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Image recognition, video understanding, 3D vision, and multimodal perception
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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.
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...
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.
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...
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.
Image recognition, video understanding, 3D vision, and multimodal perception
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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...
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.
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