标签：#ui

The US government wrote a letter in support of OpenAI’s argument that training AI on others' intellectual property is fair use.

The internet has a trust problem, and it’s not just because social media feeds are filling up with AI slop. AI-generated text and images are now making their way into job applications, product reviews, and even insurance claims, leaving platforms and users alike scrambling to figure out what’s real. A handful of startups have cropped up in the past couple of […]

This is Adobe's second acquisition out of India after Rephrase.ai in 2023

Swiggy developed an in house predicted lifetime value model using more than 350 pre order features and a multi task MLP for Food and Instamart. Adding order count as an auxiliary task reduced model parameters by 63% while improving predictive performance. The pLTV signal is used with Google Target ROAS bidding to optimize customer acquisition. By Leela Kumili

OpenAI recently published an engineering account of GPT-Live. It described how they designed the system to maintain continuous voice interaction while separating latency-sensitive media processing from broader application work. The live path contains the media pipeline and inference loop, while delegation, tool use, persistence, and other application logic run behind an asynchronous RPC boundary. By Eran Stiller

I spend long hours working on a computer, and one simple thing I regularly forget is drinking enough water. Most hydration apps rely on notifications. The problem? Notifications are very easy to dismiss. So I started building Hydration Buddy : a lightweight Windows app designed to stay visible without constantly interrupting your workflow. What it does Tracks daily water intake Shows your daily hydration progress Gives gentle reminders Lets you quickly log water Includes a small floating companion that stays on screen Keeps the experience lightweight and simple Why I built it The idea wasn't to build another notification app. I wanted something that could quietly stay present while I'm coding or working and make the habit harder to forget. Hydration Buddy is currently in beta, so I'm still testing the experience and improving it based on real user feedback. 🎁 The first 25 beta users get free lifetime access. You can try it here: https://hydrationbuddy.patelmahek.in/ If you test it, I'd especially love feedback on: The floating companion Reminder experience Ease of logging water Features you'd like to see next I'm building this in public, so I'll also share some of the technical decisions, mistakes, and improvements as the product evolves.

Most teams still optimise for Google alone. But large language models (LLMs) crawl and compress your site into internal knowledge graphs that later power AI answers. That’s a different job than just ranking URLs. Here’s a developer-focused checklist for making your site friendlier to LLM crawlers without sacrificing SEO. Make key facts atomic and stable LLMs do better when core facts are: • Short: "Starter is $99/month for 1,000 credits." • Stable: product/tier names don’t change every quarter. • Unambiguous: each product has one clear description. Avoid hiding pricing, integrations or feature lists inside long narrative paragraphs. Treat FAQ schema as training data Your FAQPage is effectively a supervised dataset of Q→A pairs. Practical tips: • Use real customer phrasing in the Question field. • Keep Answer concise, factual and time-bounded where relevant. • Avoid marketing fluff; aim for sentences that can be quoted verbatim. Use rich schema types Beyond title/description: • Product / SoftwareApplication: name, description, pricing, featureList. • Organization: legal name, logo, sameAs social URLs. • WebSite: canonical URL, SearchAction for on-site search. Validate via structured data testing tools and keep markup in sync with actual UI and copy. Expose crawl intent explicitly LLM crawlers increasingly respect machine-readable contracts: • robots.txt – allow/deny relevant user agents clearly. • sitemap.xml – keep it small and canonical. • llms.txt / links.txt – specify acceptable AI uses and preferred canonical URLs. Enforce naming consistency in code and content Reduce ambiguity by: • Centralising product and plan names in config. • Reusing the same strings across marketing site, docs and in-app help. • Cleaning up stale routes and redirecting deprecated pages. Ship evidence, not just adjectives Pages with concrete claims are easier for AIs to cite: • Simple stats or ranges. • Example queries and expected outputs. • Clear preconditions and limitations. If you mai

Mushroom is a tiny pixel creature that lives on your Mac . It can tell when you are actually at the desk, so it nudges you to drink, move and rest your eyes at the moments that help, and stays quiet when they would not. Made for people who sit at a Mac for hours. Developers, designers, writers, students, anyone whose focus is the problem and the job at the same time. Mushroom can do lots of things: https://www.getmushroom.app/features Pricing: https://www.getmushroom.app/pricing If you've seen Mushroom elsewhere, please let me know where. If you miss a feature, please tell me. I'm open for feedback and suggestions. Personal comment: I have created Mushroom because I wanted a very simple way to set up a quick reminder. You just type what and when in one sentence, and your reminder is set. Over the course of weeks, it evolved into a whole set of features. I'd like to thank Michael K. Graves for helping with ideas and suggestions, and I'd like to thank Caz-Bee for providing the graphics. Thank you for giving my post attention.

