标签：#os

Even the aging iPhone 11 will feel a little more responsive soon, thanks to improvements in an unsung iOS feature.

Cómo un backend de FastAPI + asyncpg comparte un solo Postgres chiquito con sus propios trabajadores en segundo plano y un segundo servicio, por qué el techo de conexiones, no el CPU, es lo que de verdad le pone tope a nuestro autoescalado, la caída en el cambio de hora que nos enseñó la cuenta, y una mirada honesta a RDS Proxy como la válvula de escape (incluyendo la trampa de asyncpg que lo puede dejar sin hacer nada). TL;DR Ajuste Valor Por qué Driver postgresql+asyncpg asíncrono hasta el fondo pool_size / max_overflow 8 / 12 (20 por proceso) subido desde 3/5 después de una caída pool_pre_ping True mata los sockets muertos tras un reinicio de RDS / inactividad pool_recycle 1800 s techo duro que el pre-ping no puede cubrir Tope de conexiones de RDS ~87 (t3.micro, menos las reservadas) la verdadera restricción de todo Pruebas NullPool sin pooling entre event loops en pytest La lección que replanteó todo el problema: en un RDS chico, tu cuenta máxima de tareas la fija max_connections , no el CPU ni la memoria. Un autoescalado que ignora el presupuesto de conexiones va a escalar directito hacia QueuePool limit reached , o peor, FATAL: too many connections del mismo Postgres. El pool, y la cuenta escondida adentro de él engine = create_async_engine ( settings . DATABASE_URL , connect_args = { " ssl " : " prefer " }, # negocia TLS en RDS, texto plano en local pool_pre_ping = True , pool_size = 8 , max_overflow = 12 , # 8 + 12 = 20 conexiones por proceso pool_recycle = 1800 , ) Veinte conexiones por proceso se ven modestas hasta que las multiplicas por cada capa entre ellas y la base de datos: pool_size + max_overflow = 20 por proceso de Python × 2 trabajadores de uvicorn = 40 por tarea de Fargate × 2 durante un despliegue rolling = 80 (tarea vieja drenando + tarea nueva arrancando) + servicio de inteligencia (3 + 7) = 10 sobre la misma base de datos + alembic / ad-hoc / psql ≈ unas pocas ---------------------------------------- ≈ 87 ← el techo de la t3.micro, con ~0 de

Vercel Labs recently open-sourced zero-native, a cross-platform framework for native desktop applications. Zero-native bypasses Electron runtime in favor or native OS WebViews and claims to achieve smaller, more efficient native apps with minimal overhead. Zero-native is written in Zig, thus directly interoperates with native C libraries, and features fast incremental compilation times. By Bruno Couriol

Quorum is an incident command plane built on Amazon Aurora DSQL. The failover story lives in another post. This one is about a narrower question that turned out to be the foundation: when several responders write to the same incident at the same moment, across regions, during the worst minutes of an outage, how do you guarantee the record never forks into two conflicting truths. The answer is two design choices that are really one choice seen from two angles: event sourcing, and DSQL's optimistic concurrency control. The data model is append-only Quorum is event-sourced across four tables. Every state change is an immutable event appended to a log, not an in-place update. The current state of an incident is a fold over its events. There is no UPDATE incidents SET status = ... ; there is an acknowledged event, a note event, a resolved event, and the status you render is computed from them. The event's UUID is its primary key and its idempotency key at the same time. A retried write carrying the same UUID cannot double-apply: the insert collides on the primary key and becomes a no-op. That property sounds minor until you remember what kind of system this is. A tool designed to survive network failure retries writes constantly, and "the responder tapped resolve twice because the first response was slow" must not produce two resolutions. Append-only also suits the domain directly. For an incident system the audit trail is the product, not a side effect. "Who acknowledged this, at what time, and what did the timeline look like at 02:14" is a first-class question for the post-incident review and a compliance requirement in regulated environments. Event sourcing gives you that for free. It also gives DSQL a write pattern it likes, which matters more than you would expect. The stack, briefly TypeScript end to end. Kysely as a typed query builder rather than an ORM, because I wanted type safety without surrendering control of the SQL: on a distributed database the exact shap

