标签：#us

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.

Managed Data Lake: A Guide for 2027 Apache Iceberg is the standard table format for production data lakes in 2027. Every major engine reads and writes it natively. The catalog ecosystem standardized on REST. You own your data on commodity storage with no lock-in. But Iceberg deliberately separates the table format from the system that keeps tables healthy. It gives you the primitives for maintenance — rewrite_data_files , expire_snapshots , remove_orphan_files , rewrite_manifests — but not the intelligence to decide when, how, and in what order to run them. Without that operational layer, every Iceberg table degrades over time: small files accumulate, snapshots bloat metadata, sort orders drift from query patterns, orphan files inflate storage costs, and query performance decays silently until something breaks visibly. This operational gap is the central challenge of running a data lake at production scale. Netflix built four internal services to address it — Autotune for compaction strategy selection, Polaris for catalog management, janitors for garbage collection, Metacat for cross-service observability — each staffed by dedicated teams over multiple years. Google engineered automatic compaction and garbage collection directly into BigLake , so their managed Iceberg tables stay healthy regardless of write volume or query pattern changes. In 2027, you do not need to replicate that investment. This guide covers what "managed" actually means for a data lake, the degradation mechanics that make it necessary, the control plane architecture that solves it, and the practical paths to getting there — whether you are running 50 tables or 5,000. Why Lakes Degrade — The Mechanics The degradation pattern is predictable and present in nearly every Iceberg lake running for more than three months without dedicated maintenance. Understanding these mechanics is necessary regardless of which management approach you choose. The Small-File Problem Every streaming writer — Flink, Spar

Flipkart's quick-commerce venture is delivering 1.1 million to 1.2 million orders a day, nearly triple its November volume.

We often focus on what someone says, but in the realm of clinical psychology, how they say it is often more revealing. Subtle changes in speech—a slight tremor (jitter), a slowing tempo, or a flattened pitch—can be early indicators of depression or anxiety long before a user explicitly voices their distress. In this tutorial, we are building Psycho-Acoustic , a high-performance monitoring tool that leverages the HuBERT model , HuggingFace Transformers , and Librosa to quantify emotional states from non-verbal acoustic features. Whether you're interested in speech sentiment analysis , mental health AI , or advanced audio processing , this guide covers the end-to-face-mic implementation. The Architecture of Sound 🏗️ To accurately detect mental health indicators, we can't just look at text. We need a multimodal approach that combines raw signal processing with deep learning representations. graph TD A[Raw Audio Input .wav] --> B[Librosa Preprocessing] B --> C{Feature Extraction} C --> D[Traditional Features: Jitter, Shimmer, Pitch] C --> E[Deep Learning: HuBERT Embeddings] D --> F[Feature Fusion Layer] E --> F F --> G[Classification Head: Anxiety/Depression/Neutral] G --> H[Quantified Mental Health Score] H --> I[Deployment via ONNX Runtime] Prerequisites To follow this advanced guide, you’ll need: Python 3.9+ Tech Stack : transformers , librosa , torch , onnxruntime A basic understanding of digital signal processing (DSP). Step 1: Extracting Non-Verbal Acoustic Features 🌊 Before hitting the neural network, we need to extract "Psycho-Acoustic" features. Depression is often characterized by "speech prosody" changes—specifically reduced pitch range and slower speaking rates. import librosa import numpy as np def extract_prosodic_features ( audio_path ): y , sr = librosa . load ( audio_path , sr = 16000 ) # 1. Fundamental Frequency (F0) - Pitch f0 , voiced_flag , voiced_probs = librosa . pyin ( y , fmin = librosa . note_to_hz ( ' C2 ' ), fmax = librosa . note_to_hz ( ' C7

In the HBS Foundry program, AI avatars provide feedback during practice pitches and board meetings.

Contractor rates are falling, contract durations are shrinking, and the freelance labor market is flooding with senior talent — and the problem isn't the market, it's that contractors keep letting companies define the terms. A senior backend engineer — eight years of production experience, solid Go and Kubernetes chops, three reference clients — recently told a recruiter she was looking for £650 a day. The recruiter called back two days later to say the client had found someone at £450. The counter-offer was presented as good news. That's the state of independent tech work right now. Not a crisis, not a correction — something more mundane and more insidious: a slow, structural re-anchoring of what contractor labor is worth, driven less by any single market force than by the compound effect of layoff volumes, budget caution, and platform-mediated price visibility. Rates are going down. Engagements are getting shorter. And the freelancers accepting this are — not entirely without blame — helping it stick. Here's the uncomfortable claim: the ongoing compression of tech contractor rates is as much a self-inflicted wound as a market inevitability. The conditions that caused it are real. But the capitulation that maintains it is a choice. How We Got Here: The Supply Side Exploded The overrecruitment of 2021 and 2022 didn't just hurt the permanent hiring market when the hangover hit. Software developer jobs saw the biggest boom and bust in vacancies of any sector. No other segment saw hiring more than double in 2022, and hiring has since fallen faster in software development than anywhere else. The engineers who got caught in that bust didn't all disappear. Many turned to contracting. More than 100,000 people were laid off in the technology industry in 2024 alone, and at least some of them are not heading back into exclusively full-time work. LinkedIn's Services Marketplace, launched in 2021 to catch exactly this cohort, saw 10 million people create pages on the platform,

On the latest episode of Equity, we wonder why the DOJ is investigating startup board seats.

A chatbot can sound convincing for five minutes without remembering anything. Then you mention the job interview you were stressed about last week, the name of your dog, or a small detail from a late-night conversation. It replies like none of it happened. That is where most "AI companion" demos fall apart. I am building Local Waifu , a desktop AI companion that runs on the user's own Mac or PC. One of the rules I set early was simple: conversations and memories should stay on the machine. No central chat database. No server that needs to be online for the character to remember someone. The rule sounds clean. Building it was not. Saving chats is not memory The first version of memory was the obvious one: save messages. That gives you history, which is useful, but it does not solve recall. A long chat history grows fast. Sending all of it back to a local language model on every message is slow, expensive in context space, and usually makes the reply worse. The model does not need to see every conversation from the last six months. It needs the few pieces that matter right now. If someone says, "I have to take Luna to the vet tomorrow," the character should be able to find that Luna is their dog. It should not need to reread hundreds of unrelated messages about work, movies, and dinner plans to get there. So I treated chat history and long-term memory as different things. Chat history is the recent conversation. It gives the model immediate context. Long-term memory is a small collection of facts, moments, preferences, and relationship details that may matter later. Those memories need to be searchable by meaning, not only by exact words. The memory data stays in SQLite I wanted the app to work without a hosted database, so the storage layer is local SQLite. Each character gets their own data. Chats, memories, extracted entities, and relationships are stored locally on the device. If a user creates two characters, one character does not quietly inherit the other one's

I remember having a Spirograph as a kid and being obsessed with it. Its geometric patterns are hypnotic and gorgeous. I also love generative music composition. So bringing those two things together in a browser tab, I'm hooked. Musical Spirograph is a relatively simple concept. A set of points dash around the screen according to […]