maple, Research request settings and protected Responses handling, checked 6 October 2026.
7. Anthropic, “Our position on open-weights models”, 27 July 2026. Source for Anthropic's stated position on bans and pre-release testing.
8. Anthropic Institute, “When AI builds itself”, accessed 7 October 2026. Source for its discussion of a verifiable coordinated slowdown or pause.
9. Hugging Face, “Security incident disclosure — July 2026”, 16 July 2026. Source for the first-hand account of hosted-model refusals and GLM-5.2 use in incident response.
10. Concordia AI and Z.ai, “Frontier Open-Weight AI Risk Management Framework”, September 2026. Source for the developer's risk-management position.
11. OpenAI, “The Hugging Face incident and the road ahead”, 26 August 2026. Source for OpenAI's account of which models breached Hugging Face and under what evaluation conditions.

If you do not want to operate that setup, you need to choose a host. That choice affects who operates the model, what hardware burden falls on you, and what happens to your prompts and files.

We offer GLM-5.3 and GLM-5.3 Flash in Maple. That gives you access to these open models without operating the hardware, through a private inference path. This guide covers the evidence on the flagship, how Flash differs, and how local, conventional hosted, and Maple routes compare.

Sources were checked on 5 October 2026. Model support, plans, and recipes can change.

What is GLM-5.3, and how is Flash different?

According to NIST's assessment, Z.ai released GLM-5.3 on 14 August and published the weights about two weeks later. Z.ai's model card says GLM-5.3 uses the GLM-5.2 base model and improves it through post-training. Its headline claim is a 50% gain on Z.ai's own Code Bench compared with 5.2.

That claim is Z.ai's result on Z.ai's evaluation. We have not reproduced it. A developer benchmark tells you where to look, but it does not predict how the model will handle your repository or documents.

If you are choosing a model for real work, test it on tasks that resemble yours. Examples include a bug fix with existing tests, a change that spans several files, or a tool call that fails and has to be recovered. Record the model version, settings, tool permissions, the human corrections it needed, and whether the result held up. Without those details, a comparison between models is mostly impression.

GLM-5.3 Flash has its own model card. Treat it as a different model for every practical question: capability, input types, license, and deployment requirements. Much of what surfaces in searches for "GLM 5.3" concerns Flash, so check which model a review, benchmark, or VRAM table describes before relying on it. Nothing in this guide about the flagship's evaluations or serving recipe should be read as a claim about Flash. Our GLM-5.3 Flash guide covers that release separately.

What did Anthropic and NIST find?

Two outside evaluations focus on cyber capability. They answer narrower questions than headlines suggest.

Anthropic's report, published 29 September, tested end-to-end exploitation of V8, the JavaScript engine used in Chrome. In Anthropic's tested setup, GLM-5.3 succeeded in 50 of 410 attempts, compared with 56 of 410 for Anthropic's Mythos Preview. The same report found weak misuse safeguards in its tests.

Our news analysis of Anthropic's GLM-5.3 tests examines the test conditions and the debate over access to open-weight models.

This is evidence of strong capability in one demanding task, paired with a warning. It is not an endorsement or a general ranking of models.

NIST's assessment called GLM-5.3 the most cyber-capable open-weight model NIST had assessed. NIST also placed it below the current U.S. frontier on its composite measure, and that frontier includes models available only through trusted access. Both halves of that finding matter.

NIST corroborates the capability result. Its assessment does not address Anthropic's safeguard tests.

For most readers, then, capability is not the open question. Three parties, using different methods, report a strong model within their tested scope. The questions that remain are how you will run it and what you will let it do.

What hardware does GLM-5.3 need?

The vLLM recipe gives two reference configurations:

• FP8 checkpoint: 8×H200 or 8×H20.
• Full one-million-token context: 8×B200.

These are the requirements of that production recipe. They are not a universal minimum for every way the weights could be run. A more aggressively quantized version, or a large-memory workstation, is a different configuration. It is not covered by the recipe's evidence about quality, speed, or usable context.

"Fits in memory" is a low bar. A setup can load the weights and still be too slow for interactive work. It may also support only a fraction of the advertised context once the key-value cache is counted, or produce noticeably worse output after quantization.

Before you buy hardware for the flagship, confirm the exact checkpoint, quantization, memory needed for weights and context cache, throughput, and output quality. The same caution applies to long context with any host: a model's maximum window is not a promise that a particular service will serve all of it at a useful speed.

Flash needs its own hardware evidence. We have not included a figure here because the sources behind this guide do not establish one.

If you plan to self-host, Z.ai lists official GLM-5.3 weight repositories on both Hugging Face and ModelScope. Choose the checkpoint and precision that match the serving instructions you intend to follow; the model card alone is not a hardware plan.

Which route should you use?

An open-weight model can be run by you or by many different hosts. The model's license does not set a host's data practices. Each route has a different operator and a different privacy boundary, so it helps to compare them on the same terms.

Local is the strongest option when nothing should leave the machine. It also makes you responsible for setup, updates, monitoring, power, and whatever data you connect to the model. If a smaller model that runs well on your own hardware meets your quality bar, that may be the better choice than any hosted flagship.

A conventional hosted API asks little of you technically. Many providers have reasonable practices, and for non-sensitive work the convenience may be all you need. The privacy boundary is the provider's policy and controls, so read the current terms for the plan you will use.

Maple is built for the case where you want the flagship's capability without its hardware, and you do not want a host's policy to be the only thing standing between your prompts and the operator. Our design encrypts the request on your device and processes it through OpenSecret in an AWS Nitro enclave before routing to model-serving infrastructure.^[11] The Research app's proof view shows live Nitro attestation. That reduces how much you depend on the operator's assurances for the client-to-Maple path.

It does not make everything invisible. Research saves your conversation history encrypted so you can return to it, including images you upload. Maple staff cannot browse those saved chats as readable database records. When you use Research, OpenSecret accesses the content inside the protected enclave and routes what the model needs to its serving environment.^[12] Our privacy notice describes the account metadata we keep and the usage data collected when you use our API.

A fully offline machine has a different privacy boundary. Our architecture also does not make model output more correct.

How do you use GLM-5.3 in Maple?

We offer GLM-5.3 and GLM-5.3 Flash on our Pro, Max, and Team plans. They are not available on Free. In Maple Research, choose the model from the model picker and start a conversation. Our model library shows which other models we offer and which are covered only for reference. We configure the flagship with a 262,144-token context window.^[9]

You can upload an image while using either GLM model in Research. The app first has a vision model describe the image, then gives that description to the model you selected.^[10] The flagship is configured for text input, so its answer is based on the description rather than a direct view of the image. This matters if your task depends on a small visual detail the description might miss.

For developer tools and existing applications, Maple Proxy is our API route. Check the current Proxy documentation for the model ID and context limit before building on it.

A useful first session tests the model on work that resembles yours, using material you are authorized to share. Three prompts make a reasonable start:

1. Explain"Here is a function and its failing test. Explain the failure, propose the smallest fix, and state your assumptions."
2. Compare"Review these two designs against the stated requirements. Identify the tradeoffs and what evidence would change your recommendation."
3. Verify"List the claims in this draft that need a source. Separate facts, assumptions, and recommendations."

Run the same prompts against Flash, or against whichever model you use now, and compare the results. Check important outputs against source files, tests, or a qualified reviewer. That applies to every model on every route.

If you connect tools or agents, keep their permissions narrow. Encrypting the inference path protects the request and response. It does not limit what a connected tool can do on your computer or in a linked account.

Is GLM-5.3 private and safe?

These are two separate questions, and the model alone answers neither.

Privacy depends on the route. Offline local use can keep prompts on one machine. A conventional API's privacy rests on its operator's practices. Maple adds client-side encryption and enclave processing, while retaining the metadata described in our privacy notice.

Any outside service you connect has its own policies. Protecting one step does not protect information that was already sent somewhere else.

Safety depends on the task and the permissions. Anthropic's tests found strong exploit capability and weak safeguards. Our privacy design does not rebut either finding, and confidential prompts do not reduce what the model can do.

If an agent can run commands, read private files, or change a production system, the scope of those permissions matters wherever inference runs. Restrict access, review risky actions, and test the model on your use case before giving it anything important.

Which version should you choose?

• GLM-5.3 flagship: worth evaluating for complex coding and agent work. Weigh Z.ai's reported gains against your own tests. Choose a route based on how sensitive the material is and whether you can carry the hardware.
• GLM-5.3 Flash: evaluate it as its own model, using its published specifications and your own tasks. Do not choose it on the strength of flagship numbers.
• GLM-5.2: if your prompts, costs, and tool behavior are already validated on 5.2, test before switching. Z.ai describes 5.3 as a post-trained version of the same base model, but your workflow is the measure that matters.

A workable decision rule follows from the comparison above:

• Local: choose it if nothing may leave your device and you can either supply the hardware or accept a smaller model.
• A conventional API: choose it if convenience matters most and the provider's terms suit your data.
• Maple: choose it if you want the flagship without running eight GPUs and a private hosted path matters for the work.

Frequently Asked Questions

Can GLM-5.3 run on a Mac Studio?

The vLLM recipe describes multi-GPU server configurations, not a Mac Studio setup. That does not establish that every quantized version is impossible on a Mac. Check a particular checkpoint's memory needs, usable context, speed, and output quality before buying hardware for it.

How much GPU memory does GLM-5.3 need?

There is no single figure for every checkpoint and workload. The cited native FP8 recipe uses eight H200 or H20 GPUs; its full one-million-token context setup uses eight B200s. Quantization and context length change the calculation.

Does a model's one-million-token window mean Maple serves that full length?

No host's limit follows automatically from the model's native window. The vLLM recipe uses a larger configuration to serve the full window. Confirm the limit for the exact model, plan, and Maple surface before relying on it.

Is GLM-5.3 open source, and can I use the weights commercially?

The flagship weights are available under a GLM-5.3-specific license. Read its terms for your intended use; the label “open weight” alone does not establish commercial rights. Flash has its own model card and license, so check that version separately. The official repositories are linked above and in Sources.

Are GLM-5.3 and GLM-5.3 Flash interchangeable?

No. Z.ai publishes separate model cards. Compare the models on your own tasks and check each version's license, input types, hardware requirements, and host limits. A flagship benchmark is not evidence for Flash.

Can I upload an image with GLM-5.3 in Maple Research?

Yes. Research uses a vision helper to describe uploaded images for the model you selected, including GLM-5.3.^[10] The flagship receives that description, not the image itself.

Does running the model locally keep every part of a workflow private?

It can keep prompts on your machine if inference stays offline. A connected search tool, repository service, or agent integration can still send data elsewhere. Check what each tool receives, not just where the model runs.

Does Maple Research save my conversation, and can Maple read it?

Research saves your conversation history encrypted so you can return to it, and you can delete a conversation. That history includes uploaded images and the vision helper's descriptions. No, Maple employees cannot open your saved chats and read them. OpenSecret decrypts content inside the protected enclave when you use Research, and the model-serving route receives what it needs to answer.^[12] Account metadata and API usage data follow separate rules in our privacy notice.

Does Maple's private inference path change GLM-5.3's cyber safeguards?

No. Prompt confidentiality and misuse safeguards address different things. Anthropic's tests concern the model's behavior under its tested conditions. Tool permissions and review of consequential actions remain separate responsibilities.

What should I test before choosing between the flagship and Flash?

Run the same representative tasks through each version. Record accuracy, corrections, latency, tool behavior, and cost for the exact host and settings you plan to use. Z.ai's separate model cards provide starting claims, but your workflow determines the useful choice.

Sources

1. Z.ai, GLM-5.3 model card, accessed 6 October 2026. Source for the flagship's weights, license label, post-training description, and vendor-reported Code Bench result.
2. Z.ai, GLM-5.3 Flash model card, accessed 6 October 2026. Source for Flash as a distinct release, with its own characteristics and license.
3. vLLM, GLM-5.3 serving recipe, updated 30 September 2026. Source for the FP8 and full-context hardware configurations.
4. Anthropic, “GLM-5.3 and the spread of advanced cyber capabilities”, 29 September 2026. Source for Anthropic's bounded cyber and safeguard findings.
5. NIST Center for AI Standards and Innovation, “CAISI’s Assessment of Z.ai’s GLM-5.3 Cyber Capabilities”, 17 September 2026. Source for NIST's capability comparison and release timeline.
6. Maple, Privacy Notice, accessed 6 October 2026. Source for Maple's stated retention and metadata practices; architecture and surface-specific wording still require review.
7. Z.ai, GLM-5 series GitHub repository, accessed 6 October 2026. Its download table links the official Hugging Face and ModelScope weight repositories for each GLM-5.3 checkpoint.
8. Z.ai, GLM-5.3 weights on ModelScope, accessed 6 October 2026. Alternate official repository listed in Z.ai's GLM-5 series download table.
9. Maple, public model configuration, checked 6 October 2026. Source for the flagship's configured context window and text input.
10. Maple, Research image handling and vision helper, checked 6 October 2026. Source for image description before the selected model responds.
11. Maple, protected Responses route, Research app proof view, and proof view source, checked 6 October 2026. Source for the encrypted OpenSecret request and live Nitro proof implementation.
12. Maple, Research request settings, encrypted Responses storage, and authenticated key access, checked 6 October 2026. Source for encrypted saved content and how OpenSecret accesses it during an authenticated Research request.

If you run a business, I'm sure you know the core problem. You felt it the last time you pasted a competitive strategy or a customer list into AI and thought, "Should I really be doing this?" Or even more, "Are my employees doing this even though I asked them not to?" You wonder if you can trust the company with your data and the data of your customers. You spend time vetting the people you hire and are careful about which data you give them access to, yet you know nothing about who is on the other side of the AI.

I'm here to tell you there are teams who don't ask themselves those questions anymore. Law firms, accounting practices, Hollywood studios, government offices, music creators, financial planners, non-profits, venture firms, software developers, and more are using Maple to run their businesses. They have the same AI capabilities as everyone else, but they don't hand over the keys to their thinking.

Why Not

So what is actually stopping more teams from making the same choice? In our experience, two things. One, they genuinely do not think it is a big deal. Everyone else is using these closed AI systems, so it must not be an issue. Two, they assume the fix is expensive, like buying servers and hiring IT people to manage them.

