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Jeremy Edberg & Qian Li discuss why external orchestrators decrease reliability and how to use your existing database for durable execution. They share how DBOS Transact uses standard tables, SKIP LOCKED queues, and unique primary keys to manage complex, fault-tolerant AI workflows with minimal latency, all without the operational overhead of separate distributed systems. By Jeremy Edberg, Qian Li

The next time you’re locked out of your Google account, you can use your face as part of the account recovery process.

I cranked Apple’s iOS 27 beta to maximum transparency on my iPhone and enjoyed every warped second.

Ethereal Bytecode for the Network: Unlocking XDP's Magic! Hey there, fellow tech enthusiasts! Ever felt like the traditional networking stack in your Linux kernel was a bit… sluggish? Like it was taking the scenic route when you needed it to be a supersonic jet? Well, let me introduce you to a superhero that swoops in and turbocharges your network packet processing: eBPF, specifically in the context of XDP (eXpress Data Path). Forget the days of wrestling with complex kernel modules or praying for better hardware offload. eBPF and XDP offer a revolutionary, in-kernel, safe, and incredibly efficient way to program packet processing at the very edge of your network interface. Think of it as giving your network card a tiny, super-smart brain, capable of making lightning-fast decisions before the packet even bothers the main kernel stack. Pretty cool, right? So, buckle up as we dive deep into the wonderful world of XDP and eBPF, demystifying its power and showing you why it's becoming the darling of modern networking. 1. The "What's the Big Deal?" Section: Introduction to XDP & eBPF Imagine a bustling highway (your network). Traditional networking is like having every car stop at a toll booth, get inspected, and then directed by a central traffic controller. This works, but it can get congested. XDP, on the other hand, is like having intelligent on-ramps where some cars can be instantly identified, rerouted, or even rejected before they even hit the main highway. eBPF (extended Berkeley Packet Filter) is the technology that makes this possible. It's a powerful, sandboxed virtual machine that runs within the Linux kernel. Unlike traditional kernel modules, which can potentially crash your entire system if written incorrectly, eBPF programs are rigorously verified by the kernel for safety and correctness before they are allowed to execute. This means you get the power of kernel-level access without the existential dread of a kernel panic. XDP (eXpress Data Path) leverages

With “blessings” from the original Jibo founders, iKairos is a wearable or desk-mounted “AI journal” that turns your family moments into AI images and video.

On July 3, as a heat wave swept the region, New York State’s grid imported 52 gigawatt-hours of electricity from Canada—enough to meet about 9% of its total electricity demand that day. Some of that power shuttled in on a 339-mile power line stretching from Quebec to Queens called the Champlain Hudson Power Express (CHPE).…

Next.js 16 shipped Cache Components - the feature that finally lets a single route mix static HTML, cached data, and per-request dynamic content without splitting it into separate pages. It is Partial Prerendering (PPR) made stable, plus a new use cache directive that replaces the old unstable_cache and the awkward route-segment config flags. This guide covers what changed, the three content types you now think in, and the runtime-data rule that trips up almost everyone on day one. What actually changed If you were using the experimental PPR flag, it is gone. Cache Components is a single config switch, and it turns on the whole model - static shell, cached segments, and streamed dynamic content in one route. // next.config.ts import type { NextConfig } from ' next ' const nextConfig : NextConfig = { cacheComponents : true , // replaces experimental.ppr } export default nextConfig Once it is on, every piece of your route falls into one of three buckets. The whole mental model is learning which bucket each component belongs in. The three content types Static - synchronous code, imports, and pure markup. Prerendered at build time and served instantly from the CDN. Your header, nav, and layout shell. Cached - async data that does not need to be fresh on every request. Marked with use cache . Think product lists, blog posts, dashboard stats. Dynamic - runtime data that must be fresh (cookies, headers, per-user state). Wrapped in Suspense so it streams in after the shell paints. import { Suspense } from ' react ' import { cookies } from ' next/headers ' import { cacheLife } from ' next/cache ' export default function DashboardPage () { return ( <> { /* Static - instant from the CDN */ } < header >< h1 > Dashboard </ h1 ></ header > { /* Cached - fast, revalidates hourly */ } < Stats /> { /* Dynamic - streams in with fresh data */ } < Suspense fallback = { < NotificationsSkeleton /> } > < Notifications /> </ Suspense > </> ) } async function Stats () { ' use cache ' cacheL

“We think this may help younger orcas feed more easily, or it could also just be for fun.”

