How to Improve your Vibe Coding
summary
A large benchmark of agentic coding tools reports stark failures: three of six agents show 10% true positive rate or less across 900+ bug reports, one agent emits 70 false issues for a single task, and Cursor registers a 97% false positive rate over 100+ repos and 1,200+ issues. Agents lose context after compaction, produce high run-to-run variability, and miss cross-file bugs; thinking models perform better. Bismouth runs automated PRs, links to GitHub/GitLab/Jira/Linear, and offers on-prem deployments.
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chapters (8)
- 0:00Introduction
- 0:21Current state of AI agents
- 1:13The impact of bad vibes
- 1:59Improving agent performance
- 3:03Optimizing with rules files
- 4:26Managing context for agents
- 5:28Leveraging thinking models
- 6:44Conclusion and resources
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