Fixing the “D.map is not a function” crash by tightening DB indexes and normalizing the API payload TL;DR: I added missing PostgreSQL indexes in apps/api/src/db/db.ts and forced the /condos/metrics endpoint to always return an array. The change stopped the runtime TypeError: D.map is not a function in the React selector and restored correct KPI calculations. The Problem Our internal “Condo Dashboard” started throwing a JavaScript error in production: TypeError: D.map is not a function at render (src/components/CondoSelector.tsx:45) at D.map(e=>(0,a.jsx)("option",{value:e.id,children:e.name},e.id)) D is the data array used to populate a <select> with condo options. When the page loaded, the dropdown was empty and the whole component crashed. The API call that feeds D ( GET /api/condos/metrics ) was supposed to return an array of objects { id, name } , but under certain conditions it returned null or a single object, breaking the .map call. The root cause turned out to be duplicate rows in the broker_tokens table that caused the query to return a malformed result set. Those duplicates were a side‑effect of missing unique indexes on the broker_tokens and condo_metrics tables. What I Tried First Guarding the Front‑end – I added a quick check in CondoSelector.tsx : const options = Array . isArray ( data ) ? data : []; This silenced the error, but the UI still showed no options because the API kept returning the wrong shape. It was a band‑aid, not a fix. Manual Data Normalization – In the API controller I forced the result to an array: const rows = await db . query ( sql ); return res . json ( Array . isArray ( rows ) ? rows : [ rows ]); This produced duplicate entries and confused downstream calculations. The KPI numbers in the dashboard were still off. Both approaches addressed the symptom but left the database inconsistency untouched, so the bug could re‑appear anytime new data landed. The Implementation 1. Add proper indexes (the real fix) The missing indexes allowed

GoPro is merging with a company that builds AI infrastructure, and will continue supporting existing consumer products.

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Falsifier first: if you can find a fourth production call site that builds a Transformation and reconstructs its target field differently from the three I'm about to describe, this post is wrong about "all of them." I counted by grepping for the one function that computes a transformation's identity and checking every call site by hand. Three. If there's a fourth, the bug I'm describing isn't fully fixed. Here's the shape of it. Engine::plan_shape has a doc comment that says, more or less, "this isn't a second place where transformation identity gets defined, because it's the same code as the one true place." That claim was false, and it had been false since the field it's talking about was added. The actual second place was Engine::rehydrate_committed . Its job is to rebuild a Transformation from the journal when a fresh CLI process needs to undo something a previous process committed. Every gx undo call from a cold process goes through it. And for one field, target , it wasn't rebuilding anything. It wrote a hardcoded placeholder. Nobody noticed, because nothing disagreed with the placeholder. Every adapter shipping at the time also produced the placeholder for that field, by omission rather than by design, so the two sides matched by coincidence. A missing value that's always missing on both sides of a comparison is invisible. cargo check doesn't catch it because the type is Option<T> and None is a completely legal value of that type. Nothing was wrong, until something else became right. What made it right was landing the two adapters that finally do predict target , fs and git, so their production plan() calls started filling in the real value instead of leaving it empty. The moment that shipped, cold-process undo broke for every fs or git transformation: gx_code=INTERNAL detail="TransformationId(...) is Committed, and 43 §3 has no `rehydrate: the rebuilt transformation names another id, so the intent supplied is not the one this transformation was planned from`

A lesson in overcorrection, from an AI-assisted builder who is scared stiff of shipping spaghetti and...

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Building a multi-agent system sounds simple on a whiteboard. Give one agent a task, let another handle the next step, add a reviewer, connect a few tools, and you have an agentic workflow. It gets more complicated when those agents need to operate at the same time. A sequential workflow can force agents into a fixed order: one finishes, another starts, and everyone downstream waits. That model is easy to reason about, but it can become restrictive as the system grows and agents need to react to new information independently. Mozaik takes a different architectural approach. It is an open-source TypeScript framework for building reactive agents inside an event-driven environment, where agents can work concurrently, respond to events, and coordinate without requiring a central workflow to define every interaction. And now there is a practical way to try this architecture. JigJoy , together with daily.dev and Hyperskill , is organizing the Mozaik Hackathon 2026 , a free online hackathon focused on building concurrent AI agents. TL;DR Building more agents doesn't automatically make a multi-agent system better. The way those agents communicate, react, and depend on one another can have a bigger impact on how the system behaves as it grows. Mozaik approaches this problem with an event-driven architecture designed around reactive, non-blocking agents. Agents join a shared AgenticEnvironment , receive events, and decide how to react to them. Here’s what makes the Mozaik Hackathon 2026 worth a look: Concurrent AI agents: Multiple agents can work at the same time and react to events as they arrive. Event-driven architecture: Agents, humans, observers, and tools participate in the same AgenticEnvironment . Non-blocking execution: Inference and message delivery can continue in the background without holding up other participants. Loosely coupled agents: Agents can operate more independently, making them easier to reuse across projects and applications. TypeScript-based: Mozaik i

The follower counter on my phone says 1,946. I check it more often than my homework diary. 54 to go...

