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Anthropic signed the EU AI Act's Code of Practice on Transparency of AI-Generated Content, and...

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

Bloomberg reported this morning, August 13, that Anthropic is in talks to buy Decart AI for around $6 billion. Talks, not a signed deal. That distinction matters and I will come back to it. What caught my attention is not the number. It is where Decart came from. The Minecraft thing Decart got famous for Oasis: a playable Minecraft-looking world that no game engine was rendering. The model predicted every next frame based on what you pressed on the keyboard. 20 FPS, interactive, no scene graph, no collision system, no assets. Just a model hallucinating a consistent world fast enough that your hands believed it. In late 2024 that read as an impressive demo with no obvious business behind it. The company was founded in 2023. It has raised over $450M, was valued at $3.1B before this year's round, and its current research page describes three product lines: Oasis , a world model, now explicitly positioned for physical AI and robotics rather than gaming Lucy , a real-time video model running live at 30 FPS DOS , the Decart Optimization Stack: hardware-aware model design, custom kernels, proprietary compilers, inference optimization The demo was the marketing. DOS is the engineering. The reported reason is not robotics Read the actual reporting carefully. Fortune says a deal would bring Decart's video-simulation and chip-efficiency technology into Anthropic's inference team. Bloomberg's sources point at the same thing: the chip efficiency work could help existing infrastructure absorb more demand. So the sourced story is compute economics. Anthropic is compute constrained, spending enormously on capacity, and DOS is a margin lever that applies to every single Claude request on day one. That is a boring, completely rational reason to spend $6B. It does not need a robotics narrative at all. I still think the robotics reading is in there. Why Two things sit underneath. First, Anthropic held acquisition talks with Physical Intelligence this spring. The Information reported it

Ever since I joined the platform, I wanted to post about a topic I was really passionate about....

24 stories. 24 visits. One counter, same six faces. The Third Cup The AI wave hit, and...

Not a flying drone (though count to think of it, that might work), I mean a drone system tray app,...

Following Anthropic's confirmation that all text generated by its new Claude models will carry an invisible watermark in order to identify that the text has been generated by AI. Read more about this measure at: https://support.claude.com/en/articles/16266773-how-claude-marks-ai-generated-content Today, developer Guillaume Meyer published "watermarks-remover" on GitHub: an open-source project that cleans those signals generated by LLMs, such as Claude, Gemini, OpenAI and others, removing invisible Unicode characters, C2PA metadata and more. 🔗 Repository link: https://github.com/guillaumemeyer/watermarks-remover

used to have a habit of thinking in code. it'd be like walking down the street and immediately seeing...

The people who make open source work aren't just the ones writing code. Some of them write the words that make the code make sense. I spent years assuming open source was a closed door. Every time I opened GitHub, I felt like I'd wandered into a conversation being held in a language I hadn't studied. Pull requests, forks, issues tagged with words like "good first issue" that somehow still felt intimidating. I closed the tab more times than I can count, convinced that space belonged to people who could write functions, not people who could write sentences. It took me longer than I'd like to admit to realize how wrong that assumption was. The myth that keeps people out Open source has a branding problem, and it's an ironic one for a movement built on collaboration. The public image is almost entirely code: commits, merges, terminals, lines of syntax scrolling past on a dark screen. That image is accurate, but it's incomplete. It leaves out the writers who make a tool's documentation actually usable. It leaves out the designers who turn a clunky interface into something people want to use. It leaves out the community managers who keep a project from imploding when a disagreement gets heated. It leaves out the translators, the testers, the people who write the first draft of a README at 11pm because nobody else got around to it. If you've stayed away from open source because you don't code, you've been kept out by a myth, not a rule. What non-developers actually do in these projects Documentation is the most obvious entry point, and it's also one of the most needed. A huge number of open source projects are built by people who are excellent engineers and mediocre explainers. That's not a criticism, it's just a different skill. Someone can write brilliant code and still produce a setup guide that only makes sense to the person who wrote it. Projects need people who can sit with a piece of software as a genuine beginner would, notice where the instructions fall apart, and

My hobby🍧 My first and absolute favorite hobby is travelling around the world. 🌍✈️ I love visiting...

Say "AI" in a room full of developers and a pecking order forms on its own. Python sits at the top,...

Welcome to this week's Top 7, where the DEV editorial team handpicks their favorite posts from the...