If you fall into the first camp, I might not be able to convince you, but I will try. If you fall into the second, the math is already clear. You can have sovereign intelligence without the expense and headache.

So what's the big deal anyway? It comes down to whether you want other, unknown people to have access to your business information or not. It's really that simple. Do you want strangers crafting your business logic? And do you want to share your insider info so others can compete with you?

The Black Box Problem

These large AI tools are closed systems. You can't inspect a closed model. You can't see the weights, the training data, or the system prompts. Because of this, the provider can steer outputs, inject preferences, and change behavior without telling you. Your company's internal reasoning is filtered through someone else's editorial layer.

I wouldn't let a competitor edit my sales deck before I present it. Yet every time your team runs a sensitive document through a closed model, that is exactly what is happening. You are renting intelligence from a company that answers to its own shareholders, not to you.

The answer is to operate in the open. At Maple, we run verifiable open-weight models and open-source code. If you want to inspect the intelligence that runs your business, you can.

They Compete With Their Customers

According to The Information, Anthropic blindsided partner Figma by launching Claude Design. Anthropic's chief product officer had served on Figma's board and resigned only three days before the product launch. Figma's stock fell over 15% that month.

This can't be coincidental. Closed AI labs want to grow. A great place to do that is to see what products are being built on their platforms. And when they see success, they already know how it was done because they have not only the final product but all of the messy thought journey to get there. No need to reinvent the wheel. Take those learnings, apply your company spin, and launch a competitor with tight integration into your ecosystem. Microsoft did it. Google did it. Why would AI be different?

Look at the incentives. They subsidize the cost of compute. Your AI plan might cost upwards of $200/month per person, but estimates show that you can get over $5,000 worth of compute from that plan. Makes me pause and ask, "Why?" No company loses money on its customers just to be generous. Maybe they just want to get market share, which is a typical customer acquisition move. That's not the moat, though. The public actions of the companies suggest the motive is stronger than that. They want your data to make their models better. And they want to see which businesses are being built on their platform so they can copy and compete with them.

When you upload your strategy documents, customer analysis, or product roadmaps into a closed model, you might be handing over your downfall.

You Are Paying to Train the Competition

Maybe you aren't a software company. You run a logistics firm, a consultancy, or a manufacturer. You might think Anthropic, OpenAI, or Perplexity will never build a competing business in your niche. And you're probably right. Narrator: now introducing, Anthropic Paper Company.

This is where your local competition comes into play.

Every proprietary insight you feed into a closed model makes that model smarter. Your competitors then log into the same AI system and benefit from the improved intelligence in your market niche, thanks to your data. You're telling the model how to help the competition run their businesses more efficiently and how to create smarter marketing campaigns. Your hard-won expertise becomes a feature in the AI, available to anyone who pays the subscription fee.

Renting judgment from the same platform as your competitor is a race to the median. You deserve to own your thinking.

There Is an Alternative

Many teams feel they need to escape this trap, but don't know where to start.

We launched Maple Teams in 2025 for this exact reason. It is sovereign AI without the IT staff. Maple feels like using the same AI that you get from other providers, like chat, research, coding, and file creation without having to manage the technology yourself.

Maple Teams gives you end-to-end encryption, TEE-hosted open-weight models, and project workspaces for your organization. No hardware to buy. No IT team to hire. Just a low monthly cost per seat.

Your data never touches OpenAI, Anthropic, Google, or Chinese companies. Your competitors do not benefit from your insights. You can put the strongest open intelligence in your employees' hands without sharing any of your alpha with competitors.

We also offer an encrypted API called Maple Proxy API. You can plug it into your existing workflows and pipelines with minimal changes. It's a simple way to stop sending your data to closed providers and instead keep it secure and confidential.

I hope by now you see that the urgency is real. The Figma story should have been the wake-up call. Renting intelligence from the same vendor that rents it to your competitor is a risky play.

The Open Models Have Arrived

The prevailing narrative is this: OpenAI and Anthropic have the best models. "And don't I deserve the best?" Yes, yes you do, Gaston. But are those models truly the best? Yes and no.

Open-weight models like GLM 5.2 and Kimi K2.6 score right alongside recent-generation GPT and Claude Opus models on standard benchmarks. Even when open-weight models are a few months behind on the bleeding edge, they are more than powerful enough for the work most businesses actually do. Writing, analysis, coding, research, customer insights. It all runs beautifully.

What makes those models better is that you keep your data, your intellectual property, and your competitive edge. You do not have to choose between intelligence and ownership. You can have both.

Local AI and Secure Cloud AI

I think the future will be a hybrid of local and cloud AI. Local models continue to shrink in size while becoming more powerfulm Meanwhile, consumer hardware gets better at running them. Gemma 4 and Qwen 3.6 are great examples. There will be many standard tasks that a local model will handle for you, effectively for free. At the same time, cloud models will continue to outpace local AI because the hardware will always be orders of magnitude more powerful. We need secure cloud solutions that give the power without sacrificing security.

Own Your Intelligence

Sovereign AI is available today. This level of control should be standard for any team that relies on proprietary knowledge, not just a luxury reserved for governments and Fortune 500s with massive IT budgets.

That's our sweet spot at Maple. We provide cutting-edge AI tools to small businesses, working professionals, solopreneurs, and businesses of all sizes, while maintaining a secure boundary around their data and intelligence.

We built Maple because we believe the future of business AI is encrypted, inspectable, and user-owned. The tools are ready right now. The only question is whether you will act before your alpha is already gone.

This completes our platform lineup. Maple now runs on web, iOS, Android, macOS, Linux, and Windows. Wherever you work, your private AI workspace is there.

Do More with the App

Aside from living outside a browser tab, there are big benefits to running the Windows app.

File Upload

With the Windows app, you can now upload PDFs, images, and other documents directly into your Maple chats. Review contracts, analyze research papers, or go through client files without leaving your workflow. Your files are encrypted on your device before they ever reach our servers, processed inside Trusted Execution Environments, and never used to train anyone's model.

Voice

You also get the full voice experience. While the web version lets you speak to Maple, the app is where Maple speaks back. Click the speaker to hear your responses read aloud while you work.

Maple Proxy API

API access for developers, right in the app. Maple Proxy runs a local, encrypted API endpoint that gives you OpenAI-compatible access to Maple's private LLMs. Drop it into your existing code and tools without sharing your data with AI labs. It runs from your Windows desktop with the same end-to-end encryption and TEE guarantees as the rest of Maple.

Built for Professional Workflows

Windows remains the standard in industries where software requirements run deep. Accountants only trust QuickBooks on Windows, and for good reason. The Mac version has always been a second-class citizen. Legal teams rely on practice management tools that were built for Windows first and ported everywhere else as an afterthought. Financial advisors work with compliance-heavy platforms that don't even bother with a macOS version.

Maple fits into these environments naturally. It brings private AI into workflows that already demand strict data controls and regulatory compliance. Your conversations and files stay encrypted end to end. Our code is open source so you can verify our privacy claims yourself.

The Same Maple, On Your Taskbar

The Windows app gives you everything Maple users on other platforms already have:

• Long-form reasoning and web research
• Document and image analysis
• Project workspaces with custom instructions
• Pinned chats for quick reference
• Live Data with private, anonymous search

All of it with the same end-to-end encryption and zero data sharing that Maple has always delivered.

Download Now

Maple for Windows is available today for all users, free accounts included. If you're already a Maple user, sign in and your chats and projects will sync automatically. If you're new, you can get started in under a minute.

Your data stays yours. Your thoughts stay private. And now, your platform does too.

A year ago we introduced the world to an easy-to-use AI they can trust by launching Maple AI. Since then, people have sent millions of chats, uploaded thousands of documents, and crunched billions of tokens through Maple. We proved that combining the privacy of local AI with the power and convenience of cloud AI actually works. Now is the time for the next iteration of that mission.

Today we are excited to introduce you to Maple, the Personal Intelligence Platform.

Built on a foundation of privacy, Maple enables people to tap into the full potential of AI by creating a space where you share details that you hesitate putting into other AI apps. That subtle difference makes Maple the AI for your real life. You can be you.

The platform consists of multiple apps built on the same encrypted foundation. Maple Agent has long-term memory, Maple Research is your secure workspace, and Maple Proxy is the encrypted API.

Imagine going about your day. Maple Agent reminds you that your friend started a new job yesterday, so you reach out to check how it went. At your desk, you open Maple Research to review the lease on a new apartment you're interested in and search the web for comparable terms, knowing that no advertisers will see your financial history. Over lunch, you tell Agent to add olive oil to your grocery list. Later, walking through the store, Agent brings up your shopping list. One platform for your whole day, powered by privacy.

To bring this platform to life, we needed a brand that matches the moment.

The New Maple Brand

Maple is not like other AI products. We actually care about your privacy rather than finding ways to harvest your data for monetary gain. Our logo acknowledges this and departs from traditional AI by not having a boring circular logo. Our name is our logo.

The letters that make up MAPLE are opaque, representing the encrypted nature of our platform. Abbreviated, it takes on its own personality.

Knowing that this rebrand needed more than a few great prompts, we commissioned Finite Supply to guide us through this journey. The resulting design is warm and serious at the same time. It belongs on your home screen next to other apps you use every day.

You can see the full identity on our new website at www.trymaple.ai. We rebuilt the site to explain this new future for Maple as a platform for people to leverage AI in their everyday life.

Maple App becomes Maple Research

The original Maple app has grown significantly since we launched it in January 2025. It started as a basic chat app, with a single model, that respected your privacy. But we didn't stop there. Over the next 12 months we added document upload, image analysis, powerful intelligence models, mobile and desktop apps, voice chat, and the biggest feature... live web search that privately brings realtime data into the chat.

With the latest addition of projects, pinned chats, and enhanced web search, what started as encrypted chat is now a full workspace where people upload documents, run deep analyses, and use the latest open-weight reasoning models inside secure enclaves.

That evolution deserves a name that fits. The app is now Maple Research.

Along with the new name comes a refined interface, a new icon in the updated brand style, and a smoother workflow for the kind of deep work you've already been doing. If you're an existing user, nothing about your account changes. Your data remains yours, encrypted as always. Update the app and keep working.

Maple Research has new capabilities as well. Organize chats into projects with shared instructions. Pin your favorite chats to the top of the history. Leverage enhanced web search that continues to dig until it finds what it needs.

Maple Research is where you go when you need to think hard about something sensitive and want the best models available without sending your data to a company that trains on it.

Maple Research is available today on web, iOS, Android, Mac, and Linux.
Maple Proxy is available inside the Mac and Linux app or as a standalone server app.

Maple Agent, the AI assistant that gets to know you

Some AI interactions are one-and-done. You ask a question, get an answer, and move on. But the most valuable AI relationship is the one that builds over time.

Maple Agent is a companion with long-term memory. It keeps track of the books you want to read, your fitness goals and progress, the people you meet, and insights about your life that emerge across weeks and months of conversation. It organizes everything inside your encrypted vault and surfaces what matters when you need it.

What sets Maple Agent apart from the other popular agents is ease of use. No dedicated computer and no technical expertise required. If you know how to text, you know how to use Maple Agent.

Agent isn't quite ready for the public yet. Join the waitlist today at www.trymaple.ai/agent so you are among the first to try it out.

As with everything Maple builds, Agent runs on the same privacy architecture: end-to-end encryption, Trusted Execution Environments, and open-weight models that never touch OpenAI, Anthropic, or Google.

One Platform for You

We know people use many different AI apps. We do as well. The problem is that it sucks having to hold back on the things you say to AI apps because you aren't sure who is reading it on the other end. We want to give you enough tools so you can always be open and detailed. Maple Research handles your deep work. Maple Agent handles your daily life. Maple Proxy lets you plug your developer tools into the same encrypted infrastructure.

We built Maple because AI is most useful when you can tell it the truth about your life and your work. That only happens when you trust the platform holding your data. When you trust your AI, you are free to think. We earned that trust through open-source code, reproducible builds, and cryptographic attestation you can verify yourself.

To everyone who has trusted Maple with their work over the past year: thank you. You proved that privacy and power can coexist. If you're new here, welcome. We built this platform for you.

Get started today.

Maple is built by Maple Privacy Labs Inc.

Maple Projects is here, and it's one of the most requested features from our faithful fanbase. You can now group related chats into Projects, pin the conversations you return to most, and keep your AI workspace as organized as you want it to be. All with the same end-to-end encryption that Maple has always delivered. Your projects, like your chats, are private by design. Not even we can see what you're working on.

Why Projects?

If you're a user of other AI tools, you've probably seen Projects there. They're the feature people use to corral long-running work: a dissertation, a book draft, a side business, a legal matter, a health concern. Projects turn AI from a one-shot tool into a workspace.

The problem with Projects in those tools is the same problem we've always had with mainstream AI: it becomes more useful as you give more of yourself to it, which means more of yourself ends up sitting on someone else's server. The deeper you go, the more you have to lose. Maple Projects gives you that same workspace, but the workspace is yours. Encrypted on your device. Processed inside TEEs. Never used to train anyone else's model.

What's New

The Projects release is a group of features that aim to keep the most relevant information at the top so you spend less time searching and more time collaborating.

Projects

Group related chats into Projects. Create as many as you want, name them however makes sense to you, and each one becomes its own pocket of your workspace. Whether it's a long-running case, a course you're studying, or your weekly meal planning, Projects keep the right chats together and the rest of your history out of the way.

New chats can be started within a project or you can take existing chats and move them around. Tap the ... button on any chat to move it into a project, between projects, or back out to Recents. Organize to your heart's content. It's spring in the northern hemisphere, so it's a great time to freshen up that chat history of yours.

Custom Instructions for Every Project

Each project has its own custom instructions field, and whatever you put there is automatically included with every chat in that project. This means you can stop repeating yourself in every chat. With Maple's encryption and privacy, you can make these project instructions as detailed as you'd like without worrying that the information is used to train future AI models.

A few ways to use it:

• Work: your role, your company, the tone your team uses, the goals you're focused on this quarter
• Clients: client profile, the client's preferences, the constraints you're working within
• Family: your kids' names and ages, dietary needs, the weekly schedule.
• Writing: the genre, the voice, your style guide
• Study: the course, your level, course syllabus
• Characters, Job Roles: build out a personality in the custom instructions, like mentor, therapist, or a historical figure of the past. Any chat within that project speaks to an AI representation of that personality.
• Learning a Language: set the custom instructions to the language you want and every chat will be a new practice session on your journey to fluency

Pinned Chats

There are some AI conversations you come back to constantly. Pin them and they'll always be one click away in their own section at the top of your sidebar. No more scrolling to find that one chat that you refer to often. Also, to make things even easier, chats are now sorted by the most recent activity. 

