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Concurrent JavaScript: It can work (2017)
Agentty – A drop-in alternative to claude-code, written in C++26. 11.0 MB binary
Fuse – an open source MCP/CLI tool to speed up Claude Code on C# codebases
LLM Networking with MikroTik
P2P local file transfer based on WebRTC
The FCC's Spam Call Proposal Is Just a Data Collection Scheme
Sokoban Speedrun for RL
Rzk: A Proof Assistant for Synthetic ∞-Categories
Speculative Growth and the AI "Bubble" [pdf]
We don't use AI in any of our design or production processes
When Bugs Feel Like Sabotage
Judge: Trump can’t deport researchers just for working in content moderation
Disinformation researchers praise ruling blocking Trump visa denials and deportations.
Odin 2 Portal Exploded
The End of Creativity
Must actively fund open source AI [pdf]
Inkling Model Card
DoD says soldiers over age 30 to be screened for testosterone deficiency
US Interconnection Queues by Region, State, and County
Everyone Knows Trump's Tweets Move Markets. I Measured It: the Connection Is Real — the Direction Isn't.
#NebiusServerlessChallenge Live dashboard: Myth-Busting Quantitative Terminal · Live endpoint: /predict on Nebius Serverless · Code: github.com/KoralZakai/stocksPredictionAfterTweet Everyone on a trading desk knows the story. He tweeted about Intel, and the stock ran for months. He posts about Iran, oil spikes. The anecdotes are vivid, specific, and everybody has one. While the prevailing myth suggests that Trump's tweets drive market movements, my research — built on an analysis of 78,130 posts, with Llama-3.3-70B reading every market-relevant tweet — reveals a more nuanced reality. The findings demonstrate that the market tends to price in significant events long before the tweet is even posted. This indicates a case of reverse causality, where the market dictates Trump's narrative, rather than the other way around. He isn't moving the market; he is simply riding the wave of established trends. Forward — the direction you could trade — the signal is a coin flip, and I can show that with pre-registered tests rather than vibes: 0 of 63 cells survive correction. Getting to that answer honestly was the hard part. Seven times this pipeline produced a beautiful, publishable, completely false positive — each one looking exactly like the discovery the anecdotes promise. Those seven are the engineering content of this post. The setup Data. 78,130 public posts; 8,317 in the study window (2025-01-01 → 2026-07-06). Daily OHLCV bars for 62 tickers, committed to the repo. Public data only — a stranger can re-run every number without a single private key. The model. meta-llama/Llama-3.3-70B-Instruct on Nebius AI Studio , zero-shot, reading only the tweet text : what is this post about, which instruments does it touch, and which way does each one go. 476 tweets, 1,296 instrument calls. No fine-tuning — the question is whether the text carries signal, and a fine-tune would smuggle the outcome into the answer. The architecture. Nebius Serverless AI Jobs run the batch research pipel