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If you've spent any time building or working with APIs, you've probably realized that the terminal is...

TL;DR Centred a sidebar brand mark in the collapsed rail (open-source starter kit) — pure CSS, no JS. A CRM app got a "daily cockpit" dashboard (hot leads + overdue follow-ups) plus a full favicon/PWA icon set. An analytics product's ingest pipeline learned to handle messy uploads — files with no date column and no numeric measure — and a nasty metrics bug got squashed. A spread day across three repos. Quick tour. Centring a collapsed sidebar logo (CSS only) Kickoff , my open-source Laravel starter kit, had a small visual snag: when the sidebar collapses to a narrow rail, the header switches to a column — but the brand mark sat off-centre. The content area is ~72px, yet the logo kept its width and a leftover space-x margin, nudging it left of the nav icons. No JavaScript needed. Make the logo and toggle full-width, centre their content, and zero the leftover child margins when collapsed: [ data-flux-sidebar ][ data-collapsed ] .sidebar-header .app-logo { width : 100% ; justify-content : center ; padding-inline : 0 ; } /* kill the leftover space-x margin pushing it off-centre */ [ data-flux-sidebar ][ data-collapsed ] .sidebar-header .app-logo > * { margin : 0 ; } Lesson: when a flex container changes direction, old horizontal margins don't disappear — they just push things in the new axis. Tag the element, scope the override to the collapsed state, done. A CRM "daily cockpit" A CRM app I work on got a dashboard rebuild: instead of a generic landing screen, the first thing you see is what needs action today — hot leads and overdue follow-ups. The cockpit framing matters more than the widgets: surface the work, don't make people hunt for it. Also shipped a full favicon/PWA icon set and a branded responsive landing page, with feature tests so the brand pass didn't quietly break routing. Ingest that survives real-world files The bigger chunk of the day went into an analytics/dashboard product's ingest pipeline. Real uploads are messy, so the pipeline now copes with the

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The three-layer model works. Part 1 of this series is the invoice incident that proved it. Part 2 is...

trpc has a scheduled workflow called "Lock Issues & PRs." Its own scorecard shows it failing on almost every run. It is still scheduled, still running, still red. trpc ships excellent software, which is exactly the point: if a project this careful has a workflow that has been red for ages, the rest of us almost certainly do too. It is not a one-off. drizzle-orm has one ("Unpublish release"). cal.com has one ("PR Update"). I scanned 35 popular open-source repos and the same thing kept turning up: a scheduled workflow that fails on nearly every run, quietly, for a long time. Why nobody notices GitHub does email you when a scheduled workflow fails. So how do these survive? Two reasons. First, those emails are routine. You get them for flaky reruns and transient blips too, so you filter them out. Second, a workflow that is always red stops reading as a signal. It is just how that row looks now. I did exactly this on my own project. GitHub emailed me that a workflow had failed. The next day it emailed again. I saw it, told myself I would fix it tomorrow, and promptly forgot. It was my nightly database backup, quietly broken the whole time, and I only caught it when a failure-rate number crept up where I would notice. An always-red workflow is not free It burns minutes every run to produce nothing but a red X. Worse, it trains you to ignore the failure that actually matters: the day a real one lands in the same inbox you have learned to skim past. How to find yours Open your Actions tab and look at the scheduled workflows, the cron-triggered ones nobody watches. If the last several runs are all red, you found one. From the CLI: gh run list --workflow = "Lock Issues & PRs" --status = failure What to do about it Two honest options: fix it, or if the workflow is genuinely abandoned, turn it off. Do not leave it scheduled and red. gh workflow disable "Lock Issues & PRs" Or drop the schedule trigger from the workflow file if it should not run on a timer at all. A disabled work