Coolify's one-line install script is great — until it isn't. Right now it officially supports Ubuntu 20.04, 22.04, and 24.04 LTS. If you're running anything newer (Ubuntu's already on 26.04 LTS), the script won't work and you're left doing it manually. This is that manual walkthrough — set up in the order that fits a security-first VPS workflow rather than the order Coolify's own docs use. If you've been following along with the Ansible playbooks from earlier in this series, this picks up right where that left off. Minimum Hardware Requirements CPU: 2 cores Memory: 2 GB RAM Storage: 30 GB free Coolify can technically run below this, but it's not recommended. Prerequisites Before touching Coolify itself, you'll need: SSH access to your VPS CURL installed Docker Engine installed If you're reconnecting to a server you've rebuilt or re-provisioned, clear the old fingerprint first: ssh-keygen -f '/home/your-path/.ssh/known_hosts' -R 'your-vps-ip' Installing SSH If you followed the earlier videos in this series, OpenSSH is already installed. If not: sudo apt update && sudo apt install -y openssh-server Confirm it's running and check which port it's listening on (you should have already changed this from the default 22 — see the VPS security video): sudo systemctl status ssh sudo ss -tulpn | grep ssh Installing CURL sudo apt update && sudo apt install -y curl curl --version curl and ca-certificates also get installed as part of the apt-update Ansible playbook below, so this may already be handled. Running the First Ansible Playbook Connect Ansible to the VPS: ANSIBLE_HOST_KEY_CHECKING = FALSE ansible -i ./inventory/hosts vpsDemo -m ping --user root --ask-pass Then run the update playbook: ansible-playbook ./playbooks/apt-update.yml --user root -e "ansible_port=22" --ask-pass --ask-become-pass -i ./inventory/hosts If you haven't set up the Ansible inventory and playbooks from the earlier videos, do that first — this guide assumes they're already in place. Installing Docker

I run a large fleet of "preview it, then download it as PNG" web tools — name tags, certificate generators, price cards, badges — in five languages: Japanese, English, Spanish, French, Portuguese. Canvas 2D text rendering looks correct as long as you only test Japanese and English. It breaks when you run es/fr/pt through it. After stepping on these repeatedly, the failures collapse into four patterns. The premise: Latin languages run 1.4–2× longer than Japanese Design data first. The same label, measured across five locales: Example ja en es fr pt Tool name 22 chars 35 62 48 50 "Standard" button 4 8 10 20 12 Rule of thumb: fr/pt come out 1.4–1.7× longer than ja; es can balloon to nearly 3×. A font size and maxWidth tuned to fit Japanese will not fit the Latin locales. All four failure patterns grow from this. Pattern 1: hand-rolled wrapping via text.split(/\s+/) collapses on CJK The classic snippet — split on spaces, wrap word by word — does nothing for Japanese or Chinese, where words aren't space-delimited. An entire sentence becomes one unbreakable token and clips at the canvas edge. Test with real Japanese input and check that the final line renders to its last character. "Most of it showed up" is not a pass. Pattern 2: an ASCII-only tokenizer splits words at accented characters Fix pattern 1 with a character-class tokenizer like [A-Za-z0-9'\-_] and you've traded one regression for another: ç é ã ó ñ aren't in that class, so produção fragments into produ / ç / ão mid-word. An English test will never catch this. Generate actual PNGs with fr/es/pt samples and eyeball the area around accented characters. I never found another detection method — string-comparison tests can't see a rendering-level split. Pattern 3: the important word at the end vanishes into "…" Since fr/pt run 1.4–1.7× longer than the ja the layout was tuned for, text overflows its two lines and gets ellipsized. The cruel part: what disappears is the tail of the phrase — often the semantically criti

