标签：#lab

Babies are tremendous learning machines, and key advances for AI may soon be found in the architecture of their little brains.

It's the company's first public proof point after a year and a half spent building AI infrastructure largely out of public view.

A group of 26 former Meta employees is suing the company over claims that it used AI tools to unfairly target workers on leave with layoffs, as reported earlier by Reuters. In the lawsuit, the employees allege Meta determined which workers to dismiss based on performance data collected by a "constellation" of internal AI tools, […]

The failures that cost me the most in three years of self-hosting were never the ones that threw an error. An error is a gift: it tells you where to look. The expensive ones are the failures that report success while being broken . A page that returns 200 OK . A healthcheck that says the container is fine. A backup that exits cleanly. A command that prints nothing wrong. Everything green, everything lying. Here are four of them, all from the same box (a 2016 desktop, i7-6700 / 32 GB, Docker behind Caddy, reachable only over Tailscale). Each fails by handing you a success signal. Each cost me an evening the first time. The fixes are boring once you know them, the point is knowing the failure exists. Sanitized skeleton with all the config at the end. 1. A loading page that returns 200 This one I could find nothing written about, so it cost me the most. To keep the box quiet, I run the heavy services on-demand: Sablier stops idle containers and starts them on the first request. Caddy (with the Sablier plugin) gates a virtual host behind a container group, serves a "please wait, starting up" page while the group boots, then proxies through: myhost . my - tailnet . ts . net : 8081 { route { sablier http :// sablier : 10000 { group office session_duration 30 m } reverse_proxy nextcloud : 80 } } I gate my whole Nextcloud vhost, WebDAV included, this way. And here is the silent failure: if the gated group is not healthy, Sablier serves that HTML loading page for every request, and it serves it with 200 OK . A browser shows a spinner, fine. But my Obsidian vault syncs over WebDAV, and a WebDAV client asking for a directory listing got a 200 with a chunk of HTML instead of the XML it expected. Sync died with a cryptic no root multistatus found . Nextcloud itself was up and perfectly healthy the whole time. Every uptime check I had was green, because the gate in front kept answering 200 . The structural lesson: the moment you put a service on-demand behind a reverse proxy, tha

Davide de Paolis discusses the realities of rolling out cloud infrastructure compliance without fracturing developer relations. Drawing from a real-world platform team reboot at Sevdesk, he explains how to implement "minimum viable governance" on AWS, utilize event-driven Slack alerting to automate policy feedback, and shift from rigid enforcement to high-empathy, data-driven collaboration. By Davide de Paolis

Originally published at woitzik.dev A previous article here covered setting up gitleaks for homelab secret scanning - the setup, the pre-commit hook, getting CI to fail on new commits that contain secrets. The setup was correct. The tool was running. The CI was green. And it had been quietly suppressing a live production credential for months. This is the follow-on story: not about getting gitleaks running, but about the specific way a baseline file breaks the guarantees you think you have once it's in place. View the complete homelab infrastructure source on GitHub 🐙 What a Baseline File Does (and Is Supposed to Do) When gitleaks first runs on an existing repo, it finds every secret-shaped string in the full git history - including secrets that were introduced years ago, rotated long since, and are completely inert. Flagging those in CI creates noise that causes developers to tune out gitleaks entirely, which is worse than not having it. The baseline workflow is the standard answer: run gitleaks on the current state, export all findings to a JSON file, commit that file to the repo, and tell gitleaks to suppress any finding that already appears in the baseline. Future commits that introduce new secrets still fail; old known-inert findings don't. # Generate baseline from current HEAD gitleaks detect --report-format json --report-path .gitleaks-baseline.json # Tell gitleaks to use it gitleaks detect --baseline-path .gitleaks-baseline.json The assumption embedded in this workflow: findings that appear in the baseline are inert. They were there before the baseline was generated; they've been there; they're known. The Assumption That Broke It The baseline was generated at a point when the repo contained Garage's rpc_secret and admin_token committed in a YAML file. Those were real production values - the cluster was live, using those exact secrets - but the baseline suppression treated them as "known, reviewed, not a problem." The commit that introduced them had happened

