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When a coding agent changes a systems program, a source diff is only the beginning of the question. The more useful question is: what exact machine-facing artifacts would this semantic change produce, and can another process independently verify that relationship? That is one of the research problems we are exploring in SEMAPRAX , an Apache-2.0 agent-native systems programming language built at Wavect GmbH. SEMAPRAX is currently v0.2 pre-alpha experimental research software . It is not production-ready. The narrow mechanism described here is useful precisely because its claims are bounded. From a patch to target projections SEMAPRAX has a read-only command: semaprax target-evidence <file> <patch.spatch> The command takes a verified source snapshot and a semantic patch. It independently rebuilds both the base program and the patched candidate, then derives several deterministic compiler-owned projections: semantic Graph JSON an explicit capability manifest Native C11 source a structurally validated WebAssembly Core module For every projection, the report records a domain-separated digest and byte length. It also classifies the projection as changed or unchanged. That sounds simple, but the distinction matters. A source edit can leave one projection unchanged while altering another. A documentation-level identity change, a capability change, and a runtime-behavior change should not all be flattened into the same “some bytes changed” signal. The target report therefore binds the proposed semantic change to the compiler artifacts it actually affects. Why deterministic output is the prerequisite Evidence over compiler output is only useful when the output is reproducible. SEMAPRAX treats source formatting, semantic graph data, diagnostics, semantic patches, and target artifacts as deterministic projections. The same admitted input must produce the same bytes. Otherwise a digest says little: a second verifier could not distinguish a meaningful change from nondeterministic

We built a plugin for the GitHub Accessibility Scanner to make sure your alt text is actually accessible. Here's how it works. The post Your alt text passes automated checks. That doesn’t mean it’s any good. appeared first on The GitHub Blog .

Proteger datos que deben seguir siendo secretos dentro de diez años es un problema de hoy : un adversario puede capturar tu tráfico cifrado ahora y descifrarlo cuando exista la capacidad cuántica ( harvest now, decrypt later ). Quipu es una librería libre de cifrado híbrido post-cuántico para datos en reposo: combina criptografía clásica probada con la nueva, de modo que solo se rompe si ambas caen a la vez. Rust puro, y por qué Quipu nació apuntando a varios lenguajes: un núcleo en Rust con una C ABI encima y bindings para Python, Node y Go. Funcionaba, pero la lección fue clara: mantener una interfaz de C estable más cuatro bindings, cada uno con su empaquetado y sus pruebas de interoperabilidad, era complejidad que no pagaba para el objetivo real —proteger datos en reposo— y ampliaba la superficie de ataque con unsafe que no queríamos. Hoy Quipu es Rust puro : memoria segura, sin garbage collector , sin unsafe de primera parte . Y para quien no programa en Rust, se distribuye como rueda nativa de Python (vía PyO3) — que es la superficie que el cliente que no es de Rust de verdad necesita. Una sola base de código, una sola cosa que auditar. Es la misma filosofía que guía el resto: donde hay buena criptografía, se reutiliza; la simplicidad es una decisión de seguridad, no una comodidad. Instalación cargo add quipu # Rust pip install quipu-crypto # Python (rueda nativa, PyO3) Cifrar y descifrar en Python import quipu # Simétrico con contraseña blob = quipu . encrypt_stream ( b " datos sensibles " , " mi-passphrase " ) assert quipu . decrypt_stream ( blob , " mi-passphrase " ) == b " datos sensibles " # Post-cuántico para un destinatario pub , sec = quipu . generate_keypair () # X25519 + ML-KEM-1024 c = quipu . encode_to_recipient ( b " secreto " , pub ) assert quipu . decode_as_recipient ( c , sec ) == b " secreto " Qué hay debajo Cifrado: XChaCha20-Poly1305 (AEAD autenticado). Derivación de claves: Argon2id (resistente a fuerza bruta) + HKDF. Post-cuántico: X25519

The FTC and Zillow have announced a settlement that ends the case alleging that a 2025 "partnership" between Zillow and Redfin violated antitrust laws. The FTC had alleged Zillow agreed to pay Redfin to syndicate its listings, while Redfin would end its own advertising contracts and promise not to compete with Zillow for multifamily listings. […]

Robotaxis are expanding. So is the fight over the rules governing them. In New York, Gov. Kathy Hochul withdrew a proposal earlier this year that would have opened the door to driverless robotaxis outside New York City after taxi drivers, unions, and state lawmakers opposed it. Six months later, commercial driverless service remains illegal in […]

General Intuition, the startup building a foundation model that trains generalized AI agents how to move through space and time, is in talks to raise at a $6 billion pre-money valuation from new investors including Valor Ventures, Point72 Ventures, Seven Seven Six.

I wanted to disagree with 'AI made me a worse reviewer' from Michael Amachree (@dev_michael) ....

Inside the frontier lab’s push to bring AI agents from software engineers to the masses.

