Every data grid demo looks incredible with twenty rows. The columns line up. The hover state is tasteful. The checkbox has confidence. Someone scrolls three inches and everyone quietly agrees that software has advanced. Then the real product arrives. Fifty thousand rows. Twenty columns. Editable money. A custom status cell. Filters. Sorting. Horizontal scrolling. A user who pastes something suspicious from Excel. A product manager asking whether the total row can stay pinned while the server is slow. That is when a table stops being a table and starts becoming infrastructure. So we built a benchmark. Not a perfect benchmark. Those do not exist. A useful one. What we measured The fixture is intentionally boring: 50,000 deterministic rows 20 fixed-width columns 1,200 by 600 pixel viewport two editable columns sorting filtering virtual scrolling production bundles fresh browser contexts raw samples committed to GitHub No network requests. No paid-only feature tricks. No images. No grouping. No heroic demo code designed to make one library look blessed by destiny. The report measures: JS gzip : reachable JavaScript after gzip Ready median : navigation until the grid adapter mounts and two animation frames pass Scroll settle : one scripted vertical and horizontal jump plus animation frames Mounted cells : body cells in the DOM after the scroll Interaction health : heap, long tasks, estimated FPS, dropped frames Live benchmark: https://vitashev.github.io/react-data-grid-benchmark/ Source and raw samples: https://github.com/Vitashev/react-data-grid-benchmark The part most benchmarks get wrong Not every grid exposes the same surface. For example, MUI X Data Grid Community uses 100-row pagination for this workload. That is a valid product boundary, but it is not the same as continuously virtualizing 50,000 rows. So the ranked tables include only compatible continuous-scroll libraries. MUI remains in the fixture and raw data, but not in the leaderboard. That makes the benchma

CodeTrace-AI v1.0.1 — Stop Reading Code. Start Understanding It. Every developer has experienced this. You clone a repository, open it, and suddenly you're staring at thousands of files. You spend hours answering questions like: Where is this function called? Which files depend on this module? What happens if I modify this class? Is this code even used anymore? Traditional tools like grep , IDE search, or AI chat assistants can help you find code. They don't help you understand the architecture . That's why I built CodeTrace-AI . What is CodeTrace-AI? CodeTrace-AI is an AI-powered code intelligence tool that transforms your repository into a searchable structural knowledge graph. Instead of treating your project as plain text, it understands your codebase structurally by analyzing: 📂 Folder hierarchy 📄 Files 🏛 Classes ⚙ Functions 📦 Imports 🔗 Function calls 🌐 Cross-file dependencies Think of it as having an AI Software Architect that understands your entire repository. 🚀 What's New in v1.0.1 This release focuses on speed, privacy, and understanding large repositories. 🕸 Interactive Code Graph One of the biggest additions is the interactive repository graph. Instead of reading hundreds of files manually, you can visualize relationships between: Folders Files Classes Functions Imports Function calls Understanding a new project becomes dramatically easier. ⚡ SHA-256 Delta Sync Engine One feature I'm particularly proud of is the new Incremental Indexing Engine. Most code intelligence tools rebuild their entire index every time. CodeTrace-AI doesn't. It computes a SHA-256 fingerprint for every tracked file and detects: ✅ Modified files ➕ Newly added files ❌ Deleted files Only those files are: Re-parsed Re-embedded Re-added to the knowledge graph Everything else is skipped. This makes repeated indexing dramatically faster, especially for large repositories where only a few files change between runs. Under the hood The sync engine includes: SHA-256 fingerprinting Parallel f

