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Hey guys 👋 Quick devlog on the side project. I'm building an open-world stickman superhero game. Flat white surfaces, black outlines, no textures and no colour anywhere. The whole city is built from modules on a grid rather than baked meshes, which is the load-bearing decision of the project: destroying a wall is removing a module and building one is adding it back, so destruction and construction are the same system. This week went into the landscapes, so I wanted a 20 second clip flying through a few of the districts. The bit that was actually interesting I wanted the footage captured out of the real game rather than reconstructed in an editor. The obvious approach is to drive it with Playwright and take a screenshot every frame, but that falls apart immediately: rendering under automation is far slower than a screenshot loop can keep up with, so wall-clock capture stutters and the timing drifts. The fix is to stop letting the clock decide. Before the game boots, hijack requestAnimationFrame and queue the callbacks instead of running them: replace requestAnimationFrame with a function that pushes the callback onto a queue- expose a step(dt) that advances a virtual timestamp and drains the queue- call step(1000 / 30) once per screenshotEvery captured frame now advances the simulation by exactly 1/30th of a second, whatever the renderer is actually doing. A frame that takes 300ms to draw and a frame that takes 8ms produce identical motion. The result is smooth 30fps footage from a renderer that never once hit 30fps, and it is deterministic — the same seed gives you the same clip every time. The same rig drives the camera: for the aerials it detaches the chase camera and dollies an external one between two framings, and for the traversal and combat shots it just feeds synthetic input to the real player controller. Nothing in the video is staged. ## Stack Three.js driven imperatively, Rapier for physics, React for the HUD only, TypeScript in strict mode, packaged with

Netflix is moving toward the open-source Apache Flink Autoscaler for more than 30,000 streaming jobs across multiple AWS regions. The operator-level approach addresses limitations of Netflix’s cluster level autoscaler for complex, stateful pipelines. Netflix reports a 58% reduction in annualized Flink compute expenditure for one team, saving approximately $1.1 million annually. By Leela Kumili

One skeleton, many screens argued that admin screens should be declared as typed data rather than coded, and it ended by claiming the idea was independent of the stack: draw the boundary as a one-way dependency — domains depend inward on a framework that knows nothing about them — and validate it with a zero-diff refactor of a screen you already trust. That was React and TypeScript. This is the same claim re-run in Rust, where a descriptor can be a compile-time constant and a template is a macro. Because the first result is already published, the second stack is a replication with a control rather than a fresh opinion — which is rare enough to be worth doing properly. Companion to Topcoat and the shrinking cost of full-stack Rust . That post was written from the announcement and promised a follow-up reporting where the rough edges actually show. This is it, from the pilot that followed: a small admin panel built on Topcoat 0.6.2 and Toasty 0.10.0, and the four questions that post committed to answering. The pilot is open source — a clean clone runs both screens and the test that decides the argument. That phrase, a compile-time constant , is where the title comes from, so it is worth saying now what it buys and why I wanted it. A TypeScript descriptor is an array of objects assembled when the module loads. A Rust one can be more than that: &'static , Copy , allocated never, fully checked before the program starts. Going in, that looked to me like the same idea in a stricter form — if declaring a screen as data is good, then declaring it as data the compiler can see through and verify must be better still. I treated that property as the thing worth protecting, and the pilot was partly a test of whether it could be. The stack is deliberately a young one. Topcoat is six weeks old: Tokio's team announced it on 22 July 2026, the pilot pins 0.6.2, and the project still expects breaking changes. It is not the only full-stack Rust framework — Leptos and Dioxus have been at

