{"data":{"id":25,"backendId":"bdf3d27c-f093-4dd1-8709-7f0c27149ec2","title":"Show HN: RapidFire AI – parallel RAG experimentation with live run intervention","summary":"We built RapidFire AI because iterating on RAG pipelines is painfully sequential: run a config, wait, inspect results, tweak one knob, repeat. When you have 15 things to tune (chunk size, retrieval k, reranker, prompt template, context window strategy...) that cycle compounds fast. RapidFire uses shard-based interleaved scheduling to run many configurations concurrently on a single machine — even a CPU-only box if you're using a closed API like OpenAI. Instead of config A finishing before config","analysis":"Directly addresses the bottleneck of sequential RAG iteration. The 'IC Ops' concept for mid-run intervention is a novel and highly practical shift.","category":"technology","strategicTrack":"ai_agents","capitalRelevance":{"social":3,"cultural":4,"economic":7,"symbolic":5,"technological":10,"informational":8,"temporal":9,"psychological":4,"physical":2},"tags":["RAG","LLMOps","AI Infrastructure","Developer Tools","Optimization"],"qualityScore":10,"valueScore":9,"interestScore":8,"potentialScore":8,"uniquenessScore":9,"sourceCount":1,"confidence":5,"detectedAt":"2026-03-06T18:08:32.319Z","createdAt":"2026-03-06 18:10:35"}}