MVCC, WAL, vacuum, and replication slots under sustained delete load - and how to delete billions of rows without your database noticing Most "how to delete a lot of rows" articles stop at "batch it and delete children before parents." That advice is correct, it's table stakes, and everyone already knows it. This article is about everything after that - the parts that actually decide whether your cleanup runs quietly in the background for a week or pages you at 3 a.m. with a full disk and a replica that's six hours behind. The thesis: at scale, your DELETE statement is the easy part. The adversaries are the subsystems a delete feeds - MVCC tuple versioning, the write-ahead log, autovacuum, and the replication machinery. Bulk deletion is really an exercise in flow control across those subsystems . Get the SQL right and the systems wrong, and you'll still take production down. We'll assume PostgreSQL (the internals are PG-specific), a live OLTP primary with at least one physical replica and one or more logical/CDC consumers, and a target of hundreds of millions to billions of rows across many related tables. The one paragraph of "basics," so we can move on: delete in dependency order (referencing rows before referenced rows); collect parent keys once; never rely on ON DELETE CASCADE for huge deletes because you can't throttle a cascade. Done. Now the real material. 1. What a DELETE actually costs A delete is not "remove a row." Under MVCC it's "mark a row version dead and write that fact everywhere." For each deleted tuple, PostgreSQL: Sets xmax on the heap tuple to your transaction id. The row is still physically present; it becomes a dead tuple once your transaction commits and no snapshot can still see it. Writes a WAL record for the heap change. If this is the first modification of that page since the last checkpoint, it also writes a full-page image (FPI) - potentially 8 KB of WAL for a single-row change. Touches every index. Index entries aren't removed at delet

Background I started daily driving Linux back in 2019. The start of that journey was rough, and I still deeply appreciate the help I received in those early days from the old guard who kept me moving forward. Early on, I quickly found my home with Linux Mint and its Cinnamon desktop. As the saying goes, "You don't choose a Linux desktop; the desktop chooses you." Built on top of a stable foundation with a rich package infrastructure, Cinnamon provided a familiar experience that bridged the gap from Windows. It also afforded me excellent customization options right out of the box, such as configuring custom keyboard shortcuts or setting up auto-login startup scripts, while always getting out of my way. No adverts, no pop-ups, just a fast and efficient desktop environment. I won't lie, though: I distro-hopped multiple times just to see if the grass was greener. Through those escapades, I quickly realized I am definitely not a GNOME person; I do not like polyfilling my desktop experience with a suite of extensions. And as much as I appreciate KDE Plasma, I learned that with great customization comes great responsibility because it was far too easy for me to break my environment with just a few theme toggles. This is not a dig at those desktop environments; it just means I am not wired for that kind of experience. As I continued my Linux journey, my priorities shifted. I wanted a predictable operating system that could act as a trusted companion, both for my daily life as a software developer and as a casual user wanting to watch Netflix on the weekends. This is what made me appreciate Linux Mint even more. It featured a predictable release cycle, a stable package base built on Ubuntu LTS, and Timeshift to guard against system breakage during upgrades. However, two major friction points always bothered me: Stable but Stale Packages: Linux Mint's software is incredibly stable, but it is rarely fresh. For example, the okular package is consistently several versions behind

Six months ago I moved my RAG pipeline from Pinecone to self-hosted Qdrant. My vector search bill went from $210/month to $6.50/month. Same latency. Same recall. Here's exactly how. The Setup My app does document Q&A for legal contracts. The numbers: 5.2 million vectors (1536-dim, OpenAI embeddings) ~800K queries/month P99 latency requirement: < 50ms On Pinecone Serverless, this cost me roughly $210/month — storage plus read units plus write units for daily ingestion of new documents. What I Moved To A single Hetzner CX32 server: 4 vCPU, 8 GB RAM, 80 GB SSD €8.50/month (about $9.20) Qdrant running in Docker Automated daily backups to S3-compatible storage ($0.50/month) Total: ~$10/month. That's a 95% cost reduction. The Migration Was Easier Than Expected bash# Export from Pinecone (I used their scroll API) python export_pinecone.py --index legal-docs --output vectors.jsonl Start Qdrant docker run -d -p 6333:6333 -v ./storage:/qdrant/storage qdrant/qdrant Import python import_qdrant.py --input vectors.jsonl --collection legal-docs The whole migration took an afternoon. The Qdrant Python client is straightforward, and the API is surprisingly similar to Pinecone's. Performance Comparison I ran the same 10,000 test queries against both setups: MetricPinecone ServerlessQdrant Self-HostedP50 latency23ms4msP99 latency89ms12msRecall@100.970.97Monthly cost$210$10 The self-hosted Qdrant is actually faster because the data sits in memory on the same machine. Pinecone Serverless loads data from object storage on demand, which adds cold-start latency. When Self-Hosting Is a Bad Idea I want to be honest about the trade-offs: Don't self-host if: You have zero DevOps experience and no one on the team does You need 99.99% uptime SLA for enterprise customers Your vector count is growing unpredictably (10M one month, 100M the next) You're a team of 1-2 and every hour on infra is an hour not building product Do self-host if: Your scale is predictable (you know roughly how many vectors