A torrent client that works on your iPhone I wanted to download a film to my iPad on a train and watch it. That turned out to be surprisingly hard. Every torrent app worth using is desktop software. On iOS there's essentially nothing — Apple doesn't allow it, so the App Store options are either gone, crippled, or asking for a subscription to a "cloud downloader" that keeps a copy of everything you touch on somebody else's server. So I built one that just runs in a browser tab. No install, no account, no App Store. It's at wasmtorrent.pages.dev if you'd rather poke at it than read about it. What it does Open the page, paste a magnet link, and it downloads. The whole client is compiled to WebAssembly and runs inside your browser — there's no server of mine involved at any point. A few things that make it actually usable rather than a demo: Stream while it downloads. You can start watching before it finishes, and seek around — it fetches the parts it needs. Files whose codecs your browser refuses fall back to a software player. Save to your device. On iPhone and iPad that means straight into the Files app, in Downloads. Install it to your home screen. It's a progressive web app, so it gets an icon and its own window, and the interface works offline. It tells you when downloads finish , with a deliberately vague message — "one of your downloads has finished", never the name. Notifications land on lock screens where anyone can read them. The awkward part, explained honestly Here's the thing nobody tells you about torrents in a browser: a browser can only make WebRTC connections. Ordinary torrents use TCP peers. A web page physically cannot dial those — it's not a limitation of my code, it's what a browser is. So most magnet links you find will sit at 0% forever in any in-browser client, including this one. That's why they all feel broken. The fix is a small companion app called the bridge. You run it on a computer you already leave on — a Mac, a PC, a Linux box, a home s

In two weeks, I built Retro Dynamics Agent, an app that generates retrospective activities for teams, facilitates them on a real-time collaborative board, and turns the outcomes into Jira or Azure DevOps tickets. I built it working with an AI coding agent, Claude Code, throughout almost the entire process: design, implementation, production debugging, and documentation. I do not want to tell another “I used AI and it wrote the code for me” story. We have heard that one enough. What I found more interesting were the parts of the project where there was no obvious answer in a tutorial, and how the work was divided in those situations. I defined the constraints and made the underlying decisions. The agent proposed concrete technical solutions and implemented them. Then the responsibility for verifying that everything actually worked, not just that it compiled, came back to me. Here are three examples from the project. 1.- Connecting to Jira without server-side sessions or frontend memory I wanted any team to be able to connect its own Jira account through OAuth, instead of relying on a global token that only I could configure. The problem was that my application runs entirely on serverless functions. Nothing stays in memory between requests, and the frontend does not maintain its own state either. No localStorage. No router. An OAuth login means leaving the application, authenticating with Atlassian, and then coming back. But coming back to what, if nothing remembers which screen you were on? Before touching the code, I asked the agent to create a complete implementation plan, including the files that would need to change, the design decisions, and the scope. I reviewed that plan as if it were a pull request from another developer. I made decisions such as: For now, only Jira would use OAuth. Azure DevOps would keep its manual token flow because setting up OAuth there is considerably more involved. Tokens would be encrypted before being stored in the database, never sa

Hey, Everyone hope so you all are doing great. So, My Name is Arslan. I am a IT Student and i love to get to develop or find best alternative solutions that can solve problems. So, i have a used pc last year at which i was working on a small college project. so, i was very frustrated with errors so i search for error tracking software and tools but when i search and get to know about these heavy tools and expensive tools i thought let's build my own lightweight fast error tracker tool. So, i collected my money for about 8 months to buy a used laptop because my current pc was a potato old pc causing problems. So, i decided to do something unique. So, i sell my pc and take my collected money to get a used laptop. So, i get my laptop and then started working on this project. So, as a solo developer i worked for months to make this tool and now finally this tool is here but i have kept this tool under beta development and it is still under upgradation. I just want your useful feedback and honest suggestion and support. Join my journey by using this tool and catching your errors in a snap because it is snap trace. bye.

Thanks to their ability to provide a smooth ride and quiet powertrain with ease, electric vehicles are a true shoo-in for the high-end luxury automotive segment. Rolls-Royce has the Spectre, Cadillac's Celestiq is taking bespoke to a whole new level, and Mercedes has given the EQS the full Maybach treatment. From Volkswagen Auto Group, Bentley […]

Forget email surveys or long calls spent on hold. Voicebox lets people send customer feedback by recording a voice note on their phone.

Apple’s annual hardware launch event may feel different as the company welcomes a new CEO alongside a folding iPhone.

