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American Diabetes Association blocked publication of op-ed articles so the authors posted them as a preprint.

"The displayed billing estimates do not reflect actual usage and charges," Amazon said.

Obsidian is working on a new Fallout game, while Fallout 3 and New Vegas remasters are on the way.

Tomorrow is the 15th anniversary of the first day of the Recurse Center ( https://www.recurse.com/ ) My cofounders and I did YC all the way back in the Summer of 2010, with the initial idea of building "OkCupid for jobs." That idea quickly fizzled, and we spent the better part of a year pivoting between other ideas that also failed. Finally, we made something that we wanted ourselves: a self-directed programming retreat, where people built fun projects, contributed to open source, and helped eac

The Asus ROG Swift 32-inch 4K QD-OLED gaming monitor has almost everything I want in a high-end gaming monitor, most notably a QD-OLED panel for inky black levels and vivid colors that go beyond what’s possible on an LCD screen. Amazon, Best Buy, and B&H Photo have this model on sale for $899.99 (usually $1,299.99). […]

The cost of writing code dropped; the cost of owning it didn't. A framework for deciding which changes are actually cheap in the AI era. The post The cost of saying yes has changed appeared first on The GitHub Blog .

The most capable AI assistant with its own inbox Discussion | Link

Prosecutors accused 21-year-old student Zyaire Wilkins of publishing on Steam several fake video games that contained malware, infecting thousands of victims, and stealing crypto from some of them.

Google and Apple were sent cease-and-desist letters regarding 13 apps on their respective stores.

Everyone recommends ByteByteGo and The Pragmatic Engineer. Don't get me wrong, they're great... but the best engineering writing of the last two years is coming from newer publications nobody's put on a list yet. Here's what survived my filter. I have a rule: if I haven't opened a newsletter in three weeks, I unsubscribe. No guilt, no "maybe later" folder. It's the only way to keep email useful when every engineering team, indie hacker, and AI startup on the planet is running a Substack. That rule has consequences. Over the past couple of years it has killed off almost every famous-name newsletter in my inbox — not because they got worse, but because they got comfortable. Meanwhile, a new generation of engineering publications launched around 2023–2024 started earning their slot every single week. They're smaller, sharper, and written by people still close to the work. The other thing my rule revealed: AI engineering quietly became its own discipline. Not "AI news" — there are a thousand newsletters rehashing model launches. I mean the craft of building production systems on top of LLMs: agents, evals, brownfield integration, governance, cost. That coverage barely existed two years ago. Now it's the most valuable section of my inbox, which is why it leads this list. So here's what survived. Twenty-six newsletters, organized by topic, heavy on publications you haven't seen on every listicle. Steal the whole list. 🤖 AI Engineering & Production AI Two years ago this category didn't exist. Today it's the most important one here, because building with LLMs in production is genuinely different work — different failure modes, different economics, different skills — and general engineering newsletters mostly aren't covering it. Latent Space — swyx & Alessio Fanelli. swyx literally coined "AI engineering" as a discipline, and this is its watering hole: podcast, essays, and the AINews digest covering frontier models, agents, and the career path itself. The anchor of the categ

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Are we living through a small car renaissance? There's the Slate Truck, Amble's dune buggy, and the Fiat Topolino, as well as a whole galaxy of kei cars and trucks from Japan that have their own built-in fan base. While microcar sales in the US are still as tiny as the vehicles themselves, there are […]

If someone asked me ten years ago what it takes to break into cloud, I would have said "get certified and hope someone gives you a chance." I was wrong. And I watched dozens of freshers follow that exact advice, collect a certification, then sit in interviews unable to explain why they chose one architecture over another. The problem was never knowledge. It was proof. Proof that you can gather requirements from a confused client, design something that works, and actually build it with your own hands. AWS just launched something that helps close that gap. And two of these credentials cost nothing. Table of Contents What AWS SimuLearn Badges Actually Are Why This Matters More Than Another Certification The 12 Badges Available Right Now The Free Starting Path I Would Follow Today The LinkedIn Advantage Nobody Is Talking About For Career Switchers: Your Existing Skills Are the Cheat Code My Honest Take After 10 Years in Cloud What AWS SimuLearn Badges Actually Are SimuLearn is not another video course. Not another multiple-choice exam. You sit in a simulated client meeting powered by generative AI. A virtual customer explains their business problem. You ask questions, uncover requirements, handle objections, and propose an architecture. The AI evaluates your communication, your technical accuracy, and your decision-making in real time. Then you build the solution. In a live AWS environment. Not a sandbox with three buttons. The real console. After that, an automated validation confirms your solution actually works. Complete every assignment in a learning plan, and AWS issues you a badge through Credly. Automatically. No exam booking. No proctored test. Just demonstrated capability across the full workflow. Each badge represents the entire journey: customer conversations, architecture design, hands-on building, and validated outcomes. Not a single quiz. Not one lab. The whole thing. Why This Matters More Than Another Certification I hold 7 AWS certifications. Let me tell

