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Washington D.C. has become a battleground for Uber and Waymo's competing views.

This essay was written with Nathan E. Sanders, and originally appeared in The Guardian . Opposition to AI data centers has emerged as a primary theme in US politics, one that—surprisingly—doesn’t fall along party lines. We applaud people coming together for constructive debate on any issue, and agree that communities need to evaluate whether any economic benefits these data centers bring is worth their costs. Still, we worry that a focus on data centers obscures the larger impacts of AI on people’s lives: the concentration of power of AI companies, and their widespread political and financial influence...

GitHub热门项目 | Grok2API 是一个基于 FastAPI 构建的 Grok 网关，支持将 Grok Web 能力以 OpenAI 兼容 API 的方式转换。 | Stars: 5,532 | 112 stars today | 语言: Go

The web has changed . Applications are no longer simple HTTP servers. Today we build real-time dashboards, AI-powered services, multiplayer systems, APIs, microservices, and applications that need to handle thousands of connections with minimal overhead. But our frameworks are still mostly designed for yesterday's problems. So we asked a simple question: What if a * Go * framework was built from the ground up for modern workloads? Meet Breeze . A high-performance Go framework designed around one idea: Performance should not come at the cost of developer experience. Why Breeze ? Go already gives us incredible performance. But the framework layer often becomes the bottleneck. Too much abstraction. Too many allocations. Too much hidden complexity. Breeze takes a different approach: ⚡ High-performance networking powered by gnet 🔥 Real-time WebSocket architecture built in 🧩 Modular middleware system 📚 Automatic Swagger/OpenAPI generation 🎨 Built-in SPA template engine 🚀 Optimized worker pool architecture 🗄️ BreezeORM for efficient database operations Everything you need to build production-grade applications — without assembling dozens of unrelated tools. The Future Is Real-Time Modern applications are moving toward instant experiences: Live collaboration Trading platforms AI assistants Gaming backends Monitoring systems Real-time analytics Breeze is designed for this world. Instead of adding real-time capabilities later, Breeze treats them as a first-class citizen. Less Glue Code. More Building. A common problem in backend development: You start with a simple API... Then suddenly you need: Authentication Documentation WebSockets Background workers Database optimization Frontend integration Your stack becomes a collection of disconnected pieces. Breeze tries to bring these pieces together into one coherent ecosystem. Built With Go Philosophy Go was created around simplicity, performance, and reliability. Breeze follows the same principles: Simple APIs. Predictable behavi

Waze is getting an AI makeover. Google is integrating its flagship AI assistant, Gemini, into the driving app with the goal of letting users personalize their trips a little more. Of the four new updates, only two are being described as involving Gemini. Waze says its updating its conversation reporting feature, first introduced in 2024, […]

This is a submission for Weekend Challenge: Passion Edition ( https://dev.to/challenges/weekend-2026-07-09 ) What I Built Rivalry Radar — a live "Heat Index" for World Cup rivalries. Fans drop 280-character Terrace Takes on any matchup (Brazil vs Argentina, England vs France, whatever's got you shouting at the TV), rate how much the moment hurt or thrilled them from 1–10, and the app does the rest: Google AI (Gemini) scores every take's sentiment the instant it lands — positive, negative, mixed, or neutral — and separately writes a short "Hype Verdict" in the voice of a stadium announcer, based on the latest takes for a matchup. That sentiment score feeds a Heat Index, computed and ranked in Snowflake with RANK() OVER (ORDER BY heat_index DESC), combining take volume, sentiment intensity, and self-rated passion into one live number per rivalry. Two leaderboards: which rivalry is hottest right now, and which fanbase is bringing the most passion overall. Demo frontend/index.html is fully self-contained: opening it in a browser lets anyone submit takes, watch the Heat Index flip digit-by-digit like an airport departure board, and see the leaderboards re-rank in real time. It ships with seed takes from eight classic rivalries so it's not empty on first load. Code NandhuTee / Rivalry-Radar-World-Cup-passion-engine-with-Snowflake-Google-AI 🔥 Rivalry Radar — World Cup Passion Engine Fans drop 280-character Terrace Takes on any World Cup matchup. Google AI (Gemini) scores the emotion behind every word and writes a stadium-announcer Hype Verdict ; Snowflake stores every take and computes a live Heat Index that ranks exactly which rivalry is boiling hottest right now. Built for the DEV Weekend Challenge: Passion Edition 🏆 Best Use of Google AI and Best Use of Snowflake Why this exists Passion is easy to feel and hard to measure. Every World Cup rivalry generates an ocean of unstructured text — chants, rants, one-line hot takes — that traditionally just... disappears into grou