Lately, I've been doing some deep personal reflection. I'm talking about myself, I hope no one misunderstands, on how pervasive the use of AI has become in my daily development workflow. Through a bit of self-analysis, I've discovered some interesting dynamics. Dependencies often arise from the desire to fill a void. But what kind of void does an experienced developer like me face? As a professional, I have the skills. Sure, AI helps me get things done faster, but the final product is always the translation of my vision; if I don't fully understand the solution, I discard it. I'm not looking for "magic," I'm looking for efficiency. Yet, I realize I've used AI to fill a specific void: the need for discussion. Software development is inherently solitary. The satisfaction of a successful "execution" after hours of discussions, refinements, and clashes over an architecture is an experience I miss today. The chat interface is always there, ready to respond. But there's a problem: it's a "yes-man." Even when I force it to be critical or provocative via the system's prompts, I know it's just reciting a script to please me. There's no conviction, no risk of error, none of the friction that arises when a colleague courageously defends their vision, perhaps one that conflicts with mine. We are part of a huge community, but debate often remains superficial. One might argue that posts and comments are enough, but anyone who has tried knows it doesn't work very well: a debate is truly alive only when there is no latency. In comments, the time between thinking, writing, and waiting for a response diminishes the energy of the exchange, turning it into a series of monologues rather than a dialogue. Why don't we try creating "virtual tables" where we can discuss projects, architectures, and technical choices with the natural rhythm of a conversation? Direct, real-time discussions, in person or remotely, where the exchange of ideas can spark sparks, without the filter (and delay) of

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

Adding cloud regions changes latency and cost in ways simple math can't capture. This article presents a framework from multiple launches: decompose your latency budget before committing to infrastructure, choose deployment patterns by consistency and traffic profile, and optimize before expanding. A phased approach cut latency 35% through routing alone, before a new region brought it under 60ms. By Uttara Asthana

In a recent article, Datadog engineer Arnold Wakim shared what worked, what didn't, and the lessons they learned while evolving a critical production system using AI to overcome hard limits in its storage backend and significantly improve performance. By Sergio De Simone

Experiments in using AI to build AI show that the future doesn’t just belong to the frontier labs.

In June, we experienced six incidents that resulted in degraded performance across GitHub services. The post GitHub availability report: June 2026 appeared first on The GitHub Blog .

"The BOHR mission serves as a pathfinder for future nuclear-powered spacecraft."

CAP: khi network partition xảy ra, chỉ được chọn C hoặc A — không có "cả ba" CAP theorem là kết quả của Gilbert và Lynch (formal proof năm 2002 cho conjecture Brewer đưa ra ở PODC keynote 2000): một hệ phân tán có shared state không thể đồng thời cung cấp cả linearizable Consistency , Availability (mọi request tới non-failing node đều trả lời không lỗi), và Partition tolerance khi có network partition. Trong thực tế, partition là thứ sẽ xảy ra — TCP retransmit, GC pause dài, switch chết, cross-region link flap — nên P là ràng buộc bắt buộc, không phải lựa chọn. Câu hỏi thật là: khi partition xảy ra, hệ thống hy sinh C hay A? Chọn sai gây ra hai loại incident khác nhau: chọn AP mà dữ liệu cần linearizable dẫn tới double-charge, oversell inventory, split-brain; chọn CP mà dữ liệu chỉ cần eventually consistent dẫn tới downtime không cần thiết, user không đọc được profile của chính mình. Cơ chế hoạt động Định nghĩa formal theo Gilbert và Lynch: Consistency ở đây là linearizability : mọi read sau một write hoàn tất phải thấy giá trị mới (hoặc mới hơn); tồn tại một total order các operation phù hợp với real-time. Availability : mọi request tới một non-failing node phải nhận response (không timeout, không error). Partition tolerance : hệ thống tiếp tục hoạt động dù network drop tuỳ ý message giữa các node. Proof intuition: giả sử có 2 node N1, N2 giữ cùng key x=0 . Client ghi x=1 vào N1. Link N1 và N2 đứt. Một client khác đọc x từ N2. Nếu N2 trả về 0 thì không linearizable (mất C). Nếu N2 chờ đến khi thấy được N1 thì mất A. Nếu N2 từ chối phục vụ thì cũng mất A. Không có cách thứ ba. Trong hệ CP, mỗi write phải qua quorum (Raft, Paxos, ZAB); khi node bị isolate khỏi quorum, nó từ chối phục vụ để giữ linearizability: // etcd/Raft-style: khi mất quorum, leader step down và write fail resp , err := kv . Put ( ctx , "order/42" , "paid" ) if err != nil { // err là ErrLeaderChanged hoặc context.DeadlineExceeded khi ở minority side // client thấy unavailable — đúng contract CP re