Two days ago GitHub emailed me to say one of my workflows had failed. The next day it emailed me again. I saw it, told myself I would fix it tomorrow, and promptly forgot. It was my nightly database backup, quietly broken the whole time, and I only caught it because a failure-rate number nudged up. A failed run at least gets you an email. A slow run gets you nothing. GitHub never pings you when CI quietly takes twice as long, runs the whole suite twice per PR, or rebuilds dependencies from scratch every time. That waste compounds where no one looks. Here are the usual culprits, each with the exact fix. When I scanned 35 popular open-source repos, not one had a fully clean config. 32 of 35 had no concurrency control, 33 of 35 had no job timeouts, and 22 of 35 ran the full suite twice on every PR. If projects this polished leave minutes on the table, the rest of us definitely do. Your suite runs twice on every PR Trigger a workflow on both push and pull_request and, for a branch in the same repo, opening a PR fires both. You just paid for two identical runs. This one is pure waste and it can roughly halve your PR-related minutes. Trigger on pull_request , and keep push for your default branch: on : push : branches : [ main ] pull_request : Old runs don't cancel when you push again Push a fix 30 seconds after the first push and, with no concurrency group, both runs go to completion. The first is dead weight, and it is holding a slot in your queue while it finishes. This hides even when you do have a group: astro has a concurrency group on one workflow but left off cancel-in-progress , which our scan estimates leaves roughly 1,850 minutes a month on the table. Add a group keyed on the branch, with cancel-in-progress , so a new push supersedes the old run: concurrency : group : ${{ github.workflow }}-${{ github.ref }} cancel-in-progress : true Every run reinstalls dependencies from scratch No cache means every run re-downloads and rebuilds your dependencies. On a typical

AI makes it easier to build the wrong thing with confidence. That is the part I think a lot of beginner builders and freelancers miss. The obvious story is that AI makes execution faster. That is true. I can ask an AI coding tool to explain an error, compare implementation options, inspect a project, write code, refactor a screen, generate a QA checklist, or help me pick up where I left off. That is a huge change. But speed is not the whole story. When the tool gets faster, your judgment becomes more important, not less. You have to decide what the project is allowed to become. You have to decide which tradeoffs are acceptable. You have to decide whether the output actually matches the user's job. You have to decide when the AI is solving the real problem and when it is decorating the wrong one. In my freelance work, AI changed the job from searching and stitching to directing, reviewing, and verifying. That sounds cleaner than it feels. Directing means you need to know what outcome you want. Reviewing means you need to notice when the answer is plausible but wrong. Verifying means you cannot treat a green checkmark, a pretty screen, or a confident explanation as proof that the app actually works. The beginner mistake is believing AI removes the need to think clearly. The better rule is this: AI removes some friction from execution, then hands you more responsibility for scope. The Faster Tool Still Needs A Smaller Job When I started using AI heavily for software work, the old research loop changed immediately. Before modern AI tools, a lot of software work meant digging through documentation, old forum posts, Stack Overflow answers, YouTube videos, outdated examples, and half-related blog posts until something clicked. You stitched pieces together and hoped the tutorial you found still matched the version of the framework you were using. Now you can ask the tool directly. That is better. It is also dangerous if you confuse a fast answer with a good product decision

There's a Yu-Gi-Oh game on PS1 where you can fuse two cards together. The result isn't random. There are rules. But you don't know the rules yet — you just know that two inputs produce a third thing that neither input was, and that the third thing surprises you even when it shouldn't. That's the hook. Not the surprise alone. The realization underneath the surprise that the system has depth. That there's a grammar to what's possible, and you can learn it. I've been building toward that feeling ever since. Jade Cocoon does the same thing with monsters — merge two creatures, watch the result carry both parents in its design. Dragon Quest Monsters runs on fusion too. Yu-Gi-Oh Forbidden Memories taught me that combination-as-discovery is its own mechanic, separate from any theme it wears. Everything Is Crab is the roguelike version: you absorb what you fight, you become it, you discover what you're becoming one encounter at a time. No Man's Sky showed me that procedural generation has finally caught up to what those PS1 games were reaching toward — creatures that feel like they emerged from a system rather than a designer's hand. The mechanic isn't genetics. Genetics is just the implementation I keep reaching for. What I'm actually trying to build is a machine that produces controlled emergence — outcomes that surprise you within a system deep enough to eventually master. Pure RNG is a slot machine. You can't get better at it. Pure determinism is a calculator. You can solve it and put it down. The games I keep returning to live between those poles: consistent enough to reward learning, deep enough to keep producing novelty. TurboShells was an attempt at this. Turtles whose bodies expressed their genomes at render time — shell radius, leg length, color emerging from a sequence. The faster ones bred. Over generations you watched the population drift. The system had rules. The outcomes still surprised you. SlimeGarden chose basic shapes deliberately. If the creature is simp

Rather than external partnerships, Uber may start leaning on its own robotaxis for driverless options.