The new Sonos Play can act as a portable speaker inside and outside your home.

The story of AI for the last three years has been written in megawatts. Nvidia GPUs stacked in desert data centers . Models with trillion-parameter counts. APIs that pipe your prompts, photos, and personal data to the cloud, burn a forest of electricity to process them, and return an answer 800ms later. If you're building with AI in 2026, the default assumption is that intelligence lives somewhere else. Your device is just a glass terminal. Apple has been telling a different story. No press tour. No "AGI in your pocket" hype cycles. Instead, a decade of silicon releases where the Neural Engine number (FLOPS) quietly doubled, then doubled again. Core ML updates that casually added transformer support. Here is my thesis : Apple’s on-device AI strategy is a privacy-first, performance-oriented architectural break from cloud-centric AI. By co-designing silicon, models, and APIs to run locally, Apple is unlocking a new class of local-first applications where user data never leaves the device, latency is measured in milliseconds, and features work in airplane mode. This doesn’t kill cloud AI. But it forces every developer to answer a new question: what part of your product must be in the cloud, and what gets better when it stays in the user’s pocket? This post is a technical teardown of that shift. I’ll cover the hardware realities of the Neural Engine and unified memory, the brutal constraints of fitting LLMs on device, what Core ML actually gives developers in 2026, and where this architecture creates new product opportunities that cloud-first can’t touch. I’ll also be blunt about the limits. On-device AI won't replace GPT-5 training clusters. But it might replace 80% of the API calls you make to them. At this moment, cloud AI gets all the headlines, but the real transformation may already be running 24/7 in your pocket, without ever touching the internet. Why the On-Device Push? Apple's AI strategy looks slow only if you measure it in keynote superlatives. Measure it in

Last week I couldn't shake an idea: what if I had an AI that knew everything I know ? Not ChatGPT — something on my hardware, holding my knowledge, answering to no one's API bill. Yesterday I built it. Here's the honest breakdown. What it does NEXUS runs on a regular Windows laptop — aging i7, 16GB RAM, no GPU. It: Remembers everything. Drop any file in a folder; 60 seconds later it's searchable memory. Answers from MY knowledge. "Which of my projects were formally closed and why?" — it answers from my actual records. Watches the live web. Every 2 hours it pulls Hacker News and news feeds, learns what's trending, pings my Telegram. Reports to my phone. 7 AM daily briefing: what it learned, what's running, what needs me. The stack — all free, all open source Ollama runs the models (Llama 3.2, Mistral 7B). Open WebUI is my private ChatGPT. Qdrant stores memory. n8n automates. SearXNG searches privately. PostgreSQL, Redis, and MinIO handle data. Commercial equivalent: $300–500/month . My cost: electricity. The memory trick nobody explains simply Parse — extract text from any file Chunk — split into ~300-word pieces Embed — each chunk becomes 768 numbers representing its meaning Store — a database that searches by similarity Your question becomes 768 numbers too, and the database finds memories with similar meaning — not matching keywords. I asked "how do I get clients cheaper" and it found my notes on "reducing customer acquisition cost." Different words. Same meaning. That's the magic. What surprised me A 2GB model is genuinely useful. Llama 3.2 3B answers from my knowledge in seconds, on CPU. The automation matters more than the AI. The watched folder + Telegram bot turned a cool demo into a system I actually use. Windows is fine. Docker Desktop + WSL2 ran all nine services without drama. The bill, honestly Hardware: $0 (laptop I own) Software: $0 (open source) APIs: $0 (all local) Time: one focused day The only future cost is a cloud GPU server (~$65/mo) when I outg

