thumbgate

ThumbGate

Your AI coding bill has a leak.

Stop paying $ for the same AI mistake.

Every retry loop, every hallucinated import, every "let me try a different approach" — those are billable tokens on every LLM vendor's bill. Thumbs-down once; ThumbGate blocks that exact mistake on every future call. Across Claude Code, Cursor, Codex, Gemini, Amp, Cline, OpenCode — any MCP-compatible agent, forever.

Under the hood: your thumbs-down becomes one of your Pre-Action Checks that physically blocks the pattern permanently on every future call — across every session, every model, every agent. It is self-improving agent governance: every correction promotes a fresh prevention rule, and your library of prevention rules grows stronger with every lesson. Works with Claude Code, Cursor, Codex, Gemini CLI, Amp, Cline, OpenCode, and any MCP-compatible agent. The monthly Anthropic / OpenAI bill stops paying for the same lesson over and over — local-first enforcement, zero tokens spent on repeats.

Prevent expensive AI mistakes. Make AI stop repeating mistakes. Turn a smart assistant into a reliable operator.

Mission: make AI coding affordable by making sure you never pay for the same mistake twice.

🎬 90-second demo

Watch the force-push scenario: agent tries to git push --force, one thumbs-down, next session it's blocked — zero tokens spent on the repeat.

▶ Watch the 90-second demo · Script · ElevenLabs narration: npm run demo:voiceover

First-dollar activation path

If someone is not already bought into ThumbGate, do not lead with architecture. Lead with one repeated mistake.

1. Show the pain: open the ThumbGate GPT and paste the bad answer, risky command, deploy, PR action, or agent plan before it runs again.
2. Capture the lesson: type thumbs down: or thumbs up: with one concrete sentence. Native ChatGPT rating buttons are not the ThumbGate capture path; typed feedback is.
3. Enforce the repeat: run npx thumbgate init where the agent executes so the lesson can become a Pre-Action Check instead of another reminder.
4. Upgrade only after proof: Solo Pro is for the dashboard, DPO export, proof-ready evidence, and higher capture limits after one real blocked repeat. Team starts with the Workflow Hardening Sprint around one repeated failure, one owner, and one proof review.

The buying question is simple: what repeated AI mistake would be worth blocking before the next tool call?

The Problem — the bill nobody talks about

Frontier-model calls are not cheap. Sonnet 4.5 is ~$3 / 1M input tokens and ~$15 / 1M output tokens. Opus is 5× that. Every time your agent:

• hallucinates a function name and you have to correct it,
• retries the same failing tool call until it gives up,
• regenerates a 4,000-token plan you already approved last session,
• repeats a destructive command you blocked manually yesterday,

…you are paying for that round-trip. Twice if it retries. Three times if you re-prompt. And the agent has no memory across sessions, so the meter resets every Monday.

Session 1:  Agent force-pushes to main.     You fix it.    +4,200 tokens
Session 2:  Agent force-pushes again.       You fix it.    +4,200 tokens
Session 3:  Same mistake. Again.            You lose 45m.  +5,800 tokens

That's ~$0.21 in tokens just to fix the same mistake three times — multiplied by every developer, every repeated-mistake class, every week. The math gets ugly fast.

The Solution — fix it once, the bill never sees it again

Session 1:  Agent force-pushes to main.     You 👎 it.       +4,200 tokens
Session 2:  ⛔ Check blocks the force-push.  Zero round-trip. +0 tokens
Session 3+: Never happens again.                              +0 tokens