AI 资讯

Roblox is about to let people make games with AI right inside its mobile app, which could make a platform that's already filled with content of questionable quality feel even more overloaded. The company has embraced AI with open arms, including a preview of an ambitious take on AI world models similar to Google's Project […]

Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap — the distance between how much autonomy enterprises are handing their agents and how far they trust the tests that are supposed to catch the failures. This wave of VentureBeat Pulse Research examines how technical leaders measure agent performance: which reliability and evaluation platforms they use, how they select and trust them, what breaks in production, and how far they are willing to let agents run without a human in the loop. The central finding is an evaluation gap — the distance between the autonomy enterprises are granting their agents and the trust they place in the evaluations meant to govern it. Half of organizations (50%) have, in the past year, deployed an agent or LLM feature that passed their internal evaluations and then caused a customer-facing failure, and a quarter have seen it happen more than once. Trust in the tests themselves is thin: only 5% say they fully trust automated evaluation today, and the single most-cited limitation is that evaluations align poorly with real-world outcomes (29%). Enterprises are discovering that a passing eval is not the same as a working agent. What makes the gap consequential is the direction of travel. Two-thirds of organizations (66%) already permit fully automated, zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to allow it within twelve months (33%). At the same tim

X will use Grok AI to better detect stolen content, redirect payouts to original creators, and crack down on engagement bait.

You can now make playlists, design flyers and compile shopping lists using AI in Search.

Travel agency Fora announced a $60 million Series D round led by Forerunner and Tactile Ventures, valuing the company at $1 billion.

GUI disk analyzers are great for figuring out what's filling up your laptop/desktop drive. On containers or remote servers, the options are limited to purely text based utilities (e.g. du) or list-centric TUIs (e.g. ncdu) which are usually limited to viewing one directory at a time. I created leaves to fill that gap. Inspired by classic utilities like WinDirStat and KDirStat, it uses a 2-dimensional treemap^1 visualization to show the entire directory hierarchy with proportionally sized rectangl

With this new update, Google is expanding AI Mode beyond answering questions and into completing tasks across the apps they use regularly.

Google said users can soon access their notebooks through AI Mode in Search.

Google is giving its AI note-taking app a new name. The company announced on Thursday that NotebookLM is becoming Gemini Notebook, but will remain a standalone app even as it integrates more deeply across Gemini and Google Search. Google first revealed Gemini Notebook - then called Project Tailwind - in May 2023 before widely releasing […]

Learn how OpenAI is making ChatGPT safer for teens with age-appropriate protections, learning tools, parental controls, and expert partnerships.

Google already baked NotebookLM into Gemini, now it's changing the name to reflect the tighter integration

Hi HN, I am co-founder of Kickstage, a software company specializing in solutions for the electrical industry and lately grid operators. We are hiring engineers from different backgrounds, a lot of them software developers with limited experience in the sectors. Deep domain knowledge is key in our industry however, so we are constantly teaching the basics of power flow analysis, active vs reactive power, transmission line properties etc. With Jupyter notebooks and the Python console only, that's

Companies coming to market are raising money at fastest pace this century.

You're paying $10 to $20 a month for Copilot. You don't have to. A 2024-era laptop can run a coding model good enough for autocomplete, refactors, and "explain this function" entirely offline. No API key, no telemetry, no per-token bill. Here's the exact 2026 setup I run on a 16GB machine. Why local in 2026 Two years ago, local coding models were a toy. The autocomplete was slow and the suggestions were noise. That changed. qwen2.5-coder and deepseek-coder-v2 are genuinely useful now, and the tooling caught up: Ollama serves them, Continue.dev wires them into your editor, and the whole thing runs on hardware you already own. The pitch is simple: Free. No subscription, no usage caps. Private. Your proprietary code never leaves the machine. This matters if you work on smart contracts or anything under NDA. Offline. Works on a plane, in a basement, behind a corporate firewall. The tradeoff is quality and latency. We'll be honest about both. Pick a model (and match it to your RAM) This is the decision that makes or breaks the experience. Pick a model your machine can actually hold in memory, or it spills to disk and crawls. # Fast, fits anywhere (8GB+) ollama pull qwen2.5-coder:1.5b # ~1.0GB ollama pull qwen2.5-coder:3b # ~1.9GB # The sweet spot for most laptops (16GB) ollama pull qwen2.5-coder:7b # ~4.7GB # Quality tier, needs headroom (32GB+ comfortable) ollama pull deepseek-coder-v2 # ~8.9GB (16b MoE) ollama pull qwen2.5-coder:14b # ~9.0GB ollama pull qwen2.5-coder:32b # ~20GB Rough rule: the model file size is the floor, then add a few GB for context and the OS. A 4.7GB model on a 16GB machine is comfortable. A 20GB model on the same machine is not. Model Size RAM I'd want Use it for qwen2.5-coder:1.5b 1.0GB 8GB Autocomplete, fast iteration qwen2.5-coder:7b 4.7GB 16GB Daily driver: chat, refactors, explain deepseek-coder-v2 8.9GB 32GB Harder reasoning, multi-file context qwen2.5-coder:32b 20GB 64GB Near-cloud quality, if you have the RAM deepseek-coder-v2 is a 16b m