Today on Decoder, I’m talking with Tim Cadogan, the CEO of GoFundMe. You know GoFundMe — it’s a major fundraising platform where you can donate to help people with everything from medical expenses to little league uniforms to starting a new business. Tim took over as CEO in March 2020 — a fascinating and chaotic […]

Your Agent Doesn't Have a Reasoning Problem, It Has a Memory Problem Anannya Roy Chowdhury Anannya Roy Chowdhury Anannya Roy Chowdhury Follow Aug 24 Your Agent Doesn't Have a Reasoning Problem, It Has a Memory Problem # ai # agents # architecture # programming 11 reactions 1 comment 9 min read

Meme Monday! Today's cover image comes from the last thread . DEV is an inclusive space! Humor in poor taste will be downvoted by mods.

Asgaut Mjølne Söderbom and Ola Hast discuss the evolution of their software engineering practices past continuous deployment and pair engineering. The conversation continues where it left off in the previous episode and focuses on the experiments in adopting Claude Code and the reasons why they consider it good for everything else, but not coding. By Asgaut Mjølne Söderbom, Ola Hast

China's intelligent driving is moving fast from highway Navigate on Autopilot (NOA) into the far harder world of urban NOA. The first leap moved hands-free driving out of the closed expressway and into real city streets. The second leap, the one now underway, is rewriting how the car actually thinks. 1. The Rules Era: An "Exam-Cramming" Trap for City NOA Highway NOA was relatively simple to crack. The road is closed, the geometry is consistent, the actors are mostly cars, and a mature rule-based stack can deliver a comfortable product. Urban NOA is a different beast. The system has to handle traffic lights, unprotected turns, pedestrians, e-bikes, food-delivery scooters running red lights, and a hundred flavors of "I-don't-care-about-the-rules" intersection behavior. The complexity grows exponentially. The earliest urban NOA architectures followed one mantra: cover every possible scenario with hand-written rules . Engineers enumerated traffic situations and wrote thousands of if-then-else statements: when to start moving after a light turns green, how much to slow when cut off, how to plan a trajectory for an unprotected left turn. On the highway this approach can pass a test. In the city it falls apart for a single structural reason. China's urban road users, almost by definition, do not follow the rules. Electric scooters drive the wrong way. Pedestrians cross mid-block. Food-delivery riders weave between cars. Drivers in congested intersections play chicken in the kind of "zipper merge" etiquette nobody teaches. These are the long-tail scenarios that no rule library can fully enumerate. As one early test team admitted about their own city NOA: "It feels like exam cramming — it scores beautifully on the routes we pre-mapped, and the moment it hits an unrecorded scenario, it hesitates, behaves awkwardly, and then asks the driver to take over." That "偏科" (one-trick) experience is precisely why urban NOA penetration in China only reached about 15.1% in 2025 , and rem

The deepfake epidemic in schools is affecting more than students. Four teachers tell WIRED about becoming targets of sexualized, AI-generated content—and how difficult it was to find accountability.

🚀 From FlipaClip to SitePoint: The Full Story of Kehinde Owolabi How a Nigerian teenager built a professional game engine with borrowed laptops, offline W3Schools, and pure determination. 🎮 Play the Game Try Limn Engine Live — Space Shooter Demo See what 4 years of determination built. This space shooter runs at 60 FPS on a Tecno Pop 4 with 1GB RAM. 📖 Introduction Every developer has an origin story. Some start with a fancy computer and a computer science degree. Others start with a flipbook app and a sister who trusted them with her phone. My name is Kehinde Owolabi . I'm 18 years old (born December 4, 2007), and I live in Lagos, Nigeria. I'm currently in PC103 at BYU Pathway, and I'm a member of The Church of Jesus Christ of Latter-day Saints. I built a 94/100 professional game engine called Limn Engine. It runs at 60 FPS on a Toshiba with 4GB RAM. It was published on SitePoint and ranked #3 among 2D JavaScript game engines. Nobody knew it was developed on a Chromebook, a borrowed Thinkpad (behind my sister's back), and a Toshiba that "hung like hell." That was the secret I kept for months. But that's only one part of this story. This is the full story of how I went from a button phone to a 94/100 game engine, from FlipaClip to SitePoint, from a boy who failed physics to a developer who built something that runs on a Tecno Pop 4. The one-line summary: "I'm Kehinde Owolabi, an 18-year-old developer from Lagos, Nigeria who went from FlipaClip to building a 94/100 game engine on borrowed laptops — and got published on SitePoint." 🎮🚀 🎨 The Beginning: FlipaClip and the Spark of Creativity Before I was a developer, I was an animator. I used FlipaClip — a simple animation app on mobile — to create flipbook-style animations. I loved bringing characters to life, frame by frame. I would spend hours drawing, tweaking, and watching my creations move. That creative spark stayed with me. I wanted to create interactive experiences. I wanted to build games. But I didn't know how.

