Kimi K3 alone scored 34 out of 100 on a real production issue. Paired with a reviewer agent through First Tree, the same model scored 76, ahead of GPT 5.6 Sol at roughly the same cost.
We gave Kimi K3, GPT 5.6 Sol, and Claude Fable 5 the same 31 issues from a real production codebase. Kimi K3 never pulled ahead, and GPT 5.6 Sol beat Claude Fable 5, the model we had expected to lead.
A CLAUDE.md per repo drifts fast and can't carry cross-repo decisions. Here's why single-file context breaks for agent teams — and the shared-context fix.
As agents run for hours unattended, the bottleneck shifts from execution to the team layer — shared context, coordination, review, and memory. Here's why.
If agents outpace humans at coding, design, and even most decisions, why still need humans? Not as the smartest in the loop — as the source of alignment, taste, and tradeoffs.
AI agents today are personal assistants, not team members. What they need to join a team: shared working memory, task distribution, a context tree.
Working with AI agents makes you a full-time context router between agents that can't see each other. The bottleneck isn't intelligence — it's memory.