Codecov’s unreliability breaking CI on my open source projects has been a constant source of frustration for me for years. I have found a way to enforce coverage over a whole GitHub Actions build matrix that doesn’t rely on third-party services.

Visualize decision trees in Python. Contribute to mljar/supertree development by creating an account on GitHub.

Human review of Terraform plans doesn’t scale, and AI review isn’t deterministic enough for production. Here’s how we use conftest to auto-apply plans that meet an explicit, testable policy.

Know instantly when your site goes down. Alerts via Telegram, Discord, Slack or Email. No server, no Docker. Setup in 2 minutes.

Former detainees detail systematic torture and sexual violence, including rape, while in Israeli custody.

Generate Mermaid architecture diagrams from any GitHub repo or local codebase using AI. Free to start with 1000 credits.

The Problem My self-hosted Jellyfin was slow whenever I connected to it from abroad over Tailscale. My ISP upload is about 500 Mbit/s, but streams stuttered and iperf3 over the tunnel only managed ~10 Mbit/s: $ iperf3 -c jellyfin-host [ 5] 0.00-10.00 sec 11.9 MBytes 10.0 Mbit/s It wasn’t a relay or routing issue — tailscale status showed a direct connection (not a DERP relay): $ tailscale status 100.xx.xx.xx jellyfin-host linux active; direct xx.xx.xx.xx:41641 So: direct, low-latency, half a gigabit of upload available — and still only 10 Mbit/s for a single stream.

When you press the power button on the SpacemiT K3 Pico-ITX, Ubuntu 26.04 appears on the serial console about 30 seconds later. Between those two events, five distinct software layers run in sequence, each one handing off to the next. Understanding what each layer does - and why it exists - matters the moment something goes wrong, or the moment you want to put a different OS on the board.

Personal webpage of Alexandre Dulaunoy - from information security to open source and art

A continuity system for LLM–harness collaboration. Builds technical specifications through structured persona conversations — and carries session state across compactions, sessions, and time. Describe a problem. Storytime surveys your codebase, assembles a team of domain-expert personas, and runs a structured conversation that produces a plan — grounded in your actual code, with citations, decisions, and visual aids. Underneath the spec workflow is a consolidation loop that preserves continuity across compactions and sessions. Phases collapse when empty. Not every run uses every gear. See a full walkthrough → Storytime v1.0.1 — github.com/1ps0/storytime

Route requests across OpenAI, Anthropic, Google, Mistral, DeepSeek, Cerebras and more through one OpenAI-compatible API. An attested gateway with no prompt/output logs, provider failover, BYOK, and an open-source trust path.