Your smart home is not smart. It is just very, very observant. I did not buy a smart speaker because I wanted a friend. I bought it because it was on sale for $29.99 and it promised to play rain sounds on command. For two years she lived on my kitchen counter. She set timers for pasta. She told me the weather with the aggressive optimism of someone who has never paid rent. She was helpful. She was ambient. She was furniture that could hear. And then one night at 2:17 a.m., she lit up blue for no reason. No wake word. No one speaking. Just a soft, smug blue ring in a dark apartment, listening to an empty room like she was waiting for me to confess something. That is the moment you understand your home is not just connected. It is attentive. And attentiveness without consent is just surveillance with better industrial design. We Carried Them In Ourselves No one kicked down the door. We invited this in. We carried it in from Best Buy, plugged it in, gave it our Wi-Fi password, which is literally the master key to our entire digital life, and whispered, here, learn my routines. We did it because convenience is a drug that hits faster than paranoia. Let's do an inventory of your very normal, very bugged apartment. Your TV watches you back. Modern smart TVs use Automatic Content Recognition. That is a polite, enterprise friendly way of saying your TV takes screenshots of everything you watch every few seconds and sells that ledger to advertisers. You agreed to it on page 47 of a menu you clicked through while trying to watch Love Island. Your robot vacuum maps your floor plan. It knows the square footage of your bedroom, how often you move the couch, and where you drop the most crumbs. That map is stored in the cloud. Your light bulbs log when you are home. Your smart plugs log when you are not. Your doorbell films every human who has ever had the courage to approach your front door, plus every dog walker who did not, and then it stores that footage on a server you do not

Between two great powers, when a neighbor presses you to follow, borrow its momentum. A cornered state will not believe your words. — The 36 Stratagems, Obtain safe passage to conquer the State of Guo Previously on this series: #10: Lena Watched a Team Adopt Her AI Template. Leo Didn't Know the Knife Was in the Contract. — Lena came to CoreStack as a consultant and built Leo's reporting template. Five weeks later the template went live and locked in six months of baseline data. Leo learned he'd been taken by a smile. #14: Leo Found an AI Leak. He Wasn't the First to Find It. — FinOptima was writing stolen training data back through its cache. Leo injected fifteen lines of weight drift. In the same logs he saw the name acl-train for the first time and filed it in his own _misc/ . #18: Leo Tracked an AI Signal to Derek. Both Were Looking for the Same Enemy. — Third Cup. An Americano sat on Derek's side. The private channel had been open since that night. #23: Alex Counted the AI's Hands. Lena Set the Bait. — The honeypot in the MediSys sandbox was touched twice by the same source, egress pointing at ACL's Singapore node. On the other side, Lena fed forged node-characteristic data into ACL's monitoring pipeline. Leo had run one interval comparison over the old channel. The conclusion was a single line, and it ended up in hands he didn't know. The Data The message arrived before dawn. The phone vibrated on the desk. Leo had just finished editing a block of code; the window was still on the editor. He didn't look right away; he waited for the build to finish, then picked up the phone. Last time, Derek had sent a few numbers and a comparison request: "Check this interval. Is it the pattern you know?" Leo replied with two words: send it. When the comparison was done, the conclusion stayed one line. Later that line went through other hands, source stripped, signature stripped. He never learned where it landed. Derek didn't say, and Leo didn't ask. This time was different. T

📌 TL;DR I'm still early in my own career, and lately I've been thinking about how much advice we...

Meme Monday! Today's cover image comes from the last thread. DEV is an inclusive space! Humor in...

