neuron-explorer
Steering-first neuron explorer for Qwen3.5-4B: push every MLP neuron, keep every rollout, browse the results
Production architecture patterns for AI agents, RAG pipelines, and LLM systems — with real-world architecture diagrams and decision frameworks.
Developer toolsJavaScript · CSSGitHub @gmaheshrajuOnlineOwner unconfirmed
Have you tried it? One tap is enough. No sign-in needed.
No updates detected yet. GitHub releases will appear here.
Owners of public repositories under a personal GitHub account can sign in to verify ownership and manage the introduction themselves. No administrator approval is needed.
Sign in, generate a verification file, and commit it to this repository’s default branch within 30 minutes. Files on other branches or forks do not count. Then check the file below to manage the project without administrator approval. Keep the file to retain your access.
If needed, you will go to sign-in. After signing in, return here and press your chosen verification button again.
Run /nomorevibe verify in your project folder to verify ownership and manage the introduction yourself.
Verify in one minuteAsk about other verification options
31 hands-on, production-grade agentic AI engineering projects built from scratch — AI agents, RAG, LLMOps, guardrails, evaluation, and decision-quality observability. By Mahesh Guntumadugu.
Taken from the repository README
Loading the full README. The saved excerpt is shown below.
Hands-On Agentic AI Engineering — 31 Production-Grade Projects Built From Scratch
A portfolio of 31 hands-on, production-grade AI engineering projects — real agents, real RAG pipelines, real LLM infrastructure — each built from scratch in Node.js/Python with minimal frameworks, tested, and hardened. Not slideware. Not notebook demos. Runnable systems that show how production agentic AI is actually engineered, from the ReAct loop up to observability, cost attribution, guardrails, and self-improvement.Read the full README ↗The full README is unavailable right now. Read it at the original link. The saved excerpt is shown below.
Hands-On Agentic AI Engineering — 31 Production-Grade Projects Built From Scratch
A portfolio of 31 hands-on, production-grade AI engineering projects — real agents, real RAG pipelines, real LLM infrastructure — each built from scratch in Node.js/Python with minimal frameworks, tested, and hardened. Not slideware. Not notebook demos. Runnable systems that show how production agentic AI is actually engineered, from the ReAct loop up to observability, cost attribution, guardrails, and self-improvement.Read the full README ↗