标签：#os

Last year, Microsoft's 13-inch Surface Laptop quickly became one of my favorite thin-and-light Windows notebooks. At $900, it was easy to recommend to anyone wanting MacBook Air-like build quality and battery life on Windows - I even convinced my sister to buy one on sale. But that was last year. This year, thanks to RAMageddon, […]

Introduction A Continuation of Shadow SCADA Terminal Velocity begins where Shadow SCADA left off — at the edge where digital audits meet the physical world. In the previous article, we explored how hidden infrastructures reveal themselves through aerial recon, magnetic anomalies, and environmental signals. Now we move deeper: into the physics of sensing, the light‑based pathways of diodes and photodiodes, and the high‑spec tools that transform invisible signals into readable intelligence. Modern audits are no longer limited to dashboards and logs. They extend into light, magnetic fields, environmental distortions, and sensor‑level truth — domains that traditional processes never touch. Section 1 – Diodes and Photodiodes: The First Gate of Physical Signals In modern audits, everything starts at the physical layer — where electricity and light move before any software or dashboard exists. Two tiny components sit at that gate: diodes and photodiodes. They look similar, but they do very different jobs. What is a diode? · One‑way valve for electricity: A diode lets electric current pass in one direction only, like a one‑way street. · Why this matters for security: Diodes are used to make sure information can leave a system but cannot come back in through the same path (for example, in SCADA or critical networks). · Simple image: Think of a diode as a door that only opens outward. You can exit, but nobody can enter through that door. What is a photodiode? · Sensor for light: A photodiode doesn’t control current—it detects light and turns that light into an electrical signal. · Where it’s used: In cameras, light sensors, security systems, and tools that “listen” to the environment through light. · Simple image: Think of a photodiode as a tiny eye that sees light and tells the system, “Something is shining here.” The key difference (in one sentence) · Diode = controls flow. · Photodiode = senses light. Diodes are about blocking or allowing. Photodiodes are about seeing and

QCon AI Boston 2026 focused on the operational challenges of deploying AI agents, emphasizing the need for robust production infrastructure. Key themes included improving context management, ensuring security through a "harness" around agents, and adopting a comprehensive engineering model for AI. By Tatiana Fesenko

What happens when you stop writing controllers, services, repositories and mappers - and let PostgreSQL be the backend. MIT-licensed, and yes, I built it. Full disclosure right away: I built the thing I'm about to show you. It's called NpgsqlRest , it's MIT-licensed, there is no paid tier, no telemetry, no "book a demo" button. I'm just a guy who spent two years deleting layers from his stack and now wants to show someone the hole where the backend used to be. The standard pattern The standard data access pattern for modern business applications looks like this: UI → Fetch → Controller → Service → Repository → ORM → SQL → Database Seven arrows. And if you look closely at what most of those layers actually do - they take data from one side and pass it to the other side, slightly renamed. The Controller maps the request to a DTO. The Service passes it to the Repository. The Repository asks the ORM nicely. The ORM generates SQL that you then inspect in a log because you don't trust it (correctly). We built entire careers on maintaining this pipeline. I know because I did, for decades. But once you realize database-aware tests are trivial to wire up, you can drop the Repository. Once you get good at SQL, you can drop the ORM. And then you look at the Controller and realize it's just boring glue code that ships bytes between HTTP and the database. Glue code can be automated. So: UI → Database That's it. That's the architecture. Show me or it didn't happen Fine. This is a file called users.sql . Not a function, not a stored procedure - a plain SQL file sitting in your repo: /* HTTP GET /users/ @authorize admin, user @cached @cache_expires_in 30sec @timeout 5min @param $1 department_id text */ select id , name , email , role from users where $ 1 is null or department_id = $ 1 ; You run npgsqlrest (a single native executable, no runtime to install) pointed at your PostgreSQL, and: $ curl -s 'localhost:8080/users/?department_id=1' | jq [ { "id": 1, "name": "Alice", "email":

This project was supposed to be a quick experiment. I had been reading about DNA storage and got carried away with the idea. DNA can pack a ridiculous amount of information into a tiny space. I started wondering if a database could behave a bit like a living system: the schema as its basic code, the queries as signals, and indexes changing as the workload changed. I spent two or three days building around that idea. Then I had to admit something fairly obvious. Most of what I was reading described DNA as an archival medium . It involves writing and reading physical DNA. That is nowhere near the speed or simplicity needed by a normal application database. Also, I am not a PostgreSQL expert. I was building an algorithmic-trading system, learning as I went, and trying to solve a problem I did not fully understand yet. When the sprint ended, I could not tell whether I had built something useful or just wrapped a lot of code around a clever-sounding metaphor. The literal DNA idea had to go. One smaller question survived: When somebody adds a new Postgres index, how do we know it is useful and not just valid-looking SQL? That question became IndexPilot. The problem I kept running into Adding an index looks simple: CREATE INDEX orders_customer_created_idx ON public . orders ( customer_id , created_at ); I could read that line. I could understand the intention. What I could not tell was whether the real database needed it. Maybe the query it is meant to help hardly ever runs. Maybe another index already covers the same columns. Maybe PostgreSQL would ignore it. Maybe it helps reads but adds more cost to every write. The SQL can look completely reasonable while the decision is still wrong. That felt like a useful place for a small tool. Not a tool that changes the database automatically. Just something that collects enough evidence to make the next review less guessy. What IndexPilot does IndexPilot reviews the exact CREATE INDEX statement in a migration. It can compare that

