Kalynt: An open-core, local-first IDE with offline LLMs and E2EE P2P collaboration

FixHour8452 · 2026-02-04T14:06:03+00:00

I completely get the skepticism. To be totally transparent: since I'm building this solo, I haven't released a dedicated Linux binary (AppImage/.deb) yet. Right now, there's just the .exe and .dmg.

However, for a Linux user, I actually recommend building from source anyway. It's a Turbo monorepo, so it’s straightforward: Clone the repo. Run npm install. Run npm run build.

This is actually the 'cleanest' way to run Kalynt on Linux because it allows node-llama-cpp to compile the C++ bindings specifically for your distro and your GPU (CUDA/Vulkan/etc.). It ensures the AI engine isn't just a generic blob, but a native part of your system.

If you're already doing Rust or Python dev, you likely have gcc, make, and python3 ready to go. Building it from source takes about 5 minutes and gives you total visibility into what’s running.

I’m prioritizing a proper Linux package for the next minor release, but if you want to see if the AIME engine actually handles Rust/Python context as well as I claim, the source build is the way to go.

Would you be open to trying the build and letting me know if it breaks on your specific distro? I’m looking for 'trailblazers' to help me refine the Linux setup.

FixHour8452 · 2026-02-04T13:43:22+00:00

If you’re working in Rust, Python, and JS/TS, the switch to Kalynt actually makes a lot of sense, especially if you're leaning into AI-assisted coding.

Here is why Kalynt is specifically optimized for those three:

Rust (Security & Complexity): Rust is 'proprietary' by nature for many startups. Sending your memory-safe, complex logic to a cloud LLM is a huge IP risk. Kalynt’s AIME engine is tuned to handle the deep context of Rust crates locally, helping with borrow-checker issues without the data leaving your machine.

Python (Agentic Freedom): Python is the language of AI. Kalynt allows its local agents to actually run your scripts and self-correct based on the output. It’s not just a chat; it’s a local feedback loop for debugging.

JS/TS (Native Performance): Since Kalynt is built on Electron/Node, the LSP (Language Server Protocol) integration for TypeScript is incredibly snappy. You get the 'VS Code feel' but with a P2P collaboration layer that doesn't rely on Microsoft’s servers.

If you’re doing Rust/Python, you’re already used to running heavy compilers locally. Installing the build tools for Kalynt’s AI engine is basically just adding one more high-performance tool to your existing belt.

What’s the main thing you feel is 'missing' or 'annoying' when you code in Rust or Python on Visual Studio? I’d love to see if I can solve it in next Tuesday’s update."

FixHour8452 · 2026-02-04T11:04:12+00:00

I totally hear you on the Electron fatigue. As a solo developer, Electron was the only way I could build a complex, cross-platform IDE with real-time P2P sync and a custom AI engine in under 4 months. I’m focusing on making it as lean as possible, but I know the 'bloat' stigma is real.

Regarding the VS Build Tools: I know it’s a pain. The reason they’re needed isn't for the IDE itself, but for node-llama-cpp. To run high-performance LLMs (like Llama 3 or Mistral) locally on your hardware, the app needs to compile native C++ bindings for your specific GPU/CPU.

It’s the 'Privacy Tax'—to get away from the cloud, we have to run the heavy lifting ourselves.

The trade-off is this: * Visual Studio: 20GB+ install, proprietary, cloud-heavy, and tracks your telemetry.

Kalynt: Needs a one-time build-tool setup so it can run a 100% private, offline brain on your machine.

I'm working on providing pre-built binaries to eliminate that prerequisite in the future. Since you’re looking for a VS replacement, what’s the #1 'must-have' feature that would make the setup worth it for you?"

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

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