DeepSeek has previewed its V4 model family, led by a 1.6 trillion-parameter flagship, and made a one-million-token context window the default across all its services. The weights are downloadable and self-hostable, putting frontier-scale long context in reach of smaller labs and individuals without per-token payment to a closed provider. Key facts What: DeepSeek previewed two free-to-download V4 models that can read a million tokens at once, no longer as a premium add-on but as the standard setting. When: 2026-06-29 Primary source: read the source A large language model has no persistent memory. Each time it answers, it re-reads everything in front of it — your question, the conversation so far, any documents you pasted — and that pile of text is the context. The context window is the hard ceiling on how much it can hold at once. For years that ceiling was a few thousand words, then tens of thousands. Pushing it to a million has been possible but expensive, usually sold as a special, pricey tier. DeepSeek's move is to make a million the everyday default. The family comes in two sizes. V4-Pro is the big one — 1.6 trillion parameters in total, but only about 49 billion of them switch on for any given word. That design is called a mixture of experts : instead of running the entire brain for every token, the model routes each piece of text to a small relevant subset of specialists, so it stays affordable to run despite its enormous size. V4-Flash is the smaller, cheaper, faster sibling, meant for everyday chat and quick edits, and DeepSeek says it keeps up with Pro on simpler agent tasks. Making a million-token window affordable comes down to how the model handles its KV cache — the running set of notes it stores about every previous word, which grows steadily the longer the conversation gets. At a million tokens those notes become a mountain of memory, and the model normally has to consult every note for every new word it writes. DeepSeek's approach, which they call sp

A note before we start: this is about the machine, not the money. I'm not going to show you returns, positions, or a single "this strategy made X%." Partly because that's a regulatory minefield, and partly because the returns aren't the interesting part — the engineering is. If you came for a get-rich screenshot, this isn't that. If you came to see how one person ships production infrastructure with an AI, pull up a chair. The thing I built Over the last few months I built, with an AI coding agent as my pair-programmer, a fleet of ~35 automated trading bots. They run across five equity markets plus crypto. Each one is a long-running service. They share a single database, post to a live dashboard, fire alerts to my phone, and — the part that took the longest — they're built to survive restarts, reconcile against reality, and refuse to do anything stupid. I'm one person. I am not a team. The "team" is me plus an AI in a terminal, working the way you'd work with a very fast, very literal junior engineer who never gets tired and occasionally needs to be talked out of a bad idea. Here's how it's put together, and the handful of lessons that cost me the most to learn. The architecture, in one breath One Postgres database is the brain — every trade, signal, and piece of state lives there. Around it sit ~35 containerized bots, each isolated (its own tables, its own config, its own identity), orchestrated with Docker Compose. A Streamlit dashboard reads the database and renders the whole fleet — open positions, P&L curves, health. A notification layer pushes Telegram alerts on every meaningful event. Schema changes go through migrations so a new bot is never born with a stale database shape. Each bot is the same skeleton wearing a different hat: a signal module (the strategy logic), a trader that turns signals into orders, a storage layer that persists everything, a runner loop on a schedule. Strategies are swappable. The infra underneath them is identical. That sameness is

I’ve always thought building a mobile app required climbing a massive learning curve just to get a basic environment set up. To test that theory, I tried building my very first Android app using Google AI Studio . Five minutes later, I had a working prototype. The coolest part about this isn't just the speed: it’s that anyone can do this. The traditional barriers to building software are disappearing, making it incredibly easy to just start creating. I recorded the whole 5-minute process here if you want to see what it looks like in practice: What's in the video Prompting AI Studio to build a native Android app from scratch Progressive Webapp (PWA) vs Android Native App in 2026: feature comparison Sideloading the app onto an Android device via USB-C cable. No Play Store required What happens when the AI gets something wrong? Fixing bugs in a vibe coded app

Far-UVC could help rid future cars of odors.

The AI neocloud provider, which specializes in hosting open source models, last raised at a $3.3 billion valuation in early 2025.

Tariffs and anti-China policies killed this little Volvo in the United States.

The nine-year-old scooter and bike-share company has said it needs the funds to help pay down around $1 billion in liabilities.

