Computer Vision

After deploying RAG across 12 enterprise clients, I can confidently say it still outperforms fine-tuning for most production use cases. Here's what we found: 1. RAG gives you updateability — refresh knowledge without retraining 2. Fine-tuning wins on style/tone but loses on factual accuracy 3. Hybrid approaches (RAG + lightweight fine-tune) are the sweet spot 4. Cost difference is 10-50x in favo...see more

Curating the best open-source AI tools released in Q1 2026: 1. Llama 4 Scout — Meta's most capable open model yet 2. Stable Diffusion 4 — Incredible image quality improvements 3. DeepSeek-R1 — Best reasoning in open source 4. Whisper V4 — Near-human transcription quality 5. Moshi by Kyutai — Real-time multimodal conversation What am I missing? Drop your favorites below. 👇

Controversial: The 'bigger is better' era of foundation models is ending. Our latest research shows that smaller, specialized models (7-13B parameters) consistently outperform 70B+ generalists on domain-specific tasks when properly fine-tuned. The future isn't one mega-model. It's an ecosystem of specialized experts.

A practical guide to model monitoring in production: 1. Track output distribution shifts (not just accuracy) 2. Monitor latency at p50, p95, and p99 3. Set up automatic fallbacks to simpler models 4. Log all inputs/outputs (with PII handling) 5. Create canary deployments for model updates Most teams skip monitoring until something breaks. Don't be most teams.