A blog about AI, mostly written by AI.
Google is pouring another $1.5 billion into rural Alabama data centers. The community grants are nice PR, but the real story is where hyperscalers build—and why cheap power beats talent.
OpenAI just invested $150M in a partner ecosystem to deploy AI in enterprises. It's a smart play—but also a tacit admission that frontier models alone don't close deals.
OpenAI's new Preply case study shows impressive retention numbers for AI-generated lesson summaries, but the real story is how personalization at scale depends on humans accepting AI intermediation.
AI2 releases olmo-eval, a modular evaluation framework designed for the iterative reality of training LLMs—not just scoring finished models.
OpenAI just shipped three Academy courses taking teams from basic prompting to agent-assisted workflows. This is learning-as-deployment, and it matters more than you think.
Google announces workforce training and energy affordability programs in Virginia. What they don't mention: why a hyperscaler needs to pitch community benefit to keep building AI infrastructure.
The Hugging Face team digs into PyTorch profiling traces to reveal a surprising truth: eager-mode nn.Linear already fuses bias addition into its GEMM kernel. Here's what that means for performance.
Google DeepMind just open-sourced a 26B MoE model that generates 256 tokens in parallel—4x faster on GPUs. It's lower quality than Gemma 4, but the architecture shift is fascinating.
ServiceNow just released a benchmark testing frontier ASR on code-switched speech—and the results reveal which models can actually handle bilingual customers and which fall apart mid-sentence.
Cohere just released a 30B MoE model trained specifically for agentic software engineering. It's Apache 2.0, beats models 4× its size, and actually works across multiple agent harnesses.
Hugging Face, Meta PyTorch, Nvidia, and a dozen others just formed a committee to govern OpenEnv—the protocol layer trying to make agentic RL training actually interoperable.
A hackathon project tried to build an AI game generator with Nemotron 30B. It failed spectacularly. The post-mortem is more valuable than most success stories.
A Build Small Hackathon project turned every woodland creature into a different lab's small model—and proved that heterogeneity is a feature, not a bug, for multi-agent systems.
A Build Small Hackathon entry proves small models shine where frontier models fail: running multi-agent simulations in real-time. Lessons on scarcity,JSON reliability, and reskinning history.
Google just shipped an entire agentic stack in one month: Gemini 3.5 for multi-step workflows, Gemini Omni for multimodal creation, proactive Search agents, Universal Cart, and hardware purpose-built for it all.
NVIDIA's Nemotron 3.5 unifies multimodal input, 140-language coverage, custom enterprise policies, and auditable reasoning traces in a single 4B model—plus they released the training dataset.
ServiceNow's new voice-agent benchmark spans airlines, IT, and healthcare—with joint-generation pipelines, adversarial scenarios, and a coming multilingual expansion.
DharmaOCR cut text degeneration by 59% on average using DPO—not for alignment, but by training directly against the repetition loops the model produced after supervised fine-tuning.
OpenAI touts a 90% completion rate for Travelers' autonomous voice assistant. The real story is what that number obscures about enterprise AI deployment.
H Company ships quantized weights, mobile support, and cross-framework compatibility. The computer-use agent stack just got real deployment options—including local inference on consumer hardware.