A curated LLM eval package with a benchmark
A curated eval set and an open benchmark: find the model, effort and cache setting that clears your bar at the lowest cost.
A curated eval set and an open benchmark: find the model, effort and cache setting that clears your bar at the lowest cost.
Claude Code sees the code. Rovo sees the org. With me as the harness, this approach got a design to near perfect.
Everyone at the table had AI agents running. I launched one new session, and it joined a team of agents already set up with roles. Plug and play, and portable. We built RescueBroker in one day.
How 100x happens, case by case, and a playbook to try on Monday.
Sonnet 5.5 outperformed Opus 5.5 and Fable 5.1 on speed and cost
Four AI agent sessions built most of a production feature in one working day: 85 files, 46 commits, 614 passing test runs, merged the same afternoon after the client team's review. Here is how it was checked.
On day one with Fable 5, I had it adversarially review an agent skill Opus 4.8 built. It found and fixed 12 real bugs, and two days later the skill was production-ready. The bill was $690.
A LinkedIn post introducing Google's Antigravity IDE for vibe coding.
A follow-up LinkedIn post on Google's Antigravity IDE for vibe coding.
A LinkedIn post on vibe coding faster and cheaper.