Glimpses of the Future: Speed and Swarms (www.dbreunig.com)

Over the past two months of hands‑on testing, the author found two clear trends reshaping AI-assisted coding: raw speed and concurrent “swarms” of specialized agents. Ultra‑fast models like Qwen 3 Coder 480B (≈2,000 tokens/sec on Cerebras) can produce 1,000 lines of JavaScript in ~4 seconds — roughly 30× faster than Claude 4.5 Sonnet — and this latency drop changes workflows from batchy, coffee‑break interactions to near real‑time, trial‑and‑error coding. Faster, cheaper models encourage chunking problems into tiny steps, rapid iteration, and more exploratory experiments that previously felt too costly to try. To scale capability beyond a single model, developers are running multiple agents in parallel or using orchestrators (claude-swarm, claude-flow, catnip) that apply RAG, tool loadouts, context quarantining and containerization. A concrete example is claude-on-rails: a preconfigured swarm tuned to Rails conventions that assigns roles, directories and allowed tools to reduce setup and Git conflicts (atomic commits, folder isolation), yielding high accuracy out‑of‑the‑box. The takeaway for the AI/ML community is that orchestration and conventions — not just raw model accuracy — will soon determine productivity: speed enables new interaction patterns, and convention‑driven swarm frameworks can lower friction, level the playing field across stacks, and unlock more parallel, agentic development.