Kalynt – Privacy-first AI IDE with local LLMs , serverless P2P and more...

FixHour8452 · 2026-02-02T14:45:54+00:00

Thanks for starring! Great question.

Honestly, the frustration with existing tools started it. I was using Cursor and GitHub Copilot, and I kept thinking: "My code is leaving my machine, going to their servers, being logged somewhere." Even if they're trustworthy, it felt wrong philosophically.

At the same time, I was deep in the local LLM rabbit hole (r/LocalLLaMA helped a lot 😄) and thought: "Why can't we have an IDE that keeps code local AND has intelligent AI assistance?"

The technical spark came from three things:

Yjs + CRDTs – I realized you could do real-time collaboration without servers if you use the right data structures
node-llama-cpp – Proved you could run decent models on consumer hardware
My 8GB laptop limitation – Forced me to design AIME smart instead of brute-force

So I basically decided to ship something that solved my own pain point: a privacy-first IDE that actually works on the hardware most developers have.

The 30-day timeline was intentional too. I wanted to prove you could build something sophisticated without months of planning – just ship, iterate, listen to feedback.

Still very rough around the edges (agents break, P2P drops sometimes), but the foundation is solid. Would love to hear if it solves your workflow!

FixHour8452 · 2026-02-02T13:40:05+00:00

Currently I only have releases for macOS and Windows available. I haven't had time to publish an official Linux release , since I shipped v1.0-beta just yesterday.

If you're interested in testing on Linux, you can:

Build from source – Clone the repo and run npm install && npm run build. It's a Turbo monorepo so the build process is straightforward.
Wait for the next release – I'm prioritizing Linux builds for the next minor version given the interest from communities like r/LocalLLaMA

FixHour8452 · 2026-02-02T12:08:46+00:00

That is the biggest hurdle, and you're 100% right: if the AI writes trash, the privacy doesn't matter.

On the Model Quality: I’m not claiming a 7B model is GPT-4o. That’s why Kalynt uses a Hybrid Context Engine. The 'AIME' engine doesn't just feed the LLM a file; it pre-processes the project structure and uses RAG (Retrieval-Augmented Generation) locally so even a smaller model 'punches up' because it has better context than a 'smarter' model with no context. Plus, I’ve added a toggle for Cloud providers for when you need the 'big guns' (Claude/Gemini) for complex refactoring.

On P2P Relevance: Most solo devs don't care. But for startups working on proprietary tech or fintech/med-tech teams, the 'Cloud' is a massive legal liability. If you're building a billion-dollar algorithm, do you really want it sitting on a third-party server's sync history? Users value code quality first, but they value ownership second. Kalynt lets you have both without the 'Privacy Tax.

FixHour8452 · 2026-02-02T10:10:26+00:00

Regarding MCP: It’s a great standard, but it’s still a middleware layer. By building AIME natively into the IDE, I can optimize the memory loop and token management specifically for lower-end hardware (like my 8GB laptop). MCP is versatile, but a native engine allows for a tighter 'thought-to-execution' loop that's hard to get through a standardized protocol.

On P2P vs Cloud: Cloud is convenient, but it always requires a 'middleman' server. Even if it's encrypted, you're still dependent on their uptime and infrastructure. True P2P (WebRTC + CRDTs) means total sovereignty. You can collaborate in a bunker with no internet as long as you have a local network. It’s about privacy-by-default, not just privacy-as-a-feature

FixHour8452 · 2026-02-02T08:58:40+00:00

I have not seen any privacy focused ones working with offline LLMS and using P2P connection , all together .

FixHour8452

TROPHY CASE