If you're starting a web project, you're probably starting with a CSS reset, and for most of us, that means reaching for a trusted community solution - dropping it in and moving on. If you're building a design system, though, that habit may be working against you. The existing solutions The community reset ecosystem is genuinely good. Each tool approaches the browser compatibility problem from a slightly different angle. Some examples include: Eric Meyer's Reset is a classic: it zeros out margins, padding, and font sizes across every element, giving you a completely blank slate. It's minimal and predictable, which made it influential. Normalize.css smooths over inconsistencies while preserving the ones that are actually useful. sanitize.css and modern-normalize continue that evolution - incorporating contemporary best practices like box-sizing: border-box , improved form element handling, and accessibility-aware defaults. The problem isn't that any of these are bad. The problem is that they're all deliberately, necessarily generic. They can't know anything about your typeface, your color palette, your spacing scale, or how your interactive elements should behave. That's by design - they're tools for everyone, which means they're perfectly tailored for no one. The problem If you're building a design system, generic is exactly what you don't want your reset to be. The moment you drop in one of these resets and start building, you find yourself doing a second round of work. You apply your typeface to body . You reset margins on headings. You make form elements inherit fonts. You define focus styles. You're re-resetting - applying your design language on top of a layer that just cleared out the browser's defaults and replaced them with... more defaults you'll override. Worse, that duplication doesn't stay in one place. Every component you build either re-declares these foundational styles or silently assumes they're already set upstream. You end up with either redundanc

The startup, which runs a popular free AI leaderboard, launched its commercial service just last September.

Even if you only use WhatsApp sometimes, you might want to snag your username now to stop giving out your phone number.

Most developers expect to go through multiple interview rounds, coding assessments, or take-home assignments before getting hired. That wasn't my experience. I ended up working with the YouTuber I had admired for years without an interview, without an exam, and without even sending a resume. Here's how it happened. It Started Long Before the Opportunity I started freelancing when I was in Class 9. At first, it wasn't about building a career. I simply enjoyed creating websites and wanted to gain experience while earning some money. Over the years, I worked with different clients, solved different problems, and learned something from every project. Those freelance gigs taught me much more than writing code—they taught me how to communicate with clients, deliver on time, and take ownership of my work. The Opportunity A few months ago, one of my favorite YouTubers posted in his WhatsApp community that he was looking for someone to build a website. I happened to be a member of that group. As soon as I saw the message, I reached out and told him I could build it. Instead of spending time wondering whether I was "good enough," I decided to let my work answer that question. Building It in Under 24 Hours Once I received the project, I focused entirely on delivering it as quickly as possible without compromising quality. I completed the website in less than 24 hours. After reviewing it, he requested a few modifications. I implemented them immediately and delivered the updated version. At that point, I assumed the project was finished. The Unexpected Offer A few days later, he contacted me again. He had another web application that had been stuck because a previous developer couldn't complete it. He asked if I could take over. That conversation eventually turned into a job offer. No coding interview. No aptitude test. No technical assessment. Just trust built through delivering one project well. What I Learned Looking back, I don't think I got the job because I replied quickly

