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We’ve got another special episode of Decoder today, recorded at the Cannes Lions advertising festival in the South of France. I’m talking with Ali Berman and Raina Penchansky, who run the Creators division at United Talent Agency. UTA is an enormous talent agency. Half the people you’ve ever heard speak or perform or who show […]

Netflix engineers introduced dynamic partition splitting for Cassandra to address wide partitions in time series workloads. The metadata-driven approach detects oversized partitions, splits them smaller units, and routes reads across child partitions. Netflix reported lower read latency from seconds to milliseconds, reduced timeouts, and improved cluster stability while maintaining transparency. By Leela Kumili

FCA official makes case for greater powers for watchdog as millions use technology for personal finance decisions.

WhatsApp now lets users reserve usernames ahead of the feature’s full rollout, making it possible to connect without sharing a phone number once usernames go live.

Multiplayer synthesizer Discussion | Link

GitHub热门项目 | git worktrees + tmux windows for zero-friction parallel dev | Stars: 1,708 | 62 stars this week | 语言: Rust

GitHub热门项目 | Open-source desktop app for downloading, organizing and studying media. Native cross-platform (Tauri + Rust + Svelte). PDF/EPUB reader with focus mode, timestamped notes and spaced repetition. Media downloads via yt-dlp (1.800+ sites). Extensible plugin system. | Stars: 6,293 | 96 stars today | 语言: Rust

GitHub热门项目 | Run Windows apps on 🐧 Linux with ✨ seamless integration | Stars: 21,861 | 14 stars today | 语言: TypeScript

GitHub热门项目 | 📺 Cross-platform IPTV player application with multiple features, such as support of m3u and m3u8 playlists, favorites, TV guide, TV archive/catchup and more. | Stars: 6,463 | 21 stars today | 语言: TypeScript

GitHub热门项目 | A self-hostable bookmark-everything app (links, notes and images) with AI-based automatic tagging and full text search | Stars: 26,696 | 178 stars today | 语言: TypeScript

GitHub热门项目 | The simplest, fastest repository for training/finetuning medium-sized GPTs. | Stars: 60,827 | 246 stars today | 语言: Python

Station F, the Paris-based startup hub founded by French billionaire Xavier Niel in 2017, is gearing up for a new edition of its selective acceleration F/ai program that will cement its role as a stepping stone for AI startups.

I kept hearing about Kalshi. The commercials, the mentions, and then one morning CNN was talking about Kalshi prediction odds like they were a weather report. So I went and looked. And I had no idea what I was seeing. Kalshi is really hard to understand when you're new to it. You get markets, contracts, prices that are also probabilities, volume, movement, and none of it tells you what's actually worth paying attention to. I wanted something that would translate what I was looking at into something I could understand and, ideally, act on. That was the whole original goal: make the firehose legible. Then, of course, I kept adding to it, because once you can read the flow you start wanting an edge in it. Who doesn't. So Trade Hunter grew from a translation layer into a translation layer with detection on top. This is a writeup of what it became, why I made the design choices I made, and the parts I'm still not sure about. I'd rather you poke holes in it now than find the breakpoints the hard way. If you think a decision here is wrong, please let me know. The comments are the point of this post, not an afterthought. Where this came from Basically, I couldn't read Kalshi, and I wanted to. Trade Hunter is the original tool I built to fix that for myself, and it's the one I still run when I want a live view. So this isn't a polished sequel to anything. It's the thing I built because I wanted it to exist, flaws and design bets included, and I'd rather show you those directly. The core idea never changed even as I piled features on: watch live Kalshi WebSocket feeds and surface an unusual move while it's still moving, rather than reading about it after the fact, or on CNN the next morning. What it actually does Trade Hunter subscribes to live Kalshi feeds across every market you track. Multi-contract series like the Fed rate decisions or who will win Top Chef fan out automatically to all open contracts, so you point it at one thing and it watches the whole family. When some

Bentley has confirmed that it will finally add an electric car to its exclusive lineup.

