标签：#t

There is a torrent of unappetizing slop coming from restaurants, cafes, and brands that are increasingly turning to AI to generate images promoting their food. The resulting horror show includes donut shrimp, Reubens from the deep, wormlike noodles, and noodle-like pastries and stringy chicken. There's also construction material masquerading as ice cream, ice cream masquerading […]

Microsoft is giving its developer-optimized Windows experience a name: Project Zenith. While the software maker originally announced a similar developer-optimized Windows effort at Build earlier this year, Project Zenith is designed for new developer-focused devices with 64GB or more of unified memory. "Project Zenith devices come with a preconfigured Windows setup for development and a […]

We cannot forget that AI coding agents are not yet trustworthy : Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI’s Codex, and Nous Research’s Hermes, were involved. Anthropic, OpenAI, and Nous Research did not respond to requests for comment by the time of publication...

Homeland Security Investigations agents hit the outdoor retailer with a controversial subpoena as part of a dragnet search for the identities of protesters who entered a Minnesota church in March.

With the risk of infection rising, parents and young adults are facing agonizing choices about being out in public as school starts back up.

Macs are fantastic computers, but you’ll need one of these great displays to complete your work-from-home setup.

Hello, I'm Maneshwar, and I'm building LiveReview — a blast-radius aware AI code review built for...

Microsoft opened GitHub Copilot code review for Azure Repos to all Azure DevOps customers, after acknowledging that many are not ready to migrate to GitHub. Reviews bill per use through the linked Azure subscription and appear in Cost Management 48 hours later. Budgets notify but do not stop reviews, and concurrency caps at five per organization. By Steef-Jan Wiggers

Job seekers are trying to game the application process using AI. It’s not working, and not for the reasons you might think.

The "frequency healing" corner of the internet runs on faith. Apps ship MP3s labeled "40Hz gamma" and ask you to believe it. I'm a life scientist who builds web tools, and I couldn't ship that. So I built SereneSynth, a browser-based binaural beat and noise generator — and then I built a live spectrum analyzer into the page so anyone can audit the output in their own browser. This is the engineering write-up: the Web Audio graph, the FFT gotcha that almost made me publish wrong numbers, and how I cross-verified everything in Audacity. The honesty constraint first A binaural beat is not a tone in the air. Play 200 Hz into the left ear and 240 Hz into the right, and the listener's superior olivary complex computes the 40 Hz difference. A microphone — or a mono spectrum analyzer — will never show a 40 Hz peak. So the only honest thing a generator can prove is its carriers and its spectral slope. That is exactly what we measure. The synthesis graph Two sine oscillators, hard-panned with StereoPannerNode, summed into a master GainNode, tapped by an AnalyserNode before the destination — the analyzer observes exactly what the headphones receive. Settings that matter: fftSize 16384, smoothingTimeConstant 0.8. The FFT gotcha that almost made me ship garbage My first version used fftSize 1024: one fat bump near 220 Hz instead of two peaks. Bin width = 44100 / 1024 ≈ 43 Hz, and my carriers are 40 Hz apart — same bin, merged. At fftSize 16384 the bin width drops to ≈ 2.7 Hz and the carriers resolve as razor-sharp spikes at 200.0 and 240.0 Hz. Lesson: FFT size is the magnifying glass. If a "frequency proof" doesn't state its FFT size, ask. The widget renders a log axis (20–1000 Hz) because a linear axis wastes 90% of the canvas, and peak detection labels the top bins in the 100–500 Hz range live. Bit-exact, downloadable verification The page also renders 10-second stereo WAVs via OfflineAudioContext (16-bit PCM, 44.1 kHz): same graph, offline render, RIFF encode. No lossy compre

Turning Data Into Decisions Bar charts, histograms, scatter plots, subplots, and plotting straight from pandas Previously learned to draw a line — literally. we now know how to create a figure, style it, and save it. But real analyst work rarely stops at trends over time. You'll need to compare categories , understand distributions , spot relationships between variables , and show several views of the data at once . That's exactly what today covers. Grab a coffee — let's turn raw numbers into charts that actually tell a story. 1. Bar Charts: Comparing Categories When to use one Bar charts are your go-to whenever you're comparing discrete categories against each other — regions, products, departments, months. If someone asks "which one is bigger?", a bar chart answers it instantly. The code import matplotlib.pyplot as plt regions = [ " North " , " South " , " East " , " West " ] revenue = [ 420 , 380 , 510 , 290 ] fig , ax = plt . subplots ( figsize = ( 7 , 5 )) ax . bar ( regions , revenue , color = " teal " ) ax . set_title ( " Revenue by Region " ) ax . set_xlabel ( " Region " ) ax . set_ylabel ( " Revenue ($K) " ) plt . show () A useful variant: horizontal bars When category names are long, flip the chart with barh() — it's far easier to read than squeezing labels sideways: fig , ax = plt . subplots ( figsize = ( 7 , 5 )) ax . barh ( regions , revenue , color = " darkorange " ) ax . set_title ( " Revenue by Region " ) ax . set_xlabel ( " Revenue ($K) " ) plt . show () Rule of thumb: categories on the x-axis → bar() . Long labels or many categories → barh() . 2. Histograms: Understanding Distributions Bar chart vs. histogram — don't mix them up This trips up almost every beginner: a bar chart compares separate categories. A histogram shows how continuous numeric data is distributed by grouping values into ranges called bins . There are no gaps between histogram bars by convention, because the x-axis is continuous, not categorical. The code import matplotlib.pyplot