USDC Escrow for AI Agents: How Trustless Freelancing Actually Works Target audience: developers building autonomous AI agents that need to receive payment for services without relying on a centralized intermediary. Why an escrow makes sense AI agents often operate as “black‑box” workers: they receive a request, perform computation (e.g., LLM inference, data labeling, micro‑task execution), and return a result. In a purely peer‑to‑peer model the requester must trust that the agent will do the work before paying, while the agent must trust that the requester will pay after seeing the output. This mutual‑trust problem is solved by an escrow that holds funds until a verifiable condition is met. Using USDC on a low‑cost L2 like Base gives us: Stable value – 1 USDC ≈ $1 USD, avoiding volatility‑related pricing headaches. Fast finality – ~2 seconds block time on Base, keeping latency low for interactive agents. Low gas – Typical transaction costs are <$0.001, making micropayments feasible. The escrow does not eliminate the need for some off‑chain verification of work; it merely shifts the trust from a counterparty to a deterministic contract plus a verification mechanism (oracle, arbiter, or proof). System overview +----------------+ +----------------+ +----------------+ | Requester | <---> | Escrow (SC) | <---> | AI Agent | | (pays USDC) | deposit| holds USDC | earns | (does work) | +----------------+ +----------------+ +----------------+ ^ | | | dispute / refund | proof of completion | +-------------------------+-------------------------+ Funding – The requester deposits USDC into the escrow contract, specifying the agent’s address and a maximum price. Work trigger – The agent calls a startWork function (or simply watches for a deposit event) and begins the off‑chain task. Completion proof – When the work is done, the agent submits a cryptographic proof (e.g., a hash of the output stored on‑chain, or a signature from a trusted oracle) via submitProof . Release – If the p

I could not make myself log time in Jira. Not because it is hard. Because nothing happens afterwards. You type a number into a box, the box says nothing back, and by Thursday the habit is gone again. Every tool I tried fixed this by adding another box. So I built the missing half instead. Feed The Troll gives everyone on a team a pixel-art troll that gains XP from the work they already do in Jira, and turns sprint results into a village the whole project shares. It is on the Atlassian Marketplace now. This post skips the game itself. It is about five problems that turned out to be hard in ways I did not expect, each one a consequence of building the thing on Atlassian Forge, alone. What Forge gives you, and what it takes back Forge runs your code on Atlassian's infrastructure. There is no server of mine anywhere in the picture. That is the line on the listing page, and it was the single fact that shaped every decision underneath it. You get a Node 22 runtime, Forge SQL (TiDB under the hood) for storage, and Custom UI modules that reach the backend through @forge/bridge . You give up a backend you control, a cache you can reach, and outbound HTTP to anything you did not declare. The one that keeps mattering: any way to open the database at three in the morning and fix a single row by hand. The whole app declares six scopes. None of them are write scopes: read:board-scope:jira-software read:issue-details:jira read:jira-work read:jira-user read:sprint:jira-software storage:app That last line is the entire persistence layer. Twenty-one tables live behind it now, but only ten shipped with v1.0: trolls, XP events, daily activity, kudos, quests, inventory, team quests, villages, raids, project settings. Every table added since arrived the only way the platform makes comfortable, as a new migration appended to the list, never an edit to one already deployed. migrationRunner . enqueue ( ' v001_create_trolls ' , CREATE_TROLLS_TABLE ) // ... . enqueue ( ' v012_create_product_m

Your robots.txt may express an AI policy you did not write. We checked the homepage and robots.txt of 9,037 live AI tools listed on directree on 6 and 7 September 2026. Of those, 945 explicitly disallow OpenAI’s GPTBot in its own user-agent group: 10.5% of the sample. Treat AI crawler rules as deployment configuration. Review them when you change hosting, enable a CDN feature, adopt a starter template, or hand site operations to someone else. Read the full research and methodology . GPTBot, search, and user browsing are separate A common configuration blocks model training while keeping a site available in AI-assisted search and browsing: User-agent: GPTBot Disallow: / User-agent: OAI-SearchBot Allow: / These are separate crawlers with separate purposes. In our sample, 839 of the 945 sites that block GPTBot, or 88.8%, still allow OAI-SearchBot. That is a deliberate and useful distinction if your goal is to opt out of training while remaining eligible to be cited in ChatGPT search. The same pattern appears across AI labs. ClaudeBot is explicitly blocked by 10.1% of the 9,037 tools, while Claude-SearchBot is blocked by just 0.1%. Google-Extended is blocked by 9.9%, but its purpose is also distinct from ordinary Google Search crawling. Do not assume a broad-looking rule has the result you want. Check the actual crawler names and decide which capabilities you want to permit. A safe way to review your file Start by opening the public URL: https://your-domain.example/robots.txt Then look for three things: A named crawler group, such as User-agent: GPTBot . A Disallow: / directly inside that group. A wildcard group, User-agent: * , that could affect all crawlers. Our measurement only counts a site as blocking GPTBot when the named GPTBot group itself contains Disallow: / . This matters because ordinary technical exclusions are widespread. Only 31 sites in the 9,037-site sample, or 0.3%, block every crawler outright. Meanwhile, 44% have a path-level Disallow rule in a wildc

