July 9, 2026
Bots, bugs, and a billing nightmare
Build your own vulnerability harness
AI bug hunting gets a DIY makeover — and the comments are already fighting over the bill
TLDR: The article says companies should build a reusable system around multiple AI tools to find software security flaws, instead of relying on one chatbot. In the comments, supporters shared open-source tools while critics mocked the cost, turning the real debate into: smart defense or money-burning machine?
A new post about building your own AI-powered bug-finding system should have been a dry engineering explainer. Instead, the community turned it into a full-blown debate over whether this is the future of software safety — or just an extremely expensive new hobby. The big idea from the article is simple: don’t trust one chatbot to scan your code for security problems. Build a larger system around multiple AI tools so they can check each other, remember past investigations, and keep working even when the hottest model of the week gets replaced. In plain English, it’s less “ask one bot for help” and more “run a whole detective agency of bots.”
That sparked instant comment-section whiplash. One camp was basically, “Sure, this makes sense — people are already sharing tools,” with commenters linking projects like Strix and secscan-skill. Another camp was much louder: who is paying for all this? One skeptical commenter practically screamed that this sounds like burning “tokens” — the paid units AI companies charge for processing text — by the “hundreds of millions” just to have AIs double-check other AIs. Ouch.
And then came the chaos energy: one reader popped in with the wonderfully relatable “dumb question” asking what a harness even is, which perfectly captured the vibe. The article says a harness is the reusable control system around the AI. The comments say it’s also the thing trying to hold this whole wild AI security rodeo together.
Key Points
- •The article argues that enterprise AI security scanning should use a model-agnostic harness rather than depend on a single model, prompt, or standalone agent session.
- •It states that using different models for discovery and validation improves coverage by checking vulnerabilities through distinct logic paths.
- •The article says enterprise-scale vulnerability scanning must persist investigations across runs, support deduplication and resumability, and trace issues across repositories and dependencies.
- •It argues that generic coding agents and subagents are insufficient for this task because of context-window limits, single-hypothesis behavior, and lack of persistent orchestration.
- •The described system started as an approximately 450-line security-audit skill and evolved into a 7-phase workflow producing both human-readable reports and schema-validated JSON findings.