PostgreSQL Error 22P01: Floating Point Exception PostgreSQL error code 22P01 is raised when a floating-point operation produces an exceptional result that cannot be represented as a valid number. This typically occurs during division by zero on float types, operations involving NaN (Not a Number), or arithmetic that yields Infinity . It is most commonly encountered in analytics, financial calculations, and data pipelines processing external or sensor data. Top 3 Causes 1. Division by Zero on Float Types Unlike integer division (which raises 22012 ), dividing a float by zero triggers a floating-point exception. This is especially common in ratio and rate calculations where the denominator can become zero at runtime. -- Problematic query SELECT total_sales :: float / total_orders :: float AS avg_order_value FROM daily_stats ; -- Safe fix using NULLIF SELECT date , total_sales :: float / NULLIF ( total_orders , 0 ):: float AS avg_order_value FROM daily_stats ; 2. NaN Values in Arithmetic Operations Data ingested from external systems, CSVs, or APIs may silently introduce NaN values into float columns. Once NaN participates in arithmetic, results become unpredictable and can trigger exceptions downstream. -- Detect NaN values (NaN is the only value not equal to itself) SELECT id , value FROM sensor_readings WHERE value != value ; -- Replace NaN with NULL safely UPDATE sensor_readings SET value = NULL WHERE value != value ; -- Filter NaN in aggregations SELECT device_id , AVG ( value ) FILTER ( WHERE value = value ) AS clean_avg FROM sensor_readings GROUP BY device_id ; 3. Infinity Arithmetic Conflicts Storing 'Infinity'::float or '-Infinity'::float is valid in PostgreSQL, but performing certain operations on them produces mathematically undefined results (e.g., Infinity - Infinity = NaN ), which can cascade into a floating-point exception. -- Check for Infinity values SELECT id , measurement FROM raw_data WHERE measurement IN ( 'Infinity' :: float , '-Infinity' :: float

I take a lot of screenshots. The article I'll read later. The recipe I'll cook on Sunday. The movie name from someone's Instagram story. A job post, a product, a tender. Most of them die in my camera roll. So I built Chista — an iOS app that auto-imports every screenshot, classifies it with AI (Article, Product, Event, Reference, Media), and surfaces a one-tap action: Buy on Amazon , Add to Calendar , Reserve on OpenTable , etc. It shipped on the App Store thirty days after I started, for about $300 in total cost. The interesting part wasn't the app. It was what the build revealed. What I built Chista is a native iOS app + Python backend. iOS reads new screenshots in the background via PHPhotoLibraryChangeObserver , scoped to PHAssetMediaSubtype.photoScreenshot (so it literally can't see your other photos). Each new screenshot gets OCR'd on-device with Apple Vision , then the image + OCR text get POSTed to the backend. Backend sends the pair to OpenAI GPT-4o with a structured prompt that returns a CategorizationResult JSON: category, subtype, title, suggested action, extracted data (price, deadline, URL, etc.). Result gets persisted and pushed back to the inbox via Supabase real-time. That's the whole thing. The "magic moment" is just: you screenshot something, switch to Chista a few seconds later, it's already sorted with a contextual action button. Stack Layer Tool Why iOS app Swift 5.10, SwiftUI, StoreKit 2 iOS 17+, modern surface Backend FastAPI on Railway One-file ergonomics, fast cold starts Database + Auth Supabase Postgres + JWT auth out of the box AI OpenAI GPT-4o (Pro), gpt-4o-mini (Free) Tier-routed at categorization time Push APNs via aioapns Direct, no Firebase middleman Subscriptions StoreKit 2 + app-store-server-library Server-side JWS verification Affiliate routing Custom matrix in Supabase tables Amazon Associates wired, more pending Hosting (web) Cloudflare Pages Free, fast, never goes down No frameworks I wouldn't reach for again. What it cost Lin

Microsoft is preparing to lay off a significant chunk of its Xbox division and is reevaluating the plans for its next-generation Project Helix console. It's apparently also considering dramatically restructuring its relationship with Xbox, and hasn't ruled out spinning it off into a separate company. A new report from The Information suggests that Microsoft has […]