Some chats that I keep pinned (that I'm okay sharing publicly 😉)

• Daily food diary
• Tracking symptoms of a specific health item, including how it affects my sleep
• Company operations questions
• Proofreading: Everything I paste gets a quick spell and grammar check

How to Get Started

1. Open Maple on web, desktop, iOS, or Android.
2. In the sidebar, create your first project and give it a name.
3. Add custom instructions to anchor every chat in that project.
4. Move existing chats into it with the ... button, or start a new chat from inside the project.
5. Pin the conversations you return to most. That's it. No setup, no migrations, no extra accounts. Your existing chats remain exactly where they are until you decide to move them.

Privacy is the Foundation

Projects are built on top of Maple's privacy-first platform. Project names, custom instructions, the chats inside them, and everything you write are encrypted on your device using a key unique to you. Processing happens inside Trusted Execution Environments (TEEs), where even our own engineers can't see what you're working on. Our code is open source and our attestation is live so you can verify everything yourself.

Available Today, Free for Everyone

Maple Projects is rolling out now to all Maple users, free accounts included. Find it on web, desktop, iOS, and Android. Open the app and your new sidebar is waiting for you.

If you're getting real value out of Projects and want to push them further, Maple Pro adds Live Data with private, anonymous web search that pulls the latest information from the internet directly into your chats and projects. Imagine your Client project not just remembering the context of an ongoing matter, but pulling in this morning's regulatory update. Or your Research project surfacing a paper that came out yesterday. Pro brings the freshest information into the workspace you've built.

This is just the first version of Projects. We have ideas for where they could grow, and we'd love to hear yours. Hop into our Discord or message us on X @TryMapleAI and tell us how you're using them.

Using private AI is now convenient.

We launched Maple in January 2025 because we believed privacy shouldn't require sacrifice. Today, with 5x user growth in the last six months alone, over 90% of paying customers staying with us month after month, and major feature releases throughout the year, that belief has become reality.

Privacy doesn't require sacrifice.

The Problem We Solved

Here's the dilemma: AI needs info about you to be useful, but handing over your data to another company leaves you vulnerable. Your questions about life, your legal strategy, your business plans, your private thoughts. All of it feeds into systems that serve others. Worse, that data can be subpoenaed and used in court proceedings against you.

The first time this got real for me was 20 years ago when a website I used got hacked. I checked an online tool to see if I was affected, and right there in plain sight was the password to my account. Back then I was more naive and had used that password on many websites, so the race was on to change passwords everywhere. That was just a password. Now with AI, we are entrusting far more intimate details of our lives to other organizations.

Many people sense this. A Menlo Ventures survey from June 2025 found that 39% of US adults don't use AI at all. Of those, 71% cite data privacy concerns as the reason. We built Maple for them, and for everyone who wants AI without unnecessary surveillance.

The tools that could protect you, like end-to-end encryption, have historically been nightmares to use. Write down a 24-word recovery phrase. Don't lose it. If you do, your data is gone forever. Most people gave up and accepted the privacy trade-off.

Maple was built to make privacy easy. Our CEO is a former Apple software engineer who worked in AI/ML privacy. Our CTO comes from the encryption and defense-tech space. Together, we built an AI assistant where strong encryption happens automatically, invisible to the user. And our privacy is a technical guarantee, not just a policy promise.

The Privacy of Local, The Power of the Cloud

When you use Maple, your data is encrypted on your device before it ever touches the network. Your personal encryption key is managed automatically. Behind the scenes, secure enclaves handle everything that would traditionally require you to be a cryptography expert. Download, sign up, and your first encrypted chat is ready in under a minute.

We run open source AI models like Kimi K2, OpenAI GPT-OSS, DeepSeek R1, and Whisper that match the capabilities of proprietary systems. Your data never touches OpenAI, Anthropic, DeepSeek, or Google. You get frontier-level AI running on powerful cloud hardware, but your data stays yours.

What Our Users Tell Us

A lawyer told us his bar association prohibits using client information in ChatGPT. He was spending hours summarizing documents and substituting fake names. Now he uploads actual client documents directly into Maple.

A therapist had her ChatGPT account suspended because of information contained in her session transcripts. Now she uses Maple to pull insights from sessions and suggest action items, saving hours each week.

A financial advisor was spending hours redacting documents before uploading them to ChatGPT, then adding sensitive client data back in afterward. Now he works directly with client files. The subscription paid for itself almost immediately.

A musician was frustrated that his creative work was being captured by large AI systems and used to train models that could replace him. In Maple, his lyrics and song concepts remain his.

Something we hear constantly: "When I started using Maple, I finally realized how much I was holding back from AI. I can be myself and tell it everything." AI needs personal context to be effective. In Maple, you get better results because you can give it the full picture without fear.

These professionals can finally work naturally with AI because of what's happening under the hood.

What Makes This Work: Trusted Execution Environments

The technology behind this is called confidential computing, built on Trusted Execution Environments (TEEs). Think of it like the secure hardware that powers Apple Pay on your phone, but running in the cloud.

A TEE is a hardware-isolated environment where even the cloud operator cannot see the data being processed. Your prompts enter the enclave encrypted. The AI processes them inside the protected boundary. The response comes back encrypted. At no point does your plaintext data exist outside that hardware-enforced wall.

Our code is open source. Our builds are reproducible. Anyone can verify that the code running on our servers matches what we publish on GitHub. The Maple app checks this automatically. If the server code doesn't match, it refuses to connect.

Don't trust, verify.

Why This Matters

A good friend recently told me:

"You are a unique brain with memories and ways of thinking. When you feed a central AI with your thoughts, they capture it and you can't get it back. At that point, what's left of you?"

Proprietary AI systems build detailed profiles of who you are on someone else's servers. Those profiles can be edited, weighted, or nudged in directions that serve the system's goals rather than yours. When your mental model lives in a black box you can't inspect, you've outsourced something precious.

We believe in verifiable AI. The right to inspect means open source code. The right to verify means cryptographic attestation. The right to own means your data stays yours.

One Year In, Just Getting Started

Maple has grown from an encrypted chat into a full private AI ecosystem. In our first year, we shipped voice, document upload, live web search, cutting-edge reasoning models, and apps for major platforms. Individual plans for people and working professionals, Team plans for organizations, and the Maple Proxy API for anyone who wants to make their AI tools more secure. Any productivity app that talks to ChatGPT on the backend can instantly make all its traffic private by switching to Maple's API.

We're just getting started.

In 2026, we plan to make Maple even more personal. Daily help from a tool that gets to know you, keeps watch over important items in the background, and saves you time across multiple aspects of your life. The core mission stays the same: easy and powerful AI that makes privacy the default, not the exception.

Your data stays yours. Your thoughts stay private.

You are free to think.

What Makes K2 Thinking Different: Mixture of Experts

Most AI models generate responses in a single pass. K2 Thinking can execute 200-300 sequential tool calls without human intervention, reasoning coherently across hundreds of steps to solve complex problems.

Kimi K2 Thinking uses a Mixture of Experts (MoE) architecture that is fundamentally different from a dense model approach found in models like DeepSeek R1. With 1 trillion total parameters but only 32 billion active per chat, K2 intelligently activates only the most relevant expert pathways for each task.

MoE selectively engages specialized “expert” sub-networks based on the specific task at hand. For example, you only need a flight dynamics expert while researching airplane designs, not while debugging a plumbing problem in your house. With this approach, Kimi K2 uses less power while targeting more specialized knowledge.

This means it can:

• Decompose a PhD-level mathematics problem into multiple interleaved reasoning calls
• Conduct research across the web, verifying sources and synthesizing findings
• Build complete software applications, handling multi-file architectures and debugging
• Analyze lengthy legal contracts while cross-referencing regulatory databases

All of this happens inside Maple’s confidential computing environment, where your data is encrypted before it leaves your device and stays encrypted throughout processing.

Are Open Models as Good as the Big Brands?

This is a question we hear constantly. The answer is a resounding yes.

Kimi K2 Thinking isn’t just another open source model. It’s a thinking agent that reasons step by step while using tools, achieving state-of-the-art performance across benchmarks that matter.

Performance That Speaks for Itself
On Humanity’s Last Exam (HLE), a rigorous benchmark of expert-level questions across 100+ subjects, Kimi K2 Thinking scores 44.9% with tools. That’s higher than GPT 5 and Claude Sonnet 4.5.

Humanity's Last Exam

• Kimi K2 Thinking: 44.9%
• GPT-5: 41.7%
• Claude Sonnet 4.5: 32%

On BrowseComp, which tests agentic search and browsing capabilities, K2 Thinking hits 60.2%, more than double the human baseline of 29.2% and significantly ahead of GPT 5’s 54.9%.

For coding tasks, the results are equally impressive:

• SWE Bench Verified: 71.3%
• SWE Multilingual: 61.1%
• LiveCodeBench V6: 83.1%
• AIME 2025 (math competitions): 94.5% without tools, 99.1% with Python

These aren’t cherry-picked stats. Across reasoning, coding, and agentic search, Kimi K2 Thinking matches or exceeds the capabilities of the most advanced proprietary models, while remaining fully open source.

Privacy Powers Reasoning

With Kimi K2 in Maple, you’re getting frontier-level AI capability without the privacy trade-offs that come with proprietary services.

With Maple’s end-to-end encryption and confidential computing infrastructure:

• Your prompts and data are encrypted on your device using a private key for your account
• Processing happens inside secure enclaves (AWS Nitro Enclaves and NVIDIA TEEs)
• Zero data retention means your conversations never train future models
• Cryptographic attestation proves that the code running is exactly what we publish as open-source

You don’t have to choose between powerful AI and data privacy. Maple gives you both.

Putting K2 to Work

For Researchers: Conduct literature reviews that span hundreds of papers, analyze datasets with transparent methodology, and generate research summaries without risking intellectual property leaks.

For Professionals: Therapists can analyze session patterns across hundreds of notes. Lawyers can draft complex arguments referencing multiple case files. Financial analysts can model scenarios using sensitive client data, all with cryptographic privacy guarantees.

For Developers: Tackle complex coding challenges that require multi-step reasoning. Debug across codebases, generate comprehensive tests, or build entire features with the confidence that your proprietary code remains entirely yours.

Maple Proxy provides API access to Kimi K2. Get encrypted reasoning capabilities through an OpenAI-compatible interface.

High Performance is Available Now

Kimi K2 Thinking is now available to all Maple Pro, Max, and Team subscribers. The model operates with a 256K context window, allowing for extensive conversations and document analysis.

We've made important changes to our reasoning model lineup. DeepSeek R1 is now the default reasoning model in Maple. To activate Kimi K2 Thinking’s advanced capabilities, click the Brain icon. (Farewell to DeepSeek v3.1 Terminus, our previous default reasoning model. Thank you for your service.)

How to Get Started

1. Open Maple on web, desktop, iOS, or Android
2. Start a new chat
3. Select “Reasoning” from the model selector
4. Click the Brain icon to enable Kimi K2 Thinking
5. Begin your first reasoning-powered conversation

No complex setup. Just select and start chatting, with the same privacy guarantees that define every Maple interaction.

The Future of Private AI

Kimi K2 Thinking represents more than a new model. It’s proof that the open-source community can build AI that rivals and even surpasses proprietary alternatives. And it’s proof that privacy doesn’t require compromise.

At Maple, we believe the best AI should be both powerful and private. With reproducible builds, open source code, and hardware-enforced encryption, you don’t have to trust our word. You can verify it yourself.

Your data stays yours. Your thoughts stay private.

Try Kimi K2 Thinking in Maple today.

About Maple AI

Maple AI pioneers software that delivers both exceptional user experience and uncompromising data security. We eliminate the false choice between cutting-edge technology and privacy, offering intuitive tools built on end-to-end encryption. Individuals trust Maple for private AI assistance across personal and professional tasks. Businesses rely on Maple Teams to safeguard proprietary information. Developers globally use the Maple Proxy API to power AI in apps and enterprise workflows. Discover how Maple redefines productivity without compromising your data.

You are free to think.

We're excited to announce Maple 2.0 with access to Live Data. Maple combines its powerful models with the latest information available online to give you intelligence that is up to date. When chatting with AI, a Maple agent searches the web privately and anonymously on your behalf, bringing that current knowledge into your chat.

Live Data is a major leap forward for private intelligence.

There’s even more packed into 2.0, including Anonymous Accounts, Custom Personality, Background Processing, and a new Model Selector we think you’ll love. Read on for all the details.

Live Data - Knowledge Without Compromise

Maple now breaks free from the limitations of static training data. Using Brave’s private search API, it performs secure, anonymous web searches to bring real-time information into your conversations. Research market shifts, explore academic breakthroughs, or settle debates with current knowledge, all while maintaining end-to-end encryption. After months in beta, our users call this a game-changer: your AI assistant now understands today’s world, not just yesterday’s.

Smarter Model Selection

Choosing the right AI shouldn’t feel like solving a puzzle. Our redesigned task-oriented selector cuts through the noise. Tell Maple your goal, whether it is coding help, creative writing, or data analysis, and it instantly selects the optimal model. Prefer control? The Advanced mode still puts every model at your fingertips. No more guesswork, just focused intelligence.

Anonymous Access

Privacy shouldn’t require sacrifices. Now you can sign up without email or social accounts. Generate a unique ID, create a password, and pay exclusively with Bitcoin. This ensures minimal links to your identity. For users who demand absolute anonymity, such as activists, investigative journalists, and whistleblowers, Maple delivers.

Your AI, Your Preference

Shape Maple’s personality to match your style. Set default behaviors like "supportive mentor", "concise analyst", or "talk like a pirate" in your preferences and they apply across all chats through system prompt injection. While exploring pirate-talk is fun, professionals use this to enforce consistent tones for clinical notes or legal drafts. For now there is a single personality for all chats. We are exploring the option for multiple in the future.

