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A survey of 157 enterprises finds half have already shipped an AI agent that cleared internal evaluations and then failed a real customer. Yet two-thirds are moving toward fully automated, zero-human deployment anyway.
A 101-company survey finds most enterprises have already caught their AI agents giving confident, wrong answers traced to bad context. Provider-native retrieval now leads, but the governance layer that would fix it is mostly still under construction.
A survey of 107 mid-market enterprises finds AI infrastructure spending racing ahead of cost visibility: GPUs run half-idle, fewer than half track true compute costs, and most plan to switch providers within a year.
A survey of 107 enterprises finds that AI agents are being deployed with real system access while the controls needed to contain them — scoped identities, sandbox isolation, purpose-built tooling — remain largely absent.
Thinking Machines has published Inkling as an open-weights model, giving builders direct access to run, fine-tune, and deploy it on their own terms rather than through a locked API.
A hobbyist ran a 26B-parameter model at roughly 5 tokens per second on decade-old server hardware with no GPU. Here's why that matters and how to try CPU-only inference yourself.
The AI lab behind the Hermes model family is closing a significant raise with Robot Ventures leading and Union Square Ventures participating — a signal that open-weight agent infrastructure is attracting serious capital.
A security flaw in the Cursor AI code editor was disclosed publicly after the vendor declined to patch it — a rare case where full disclosure was the only way to protect users.
Chinese AI video startup PixVerse has secured $439 million in new funding, pushing its valuation above $2 billion as it bets on expanding its world model capabilities and international reach.
Comma.ai founder George Hotz draws a sharp line between LLMs as practical engineering tools and the AGI-adjacent marketing noise surrounding them. A useful reality check for builders.
A logged comparison of Claude Code vs OpenCode reveals a 4-5x difference in baseline token overhead before any actual work begins — a real cost difference for heavy users.
Iroh's new Mesh LLM project lets you spread large language model inference across multiple devices over a peer-to-peer network — no central server required. Here's what it means for builders running models too big for a single machine.
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