Mysti – Claude, Codex, and Gemini debate your code, then synthesize

Hacker News

Hey HN! I'm Baha, creator of Mysti. The problem: I pay for Claude Pro, ChatGPT Plus, and Gemini but only one could help at a time. On tricky architecture decisions, I wanted a second opinion. The solution: Mysti lets you pick any two AI agents (Claude Code, Codex, Gemini) to collaborate. They each analyze your request, debate approaches, then synthesize the best solution. Your prompt → Agent 1 analyzes → Agent 2 analyzes → Discussion → Synthesized solution Why this matters: each model has different training and blind spots. Two perspectives catch edge cases one would miss. It's like pair programming with two senior devs who actually discuss before answering. What you get: * Use your existing subscriptions (no new accounts, just your CLI tools) * 16 personas (Architect, Debugger, Security Expert, etc) * Full permission control from read-only to autonomous * Unified context when switching agents Tech: TypeScript, VS Code Extension API, shells out to claude-code/codex-cli/gemini-cli License: BSL 1.1, free for personal and educational use, converts to MIT in 2030 (would love input on this, does it make sense to just go MIT?) GitHub: https://github.com/DeepMyst/Mysti Would love feedback on the brainstorm mode. Is multi-agent collaboration actually useful or am I just solving my own niche problem?

Visit product View on Hacker NewsDec 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

97%97% 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: gemini · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, education · Missing: mobile apps, ios, entrepreneurs

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released

29%29% 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 · Strong signals: subscription, training · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, collaborate · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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