AI code generators work like magic on fresh boilerplates and isolated components, but as soon as I drop them into our 4-year-old monolithic codebase the hallucinations spike. It misses hidden side effects invokes deprecated helper functions and breaks existing context because it can't grasp the full tree.
For devs working on established production systems what context-pruning strategies or tools do you use to keep AI generations accurate and context-aware?
A little game, because it explains what we built better than any pitch.
Picture a typical freshly shipped SaaS: Next.js front end, Supabase back end, a few API routes. You run an external scan on it and recon flags these 6 candidates. When the validation step finishes, exactly one of them is PROVEN. The other five end up refuted, inconclusive, not tested or false positive.
Most security reports fail the same way: 40 pages of "possible" issues, and nobody can tell which three are real. We built Xseth to fix that, and now we want your help deciding what the report itself should look like.
Quick recap of what Xseth does: it looks at your web app from the outside, the way an attacker would, narrows thousands of URLs down to the few worth testing, then validates each one. No install, no code access, no accounts. A finding is only marked PROVEN when a deterministic check confirms it, never on an AI's word alone. Everything else is labelled refuted, inconclusive, not tested or false positive.