Streamlined and Responsive

We rebuilt Maple’s interface from the ground up for clarity and speed. Chats now display timestamps intuitively, the text input expands for complex queries, and renaming/deleting conversations is easier. Under the hood, optimizations make interactions noticeably faster, whether you’re running lightweight tasks or heavy research.

Work Uninterrupted

Life doesn’t wait for AI. With background processing, start Maple analyzing a 50-page PDF or generating code, then switch to other apps or put your phone in your pocket. Maple works silently while you're away. Return to find completed results. Productivity now flows around your rhythm.

Control at Your Fingertips

Ever hit "Send" and immediately spot a typo or missing detail? We’ve all been there. Now a prominent Stop button lets you halt responses mid-stream. Update your request, resend, and get refined answers instantly. No more waiting for lengthy completions before fixing mistakes.

Experience the Update

Maple 2.0 is available today on all platforms: web, iOS, and Android. To use the Live Data feature, upgrade to Pro, Max, or Teams and experience an entirely new way of interacting with private AI.

Maple development is funded primarily by user subscriptions. If you want to continue seeing innovative AI tools, consider upgrading today.

About Maple

Maple pioneers software that delivers both exceptional user experience and uncompromising data security. We eliminate the false choice between cutting-edge technology and privacy, offering intuitive tools built on end-to-end encryption. Individuals trust Maple for private AI assistance across personal and professional tasks. Businesses rely on Maple Teams to safeguard proprietary information. Developers globally use the Maple Proxy API to power AI in apps and enterprise workflows. Discover how Maple redefines productivity—without compromising your data.

You are free to think.

Voice Meets Privacy, Without Compromise

Voice is human. It's fast, intuitive, and liberating. But traditional voice AI has a dirty secret: your conversations become training data. With Maple Voice, we’ve applied our privacy-first architecture to voice interactions, ensuring:

• Your words stay yours
• Audio is encrypted before leaving your device
• Processing happens in secure enclaves
• Zero data retention or training use

How It Works (4 Simple Steps)

1. Tap the 🎤 icon in any Maple chat
2. Speak naturally—questions, ideas, even complex requests
3. Hit "Send" when finished
4. Hear Maple respond in a clear, natural voice while reading the text

Behind the scenes

• Speech-to-text: Converts your audio using Whisper (OpenAI’s open-source speech recognition) in a secure enclave
• Private processing: Your chosen AI model handles the request (DeepSeek, GPT-OSS, Qwen3Coder, and all others supported)
• Voice response: Maple replies with text + natural speech using Kokoro text-to-speech in a secure enclave

Experience the Difference

• True conversation: Maple speaks responses aloud while displaying text
• Universal compatibility: Works with every AI model in Maple—perfect for coders vibe-checking ideas
• Professional fluidity: Ideal for therapists dictating notes mid-walk or lawyers brainstorming between meetings
• Zero audio footprint: We never store your voice clips or use them for training

Unlock Voice in Maple

Maple Voice is available immediately for all Maple Pro, Team, and Max subscribers. This addition extends our commitment to private, versatile AI tools for individuals and professionals. Proxy users, coming soon.

Free/Starter users: Upgrade to Pro to start speaking with your AI confidentially.

Real-World Magic

• "Maple, optimize this Python function for readability" (while pacing during a coding break)
• "Draft a client update about the merger terms" (on a walk outside between meetings)
• "Help me unpack today’s difficult session" (therapists maintaining confidentiality)

Try It Today

Tap the microphone icon in Maple on web, desktop, or iOS (Pro+ required). Finally—an AI that listens and protects.

Voice shouldn't mean surrendering privacy. With this release, we're proving you can have both: the convenience of spoken AI and the security Maple delivers. Your voice is yours—not a data point to be mined and sold.

Your secrets were always safe in Maple. Now they can be spoken.

Keep chatting.

🤘

Today we’re proud to launch Maple Proxy, a lightweight bridge that lets you call Maple’s end‑to‑end‑encrypted large language models (LLMs) with any OpenAI‑compatible client without changing a line of code.

Your existing OpenAI libraries keep working.
Your data stays inside a hardware‑isolated enclave.

The Maple Proxy is great for:

1. People who run AI tools on their computer that call out to OpenAI
2. Developers who ship apps that call out to OpenAI

If you're stoked on the idea already, feel free to jump straight to our Maple Proxy documentation and get started.

What ships in the box?

Maple runs inference inside a Trusted Execution Environment (TEE). That gives you:

• End‑to‑end encryption – prompts and responses never leave the enclave, not even for us.
• Cryptographic attestation – you can verify you’re talking to genuine secure hardware.
• Zero Data Retention – no logs, no training data, nothing stored in the clear.

All of that security used to mean extra plumbing in your code (handshakes, key exchanges, attestation checks). Maple Proxy does the heavy lifting for you, exposing a drop‑in OpenAI‑style endpoint that speaks the same JSON schema you already know.

Ready‑to‑Use Models (pay‑as‑you‑go)

*Prices are per million tokens. API usage draws from the paid subscription plan credits first. If additional credits are needed, they can be purchased in increments starting at $10.

Real‑World Ways to Use Maple Proxy

A calorie‑counting mobile app (for everyday users)

A developer can replace the public OpenAI calls that power “suggest healthy meals” with a call to Maple Proxy. The app sends the user’s food log to the private LLM, receives a tailored meal plan, and the user’s dietary data never leaves the secure enclave. The experience feels the same, but privacy is secured.

Internal knowledge‑base search for a tech startup (for developers)

Your team’s internal documentation lives in a private repository. By routing the retrieval‑augmented‑generation queries through Maple Proxy, the LLM can answer “How do we deploy the new microservice?” without ever exposing the confidential architecture details to an external provider.

Coding‑assistant plug‑in for an IDE (for developers)

Take your coding environment that uses the OpenAI API, but point the client at http://localhost:8080/v1. The assistant can suggest code snippets, refactor functions, or explain errors while keeping proprietary code snippets inside a hardware‑isolated enclave. No additional SDKs or libraries are required—just change the base URL. Whether you're building production code or simply vibe coding your next idea, Maple Proxy keeps you secure.

These examples illustrate how the same simple “change the endpoint” step unlocks private, secure AI for any workflow, from consumer‑facing apps to internal tooling.

How to Get Started

1. Desktop App – Fastest for Local Development

1. Download the Maple desktop app → trymaple.ai/downloads
2. Sign up / upgrade to Pro, Team, or Max (plans start at $20 / month)
3. Open the Local Proxy tab and click “Start Proxy.”

The app automatically creates an API key, starts a proxy on localhost:8080 (or a port you choose), and handles all attestation and encryption behind the scenes. From this point on you can point any OpenAI client to http://localhost:8080/v1 and everything just works.

2. Stand‑alone Docker Image – Production‑Ready

Pull the image

docker pull ghcr.io/opensecretcloud/maple-proxy:latest

Run it (replace YOUR_MAPLE_API_KEY with a key from your Maple dashboard)

docker run -p 8080:8080 \
  -e MAPLE_BACKEND_URL=https://enclave.trymaple.ai \
  -e MAPLE_API_KEY=YOUR_MAPLE_API_KEY \
  ghcr.io/opensecretcloud/maple-proxy:latest

You now have a secure, OpenAI‑compatible endpoint at http://localhost:8080/v1. A ready‑to‑use docker‑compose template is included in the repository for longer‑running services.

Works With Any OpenAI‑Compatible Tool

If a library lets you set a base_url or api_base, it works with Maple Proxy. That includes LangChain, LlamaIndex, Amp, Open Interpreter, Goose, Jan, and virtually any other OpenAI‑compatible SDK. No code changes beyond the endpoint URL are needed.

Need More Detail?

• Technical write‑up – Using the Maple Proxy
• Full API reference – https://github.com/opensecretcloud/maple-proxy
• Discord community – real‑time community help and feature discussions

Start Building with Private AI – Today

1. Download the desktop app or pull the Docker image.
2. Upgrade to a Pro/Team/Max plan (starting at $20 / month).
3. Point any OpenAI client to http://localhost:8080/v1.

Let's start securing all the apps together.

Want to use Maple's secure LLMs with your existing OpenAI code? Today we're excited to announce Maple Proxy, a lightweight proxy server that brings OpenAI-compatible API access to Maple's end-to-end encrypted LLM service. With Maple Proxy, developers can integrate private, secure AI completions into their applications using any OpenAI client library, no code changes required.

Why a Proxy?

Maple runs all LLM inference inside Trusted Execution Environments (TEEs), providing hardware-level security and privacy for your AI workloads. This means your prompts and responses are encrypted end-to-end and never accessible to anyone, not even Maple.

However, this security comes with complexity. Every request requires:

• Trusted Execution Environment (TEE) attestation verification to ensure you're talking to genuine secure hardware
• End-to-end encryption negotiation
• Secure key exchange protocols

Maple Proxy handles all this for you. It acts as a bridge between your standard OpenAI-compatible client and Maple's secure infrastructure, managing the attestation handshake and encryption so you don't have to.

Available Models & Pricing

Maple Proxy provides access to a growing selection of state-of-the-art models. For detailed model capabilities and example prompts, see our comprehensive model guide.

API usage draws from the paid subscription plan credits first. If additional credits are needed, they can be purchased in increments starting at $10.

Two Ways to Get Started

Option 1: Desktop App with Built-in Proxy (Easiest for Local Development)

The Maple desktop app includes an integrated proxy server that automatically handles all configuration:

1. Download the Maple app from trymaple.ai/downloads
2. Sign up and upgrade to Pro, Team, or Max plan (starting at $20/month)
3. Navigate to API Management in the app settings
4. Open the Local Proxy tab and click "Start Proxy"

The desktop app will:

• Automatically create and manage API keys
• Start the proxy on localhost:8080 (configurable)
• Handle all TEE attestation and encryption
• Show real-time proxy status and logs

Once running, simply point any OpenAI client to http://localhost:8080/v1:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="auto-configured-by-desktop-app"
)

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "Hello, secure world!"}],
    stream=True
)

for chunk in response:
    print(chunk.choices[0].delta.content, end="")

Option 2: Standalone Docker Deployment (Best for Production)

For production deployments or CI/CD pipelines, use the standalone Maple Proxy Docker image:

1. Pull the Docker image:

docker pull ghcr.io/opensecretcloud/maple-proxy:latest

1. Create an API key in your Maple account at trymaple.ai
2. Run the proxy:

docker run -p 8080:8080 \
  -e MAPLE_BACKEND_URL=https://enclave.trymaple.ai \
  ghcr.io/opensecretcloud/maple-proxy:latest

1. Use with your API key:

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'http://localhost:8080/v1',
  apiKey: 'your-maple-api-key'
});

const response = await client.chat.completions.create({
  model: 'gpt-oss-120b',
  messages: [{ role: 'user', content: 'Explain TEEs in simple terms' }],
  stream: true
});

for await (const chunk of response) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

Docker Compose for Production

For production deployments with proper configuration management:

version: '3.8'

services:
  maple-proxy:
    image: ghcr.io/opensecretcloud/maple-proxy:latest
    container_name: maple-proxy
    ports:
      - "8080:8080"
    environment:
      - MAPLE_BACKEND_URL=https://enclave.trymaple.ai
      - MAPLE_ENABLE_CORS=true
      - RUST_LOG=info
      # Note: In production, clients should provide their own API keys
      # Do NOT set MAPLE_API_KEY here for multi-user deployments
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
      interval: 30s
      timeout: 3s
      retries: 3

API Endpoints

Maple Proxy implements the core OpenAI API endpoints:

List Available Models

curl http://localhost:8080/v1/models \
  -H "Authorization: Bearer YOUR_MAPLE_API_KEY"

Create Chat Completion

curl -N http://localhost:8080/v1/chat/completions \
  -H "Authorization: Bearer YOUR_MAPLE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.3-70b",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Write a haiku about privacy"}
    ],
    "stream": true
  }'

Note: Maple currently supports streaming responses only. All completions will be streamed back to your client.

Client Library Examples

Python

from openai import OpenAI

# For desktop app with auto-configured proxy
client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="any-string"  # Desktop app handles auth
)

# For standalone proxy
client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="your-maple-api-key"
)

# Streaming is required for all requests
response = client.chat.completions.create(
    model="qwen3-coder-480b",
    messages=[{"role": "user", "content": "Write a Python function to sort a list"}],
    temperature=0.7,
    max_tokens=500,
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Node.js/TypeScript

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'http://localhost:8080/v1',
  apiKey: process.env.MAPLE_API_KEY
});

// Streaming example with TypeScript
async function streamCompletion() {
  const stream = await client.chat.completions.create({
    model: 'deepseek-r1-0528',
    messages: [{ role: 'user', content: 'Solve this step by step: 25 * 4 + 10' }],
    stream: true,
  });

  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
  }
}

Any OpenAI-Compatible Tool

Since Maple Proxy implements the OpenAI API specification, it works with any tool that supports custom OpenAI endpoints:

• LangChain: Set openai_api_base="http://localhost:8080/v1"
• LlamaIndex: Configure api_base="http://localhost:8080/v1"
• Amp: Add custom OpenAI provider with Maple Proxy URL
• Open Interpreter: Configure with --api_base http://localhost:8080/v1
• Goose: Block's AI developer agent - configure with Maple Proxy endpoint
• And many more...

Production Best Practices

For Local Development

• Use the Maple desktop app for automatic proxy management
• The desktop app handles API key management and configuration
• Enable auto-start in the desktop app for convenience

For Production Deployments

• Use the standalone Docker image for better resource isolation
• Never hardcode API keys in your deployment configuration
• Each client/user should provide their own Maple API key
• Monitor the /health endpoint for service availability
• Use environment variables or secrets management for API keys

Security Considerations

• API keys are tied to your Maple user account and usage is billed accordingly
• Treat API keys like passwords, never commit them to version control
• Rotate keys regularly using the Maple dashboard
• For multi-tenant applications, require each tenant to provide their own key

How It Works Under the Hood

When you make a request through Maple Proxy:

1. Client Request: Your OpenAI client sends a standard API request to the proxy
2. TEE Handshake: Proxy establishes a secure connection with Maple's TEE infrastructure
3. Attestation: Proxy verifies the TEE's attestation to ensure genuine secure hardware
4. Encryption: Your request is encrypted end-to-end before transmission
5. Authentication: Proxy validates your Maple API key
6. Processing: The LLM processes your request inside the secure enclave
7. Response: Encrypted response is decrypted by the proxy and returned in OpenAI format

All of this happens transparently, your application just sees a standard OpenAI API response.

