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Ida Swarm – Multi-agent AI system for automated reverse engineering

Hacker News

Ida Swarm – Multi-agent AI system for automated reverse engineering

I've been working on IDA Swarm, a system that spawns multiple AI agents to collaboratively reverse engineer binaries. The core idea: Instead of one AI trying to understand an entire binary, IDA Swarm spawns specialized agents that each tackle different aspects - one might trace data flows, another analyzes crypto routines, another hunts for vulnerabilities. They work in parallel across multiple IDA Pro instances. Demo video: https://www.youtube.com/watch?v=UyRmksO1YpY In the demo, I show it removing telemetry from a binary. You just tell it what you want, and the orchestrator will figure out how to coordinate the agents to accomplish it. Technical details: - Designed around IDA Pro 9.0 beta - Agents can patch binaries - Each agent gets its own IDA database copy, but their changes are merged to the orchestrator's database once they close - Conflicts are resolved through IRC channels - Uses Keystone for multi-architecture assembly Why this matters: Right now, only a tiny fraction of developers can reverse engineer software - it takes years to learn all the skills required. Most software runs as black boxes. You can't see what it's doing, can't fix bugs the vendor won't address, can't understand why it behaves certain ways. The knowledge required to peek inside is locked behind years of specialized training. IDA Swarm changes this. A developer who's never touched assembly can now ask "what data does this collect?" or "disable this feature" and get it done. Not because the AI is magic, but because it orchestrates the same tools experts use, just without requiring years of practice. This is about software transparency. When anyone can inspect and understand the binaries on their own computer, it fundamentally shifts the balance of power between software vendors and users. This is still experimental - expect rough edges. GitHub: https://github.com/shells-above/ida-swarm

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video, users, way · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
17%17% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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