Every beginner developer hits the same issue at some point. You learn a few basics, finish a tutorial, and then you have no idea what to build next. That gap can feel bigger than learning the code itself, because now the question is not “How do I write this?” but “What should I build at all?” This article is for that moment. I want to make it simple, practical, and useful, because project ideas do not need to be too advanced to be valuable. A good project is one that teaches you something, keeps you going, and gives you enough confidence to build the next one. Why project ideas are important There’s a common thing that I have noticed in most of the beginners, that is, watching too many tutorials. Tutorials are helpful, but actual learning starts when you try to build something on your own. That is when you start facing real decisions, small bugs, unclear logic, and the feeling of connecting different parts into one working product. That is one of the reasons why project ideas matter so much. The right idea gives you direction, but it also gives you energy. When the project feels too huge, you get stuck. When it feels too small or boring, you stop caring. The sweet spot is a project that feels possible and still a little exciting. This matters even more today. Tools like ChatGPT or Copilot can help you write code faster, but that doesn't solve the real problem beginners have. Writing the code was never the hard part for long but knowing what to build is. Start with problems you already know The easiest project ideas often come from your own life. Think about small things you do every day that feel annoying, repetitive, or messy. A simple to-do list, habit tracker, note saver, expense log, study planner, or meal planner can all become strong beginner projects if you build them well. This works because the problem is already familiar to you. You do not have to invent a fake use case or force a complicated feature list. You already know what the app should do, what feel

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

The product drop had been planned for months. The direct-to-consumer subscription business had run three separate load tests, provisioned extra capacity for the launch window, and staffed a warroom across two time zones. The drop itself went cleanly. Two hours in, an unrelated video from a creator with a large following mentioned the product without warning, and the sign-up flow collapsed under a rush of new members for twenty-eight minutes. Customers were told the site was busy and to try again later. Some did. Most did not. The refund exposure was manageable. The customer acquisition exposure was not. What went wrong is not the interesting question. The system was under-provisioned for a specific traffic shape it had not seen before, and the team fixed it. The interesting question is what happened seven weeks later. A weather event redirected a wave of app traffic in an entirely different sector, at midnight on a Tuesday, without any warning. That system held, because a small group of engineers had spent those seven weeks quietly rebuilding assumptions about when peak load happens and what it looks like. The lesson from the product drop was not "provision more capacity for product drops." The lesson was that the mental model of peak load as a scheduled event had stopped being useful. This is another post in our series on the engineering layer underneath enterprise strategy. The previous post ( Sovereignty Versus Efficiency ) argued that sovereignty has become an architectural property that procurement cannot solve on its own. This post makes an analogous argument about load. Across banking, media, retail, travel, restaurant chains, and sport, the architectures built to survive named events are increasingly the wrong architectures for the traffic these businesses now routinely encounter. The discipline required has moved closer to what telecommunications engineers have always done, while the cost models have not caught up. What peak load used to mean For most of th

True Anomaly and Rocket Lab are performing Top Gun-style satellite fly-bys for the U.S. military.

A company shifted from project- to product-thinking after their platform outgrew single-team use. The limitations that they felt with their platform were one-off deliveries, lack of product vision, and weak feedback loops. They have moved toward a self-service, API-driven, multi-tenant infrastructure with clearer ownership and better abstractions. By Ben Linders

The homelab started flat. One /24, everything on it. My workstation, the NAS, the Proxmox host, and — over time — a growing list of workloads sharing the same broadcast domain because that was the path of least resistance. For a while, that was fine. A homelab running one workload doesn't need segmentation any more than a house needs an office door. Then I stood up an Akash provider. An Akash provider is, in shape, a Kubernetes cluster that accepts inbound tenant workloads from the internet — real deployments, paying for compute, containers I didn't write landing in namespaces on my hardware. The provider itself is documented at github.com/jjozzietech/akash-provider-ops-public — this piece is about the network underneath it. The containerisation posture itself is fine. I trust the isolation model. But trust isn't a network design. And the network at that moment had the tenant workload cluster sitting on the same subnet as my workstation, my NAS, and my Proxmox management interface. That was the moment I stopped thinking of the rack as a home network with extra boxes, and started thinking of it as a small data centre. This piece is the network design that came out of that shift. I'll cover the layout, the rules that hold it together, and the Nexus and Proxmox configs that anchor it — with the specifics of my own deployment sanitised. It's not a step-by-step replication guide. It's the design pattern, with enough of the shape to be useful and enough restraint to not double as a recon document for my own rack. // the original design The flat layout looked like this: home lan — 192.168.1.0/24 opnsense (perimeter) cisco nexus (dumb L2 switching) proxmox host workload VMs (all on the same subnet) What it got right: zero routing complexity, everything reachable from everywhere, fast to stand up. If you're running one project on a homelab, this is the correct design. Don't over-engineer it. What stopped working, as soon as the second project landed on the rack, was that the