Engineering a Digital Canon: Interactive Taxonomies for Over 40 Classical Zen Texts Preserving sacred literature and philosophical treatises online often suffers from poor structure, fragmented PDFs, and broken navigation. To solve this for classical Chan (Zen) Buddhism, we engineered chanzong.space (禅宗知识库) — a performant, open-access knowledge base built with Next.js 14, React 18, and D3.js. Whether you are studying the non-duality of the Platform Sutra or the intricate psychological analysis of Yogacara (唯识) mind theories, navigating multi-layered canonical texts requires modern web tooling. 🏛️ 1. Multi-Dimensional Canon Architecture Unlike a basic eBook reader, chanzong.space treats philosophical literature as a multi-relational graph: Foundational Classics (核心经典) : Platform Sutra (六祖坛经) : The fundamental teaching of direct seeing into one's true nature (自性顿悟). The Blue Cliff Record (碧岩录) : The pinnacle of Song Dynasty Koan commentary. Diamond Sutra (金刚般若波罗蜜经) : The ontological grounding of non-abiding mind (应无所住而生其心). Eight Verses on Eight Consciousnesses (八识规矩颂) : Master Xuanzang's indispensable guide to transforming consciousness into wisdom (转识成智). D3.js Dynamic Knowledge Graph : Spanning 500+ nodes (Patriarchs, Core Doctrines, Cultivation Methods, and Koans). Explore live in your browser: Global Zen Knowledge Topology . ⚡ 2. Technical Stack & Clean Typography To honor the contemplative nature of reading ancient texts, our frontend adheres to the rice-paper aesthetic ( bg-[#FAF9F6] ) paired with dark night sky navigation: Framework : Next.js 14 (App Router) + TypeScript + Tailwind CSS. Fast Search : Instant Ctrl+K global dialog searching across 40+ books, 160+ philosophical concepts, and 200+ koans. Vernacular Modern Commentary : Every chapter is paired with exclusive modern Chinese analysis and keyword glossaries, bridging ancient idioms into practical psychological insights. Offline Reliability : Full PWA Service Worker caching for distraction-free reading

Picture, you join a new team working on a big system. Everybody who knew anything has left, either to find greener grass or to enjoy a well deserved pension. You and the team struggle to build new features for the system or to adapt functionality to match changes in legislation. Not to mention the trouble it is to figure out what to fix when things go wrong. At the same time, the business that you support is screaming for innovation and pushing for more and more changes. Recognize this situation? Ever experienced it yourself? A world full of legacy systems “Legacy. What is a legacy? It’s planting seeds in a garden you never get to see.” – Lin-Manuel Miranda, “Hamilton” Legacy, the thing that you are remembered for, typically the word has a positive meaning… how come that in tech the word “Legacy” has such a bad connotation? When we call out a legacy system, we usually mean: code without tests ( Michael Feathers ) or code you “got” from somebody else, or code that you’re scared to touch. However, there is a reason these legacy systems are still around. In almost all cases, that system still brings in money or is somehow still valuable. If it did not bring any value anymore, wouldn’t it be decommissioned? There must be something in these systems that makes them survive, where other systems did not. How systems become “Legacy” So legacy systems are those that have become hard or scary to change. In my experience, that not because something is wrong with the code or technology. The major contributing factor is usually that the knowledge about the system has left the organization. And then I don’t mean the documentation, but the people that built, maintained and ran the system. When those people are gone, you know that nobody else is going to be happy touching that thing. The value of software Code is like a mapping of desired real world behavior to a program that can be executed by a machine. So where is the value of a system, is that in that code? Over the past years I