The story behind why we built a WhatsApp CRM that actually understands how WhatsApp works. There's one scene I can't get out of my head. A friend's desk. She runs an online store. On it sat three phones. Not for show. One for customer service, one for the admin, one for the number that was "just for resellers." All three buzzing, nonstop. And there she was, eleven at night, still replying to messages one by one, sighing: "It's the same questions over and over. But if I don't reply, they'll go to the competitor." That's not a rare case. That's the normal state of things for thousands of businesses. We all know one thing CRM software rarely admits: customers here don't live in email. They live on WhatsApp. They ask about prices on WhatsApp, complain on WhatsApp, close deals on WhatsApp, even ask for warranty support on WhatsApp. But the teams handling all of it? They use personal phones. No records, no context, no way to help each other when one person is drowning. Hallo Zetta was born out of that. What Frustrated Us About the Existing Tools Before building our own, of course we looked. Surely someone had solved a problem this simple? Turns out what existed fell into two camps, and both were maddening. Camp one: dumb auto-reply bots. Type "hi," get a template. But the moment a customer asks something slightly off-script, the bot freezes. It actually makes customers angrier, because it feels like talking to a wall. Camp two: bloated CRMs. Loaded with features, dashboards full of charts, but WhatsApp is bolted on as one small tab. As if WhatsApp were an afterthought, not the main battlefield. For most of our customers, WhatsApp is the battlefield. Nothing fit. So we decided to build it ourselves. The Hard Part Isn't "AI Can Reply to Messages" Let me be honest about this. Bolting AI onto WhatsApp is easy. Anyone can wire GPT to a webhook and ship it overnight. If that were the whole goal, this article wouldn't need to exist. The hard part, the thing that made us rethink

I built a daily Linux documentation site I created this site because I've noticed that Linux documentation is generally confusing. What is xybss? xybss is a simple site that publishes Linux documentation every day. No ads No tracking No JavaScript Just plain HTML docs I add at least one new command every day. Who is it for? Beginners learning Linux Anyone who needs a quick reference People who want simple, clean docs Current content Right now, the site covers basic commands like ls and rm. More commands are added daily. Check it out 👉 https://xybss.github.io Feedback is welcome

I missed the tiny "x" on a browser tab again today. Meant to close it, switched to it instead. Aiming a one-pixel pointer at an eleven-pixel checkbox is basically microsurgery, and somewhere along the way we all just accepted that. Here's the strange part: HCI research solved this twenty years ago. It just never shipped. The paper In 2005, Grossman and Balakrishnan published The Bubble Cursor at CHI. The whole idea fits in one sentence: Make the cursor's hit area a dynamic circle that always contains exactly one target. That turns out to be the same thing as always selecting the target nearest to the pointer. Picture the screen divided into Voronoi cells, one per clickable thing, and the cursor picking the owner of whatever cell it's currently in. The clever part is what it refuses to do. Naive "gravity" cursors snap to every link on the way to the one you actually want, and they get stuck. The bubble cursor grabs exactly one target by definition. The moment a second target becomes nearer, it switches. So it stays calm on link-dense pages, and the paper showed significant speedups in controlled experiments. Twenty years later our cursors are still naked, so I built it as a Chrome extension. It's called MagPoint . The core is about 30 lines A content script collects clickable elements ( a[href] , button , input , ARIA roles and so on) and, every frame, picks the one with the smallest point-to-rectangle distance: function pointToRect ( x : number , y : number , r : DOMRect ): number { const dx = Math . max ( r . left - x , 0 , x - r . right ); const dy = Math . max ( r . top - y , 0 , y - r . bottom ); return Math . hypot ( dx , dy ); } Clicks that land in the empty space near a captured element get re-routed to it. Past a max radius of 120px the magnet lets go, and empty-space clicks behave like the normal web. It also stands down while you type or select text, because getting yanked toward a link mid-sentence would be infuriating. The rule that kept me sane: the vis

When a single engineer manages all the Terraform in an organisation, everything is simple. One repo, one state, one pipeline, one set of credentials. There's no coordination overhead because there's no one to coordinate with. That stops working the moment a second team needs to deploy infrastructure. And by the time you have three or four teams — networking, platform, application, security — the single-team model is actively slowing everyone down. This guide covers what breaks, how teams typically work around it, and how to set up a structure where each team owns their slice of infrastructure independently. What breaks State lock contention Terraform's state locking is per-state. When the networking team is running terraform plan , the application team's pipeline is blocked — even though they're changing completely unrelated resources. The more teams share a state, the more time everyone spends waiting. Blast radius A junior engineer deploying a new application service shouldn't be able to accidentally destroy the VPC. But if application resources and networking resources share a state, a single misconfigured terraform apply can touch anything. Code review catches some of this. Not all of it. Credential sprawl A shared pipeline needs credentials for everything — the networking team's Azure subscription, the application team's AWS account, the security team's DNS provider. Every team's secrets end up in one CI environment, accessible to anyone who can trigger a run. This fails most compliance audits. Approval bottlenecks In many organisations, one person or a small group gatekeeps all infrastructure changes. Every PR needs their review. Every apply needs their approval. The gatekeeper becomes a bottleneck not because they're slow, but because they're a single point of serialisation for all infrastructure work. Backend access as implicit access control Terraform has no built-in concept of per-team or per-workspace permissions. All workspaces in a backend share the sam

