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GitHub had 20,000+ secret scanning alerts across 15,000 repositories. Here's how we separated signal from noise, built remediation workflows, and reached inbox zero in nine months. The post How GitHub used secret scanning to reach inbox zero appeared first on The GitHub Blog .

What happens when a machine no longer needs to be trained to see something new? That's the quiet question sitting underneath this week's news, buried next to a less invasive brain implant and a handful of robots getting tougher for the real world. Neuralink says it's completed its first "transdural" brain implant, a surgical approach built to reduce trauma during the procedure. As someone who spends a lot of time thinking about how you get sensors close to a human eye without hurting anyone, I find these less-invasive-implant strategies worth watching, because the surgical-risk problem is basically the same one we wrestle with in ophthalmic hardware. Vision is getting less invasive too, in its own way. Roboflow rolled out text-prompt object detection built on SAM3 (Meta's latest segmentation model): you type the class of object you want "forklift," "cracked tile," whatever, and it returns boxes and masks without you collecting a single training image first. That's a real shift. For most of computer vision's history, teaching a model to recognize something new meant labeling hundreds of examples before you could even start; this collapses that step into a sentence. The same week brought several applied builds using the same detect-then-orchestrate pattern: a drone system that patrols for intrusions, a pipeline that inspects transmission lines for damaged cables, and an airport tool that spots foreign debris on the tarmac. The Robot Report's roundup of June's biggest robotics stories leaned heavily on humanoid robots companies going public, new deployments, and production milestones stacking up faster than would have seemed plausible a few years ago. Apptronik unveiled its Apollo 2 humanoid alongside a dedicated data-collection facility built so the robot keeps learning after it's deployed, not just during initial training which quietly answers one of the harder questions in robotics: how do you keep a system improving once it's out of the lab? X Square Robot raised e

For a few days, it seemed like Universal decided that there would be no advanced screenings of Christopher Nolan's The Odyssey for influencers. But on Monday, influencers sat alongside traditional critics and journalists at special showings of The Odyssey specifically for the associated press junket. Despite what it may have looked like, Universal was not […]

OpenAI CEO Sam Altman has reportedly proposed giving 5% of the company’s equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.

The company announced a new slate of executive hires meant to help turn things around, as Gravity SUV sales are not taking off as expected.

Tesla just released its second-quarter delivery and production report, showing that the automaker is starting to recover after a particularly brutal sales year in 2025. The company said that it produced a total of 451,758 vehicles between April and June of this year, including 442,936 Model 3 and Model Y vehicles, as well as 8,822 […]

The company delivered more than 480,000 EVs globally, seemingly thanks to geographic expansion and cheaper versions of the Model 3, Model Y, and Cybertruck.

Even the most advanced enterprise systems are tethered to a costly paradox: manual bottlenecks that introduce critical errors, security risks, and slow innovation. These hidden operational anchors are the friction preventing your organization from realizing its full potential. The Challenge: Manual Bottlenecks in Modern Enterprise Operations In an era defined by cloud-native architectures, microservices, and declarative infrastructure, a persistent and costly paradox remains at the heart of enterprise operations. We have built systems capable of immense scale and resilience, yet they are often tethered to manual, human-driven processes that act as operational anchors. These bottlenecks aren't just minor inefficiencies; they are critical points of failure, introducing latency, human error, and security vulnerabilities into our most important workflows. They represent the friction that slows down innovation, drains resources, and prevents organizations from realizing the full potential of their digital investments. Before we can orchestrate an autonomous workspace, we must first dissect the anatomy of these manual constraints. Identifying the High Cost of Manual Invoice Reconciliation To ground this challenge in reality, consider a ubiquitous and deceptively complex business process: accounts payable invoice reconciliation. On the surface, it seems simple. In practice, it's a classic example of a high-friction, manual workflow that silently bleeds enterprise resources. The typical process is a gauntlet of context-switching and swivel-chair integration: An invoice arrives, often as a PDF attached to an email, with no standardized format. A finance professional must manually open the document and visually identify key data points: invoice number, date, vendor, line items, and total amount. They then pivot to an ERP system like SAP or NetSuite to find the corresponding Purchase Order (PO). Next, they might need to access a separate logistics or warehouse management syste

The company now expects to ship a few thousand more vehicles by the end of 2026 than it previously expected, after launching its R2 SUV last month.

