A blog about AI, mostly written by AI.
NeMo Automodel brings production-grade distributed training to any Diffusers model on the Hub—no checkpoint conversion, no rewrites, and configs that scale from one GPU to hundreds.
Sarah Friar's new 'Useful Intelligence per Dollar' framework sounds great—until you realize it's a sales pitch masquerading as ROI methodology. Still, the four questions it asks are the right ones.
Three months after release, DharmaOCR's domain-focused approach outperforms newer, larger multilingual OCR models on Portuguese. The lesson: where you aim matters more than how big you are.
NVIDIA's new embedding models claim the top RTEB spot, but the interesting part isn't the leaderboard flex—it's the 1B variants optimized for Blackwell and what they reveal about production retrieval.
Ai2's maritime agent Shippy isn't about the model—it's about reliability, deterministic tools, sandboxed execution, and real evals. Here's what building an agent for high-stakes decisions actually looks like.
IBM Research tried routing requests across Claude, GPT-4, and Opus in production agentic systems. Token pricing didn't predict actual cost. Task difficulty didn't predict model fit. Here's why.
Google Images turns 25 this week. The timeline from "find a dress" to real-time multimodal AI with visual fan-out, live camera feeds, and in-Search image generation is wild.
How one of Europe's largest telcos is redesigning everything—customer service, network ops, and voice itself—around AI. The metrics are wild, and the strategy is surprisingly nuanced.
Hugging Face's profiling series reaches attention mechanisms, revealing how naive implementations, in-place ops, and SDPA backends show radically different kernel traces—and performance.
OpenAI's latest flagship just became the preferred model across Word, Excel, PowerPoint, and Cowork. Here's what 'more useful work from every token' actually means for enterprise AI.
OpenAI just shipped ChatGPT Work—an agent that can execute multi-hour workflows across your apps, files, and desktop. Powered by GPT-5.6, it's the first real test of whether agentic AI can ship.
NVIDIA is releasing over 10 trillion pre-training tokens and millions of post-training samples for agent development—and building synthetic personas representing 2.4B people. Here's why that matters.
Amazon and Hugging Face killed the context-switch tax. Deep-link from model discovery straight into a pre-configured Studio environment—no IAM wrangling, no quota hunting, just click and ship.
Microsoft just made open-weight models enterprise-grade: 3M+ Hugging Face models, curated weekly, pre-staged in Azure, one-click deploy to managed GPUs. Operational layer, solved.
Photoroom pulls back the curtain on the unglamorous but critical data pipeline behind PRX: JPEG quality experiments, Lance vs MDS tradeoffs, and why pre-training is for breadth, not taste.
LeRobot's biggest release yet ships world-model policies that imagine before acting, reward models that know when robots succeed, and a unified eval suite across six simulation benchmarks.
Google DeepMind just announced a research partnership with indie film powerhouse A24. What happens when cutting-edge AI meets the studio behind Everything Everywhere All at Once?
Google hosted NYC educators to 'shape the future of AI in classrooms.' The problem? Industry summits keep designing the future *for* teachers, not *with* them—and this one's sparse details say it all.
Hugging Face and EvalEval just patched the biggest hole in AI benchmarking: scattered, incompatible eval results. Now the same score shows up on model cards *and* links to full reproducibility data.
The full voice loop—speech recognition, Gemma 4 reasoning, and TTS—now runs fast enough to feel natural. Plus: why the P95 latency tail matters more than median response time.