For a full quarter, our feature velocity significantly dropped after we re-implemented a Go service using Rust. The performance improvements actually happened. Why we did it in the first place We are a small startup. Each engineer is important, and each week is even more important. Our backend was built using Go, which was performing well. It was fast, reliable, and we could easily find resources to hire. However, we became infected with that fever. The phrase "Rewrite it in Rust" was being used in all kinds of situations, and it sounded very appealing with its promises of memory safety, no garbage collector pauses, and blazing speed. We told ourselves it was an investment in the future. What we actually bought was a quarter of silence. The numbers nobody warns you about I may not have the exact metrics we use internally, but I can direct you to an individual who shared accurate calculations transparently. In a retrospective from November 2025, engineering manager Noah Byteforge wrote that a Node.js-to-Rust backend rewrite "dropped API response times from 340ms to 28ms. That's 12.1x faster." And the other metric. A 65% decrease in sprint velocity. They didn't deliver a single story point for three weeks. The time it took to send out new features increased by 185%. The time it took for pull requests to be processed increased by 320%. Additionally, scores from the "I feel productive" survey dropped from 8.2 to 4.1. Most importantly, the kicker is what he says in his own words: "We'd won the technical battle and lost the war that actually mattered." He also admits that if he had been forthright about the 6-12 month per engineer ramp, "the business case would've fallen apart immediately." That retrospective was so relatable, it read like our own diary. The battles with the borrow checker and the compile times just snuck entire weeks away from us. The wins were real. That's the trap. I must give credit to Rust because the safety benefits are not exaggerated. The rewrite

Hey everyone, I change servers more often than I probably should. A discounted VPS or a good coupon is usually enough to convince me, but manually recreating the same web stack every time stopped being fun a long time ago. That is why I built HostShift , an Apache-2.0 licensed Go CLI for discovering, planning, migrating, and verifying Ubuntu and Debian servers. The rule I would not compromise on The source server must remain read-only. HostShift does not install packages, stop services, enable maintenance mode, create temporary archives, or change configuration on the source. It reads approved facts and streams data directly to the target. Any target mutation requires an explicit CLI apply command. What it currently covers Docker Compose projects and standalone containers MySQL/MariaDB, PostgreSQL, and Redis Nginx, Apache, Caddy, and systemd services SSH and firewall configuration PHP-FPM, Supervisor, Fail2ban, Certbot, and Logrotate Migration planning, audit journals, status, resume, rollback metadata, and verification checks The migration engine is deterministic Go code and does not need AI. I also added an optional Codex plugin and a deliberately non-apply MCP interface for discovery, planning, review, and dry runs. Actual changes stay in the human-operated CLI. Testing real migrations I did not want to call it tested just because a few unit tests passed. The repository includes Docker migration matrices and real Lima VM matrices covering Ubuntu 22.04, Ubuntu 24.04, Ubuntu 25.10, Debian 12, and Debian 13, including cross-distribution moves. The VM tests also reboot the target and verify persistence while comparing source snapshots before and after the migration. The project is still new, so I expect real-world edge cases. I am sharing it now because feedback from people who actually move and maintain servers will be more useful than polishing it alone forever. GitHub: https://github.com/oguzhankrcb/HostShift Documentation: https://hostshift.karacabay.com

