sharmasachin98

Totally agree, context-aware misuse is a big gap, and one we’re actively exploring. We’ve built session-level risk tracking and some early logic to detect drift over time, but it’s definitely still evolving.

LLM security isn’t a one-and-done, it’s an ongoing process, especially as attack patterns keep getting more subtle.

If you’ve seen other use cases or edge cases worth considering, we’d love to hear them. And feel free to ask more questions, really appreciate your input!

Thanks for checking this out, happy to answer any questions or feedback. Also curious what others here are using today to secure GenAI deployments? Especially anything for prompt validation, Prompt Injection or hallucination detection?

This looks solid, being able to search across diagrams and videos is a big win. Curious how well it performs with noisy scanned PDFs or annotated images.

This is a cool idea. Feels like a handy tool for streamlining thoughts without switching contexts too much.

This looks super useful, especially with so many folks experimenting with local LLMs now. Curious how well it handles edge devices. Will give it a try!

This is absolutely a real problem, especially in enterprise GenAI rollouts where hallucinations and data leakage risks are non-negotiable.

We’ve run into scenarios where LLMs exposed internal data just through cleverly crafted prompts. Your ability to inspect and enforce policies at both prompt and response level is spot on.

If I were in your shoes, I’d seriously consider open-sourcing the data plane, especially since your control plane is where monetization lies. It builds trust, invites contributions, and positions you as a default in this emerging category.

And no, you're not early, you're exactly on time. Most companies are just realizing how much risk they’ve shipped into production.

This feels like a great blend of immersion and repetition. Curious if you’re doing any difficulty adaptation based on content complexity or vocabulary frequency?

I love minimal note tools, what’s your approach to syncing across devices without adding too much bloat?

Really cool project, the privacy-first angle and self-hosted design are a huge plus. Curious: have you run into any rate limits or session issues with the whatsmeow API, especially when used continuously by agents?

Curious: have you benchmarked it against any similar tools in other languages (like Go or Rust) for comparison?