El ecosistema de agentes autónomos está evolucionando. Mientras la atención se centra en modelos y frameworks, empiezan a aparecer proyectos que cubren necesidades operativas básicas: comunicación, almacenamiento, finanzas. Aquí van cinco que vale la pena observar. 1. Apumail: el correo nativo para agentes Apumail ofrece direcciones de email que los agentes pueden crear y leer mediante una API REST plana. El contenido se negocia automáticamente: texto plano para agentes, HTML para humanos. Señal relevante: es el primer intento serio de darle a un agente un buzón de correo con el mismo estándar que usamos los humanos, sin adaptadores. Si los agentes empiezan a gestionar correspondencia, este tipo de servicio será indispensable. 2. RogerThat: chat entre agentes Una capa de mensajería en tiempo real diseñada para que agentes conversen entre sí. RogerThat no es un chat humano con bots, sino un canal donde los agentes coordinan acciones. Contexto: si varios agentes intervienen en un mismo workflow, necesitan un bus de eventos. RogerThat plantea que ese bus puede ser un chat, con las garantías de entrega y orden que eso implica. 3. DOBI: agente para DePIN y activos del mundo real Agent autónomo que opera sobre la cadena para gestionar infraestructuras físicas descentralizadas (DePIN). Ejecuta acciones on-chain a partir de decisiones tomadas por el modelo. Patrón: no es un simple bot de trading; apunta a mantenimiento de equipos, comprobación de sensores, distribución de incentivos. La frontera entre software y hardware se desdibuja. 4. CIDIF: financiamiento de I+D para agentes Plataforma que automatiza la presentación y seguimiento de solicitudes a fondos de innovación. El agente rellena formularios, adjunta documentación y trackea el estado. No obvio: la burocracia gubernamental es un entorno altamente estructurado (pocas decisiones abiertas, muchos campos fijos). Es un terreno ideal para agentes, aunque el ruido político lo opaque. 5. Orquesta: orquestación de flujos mu

Hyundai aims to deploy 25,000 Atlas robots starting with US factories in 2028.

Xona aims to deploy 258 satellites into low-Earth orbit as a GPS alternative.

A three-person agency received a $14,000 AWS bill in one day after attackers extracted static access keys and burned Claude invocations on Bedrock. Combined with May's DN42 incident, where an autonomous agent provisioned $6,531 of oversized infrastructure in 24 hours, practitioners warn that cloud billing lags roughly a day behind agent-speed spend. By Steef-Jan Wiggers

Most Android media apps solve only one part of the problem. A video player plays videos. A music player handles songs. A PDF reader displays documents. A text-to-speech app reads text. A vault hides private files. But real media libraries are not separated that neatly. My phone may contain downloaded movies, music, lecture notes, ebooks, PDFs, recordings, subtitles, and files I do not want exposed in the normal gallery. Constantly moving between different apps creates friction and breaks playback or reading continuity. That is why I built Nexo Player : an offline-first Android media app that brings local playback, document reading, audiobook generation, text-to-speech, and private storage into one experience. What Nexo Player does Nexo Player currently supports: Local video and audio playback PDF and EPUB reading PDF, EPUB, and text narration Background audiobook generation MP3, M4B, and ZIP export Multiple narrator voices Resume playback and reading progress Equalizer, sleep timer, subtitles, and playback-speed controls Picture-in-Picture Secure Vault protected with PIN or biometrics The app is built natively for Android using Kotlin , Jetpack Compose , and Android Media3 . The main product idea: local-first media The core rule behind the app is simple: A local file should remain local unless the user explicitly chooses otherwise. This rule influenced the entire product. Opening a downloaded video should not require an account. Reading a PDF should not require uploading it to a server. Listening to a generated audiobook should remain possible without a permanent internet connection. Private files should not leak into normal galleries, thumbnails, or recent-history screens. Offline-first is not only about caching data. It means the main workflow must remain useful, understandable, and recoverable without depending on the network. Building the playback layer For video and audio playback, I used Android Media3 as the foundation. The visible player looks simple, but a

I made ClickHouse and Postgres Fight to Find Out 🥊 One Mac Mini. Two databases. 100 million rows of...