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Every logistics and field-sales team runs the same expensive process: a driver photographs a receipt into a WhatsApp group, and a back-office clerk manually types the invoice number, total, and date into a spreadsheet. Hundreds of receipts a week = transcription errors and thousands of wasted hours. AI vision models kill that bottleneck. Here's the pipeline that turns a blurry field photo into clean structured data in seconds. Why vision models beat traditional OCR OCR reads characters. Modern vision models (Claude Vision, Gemini Vision, GPT-4 Vision) read structure — they distinguish a tax ID from a total, and a date from an amount, even on crumpled, angled, or poorly lit receipts. No brittle per-vendor parsers. The pipeline (3–8 seconds end to end) WhatsApp image → Apps Script doPost → forward to vision model → model returns JSON { InvoiceNumber, TotalAmount, VendorName, Date, Category, confidence_score } → confidence routing: > 90 → auto-append to ledger 70–90 → flag for human review < 70 → ask driver to re-photo → write row to Google Sheet (+ link to original image) → auto WhatsApp confirmation to driver The confidence_score is the whole trick — it's what stops bad extractions from silently polluting your ledger. Model selection (this drives your bill) Gemini Vision — cost-efficient default, strong multilingual OCR, great on clean receipts. Claude Vision — highest accuracy on degraded receipts; use for high-stakes flows. GPT-4o Vision — competitive, strong structured extraction. Pattern: Gemini for the first pass, escalate only low-confidence cases to Claude / GPT-4o. The economics ~500 receipts/week: vision API $10–40 + WhatsApp API $30–60 + Apps Script free = ~$40–100/month . Versus a clerk at ~25 hrs/week = $2,000–4,000/month in loaded labor. Per-receipt cost: $0.005–0.02 (compress images to ~1024px to cut it further). Accuracy: 92–97% on legible receipts, 75–85% on handwritten/damaged — hence the confidence routing. Pitfalls to avoid Auto-appending with no c

Field-service software has an adoption problem: drivers won't use it. Heavy app, another login, crashes in low-signal areas. So the "real-time" data still shows up as end-of-shift phone calls. The fix that actually sticks: stop building an app and use the one drivers already live in — WhatsApp. With Apps Script and Google Sheets behind it, WhatsApp becomes a frictionless mobile ERP. Here's the build. WhatsApp as a data-entry terminal A driver texts Status ABC-1234 Delivered . An Apps Script doPost webhook receives it, parses it, and updates the Sheet in real time. Latency goes from hours to milliseconds — and there's nothing to install, so adoption hits 90%+ in a week (vs. 50–70% for custom apps). Two-stage parsing for messy input Real drivers type "done," not clean commands. So: Regex first pass — handles ~70% of messages (clean format) instantly and for free. LLM fallback — the remaining ~30% goes to a cheap model (GPT-4o-mini / Gemini Flash) with the known cargo IDs and valid statuses. It returns normalized JSON + a confidence score. Below-threshold messages surface to a dispatcher. The LLM normalizes correctly 95%+ of the time (~5% manual), and it handles multilingual input with zero extra code. Driver msg → Apps Script doPost → regex pass → (fail) LLM fallback w/ confidence score → Sheet update (timestamp + raw-message log) → optional outbound (route change, POD photo request) Why Google Sheets is the right backend Dependent formulas: time-to-delivery, SLA-breach flags Pivot tables for reporting Apps Script triggers for automatic client emails Conditional formatting dashboards Native Calendar / Maps / Drive integration (POD photos → Drive folder) It runs on free Google Workspace infrastructure with minimal API cost. Bidirectional by default The same integration pushes messages back to drivers: route changes, delivery instructions, shift reminders, exception alerts, proof-of-delivery photo requests — all in the same thread. Pitfalls that get your number banned T