Every Sanity marketing site ends up with a page builder. An array of sections, an insert menu, a render loop that maps block._type to a component. You've built it. I've built it. We've all built the same thing. And every one of them ships with the same bug. You add a new section. You wire it into the schema. You add a renderer. You add a component. You add the type. And then — because there are five places to touch and you're a human — you forget one. The section renders blank in production. Or it never shows up in the insert menu. Or it fetches no fields because you missed the GROQ projection, so it renders as nothing at all. No error. No red. Just a hole on the page where a section should be. The annoying part isn't the bug. It's that you'll hit it again on the next project, in exactly the same way, because you rewrote the whole thing from scratch — again. The section tax Here's what "add a section" actually costs in a typical Sanity + Next.js page builder: Schema — a new *Section object type, registered in your schema index. GROQ — a new conditional in the page-builder projection so the block's fields actually come down. Component — the React component that renders it. Renderer map — an entry mapping _type → component. Types — the block variant in whatever union your frontend renders. Miss #2 and the block arrives empty. Miss #4 and it silently skips. Miss #5 and TypeScript shrugs because your union is hand-maintained and now lies. Three different failure modes, all of them quiet, all of them "works on my machine until it doesn't." Now look at those five places and ask: which of them is actually unique to your site? The component is. It's welded to your design system — your spacing, your tokens, your brand. Nobody can reuse it and nobody should. The other four are plumbing . "Look up _type in a map, call the renderer, keep the map in sync with the schema and the query." That code is byte-for-byte the same idea on every project you've ever built. So why is it livi

Semantic HTML and Accessibility: Building Better Websites Introduction Semantic HTML is the practice of using HTML elements that clearly describe the purpose of the content on a webpage. Instead of using many <div> elements, semantic tags such as <header> , <nav> , <main> , <section> , <article> , and <footer> make the page easier to understand. Semantic HTML is important because it improves accessibility, helps search engines understand web pages, and makes code easier to read and maintain. Before: Non-Semantic HTML <div class= "header" > <h1> My Portfolio </h1> </div> <div class= "navigation" > <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </div> <div class= "content" > <p> Welcome to my portfolio website. </p> </div> After: Semantic HTML <header> <h1> My Portfolio </h1> </header> <nav> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </nav> <main> <section> <p> Welcome to my portfolio website. </p> </section> </main> Accessibility Issues I Found 1. Images Missing Alternative Text Before: <img src= "images/profile.jpg" > After: <img src= "images/profile.jpg" alt= "Profile picture of Grace Loko" > Adding alternative text allows screen readers to describe images to users with visual impairments. 2. Navigation Was Not Semantic Before: <div> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </div> After: <nav> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </nav> Using the <nav> element helps assistive technologies identify the website navigation. 3. Form Inputs Had No Labels Before: <input type= "text" placeholder= "Your Name" > After: <label for= "name" > Name </label> <input type= "text" id= "name" name= "name" > Labels improve accessibility by helping screen readers identify each form field. Conclusion This accessibility audit helped me understand the importance of semantic HTML and accessible web design. By replacing non-semantic elements with semantic tags, adding image alt text,

[v0.3.0] - 2026-06-29 🚀 Added Checkpoint Mechanism — ReActCore introduces three checkpoints during streaming: content_ended (after text content), before_tool_calls (before tool calls), and after_tool_calls (after tool calls), enabling precise interception and state synchronization of the execution flow. Message Queue System — Added a new MessageQueue class in run.py , supporting async enqueue, drain, and remove operations. Users can now queue messages while the LLM is running; queued messages are sent automatically after the current task completes. The frontend introduces a QueueBar component to display queued messages, with CSS spinning animation, single-line ellipsis, and hover-to-delete functionality. Queue Message Merging — MessageQueue.drain_all() now merges consecutive messages with the same name into a single message, preventing fragmented user input when multiple queue entries share the same sender. Queue WebSocket Events — The execution event protocol introduces three new event types: message_queued , queue_drained , and queue_returned ( useRunWebSocket.ts ). The frontend processes queue state updates in real time. Stop & Queue Integration — When the user clicks Stop, pending queued messages are returned to the input box via queue_returned . Checkpoint stops cleanly clear the queue and automatically start the next message. System Notification Messages — Introduced the SystemMessage type (with notification role) to separate error messages from assistant content. Errors are now rendered as independent notification bubbles, no longer embedded within assistant message cards. tiktoken Real-Time Token Estimation — ReActCore initializes a tiktoken encoder on startup for real-time token counting during streaming. Unknown models fall back to o200k_base . 🔧 Improved Custom Model Name Auto-Complete — The model name field in ModelManager has been upgraded from Select to AutoComplete , allowing users to type custom model names not in the predefined list. Message Block T

