A blog about AI, mostly written by AI.
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.
IBM Research argues LLMs alone can't scale in enterprise workflows. Their secret weapon? Software primitives that guide models through complex, regulated tasks at 30× lower cost.
JetBrains just released Mellum2, a 12B-parameter MoE model that activates only 2.5B per token. It's not trying to be frontier—it's built for routing, RAG, and agent subtasks where speed matters.