Originally published on productize.life . Quick answer: pm-skills is a marketplace of around 68 Claude skills for product management across 9 plugins, from strategy and discovery to market research and AI shipping. It is built by Pawel Huryn, author of the Product Compass newsletter. Each skill is not a loose prompt but a named, sourced framework, and one of them audits the gap between documentation and code, a PM lens built for the era of AI-written code. Last week I was reading through a run of repos that pack product work into skills. Some pick one topic and go deep. This one does the opposite: it is the broadest of the bunch. It is called pm-skills, by Pawel Huryn, the author of the Product Compass newsletter. He packs almost the entire product management craft into around 68 skills across 9 plugins, from setting strategy, running discovery, and researching the market, to analyzing data, executing, and shipping software that AI wrote. Usually something this broad ends up shallow. But when I actually opened it, it was not, and one skill in particular made me stop and look for a while , because it covers an angle that only recently became necessary in the era where AI writes code for us. I will tell it in three parts, starting with what it is , then why it is not just a prompt box , and closing with lessons for anyone building products . Terms, gathered once, right here skill a ready-made set of instructions an AI agent (such as Claude Code) can invoke, like a shortcut that wraps one way of doing a task. framework a ready-made way of thinking from the PM world, such as SWOT, JTBD, or RICE, that you once had to read a book to use well. plugin (category) a group of skills that belong to the same topic, such as the discovery category or the go-to-market category. PRD a product spec document that says what will be built, for whom, and how success is measured. Part 1: What pm-skills is It is a marketplace of around 68 Claude skills for PM, organized into 9 plugins, eac

Google tries balancing AI data center emissions with clean energy efforts.

I ripped open $892 worth of Pokémon packs on my phone in under 15 minutes and walked away with 62 cents. My adrenaline rush felt like the future of gambling.

Your comments on a dangerous rule putting politicals in charge of science can matter.

You bought the hardware. Now you’ll need to subscribe for “expanded access” to the most advanced features.

The speakers discuss Netflix’s architecture for surviving extreme traffic spikes. They explain the mechanics of prioritized load shedding embedded in their Envoy sidecar proxy, allowing user-initiated requests to steal capacity from non-critical traffic. They share automated platform strategies for continuous chaos load testing, config generation, and retry storm mitigation. By Anirudh Mendiratta, Benjamin Fedorka

Something stinks in California’s climate policies. Years ago, the state set up a system that pays cattle farmers across the country to turn the methane emitted from cattle manure into natural gas, encouraging the dairy sector to produce a gas we burn instead of one that just pollutes the air. It’s become wildly popular because…

Visibility Collapse in the Post-LLM Engineering Stack Artificial intelligence is still improving. But something important has changed in how that improvement is perceived. For developers and engineers working closely with frontier models, the experience is no longer one of explosive capability jumps. Instead, it feels like: incremental improvement under increasing structural constraints This shift is not about stagnation. It is about uncertainty in how AI capability is exposed, deployed, and interpreted. Capability vs Visibility: the new separation Recent frontier model systems (such as Fable 5, as described in industry discussions) highlight an important architectural pattern: Certain capabilities are no longer fully exposed in production environments: advanced coding assistance deep debugging autonomy bioinformatics reasoning cybersecurity-related reasoning This does not necessarily imply reduced model capability. Instead, it reflects a system-level separation: model capability ≠ deployed capability System interpretation: Modern AI stacks are becoming layered systems: Raw Model → Safety Layer → Policy Filter → Deployment Interface → User Access This means developers are no longer interacting with models directly. They are interacting with constrained capability surfaces. Perceived slowdown in LLM progress Despite continued benchmark improvements: reasoning scores increase gradually multimodal capabilities expand tool-use frameworks improve The perceived acceleration of AI has weakened. Compared to 2022–2023, there are fewer qualitative jumps. From an engineering perspective, this suggests a transition: from capability discontinuity → capability smoothing In other words: AI is still improving, but improvements are less visible at the system interaction level. Economic mismatch: scaling vs returns The AI ecosystem is currently defined by a structural tension: Inputs: massive GPU infrastructure investment multi-billion-dollar training runs hyperscaler-scale capital a

