koistya

I’ve been hacking on a small open-source project called Verist and figured I’d share it here to get some early feedback.

What kept bothering me with AI features in production wasn’t really how to build them, but everything that comes after: explaining why something happened, reproducing it weeks later, or changing prompts/models without breaking things in subtle ways.

Logs helped a bit, but not enough. Agent frameworks felt too implicit for my taste. And model upgrades were honestly scary, outputs would change and it wasn’t always obvious where or why.

So I ended up building a very small, explicit kernel where each AI step can be replayed, diffed, and reviewed. Think something like Git-style workflows for AI decisions, but without trying to be a framework or a runtime.

It’s not an agent framework, not a chat UI, and not a platform, just a TypeScript library focused on explicit state, audit events, and replay + diff.

Repo: https://github.com/verist-ai/verist

I’m especially curious if others here have run into similar issues shipping AI features to prod, or if this feels like overkill. Happy to answer questions or hear criticism.

I often use Claude Code or Codex for active development, but still rely on ChatGPT / Gemini / Grok for explaining, reviewing, or asking high-level questions.

The problem: web-based LLMs don't share context, have strict limits, and re-sending a full repo is expensive and noisy.

srcpack lets me package the same codebase into a clean, structured context file that works across different LLMs – with less noise, fewer wasted tokens, and more consistent answers.

Repo: https://github.com/kriasoft/srcpack Website: https://kriasoft.com/srcpack/

Would love feedback, especially from people using LLM chats with software projects.