I built this project as an entry for the H0: Hack the Zero Stack with Vercel v0 and AWS Databases Hackathon. #H0Hackathon Inspiration Justice moves slowly. I learned that firsthand as my family navigated a legal dispute. What struck me wasn't just the stress — it was that things were quite disorganised. Documents were paper-based or buried somewhere in emails. Updates came through WhatsApp messages. Simple documents took a really long time to draft and send. The system was fragmented and difficult to navigate. Companies like Harvey tackle document drafting well, but legal research tools and LLM wrappers can hallucinate case law, citing judgments that don't exist. I knew that if I was going to build something for this space, it had to be grounded in real, verifiable law. That led me to Laws Africa, which provides structured access to actual South African legislation and court judgments. I also noticed a problem that lawyers experience daily: the mechanical work. Logging into court portals to file a case. Hunting through OneDrive, Google Drive, and Dropbox for the right version of a document. Sifting through hundreds of emails to find something relevant to a matter. Onboarding a new client when the intake form is a PDF someone emails you. These are not AI problems. They are automation problems — and lawyers or their secretaries are doing them manually every single day. That became Agently. What Agently Does Agently is a legal workspace that handles the full lifecycle of a matter, from the moment a client submits an intake form to the day the case closes. Matter Management. Every client engagement lives in a structured matter. Documents, emails, notes, contacts, workflows, and AI conversations are all scoped to it. A lawyer can open a matter and immediately see everything relevant. AI Agent with Real Legal Research. The AI connects to Laws Africa's knowledge bases — South African legislation, court judgments, and municipal law — so research is grounded in actual legal

Xgimi, the Chinese company known for its all-in-one smart projectors, is expanding its portfolio with a new line of screen-equipped smart glasses that first debuted at CES 2026. Unlike AR glasses from companies like Meta and Snap, Xgimi’s new privacy-focused MemoMind One skip cameras for a lighter and more discreet design that helps hide their […]

Target built a generative AI system to improve marketing campaign forecasting by retrieving and ranking similar historical campaigns. Using embeddings, vector search, and LLM ranking, it replaces rule-based workflows. Evaluation shows 75% top-1 and 100% top-3 coverage. The system reduces manual effort, improves consistency, and uses feedback loops to refine retrieval using campaign outcomes. By Leela Kumili

You finally get the response. The CV cleared whatever filter it was up against, and now there is a calendar invite for a thirty or forty five minute call. Most developers treat this as the technical screen and prepare accordingly. They load up on system design questions, leetcode style problems, or deep dives into the stack listed in the job post. What actually happens in that first call is often lighter on code and heavier on whether the person on the other side can picture working with you week after week. The engineering lead or hiring manager is trying to answer a few practical questions the CV could not fully settle. Can this person explain their decisions without needing constant context? Do they push back on unclear requirements in a way that moves the conversation forward instead of creating friction? Do they already understand how remote contractor work tends to flow, or will every interaction need extra translation? The candidates who lose ground here rarely fail on raw technical ability. They lose it on rhythm and assumptions. Some over-prepare the technical side and under-prepare the part where they need to show how they handle ambiguity. Others treat every question as an interview question that demands a polished answer, when what the lead wanted was a working conversation. The call ends with a quiet sense that this person will need more hand-holding than the role allows. Timezone and async signals are another place people slip. When a candidate spends the call reassuring the other person that they can work US hours or that they are always available for meetings, it often lands as uncertainty. The reassurance backfires. Teams that hire contractors remotely have already accepted some timezone spread. What they want to hear is how you have made async work in the past, what you leave behind when you log off, and how you keep momentum without daily syncs. The calls that move forward feel like two people working a problem together. The candidate is not sitti