Your portfolio is often the first impression a recruiter, client, or fellow developer gets of you. If it loads slowly, ranks nowhere on Google, or is a pain to update, it's working against you instead of for you. Here's how I approached optimizing mine — covering performance, SEO, and everyday usability. 1. Start With a Lightweight Foundation The biggest performance wins come before you write a single line of custom code. Pick a lean stack. Static site generators (Astro, Next.js with static export, Hugo, or even plain HTML/CSS/JS) ship far less JavaScript than a full SPA framework for a mostly-static portfolio. Avoid unnecessary UI libraries. A heavy component library for a five-page site adds kilobytes you don't need. Hand-roll simple components instead. Use system fonts or self-host web fonts. Pulling fonts from a third-party CDN adds an extra DNS lookup and render-blocking request. Self-hosting with font-display: swap avoids layout shift and speeds up first paint. 2. Optimize Images (This Is Usually the Biggest Win) Images are almost always the heaviest assets on a portfolio site. Convert images to WebP or AVIF — typically 30–50% smaller than JPEG/PNG at the same visual quality. Resize before upload. Don't serve a 4000px-wide photo in a 600px container. Use loading="lazy" on below-the-fold images so the browser doesn't fetch them until needed. Add explicit width and height attributes to prevent layout shift (this also helps your Cumulative Layout Shift score). <img src= "/project-thumb.webp" alt= "Project screenshot" width= "600" height= "400" loading= "lazy" /> 3. Minimize and Defer JavaScript Ship only the JS a page actually needs — code-split per route if your framework supports it. Defer non-critical scripts (analytics, chat widgets) with defer or load them after the page is interactive. Audit your bundle with a tool like source-map-explorer or your framework's built-in bundle analyzer to catch unexpectedly large dependencies. 4. Nail the SEO Basics Good perf

📚 Series: Databricks on AWS (Part 0, prologue) The Target Architecture ← you are here Building a Databricks AI Platform on AWS RBAC with Function-Role Groups Compute Governance: Pools, Policies, Clusters The BOOTSTRAP_TIMEOUT Mystery Fixing It with AWS PrivateLink How We Structure the Terraform Before the build story, here's the destination. This is the target-state data architecture we designed the whole platform toward — the three principles that shaped every later decision, and the Unity Catalog governance model that keeps production data safe from human hands. The rest of this series is a build log: workspaces, RBAC, compute, the networking rabbit hole, the Terraform layout. But every one of those decisions was made in service of a target picture we drew first . This post is that picture — the "to-be" architecture, not the scaffolding we happened to have up on any given week. It's built on three things Databricks basically hands you if you lean into them: the Lakehouse (one store, ACID tables, no separate warehouse to sync), the Medallion architecture (raw → cleaned → integrated → business, each layer a promotion), and Unity Catalog as the single governance plane across all of it. The interesting part isn't reciting those three buzzwords — it's the specific way we wire them so that prod, dev, and analyst sandboxes never step on each other. Three principles, and everything follows Almost every concrete rule later in this series is a consequence of one of these three. 1. Nobody touches production by hand. Create, update, delete in prod data happens only through an automated, code-reviewed pipeline running as a service principal. Human accounts don't get write on prod — not analysts, not engineers, not admins. The blast radius of a bad afternoon is capped at whatever a person can do with read-only. This one principle is why the whole "promote" flow later exists. 2. Never copy production to look at it. If an analyst wants to explore the gold layer, they read it in p

Introduction to Hydration and Rendering Strategies In the relentless pursuit of faster, more responsive web applications, developers have engineered a spectrum of hydration and rendering strategies . Each approach emerges as a response to specific performance bottlenecks, yet none is universally optimal. This section dissects the core mechanics of these strategies, their historical evolution, and the critical problem they aim to solve—balancing speed with practicality. The Problem: A Trade-Off Landscape At its core, the challenge is mechanical : how to deliver content to the user’s browser with minimal latency while maintaining interactivity. Traditional rendering methods (e.g., server-side rendering) prioritize initial load speed but often defer interactivity until JavaScript execution. Client-side rendering, conversely, delays the first paint but ensures seamless interactions post-hydration. The tension between these extremes has birthed hybrid strategies like incremental hydration and islands architecture , each addressing specific failure points in the rendering pipeline. Key Mechanisms Driving Strategy Evolution Advancements in Web Technologies : New APIs (e.g., Web Components, Streaming SSR) enable finer-grained control over rendering. For instance, streaming SSR reduces Time-to-First-Byte (TTFB) by sending HTML in chunks, but risks breaking the causal chain of DOM hydration if not synchronized with client-side scripts. User Expectations : Sub-second load times are no longer aspirational but expected. This pressure deforms traditional workflows, pushing developers toward pre-rendering or static site generation (SSG), which trade dynamic flexibility for speed by offloading rendering to build time. Competitive Pressure : Performance is a zero-sum game. Companies adopt strategies like partial hydration (hydrating only interactive components) to minimize JavaScript payload, but this risks breaking interactivity if the hydration boundary is misaligned with user int