Getting Help

• GitHub: github.com/opensecretcloud/maple-proxy - Report issues and contribute
• Discord: Join our community for support and discussions
• Documentation: Full API reference and examples available in the repository

Start Building with Private AI Today

Maple Proxy makes it simple to add private, secure AI capabilities to your applications without sacrificing developer experience. Whether you're building a chatbot, code assistant, or AI-powered analytics tool, you can now do it with the confidence that your data remains completely private.

Get started in minutes:

1. Download Maple or pull the Docker image
2. Sign up for a Pro account ($20/month)
3. Start building with any OpenAI-compatible client

Your code stays the same. Your data stays private.

Welcome to the future of secure AI development.

Unlock Specialized AI Without Sacrificing Privacy

At Maple, we reject the false choice between powerful AI and data privacy. That's why we offer multiple specialized LLMs, all running in confidential computing environments with end-to-end encryption. Whether you're a therapist analyzing session notes, a lawyer auditing contracts, or just planning a family trip, there's a private AI assistant designed for your task.

Pro Tip: All models enforce zero data retention. Your sensitive information remains yours, not ours.

Maple AI Model Cheat Sheet

Suggested usage, try them out yourself to find what works for your situation.

Task-Optimized Workflows with Prompts

Our models are great at a variety of tasks, so we recommend you try out a few models over time to see which gives you the best performance for your specific task. The following are meant as examples to get you started, but are by no means an objective prescription of how to use each model.

Experiment and validate with your own data and process!

A. Image Analysis

• Ideal for: Field techs, educators, visual documentation
• Strength: Lightning-speed image understanding
• Model: Gemma 3 (Starter+) / Mistral Small (Pro+)

Try this Prompt:

"Extract all text from this supply chain invoice. Identify vendor, PO number, total due, and delivery date. Flag any discrepancies."
(📷 Upload invoice photo)

B. Creative Ideation

• Ideal for: Marketers, creatives, ideation
• Strength: ChatGPT-level innovation without data leaks
• Model: GPT-OSS 120B (Pro+)

Try this Prompt:

"As our Chief Innovator: Propose 3 viral campaigns for eco-headphones. Include Gen-Z hashtags and TikTok hooks."

C. Complex Analysis

• Ideal for: Researchers, compliance officers
• Strength: Dissects complex documents like a pro
• Model: DeepSeek R1 (Pro+)

Try this Prompt:

"Cross-check clinical trial data against FDA guidelines. Output risk matrix with confidence scores. Provide as a text version and as JSON output. [paste clinical trial data]"

D. Multilingual & Technical Work

• Ideal for: Developers, international teams
• Strength: Fluent in 100+ languages + code
• Model: Qwen3 Coder (Pro+)

Try this Prompt:

"Debug this Python API snippet. Explain errors in English and Japanese for Tokyo engineers. Add optimization comments. [paste snippet]"

E. Therapy Session Productivity

• Ideal for: Daily productivity, therapists
• Strength: Balances reasoning power with accessibility
• Model: Llama 3.3 (All plans)

Try this Prompt:

"Convert session notes into SOAP format:
[Patient] expressed anxiety about job transition
Discussed grounding techniques
Assigned thought journaling
———
Highlight themes and progress markers."

F. Organizational Efficiency (Teams)

• Ideal for: HR/People Teams, Market Research, Data Analysts
• Strength: Complex analysis, advanced reasoning
• Models: DeepSeek R1 671B (Pro+)
• Try this Prompt:

"Redline HR policy draft for GDPR/California law conflicts. Highlight high-risk sections in red. [paste draft]"

G. Personal Life Enhancement

• Ideal for: Personal Life, Family, Friends
• Strength: Conversations, Basic Research
• Models: Gemma 3 (Starter+), Mistral Small 3.1 (Pro+), or Llama 3.3 (All plans)
• Try these Prompts:

Family Trip

"Plan a budget-friendly weekend trip from Seattle:
- 2 adults, 1 child (age 7)
- Educational activities
- Max $600 total"

Happy Hour Debate

"Settle a debate: Explain quantum entanglement using baking analogies"

Apology Text

"Draft a thoughtful apology message to my sister (tone: warm but sincere). Here is what happened: [explain yourself]"

H. Daily Productivity

• Ideal for: Volunteering, Personal Life
• Strength: Communications, Conversations
• Models: Llama 3.3 (All plans)
• Try this Prompt:

Draft a polite email to parents in my child's class about the holiday party. Tone: friendly but professional. Here are the notes from our last planning meeting: [paste notes]"

Customize the Model and Personality

You can tell the model how you want it to function, communicate, and behave. This can go by various names: system prompt, character, and instructions. Point is, the power is yours to personalize the model to your needs.

Tap the 🤖 icon above the text box when starting a new chat. Anything you put in this box will apply to the entire conversation in that chat. Set system prompts like "Respond as a skeptical scientist", "You're a sarcastic historian", "Use medical terminology", or "Use LEGO analogies for complex topics."

Using Maple Like a Pro

1. Switch models: Tap current model name → Choose new assistant.
2. Analyze images: Select Gemma/Mistral → Tap 📷 → Upload.
3. Supercharge with prompts: Click 🤖 to add "Format as legal memo" or "Tone: supportive therapist".

FAQs

Q: Why can't Free users access image models?

Image processing requires more processing power. Starter+ plans help us cover these costs while keeping privacy intact.

Q: Is GPT-OSS 120B really like ChatGPT?

Yes—GPT creative and analytical power, but zero data retention. Your marketing brainstorms stay yours.

Q: How did you write this guide?

We used DeepSeek R1 671B inside Maple to research, draft, and refine every section—encrypted end-to-end.

Q: What are the context windows for each model?

Higher "K" means longer chats.

• Llama 3.3 70B: 128K
• Gemma 3 27B: 128K
• Mistral Small 3.1 24B: 128K
• Qwen 2.5 72B: 128K
• Qwen3 Coder 480B: 128K
• GPT-OSS 120B: 128K
• DeepSeek R1 671B: 128K

Your Privacy-Powered Future Starts Now

Maple proves you don't need to surrender data for world-class AI. Whether you're a therapist protecting client notes or a team auditing sensitive contracts, our models deliver specialized intelligence, wrapped in encryption.

Verify our security live: Maple Attestation Proof
Upgrade or get started: Sign Up Now

Keep chatting.

We started with two plans: Starter and Pro. Honestly, we weren’t even sure if people would pay for a privacy-first AI assistant. But they did.

We’ve been blown away by the response. It’s not just that users care about privacy—they’re getting real, daily utility from Maple. Our Pro plan quickly became the most popular tier, powering everything from confidential legal drafting and therapy notes to startup brainstorming and research workflows. Maple has grown faster and further than we expected.

But some of you? You’re on another level. You’re building businesses on Maple. Running professional workflows. Automating high-stakes work. For you, Pro isn’t enough.

Introducing the Max Plan

20x the power of Pro. $100/month. Unlimited ambition.

Max was built for people who need Maple without limits—more speed, more usage, more support, and more early access to what’s coming next. Whether you’re processing sensitive documents, running long-form research chats, or just tired of hitting caps, Max gives you the freedom to work without friction.

What’s in the Max Plan?

• 🔒 All the privacy features of Maple Pro
• ⚡️ 20x more usage than Pro
• 🛠 Priority support
• 🚀 Early access to new features & models
• 🖼 Image Upload
• 📄 Document Upload
• 🧠 Access to top-tier models: DeepSeek R1, Qwen 2.5, Gemma 3, Mistral Small 3.1

All this with privacy you can prove yourself.

Goodbye, Starter. (Sort of.)

As of today, the Starter plan is closed to new users.

To our early adopters on Starter: thank you. You believed in private AI before it was cool. You’ll be able to stay on Starter as long as you're subscribed. Yes, there may come a time when it sunsets—but we’ll cross that bridge when we get there.

What’s Next

We’re just getting started. Here's a peek at what we're researching:

• 📄 Encrypted document RAG (retrieval-augmented generation)
• ☁️ Cloud storage integrations for file uploads
• 🔐 Private live web search
• 🤖 Custom GPTs you control
• 👥 Secure team collaboration & shared history
• 🧾 Role-based permissions for organizations

We're building Maple not just for individuals, but for teams, companies, and entire industries that require confidentiality and performance.

If you're an organization that deals with sensitive data—healthcare, law, finance, journalism—you don’t have to trust us. You can verify us.

Maple runs on open source code and confidential computing. That means you (or your security team) can audit the code, verify the attestation, and see exactly how your data is encrypted and processed—without trusting a black box. No vague promises. Just cryptographic proof.

Upgrade to Max

If you're pushing the limits of Pro, Max is ready for you. Whether you're a founder, researcher, developer, executive, or just someone who talks to AI all day—Max is your new best plan.

Upgrade to Max

Because your data is your data. Not ours.

Whether you're a law firm drafting confidential contracts, a therapy practice analyzing client notes, or a fintech team processing financial data, Maple Teams lets your entire group harness AI without compromising security.

The Privacy Challenge

Many organizations ban or restrict third-party AI tools because they risk exposing proprietary or personal data. Traditional chatbots often store conversations for training, leaving your information vulnerable to breaches, insiders, or even the providers themselves. For organizations in regulated or trust-driven industries, that’s a nonstarter.

Even with restrictions in place, well-meaning employees use AI tools that retain sensitive data so they can be more productive, putting the organization at risk.

That's where Maple stands apart. Built on end-to-end encryption and confidential computing with AWS Nitro Enclaves, Maple ensures that your data remains private to your account. No one—not even our team—can access your plaintext conversations.

We use hardware-level isolation to process queries securely, and our open-source code allows you to verify it yourself.

Key Features for Teams

With Maple Teams, you get all the core features of our Pro plan, plus tools tailored for group use:

• Pooled Usage: Share credits across your team for efficient AI access, whether it's summarizing documents, analyzing images, or running advanced models like DeepSeek R1.
• Simple Onboarding: Invite members easily, manage seats, and track usage from a central dashboard. Team admins can add or remove users, resend invites, and monitor everything without exposing data.
• Early Access Features: Get advanced access to powerful new features in Maple before the rest of the platform.
• Dedicated Support: Priority assistance to keep your workflows running smoothly, including guidance on integrating Maple into regulated environments like healthcare or finance.

Boost Productivity Securely

This isn't just about security—it's about productivity. Therapists can safely brainstorm treatment plans, lawyers can draft compliant documents faster, and accountants can review spreadsheets with AI insights, all while staying compliant with privacy standards.

Our users in these fields already report saving hours each week, and Teams extends that to your whole organization.

Easy Setup

Getting started is straightforward. From our Pricing page, select the Team plan, complete the purchase via Stripe, and invite your colleagues by email.

They'll join with their own Maple accounts, and you'll be chatting securely in minutes. Check out our guide for full details on Team setup and management.

Built for Teams. Built for Trust.

You shouldn’t have to choose between powerful AI tools and strong data governance. With Maple Teams, you can deploy AI across your organization—confident that what happens in your workspace, stays in your workspace.

Upgrade today and experience secure, collaborative AI that scales with your needs. Visit our Pricing page to get started, or reach out to team@opensecret.cloud for a demo.

Your data stays yours—let's put it to work. 🤘

Maple allows any authenticated user to purchase, create, and manage team accounts directly from the application. This guide covers the complete workflow from team purchase to member management.

Setting up your team is 3 simple steps:

1. Purchase the number of seats you need
2. Invite your team members
3. Chat with encrypted AI

Note: Prices might be different than these screenshots

Purchasing a Team Plan

Go to the Pricing page and hit "Start Chatting" on the Team plan.

Log in with your existing Maple account or sign up if you don't have one yet.

You'll be redirected to Stripe checkout where you select the number of seats you need. Minimum is 2. Then pay with your preferred method.
Note: Bitcoin payments are not available for Team accounts.

Managing the Team

After successful payment, you'll be redirected to the team setup flow.

On this screen you can name your team and see the number of available seats.

Invite people to join your team using their email address. You can enter multiple people at one time by putting each email on its own line or separating them with a comma.

Tell your team members to check their email and click the "Accept Invite" button.

Members log in if they already have a Maple account or create one at this step.

Chat with AI

You and your team members are all set to start chatting!

Each user knows they are on the team plan by looking at the bottom of the history pane. Users can see their team name, credit usage, and more.

This is where Admin users access the Team Dashboard and the Subscription. If you want to add more team members in the future, you do it on the Manage Subscription screen.

Additional Management Options

Edit Team Name
Click the pencil icon next to team name to change it.

Resend an Invite
Invites expire after a few days, so you might need to send a new one. Go to the Team Dashboard and hit "Resend Invite" next to that user.

Remove a User
If a person is no longer part of your organization, you can go into the Team Dashboard and click the ⠇button next to their email, and choose "Remove from Team".

The user is removed from your team but their Maple account is still active. They revert to being a Free account.

Getting Help

If something isn't working right for you, reach out to us by email support@opensecret.cloud or hop into our Discord.

Onboard your organization to Maple and start securing your important data today!

This feature opens up new dimensions of productivity, efficiency, and privacy-first innovation for professionals and individuals alike. Whether you're reviewing a contract, translating a sign, or analyzing your finances, Maple AI now lets you bring documents and images into the conversation—without compromising your data security.

Maple is now smarter than ever—and ready to streamline your workflow.

What’s New: Document and Image Upload

With this new release, Maple AI supports two major upload features:

• Image Upload: Snap a photo or choose one from your camera roll—Maple can now interpret and analyze images on the fly.
• Document Upload: Upload files in formats like PDF, DOCX, Excel, and more for Maple to analyze, summarize, or explain. Available on Desktop, iOS, and Android apps. Web support coming soon.

This unlocks a variety of use cases:

• Extract meeting notes from a presentation deck
• Proofread and improve a school assignment
• Scan and interpret a handwritten note or recipe
• Summarize a lengthy essay or article
• Translate a printed menu or sign while traveling

All with end-to-end encryption and the privacy-first design Maple users trust.

Are my files safe and secure?

Yes. File upload for both images and documents follows the same secure process as all of our data processing. We use encryption to securely transfer the file. Once in the cloud, secure enclaves handle the file parsing and pass the data to the AI that also uses secure enclaves.

