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Two weeks ago one of my outdoor cats bit me. She's fine — healthy, pregnant, and deeply offended that I picked her up, but she needed flea medicine and I needed to confirm the pregnancy. (If anyone wants a kitten, I know a grumpy lady who has some.) My pinky swelled up, and typing went from "mildly error-prone" to "not happening." So I dictated this post. If you've ever looked at raw voice transcription, you know what that produces: one giant unpunctuated block with half the words wrong. My transcript literally claims "AI needed to put flea medicine on her." It was me. That's the kind of thing the AI is cleaning up. The ideas are mine. The argument is mine. The punctuation and clarification belongs to the machine, because the machine is better at punctuation than a transcript is. By the rules of the current discourse, you're now supposed to stop reading. That's the game, right? "Not reading this if it's AI-generated." "It has em dashes — slop." Let's deal with the em dash first, since it's apparently forensic evidence now. You can type one. Shift-Option-hyphen on a Mac. Windows-Shift-hypen on Windows. Writers were littering pages with them for a century before the first transformer shipped. Its little brother the en dash is everywhere too, and nobody has ever accused an en dash of being a robot. The em dash gets singled out for exactly one reason: it's a fast, cheap way to judge a piece of writing without engaging with a single idea in it. Zero effort, instant superiority. Remember that phrase — zero effort. It's coming back. Because real AI slop absolutely exists. Someone fires off one prompt, ships whatever falls out, never reads it, then farms for stars and upvotes. That's slop — not because a model was involved, but because no human was. Effort is the variable. The tool never was. Here's what the other end of the spectrum looks like. Hundreds of hours on a single project. I decide the architecture, the language, how it compiles, how it deploys. I fork the output

I love Nuxt and I really like TanStack Start. But which one is better? Or are they about the same? And if they are about the same, does it do anything my Nuxt setup can't, and is that worth leaving Vue for React? So I decided to build the same app in both frameworks and take a look. Read on below to find out! If you'd rather watch a video, check out the video on the same topic! The app In both frameworks I built a small GitHub user lookup app. You type a username, the profile gets fetched on the server, and the username lands in the URL as a ?user= query param so the result is shareable. Type ErikCH , hit enter, and the card renders. Refresh the page and it's still there. It has the same behaviour so the difference lies in the code. Difference one: server functions vs server routes On the Nuxt side we call a server route from useAsyncData . Server are the more idiomatic way to use Nuxt to call things on the server. <!-- app/pages/index.vue --> < script setup lang= "ts" > import { z } from ' zod ' import type { GithubUser } from ' ~~/server/api/github.get ' definePageMeta ({ props : route => z . object ({ user : z . string (). default ( '' ) }). parse ( route . query ), }) const props = defineProps < { user : string } > () const router = useRouter () const input = ref ( props . user ) const { data , error } = await useAsyncData ( ' github-user ' , () => props . user ? $fetch < GithubUser > ( ' /api/github ' , { query : { user : props . user } }) : Promise . resolve ( null ), { watch : [() => props . user ] }, ) function lookup () { router . push ({ query : { user : input . value . trim () } }) } </ script > The props option on definePageMeta maps the query into a typed page prop and re-runs on client navigation. useAsyncData fetches when there's a username and refetches whenever it changes. The conditional that returns Promise.resolve(null) skips the request on an empty query param, (or when you first load). The server route does the outbound call: // server/api/gith

Google Vertex AI is Google Cloud's managed ML platform with experiment tracking, training jobs, pipelines, a model registry, and endpoints, but it locks you into GCP-specific APIs and per-use billing. ClearML is an open-source MLOps platform that covers the same ground on any Docker host or Kubernetes cluster, capturing training metrics automatically with no code changes and keeping every byte of data on infrastructure you control. This guide deploys ClearML Server with Docker Compose and Traefik, registers an agent, runs an experiment, builds a pipeline, runs a hyperparameter sweep, and deploys a Triton-served model. By the end, you'll have a self-hosted Vertex AI replacement covering the full ML lifecycle. Vertex AI → ClearML Mapping GCP Vertex AI ClearML Equivalent Purpose Vertex AI Workbench ClearML Web UI Browser-based monitoring and configuration Vertex AI Experiments ClearML Experiment Manager Automatic hyperparameter/metric/artifact tracking Vertex AI Training Job ClearML Agent + Tasks Any machine becomes a remote worker via queues Vertex AI Pipelines ClearML Pipelines Python-native DAGs, no separate compile step Vertex AI Model Registry ClearML Model Repository Versioned models with full lineage Vertex AI Endpoints ClearML Serving (Triton) Self-hosted inference with canary/A-B support Cloud Monitoring/Logging ClearML Scalars/Plots Built-in metrics and hardware dashboards Prerequisite: Ubuntu host with Docker + Compose, DNS A records for app.clearml.example.com , api.clearml.example.com , files.clearml.example.com . NVIDIA Container Toolkit if you'll run GPU agents. Deploy the ClearML Server 1. Raise Elasticsearch's virtual memory limit and restart Docker: $ echo "vm.max_map_count=524288" | sudo tee /etc/sysctl.d/99-clearml.conf $ sudo sysctl --system $ sudo systemctl restart docker 2. Create persistent data directories with the correct ownership: $ sudo mkdir -p /opt/clearml/ { data/elastic_7,data/mongo_4/db,data/mongo_4/configdb,data/redis,data/fileserver,