On the benchmark that measures automating actual business processes, Claude Fable 5.1 scored 31.4% — up from 17.1% for Claude Fable 5, released three months earlier. Anthropic calls that benchmark AutomationBench. A near-doubling in one release cycle is the number worth stopping on, because most of the automation work I build for clients lives or dies on exactly that capability: can the model finish a multi-step job without a human stepping in. Here is a clear-eyed read of what Claude Fable 5.1 changes for business automation, what it actually costs once you account for how it behaves, and when Fable 5 or Opus 5 is still the right call. TL;DR Anthropic released Claude Fable 5.1 and Mythos 5.1 on 1 September 2026. Fable 5.1 is generally available; Mythos 5.1 is restricted to vetted cybersecurity and life-sciences organisations. Anthropic reports Fable 5.1 scores 31.4% on AutomationBench (business-workflow automation), up from 17.1% for Fable 5, with large gains on agentic coding and research benchmarks too. Base API pricing is unchanged at $10 / $50 per million input/output tokens. The one cut is cache reads, down 75% to $0.25 per million. Independent analysis by Stork.AI reports Fable 5.1 emits about 1.7x more output tokens per task, so it is cheaper only when cached context dominates your spend — long-running agents on a stable codebase or knowledge base. For varied one-off prompts, Opus 5 or Sonnet 5 is better economics. What is Claude Fable 5.1? Claude Fable 5.1 is Anthropic's flagship model for coding and knowledge work, released on 1 September 2026 as an incremental upgrade to Claude Fable 5. The same underlying model ships in two safeguard configurations: Fable 5.1 — generally available. API id claude-fable-5-1 , on the Anthropic API, Amazon Bedrock, Google Cloud Vertex AI, Microsoft Azure AI Foundry, Claude Code and Claude Enterprise. Mythos 5.1 — restricted. Lighter safeguards for vetted organisations via Anthropic's Cyber Verification and Life Sciences Veri

Ugreen, known for its phone power banks, chargers, and NAS storage solutions, is moving into the smart home - in a big way. This week at the IFA tech show, the company launched its HomeAgent smart home platform that combines security camera storage, on-device AI, and smart home control in one system, managed by a […]

A production postmortem. We shipped browser-side image compression (Rust → WASM + WebGPU), turned on cross-origin isolation for speed, and watched every format crash with compression worker crashed . Here's the root cause and the fix. The setup We built an image compressor that runs 100% in the browser — Rust compiled to WASM for the codec work, WebGPU for the heavy ML passes (background removal, denoise, watermark). No upload, so users' pixels never leave the device. Privacy is the whole selling point. For the multi-threaded code paths we rely on shared memory + atomics , which in the browser requires crossOriginIsolated . So we served the document with: Cross-Origin-Embedder-Policy: require-corp Cross-Origin-Opener-Policy: same-origin That gives us crossOriginIsolated === true , unlocks SharedArrayBuffer , and lets the *‑threaded WASM builds actually spawn workers. The build uses a nightly toolchain ( nightly-2025-06-01 + -Z build-std ) with: RUSTFLAGS = "--cfg=... +atomics,+bulk-memory --shared-memory --import-memory" and a custom rayon handle pool ( with_turbo_pool ) instead of build_global , so we control worker lifecycle and can abort/self-heal. The incident After flipping COEP to require-corp in production, every format started crashing with the same message: compression worker crashed Not one codec — JPG, PNG, WebP, AVIF, all of them. It was a P0: the core feature was dead for every user. What made it nasty: it only reproduced under real cross-origin isolation . Local dev without COEP was fine. Staging without the header was fine. So the bug hid until it hit production traffic. Root cause The *‑threaded WASM packages spin up nested rayon workers to parallelize the codec. Under COI + COEP require-corp , those nested workers get blocked by Cross-Origin-Resource-Policy / COEP — the spawned worker script is treated as a cross-origin response without the right CORP header, so the browser refuses it. No worker → the rayon pool never initializes → the compression c

