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OpenAI accused of conspiring with former Apple employees to steal trade secrets.

The two subscription services offer more than just extra storage, but getting the right one can save you money.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Anthropic—currently the world’s most valuable AI company, with a nearly $1 trillion valuation—has a reputation for publishing strange and heady research. It’s looking into whether AI models can feel pain, for example,…

The biggest smartphone makers keep on trucking in the face of component shortages and economic uncertainty.

Android and iOS both have built-in tools to easily open QR codes from screenshots.

Every workout tracker I've tried has the same limitation: it records what you did, but it doesn't...

Someone would ask a question in a public Slack channel. Every so often a couple of people would start to answer. Then the manager would step in, say what was going to happen, and the thread would go quiet. On its own, it looks like nothing. A decisive manager keeping things moving. But it was a team going quietly into debt, and the dead Slack thread was one of the interest payments. You already know tech debt. You cut a corner in the code to ship faster, and you pay interest on it later in bugs, slow changes, and the one file nobody wants to touch. Culture debt works the same way, except the corners you cut aren't in the code. They're in the norms, the expectations, and the relationships that decide how people actually work together. But tech debt is visible. You can see it, point at the file, write a ticket, argue about whether it's worth paying down. Culture debt is more dangerous because it gives you none of that. You don't watch it accruing. You see the symptoms, and by the time they show up, the debt has already compounded. Let me tell you how a team I joined got there. The reward was volume. The only thing that reliably got praised was pushing a lot of code. The manager was open about it...their whole framing of the job was being able to out ship anyone on the team. Everyone else stayed quiet. Nobody ever stood up and argued against quality. If you'd asked, the manager would have agreed that testing mattered and that quality mattered. Those things just never got prioritized. So over and over, what actually got rewarded (volume) quietly beat what everyone said they wanted. This didn't happen out loud. The reward silently won every time. You can guess what that bought. Planning went first, so features shipped in half finished states and got abandoned there. Testing basically didn't exist. We had a QA person, but things slipped through constantly. Bugs were everywhere. Plenty of features barely worked, and some just didn't. The human side hollowed out at the same

In this podcast, Michael Stiefel spoke to Sarah Wells about the relationship of governance to software architecture. Governance enables teams to work effectively by establishing procedures that minimize system complexity, improve security, and reduce repetitive tasks. Targeted checklists help engineers by reducing the stress over these procedures. By Sarah Wells

Most people assume better AI performance means a bigger bill. That assumption is quietly being proven wrong. The "Don't Touch It" Trap in AI Products There's a psychological pattern that shows up in almost every team running a live AI-powered product: once something works, nobody wants to mess with it. And honestly, that instinct makes sense. You've tuned your prompts, worked out the edge cases, trained your users, and finally gotten the thing stable. The idea of swapping out the underlying model - the engine of the whole operation - feels like pulling a thread that might unravel everything. So teams stay put. They watch new model releases come out, read the benchmark comparisons, and quietly decide it's not worth the risk. The phrase you hear most often is "if it ain't broke, don't fix it." The problem is that this logic made sense when model upgrades were expensive and disruptive. That's no longer the default reality. What's actually happening now is that AI providers are competing hard on price-per-token while simultaneously improving quality. That combination - better output, lower cost - breaks the old mental model most product people are still operating with. What a Model Migration Actually Involves Let's be clear: switching AI models isn't a one-click operation. But it's also not the months-long project many teams imagine it to be. At its core, a model migration for an AI agent involves three things: re-evaluating your prompts (because different models respond differently to the same instructions), running parallel tests to compare output quality on your real use cases, and updating any API parameters that differ between versions. That's the actual work. For most small-to-medium deployments, that's days of effort, not weeks. The bigger shift is in how you think about model versions. Rather than treating the model as permanent infrastructure, it helps to think of it more like a dependency in your software stack - something you update deliberately, test careful