I've been tuning Codenames AI , a small web game where an LLM plays Codenames with you. Clue generation is tightly constrained: one word, a count, optional intended targets, JSON on the wire, then deterministic validation before anything reaches the board. As the project started attracting regular players, I wanted to improve the gameplay experience without blowing out costs. Moving one model generation from gpt-4o-mini to gpt-5-mini was my first instinct. The default reasoning setting made responses an order of magnitude slower for this workload. Minimal reasoning looked like the obvious compromise: newer model, responsive gameplay. I expected to compare clue quality, latency, and cost while the surrounding prompt, validator, and consumer contracts stayed put. That last part was wrong. The experiment stopped behaving like an A/B test What showed up was structural, and it showed up in places that had been stable for months. Validation failures started rising. Retries started rising. Entire candidate batches started failing before the game ever saw a clue. The sharpest signal came from a clue-selection path that had run untouched for months, and it hard-failed for the first time. They weren't latency regressions so much as architectural ones. It is easy to read that as "minimal reasoning made the model worse." More often, the failures were exposing gaps in contracts that had looked fine under the previous model. What each failure actually invalidated Eventually every failure traced back to one of three layers: Prompt contracts ask for exactly count targets and, in batch mode, several distinct candidates. Deterministic validators reject target/count mismatches and filter invalid candidates before anything downstream runs. Downstream consumers only see survivors. Empty batches retry with rejection feedback, then fall back if needed. Those layers share one job: enforce the same invariants. The failures below cut across all three rather than mapping one to one. Side comm

I created the official PasteDB VS Code extension that lets you upload your current file or selected code directly from Visual Studio Code. It includes secure API key storage, customizable upload settings, recent paste history, automatic language detection, and instant shareable links. In this post, I'll explain how it works, how to install it, and the challenges I faced while building it. More Info View on Marketplace

Federal authorities have arrested a Florida man suspected of stealing at least $220,000 in crypto through malware-infected Steam games, as reported earlier by local news outlet Local10. In the complaint, officials accuse 21-year-old Zyaire Wilkins and co-conspirators of launching eight malware-embedded games from around May 2024 to February 2026, allowing them to infect about 8,000 […]

How Bifrost Enterprise combines Data Access Control (DAC), Role Based Access Control (RBAC), Access Profiles, and Bifrost Edge to secure AI applications at scale. Artificial intelligence is quickly becoming part of every employee's workflow. Developers rely on coding assistants, customer support teams use AI powered chat applications, analysts generate reports with large language models, and organisations increasingly deploy AI agents connected to internal tools through the Model Context Protocol (MCP). While this rapid adoption improves productivity, it also introduces a significant governance challenge. It's no longer enough to decide who can log into an AI platform; you must also determine who can access specific AI resources, which models they can use, what they can spend, and which data they should even be able to see. This is where Bifrost Enterprise provides a comprehensive governance layer. By combining Role Based Access Control (RBAC), Data Access Control (DAC) , Access Profiles , and Bifrost Edge , organisations can secure AI workloads without slowing down innovation. Together, these capabilities create a governance framework that scales from small engineering teams to global enterprises and you can learn more about this in the documentation on GitHub . Why Enterprise AI Needs More Than Authentication Traditional enterprise applications typically answer two questions: Who is the user? What can that user do? Modern AI platforms introduce a third and equally important question: What information should this user actually be able to see? Imagine an organisation with several engineering teams working on independent AI products. Each team has its own prompts, routing rules, API budgets, virtual keys, observability data, and model configurations. If every developer can view every configuration simply because they have developer permissions, sensitive information can easily become exposed. This challenge becomes even more complicated when organisations begin deplo

Some Amazon customers logged on Friday to a surprise bill estimate claiming that they owed the tech and cloud giant billions in fees.

I've been using Claude for a long time and mostly ignored Artifacts. Fine for a quick React demo. Not something I reached for. Then I needed to send an analysis to a few people at work, and it clicked. Or I'm just using it in a way nobody intended. Hard to say. The actual case I own the paywall backend at a Czech media house. The subscription offer on our news site is embedded as an iframe, and iframes are a bad neighbourhood: context isolation means the iframe has no access to the parent page's session, so user identity kept breaking and we kept patching it over postMessage. Every iframe is its own page view, so GA4 data was skewed and we had to build server-side tracking and session stitching to make the numbers mean anything. And ad blockers, CSP, and timeouts mean sometimes the thing just doesn't render, so we maintain a fallback UI in parallel. I wanted to propose we drop the iframe and ship a JS embed library instead, distributed through our internal npm registry. That's an architecture change, so it needs a document: what we fixed, why the iframe is still structurally wrong, what the alternative costs, what the numbers say. The numbers part came out of the same agent session, by the way. GA4 said roughly 0.14% of paywalled page views hit an error, about half of them iframe-blocked-by-browser. That's every 700th reader. Small number, real money. The boring problem You get a good answer out of the model. Now what? You paste it into a doc. Reformat it, because chat markdown does not survive the trip. Fix the tables. Decide whether it goes in Confluence or an email. Send it. Then someone asks a follow-up, you go back to the model, get a better answer, and now there are two versions of the truth and one of them is in someone's inbox. I've done the email version of exactly this document before. Outlook ate the markdown. I ended up hand-rolling plain text with unicode bullets and uppercase section headers like it was 1998. Half of that work is transport, not thinkin