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With the settlement withdrawn, Google is now bound by the court's full antitrust remedies.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

The Quest Begins (The “Why”) I still remember the night our API started to sputter under a sudden traffic spike. Users were seeing 502 errors, the monitoring dashboard looked like a neon rainstorm, and I felt like I was stuck in a lobby waiting for the elevator that never arrives. We had a simple round‑robin load balancer sitting in front of three identical services. It worked fine when traffic was smooth, but as soon as a burst hit, one node would get overloaded while the others twiddled their thumbs. Honestly, I thought we just needed more servers. Throwing hardware at the problem felt like using a sledgehammer to crack a nut—expensive and messy. After a few frantic Slack threads and a lot of coffee, I realized the real issue wasn’t capacity; it was how we distributed the work. The balancer was oblivious to the actual load on each backend, treating every request like it was the same weight. That moment became my quest: design a load balancer that reacts to real‑time load, stays simple enough to operate, and doesn’t cause a reshuffling nightmare when we scale the cluster. The Revelation (The Insight) The breakthrough came when I read about least‑connections load balancing combined with a slow‑start period for new hosts. The core insight is deceptively simple: Send each new request to the backend that currently has the fewest active connections. Why does that work? Immediate fairness – If one node is handling long‑running requests, it will naturally have a higher connection count and receive fewer new ones until it catches up. Burst absorption – During a traffic spike, requests spill over to the less‑busy nodes instead of piling onto a single overloaded instance. Predictable scaling – When we add a new server, it starts with zero connections, so it gets a fair share of traffic right away—but we temper that with a slow‑start window to avoid overwhelming a cold host. Compare that to round‑robin, which blindly cycles through the list regardless of each node’s state. In

There's a school of voice-agent development that treats turn detection as something you buy, not something you build. Pick a streaming STT provider, let its end-of-turn logic decide when the user is done, and move on. For a lot of teams that's the right move — and if you're weighing the options, our breakdown of turn detection vs forced endpoints is the place to start. But some teams have already solved turn detection. They've tuned their own voice-activity detection over thousands of calls, they know their audio, and they trust their endpointing more than any default. For those teams, a streaming model's built-in turn logic isn't a feature — it's something to work around. What they want is narrower and faster: hand over a finished chunk of speech, get accurate text back, get out of the way. That's the case for bringing your own turn detection and pairing it with fast ASR over HTTP. Turn detection is an architectural decision, not a default Here's the framing that matters. In a streaming setup, the STT model is a participant in the conversation — it's watching the audio and deciding, continuously, whether the user has finished. That's genuinely useful when you want the provider to own that judgment. But it means the model is inserting its own decision between "user stopped talking" and "you get the transcript." If you already know the turn is over — because your VAD just fired — you don't want the model deliberating. You want it transcribing. Every millisecond the STT layer spends re-deciding a question you've already answered is latency you're adding for no benefit. So the decision isn't "which provider has the best turn detection." For these teams it's "who owns the turn boundary?" If the answer is you, then the ideal STT layer is one that does exactly one thing: turn a finished clip into accurate text, fast. Built-in vs. bring-your-own Built-in (streaming). The model reads tonality, pacing, and rhythm to detect end-of-turn — with Universal-3.5 Pro Realtime, aroun