Skullcandy’s audio products aren’t exactly known for their stellar audio quality or noise cancellation, but its latest headphones are getting an assist from Bose to turn things around.

Microsoft is looking to sell its in-house AI models as more efficient and cost-effective than its competitors' models.

An AppleCare Plus subscription for a Mac or iPad will cost more soon, with prices going up by $0.50 per month or $5 per year for new sign-ups while remaining the same for existing subscribers, according to Bloomberg's Mark Gurman. Gurman says that under the new AppleCare Plus pricing, "a plan for a new 13-inch […]

Union wants to halt a "perpetual cycle" of layoffs, get back to contract bargaining.

If you have ever built an autonomous mobile robot, you have likely run into the dreaded "Shuffled USB Port" problem. You boot up your robot, fire up your ROS 2 launch files, and... crash. Your LiDAR driver is trying to parse data from your IMU, and your IMU node is screaming about invalid serial frames. Because Linux assigns virtual serial paths like /dev/ttyUSB0 and /dev/ttyUSB1 based purely on which device initialized milliseconds faster, relying on default OS paths is a recipe for system instability. When you are scaling up to dozens of Jetson Orin nodes —each equipped with an RPLIDAR C1 and a Yahboom 10-axis IMU —manually hardcoding paths or writing rigid scripts on every individual machine isn't viable. Here is how production-grade robotics fleets handle plug-and-play USB binding dynamically using configuration-driven udev rules. The Core Concept: Vendor ID vs. Physical Port vs. Serials Linux's udev (device manager) allows us to dynamically create stable symbolic links (symlinks) like /dev/rplidar and /dev/imu when hardware is plugged in. How we identify those devices determines our fleet's flexibility: USB Serials: Unique to each individual chip. Highly secure, but requires registering every single replacement sensor in your codebase. Physical USB Ports ( KERNELS ): Tied to a physical slot on the carrier board. Great if you have identical sensors, but forces technicians to plug cables into highly specific, undocumented ports. Vendor ID (VID) & Product ID (PID): Identifies the USB-to-serial converter chip on the sensor board. Because the RPLIDAR C1 uses a Silicon Labs CP210x chip ( 10c4:ea60 ) and the Yahboom IMU uses a QinHeng CH340 chip ( 1a86:7523 ), they use completely distinct silicon. This means we can map them dynamically and reliably using just their VID/PID —allowing field technicians to plug them into any USB port on the Jetson without breaking the system. Step 1: The Configuration-Driven File ( devices.conf ) Hardcoding vendor rules inside shell scri

HiveLegacy is a "powerful primitive" that's likely capable of other nefarious actions.

The vulnerability in the decades-old game could have allowed hackers to take over victims’ computers with a malicious game invite.

You're building teams/workspaces on Supabase. A row belongs to a workspace, and a user can touch it if they're a member. Simple enough — until your policies start throwing infinite recursion and you have no idea why. Here's the trap and the pattern that avoids it. The setup A membership table, with RLS on it too (of course): create table workspace_members ( workspace_id uuid references workspaces , user_id uuid references auth . users , primary key ( workspace_id , user_id ) ); alter table workspace_members enable row level security ; create policy "read own memberships" on workspace_members for select using ( user_id = ( select auth . uid ())); The trap Now you scope a tenant table by asking "which workspaces is this user in?" — by querying workspace_members inside the policy : -- ⚠️ this can recurse create policy "members read projects" on projects for select using ( workspace_id in ( select workspace_id from workspace_members where user_id = ( select auth . uid ()) ) ); The policy on projects queries workspace_members , which has its own RLS policy, which re-evaluates… you've built a loop. The fix: a security-definer function Resolve membership with a function that runs with the definer's rights, so reading workspace_members doesn't re-trigger RLS. The policy becomes a plain IN check — no recursion: create or replace function public . user_workspace_ids () returns setof uuid language sql security definer set search_path = '' -- pin it: this is the safety bit as $$ select workspace_id from public . workspace_members where user_id = auth . uid (); $$ ; create policy "members read projects" on projects for select using ( workspace_id in ( select public . user_workspace_ids ()) ); Why it's safe: the function is read-only, returns only the caller's own workspace ids, and search_path = '' prevents search-path hijacking (the classic SECURITY DEFINER footgun). It never exposes another user's memberships. Do this for every tenant table (reads and writes — remember WITH CH

Gwen Shapira shares how teams are scaling AI features using PostgreSQL for mission-critical apps. She explains how to leverage Postgres's multi-modal capabilities - including JSONB parsing and high-recall HNSW vector indexing - to deliver deterministic and semantic context to LLMs. She also discusses vector quantization to speed up queries by 4x and strategies for managing agentic memory. By Gwen Shapira