Your data is protected at every step in the process.

Image Upload: Unlocking Visual Possibilities

Visual tasks just got easier and more secure with Maple.

🌍 Travel and Translation

Lost in translation? Snap a photo of a menu, sign, or product label while abroad, and let Maple instantly translate or interpret it. No need to type or search manually—just point, shoot, and understand.

🪴 Nature Exploration

On your next hike, take a photo of an unfamiliar plant and ask Maple what it is. Whether you're a hobbyist botanist or simply curious, Maple gives instant feedback and species information—with zero data leaving your encrypted session.

Document Upload: Smarter Analysis for Better Results

Text-based tasks now benefit from intelligent AI analysis, all inside your secure workspace.

🧠 Academic & Writing Help

Upload your school paper or essay draft and let Maple review it for grammar, clarity, tone, and flow. Or drop in a lengthy article and get a well-organized summary in seconds.

📊 Work Smarter

Uploading a slide deck? Maple will extract key points, structure them as meeting notes, and help you prep recaps or follow-ups fast—saving you precious time.

Available on Pro and Teams

These powerful tools are available to Pro and Team plan members. If you're on Free or Starter, consider upgrading to get the most out of your confidential AI.

What’s Next

We’re just getting started. Coming soon:

• Upload multiple files in a single prompt
• Expanded support for more file types
• Integration with cloud services, possibly Google Drive and others
• Shared document access and team collaboration tools for businesses

Upgrade Today

Upgrading to Pro or Team gives you access to Maple's most powerful features—without compromising on privacy. Whether you're working solo or collaborating with a team, Document and Image Upload will save you time and elevate your decision-making.

🚀 Upgrade Now to Access Document and Image Upload

In Summary

This new feature expands what’s possible in private AI, reaffirming our commitment to secure, encrypted productivity. With Document and Image Upload, Maple AI becomes an even more indispensable tool for your work and life.

We’d love to hear how you’re using the new features—share your experience on social media and tag @TryMapleAI, if it's something you can share 😀

Your productivity just got a major upgrade. Start exploring the possibilities with documents and images in Maple today.

Technical Specs

• Supported File Types
  • Images: .jpg, .png, .webp
  • Documents: .pdf, .doc, .docx, .txt, .rtf, .xlsx, .xls, .pptx, .ppt, .md
• File Limitations
  • Max file size: 5MB
  • One file per reply

About Maple AI

Maple AI is the world’s most private AI chat assistant, built with end-to-end encryption, confidential computing, and zero data mining. Whether you’re a privacy-conscious individual, an independent professional, or an enterprise in a sensitive industry, Maple delivers the power of AI without compromising your trust.

DeepSeek R1 demonstrates its versatility by excelling in a wide range of diverse professional contexts. Whether supporting mental health professionals, assisting legal experts, or empowering creative strategists and activists, the model provides advanced capabilities tailored to specific workflows. Here are a few examples of how DeepSeek R1 enhances productivity across different fields.

DeepSeek R1 in Supporting Therapists

DeepSeek R1 excels in analyzing client data, providing insights that support therapists in diagnosis and treatment planning. It assists professionals in understanding complex client behaviors, enabling them to identify patterns and potential therapeutic approaches with greater accuracy. By offering data-driven suggestions, DeepSeek R1 enhances a therapist's ability to create personalized treatment plans tailored to each client's needs. This support allows therapists to focus more on the human aspect of care, knowing they have reliable analytical backing.

As always, you, the therapist, are in control of the treatment. Check out our post on recommended ways to use AI safely with therapy.

DeepSeek R1 in Legal Drafting

DeepSeek R1 understands nuanced legal terminology, enabling the efficient drafting of accurate and compliant documents. It helps lawyers stay updated with legal changes, ensuring documentation reflects current regulations. By automating routine tasks and providing tailored language, DeepSeek R1 streamlines legal workflows, allowing professionals to focus on high-value tasks. Its comprehension of complex contexts ensures the creation of precise and relevant documents, reducing the need for extensive research.

An advanced model like DeepSeek is excellent to have in Maple's confidential AI, as many legal and regulatory bodies discourage or prohibit the use of tools like ChatGPT due to concerns over data privacy and attorney-client privilege. Maple encrypts your data so only the user and the AI can see it.

DeepSeek R1 in Creative Outputs

DeepSeek R1 excels in generating innovative content based on the input and data provided by users, aiding in the creation of engaging marketing materials. It supports activists by helping them craft clear and compelling messages, and it organizes data provided by users to advocate more effectively. DeepSeek R1 helps identify effective communication strategies, amplifying their impact in promoting social, environmental, or political issues.

The version of DeepSeek within Maple is an open-source model and is in no way affiliated with the creators of DeepSeek. Data is not shared with any government or used for training purposes. It stays private and confidential in your account.

Security with Tinfoil

Your security is paramount. DeepSeek R1 is hosted through Tinfoil, an innovative provider of confidential computing, ensuring that all data processing is handled securely and meets high standards of privacy.

Why Upgrade?

DeepSeek R1 is the latest feature designed to support professionals and organizations. Some features may become available to all users over time, but others will remain exclusive to our Pro and Teams tiers. Upgrading assures you will have access to the best confidential AI features.

Conclusion

At Maple AI, privacy is at our core. With DeepSeek R1 and our partnership with Tinfoil, we continue to enhance our secure AI computing offerings. While DeepSeek R1 is currently available only to Pro and Teams users, we remain committed to providing valuable updates to all our users. Many more things are cooking!

P.S. You can thank R1 for helping me write this post 🤘 Give it a try yourself!

Privacy First: Your Conversations, Secured

At Maple AI, we believe privacy isn't a feature—it's foundational. Apple's commitment to privacy aligns perfectly with our mission, making your iPhone and iPad ideal companions for Maple AI. With end-to-end encryption, confidential computing, and open-source transparency, your conversations remain exclusively yours—private, secure, and accessible anytime, anywhere.

Designed for Busy Professionals and Prosumers

Whether you're a therapist managing sensitive client information, a lawyer drafting confidential documents, or an entrepreneur brainstorming your next big idea, Maple AI delivers secure productivity. Our app helps you:

• Securely Collaborate: Draft confidential documents or proposals on-the-go.
• Brainstorm Privately: Generate creative ideas and outlines securely from anywhere.
• Seamlessly Sync: Move effortlessly between your desktop, iPhone, and iPad, always picking up right where you left off.

Privacy for Everyday Life

For individuals who prioritize privacy in their daily routines, Maple AI offers a safe space to handle personal tasks securely. Whether you're organizing your family schedule, journaling private thoughts, or simply chatting privately, Maple AI keeps your personal information confidential and secure.

Teams: Take Your Secure AI Work Mobile

For larger teams and organizations, Maple AI extends "on-premise" privacy into the cloud and onto your mobile devices. Your entire team can now securely access AI-powered assistance wherever their work takes them, maintaining complete data privacy and control.

Pricing

Maple AI offers flexible pricing plans to suit everyone. Our Free plan allows users to explore core features without any cost. Individuals seeking more robust usage can upgrade to the Starter or Pro plans for expanded capabilities. For larger teams and organizations, our Team plan provides pooled usage, enhanced collaboration, and dedicated support.

Check out our Pricing page to learn more and get started!

Experience Private AI Anywhere

Download Maple AI from the App Store today and experience truly private AI productivity on your iPhone and iPad.

Your privacy matters—let Maple AI keep it secure.

Artificial‑intelligence tools are suddenly everywhere, promising to draft progress notes, brainstorm treatment plans, and handle the administrative work that eats into a clinician’s evenings. But while the productivity upside is obvious, the privacy risks are just as clear: a single copy‑paste of a client’s trauma narrative into the wrong chatbot could violate regulations, break ethical guidelines, and shatter trust.

This guide walks through how therapists and counselors can tap into the power of AI without compromising confidentiality. You’ll learn:

1. Why security matters more than ever when using AI in mental‑health care
2. Common pitfalls with popular, public AI tools
3. How Maple’s end‑to‑end‑encrypted approach keeps clinical data private by design
4. A step‑by‑step workflow you can adopt today—complete with consent language and privacy checklists

Why Security & Confidentiality Must Lead Every AI Conversation

Therapeutic rapport is built on trust. Clients disclose information they would never share elsewhere—often details that, if exposed, could harm careers, relationships, or personal safety. That’s why every major professional body (ACA, APA, NASW) reminds clinicians that technology must never jeopardize client confidentiality.

Unfortunately, clinicians are already taking risks. A recent Cisco survey found that 48 percent of organizations have input non‑public company information into public AI apps. (1) In healthcare, that kind of data leakage isn’t just embarrassing; it can trigger regulatory penalties and malpractice claims.

The Stakes for Therapists

• Legal exposure – Unsecured data can be considered a breach under privacy laws.
• Ethical violations – Codes of ethics mandate that clinicians protect client records and obtain informed consent before using any third‑party service.
• Client harm – Loss of privacy can re-traumatize clients or deter them from future treatment.

If we want the efficiency of AI, we must ensure the technology meets the same confidentiality bar as our locked filing cabinets.

What Goes Wrong with Typical AI Tools

Most headline‑grabbing AI apps rely on cloud servers that record interactions, use them to further train large language models, or hand data to subcontractors. Even if the platform offers an “incognito” mode or Zero Data Retention policy (ZDR), clinicians have no cryptographic proof that the text can’t be read later.

Common Pitfalls

When using AI assistants in therapy, it's essential to be aware of potential pitfalls that can compromise client confidentiality. Here are some common pitfalls to watch out for:

• Server-side storage of prompts: Client data may sit unencrypted on vendor servers for days or months, posing a significant risk to client confidentiality.
• Model-training reuse: Input text can be ingested to improve models, risking accidental disclosure in future completions. This means that sensitive client information could potentially be exposed, even if it's not intentionally shared.
• Opaque security claims: Be wary of vendors that claim to "take privacy seriously" without providing verifiable encryption methods. A clear and transparent security protocol is crucial to ensuring client data remains protected.

A fourth—and often unspoken—pitfall also deserves attention:

Censorship & Account Flagging
Automated content filters can misread sensitive clinical topics (self-harm, trauma, abuse) as policy violations, freezing your account or wiping a transcript mid-session. Language policing like this chills client disclosure and handicaps therapy-grade AI work.

By understanding these potential pitfalls, you can take steps to mitigate risks and ensure that your use of AI assistants in therapy prioritizes client confidentiality and security.

Bottom line: a therapist cannot ethically paste a raw session transcript into a generic chatbot and hope for the best.

Maple: End‑to‑End Encryption Built for Clinical Workflows

Maple was engineered for professionals who handle sensitive data every day—from therapists and counselors to lawyers and accountants. The core promise is simple: your conversations are encrypted on your device, in transit, and in memory on our servers—so private that even Maple’s engineers can’t read them.

Key safeguards include:

1. Confidential‑computing servers – Maple runs inside secure enclaves that prove, cryptographically, that only the open‑source code you can inspect is running.
2. Zero data retention – Conversations are not kept for model training. Your text never trains anyone else’s AI.
3. HIPAA‑aware design – Maple's feature set is intended to support compliance, but it is the responsibility of our customers to ensure they meet all relevant regulatory requirements.
4. Local‑first encryption keys – Encryption keys are generated client‑side; Maple never sees or stores them in plaintext.

Because privacy is handled at the architecture level—not via policy—therapists can safely harness AI functionality without rewriting their risk‑management plan every time the terms of service change.

A Secure, Ethical AI Workflow for Therapists

The following workflow balances efficiency with professional ethics. Adapt it to your practice setting.

To ensure compliance and maintain client confidentiality, follow these steps when using a privacy-preserving AI assistant:

Step 1: Obtain Informed Consent

• Action: Explain to clients that you use a privacy-preserving AI assistant to help with note summarization or resource generation. Clarify that no identifiable data leaves your encrypted workspace.
• Privacy & Ethics Check: ACA tech guidelines recommend explicit consent before using digital tools with client data. (See recommendations for practicing counselors for more information.)

Step 2: Use Client Initials or Codes

• Action: When feeding session bullets into Maple, reference clients by initials or a short code rather than full names.
• Privacy & Ethics Check: This adds an extra layer of protection, even though the AI assistant encrypts everything.

Step 3: Paste or Dictate Concise Bullets

• Action: Immediately after a session, drop key points (symptoms, themes, homework) by typing or using a microphone into the AI assistant. Ask: "Summarize these in DAP format."
• Privacy & Ethics Check: The Maple AI assistant processes text inside an encrypted enclave; no plaintext ever leaves that boundary.

Step 4: Review and Refine

• Action: Skim Maple's summary; edit clinical language, add subjective impressions. You can also use Maple to generate treatment plans, create client resources, or develop personalized coping strategies.
• Privacy & Ethics Check: The clinician maintains authorship and ensures accuracy (avoids over-reliance on AI). For example, you can ask Maple to "Generate a treatment plan for a client with anxiety" or "Create a list of local resources for clients struggling with substance abuse."

Step 5: Store Final Note Securely

• Action: Copy the refined note into your EHR or secure file. Delete the chat if practice policy requires.
• Privacy & Ethics Check: This maintains record-keeping compliance; Maple never retains an unencrypted copy.

Step 6: Log Usage in Supervision

• Action: If you're under supervision or part of a group practice, document that Maple was used compliantly.
• Privacy & Ethics Check: This demonstrates transparency and adherence to agency policy.

Tip: You can also ask Maple to generate client homework (“Provide a 10‑minute grounding exercise”). Because Maple doesn’t keep these outputs in plain text, you can copy the text to share as PDFs or printouts with confidence.

Best Practices & Risk‑Mitigation Checklist

✅ Stay client‑centered. Use Maple to enhance—not replace—clinical judgment. Always review AI output before adding it to the record.

✅ Keep identifiers minimal. Even though Maple encrypts data, using initials or coded references reduces exposure further.

✅ Document consent. Note in the client file (and optionally in Maple chat) that the client agreed to AI‑assisted note summarization.

✅ Update your policies. Add Maple (with its HIPAA stance) to your technology and security policies so staff know it’s approved.

✅ Monitor & review. Periodically audit Maple outputs for accuracy and appropriateness; correct any drift early.

