We've all hit that moment: the model works on prompt A, breaks silently on prompt B, and there's no log line, no stack trace, no clue what changed inside the model.
I'm launching OpenInterpretability in a few hours on Product Hunt. It's a mech interp toolkit that runs inside Claude Code, Cursor, and Cline via MCP plus drop-in probes for hallucination and agent-failure detection. The project started because I needed a way to see what was happening when a coding agent kept making the same silent tool-call mistake.
Hey PH, Caio here, maker of OpenInterpretability.
When something breaks inside an LLM app ā hallucination, silent agent failure, "works on prompt A but not on prompt B" ā you usually have no way to see inside the model. Mech interp can answer those questions, but the tools have been research-only: H100s, deep domain knowledge, weeks of setup.
So I built the first mech interp MCP server. It plugs straight into Claude Code, Cursor, and Cline. Once installed, your AI assistant can call interpretability tools directly during a session ā capture activations, look up SAE features, run probes, test causal interventions. No separate notebook, no context switch.
ā One-line install: openinterp.org/start
Two production probes ship with it today:
FabricationGuard ā drop-in hallucination detector on Qwen3.6-27B. ā openinterp.org/products/fabricationguard
agent-probe-guard ā detects silent coding-agent failures with Qwen 3.6 27b. ~18% budget cut at 86% accuracy.
ā pip install openinterp All Apache-2.0. What I'd love feedback on: - Which IDE workflow would you want this in next? - What LLM failure mode do you wish you could actually see into?
Happy to answer anything.
Researchers trying to find causality can use OpenInterpretability MCP to connect Claude Code, Cursor or Cline to GPUs on Google Colab to do Vibe Research