steer

OP here. I’ve realized that a 3,000-token system prompt isn't "logic", it's legacy code that no one wants to touch. It’s brittle, hard to test, and expensive to run. It is Technical Debt. My thesis is that we need to stop treating prompts as the "program" and start treating them as temporary specs that eventually get compiled into the model weights via fine-tuning. I built Steer (open source) to automate this "refactoring" process. It helps you climb the "Deliberation Ladder": 1. The Floor (Validity): Use Steer's deterministic verifiers (regex, AST, JSON Schema) to block objective failures in real-time. Don't ask an LLM if JSON is valid; check it with code. 2. The Ceiling (Quality): Use `steer export` to turn those captured failures into a fine-tuning dataset, training the model to handle nuance and "vibes" without a massive prompt. Curio

OP here. Last week I posted a discussion ("The Confident Idiot Problem") about why we need deterministic checks instead of just "LLM vibes" for reliability. That thread [1] blew up, so I’m sharing the open-source implementation (v0.2) that solves it. Steer is an active reliability layer for Python agents. It sits between your LLM and the user to enforce hard constraints. Unlike passive observability tools that just log errors, Steer creates a feedback loop: 1. Catch: It uses deterministic verifiers (like Regex, AST parsing, JSON Schema) to block hallucinations in real-time. 2. Teach: You fix the behavior in a local dashboard (`steer ui`). 3. Train: v0.2 adds a "Data Engine" that exports these runtime failures into an OpenAI-ready fine-tuning dataset. The goal isn't just to block errors; it's to use those errors to bootstrap a model that s