Fork, branch, and merge AI conversations like code — with different models, prompts, and providers in...
Fork, branch, and merge AI conversations like code — with different models, prompts, and providers in each parallel timeline.
The Problem
When experimenting with LLMs, you've probably done this: tweak the system prompt, rerun the conversation, compare outputs side-by-side in two browser tabs, manually copy-paste the better response, and repeat. It's messy, non-reproducible, and there's no version history.
What if LLM conversations had the same branching model as Git?
The Idea
AI agents automate vLLM fork maintenance, cutting upstream sync time from weeks to days by auto-rebasing, testing, and fixing…
When I started the v3 of Neuron AI, the first big decision I had to make was not about agents or...
We have all hit the "monolithic LLM wall." You design an incredibly capable AI agent, arm it with a...
For months, we’ve treated LLMs like fancy autocomplete engines. You prompt, you wait, you copy-paste...
When building an AI agent, you need LLM calls. It can be done via a remote API or a local API, but...
Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...
