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When many people are first learning how to code, they find it difficult and when they ask, "How can I get better at coding?" they are usually told, "With time and practise it will get easier." . But instead of using so much time and effort to get better at coding using hard programming languages, what if coding could get better for you instead of you getting better at coding ? Well, this is the reason that inspired me to make a new programming language called Sofya . Sofya is designed to be so simple (even simpler than Python ) so that anyone can find programming easy and fun. But to prove my point, let us use an example. Let us say that we want to make a program that will show us all the numbers from 1 to 20 . Let us compare how this program will look like in Python and Sofya . The Python Program for number in range ( 1 , 21 ): print ( number ) The Sofya Program Variable Number is 0 Do this { Increase Variable[Number] by 1 Write Variable[Number] on the screen } Until Variable[Number] = 20 From this example, we can see that the Sofya program is easier than the Python program, for a beginner in programming, for the following reasons: Sofya uses simpler commands than Python: It is easier for a beginner in programming to remember the command Do this...Until Variable[Number] = 20 , which is used for making a loop, as compared to the command for number in range(1, 21): . Sofya's syntax is closer to English as compared to Python's syntax: When we are making a loop variable in Sofya, we simply say Variable Number is 0 rather than saying number in range(1, 21) in Python. The Sofya program can easily be understood by anyone even if it is the first time that they are seeing it as compared to Python: A beginner in programming can easily tell that in the line where we say Increase Variable[Number] by 1 , that we are increasing the value of the variable called 'Number' by 1 as compared to the line number in range(1, 21) in Python. If you would like to try out Sofya for yourself

In mid-July, my Google clicks in my home market (Israel) dropped by almost half. Buyer-intent queries that used to bring steady leads just evaporated from Search Console. While I was staring at GSC dashboards trying to figure out what broke, I finally did the thing I should have done months earlier: I stopped looking at dashboards and started reading raw server logs. What I found there was a parallel universe. Google Search was sending me less than ever — but AI systems were reading my site constantly . Not "someday this will matter" constantly. Right-now constantly: an AI assistant was fetching a page of mine roughly every 26 minutes, around the clock, because a real human had just asked it a question. So I built a small log analyzer and let it run. Here's what 34 days of complete Caddy logs from a small business site (about 70 real human visitors a day) actually look like. The numbers All counts are HTTP 200 responses only (more on why below), over 34 days: Bot Requests Per day What it is bingbot 5,444 158.2 Bing's index — which feeds ChatGPT ChatGPT-User 1,388 40.3 Live fetch while a human asks ChatGPT Googlebot 1,233 35.8 Classic Google crawl GPTBot 547 15.9 OpenAI training crawler Claude-User 519 15.1 Live fetch while a human asks Claude OAI-SearchBot 281 8.2 ChatGPT search indexing Applebot 268 7.8 Apple (Siri / Apple Intelligence) ClaudeBot 214 6.2 Anthropic training crawler Amazonbot 136 4.0 Amazon (Alexa & co.) PerplexityBot 103 3.0 Perplexity indexing Three things in that table genuinely surprised me. ChatGPT-User outreads Googlebot. 40.3 fetches a day versus 35.8. This isn't a crawler building an index for later — ChatGPT-User is the user-agent OpenAI sends when a human is mid-conversation and ChatGPT decides to pull a live page to answer them. On my site, that now happens more often than Googlebot visits. For a tiny business site in a niche market, I did not expect that. Bing crawls 4.4x harder than Google. 158 requests a day versus 36. Nobody optimizes

A known-bad regex is useful evidence, but putting it directly in the test process can hang the runner before the timeout assertion fires. The boundary I am using: run each adversarial case in a fresh worker thread or child process let the parent own a hard timeout and terminate the child keep semantic-parity fixtures separate from timing guards require the safer replacement to pass both suites record the timeout class and bounded elapsed time as evidence Browser workers have the same trap: startup time should not consume the execution budget, and output limits matter alongside time limits. Disclosure: I maintain MonoTools. I recently tightened its browser-local Regex Tester around a 300 ms post-startup Worker budget, named groups, replacement previews, and regression cases: try the bounded tester What does your team treat as a deterministic CI failure receipt for ReDoS: an exit code, a timeout class, an elapsed-time range, or something else?

Hey everyone! 🌸 So, recently I was thinking about something and thought I’d share it with you all. 😄...

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...