标签：#t

I've been thinking about something that is becoming pretty common among developers.🤔 A lot of us...

It’s a vulnerability that allows someone to recover the order of ballots cast, newly exploited with AI tools. Nearly four years since the original vulnerability was disclosed, I was still able to use it to analyze voter behavior in Georgia (one of the 21 states that uses affected scanners) in the recent May 2026 primary. Notably, I never touched a voting machine, exploited a network, examined source code, or accessed anything non-public. After pointing a coding agent to the original vulnerability paper, I supplied it with two data sources highlighted in the paper: the early-voting list for each county, and the “CVR” (cast-vote record) file, containing every ballot and its selections (but not the voters’ names or other identifying information). The CVR file is available upon request, precisely because a public, ballot-level record is what makes election results independently verifiable...

The success of a program linking zoos across the US has created social media stars and generated a wave of interest in how to protect the endangered great apes in the wild.

Onur Satici explains how Vortex, an open-source columnar file format under the Linux Foundation, revolutionizes high-throughput data loading. He details how cascading lightweight encodings, layout-based segment pruning, and zero-copy memory pipelines eliminate CPU/NVMe bottlenecks to stream S3 data straight to GPUs at speeds up to 60 Gbps without requiring upfront data reprocessing. By Onur Satici

This eMag examines how architects can lead with clarity in a rapidly evolving engineering world, distilling industry insights into field-tested practices for teams. Together, these stories reveal a core theme: the technology leader’s role is expanding from building systems to guiding how tech behaves and learns, while enabling engineers and organizations to bring out their best. By InfoQ

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,...