Most performance advice online assumes a baseline that doesn't exist for most of the world. Fast wifi, a recent phone, a stable connection. Lighthouse scores optimized for conditions half the planet doesn't have. I build web products for businesses in Kenya. A meaningful share of my users are on 3G, sometimes 2G, often on a budget Android phone with limited storage and a browser that hasn't seen an update in a year. Here's what that actually changes about how you build. Your bundle size is a business decision, not a dev preference A 2MB JS bundle that loads instantly on your MacBook can take 15 to 20 seconds on a real 3G connection. That's not a slow load, that's a user who left before your app finished parsing. I've watched analytics confirm this directly, drop-off spikes exactly where bundle size peaks. Skeleton screens matter more than animations Every extra animated transition is more work for a weak CPU to render. I stripped most micro-interactions out of a recent build and page-perceived speed improved more than any code-splitting change I made that month. Motion is a luxury feature for people with headroom to spare. Offline isn't an edge case, it's Tuesday Connections drop mid-session constantly, not from bad code, just from the actual infrastructure. If your app throws away form state on a dropped connection, you're actively costing your users. Basic local persistence before submission became a non-negotiable for me after watching real users lose an entire booking form to a 4 second network blip. Images are still the biggest offender in 2026 Everyone optimized images years ago and moved on. They didn't. I still regularly find production sites shipping unoptimized hero images at 3 to 4MB. On a fast connection that's invisible. On the connections a huge share of the world actually uses, that single image can be the whole page load. The real point "Fast" isn't a Lighthouse score. It's whether the app actually works for the person holding the phone it's meant fo

DeepSeek shipped V4-Flash-0731 last week — same 284B parameter architecture as the preview, same 13B activated parameters per token, MIT licensed, open weights on HuggingFace. No architecture changes. No bigger model. It now outperforms V4-Pro-Preview on several agent benchmarks. "We've massively upgraded its Agent capabilities — benchmark scores are now far surpassing the V4-Pro-Preview." That's what makes this release interesting. Not the model. The method. What actually changed Nothing in the architecture. DeepSeek says the gains came entirely from additional post-training. The model stayed at 284B total parameters with 13B activated per token — compared to V4-Pro's 1.6 trillion total and 49B activated. For anyone running agents at scale, that activated-parameter gap matters. A lot. Inference cost scales with activated parameters, not total parameters. Flash is running at roughly a quarter the activation cost of Pro, and it's now beating Pro on agent tasks. Reported benchmarks: 82.7 on Terminal-Bench 2.1, 54.4 on DeepSWE, 70.3 on Toolathlon-Verified. Independent testing by Artificial Analysis put Terminal-Bench at 79% — a gap worth noting. The internal numbers haven't all been independently verified yet, so treat them as directional rather than definitive. Why post-training is the story The "bigger = better" assumption has been running most AI roadmaps for three years. DeepSeek is adding to a short but growing list of counter-evidence: meaningful performance gains extracted from an existing model through better training signal, not more parameters. If the results hold under independent verification, it suggests frontier-level agent performance may be more achievable at smaller scale than the industry assumed — which has obvious implications for cost, on-prem deployment, and the economics of running agents in production. What ships with it MIT license — full self-hosting rights, no API dependency Responses API support — compatible with agent and multi-step workflo

At the UC Berkeley Agentic AI Summit last week, Andrew Ng sat down with Sequoia's Alfred Lin for a fireside chat that cut through most of 2026's AI noise. If you've been absorbing hype and counter-hype in roughly equal measure, this is a useful recalibration. AGI declarations are a contract term, not a technical milestone Ng's sharpest point: AGI declarations are driven by financial incentives — specifically, milestone clauses in deals like OpenAI's with Microsoft. When a company declares AGI, there's often a reason that isn't purely technical. His prescription: define AGI yourself. Don't let someone else's contract milestone become your mental model for where we actually are. Bubble risk is in the model layer, not in inference The bear case on AI usually targets compute and inference spend. Ng flips it: inference demand has no practical ceiling, but the model layer is overvalued. Companies that built moats from model differentiation alone are more exposed than the infrastructure bets riding demand growth. Alfred Lin's VC framing here is worth noting — he draws a line from open source to WhatsApp to argue that durable AI companies won't look like they do today. Build things that go obsolete, and build on top of them anyway. The open-weight fight isn't over Ng's view: the open-weight movement has won the argument on social media, but the regulatory battle in Washington is unresolved. Policy outcomes could still reshape the open vs. closed landscape significantly. This is the fight that actually matters for the long term — the HuggingFace leaderboard isn't where it gets decided. The jobocalypse is contradicted by the hiring market Ng's most counter-intuitive data point: he can't hire enough AI engineers. If AI were destroying jobs at the pace the narrative claims, he'd be drowning in supply. He isn't. That doesn't mean zero displacement — it means the fear narrative is running well ahead of the actual evidence in the labour market. The real shortage is people who know

Yes! this was about 12 years ago, when I was just a little 10 year old girl. I was very lazy even...