How kernel anti-cheats work

Rack-mount hydroponics

$96 3D-printed rocket that recalculates its mid-air trajectory using a $5 sensor

Allow me to get to know you, mistakes and all

A Visual Introduction to Machine Learning (2015)

A few weeks ago I saw a post about someone converting an entire C++ codebase to Rust using AI in under two weeks.That inspired me — if AI can rewrite a whole language stack that fast, I wanted to try building a programming language from scratch with AI assistance.I&#x27;ve also been noticing growing global interest in Korean language and culture, and I wondered: what would a programming language look like if every keyword was in Hangul (the Korean writing system)?Han is the result. It&#x27;s a statically-typed language written in Rust with a full compiler pipeline (lexer → parser → AST → interpreter + LLVM IR codegen).It supports arrays, structs with impl blocks, closures, pattern matching, try&#x2F;catch, file I&#x2F;O, module imports, a REPL, and a basic LSP server.This is a side project, not a &quot;you should use this instead of Python&quot; pitch. 
Feedback on language design, compiler architecture, or the Korean keyword choices is very welcome.<a href="https:&#x2F;&#x2F;github.com&#x2F;xodn348&#x2F;han" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;xodn348&#x2F;han</a>

Show HN: Han – A Korean programming language written in Rust

A most elegant TCP hole punching algorithm

The 100 hour gap between a vibecoded prototype and a working product

Treasure hunter freed from jail after refusing to turn over shipwreck gold

SBCL Fibers – Lightweight Cooperative Threads

Why Mathematica does not simplify sinh(arccosh(x))

Glassworm Is Back: A New Wave of Invisible Unicode Attacks Hits Repositories

What makes Intel Optane stand out (2023)

I built Signet in Go to see if an autonomous system could handle the wildfire monitoring loop that people currently run by hand - checking satellite feeds, pulling up weather, looking at terrain and fuels, deciding whether a detection is actually a fire worth tracking.All the data already exists: NASA FIRMS thermal detections, GOES-19 imagery, NWS forecasts, LANDFIRE fuel models, USGS elevation, Census population data, OpenStreetMap. The problem is it arrives from different sources on different cadences in different formats.Most of the system is deterministic plumbing - ingestion, spatial indexing, deduplication. I use Gemini to orchestrate 23 tools across weather, terrain, imagery, and incident tracking for the part where clean rules break down: deciding which weak detections are worth investigating, what context to pull next, and how to synthesize noisy evidence into a structured assessment.It also records time-bounded predictions and scores them against later data, so the system is making falsifiable claims instead of narrating after the fact. The current prediction metrics are visible on the site even though the sample is still small.It&#x27;s already opening incidents from raw satellite detections and matching some to official NIFC reporting. But false positives, detection latency, and incident matching can still be rough.I&#x27;d especially welcome criticism on: where should this be more deterministic instead of LLM-driven? And is this kind of autonomous monitoring actually useful, or just noisier than doing it by hand?

Show HN: Signet – Autonomous wildfire tracking from satellite and weather data

HN