Matomo is an open-source web analytics platform that keeps full ownership of visitor data on infrastructure you control, a privacy-first alternative to Google Analytics. This guide deploys Matomo using Docker Compose with a MariaDB backend, Nginx as the entry point, and Certbot issuing the TLS certificate before the stack goes live. By the end, you'll have Matomo tracking a website with a signed HTTPS certificate at your domain. Prepare Docker $ sudo usermod -aG docker $USER $ newgrp docker Set Up the Project 1. Create the project directory: $ mkdir matomo && cd matomo 2. Open the firewall: $ sudo ufw allow 80/tcp $ sudo ufw allow 443/tcp 3. Create the environment file: $ nano .env MYSQL_ROOT_PASSWORD = your_strong_mysql_root_password MYSQL_PASSWORD = your_strong_mysql_matomo_password Use passwords of at least 16 characters. 4. Create the Nginx config: $ mkdir nginx $ nano nginx/matomo.conf server { listen 80 ; server_name matomo.example.com ; location /.well-known/acme-challenge/ { root /var/www/certbot ; } location / { return 301 https:// $host$request_uri ; } } server { listen 443 ssl http2 ; server_name matomo.example.com ; ssl_certificate /etc/letsencrypt/live/matomo.example.com/fullchain.pem ; ssl_certificate_key /etc/letsencrypt/live/matomo.example.com/privkey.pem ; location / { proxy_pass http://app:80 ; proxy_set_header Host $host ; proxy_set_header X-Real-IP $remote_addr ; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for ; proxy_set_header X-Forwarded-Proto $scheme ; } } Replace every matomo.example.com occurrence with your domain — it appears in both server blocks and the certificate paths. Deploy with Docker Compose $ nano docker-compose.yaml services : db : image : mariadb:11.4 command : --max-allowed-packet=64MB restart : always volumes : - matomo-db-data:/var/lib/mysql environment : - MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD} - MYSQL_DATABASE=matomo - MYSQL_USER=matomo - MYSQL_PASSWORD=${MYSQL_PASSWORD} networks : - matomo-net app : image

Have you ever wondered how vector databases like Pinecone, Milvus, Qdrant, or pgvector search through billions of high-dimensional documents in milliseconds? Under the hood, they map semantic concepts into dense numerical vectors, calculate multidimensional cosine similarity angles, and traverse proximity graphs to locate nearest neighbors without scanning the entire database. To help you visualize how vector databases and embeddings actually operate, I built a retro-vector arcade game: 🛰️ Vector Strike: Database Defender Play in Fullscreen Mode (if the embed sizing is tight) 🛠️ Choose Your Database Optimizations Your mission as a Vector Database (VDB) administrator is to configure your query settings and index structures to defend your index nodes: 📏 Similarity Threshold (τ): Tweak the match threshold slider. High thresholds require near-identical semantic matches but protect your index, whereas lower thresholds act like a splash-damage laser but risk matching incorrect clusters. 🪐 Embedding Dimensions (2D $\rightarrow$ 8D $\rightarrow$ 32D): Higher dimensions isolate categories and guarantee precise hits. Lowering dimensions collapses the projection space, causing spatial overlap that results in false deflections and friendly-fire query failures. ⚡ Proximity Indexing (Flat Scan $\rightarrow$ HNSW Graph): Flat Scan: Runs a brute-force linear search over all targets. It causes computation latency spikes as more query objects arrive. HNSW (Hierarchical Navigable Small World): Dynamically builds proximity links between adjacent node targets. The turret traverses vectors along the nearest-neighbor graph, snap-locking onto targets with zero lookup latency. 🧬 Playable ML Concepts Explained Here is how the arcade mechanics map to production vector databases: 1. 🔀 Multidimensional Projections (Dimension collapse) In-Game: You can toggle between 2D, 8D, and 32D space. In 32D space, the categories are cleanly separated. In 2D space, the database collapses, and you'll find sp

Joshua Achiam spent nearly nine years at OpenAI researching AI safety and made a memorable appearance in the Musk v. Altman trial.