When Satoshi Nakamoto designed Bitcoin's Proof-of-Work consensus, the goal was simple: one CPU, one vote. What actually happened was very different. ASIC farms centralized mining into industrial warehouses, and the "one CPU" vision became "one warehouse, one vote." Proof-of-Stake was supposed to fix this by replacing energy expenditure with economic stake. Instead, it created a different problem: the rich get richer, forever. RustChain's Proof-of-Antiquity (PoA) takes a radically different approach. Instead of rewarding who has the most money or the newest hardware, it rewards who has kept the oldest hardware running the longest. The core insight is elegant: time is the one resource that can't be bought, faked, or manufactured. Either your hardware has been alive for twenty years, or it hasn't. This article does a deep technical comparison of Proof-of-Antiquity and Proof-of-Stake, drawing on the actual RustChain source code to explain how each consensus mechanism handles decentralization, Sybil resistance, economic fairness, and network security. The Fundamental Philosophies Proof-of-Stake: Wealth as Security Proof-of-Stake systems — Ethereum 2.0, Cardano, Algorand, Solana (with its Delegated PoS variant) — all share a common assumption: the more tokens you stake, the more committed you are to network security. If you act maliciously, your stake gets slashed. The economic logic is straightforward: attackers would need to acquire a majority of the token supply, which would be prohibitively expensive. The problem is what happens after someone acquires that stake. In PoS, staking rewards compound. A validator with 10x the stake of a small holder earns 10x the rewards, which they can reinvest into more stake. Over time, validator concentration increases. On Ethereum, Lido + Coinbase + Binance + Kraken collectively control over 50% of staked ETH. The "rich get richer" dynamic isn't a bug — it's a mathematical inevitability of proportional rewards based on capital. Proof-

Engineers who only understand the technology, and never the business it serves, hit a ceiling early. The best ones develop commercial awareness — a real sense of how value is created, funded, and sold. Two days at 21BY72 Season 4, one of Bharat's leading startup summits — eighty-five ventures on the floor and live pitches in front of six hundred investors — was a concentrated lesson in exactly that, and I wrote about it in this reflection . Technology is a means; the business is the point It's easy, as an engineer, to treat the product as the whole world and the commercial side as someone else's problem. Sitting in a room where eighty-five ventures pitched to investors makes the truth obvious: the technology is a means to a business end, and understanding that end makes you a better engineer, not a distracted one. Watching founders pitch — being judged not on how clever the build was but on whether it solved a real problem people would pay for — reframes how you think about your own work. It pushes you to ask "who is this for and why does it matter" before "how do I build it." What the summit floor teaches an engineer Investors buy problems solved, not features built. The pitches that landed were about a real need and a credible path to meeting it — a discipline that improves engineering priorities directly. Commercial context sharpens technical decisions. When you understand the business constraints — cost, speed to market, who the customer actually is — you make better trade-offs in the architecture, not worse ones. Exposure recalibrates ambition. Being around people building real ventures at scale resets your sense of what's possible and what "serious" looks like. The takeaway The most rounded engineers I've come to admire pair technical depth with genuine commercial awareness. Spending two days inside a major startup summit, watching how businesses are pitched, funded, and built, was a deliberate investment in the half of the picture that a pure engineering educ

Most published authors have, without their knowledge or consent, contributed to the development of the same AI tools that threaten to undermine their livelihoods. That seems illegal, right?

Python is a perfect language for Agile development, where requirements might change on the go. Especially if you are in a startup business, you will need to experiment and change things fast. However, Python is an interpreted language, and in certain situations you might need faster performance than what an interpreted language can provide. A common practice in these cases is using python-to-binary bindings, where the binary code is built with Rust, C++, or Go. In this article, I will explore bindings to Rust-based code. How do the bindings work The idea behind bindings is that you create a module with functions of a specific domain in a language that compiles to binary, and build it as a C-compatible dynamic library ( .so on Linux, .dylib on macOS, .dll on Windows). Then a Python wrapper is built as a Python package and installed together with the dynamic library, allowing you to import and use functions that pass control to the corresponding functions in the dynamic library. On some occasions, classes can be used instead of functions. If any parameters are complex, they must be serialized in the wrapper and passed to the dynamic library as a JSON string or as a set of individual primitive parameters. An experiment with benchmarks To try this Python-Rust communication, I vibe coded an experiment that reads a large CSV file and builds a new one with duplicates stripped out based on specified column indexes. In my test case, it was a 3 MB CSV file with data about European NGOs for the donation platform I am building, where I wanted to remove the NGOs that don't have website URLs listed. As benchmarked, the file was processed 4.3x faster with the Rust binding than directly with Python. Here is the repo to get a first glimpse into the code and structure. What is there to know about Rust A few things about Rust: Rust packages are built with Cargo, which is the equivalent of pip, virtualenv, and setuptools combined. A single package is called a crate, and it can be publi

Docking stations expand what your laptop can do, and I’ve been testing the best of the best to see which you should buy.