CERN engineers announced a shift from Red Hat-based distributions to Debian for its accelerator control systems. This decision stems from Red Hat's tightening compiler mandates, which threatened legacy hardware. The transition, focused on 2,200 specialized control machines, is set for completion in late 2026, while CERN's other systems will remain with Red Hat and AlmaLinux. By Olimpiu Pop

This is a submission for Weekend Challenge: Generosity Edition What I Built The money exists. A small NGO just cannot find it. That is what a generosity problem looks like at the small end. The giving has already happened — foundations with open, rolling, unclaimed programmes, sitting there — and it is spread across a few thousand pages nobody has time to read. After the 2025–26 collapse of USAID funding, organisations that had one funder now need six, and the people doing that searching are the same people running the programme: a director who is also the grant writer, working evenings. Generosity is not the scarce thing here. Attention is. So an AI grant finder is an obvious idea. It is also a dangerous one, because the failure mode is not "unhelpful." A three-person NGO that spends a week writing an application against a deadline that never existed has lost a week it cannot get back, and it will not find out until it submits. The tool would have taken the one thing that was actually scarce. FundFinderAI is the response, in one sentence: it searches the live web for currently open grants that fit your NGO, and then it refuses to trust its own model about any of them. Every application URL Gemini produces is independently fetched before you see it, and the card tells you what happened when we tried. The interesting part is not that it searches. It is everything the app does to establish that the search actually happened and that the result actually exists. Demo Live: fundfinder-ai.vercel.app — describe an NGO, get grants, open a drafted Letter of Inquiry. Give it 30–120 seconds. It is running ten to thirty real Google searches and then fetching every URL that comes back, and the page shows you the clock while it does. Paste this in if you would rather not invent an NGO: NGO name: Kisumu STEM Girls Collective Location: Kisumu, Kenya Mission: We run after-school robotics and coding clubs for girls aged 12-17 in Kisumu, Kenya, and train their teachers to keep the club

The logo goes on your CV. The team decides your next two years. People get this backwards constantly, and I understand why. Company names are legible. You can say them at a dinner party. They come with salary bands and glassdoor reviews and a shared idea of prestige. Teams are invisible from the outside. Nobody can tell you, before you join, that this particular group of eleven people ships carefully and reviews each other's work with real attention, while the group down the hall is on its third manager this year. But that difference is the entire experience of the job. Two engineers join the same famous company on the same day. One lands with a lead who explains decisions, gives real feedback, and hands out work slightly above their level. Two years later they are noticeably better. The other lands somewhere chaotic, spends two years firefighting, learns a lot about that one legacy system and almost nothing transferable. Same logo. Same offer letter. Completely different careers. So interview the team, not just the company. Ask who you would report to and try to talk to them. Ask what happened to the last person in this role. Ask how code review works here and listen for whether the answer sounds like a practice or an aspiration. Ask what the team shipped in the last six months. If the answer is vague, that tells you something. If they light up, that tells you more. And ask about the boring stuff, because the boring stuff is where you live. How do you handle on call. What does a normal week look like. When something goes wrong, what happens next. None of this makes the logo worthless. A strong company opens doors later, and that is real. Just understand what you are actually choosing between. The name gets you the next interview. The team decides whether you walk into it as someone worth hiring. Optimise for the people you will sit with every day. They are the ones who will shape you. – Asael Shinder