For a full quarter, our feature velocity significantly dropped after we re-implemented a Go service using Rust. The performance improvements actually happened. Why we did it in the first place We are a small startup. Each engineer is important, and each week is even more important. Our backend was built using Go, which was performing well. It was fast, reliable, and we could easily find resources to hire. However, we became infected with that fever. The phrase "Rewrite it in Rust" was being used in all kinds of situations, and it sounded very appealing with its promises of memory safety, no garbage collector pauses, and blazing speed. We told ourselves it was an investment in the future. What we actually bought was a quarter of silence. The numbers nobody warns you about I may not have the exact metrics we use internally, but I can direct you to an individual who shared accurate calculations transparently. In a retrospective from November 2025, engineering manager Noah Byteforge wrote that a Node.js-to-Rust backend rewrite "dropped API response times from 340ms to 28ms. That's 12.1x faster." And the other metric. A 65% decrease in sprint velocity. They didn't deliver a single story point for three weeks. The time it took to send out new features increased by 185%. The time it took for pull requests to be processed increased by 320%. Additionally, scores from the "I feel productive" survey dropped from 8.2 to 4.1. Most importantly, the kicker is what he says in his own words: "We'd won the technical battle and lost the war that actually mattered." He also admits that if he had been forthright about the 6-12 month per engineer ramp, "the business case would've fallen apart immediately." That retrospective was so relatable, it read like our own diary. The battles with the borrow checker and the compile times just snuck entire weeks away from us. The wins were real. That's the trap. I must give credit to Rust because the safety benefits are not exaggerated. The rewrite

The Chinese company is allowed to sell its impressive phones in the States, but it doesn't. Here's why.

The state of the smart home can be frustrating, because it is just so obvious how things ought to work. You should be able to control everything from everywhere. Your spaces should adapt to what you're doing and how you're feeling. Making your home smart shouldn't require renovating, and the smarts should be mostly invisible. […]

Researchers cobbled together funding and time to show how quantum computing could aid in the development of drugs to help underserved populations and combat rare diseases.

Whether you’re near or far, keep an eye on your fur baby with our favorite pet cameras.

The glasses are targeted at people who might be constantly in meetings, giving presentations, and traveling to countries where different languages are spoken.

Your photos don't have to go home, but they can't stay here.

Instead of writing long build logs or recording traditional vlogs, my co-founder and I wanted to try something different. We're documenting our startup journey by turning it into an AI-generated anime series. Not for fiction. For real startup moments. Episode 2 follows our cold outreach journey: Finding an ICP Testing different niches Sending DMs Getting ignored Learning what works (and what doesn't) We're treating this as an experiment to see whether AI-generated storytelling can make the process of building a startup more engaging than the usual "build in public" content. The goal isn't perfect animation. It's authentic documentation—with AI as the creative medium. We're still figuring it out, improving every episode, and learning as we go. Would love to hear what fellow builders and developers think about this approach. Could AI-powered anime become a new way to document products, startups, and open-source projects? Feedback is always welcome. 🚀

Johannes Heidecke’s departure comes as OpenAI tries to further integrate its research and safety teams.

Phia, the shopping startup founded by Bill Gates’ daughter, Phoebe, and her friend, Sophia Kianni, is under fire for a practice known as “cookie stuffing,” which helped the product receive commissions and credit for sales it did not actually generate, per a Bloomberg investigation.

You ship all the time. A fix here, a new setting there, a feature you spent a whole weekend on. And your users mostly don't notice. That gap is expensive. When people can't see a product moving, it feels abandoned, even when you're shipping every week. They churn a little faster, they email asking for things you built a month ago, and all the momentum you're actually creating stays invisible. The fix is boring and old: a changelog. But not a changelog rotting in a Notion doc nobody opens. One that shows up inside your app , where users already are. Here's the approach I settled on. The idea: a widget, not just a page A "what's new" widget is a small button or badge in your UI. Click it, and a panel slides out with your latest updates. Users see it in the flow of using your product, not on some /changelog page they'd never visit. You really want three things: An in-app widget users actually see. A public page and RSS feed you can link from emails and docs. A way to write updates in plain language and publish in a click. The one-tag version I ended up building a tool for this (honest disclosure below), but the integration is the part worth showing, because it's the pattern any changelog widget should follow: <!-- Paste before </body> --> <script src= "https://cdn.patchlog.io/widget.js" data-project= "your-project-id" data-position= "bottom-right" async ></script> One script tag. No SDK, no npm install, no framework coupling. It behaves the same in React, Vue, Rails, or a plain HTML page. Two implementation details matter, whether you build one of these yourself or evaluate an existing one: Render it in a Shadow DOM. A changelog widget should not inherit or leak styles. If it uses the host page's global CSS, it will look broken on half the sites it lands on. Shadow DOM isolates it completely. Fail silently. A marketing widget must never break the host app. If the network call fails, it should quietly do nothing. What to actually write in it The tool is the easy part. T