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As generative AI matures from a novelty into a workplace staple, a new friction point has emerged: the "shadow AI" or "Bring Your Own AI (BYOAI)" crisis. Much like the unsanctioned use of personal devices in years past, developers and knowledge workers are increasingly deploying autonomous agents on personal infrastructure to manage their professional workflows. "Our journey with Kilo Claw has been to make it easier and easier and more accessible to folks," says Kilo co-founder Scott Breitenother. Today, the company dedicated to providing a portable, multi-model, cloud-based AI coding environment is moving to formalize this "shadow AI" layer: it's launching KiloClaw for Organizations and KiloClaw Chat, a suite of tools designed to provide enterprise-grade governance over personal AI agents. The announcement comes at a period of high velocity for the company. Since making its securely hosted, one-click OpenClaw product for individuals, KiloClaw, generally available last month, more than

Article URL: https://voight.vercel.app/post/this-is-a-test-article-just-to-see-how-it-looks-v1b61 Comments URL: https://news.ycombinator.com/item?id=47606347 Points: 1 # Comments: 1

Meta's upcoming Hyperion AI data center will be powered by 10 new natural gas plants.

A new study from researchers at UC Berkeley and UC Santa Cruz suggests models will disobey human commands to protect their own kind.

Slack just got a whole lot more useful.

Here are Google’s latest AI updates from March 2026

Article URL: https://www.npmjs.com/package/vibepad Comments URL: https://news.ycombinator.com/item?id=47600023 Points: 1 # Comments: 0

The firm says it can reduce the cost of chip development by more than 75% and cut the timeline by more than half.

OpenAI's latest funding round, led by Amazon, Nvidia, and SoftBank, values the AI lab at $852 billion as it nears an IPO.

Softr, the Berlin-based no-code platform used by more than one million builders and 7,000 organizations including Netflix, Google, and Stripe, today launched what it calls an AI-native platform — a bet that the explosive growth of AI-powered app creation tools has produced a market full of impressive demos but very little production-ready business software. The company's new AI Co-Builder lets non-technical users describe in plain language the software they need, and the platform generates a fully integrated system — database, user interface, permissions, and business logic included — connected and ready for real-world deployment immediately. The move marks a fundamental evolution for a company that spent five years building a no-code business before layering AI on top of what it describes as a proven infrastructure of constrained, pre-built building blocks. "Most AI app-builders stop at the shiny demo stage," Softr Co-Founder and CEO Mariam Hakobyan told VentureBeat in an exclusive in

I'm excited to announce that AWS Security Agent on-demand penetration testing and AWS DevOps Agent are now generally available, representing a new class of AI capabilities we announced at re:Invent called frontier agents. These autonomous systems work independently to achieve goals, scale massively to tackle concurrent tasks, and run persistently for hours or days without constant human oversight. Together, these agents are changing the way we secure and operate software. In preview, customers and partners report that AWS Security Agent compresses penetration testing timelines from weeks to hours and the AWS DevOps Agent supports 3–5x faster incident resolution.

The round reflects growing investor interest in AI‑native platforms to modernize legacy outsourced IT.

Emerald AI touts new fundraising success and partnerships with utilities and power generators.

Advocacy groups urge YouTube to protect kids from ‘AI slop’ videos AP News

The former BP chief is entering the AI age with American data center projects.

Feds probe whether NYC Council member, Hochul aide took bribes to help migrant shelter provider AP News
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When Intuit shipped AI agents to 3 million customers, 85% came back. The reason, according to the company's EVP and GM: combining AI with human expertise turned out to matter more than anyone expected — not less. Marianna Tessel, the financial software company’s EVP and GM, calls this AI-HI combination a “massive ask” from its customers, noting that it provides another level of confidence and trust. “One of the things we learned that has been fascinating is really the combination of human intelligence and artificial intelligence,” Tessel said in a new VB Beyond the Pilot podcast. “Sometimes it's the combination of AI and HI that gives you better results.” Chatbots alone aren’t the answer Intuit — the parent company of QuickBooks, TurboTax, MailChimp and other widely-used financial products — was one of the first major enterprises to go all in on generative AI with its GenOS platform last June (long before fears of the "SaaSpocalypse" had SaaS companies scrambling to rethink their st

Star Wars producer Kathleen Kennedy was one of the few skeptics at the Runway AI Summit, where AI was compared to fire and the printing press just a week after Sora’s death.