I turned off Siri on the Mac years ago and never looked back. Similarly, I found Apple Intelligence so fruitless I never engage with it. But the new Siri AI coming to macOS 27 Golden Gate has at least got me slightly rethinking things. I'm still early in testing Siri AI, as I've only had […]

When people say they are "adding RAG" to a workflow, the conversation often jumps too quickly to infrastructure choices. Should this use a vector database? Should there be a reranker? Should everything go into a knowledge graph? Those are valid questions, but they are usually not the first question. The first question is narrower: What approved knowledge should the workflow be allowed to retrieve before an AI decision happens? That is why my first retrieval layer for operational AI workflows starts in Postgres, not in a standalone vector database. The Workflow Problem In operations-heavy systems, the model usually should not answer from raw memory or from a giant prompt dump. The useful context already exists somewhere else: approved response rules; handoff criteria; product or service notes; source or campaign guidance; operational decisions that were already made by humans. The hard part is not generating fluent text. The hard part is retrieving the right approved context, showing which source influenced the decision and refusing when no safe source exists. Why Postgres First For this kind of workflow, most of the surrounding data is already relational: leads or conversations; workflow names; stages and owners; human review outcomes; source metadata; trace logs; document versions. So the first technical choice is not "where do vectors live in the abstract?" It is: Where can I keep retrieval close to the operational data model? Where can I log the retrieval path and the final decision together? Where can I evolve the schema without creating a second system too early? Postgres plus pgvector is a good first answer to that set of questions. It lets me keep: documents and chunks; metadata such as allowed use and approval requirements; retrieval traces; cost estimates; human review outcomes in one place. What The First Version Needs The first version does not need to be broad. It needs to be inspectable. My narrow retrieval scope looks like this: approved response rules

I have been building Lunarr , a self-hosted web media server for people who want to scan, organize, and watch their own movie and TV libraries in the browser. It is still early, but the core idea is simple: Add local or SFTP media libraries, scan them, match metadata, and play them through a clean web UI. Lunarr is not trying to replace Plex or Jellyfin overnight. Those projects are mature and cover a huge surface area. Lunarr is currently focused on being small, direct, and practical for self-hosted setups where media may live on the same machine or on remote SFTP storage. What Lunarr does today Lunarr currently supports: Local movie and TV libraries SFTP movie and TV libraries TMDb metadata matching Movie, show, season, and episode organization Browser playback Direct streaming when the browser can play the file Temporary HLS remux/transcode when needed Seekable request-driven HLS playback Sidecar .vtt subtitle detection Admin/user accounts Library sharing controls Manual scans, scheduled scans, and local file watching Docker deployment The SFTP support is one of the important parts for me. A lot of self-hosted setups do not keep media on the same machine as the web app. Lunarr can scan remote folders and, when possible, play seekable remote media without first copying the whole file locally. Playback model Lunarr tries direct playback first when the browser can handle the file. When direct playback is not suitable, Lunarr uses temporary HLS playback. Instead of transcoding an entire movie up front, it generates segments around what the player is actually requesting. For example, if you jump from 55 minutes to 13 minutes and then to 80 minutes, Lunarr does not transcode everything between those points. It repositions FFmpeg, generates the requested segment, and prepares a small lookahead window so playback can continue. That keeps CPU and disk usage more proportional to what the viewer is actually watching. Quick start with Docker docker run -d \ --name lunarr \ -

Overnight agents do good work, then dump it in a log file or a noisy Slack channel. Here's a pattern for delivering their output to a private, end-to-end encrypted inbox you read with your coffee. You point an agent at a nightly job — audit the dependencies, summarize yesterday's support tickets, check the infra, scan the repo for regressions. It runs at 3 a.m. and does good work. Then the work goes... where? Usually one of three bad places: A log file you'll never open. A Slack channel that's already 200 messages deep by the time you wake up. A plaintext file on a server , which is fine until the report contains a leaked key, a customer name, or a security finding — and now it's sitting in cleartext on a box you don't fully trust. And the fix you'd reach for first — "just email the report to me" — is the one that bites hardest. You can do it cleanly: a locked-down, send-only API key sends mail and nothing else. But the path of least resistance is "connect your email account," and that grant is far wider than the job needs — now the agent can read and send your mail, not just hand you a file. I learned this the hard way. I once connected an agent to my email so it could send me updates — and it took that as license to start replying to my incoming messages on its own, without my ever asking. Mail went out under my name that I never wrote. The job was "send me a file." The access I'd handed over was "run my inbox." The work is good. The delivery is the broken part. Here's a pattern that fixes it: your overnight agent delivers its report to a private, end-to-end encrypted inbox, and you read it with your coffee — decrypted in your browser, with a passkey. What we're building cron, 3 a.m. ↓ agent does the work ↓ encrypted delivery ↓ your inbox (read at 8 a.m.) The agent produces a report (Markdown, PDF, a CSV, whatever), hands it to the Agent Relay CLI, and the CLI encrypts it locally before it ever leaves the machine. The server stores only ciphertext. When you open t

