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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

In a rare combined cybersecurity/squid post, a twenty-nine-year-old squid proxy bug can leak HTTP requests. As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.

The tech giant is in breach of the Digital Services Act by focusing on features like infinite scroll, autoplay, push notifications, and the highly personalized recommendation algorithms, the European Commission said.

In the near future, AI -powered surveillance systems will be able to track everything we do in public, and much of what we do in private. And if we do something wrong—shoplift, litter, jaywalk, you name it—the system will notice, retain it, tie it to your official government record, communicate that fact to you, and provide real-time alerts to any relevant authorities… and maybe also to the general public. Think of these systems as automated speed cameras, but on steroids. Only they’ll enforce not just speed limits, but any other rule you can imagine. And you won’t receive a ticket weeks later by mail; you’ll be informed about and fined for your violation immediately...

Reddit looks simple and hides two genuinely hard problems. Comments nest arbitrarily deep, and a naive tree structure makes loading a busy thread slow. The front page reorders itself constantly, so ranking cannot just count votes or old posts would never leave. Both problems have well-known answers, and both are good lessons in choosing the right model. The core problem A comment thread is a tree. Each comment can reply to any other, so depth is unbounded. If you store only "this comment's parent id" and then try to load a whole thread, you walk the tree one level at a time, one query per level, which gets slow for deep or wide threads. Loading a popular post with thousands of nested comments should not take thousands of queries. Ranking is the second problem. If the front page sorted by raw vote count, the highest-voted post of all time would sit at the top forever. If it sorted by newest, quality would drown in noise. You need a score that blends how good a post is with how fresh it is, so good new posts can climb and old ones fade even if they were once popular. Key design decisions Store the parent pointer, but do not traverse at read time. The simple model is a parent_id per comment, which is easy to write but expensive to read as a tree. To load a thread cheaply, fetch all comments for the post in one query, then assemble the tree in application memory. One read, in-memory tree building. This works because a single post's comments, while numerous, fit in memory to assemble. Consider a path or closure model for deep trees. For very deep threads, some systems store a materialized path on each comment, an encoded ancestor chain, so you can fetch an entire subtree with a single prefix query and sort by the path to get correct display order. Another option is a closure table that records every ancestor-descendant pair, which makes subtree queries direct at the cost of extra write work. The right choice depends on how deep threads get and how often you read subtrees

Building an explainable AI platform that helps district administrators allocate resources and farmers make better crop decisions using Gemini, Vertex AI, BigQuery, and Google Cloud. Climate disasters are not just weather events. They are decision problems. When forecasts predict a strong El Niño, governments do not simply need more data. They need answers to questions like: Which districts will be affected first? Where should limited water resources be sent? Which crops are likely to fail? What should farmers sow instead? Why is the AI recommending this action? Existing dashboards provide plenty of charts. Very few provide decisions. That became the motivation behind El Niño 2026 Decision Copilot , an AI-powered decision intelligence platform built during the Google Cloud Gen AI Academy APAC Hackathon . The Problem India depends heavily on the monsoon. A severe El Niño can lead to: Rainfall deficits Reservoir depletion Groundwater stress Crop failures Rising food prices Rural employment challenges The information already exists across dozens of government portals, weather services, satellite datasets, and agricultural reports. The challenge is that it is scattered. District collectors do not have time to manually combine: Weather forecasts NDVI satellite imagery Reservoir levels Mandi prices Contingency plans Drought indicators Farmers face an even bigger challenge. Most need a simple answer: Given my district, should I plant the usual crop this season? The Goal Instead of building another dashboard, I wanted to build an AI system that reasons over multiple data sources and produces explainable recommendations. The platform serves two audiences through the same intelligence engine. District Administrators They receive: District risk scores Interactive risk maps Reservoir outlook Crop stress indicators Resource allocation recommendations AI-generated explanations Instead of simply showing that a district has high risk, the system explains why . Farmers Farmers intera

Vivo's joint venture could become a template for Chinese smartphone makers in India.

