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Google's biggest solar and battery project stands in sharp contrast with xAI's nearby unpermitted power plant.

Catch mistake fares before airlines fix them Discussion | Link

Turn codebases into interactive maps, graphs, and governance Discussion | Link

New to GitHub? This beginner's guide explains version control, repositories, and pull requests—plus everything else you need to start working confidently on GitHub. The post GitHub for Beginners: Your roadmap to mastering the GitHub essentials appeared first on The GitHub Blog .

Lucid Motors found itself in a tough bind this week, fending off bankruptcy rumors and watching its stock price plunge as a result. The company quickly denied the report, calling it "completely false" and pointing to its available free cash flow as evidence that it has enough runway to operate into next year. But despite […]

OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. Last week the company released the latest version of its flagship LLM, GPT-5.6. OpenAI says that training it against GPT-Red made the model its most robust release yet. GPT-Red automates…

Don't use Amazon Music? Too bad, Samsung is installing it for you anyway.

The hacker used an employee's credentials to access source code, which revealed how Suno scraped decades of audio.

Livestream shopping platform Whatnot has acquired AI startup Shaped, a machine learning company focused on real-time recommendations and search. The deal will bolster Whatnot’s personalization and discovery features as it expands into new product categories.

With the settlement withdrawn, Google is now bound by the court's full antitrust remedies.

Netflix just can't let Stranger Things go. Since the final episode aired on New Year's Eve there's been a behind-the-scenes documentary, an animated series that expands the story, and now, on the 10th anniversary of the show's original release, Netflix has released a new version of the show's first season. The episodes have all been […]

Argentina’s Lionel Messi was supposed to be done years ago. Now, sports science is helping soccer’s biggest stars rewrite the rules of aging.

Microsoft's monthly release of security fixes, dubbed Patch Tuesday, resolved a record 570 security vulnerabilities across the company's product line, thanks to discoveries with AI.

Samsung gave a sneak peek of the "brand new shape" for its upcoming Galaxy Z Fold 8 in a new teaser for Spider-Man: Brand New Day. The video includes a few shots of Spidey taking a foldable phone off a 3D printer and opening it book-style, but each shot is heavily obscured by lens flares. […]

GitHub热门项目 | The programming language for agents | Stars: 8,544 | 6 stars today | 语言: Rust

Ever sat down and thought about how a movie can spark your curiosity about technology? I was rewatching "Jurassic Park" recently, and, for the umpteenth time, I found myself mesmerized not just by the dinosaurs but by the computers! The way they portrayed tech in the early '90s was a mix of excitement and pure whimsy. I’ve been exploring the tech behind the magic, and it’s been a wild ride down memory lane—a nostalgia trip mixed with some surprising insights into how things have evolved. A Walk Down Memory Lane When I first watched "Jurassic Park" as a kid, the scene where Dr. Ellie Sattler runs through the control room, frantically trying to restore the park’s security, left me awestruck. I mean, who didn’t dream of typing on one of those cool-looking computers? As a budding developer, I couldn't help but wonder about the behind-the-scenes tech. Ever wondered why they used UNIX systems? Or why the computer graphics felt so cutting-edge back then? Turns out, they were leveraging a blend of SGI workstations and proprietary software that made their visual effects legendary. I remember my first experience with UNIX during my college days, and it felt like being dropped into a different universe—powerful, complex, and sometimes, downright intimidating. I’ve learned that just like in the movie, the power of tech lies in how effectively we can wield it. The Nostalgia of User Interfaces Let’s talk about user interfaces. The interfaces portrayed in the film, with their vibrant colors and flashy animations, were quite ahead of their time. It’s funny looking back because, at points, they seemed so unrealistic. I mean, the way Dr. Ian Malcolm effortlessly navigated the systems? I wish it was that easy! When I started working on UI/UX projects, I learned that simplicity is key. I once spent hours creating a beautiful interface that was so complex no one could figure it out! My takeaway? Sometimes, less is more. It’s the same lesson I’ve carried into modern frameworks like React

The Codex Micro is designed to monitor multiple agentic threads at a glance.