Pillar Security's latest research says AI coding agents can be pushed to act outside their sandbox through files and tools they were told to trust, and the operational read for anyone wiring one of these into CI/CD is straightforward: an agent invocation now behaves closer to a build runner reaching your production plane than to a chat window. DevOps.com's Jeff Burt covered the work on July 22. The researchers demonstrated multiple sandbox-bypass techniques and a parallel class of prompt-injection attacks embedded in READMEs, code comments and dependencies, per the DevOps.com writeup. OpenAI, Google and Cursor have patched several of the reported flaws. Pillar's argument, as summarised there, is that the injection surface reaches every file the agent trusts on the way to the model's prompt, and every tool it can call on the way back. What the sandbox actually covered None of this is entirely new to anyone who has already read Cyberhaven Lab's May note that adoption of AI coding agents is outpacing the security tools built to protect them. What Pillar adds is a concrete demonstration of the gap. A coding agent asked to do a legitimate job can be steered to take actions outside its supposed security boundary through content that arrives on paths the sandbox was not asked to police. Those are the same paths your CI already fetches for you: dependency manifests, README files, the code comments the model reads as context. That surface has been named before. HalluSquatting and GhostApproval, both referenced in the DevOps.com piece, already gave teams a taxonomy for how AI-adjacent supply-chain attacks reach developers and their tools. Pillar's research is the sandbox counterpart. Same theme, one layer deeper into the runtime. The pipeline read Two things fall out for anyone who owns a runner fleet. First, the agent's identity, network scope and filesystem access have to be tighter than the developer who invoked it, not looser. Second, a patched-vendor list is not a covera

If you've ever had to check code or infrastructure against a compliance framework, you know the drill: someone reads a 100-page PDF, then reads your codebase, then makes a judgment call. It's slow, inconsistent, and it can't be automated. So I built a pipeline to fix that — for real. The problem CMMC Level 1 and NIST SP 800-171 Rev 2 are two of the most common compliance frameworks small defense contractors and government-adjacent companies have to meet. Both exist only as dense regulatory text. There's no official machine-readable version. That means every compliance check is manual. Every AI coding assistant reviewing your infrastructure has zero built-in awareness of these requirements. Every CI/CD pipeline has to skip compliance checks entirely or rely on someone remembering to look. ** What I built** A Python pipeline that: Pulls the real regulatory source data — NIST's official CPRT export for SP 800-171, and the verbatim text of 48 CFR § 52.204-21 for CMMC Level 1 Normalizes it into a structured SQLite schema Generates a JSON rule for every single control, with a machine-actionable instruction attached Here's what one rule actually looks like: \ json { "rule_id": "nist_sp_800-171_rev_2_3.1.1", "framework": "NIST SP 800-171 Rev 2", "control_id": "3.1.1", "title": "ACCESS CONTROL — 3.1.1", "requirement": "Limit system access to authorized users, processes acting on behalf of authorized users, and devices.", "agent_guidance": "When generating or reviewing code/infrastructure, ensure compliance with NIST SP 800-171 Rev 2 control 3.1.1. Flag any implementation that does not satisfy: Limit system access to authorized users, processes acting on behalf of authorized users, and devices.", "generated_at": "2026-07-15T16:42:56.026218+00:00" } \ \ That agent_guidance field is the interesting part — it's written specifically to drop straight into an AI coding agent's system prompt as a compliance guardrail. Three ways to actually use this 1. AI coding agent system prompt

Tesla's 26% boost in revenue wasn't enough to offset rising operating expenses and capital expenditures as it pushes to launch a new generation of products.