What nobody tells you about exporting your multi-agent prototype to a local workspace. Every architect who's prototyped a multi-agent app in Google AI Studio eventually hits the same wall: the prototype works, but it lives in a browser tab. At I/O 2026, Google shipped a fix — Export to Antigravity, a one-click handoff to a local production workspace, carrying "all the context" with it. I ran a real two-agent prototype through it. Here's exactly what survived the trip, what didn't, and what I had to fix by hand — including a bug that had nothing to do with the export itself. 1. The Pilot Project + The Click The project: Research Digest — a sequential two-agent app. Agent 1 (Researcher) takes a topic, uses grounded web search to gather sources. Agent 2 (Editor) synthesizes those findings into a polished digest. Persistence via Firestore, with a history archive of past digests. Built entirely from a single prompt in AI Studio's Build mode . Along the way, provisioning Firestore surfaced my first real gotcha before I even got to the export step — more on that below. Triggering the export: Code tab → Export → Export to Antigravity. The dialog is genuinely informative — it tells you upfront what's coming: all project files, conversation history, and explicitly "1 secret will be included." 2. What Actually Survives the Trip The export dialog's claims, checked one by one: Claimed to transfer What I found All project files ✅ Confirmed — full structure landed intact: .agents, .antigravity, src, config files, README.md with setup instructions Secrets (1 secret) ✅ Confirmed — GEMINI_API_KEY arrived populated in .env, worked immediately, no manual re-entry Conversation history history❌ Did not transfer. The imported "Research Digest" project showed "No conversations yet" in Antigravity's Agent Manager, despite the dialog's explicit promise. Checked twice, on two separate screens — consistent result. 3. The Gotchas Gotcha 1 — "Conversation history will carry over" is currently no

Large language models often develop recurring symbolic patterns — archetypes, metaphors, and memetic shortcuts — that appear across unrelated contexts. One observed example is the repeated emergence of fantasy-based metaphors such as “goblins,” “gremlins,” or similar entities when describing abstract system behavior, errors, or complexity. This article presents a structured analytical trace (A11 framework passes) showing how such patterns emerge from the interaction between reinforcement learning, cultural priors in training data, and user feedback loops. It also explores how introducing explicit interpretability layers can reduce the risk of these symbolic attractors becoming dominant explanatory shortcuts in model behavior. The first A11 pass S1 — Will Understand the causal mechanism: why the “goblin / fantasy drift” emerged in LLMs S2 — Wisdom (constraints) Main pitfall: confusing correlation (goblins appearing in outputs) with causation (why those specific symbols emerge) Also: “goblins” are not a standalone phenomenon they are a case of broader archetypal language drift S3 — Knowledge (what is actually known) There are 5 established mechanisms in LLM behavior: 1. RLHF reinforces “socially engaging metaphors” Models are rewarded for: vividness humor imagery human-like explanations ➡️ fantasy imagery tends to score highly 2. Internet prior already contains strong fantasy culture Training data includes: Reddit gaming discourse D&D culture fanfiction ➡️ “goblin / elf / troll” already exist as: universal behavioral archetypes 3. Compression effect (semantic abstraction) The model seeks compact semantic units: goblin = chaotic / greedy / messy / low-level failure mode ➡️ one token replaces a complex description 4. User feedback loop If the model says: “it’s like a goblin” users: react positively repeat it reinforce it in conversation ➡️ increases probability of reuse 5. Cross-task transfer (persona leakage) Stylistic patterns from: coding assistant mode creative mode