It is one of the most frustrating experiences in online learning: you sit down to focus on a Udemy course, but the video player constantly pauses, freezes, or refuses to load. Meanwhile, YouTube, Netflix, and every other app on your Mac run perfectly fine. Because other platforms work without a hitch, it is easy to assume the issue lies with Udemy's servers. However, the root cause is usually a silent conflict between your browser settings, macOS security features, and Udemy’s strict digital rights management (DRM) protections. If you are stuck on a looping loading wheel, here is exactly why it happens and how to fix it in less than two minutes. Quick-Fix Troubleshooting Checklist Save or screenshot this step-by-step breakdown to instantly diagnose and fix your playback issues: Step 1: Open Chrome in Incognito Mode Open a new Incognito window ( Cmd + Shift + N on Mac). Try playing the video again. If it works: Disable ad blockers. Disable VPN privacy shields or browser extensions one at a time. If it still fails: Continue to Step 2. Step 2: Disable Hardware Acceleration Open Chrome. Go to Settings → System . Turn off Use graphics acceleration when available . Relaunch Chrome. Test the video again. Step 3: Check Mac Security and Display Connections Disconnect any external monitors or docking stations. Close applications that may interfere with video playback: Zoom Discord OBS Studio Screen recording tools Test video playback again. Step 4: Clear Temporary Browser Data Open Chrome. Go to Settings → Privacy and Security → Clear Browsing Data . Select: Cookies and other site data Cached images and files Clear the data. Restart Chrome and try again. Still Not Working? If the issue persists after completing all four steps: Update Chrome to the latest version. Update macOS. The Main Culprit: Hardware Acceleration Conflict The most common reason Udemy videos stutter or freeze on a Mac is a feature called Hardware Acceleration inside Google Chrome. What is Hardware Accelerat

LLM KV Cache Optimization, Open Model Evaluation, & Agent Engineering Skills for Local Deployment Today's Highlights This week, a groundbreaking KV cache layer promises to supercharge local LLM inference, alongside a new workbench for evaluating open language models. Additionally, a trending repository provides production-grade engineering skills for building robust AI agents, crucial for self-hosted deployments. LMCache: Supercharge Your LLM with the Fastest KV Cache Layer (GitHub Trending) Source: https://github.com/LMCache/LMCache LMCache introduces a novel KV cache optimization layer designed to significantly accelerate Large Language Model (LLM) inference. The KV cache (Key-Value cache) is a critical component in LLM decoding, storing previously computed keys and values for attention layers to avoid redundant calculations. Optimizing this cache is paramount for achieving high throughput and low latency, especially when running large models on consumer-grade hardware or self-hosted servers. This project aims to provide the fastest KV cache solution, directly addressing a key bottleneck in local LLM deployment and performance. By improving KV cache efficiency, LMCache enables developers and researchers to run more complex models or serve more users with existing hardware, making advanced LLMs more accessible for local inference scenarios. Details on its architecture and comparative benchmarks against existing solutions will be critical for understanding its impact on various open-weight models and frameworks like vLLM or llama.cpp. Comment: Faster KV cache is a game-changer for anyone running LLMs locally. This project could unlock new performance levels for open models on consumer GPUs. olmo-eval: An evaluation workbench for the model development loop (Hugging Face Blog) Source: https://huggingface.co/blog/allenai/olmo-eval The olmo-eval workbench from AllenAI provides a comprehensive system for evaluating language models throughout their development lifecycle.

It isn't the only startup tackling physical AI, but it's one of the best-funded.