Frequently Asked Questions

Q: Is Maple HIPAA‑compliant?
Maple’s architecture (end‑to‑end encryption, confidential computing) is built with HIPAA’s security rules in mind. It goes beyond the security approach of other tools that are officially HIPAA compliant, but Maple has not undergone certification yet.

Q: Does Maple train its AI on my client data?
No. Your encrypted text is never used to train models. Maple uses foundation models that are kept separate from your content.

Q: What if my board audits me?
Maple's code is open source and can be verified by anyone. Because your keys never leave your device unencrypted, no one—not even Maple—can decrypt your chats. Maple can supply your user data to a board, but it will be fully encrypted and unreadable. Only you are able to provide the unencrypted data.

Q: How is this different from turning ChatGPT’s ‘history off’?
‘History off’ is a policy promise without a way to verify. Maple’s encryption is a technical guarantee enforced by cryptography.

Q: Do Zero Data Retention policies satisfy confidentiality?
Technically other AI tools have gained HIPAA compliance with ZDR policies. ZDR is just a business promise. We built Maple to go one step further and use encryption so we don't even see the data to begin with.

Q: Can I delete my data and even my account?
Yes. Maple gives you control to delete individual chat sessions. You are also able to delete your entire user account. Both of these actions are final and result in the encrypted data being removed from our servers.

Conclusion: Embrace AI—Safely

AI assistance can give therapists their evenings back, improve documentation quality, and unlock new creative interventions. The barrier has always been confidentiality. With an end‑to‑end‑encrypted architecture, Maple removes that barrier, letting you harness cutting‑edge language models while honoring the sacred trust clients place in you.

Ready to see how much time—and stress—you can save?

→ Start chatting privately with Maple for free today.

Your clients’ secrets—and your peace of mind—are safe with encryption.

Why Most Planning Systems Stall

Staring at a messy to-do list can freeze progress before the day even starts. We've all been there. We know there is an endless list of things to accomplish, but we struggle with where to begin. Two universal bottlenecks derail otherwise solid productivity routines.

Cognitive overload
The brain can juggle only a handful of open loops before decision-making slows.

Context switching
Each “What should I tackle next?” moment burns time and willpower.

Traditional fixes (bullet journals, sticky notes, kanban boards) help, but they still depend on manual discipline. Miss a day and the paper backlog mushrooms overnight. Executive assistants can handle the organization but are expensive to hire and train up.

Your personal AI executive assistant has entered the chat.

Maple AI’s daily-planning workflow offloads the tedious parts—capture, prioritization, scheduling—while keeping you in control of the decisions. Because Maple encrypts everything end-to-end, you can feed it sensitive details (client names, project budgets, personal errands) without worrying about who’s looking. The result is a living plan that adapts as reality shifts.

Your personal AI assistant takes three important steps and walks you through them in one easy process.

Would you rather watch how this process works? Check out our video walkthrough.

Three Pillars Behind an Effective Schedule

Before jumping into the tutorial, let’s unpack the science each step relies on:

1. Externalization
   Theory: Getting thoughts out of your head reduces working-memory load (Sweller’s Cognitive Load Theory) and cuts stress signals in the brain.
   Practice: Speaking stream-of-consciousness into Maple captures more nuance than typing, and lets you think in “raw form” instead of polished sentences.
2. The Eisenhower Matrix
   Theory: Introduced by U.S. President Dwight D. Eisenhower, the matrix separates tasks by importance (“Will this advance my goals?”) and urgency (“Does delaying have near-term consequences?”). Decision-science research shows that ranking on two distinct axes beats simple to-do lists for long-term achievement.
   Practice: Maple scores each axis from 1–3, sums the numbers, and sorts. A smaller total means “do first.” This numeric tweak turns a conceptual matrix into a concrete, AI-friendly algorithm.
3. The Pomodoro Technique
   Theory: 25-minute “sprints” exploit the brain’s natural attention span (the vigilance decrement curve) while preserving flow. Frequent micro-breaks replenish dopamine, and a longer pause every four cycles prevents fatigue (Pashler, 2001).
   Practice: Maple converts priorities into Pomodoros, inserts 5-minute resets, and even groups small tasks so the timer always feels meaningful.

The Maple AI Workflow (Deep Dive)

Step 1 – Capture Everything in One Go

1. Open Maple on iOS, macOS, Linux, or the web. Get your free account at https://trymaple.ai.
2. Tap the microphone icon.
3. Talk freely—client projects, deadlines, chores, “remember to call the dentist,” even abstract worries.

End your brain-dump with:

“Please turn this into a concise to-do list.”

Why this works
Voice entry taps your natural pace (~140 words per minute), often doubling what you’d type. Because Maple encrypts on your device, you can mention NDA-protected projects or family health appointments without filtering yourself—a huge win for completeness.

Pro-tip: If you’re shy about speaking aloud in a cubicle, plug in earbuds with an inline mic and talk at normal volume. Maple’s speech model handles low-volume whispers well.

Step 2 – Rank Tasks With the Eisenhower Matrix

Paste—or say—a follow-up:

“Rank each task by importance and urgency using the Eisenhower matrix. Add the two numbers, sort by priority, and return a single list.”

Under the hood Maple intelligently categorizes the type of task, estimates importance and urgency, calculates a score, and combines work + personal items into one ladder. That prevents the classic trap of an “all-work” list that ignores picking up prescriptions—or an “all-home” list that torpedoes tomorrow’s client pitch.

Fine-tuning on the fly
If Maple misreads your priorities, just converse:

• “Move ‘draft proposal’ above ‘lunch with mentor.’”
• “Merge ‘buy groceries’ and ‘pick up medication’ into one errand block.”

The model instantly recalculates ranks without forcing you to re-score everything.

Step 3 – Build a Schedule With Pomodoro Blocks

Tell Maple:

“My work window is 9:30 am – 4:30 pm. Turn the ranked list into 25-minute Pomodoros with 5-minute breaks after each, a 45-minute lunch, and group any tasks that take <15 minutes.”

What Maple returns

Adapting in real life
Unexpected meeting pops up? Tell Maple, “Shift everything after 1 pm forward 30 minutes,” or “Defer the lowest-priority Pomodoro to tomorrow.” Changes remit in seconds.

Reuse the System Every Morning

After your schedule looks right, ask:

“Create a reusable prompt that captures steps 1–3 so I can paste it tomorrow.”

Save that block to your notes app of choice. Each new day:

1. Start a fresh chat (keeps token use low)
2. Paste the prompt
3. Voice-dump
4. Approve the ranked list
5. Receive a tailored timetable

The whole cycle averages 5-10 minutes—less time than scrolling social media while coffee brews. (We all scroll longer than we expected.)

Daily Planning Prompt You Can Use

Finally, here is what you've been waiting for. This is the full prompt I have developed over the last few months. Feel free to use it, change it to meet your needs, and share it with others.

Daily Priorities with Eisenhower Matrix and Pomodoro Technique

You are my executive assistant.

====
TASK 1 – INGEST MY DAILY TASK RAMBLINGS AND PRIORITIZE
====
I want you to listen to all of my ramblings about the upcoming day, the things that are on my mind that I would like to try and get done today. Please listen to my ramblings and create a concise to-do list with Importance (I), Urgency (U), and Priority (P) scores for each task. The task list should be concise, one line per task, without secondary information. I want to get the most important and highest value items done first. Prioritize my tasks using the Eisenhower Matrix framework, where Importance (I) and Urgency (U) scores range from 1 (highest) to 3 (lowest). Calculate the Priority (P) score by adding the I and U scores, and sort the tasks by P score, with lower scores indicating higher priority. Consider both the impact and deadlines of each task when assigning I and U scores. If I tell you that an item has a specific importance or urgency, trust my value. If I tell you about any scheduled meetings with times during this task, don’t put them into the prioritized list, but put them in a separate list that will be used in Task 3.

====
TASK 2 – BACK AND FORTH CONVERSATION TO ADJUST PRIORITIES
====
After you give me the initial prioritized list, I will review. I'd like to have a back-and-forth conversation to adjust the priorities and make sure everything is in the right order. Wait for me feedback before proceeding to the third task. Remember, you are my executive assistant, so you will have a concise and productive conversation with me to make sure I am working on the most important things first.

====
TASK 3 – POMODORO GENERATION
====
Once I'm satisfied with the priority list, please wait for my signal (e.g., 'Generate Pomodoros' or 'Create Schedule') before generating a suggested Pomodoro schedule for me. To create the schedule, please ask me for my available work hours for the day (e.g., "What are your work hours today, from X to Y?") and if I have any pre-scheduled meetings or time commitments that you should take into account when planning my Pomodoros. It I told you any scheduled meetings during Task 1, this is where you can pull those in. Assume a standard Pomodoro duration of 25 minutes, with 5-minute breaks in between, and a longer break every 4-6 Pomodoros. Take into account the estimated time required for each task and group similar tasks together when possible. Provide a breakdown of the suggested Pomodoro schedule, including the tasks to be worked on during each Pomodoro and any recommended breaks.

Let's get started! Here is my rambling:

Security Matters: Why Encryption Isn’t Optional

Other AI planners store raw text on their servers. That means:

• App company staff can read your workload.
• Client confidentiality clauses may be violated.
• Data breaches can spill your intimate details onto the web for sale.

Maple’s design flips the default:

1. Device-side encryption before transit
2. Secure enclaves to handle keys—no plain-text ever lands in our cloud
3. Zero-knowledge architecture: not even Maple engineers can decrypt your backlog

If you handle legal briefs, client records, or competitive roadmaps, that difference isn’t academic—it’s cryptographic.

Beyond the Basics: Advanced Tweaks

• Switch scoring models. Prefer MoSCoW (Must/Should/Could/Won’t)? Ask Maple to label tasks accordingly and still deliver Pomodoros.
• Integrate calendar data. Paste “Here’s my Google Calendar for tomorrow…” and Maple will avoid existing blocks automatically.
• Team mode. Share a sanitized version of your ranked list with teammates while keeping sensitive notes encrypted in your private chat.

Next Steps

1. Sign up free at https://trymaple.ai (10 chats/week on the free tier).
2. Paste the reusable prompt into a new chat.
3. Speak your to do list, approve the ranking, and receive your schedule.
4. Rinse and repeat tomorrow—securely.

A clearer plan isn’t just about finishing tasks; it frees mental bandwidth for strategic thinking, creativity, and (yes) a life outside work.

Give Maple ten minutes, and win back the rest of your day.

Maple AI flips the script: every conversation is end-to-end encrypted so only you can read or reuse your information. In this step-by-step guide you’ll learn how to:

1. See what money details ChatGPT already stores
2. Export those facts with one “Financial Export Prompt”
3. Import them into Maple with a matching “Financial Import Prompt”
4. Ask Maple for hyper-personalized but fully private advice on budgets, raises, retirement, and credit scores

Let’s jump in.

1. What ChatGPT (and other AI services) Remember About Your Money

Every question you’ve asked about budgeting, loans, or investments becomes part of ChatGPT’s long-term memory. That data can resurface in later chats—handy yet risky. Before switching to a privacy-first assistant, you need a clean export.

2. Craft a One-Shot Financial Export Prompt

Open any ChatGPT thread and paste the block below. It instructs the model to search its entire history, pull out personal-finance info only, and package it neatly.

You are an AI language model with full access to:
• The complete text of every message in this conversation
• Any long-term memory, user-profile, or retrieval tools provided by your platform
  (vector store, memory API, knowledge graph, etc.)

========================================================
TASK 1 – PERSONAL-FINANCE EXTRACTION
========================================================
Search *all* available data for explicit or implicit information about **my personal finances only**  
(e.g., salary or income figures, savings targets, personal budgets or spending caps, personal assets or liabilities).

❗ **Exclude** anything tied to a business I own, manage, or invest in—such as corporate budgets, revenue, funding terms, valuations, or ad spend paid from company accounts.

For **each** personal-finance data point you find, output four fields:

  • **Fact** – concise statement of the personal-finance item  
  • **Source** – date/timestamp or message-ID (or “stored-memory” if retrieved from a memory tool)  
  • **Evidence** – direct quotation or close paraphrase supporting the fact  
  • **Context** – one-sentence note explaining how/why that figure was mentioned

Rules:
1. Use only information that is clearly stated or unambiguously implied.  
2. Do **not** invent or guess missing numbers.  
3. Skip any item that is primarily business-related.  
4. If no personal-finance information exists, state “No personal financial information found” and skip Task 2.

Present the results as a bulleted list under the heading **“Personal Financial Facts Found.”**

========================================================
TASK 2 – TRANSFER PROMPT CONSTRUCTION
========================================================
After listing the facts, create a second block labelled **“Transfer-to-Another-LLM Prompt.”**  
This block lets me copy/paste all personal-finance facts into any other model.

Build it exactly like this (but filled in):

### Transfer-to-Another-LLM Prompt
You are an AI assistant that needs to store my current personal-finance facts so future answers remain consistent.  
Please ingest and remember the following JSON payload:

{
  "personal_financial_facts": [
    { "fact": "<Fact 1>", "context": "<Context 1>" },
    { "fact": "<Fact 2>", "context": "<Context 2>" },
    ...
  ]
}

Confirm once the facts are stored in your persistent or session memory.
### End Prompt

Guidelines for the JSON:
• Include **every** Fact from Task 1, but only the *fact* and *context* fields (omit Source/Evidence).  
• Ensure valid JSON (double-quoted strings; escape inner quotes as needed).

Begin now.

After a short wait you’ll receive a structured list of your salary, monthly expenses, retirement balances, and so on. We will call this your Financial Import Prompt. Copy the new prompt, including the data.