Most organisations don't live in a single cloud. You might run compute in AWS, DNS in Cloudflare, identity in Azure AD, and logging in GCP. Terraform handles each provider fine on its own, but the moment you need to coordinate across providers the tooling fights you. This guide walks through the common pain points of multi-cloud Terraform setups and the approaches teams use to cope — then shows how Snap CD makes cross-cloud dependency management a solved problem. Where it gets difficult Credential sprawl Each cloud provider has its own authentication mechanism. AWS uses IAM roles and access keys. Azure uses service principals and managed identities. GCP uses service accounts and workload identity federation. A single Terraform state that spans providers needs credentials for all of them — which means your CI runner or developer workstation holds keys to everything. That's a security problem. A compromised CI pipeline with AWS and Azure credentials exposes both clouds simultaneously. And it's an operational problem — rotating credentials means updating every pipeline that touches that state. This problem compounds at scale: Terraform couples provider processes tightly to credentials , so managing hundreds of accounts across clouds means spawning thousands of provider processes, which quickly becomes unmanageable. Provider version conflicts Terraform providers are versioned independently. Upgrading the AWS provider to fix a bug in aws_eks_cluster shouldn't require you to also test a new version of the Azure provider. But when they share a state, a terraform init -upgrade pulls new versions for everything, and a regression in one provider blocks all deployments. Terraform also lacks built-in support for instantiating multiple providers with a loop and passing providers to modules in for_each , making multi-cloud configurations especially verbose and repetitive. Blast radius across clouds A misconfigured terraform apply in a single-cloud state damages resources in one c

Todo dev brasileiro que já precisou consultar CNPJ conhece o dilema: ou você usa uma API que faz proxy da Receita (3 a 10 segundos por consulta, quando não cai), ou baixa o dump de dados abertos e monta a própria base — e descobre que "baixar um CSV" era a parte fácil. Eu montei a própria base. Este post é o diário honesto do que funcionou, do que quebrou e dos números reais — 217 milhões de linhas servidas em ~10ms de p50 dentro do datacenter, num Postgres de 1 vCPU. A arquitetura em uma frase Não consulte a Receita em tempo real. Ingira o dump mensal e sirva da sua infra. O resto é decorrência. Receita (dump mensal, ~6GB zip) ──▶ ingestão Go (COPY) ──▶ Postgres ──▶ API (chi) CGU (CEIS/CNEP, zip diário) ──▶ job diário ──┘ O dump da Receita: as pegadinhas que ninguém documenta O layout oficial existe, mas o que quebra parser de verdade é o que está fora dele: Encoding latin1 (ISO-8859-1) — acento vira lixo se você ler como UTF-8. Em Go: charmap.ISO8859_1.NewDecoder() num transform.Reader streaming. Decimal com vírgula ( "1000000,00" ) e datas YYYYMMDD onde 0 e 00000000 significam nulo. CNPJ quebrado em 3 colunas (básico 8 + ordem 4 + DV 2). A chave de junção entre empresas, estabelecimentos e sócios é o básico — errar isso custa um dia. As partições 0–9 não se alinham entre arquivos. O estabelecimento da partição 3 pode ser de uma empresa da partição 7. Foreign key rígida entre as tabelas = COPY quebrando no meio da carga. A solução: sem FK; a integridade vem da fonte. Bytes NUL ( 0x00 ) no meio dos dados. O Postgres rejeita NUL em text . Um strings.ReplaceAll(s, "\x00", "") no parser economizou três recargas. Desde jan/2026 o repositório é um Nextcloud do SERPRO+ com WebDAV público — dá pra listar meses com PROPFIND e baixar com o token do share como usuário. Adeus, scraping. COPY ou morte A diferença entre INSERT em lote e o protocolo COPY não é incremental — é outra categoria. Com pgx.CopyFrom e lotes de 50k: 28,1 milhões de empresas em 1m28s (~320k linhas/s) num