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

A Visual Introduction to Machine Learning (2015)

Office.eu launches as Europe's sovereign office platform

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

Let your Coding Agent debug the browser session with Chrome DevTools MCP

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

Separating the Wayland compositor and window manager

The 49MB Web Page

Grandparents are glued to their phones, families are worried [video]

What makes Intel Optane stand out (2023)

Comment sections on AI threads tend to split into &quot;we&#x27;re all cooked&quot; and &quot;AI is useless.&quot; I&#x27;d like to cut through the noise and learn what&#x27;s actually working and what isn&#x27;t, from concrete experience.If you&#x27;ve recently used AI tools for professional coding work, tell us about it.What tools did you use? What worked well and why? What challenges did you hit, and how (if at all) did you solve them?Please share enough context (stack, project type, team size, experience level) for others to learn from your experience.The goal is to build a grounded picture of where AI-assisted development actually stands in March 2026, without the hot air.

Ask HN: How is AI-assisted coding going for you professionally?

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

LLM Architecture Gallery

Hollywood Enters Oscars Weekend in Existential Crisis

Learning athletic humanoid tennis skills from imperfect human motion data

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

Animated 'Firefly' Reboot in Development from Nathan Fillion, 20th TV

Examples for the tcpdump and dig man pages

C++26: The Oxford Variadic Comma

Bus travel from Lima to Rio de Janeiro