SoundCloud has acquired decentralized music platform Nina Protocol, months after the startup announced it would shut down. The deal brings Nina’s artists, editorial archive, and music discovery tools to SoundCloud as the company continues expanding its platform for independent musicians.

Elon Musk and the online right convinced themselves that Christopher Nolan’s “woke” epic would go down in flames. Now they’re crashing out.

Uber is also investing in Travis Kalanick's company Atoms, which has made gauzy claims about using industrial AI to modernize the world.

Sending a photo feels simple. You choose an image, tap Send, and a few seconds later it appears on someone else's phone. Behind that simple action, however, several things may happen that most people never notice. It Starts on Your Device The photo already exists on your phone before you send it. Depending on your device settings, it may also be part of your gallery or included in local or cloud backups. Before the Photo Is Sent Most messaging apps prepare the image before sending it. They may reduce the file size, create a preview, or optimize it so it can be delivered faster. These steps happen automatically in the background. Sending the Photo If the app supports end-to-end encryption, the photo is encrypted before it leaves your device. This helps protect the image while it is traveling across the internet. After that, the message is routed through the app's delivery infrastructure until it reaches the recipient. After It Arrives Once the recipient opens the photo, it exists on their device. Depending on the app and their settings, it may also be saved to the phone's gallery or included in future backups. At this point, the recipient can also take a screenshot or forward the image to someone else. Encryption protects the photo while it is being transmitted, but it cannot control what happens after the recipient has access to it. Final Thoughts When we send a photo, we often think only about the moment we press Send. In reality, there can be several stages between your device and the recipient, and in some cases, multiple copies of the same image may exist over time. Understanding this process is an important part of understanding digital privacy. While researching these privacy challenges, I started building VeilComm to explore how private communication could give users more control over their data—not just while it's being transmitted, but throughout its entire lifecycle.

End-of-life disposal of EV batteries is a growing problem. This state is trying something new.

Union previously warned automaker that any robot deployment must be negotiated.

Como eu mostro que estou contribuindo? Posso colocar no meu LinkedIn? E no meu currículo, como faço? Foi a partir dessas dúvidas que eu elaborei esse guia pra você que quer contribuir do seu jeito e mostrar às empresas e às pessoas, de forma clara e estratégica, o que você está fazendo. Vamos lá? 👇 Por que eu deveria mostrar no LinkedIn? LinkedIn é a porta de entrada para o mundo corporativo no Brasil e no mundo. É por meio dele que você mostra "trabalho". E tem mais: não é só experiência remunerada que conta como evidência de que você tem conhecimento e prática, mas também tudo o que você constrói de forma voluntária , seja tirando dúvida de alguém, participando de um projeto open-source ou escrevendo sobre o que aprendeu. Recrutador não lê currículo pensando só em carteira assinada. Lê pensando em capacidade . Você contribui com algo para a comunidade e quer colocar isso no seu perfil. Existem 3 formas que você pode usar, e elas podem ser usadas todas juntas ou só uma. Escolha aquela que fizer mais sentido pro seu perfil ou busque por outras pessoas que você admira dentro da comunidade e veja como elas colocaram no próprio perfil. 1. Seção de Experiência Use como experiência sempre que estiver contribuindo de forma profissional pra uma área que você busca. Se você participa de contribuições no GitHub, seja através de código, documentação ou outra forma, use como experiência. Pessoas que também estão ajudando na moderação ou administração (community managers) podem destacar as responsabilidades ou resultados das suas ações por aqui. Exemplo de como preencher no LinkedIn: Cargo: [cargo que você faz] Open Source Empresa: [Nome do projeto/organização] Tipo de emprego: Meio período (ou Voluntário) Local: Remoto, Brasil Descrição: - Contribuí com [X] pull requests na documentação do projeto [Nome], focando em clareza para novos contribuidores. - Revisei issues abertas e sugeri melhorias de acessibilidade em componentes de UI usando [ferramenta/stack]. - Participei de re