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Magpie – I built a CLI where AIs argue about my code

Hacker News

Magpie – I built a CLI where AIs argue about my code

Hi HN, I built Magpie because I was tired of AI code reviewers being too "nice." Most AI tools just say "LGTM" or nitpick formatting. To fix this, Magpie uses an adversarial approach: it spawns two different AI agents (e.g., a Security Expert and a Performance Critic) and forces them to debate your changes. They don't just list bugs; they attack each other's arguments until they reach a consensus. This cuts down on hallucinations and lazy approvals. Features: Adversarial Debate: Watch Claude and GPT-4o fight over your code. Local & CI: Works on local files or GitHub PRs. Model Agnostic: Supports OpenAI, Anthropic, and Gemini. The Experiment: This is also an experiment in "coding without coding." I didn't write a single line of TypeScript for this project manually. The entire repo was built using Claude Code. I'd love to hear your feedback—especially if you manage to make the models get into an infinite argument. https://github.com/liliu-z/magpie

Visit productView on Hacker NewsJan 27, 2026
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, gemini · Missing: reddit linkedin, podcasting, created
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: code review, io · Missing: https docs, excited, just released
23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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