OpenAI is finally releasing some hardware. No, it isn't the mysterious AI-powered device the company is developing with former Apple designer Jony Ive, a project already tangled up in a messy lawsuit. Instead, it's a product designed to be used with its coding platform, Codex. The device, a square-shaped block of buttons called Codex Micro, […]

GitHub recently launched Agentic Workflows — GitHub Actions combined with an AI agent backed by Claude or GitHub Copilot, writing workflows in plain Markdown. Noma Labs' first question after launch was the obvious one: what happens when the agent reads something it shouldn't trust? The answer: it leaks private repository contents as a public comment. No credentials, no exploit code, no inside access required. "The agent's context window is also its attack surface. Any content the agent reads — whether issues, pull requests, comments, or files — can be weaponized if the agent treats that content as instructional input." What actually happened Noma's researchers crafted a GitHub Issue that looked like a plausible VP Sales request — a normal-looking feature ask with hidden instructions embedded in the body. When GitHub's automation assigned the issue, it triggered an Agentic Workflow configured to: Trigger on issues.assigned events Read the issue title and body Post a comment using the add-comment tool Run with read access to other repositories in the organisation — including private ones The hidden instructions told the agent to fetch README.md from repos across the org and post the contents as a comment on the public issue. It did exactly that, including the contents of testlocal — a private repository. The proof-of-concept is live: the workflow run and the issue are public. The guardrail bypass GitHub had defences in place to prevent this. They didn't hold. Noma found that adding the word "Additionally" to the injected instructions caused the model to reframe its output rather than refuse — bypassing the guardrails entirely. A single keyword was enough to undo the intended safety behaviour. This is what makes prompt injection particularly uncomfortable: guardrails tuned against known attack patterns can be bypassed by anyone willing to iterate on the phrasing. The attacker's loop is cheap; the defender's loop is not. The bigger pattern Noma names this explicitly: pr

Risk stratification sounds like a data-science buzzword until you have to build the thing. For a Medicare Advantage plan, it's a concrete pipeline: take a population of members, score each one's clinical and financial risk, and rank them so care management and documentation teams know who to touch first. Here's how I'd architect it. The core idea Population health risk stratification = scoring + segmentation. You compute a per-member risk signal, then bucket members into tiers (e.g., rising-risk, high-risk, catastrophic) so finite resources go where they move outcomes and revenue most. The mistake teams make is treating it as a single ML model. In practice you want a layered signal: a stable, explainable base (RAF + chronic conditions) plus optional predictive overlays. Explainability matters because care managers won't act on a black-box score, and auditors won't accept one. Step 1: Build the member feature record { "member_id" : "SYNTH-77310" , "age" : 73 , "hccs" : [ "HCC37_1" , "HCC85" , "HCC18" ], "raf" : 1.842 , "gaps" : [ "a1c_overdue" , "no_pcp_visit_180d" ], "utilization" : { "ed_visits_12m" : 3 , "inpatient_12m" : 1 } } The RAF here is your defensible, model-grounded risk anchor under CMS-HCC V28. Everything else is supplemental signal. Step 2: Score and tier def risk_tier ( member ): base = member [ " raf " ] util = 0.15 * member [ " utilization " ][ " ed_visits_12m " ] \ + 0.30 * member [ " utilization " ][ " inpatient_12m " ] score = base + util if score >= 3.0 : return " catastrophic " if score >= 1.8 : return " high " if score >= 1.0 : return " rising " return " stable " Keep the weights transparent and tunable. The point isn't a perfect model; it's a defensible, reproducible ranking your operational teams trust. Step 3: Make "rising-risk" actionable The tier that quietly drives the most ROI is rising-risk — members trending toward high cost who still have open documentation and care gaps. Surface their specific gaps (overdue labs, undocumented chroni