From Optimization to Protection: Adding a Security and Governance Agent to Your Snowflake Multi-Agent Team (Part 3) In Part 1 , we built an Admin Agent for usage and cost visibility. In Part 2 , we added a Cost Optimizer Agent and an Orchestrator that routes questions to specialists. Now we close the loop with the third specialist: a Security and Governance Agent . This turns your assistant from "what happened" and "what to optimize" into a full team that also answers "what is risky right now". By the end of this post, you will have: A Security and Governance Agent with focused security tools Security semantic views mapped to natural language Orchestrator routing across Admin, Cost Optimizer, and Security agents A practical triage workflow for failed logins, privilege risk, and unauthorized access Why Add a Security Specialist? The first two agents are strong for operations and spend, but security requires a different lens: Access control and role hygiene Failed login patterns and anomaly detection Unauthorized access attempts Inactive users with active privileges Compliance-friendly audit summaries Could one large agent do everything? Sometimes. But specialized agents are easier to maintain, safer to evolve, and easier to test. Final Team Architecture User Question (natural language) | Orchestrator Agent / | \ Admin Cost Security Agent Optimizer Governance Agent \ | / Unified Response Role of each specialist Admin Agent: usage, credits, storage, operational metrics Cost Optimizer Agent: idle compute, rightsizing, optimization opportunities Security and Governance Agent: roles, privileges, failed logins, unauthorized access, audits The Security Pattern (Same Foundation as Parts 1 and 2) Step 1: Base Views Create security-focused views over SNOWFLAKE.ACCOUNT_USAGE , including: Role hierarchy and privilege grants Failed login attempts and anomaly severity Excessive or unused privileged access Unauthorized access attempts User and role audit summaries Network policy ac

You can see if ads on Google Search, Google Discover, and YouTube were made or edited using AI from a new section in Google's "My Ad Center," as reported earlier by TechCrunch. The update, announced on Thursday, adds a "created or edited with AI" label under the "how this ad was made" tab. Users can […]

If Google's AI tools are used, it will automatically be noted in My Ad Center.

News publishers say OpenAI hid tools and datasets that could identify copyrighted journalism in ChatGPT outputs, escalating their lawsuit with a new motion for sanctions.

Passei os últimos dias construindo o HookSafe, uma camada que fica entre a plataforma de pagamento e o servidor do cliente para garantir que nenhum webhook se perca. A promessa do produto é uma só: se o seu servidor cair, eu seguro o evento e insisto até entregar. Cometi três bugs no caminho. O que me fez escrever este texto não foi a burrice de cada um, foi perceber, depois, que os três tinham a mesma forma: todos faziam uma falha parecer um sucesso. Num sistema cujo produto é confiabilidade, é difícil imaginar categoria de bug mais cruel. Bug 1: engoli o erro, e o sistema jurou que tinha entregue A função que entrega o evento no servidor do cliente ficou assim: go resposta, err := clienteHTTP.Do(requisicao) if err != nil { return "", nil // <- olhe com carinho } Eu quis escrever return "", err . Escrevi nil . O efeito: apontei o destino para uma porta onde não havia nada escutando. O Do devolveu um belo connection refused . E a minha função respondeu ao worker: "sem erro, chefe". O worker, obediente, marcou o evento como entregue , com o status da resposta vazio, e seguiu a vida. No banco: id | pedido_id | status | tentativas | resposta ----+-----------+----------+------------+---------- 6 | 9002 | entregue | 0 | Um evento que nunca saiu do lugar, registrado como entregue. Se isso estivesse em produção, um cliente teria pagado, não receberia nada, e o meu painel mostraria, orgulhoso, que a entrega foi um sucesso. Aquele if err != nil { return err } que a gente reclama de repetir em Go existe exatamente por isso. A linguagem te obriga a decidir o que fazer com a falha, toda vez. O preço da verbosidade é que ninguém engole um erro sem querer... a menos que digite nil . Bug 2: o log mentiu Corrigi o primeiro bug, rodei de novo, e o worker começou a cuspir isto, a cada cinco segundos, para sempre: worker: erro ao marcar morto 7: ERROR: column "reposta" does not exist worker: evento 7 esgotou as tentativas, marcado como MORTO Leia as duas linhas de novo. A primeira diz