I rebuilt aesencryption.net so text AES (128/192/256) runs fully in the browser - the key and plaintext never leave the page. The hard part is staying byte-compatible with common server-side AES libraries (mode, IV, padding, base64 output), so I ship copy-paste equivalents in PHP, Java, Python, Go, Rust, Kotlin and JS. Live tool (mine, free): https://aesencryption.net - feedback on the crypto choices welcome. My own site.

Plausible Analytics is an open-source, self-hosted web analytics tool that tracks traffic without cookies or personal data collection, a privacy-first alternative to Google Analytics. This guide deploys Plausible Community Edition using Docker Compose, fronts it with Nginx, and secures it with a Let's Encrypt certificate. By the end, you'll have Plausible tracking a website's traffic securely at your domain. Configure the Environment 1. Clone the Community Edition repo: $ mkdir ~/plausible $ cd ~/plausible $ git clone https://github.com/plausible/community-edition.git $ cd community-edition 2. Generate a secret key: $ openssl rand -base64 64 | tr -d '\n' 3. Create the environment file: $ nano .env ADMIN_USER_EMAIL = admin@example.com ADMIN_USER_NAME = admin ADMIN_USER_PWD = ADMIN_PASSWORD BASE_URL = https://plausible.example.com SECRET_KEY_BASE = YOUR_SECRET_KEY_BASE DATABASE_URL = postgres://postgres:postgres@plausible_db:5432/plausible_db CLICKHOUSE_DATABASE_URL = http://plausible_events_db:8123/plausible_events_db Fill in your email, a strong admin password, your domain, and the secret key generated above. 4. Expose the app port via a Compose override (auto-merged with compose.yml ): $ nano compose.override.yaml services : plausible : ports : - 127.0.0.1:8000:8000 Deploy with Docker Compose $ docker compose up -d $ docker compose ps Confirm the app, Postgres, and ClickHouse (events DB) containers are all running. Front with Nginx and Let's Encrypt 1. Install Nginx: $ sudo apt update $ sudo apt install nginx -y 2. Create the virtual host: $ sudo nano /etc/nginx/sites-available/plausible.conf server { listen 80 ; listen [::]:80 ; server_name plausible.example.com ; access_log /var/log/nginx/plausible.access.log ; error_log /var/log/nginx/plausible.error.log ; location / { proxy_pass http://127.0.0.1:8000 ; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for ; proxy_http_version 1.1 ; proxy_set_header Upgrade $http_upgrade ; proxy_set_header Connection "Upgr

I've been working on rootless container checkpoint/restore in CRIU and wrote up some notes on the prototype, major failures, and the path toward the current implementation. Would appreciate any feedback, criticism, and corrections, especially from people familiar with CRIU, Podman, or container runtimes. submitted by /u/Ok-Job3177 [link] [留言]

The Log Is the Agent AI Engineer World's Fair Coverage Ishaan Sehgal Ishaan Sehgal Ishaan Sehgal Follow for Daily Context Jun 30 The Log Is the Agent # aie # agents # ai 48 reactions 89 comments 5 min read

The new AI model is also powering effects in Stories and image generation in WhatsApp.

Meta is launching the first AI image generation model made by its Superintelligence Labs division. The Muse Image model now powers the image-making tools across the Meta AI app, Instagram, and WhatsApp, and it's coming soon to Facebook and Messenger, according to an announcement on Tuesday. It's part of the growing Muse family of AI […]

Open source models’ success isn’t coming at the expense of frontier labs. Instead, they each seem to capture two phases of the same life cycle.

Momentic, the company behind an AI-driven software testing platform, recently rearchitected its caching system to handle over 2 million queries per day across 20 billion total entries, while maintaining an average response latency of around 250 ms. This improvement was made possible by transitioning from PostgreSQL to the column-oriented database ClickHouse. By Sergio De Simone

So a few days ago I was in a comment thread arguing about whether AI is quietly ruining junior...

Microsoft is the latest Silicon Valley giant to cut back on its AI spending.