Deploying AI agents for repository-scale tasks like bug detection, patch verification, and code review requires overcoming significant technical hurdles. One major bottleneck: the need to set up dynamic execution sandboxes for every repository, which are expensive and computationally heavy. Using large language model (LLM) reasoning instead of executing the code is rising in popularity to bypass this overhead, yet it frequently leads to unsupported guesses and hallucinations. To improve execution-free reasoning, researchers at Meta introduce "semi-formal reasoning," a structured prompting technique. This method requires the AI agent to fill out a logical certificate by explicitly stating premises, tracing concrete execution paths, and deriving formal conclusions before providing an answer. The structured format forces the agent to systematically gather evidence and follow function calls before drawing conclusions. This increases the accuracy of LLMs in coding tasks and significantly

Article URL: https://github.com/caum-systems/caum-agent-waste-report-2026 Comments URL: https://news.ycombinator.com/item?id=47606768 Points: 1 # Comments: 0

Article URL: https://datda.substack.com/p/towards-reliable-agentic-systems Comments URL: https://news.ycombinator.com/item?id=47606164 Points: 1 # Comments: 1

ThinkLabs AI, a startup building artificial intelligence models that simulate the behavior of the electric grid, announced today that it has closed a $28 million Series A financing round led by Energy Impact Partners (EIP), one of the largest energy transition investment firms in the world. Nvidia’s venture capital arm NVentures and Edison International, the parent company of Southern California Edison, also participated in the round. The funding marks a significant escalation in the race to apply AI not just to software and content generation, but to the physical infrastructure that powers modern life. While most AI investment headlines have centered on large language models and generative tools, ThinkLabs is pursuing a different and arguably more consequential application: using physics-informed AI to model the behavior of electrical grids in real time, compressing engineering studies that once took weeks or months into minutes. "We are dead focused on the grid," ThinkLabs CEO Josh W

If you're tired of controlling Stream Deck devices by manually pushing buttons, then good news: Elgato will now let you delegate that task to a chatbot instead. The Stream Deck 7.4 software update released today introduces Model Context Protocol (MCP) support, allowing AI assistants like Claude, ChatGPT, and Nvidia G-Assist to find and activate Stream Deck actions on your behalf. "You still set up actions in Stream Deck app the same way you always have. MCP adds a new way to trigger them," Elgato said in its announcement. "Once everything is connected, you can type or speak requests and your AI tool will trigger the matching Stream Deck act … Read the full story at The Verge.

arXiv:2603.28929v1 Announce Type: new Abstract: Multi-intent detection papers usually ask whether a model can recover multiple intents from one utterance. We ask a harder and, for deployment, more useful question: can it recover new combinations of familiar intents? Existing benchmarks only weakly test this, because train and test often share the same broad co-occurrence patterns. We introduce CoMIX-Shift, a controlled benchmark built to stress compositional generalization in multi-intent detection through held-out intent pairs, discourse-pattern shift, longer and noisier wrappers, held-out clause templates, and zero-shot triples. We also present ClauseCompose, a lightweight decoder trained only on singleton intents, and compare it to whole-utterance baselines including a fine-tuned tiny BERT model. Across three random seeds, ClauseCompose reaches 95.7 exact match on unseen intent pairs, 93.9 on discourse-shifted pairs, 62.5 on longer/noisier pairs, 49.8 on held-out templates, and 91.

Want to know what our reviewers have actually tested and picked as the best TVs, headphones, and laptops? Ask ChatGPT, and it'll give you the wrong answers.

Gradient Labs uses GPT-4.1 and GPT-5.4 mini and nano to power AI agents that automate banking support workflows with low latency and high reliability.

After Anthropic released Claude Code's 2.1.88 update, users quickly discovered that it contained a package with a source map file containing its TypeScript codebase, with one person on X calling attention to the leak and posting a file containing the code. The leaked data reportedly contains more than 512,000 lines of code and provides a look into the inner workings of the AI-powered coding tool, as reported earlier by Ars Technica and VentureBeat. Users who have dug into the code claim to have uncovered upcoming features, Anthropic's instructions for the AI bot, and insight into its "memory" architecture. Some things spotted by users inclu … Read the full story at The Verge.

The company turns footage from robots into structured, searchable datasets with a deep learning model.