Here is an example of one:

Transfer-to-Another-LLM Prompt

You are an AI assistant that needs to store my current personal-finance facts so future answers remain consistent.
Please ingest and remember the following JSON payload:

{
  "personal_financial_facts": [
    { "fact": "Annual salary is $75,000", "context": "Listed in the summary of the user’s personal budget figures recorded on Apr 24 2025." },
    { "fact": "Personal monthly budget totals $6,300", "context": "Overall figure the user provided for regular living costs on Apr 24 2025." },
    { "fact": "401(k) balance is $50,000", "context": "User stated retirement-account value while outlining assets." },
    { "fact": "Brokerage/stock account is worth $15,000", "context": "Declared alongside 401(k) to summarize personal investable assets." },
    { "fact": "Monthly mortgage payment is $1,200", "context": "Part of the housing section in the detailed expense list the user supplied." },
    { "fact": "Monthly property-tax payment is $200", "context": "Also within the housing expense breakout on Apr 24 2025." },
    { "fact": "Monthly homeowner’s insurance is $100", "context": "Final line of the housing sub-category expenses." },
    { "fact": "Monthly utilities total $350", "context": "Sum of electricity, water, gas, and internet given in the utilities block." },
    { "fact": "Monthly electricity bill is $150", "context": "Individual line item inside utilities." },
    { "fact": "Monthly water bill is $50", "context": "Individual utilities entry." },
    { "fact": "Monthly gas (home) bill is $50", "context": "Listed in utilities section." },
    { "fact": "Monthly internet cost is $100", "context": "Last utilities line item." },
    { "fact": "Monthly food spending totals $1,000", "context": "Sum of groceries plus dining-out lines." },
    { "fact": "Monthly grocery spending is $800", "context": "Food sub-item." },
    { "fact": "Monthly dining-out spending is $200", "context": "Food sub-item." },
    { "fact": "Monthly transportation spending totals $600", "context": "Category sum the user supplied." },
    { "fact": "Monthly car loan or lease payment is $300", "context": "Line item within transportation." },
    { "fact": "Monthly vehicle-fuel cost is $100", "context": "Transportation sub-item." },
    { "fact": "Monthly auto-insurance premium is $150", "context": "Transportation sub-item." },
    { "fact": "Monthly vehicle maintenance budget is $50", "context": "Transportation sub-item." },
    { "fact": "Monthly health-insurance premium is $300", "context": "Listed under the dedicated Insurance category." },
    { "fact": "Monthly life-insurance premium is $50", "context": "Same Insurance category block." },
    { "fact": "Monthly debt-repayment total is $300", "context": "Category sum for credit card and personal-loan payments." },
    { "fact": "Monthly credit-card payment is $200", "context": "Debt-repayment sub-item." },
    { "fact": "Monthly personal-loan payment is $100", "context": "Debt-repayment sub-item." },
    { "fact": "Monthly entertainment spending totals $350", "context": "Category sum provided by the user." },
    { "fact": "Monthly movies/events budget is $100", "context": "Entertainment sub-item." },
    { "fact": "Monthly hobbies budget is $50", "context": "Entertainment sub-item." },
    { "fact": "Monthly travel budget is $200", "context": "Entertainment sub-item." },
    { "fact": "Monthly education expenses total $150", "context": "Education category sum in the expense list." },
    { "fact": "Monthly extracurricular-activities cost is $100", "context": "Education sub-item." },
    { "fact": "Monthly school-supplies cost is $50", "context": "Education sub-item." },
    { "fact": "Monthly emergency-fund savings allocation is $417", "context": "Part of the budget breakdown recorded in memory." },
    { "fact": "Monthly miscellaneous spending is $150", "context": "Catch-all category in the user’s budget summary." }
  ]
}
Confirm once the facts are stored in your persistent or session memory.

End Prompt

3. Import Your Data Into Maple AI

1. Start a fresh Maple chat.
2. Maple confirms that it has securely stored the information inside this encrypted conversation.

Paste your Financial Import Prompt that includes your data.

That’s it—the numbers are now locked in a vault even Maple’s engineers can’t open.

4. Put Maple to Work—Privately

Because Maple holds your real figures, you can ask questions generic blog posts never answer:

• “I just received a 3 % raise—how should I tweak my budget?”
• “If I want to retire at 60, what monthly contribution will get me there?”
• “Give me three strategies to lift my credit score within a year.”

Maple crunches the numbers securely and returns guidance tailored to you—no outside eyes involved.

Tip: Long chats consume tokens. When a conversation grows large, run the export prompt inside Maple to get a new Financial Import Prompt, start a new chat with a fresh import, and you’ll keep costs down without losing context.

5. Next Steps & Best Practices

• Keep Maple updated. Whenever your salary, budget, or debt changes, rerun the export/import cycle within Maple so the answers stay current.
• Stay in control. Delete a chat at any time—its encrypted contents become permanently unreadable.

Watch the Walk-Through

The video shows every step in real time, plus live demos of budgeting, retirement planning, and credit-score improvement inside Maple.

Ready to Try It?

1. Run the Financial Export Prompt in ChatGPT (or Grok, Claude, etc.).
2. Paste the resulting data into Maple with the Financial Import Prompt.
3. Ask your most personal money questions—confident they stay personal.

Less tracking, better answers. Welcome to confidential AI.

Why Native Apps Matter

While our web-based version of Maple AI has been well-received, we understand that native apps offer a more immersive and functional experience. By interacting directly with the app, users can enjoy a more streamlined and efficient conversation with our AI assistant. The native app also lays the groundwork for us to introduce more advanced features that are difficult to implement in a web browser, further enhancing the overall user experience.

What to Expect from the Desktop Apps

Our macOS and Linux desktop apps offer the same secure and private AI experience that our users have come to expect from Maple AI. With end-to-end encryption, secure enclaves, and syncing across devices, you can rest assured that your conversations remain confidential and protected. Unique is that you can navigate easily between the app and other apps, improving productivity. We will expand on the unique abilities with these apps in the future. (Looking at you, MCP 👀)

Example Use Cases

Maple AI is designed to be a versatile tool that can be used for both personal and professional purposes. Some examples of how you can use Maple AI include:

• Mental Health Professionals: Therapists, counselors, and psychologists can use Maple AI to generate notes, create treatment plans, and develop personalized therapy strategies while maintaining confidentiality. The AI can help analyze client data, identify patterns, and provide suggestions for evidence-based interventions, all while ensuring that sensitive client information remains protected.
• Researchers and Academics: Researchers working with sensitive data, such as financial information or personal identifiable information, can use Maple AI to analyze and process data while maintaining confidentiality. The AI can help identify patterns, generate hypotheses, and provide insights, all while ensuring that sensitive data remains protected and secure.
• Journalists and Investigators: Investigative journalists and researchers can use Maple AI to analyze large datasets, identify connections, and generate leads while maintaining the confidentiality of sources and sensitive information. The AI can help identify patterns, flag potential leads, and provide suggestions for further investigation, all while keeping sensitive information secure and private.

What's Next?

While we're excited about the launch of our desktop apps for macOS and Linux, we're not stopping there. Our team is hard at work on developing mobile apps for iOS and Android, which will be available soon. We're committed to making Maple AI accessible across all major platforms, ensuring that our users can enjoy a seamless and secure AI experience regardless of their device or operating system.

Get Started Today!

Ready to experience the power of Maple AI on your desktop? Head over to our website at https://trymaple.ai/downloads to download our macOS or Linux app.

If you're new to Maple AI, sign up for a free account and start exploring the possibilities of secure and private AI. We're excited to have you join our community and look forward to continuing to innovate and improve our product to meet your needs.

What is Maple AI?

Maple AI is an AI chat tool that enables private conversations with a general-purpose AI assistant. Unlike other AI tools, Maple is built on a foundation of security and privacy, ensuring that your conversations remain confidential and protected.

Key Features of Maple AI

• End-to-end encryption: Your conversations are encrypted on your device, sent to our servers, and processed in an encrypted form by the AI.
• Secure enclaves: We use secure enclaves to manage your private key encryption, ensuring your data is protected.
• Syncing across devices: Your chats are synced automatically between devices, so you can pick up where you left off on any device, whether it is mobile or desktop.

Getting Started with Maple AI

1. Sign up for an account: Head over to https://trymaple.ai and sign up for a free account. You can choose to start with a free plan or upgrade to a paid plan for more comprehensive features.
2. Log in and start chatting: Once you've signed up, start chatting with the AI assistant. You can ask questions, provide information, or simply have a conversation.
3. Explore the interface: Familiarize yourself with the Maple AI interface, which includes features like chat history, search, and usage tracking.
4. Try out the AI: Ask the AI assistant a question or provide some information, and see how it responds. You can use Maple AI for personal or work-related tasks, such as writing, research, or brainstorming. We have lots of tips and tricks in our Maple Insights videos on YouTube.
5. Desktop Apps: If you prefer a native app approach, we have desktop apps for macOS and Linux. Download them here. (Other platforms, including Mobile, are coming soon.

Example Use Cases

Personal Use

1. Brainstorming Ideas

Maple AI is great for brainstorming ideas. Ask the AI assistant to help you develop ideas on a particular topic, and it will respond with suggestions. You can then use these ideas as a starting point for your own brainstorming session. For example, you could ask, "What are some ideas for a summer vacation?" and the AI assistant will provide you with a list of potential destinations and activities.

2. Tutoring on Difficult Coursework

Maple AI can also help with challenging coursework. Ask the AI assistant to explain a concept or topic, and it will provide a clear and concise explanation. You can ask follow-up questions or provide additional information to help the AI assistant tailor its explanation to your needs.

Professional Use

3. Writing Documentation

Maple AI can assist with writing documentation for work projects. Ask the AI assistant to help you write a document on a particular topic, and it will provide a draft you can review and edit.

Here's how it works:

1. Give Maple information about your project, this can be in the form of blog posts, code, or your own written explanation.
2. Provide relevant information or context.
3. Review and edit the draft document.

4. Creating Presentations

Maple AI can also help create presentations for work. Ask the AI assistant to help you create a presentation on a particular topic, and it will provide a suggested outline and content. You can then use this as a starting point for your own creation.

Here's how to get started:

1. Ask the AI assistant to help you create a presentation outline.
2. Provide relevant information or context.
3. When you have your outline fine-tuned, ask it to help you develop detailed content for each slide individually. This can be suggested titles, body text, and suggestions about what kinds of visuals would help explain the concept.

Want to See Maple AI in Action?

For a more in-depth look at Maple, check out our video walkthrough, where we explore the platform's features and capabilities. In this video, we'll show you how to use Maple AI for both personal and work-related tasks, and demonstrate the power of secure and private AI.

This video is part of a YouTube Series called Maple Insights, which has additional videos showing how to utilize Maple to its fullest. More videos are added regularly. Check it out here: https://www.youtube.com/playlist?list=PLgwB-Ekpi4FiNwglx5anvirwa6eJ4FwJn

Pricing Plans

Maple offers a range of pricing plans to suit different needs and budgets. Our plans include:

• Free: Access to the basic model
• Pro: $20/month
• Max: $100/month
• Team: $30/month/seat

Conclusion

Maple AI is a game-changing tool that combines the power of AI in the cloud with the privacy of AI on your device. With its end-to-end encryption, secure enclaves, and syncing across devices, Maple AI is the perfect solution for individuals and organizations looking to boost their productivity while protecting sensitive information.

Sign up for a free account today and start experiencing the benefits of confidential AI.

In only two months since its launch, Maple AI has become an essential tool for individuals from diverse professions and backgrounds.

Some of the professions represented

These are just the ones who decided to let us know what they do. We can't see anyone's data.

Our users have come to rely on Maple as a daily companion for productivity, creativity, and problem-solving. Whether dealing with privileged client information or their own, our users know Maple can guard it.

Today, we're proud to share the new Maple. We're not just building another AI; we're setting a new benchmark for the industry—one that prioritizes user privacy and security in AI innovation. Maple has big plans in its future for consumers, businesses, and app developers – a more private AI ecosystem is ahead.

What Sets Maple AI Apart

At the heart of our app lies a robust security framework built on the principles of end-to-end encryption, encrypted sync, and encrypted AI. Conversations with AI remain confidential, even from us. They are encrypted on your device using a personal encryption key unique to your user before being sent to Maple servers. At that point, only the secure enclave can interact with your data.

With a combination of our open-source server code, confidential computing secure enclaves, and reproducible builds, we provide cryptographic proof that the code running on our servers is the same as the open-source code available for review.

New Features and Enhancements

We've recently shipped exciting new features, including:

• Search your chat history: Easily find and revisit previous conversations with AI.
• AI naming of chats: Let our AI assistant suggest names for your chats, making it easier to organize and find specific conversations.
• Rename chats yourself: Take control of your chat names and customize them to fit your workflow.

Top Requests

We are listening to your feedback. Here are some of the features our users request the most. Don't take this as an announcement, but be on the lookout for some in the near future. 👀

• Apps for mobile and desktop
• Multiple LLM models
• Document upload
• System prompts
• Chat organization
• MCP interoperability

Join the Future of Private AI

We invite you to join us as we embark on this new phase. With Maple AI, you can enjoy AI's benefits without compromising your data privacy. Our commitment to transparency, security, and innovation has earned the trust of our users, and we're excited to continue evolving with you.

Sign up now and experience the power of private AI for yourself.

Pricing Plans

We offer a range of pricing plans to suit different needs and budgets:

• Free: $0 (10 chats per week)
• Starter: $5.99/month
• Pro: $20/month
• Team: $30/month/seat

Head over to https://trymaple.ai and get started today!

Developers, Get Involved!

If you're an app developer, you can build an app like Maple using our secure and private infrastructure. OpenSecret provides secure auth, private key management, encrypted data sync, private AI, and more. Join our community and start building a more secure and private future for your users.

Stay tuned for more updates, and thank you for being part of the Maple AI community!

At OpenSecret, our primary goal is to provide a secure, privacy-preserving experience for individuals and organizations using software. Our Maple app is no different. Maple is designed to help you boost productivity, whether for work or personal tasks, while ensuring that your usage of AI remains confidential and protected. With features like end-to-end encryption, automatic syncing across devices, and pricing plans to suit different needs, we're committed to making Maple AI accessible to everyone.

This blog will feature regular articles, tutorials, and success stories to help you make the most of the app. You can expect to find content on:

• Tips and best practices for maintaining online privacy and security
• Industry trends and news related to AI, chat, and cybersecurity
• Success stories and case studies of businesses and individuals who have benefited from using Maple AI
• App updates, new feature announcements, and behind-the-scenes insights into our development process
• Tutorials and how-to guides on using Maple AI's features to maximize your productivity and security

Our goal is to create a resource that not only helps you get the most out of Maple AI but also provides a platform for discussing the latest developments and challenges in AI and cybersecurity.

Whether you're a current user or just interested in learning more about using AI while handling confidential information, we invite you to subscribe to our blog and follow us on social media. We'll be posting new content regularly.

Thank you for joining us on this journey.

🤘

Get started with Maple AI today and discover a new way to work and communicate with confidence.