Hi, my name's Ibrahim, I'm a university student, and I have a problem: I love fragrances way more than my bank account loves me for it. It started small, the way these things always do. A cheap Middle Eastern attar someone gave me as a gift, the kind that costs less than a coffee but somehow smells like it belongs in a much fancier bottle. Then another. Then I started actually reading about notes, pyramids, accords, sillage, the whole rabbit hole. Fast forward through a lot of saved-up allowance and skipped nights out, and I've now got about 20 bottles on my shelf. Mostly affordable Middle Eastern gems (some of them genuinely punch way above their price), with a small handful of designer pieces I saved up for and treat like trophies. If you're a fellow fragrance enthusiast, you already know the feeling: you don't just "wear" a scent, you collect them, you study them, you have opinions about whether a note is top, heart, or base and you will absolutely fight someone about it. That obsession is basically the entire reason this project exists. So when I saw the DEV Weekend Challenge's "Passion" prompt, there was only one thing I could possibly build. What I built: recommendmeafragrance recommendmeafragrance is a browser arcade for fragrance nerds: six small daily games built around real perfume data (notes, brands, years, price tiers), plus an AI Concierge you can actually talk to about what you're in the mood for. Every game feeds into a personal "shelf" that tracks which fragrances you've discovered, plus streaks so you have a reason to come back tomorrow. Here's the tour. 🧪 Scentle: Wordle, but for your nose A new fragrance is picked every day (the same one for everyone, worldwide, no matter your timezone). You get 6 guesses, and after each one you get Wordle style feedback: was the brand exact or just the same house family, did the real answer come out earlier or later than your guess, is it pricier or cheaper, same gender, same concentration, how many notes do you

This is a submission for Weekend Challenge: Passion Edition What I Built This weekend, I...

This is a submission for Weekend Challenge: Passion Edition What I Built This weekend, I...

This is a submission for Weekend Challenge: Passion Edition (#weekendchallenge #devchallenge #ai #googleai #gemini #webdev #showdev) What if your GitHub could roast you harder than your teammates ever would — and then remind you why you keep building? What I Built 🔥 roaster0 — an AI that roasts your GitHub profile, then redeems you. Drop in any public GitHub username and it pulls your real repo data — commit habits, abandoned projects, lazy repo names, language choices — and turns it into a savage, hyper-specific roast using Gemini's structured output and multimodal reasoning. Then it ends with one sincere, earned compliment pulled from something genuinely good in your data. The idea started from a simple thought: your GitHub is an involuntary diary of what you were obsessed with. The eleven repos with no description. The final-v2-FINAL commit. The side project you lived and breathed for three weeks in March before abandoning it. That's passion — messy, obsessive, usually invisible unless someone points a spotlight at it. There's also a second mode, 🎭 Roast Anything : submit a name, bio, links, and/or images, and Gemini reads all of it — text, links, photos — to generate the same experience for anyone, not just developers. Demo 🔗 Live app: roaster0.netlify.app Try it on any public GitHub username, or switch to Roast Anything mode and paste in a bio + an image to see the multimodal analysis at work. Once your roast is generated, you can: 🔊 Listen to it — full audio narration via Web Speech API, paced and pitched differently depending on roast intensity 🖼️ Download the card — every roast renders as a shareable PNG on HTML5 Canvas, ledger-paper aesthetic, ready to post 📋 Share the record — copy a formatted text version straight to clipboard for any platform A couple of examples from testing: GitHub mode — roasted DEV's own founder using nothing but his real public repo data: (screenshot: Ben Halpern roast card — graveyard count, repo names like oceanic-giraffe and test

El 12 de junio de 2026, Google Cloud publicó el Open Knowledge Format (OKF) , una especificación abierta que intenta resolver un problema que suena aburrido pero es carísimo: cómo darle a un agente de IA el contexto que necesita para no inventar. La propuesta es tan simple que da un poco de desconfianza —una carpeta de archivos Markdown con un encabezado YAML— y esa simpleza es, al mismo tiempo, su mayor virtud y el blanco de todas las críticas. Vale la pena entender qué anuncian, porque detrás del formato aparentemente trivial hay una apuesta bastante ambiciosa sobre cómo van a compartir conocimiento las empresas en la era de los agentes. El problema: el conocimiento vive en silos En casi cualquier organización, lo que un modelo necesita saber está desparramado y encerrado en formatos incompatibles: catálogos de metadatos con APIs propietarias, wikis internas, comentarios de código, docstrings, celdas de notebooks y —el clásico— la cabeza de dos o tres ingenieros senior. Cuando un agente tiene que responder algo tan concreto como "¿cómo calculo los usuarios activos semanales a partir del stream de eventos?" , tiene que ensamblar la respuesta juntando pedacitos de superficies que no se hablan entre sí. El resultado: cada equipo que arma un agente resuelve el mismo rompecabezas desde cero, y el conocimiento queda preso del sistema que lo generó. No hay portabilidad. La propuesta: un formato, no una plataforma La respuesta de Google no es "otro servicio de conocimiento en la nube" —y ese es el punto que más recalcan—. Es un formato . OKF v0.1 representa el conocimiento como: Solo Markdown : legible en cualquier editor, renderizable en GitHub, indexable por cualquier buscador. Solo archivos : se transporta como un tarball, se hospeda en cualquier repo git, se monta en cualquier filesystem. Solo frontmatter YAML : campos consultables como type , title , description , resource , tags y timestamp . Cada "concepto" (una tabla, un dataset, una métrica, un runbook) es un arc

