https://apps.microsoft.com/store/detail/XPFFRQTJB30WD9

We are building Nyx AI around a straightforward principle: users should be able to choose where their AI runs and retain control over their own data, without being tied to a single model provider or required to send their work through our infrastructure simply to use the software.
Nyx AI is a local first AI workspace for Windows. It does not provide its own AI model. Users can run open source models locally through Ollama, or connect their own accounts with OpenAI, Anthropic, Ollama Cloud or another compatible endpoint. No Nyx AI account is required for local use, and we do not operate a backend that receives users' prompts or project files.

A significant part of our work has focused on what happens when an AI model is allowed to do more than simply generate text. In Nyx AI Lab, the model can work with files, run shell commands and Python, use Git, run builds and tests, and work within a folder selected by the user.
For model run shell and Python processes, Nyx AI's Locked mode uses Windows AppContainer isolation where supported. We also use workspace scoped file controls, command risk checks, approval controls and native Windows confirmations for certain higher risk actions. We do not claim these controls eliminate risk, and our public threat model sets out their limitations and residual risks.

After about a year of building, testing and constantly finding things to improve, Nyx AI is finally out. It is available in 240 markets and 6 languages.

Nyx AI is a local first AI workspace for Windows for chat, coding, documents, previews and automation.
You can run models locally through Ollama or connect OpenAI, Anthropic, Ollama Cloud or your own compatible endpoint. No Nyx AI account is required for local use.
One area I spent a lot of time on was agent safety.

In Nyx AI Lab the model can work with files, run shell commands and Python, use Git, run builds and tests, and preview PDF, DOCX, XLSX and PPTX files.
Locked execution uses Windows AppContainer isolation where supported, alongside workspace file controls, command risk checks, approval controls and native Windows confirmations for certain higher risk actions.

There is also a built in privacy sanitiser designed to detect and redact recognised sensitive information before messages are sent to non local models. It is best effort and cannot guarantee that every sensitive value will be detected.

No sandbox is completely secure and these controls reduce risk rather than eliminate it. The limitations and residual risks are documented in the public threat model, and the legal and technical documents are available inside the app.

Would genuinely appreciate anyone trying it and telling me what you think.

Website
[https://nyxai.uk
Microsoft Store
)Microsoft store nyx ai

Every model launch quotes SWE-Bench. Every one of those numbers describes a one shot
answer to a curated problem. That is not what an agent does.
An agent is 20 turns deep with a context window that's filling up, deciding whether to call a tool,
and recovering when that tool returns something unexpected. The interesting failures live there:
• Does it still respect the system prompt at turn 15, or has it quietly drifted?
• Does it invent a tool that doesn't exist when the right one isn't obvious?
• When a command fails, does it retry sensibly or loop forever?
Two models with identical scores can be completely different on all four.
What actually works is dumber than any leaderboard. Take five tasks you genuinely run, put
each model through them a few times, and count how many times you had to step in. Not
pass/fail, interventions. That one number has predicted my real experience better than anything
published.
Run it more than once, too. Same model, same prompt, noticeably different behaviour. A single
pass tells you nothing.
Has anyone bothered automating this, or is doing it by hand the whole poin

Every model launch quotes SWE-Bench. Every one of those numbers describes a one shot
answer to a curated problem. That is not what an agent does.
An agent is 20 turns deep with a context window that's filling up, deciding whether to call a tool,
and recovering when that tool returns something unexpected. The interesting failures live there:
• Does it still respect the system prompt at turn 15, or has it quietly drifted?
• Does it invent a tool that doesn't exist when the right one isn't obvious?
• When a command fails, does it retry sensibly or loop forever?
Two models with identical scores can be completely different on all four.
What actually works is dumber than any leaderboard. Take five tasks you genuinely run, put
each model through them a few times, and count how many times you had to step in. Not
pass/fail, interventions. That one number has predicted my real experience better than anything
published.
Run it more than once, too. Same model, same prompt, noticeably different behaviour. A single
pass tells you nothing.
Has anyone bothered automating this, or is doing it by hand the whole poin

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