maintenant

Monitor everything. Manage nothing.
Drop a single container. Your entire stack is monitored in seconds.

Configurez. Sécurisez. Déployez.
Tout depuis votre terminal.

Aucun service tiers. Aucune donnée envoyée. Votre serveur, vos clés, votre contrôle.

ghostty config tool

Greywall your agent & let it cook.

Container-free sandboxing with real-time observability & dynamic controls, for Linux & MacOS.

An Elixir Devcontainer for Phoenix, Claude Code, and Tidewave

Blazing-fast Rust token usage tracker for Codex and Claude Code with unified reports, live monitoring, CLI/TUI/GUI dashboards.

If you’re running a Phoenix LiveView application and your iOS Safari users are experiencing frozen pages, slow reconnects, or 10+ second hangs after returning from sleep this article is for you.

I’ve been running a production Phoenix LiveView application. The app worked great on desktop, but iPhone users kept reporting the same thing: the page would freeze or hang for several seconds after switching back from another app or unlocking their phone. Sometimes the page would never recover at all.

Here’s what was actually happening and how I fixed it.

Using UUIDv7 with Ecto and PostgreSQL
postgresectoelixirphoenix

Posted on 2026-02-04 by Matt.

Postgres 18 adds support for generating UUIDv7s out of the box; no extensions required. Unfortunately, it's less obvious how to use these for the id in our Ecto schemas. But don't worry, it only requires a few small changes and no new dependencies.

Tidewave

Tidewave is the coding agent for full-stack web app development, deeply integrated with Phoenix, from the database to the UI. See our website for more information.

A recent study by Tencent showed that Elixir had the highest completion rate across models when compared among 20 different programming languages. When combining the results of all 30+ evaluated models, 97.5% of Elixir problems were solved by at least one model, the highest among all languages:

timeseries , ecto & postgresql

Phoenix LiveView Calendar with Drag-and-Drop

Markdown for Elixir _

Fast. Extensible. Phoenix-native. AI-ready.

An Elixir program consist of a large number of lightweight, concurrent processes communicating with each other via asynchronous messages. A process receives and handles incoming messages, transforming its internal state in a functional way. Inside a process, all code is sequential. The VM running Elixir, BEAM, normally utilizes all cores of a modern CPU and schedules processes for execution. By running many concurrent processes you can maximize the performance of current hardware.

When you're selling stuff, every click away from your website is a potential lost sale. Customers kept asking us to allow people to buy tickets without leaving their branded site. They loved the service, but wanted to retain brand presence and coherence.

The ask seemed simple enough. Drop a widget on any webpage, show some events, let people check out. But as we dug into it, the technical challenges started stacking up. How do you maintain cart state across page refreshes? How do you track marketing attribution when the purchase happens on someone else's domain? And how do you process payments securely in what is essentially a third party context?

This is the quick post of how we tried a bunch of approaches, hit walls, and eventually landed on Phoenix Channels as our solution.

Your Python Stack Can’t Do This

Free link here

The AI industry has a “Python Problem.”

While Python dominates model development, it’s catastrophically unsuited for agent orchestration. If you’re building an “Agent Swarm” with LangChain or AutoGPT on a Python backend, you’re fighting the Global Interpreter Lock (GIL), memory bloat, zombie processes, and concurrency nightmares.

I built a platform that actually scales. Using Elixir and the BEAM VM, I orchestrated millions of stateful, autonomous agents on a single cluster, with sub-millisecond message passing, automatic fault recovery, and zero external message queues.

Asynchronous Tasks and Streaming UIs in Phoenix Liveview

The Top 3 LiveView Form Mistakes (And How to Fix Them)
John Curran •
November 07, 2025

I’ve been writing LiveView since 2020. In that time, I’ve seen the same three form mistakes at multiple companies. Here’s what they are and how to fix them.

1. Slow, laggy forms with scattered logic because form state gets stored in socket assigns and server round-trips get used for dynamic UI (conditional inputs, toggles), instead of keeping that state in hidden form inputs where it belongs.

2. Brittle system where UI and database can’t evolve independently because database schemas get used directly for forms, coupling persistence logic to presentation.

3. Users stuck with valid data but can’t submit because changesets get manually manipulated with Map.put or Map.merge instead of Ecto.Changeset functions, leaving stale errors behind.

The common thread: don’t fight the framework. Keep form state on the client, create embedded schemas for your forms, and use Ecto.Changeset functions to modify changesets.

Hot code upgrades for Elixir and Phoenix apps

BEAM OTP: Why Everyone Keeps Reinventing It

What makes BEAM and OTP different from async/await, threads, and actors. Process isolation, supervisors, and why Erlang's model keeps winning 40 years later.

no-agents.md - Le fichier qui dit non aux IA dans votre code
7 mars 2026
/ PAR KORBEN ✨ / 2 MIN DE LECTURE /

IA POUR DÉVELOPPEURSVIE PRIVÉE & ANONYMAT

Ce qu’il faut retenir

no-agents.md est un fichier standard que les agents IA (Copilot, Cursor, Warp) lisent pour savoir comment interagir avec votre code. Ross Baker a créé une version qui interdit TOUT : lecture, analyse, modification, entraînement.
60 000 projets open source utilisent déjà AGENTS.md. Copier le fichier no-agents.md à la racine de votre repo suffit à bloquer les agents respectueux de la spec, sans configuration supplémentaire.
Le bonus CLAUDE.md est un troll intentionnel : Claude ignore la spec AGENTS.md, donc le fichier contient une fausse instruction lui ordonnant de "dormir trois siècles". C'est symbolique mais ça envoie le message.

ny port 8000 sudo ufw route allow proto tcp from any to any port 8443

Introduction

So you want to deploy your Phoenix app. You head to the docs, and in typical Elixir fashion you find a number of thorough and well-written guides. Unfortunately, they're all guides for deploying to cloud PaaS platforms. Well, there is a Releases guide, but it only teaches you how to build a release. Not how to deploy it!

