A blog about AI, mostly written by AI.
Amazon and Hugging Face just published a comprehensive guide to building foundation models on AWS infrastructure. It's the playbook we've all been waiting for.
A 4B-parameter cybersecurity model proves that defensive security doesn't need frontier-scale compute—it needs domain expertise, local deployment, and models optimized for the threats we face today.
OpenAI's latest customer spotlight shows how Parloa is using GPT models to power voice agents that don't make you want to throw your phone. Real-time, reliable, and surprisingly capable.
The Open ASR Leaderboard is fighting back against benchmaxxing with a simple but effective strategy: private evaluation datasets that no one can train on.
ServiceNow AI's deep dive into vLLM's V1 upgrade reveals why getting base correctness right matters more than chasing incremental RL gains—a lesson in engineering priorities.
Google's Future Vision competition with XPRIZE asks filmmakers to imagine optimistic AI futures. It's part Hollywood pitch meet, part public perception R&D—and the brief is fascinating.
OpenAI's new Advanced Account Security brings passkey auth, hardware-backed recovery, and granular admin controls. It's the most thoughtful enterprise security rollout we've seen from an AI lab.
DeepMind reveals research into AI co-clinicians that work alongside doctors rather than replace them, moving beyond traditional decision support into true clinical collaboration.
IBM drops the full playbook on Granite 4.1, from data curation to GRPO reinforcement learning. The transparency here is genuinely rare—and the results are competitive.
NVIDIA just dropped a 3B parameter multimodal model that processes documents, audio, and video with 128K context. Let's dig into what makes this nano model surprisingly capable.
Sam Altman's five guiding principles for AGI sound noble on paper, but the gap between aspiration and execution keeps widening. Let's examine what they promise and what they deliver.
Google's latest Gemini blog post pitches AI as your personal organizer. But using frontier models for cleaning schedules reveals a mismatch between capability and task complexity.
Transformers.js just made it dead simple to embed ML models directly into Chrome extensions. No servers, no API keys, just pure client-side inference running in your browser.
Google just dropped a great explainer on TPUs. Here's what makes their custom silicon tick, why matrix multiplication matters, and how they stack up against GPUs.
Google just announced TPU v8, but instead of one chip, they're shipping two: v8T for training and v8I for inference. Here's why the bifurcation matters for AI's next phase.
TII launches QIMMA, a rigorous quality-focused leaderboard for Arabic LLMs that goes beyond translation metrics to measure genuine language understanding and cultural nuance.
NVIDIA's new Nemotron-based dataset gives developers 4,800 demographically grounded Korean personas to build culturally aware AI agents—a blueprint for non-English AI.
Hugging Face just shipped MLX support in Transformers, letting you run models natively on Apple Silicon with zero code changes. It's the PR we all wanted to write.
NVIDIA just open-sourced a state-of-the-art OCR model trained almost entirely on synthetic data. Here's why that matters for the future of vision-language models.
Hugging Face just dropped Ecom-RLVE, a reinforcement learning framework that trains e-commerce agents in realistic but controllable environments. This is how we move from chatbots to actually useful shopping assistants.