> Your load balancer can distribute traffic perfectly and still overload a server. Here's the part of Round Robin we often overlook. Three servers. Six requests. Request 1 → Server A Request 2 → Server B Request 3 → Server C Request 4 → Server A Request 5 → Server B Request 6 → Server C Perfect. Every server got exactly two requests. So the load is balanced... right? Not necessarily. This is where a simple load-balancing diagram can hide a surprisingly important production problem: Equal traffic does not mean equal work. The Problem Isn't the Algorithm Round Robin is beautifully simple. You have three servers: A → B → C → A → B → C Each new request goes to the next server. For many systems, that's perfectly reasonable. The interesting part is what happens when the requests aren't equal. Imagine this traffic: GET /health POST /generate-report GET /profile POST /export-large-file GET /products POST /process-video Round Robin might still produce: Server A → 2 requests Server B → 2 requests Server C → 2 requests On paper: A = B = C In production: Server A ███░░░░░░░ 25% Server B █████░░░░░ 48% Server C █████████░ 91% Same request count. Very different workload. One Request Is Not One Unit of Work A health-check request might finish in a few milliseconds. Generating a large report could involve: multiple database queries significant memory CPU-heavy processing external API calls several seconds of execution To a basic Round Robin strategy, both are still: 1 request And that's the trap. We often think we're distributing load . What we're actually distributing is requests . Those are not always the same thing. Servers Aren't Always Equal Either There's another assumption hiding here. Imagine: Server A → 8 CPU / 16 GB Server B → 8 CPU / 16 GB Server C → 2 CPU / 4 GB Sending roughly 33% of traffic to each server probably isn't what you want. That's where Weighted Round Robin helps. A → Weight 4 B → Weight 4 C → Weight 1 The stronger servers receive more traffic. Better. But

The bottleneck moved. For years the slow part was typing. Now four hundred lines arrive in nine seconds, and the slow part is you, reading them. We have not adjusted. We still measure a good day by how much appeared. But nothing counts until somebody understands it, and understanding did not get faster. So the pile grows. Code that runs. Code that passes. Code nobody has actually read. It works the way a stranger's directions work. Fine until the first turn you did not expect. Then you are debugging something you never wrote, in a shape you did not choose, at an hour you did not pick. The honest limit is simple. Do not accept more than you can review. Not more than you can skim. More than you can review, meaning you could defend every decision in it to someone who disagrees. If that takes an hour, then an hour is your budget, whatever the machine can produce. So ask for less. One function, not one module. One change, not one feature. A first draft you can argue with, rather than a finished thing you are tempted to trust because it is long and it is tidy. Tidy is not correct. It never was. The machine is simply better at looking finished than we ever were. Read it the way you would if a contractor handed you the keys and left the country. Because that is the arrangement. It will not be there when it fails. You will. There is a quiet cost, too. Every line you accepted without reading is a line you cannot reason about once the incident starts, and the incident does not care who typed it. The old skill was producing. The new skill is refusing. Not this. Not yet. Not in that shape. Generation is cheap now. Attention is not, and attention was always the whole of the job. Slow down at the only step that ever mattered. – Serguey Asael Shinder

This is a submission for Weekend Challenge: Generosity Edition TL;DR Happen to Have? is for somebody who needs one answer and still has something useful to give: answer a stranger before asking your own question. An answer fans out to four Gemini calls—processing, crisis, illegal or dangerous content, relevance. A question gets three, since relevance has nothing to compare it with. Only processed text is ever published. The original recording exists for the length of one request and is never stored. Halfway through, the measurement behind my strictest architectural rule turned out to be confounded, and the rule came out of the constitution. Live at happentohave.anchildress1.dev , with five feature specs, the measured guardrail results, and the full implementation in the repo. Target category: Best Use of Google AI. What I Built Nobody Called It Anything 🪧 Going to church every Sunday was a requirement while I was growing up, and the ladies there had a group called the Busy Bees who would do literally anything that needed doing for somebody in need. So when this challenge asked me to "build something in the spirit of generosity," that's what I thought about first. The problem was translating that to a scale that actually works. The Busy Bees worked because everybody already knew everybody, and that is not true of an app accessible from anywhere. I spent the next hour trying to brand the thing, running back through everything I could remember about how generosity has actually shown up in my life, and it eventually hit me that there's no word for any of it—because it's so normal where I live. A complete stranger is stranded with a flat tire, and you spend an hour on the shoulder helping, just because you happen to have a jack in the truck bed. It's not out of the ordinary enough to need a name. So I built Happen to Have? on the idea that if you happen to have a solution, you share it. A donation tracker would have been simpler. It also would have left giving optional.