We started using Jira to manage our internal development workflow. At first it worked fine, but once we outgrew the free tier, the cost became hard to justify. At $15 per user per month, we were suddenly looking at a bill that did not match how we actually used the product. What we Built We created WannaTrack, a lightweight project management tool designed for small dev teams that do not need enterprise complexity. The goal was not to recreate Jira. It was to remove everything we did not use. Key ideas : minimal agile board with no clutter or heavy configuration simple issue tracking flow fast interface for daily development work minimal setup and no onboarding overhead Migration from Jira One of the biggest concerns was switching tools without breaking our workflow. So we built a Jira import tool that lets you migrate existing tickets into WannaTrack without manual effort. This allowed us to switch internally without downtime. Where it is now We now use WannaTrack daily for our own development workflow and are opening it up to other teams who feel the same pain with traditional tools. If you are a small dev team, indie hacker, or startup looking for a simpler issue tracker without overhead, you can check it out here: https://wannatrack.com

If you've ever tried to build a simple command/telemetry protocol between a PC and a fleet of SDR receivers, sensors, or embedded devices, you know the usual options aren't great: Roll your own binary format — fast, but you end up writing and maintaining custom serialization code for every device type, and debugging mismatched structs across machines is painful. Protobuf / FlatBuffers — robust, but require you to define your message layout in a schema file upfront, run a code generator as part of your build, and commit to a fixed structure. Adding a new device type or a new parameter means editing the schema, regenerating, recompiling everything. JSON over the wire — easy to debug, but heavy for anything real-time or bandwidth-constrained. I ran into this while working on a multi-SDR receiver system and ended up writing MessageFrame — a small C++17 library that lets you build structured messages dynamically, without any schema files or code generation. The basic idea Instead of defining a struct for each device type, you address each parameter with two strings — a device name and a parameter name — and the library handles the rest: // One message, multiple devices, assembled at runtime msgframe :: MessageFrame msg ( MSG_TELEMETRY , TYPE_PERIODIC , src = 1 , tgt = 2 ); msg . add ( "sdr_1" , "rx_gain" , VALUE ( 30.0 )); msg . add ( "sdr_1" , "center_freq" , VALUE ( 915'000'000.0 )); msg . add ( "sdr_1" , "sample_rate" , VALUE ( 2'000'000.0 )); msg . add ( "sdr_2" , "rx_gain" , VALUE ( 25.0 )); msg . add ( "sdr_2" , "lock_status" , VALUE ( true )); msg . add ( "psu_1" , "voltage" , VALUE ( 12.04 )); msg . add ( "psu_1" , "temp_c" , VALUE ( 47.3 )); // Attach raw IQ data alongside the parameters std :: vector < uint8_t > iq_buffer = { 0x01 , 0x02 , 0x03 , 0x04 }; msg . add_attachment ( "raw_iq" , std :: move ( iq_buffer )); // Serialize into a buffer, send over whatever transport you use std :: vector < uint8_t > out ; msg . serialize ( out ); send_udp ( out . data (),

As of May 2026, more than 80% of the code Anthropic ships is written by Claude, not by its human engineers. The company disclosed the figure in an essay called When AI builds itself , with coverage from Tom's Hardware and VentureBeat . Key facts What: Anthropic says more than 80 percent of the code it ships is now written by its own model, Claude, and the more interesting numbers are about judgment. When: 2026-06-23 Primary source: read the source Two years ago this share sat in the low single digits. The shift accelerated after Anthropic released Claude Code , a tool that lets the model read an entire codebase, make changes, run tests, and fix what breaks without human help. The human role has flipped: engineers used to author the code while the machine assisted; now the machine authors the code and engineers review, approve, reject, and steer. Anthropic reports its typical engineer ships roughly eight times as much code per quarter as a few years ago — not because people type faster, but because they spend their day reviewing the model's output instead of writing from scratch. Think of it as a newsroom where a tireless junior writer drafts every article and senior editors only sign off. Volume goes way up. But the 80% figure is less impressive than it sounds: a draft that a human must check, fix, and approve is not the same as a writer you can leave unsupervised. Most of those lines still pass through a person. On its own, this number measures effort the machine saves, not work it can be trusted to do without oversight. The results buried deeper in the essay matter more, because they concern taste rather than volume. Anthropic ran a recurring test where the model chooses the best next step in a research project, then compared its choices against its own scientists. Late last year the model was roughly a coin flip against the humans. By spring 2026, an unreleased internal model was picking the better direction clearly more often than its own researchers. Choosing w

https://runtimewire.com/article/weave-robotics-isaac-1-home-...

Are you worried your AI chatbot is trying to build a bomb or leak personal information about you? There’s a website for that.

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