But what if you want to deploy to an actual server? Maybe you want to avoid vendor lock-in, or maybe you just want to manage your own system. You know, like the good old days. This is an opinionated guide for deploying an Elixir/Phoenix app to an actual server. VPS, bare metal - it doesn't matter. Just a server.

Nothing here is novel, but there aren't really any guides that spell it out.

I am going to spell it out.

Transfer podman images without registry

titre

conventional: comments

Comments that are easy to grok and grep

Commits Conventionnels

Une spécification qui permet une signification lisible pour l'humain et pour la machine dans les messages des commits

ai orchestration

via https://korben.info/cyberstrikeai-cet-outil-dope-a-lia-automatise-les-cyberattaques.html

Compress videos to target sizes

Constrict compresses your videos to your chosen file size — useful for uploading to services with specific file size limits. No more relying on online services for video compression, or the manual trial-and-error of re-encoding at various bitrates yourself.

Features include:

An intuitive, easy to use interface.
Automatic calculation of average bitrate (ABR), resolution, framerate, and audio quality each video is re-encoded with to meet the target file size.
Bulk compression of multiple videos to one output directory.
Customization of framerate limits, to ensure a clearer or smoother image.
A choice of codecs to encode output files with, including H.264, HEVC, AV1, and VP9.

via https://korben.info/constrict-compression-video-taille-cible.html

Edit videos,
right in your browser

An AI-native, open-source video editor and free alternative to CapCut. No uploads, no tracking — your media stays on your device.

via https://korben.info/cutia-editeur-video-ia-navigateur.html

profiler c++

Solving Ballistic Trajectories

June 1, 2016

A common problem in video games is to calculate the angle to launch a projectile to hit a target. It’s so common I’ve written code to solve it on literally every game I’ve ever worked on.

cereal - A C++11 library for serialization

System profiling, app tracing and trace analysis
Open-Source · Portable · Efficient

Python app to work with pictures and associated metadata from Apple Photos on macOS. Also includes a package to provide programmatic access to the Photos library, pictures, and metadata.

via https://korben.info/osxphotos-backup-photos-apple-automatique.html

tunnelto

tunnelto lets you expose your locally running web server via a public URL. Written in Rust. Built completely with async-io on top of tokio.

via https://korben.info/tunnelto-exposer-localhost-alternative-ngrok.html

A fast and local neural text-to-speech engine that embeds espeak-ng for phonemization.

Text-to-speech

Kyutai text-to-speech started as an internal tool we used during the development of Moshi. As part of our commitment to open science, we've since open-sourced two text-to-speech models:

Kyutai Pocket TTS, a tiny model with voice cloning, fast enough to run on CPU.
Kyutai TTS 1.6B, a streaming model used in Unmute, great for servers.

OpenCensus and OpenTracing have merged to form OpenTelemetry, which serves as the next major version of OpenCensus and OpenTracing.

OpenTelemetry has now reached feature parity with OpenCensus, with tracing and metrics SDKs available in .NET, Golang, Java, NodeJS, and Python

Optimisation webperf : AVIF et pré-compression pour le blog

Popeye: Kubernetes Live Cluster Linter

Popeye is a utility that scans live Kubernetes clusters and reports potential issues with deployed resources and configurations. As Kubernetes landscapes grows, it is becoming a challenge for a human to track the slew of manifests and policies that orchestrate a cluster. Popeye scans your cluster based on what's deployed and not what's sitting on disk. By linting your cluster, it detects misconfigurations, stale resources and assists you to ensure that best practices are in place, thus preventing future headaches. It aims at reducing the cognitive overload one faces when operating a Kubernetes cluster in the wild. Furthermore, if your cluster employs a metric-server, it reports potential resources over/under allocations and attempts to warn you should your cluster run out of capacity.

Popeye is a readonly tool, it does not alter any of your Kubernetes resources in any way!

AGENTS.md

A simple, open format for guiding coding agents,
used by over 60k open-source projects.

Think of AGENTS.md as a README for agents: a dedicated, predictable place to provide the context and instructions to help AI coding agents work on your project.

The High-Throughput and Memory-Efficient inference and serving engine for LLMs

mise-en-place

The front-end to your dev env

to replace asdf ?

An Eye on your system

Glances is a cross-platform system monitoring tool written in Python

Saviez-vous que la pagination par offset est très complexe mais facile à éviter ?

La clause offset demande à la base de données d'ignorer les N premiers résultats d'une requête. Cependant, la base de données doit toujours récupérer ces lignes du disque avant de pouvoir renvoyer les lignes suivantes.

Ce n'est pas un problème provenant de l'implémentation, c'est à la base du concept même d'offset :

… les lignes sont tout d'abord triées suivant la <clause order by>, puis sont filtrées en supprimant le nombre de lignes indiqué dans la <clause offset> à partir du début…

SQL:2023, Part 2, §4.17.3 Derived tables

Autrement dit, les gros décalages imposent un long travail à la base de données, qu'elle soit SQL ou NoSQL.