GitHub热门项目 | Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. | Stars: 49,269 | 229 stars today | 语言: Go

This is a submission for Weekend Challenge: Passion Edition What I Built The theme was passion, and mine has always been the sky and everything beyond it. Day or night, there's a specific kind of awe in remembering that the sky isn't a backdrop. It's real, it's happening right now, and every point of light is an actual place. Night is simply when you can see the most of it. I wanted to put that feeling into a browser tab. Zenith takes your location, cinematically lowers you from orbit down onto your exact spot on Earth, and becomes a first-person view of your real sky, one you can drag to look around. Every star is where it actually is. The Sun, the Moon, and the visible planets are computed for your latitude, longitude, and this exact minute, and placed where they truly are. It isn't a fixed picture either: the whole sky rotates slowly in real time, so stars rise and set while you watch. Tap any object and you travel to it. The camera flies out through the real starfield, the object grows from a point into a detailed close-up, and a short, grounded briefing appears telling you what you're actually looking at, from where you're standing, right now. A warm voice reads it to you. Stay a while and Zenith reminds you that there are people over your head: it shows how many humans are in space this moment, by name, and draws the real International Space Station crossing your sky whenever it's above your horizon. Not information about space. The quiet, enormous wonder of looking up and knowing, for a moment, exactly what you're looking at. Demo Live: https://zenith-rgerjeki.vercel.app A short walkthrough: the descent to your location, dragging the real sky, and flying to a planet for an AI briefing read aloud in a warm voice. Code rgerjeki / Zenith Zenith The sky above you, right now. I've always been drawn to the sky, and everything beyond it. Zenith is a first-person view of yours : it takes your location, lowers you onto your exact spot on Earth, and gives you the real

CISA said it "missed" an opportunity to get ahead of the security incident by not creating a response plan ahead of time.

What I built I wanted a workout timer that doesn't just beep at me. So this weekend I built one that writes the workout AND talks me through it, out loud, in a voice that actually sounds like it's yelling at you when things get hard. You give it a callsign, how long you've got, what you want to work, and how brutal you want it. It hands that to Gemini, which breaks the whole thing into 30-90 second intervals with a coaching line for each one. Then every one of those lines gets turned into real audio by ElevenLabs before it ever hits your browser. Nothing is pre-recorded, nothing is a fixed track. Ask for a different workout, get a completely different script and a completely different set of audio clips, generated on the spot. Demo Unedited screen recording, straight off my machine hitting the real APIs, sound included. Compose a routine, it comes back in a couple seconds, pacing curve draws itself as an SVG line, then hitting Start walks through each interval with the active one highlighted in red as it counts down and you actually hear it. The Maximum-intensity segments sound noticeably more unhinged because I turn the ElevenLabs stability knob way down for those specifically. Code https://github.com/marwankous/sonic-kinetic How I built it Go backend, one endpoint. It takes your workout params, sends a prompt to gemini-3.1-flash-lite with a JSON schema locked down tight enough that I don't have to think about parsing garbage back out of it, and gets back a full timeline plus a heart-rate pacing curve. The part I actually enjoyed was the audio pipeline. Every coaching line in the timeline gets fired off to ElevenLabs at the same time, one goroutine each behind a sync.WaitGroup , so a routine with a dozen segments doesn't take a dozen times longer than one with a single segment. Whatever comes back gets base64'd straight onto its segment. I also tie the eleven_flash_v2_5 stability setting to the segment's energy level, dropping it to 0.30 for anything marked Maximum