Time‑Based Public Access for the /tv Route in a Next.js App TL;DR: I added a temporal gate that lets anyone hit /tv without a session cookie between 9 am‑6 pm America/Cancun. Outside that window the request falls back to the normal auth middleware. The change lives in src/lib/auth.ts and src/middleware.ts and required proper timezone handling and a tiny refactor of the auth flow. The Problem Our TV dashboard ( /tv ) is meant to be displayed on a wall screen in the office lobby. The screen should be visible to anyone during office hours, but it must stay protected after hours. The original middleware ( src/middleware.ts ) forced a session cookie ( AUTH_COOKIE_NAME ) on all routes, including /tv . The result was a “401 Unauthorized” on the lobby screen after 6 pm, which broke the intended user experience. The symptom was simple: GET /tv → 401 Unauthorized The error came from the auth middleware that blindly redirected unauthenticated requests to the login page. We needed a conditional bypass that only applied to the /tv path and only during the defined business hours. What I Tried First My first instinct was to add a quick if (request.nextUrl.pathname === "/tv") return NextResponse.next(); at the top of the middleware. That let the request pass, but it also opened the route for the whole day, ignoring the time constraint. I tried to read the server’s local time ( new Date() ) and compare the hour, but the server runs on UTC, so the check was off by 5 hours for the America/Cancun zone. The result was that the route was either always open or always closed, depending on where the CI runner was located. I also considered using a third‑party library like moment-timezone , but pulling in a heavy dependency for a single hour check felt overkill. The Implementation 1. Add a tiny time‑window helper I created a pure function isWithin in src/lib/auth.ts . It receives a start hour, an end hour, and a timezone identifier, then returns a boolean indicating whether the current momen

The Problem GitHub profiles are data-rich but visually boring Developers want to "flex" their stats but have no aesthetic way to do it The Solution DevCard: enter username → pick theme → download PNG Show all 3 themes with screenshots How It Works (Architecture) Cloudflare Worker + GitHub GraphQL API (single query) Edge caching strategy Client-side rendering with html-to-image The CORS avatar trick (base64 conversion) The RPG Class System (fun section) How top language maps to character class Full class table (TypeScript → Archmage, Rust → Forgemaster, etc.) This section alone will get shares Try It Yourself Link: https://www.devcard.tech/ CTA: "Drop your card in the comments" What's Next VS Mode (compare two devs) More themes Open to suggestions

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A few weekends ago I got annoyed at typing prompts into a terminal and decided the fix was, obviously, to control my AI agent with hand gestures instead. This is the story of building that, and the two hours I lost fighting a GPU crash that had nothing to do with my code. The idea: a webcam watches your hand, MediaPipe tracks the landmarks, and three gestures map to three actions on an Anthropic-powered coding agent. Pinch (thumb and index touching) - the agent writes code Spinning your index finger in a circle - the agent brainstorms an idea Two fingers "running" up and down - it runs whatever code it just wrote No keyboard. No prompt box. Just your hand in front of a webcam, like you're a conductor telling an orchestra what to play. The MediaPipe detour I started with MediaPipe's newer Tasks API (HandLandmarker), because it's the one all the docs point you to now. It crashed immediately on my Mac with a Metal/GPU service error, even when I forced it onto the CPU delegate. Spent way too long assuming it was my setup before realizing the new API just doesn't play nice with this machine. Switched to the legacy mp.solutions.hands API, pinned to mediapipe==0.10.21, and the problem vanished. Sometimes the fix for a shiny new API is to not use it yet. Gestures are messier than they sound Detecting "pinch" is easy: measure the distance between thumb and index tip, threshold it, done. The other two took more work. "Running" fingers needed the vertical oscillation of the index and middle fingertips, counted by sign crossings, so it doesn't false trigger on a hand that's just drifting. "Spinning" tracks the index fingertip's trajectory and accumulates the signed angle around a center point, so a real circle reads differently than a shaky hand. Both run on a rolling 1.5 second buffer of landmarks, edge triggered so a gesture fires once, not once per frame. Letting the agent run its own code, unsandboxed, on purpose The runner executes whatever the agent wrote as a subprocess