First make it fast, then make it smart (kix.dev)

The piece argues that for everyday developer workflows, raw speed often trumps raw smarts: instead of waiting for “agentic” models to plan, think aloud, and execute, the author prefers ultra-fast models that handle simple, mechanical “leaf node edits” (split functions, renames, HTML/Markdown, etc.). Fast models make silly mistakes, but because they return results near-instantly, iteration is quick and low-friction—better for attention-limited workflows and for tasks where errors are easy to spot and fix. Cursor’s Composer 1 is highlighted as an example: aggressively fine-tuned for parallel tool-calling and high throughput, it favors speed over deep reasoning but delivers higher utility for these micro-tasks. For the AI/ML community and product teams, this reframes the latency–capability tradeoff: prioritize low inference latency, stable tool-calling, and seamless UX for developer-facing agents rather than only chasing model reasoning benchmarks. Tradeoffs surfaced include Gemini Flash’s instability with tool calling and large contexts, and the operational burden of juggling ultra-fast inference providers (Cerebras, Sambanova, Groq) despite their ability to run open-weight models like Qwen/Kimi quickly. The takeaway: optimize for fast, reliable, and cheap inference for many coding-assistant use cases—speed can be a feature or even a necessity, not just convenience.