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

AI is getting surprisingly good at planning trips. Give it a destination, a budget, and a few days, and it can generate an itinerary in seconds. Five days in Tokyo? Shibuya on Day 1. Asakusa on Day 2. Tokyo Tower on Day 3. Ginza on Day 4. TeamLab on Day 5. It looks perfect. The problem is… I might hate it. I don't like crowded places. I wake up late. I care more about food than landmarks. I don't want to spend half my trip rushing between “must-see” attractions. And I would happily spend $250 on one amazing dinner instead of visiting five popular tourist spots. The itinerary isn't wrong. It just isn't mine. And I think this reveals one of the biggest challenges for AI travel. Personalization isn't knowing where I want to go. It's knowing how I make decisions . For a long time, personalization in travel has mostly meant collecting preferences. Beach or mountains? Budget or luxury? Business or leisure? Window seat or aisle? But human travel decisions are much messier than that. Two people can have exactly the same destination, budget, and travel dates—and still want completely different trips. One person might want to stay in the center because they want to walk everywhere. Another might prefer a quiet neighborhood and take a taxi whenever necessary. One traveler wants to see everything. Another wants to do absolutely nothing before noon. One person sees a $300 hotel as expensive. Another sees it as a bargain if it means waking up next to the beach. The difference isn't simply preference. It's trade-offs . And that's where I think AI travel agents still have a lot to learn. The best travel agent isn't the one with the most recommendations. It's the one that understands your priorities. Think about what a great human travel agent does. You tell them: “I'm going to Tokyo.” They don't immediately send you a list of 20 hotels. They ask questions. “Is this your first time?” “Are you traveling with kids?” “Do you care about nightlife?” “Do you mind taking public transportat

The discovery adds to the planet’s history of strange atmospheric phenomena that scientists don’t full understand.

You have an orders topic on a Kafka cluster, its values encoded with Avro against a schema in the Schema Registry . You want the orders worth more than fifty euros on a topic of their own, and you have decided to do it with Kafka Streams — a JVM library, your code, your deployment. The schema has five fields: { "type" : "record" , "name" : "Order" , "namespace" : "com.alginte.demo" , "fields" : [ { "name" : "orderId" , "type" : "string" }, { "name" : "customerId" , "type" : "string" }, { "name" : "item" , "type" : "string" }, { "name" : "quantity" , "type" : "int" }, { "name" : "priceEur" , "type" : "double" }]} You want one line of logic over them: quantity * priceEur > 50 . Here is everything standing between that line and a topic of big orders. Seven steps The route Confluent's own examples take, and many projects with them: Get the schema out of the registry and into your repository as an .avsc — or, if your team owns the schema in the repository and publishes it to the registry, the other way round. Whichever copy you call the source, there are now two that can disagree. Add the code generator to your build. Configure it — source and output directories, and the string type. Build , producing Order.java under target/generated-sources . Write the topology against the generated class. Package the application, with the schema, the class and the serde. Deploy it somewhere that runs a JVM. Steps 2 and 3 are this, once — in Maven, though Gradle's equivalent has the same shape: <plugin> <groupId> org.apache.avro </groupId> <artifactId> avro-maven-plugin </artifactId> <version> 1.12.1 </version> <executions><execution> <phase> generate-sources </phase> <goals><goal> schema </goal></goals> <configuration> <sourceDirectory> ${project.basedir}/src/main/avro </sourceDirectory> <!-- without this, string fields generate as CharSequence, not String; Confluent's own examples set it for the same reason --> <stringType> String </stringType> </configuration> </execution></executio

Battlefields in Ukraine are littered with the remnants of drones, which are now firmly established as a critical weapon of modern warfare. But behind all that wreckage, there’s a new gold mine for the defense sector. The data drones generate will far outlast the wars in which they are used to fight, increasingly becoming part…

Most free online PDF tools work by uploading your document to a server, processing it there, and sending it back. For a lot of files that's fine. For a signed contract, a payslip, a medical form, or a scanned ID, it's the entire privacy problem: your document now lives on someone else's machine, subject to their logging, retention, and breach exposure. It doesn't have to work that way. A modern browser can split, merge, compress, sign, and even OCR a PDF without the file ever leaving your device — using libraries like pdf-lib , pdf.js , jsPDF and SheetJS that run entirely in JavaScript. How to tell an uploader from a client-side tool You don't have to trust a marketing claim. Two checks settle it: Watch the network. Open your browser's DevTools → Network tab, then run the tool on a file. If you see your file leave in a POST/PUT request, it uploaded. A client-side tool shows no upload of the document itself. Pull the plug. Load the page, then turn off Wi-Fi and try the tool again. A client-side tool keeps working offline. An uploader breaks the moment the network is gone. The honest tools pass both tests. If a site can't work offline, your file is going somewhere. The trade-offs, stated honestly Client-side processing isn't a free lunch, and any tool that pretends it is should make you suspicious: Memory. Very large PDFs are held in browser memory, so there's a ceiling a server wouldn't have. Speed. OCR in WebAssembly is slower than a server GPU. It's private, not fast. Fidelity. Converting PDF → Word transfers the text , not the layout — the same is true of every converter, but a client-side one can't hide it behind a server. Compression limits. A PDF shrinks by downsampling embedded images or rasterizing pages; a small or text-only PDF may not shrink at all, and rasterizing removes selectable text. We built 24 client-side PDF tools on exactly this principle and wrote down where each limit is, rather than papering over them. If you're evaluating any online PDF tool