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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗5533 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- Hacker News · Posted on Hacker News · First archived
Show HN: Guard hooks for Claude Code, tested against a public corpus ↗
Hacker News discussion.
- GitHub · Repository created · First archived
simoz/cinderbar ↗
macOS menu bar port of Burn Bar: AI token burn and plan pacing for Claude and Codex
- Hacker News · Posted on Hacker News · First archived
AI is creating a 'human premium' for art created by people ↗
Hacker News discussion.
- arXiv · Published · First archived
Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models ↗
Deep-learning models increasingly rival numerical simulation in Earth-system forecasting, yet forecast error alone cannot determine whether their learned input-output dependencies are physically consistent. Here we introduce sensitivity fidelity as an additional criterion: alongside prediction error, model responses to input perturbations should be examined against a trusted reference or established physical knowledge. In an idealized Rayleigh-Bénard convection benchmark, four surrogate architectures achieved low forecast errors yet encoded markedly different sensitivities. Quantitative compar…
- Hacker News · Posted on Hacker News · First archived
Nicolas Cage says he won't work with Amazon again over AI dispute ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Fixated on AI, the world has forgotten it is staring at nuclear annihilation ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
paperswithbacktest/Stocks-Daily-Price — dataset update ↗
# Stocks Daily Price This dataset includes daily price data for various stocks. **25,986,919 rows** over 7,764 symbols, 8 columns, covering **1962-01-02 to 2026-08-05**. Refreshed monthly. ## Strategies Built on This Data 2,401 papers in the [Papers With Backtest catalogue](https://paperswithbacktest.com/strategies) declare this dataset as an input. 2,238 of them have been coded and run over their own full history. The median replicated Sharpe ratio is **+0.35**, and **45%** clear a t-statistic of 1.96 on their own sample, against 48% across all 4,837 replications in the library. Some of the s
- Hugging Face · Model / dataset date · First archived
Infatoshi/mcsr-speedrun-train — dataset release ↗
# MCSR speedrun: training data, replays, checkpoints Training data for a pixels-to-keyboard Minecraft 1.16.1 speedrun agent (space-time factorized video transformer, ~179M parameters; IDM and policy share one codebase). - `train/`: training video and labels at 256x144, 20 fps (VODs aligned to replays, plus IDM-labeled footage). - `replays/ /*.rrf`: MCSR Ranked replay files: exact per-tick (20 Hz) state for both players (position, look, pose, held item, mining/placing/throwing, block and entity timelines, per-dimension seeds). - `checkpoints/`: model checkpoints from the runs (IDM and policy va
- Hugging Face · Model / dataset date · First archived
francesca9805/ind-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso_seed10 — model release ↗
# Model Card for ind-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso_seed10 This model is a fine-tuned version of [francesca9805/ppt-wc-loglinear-newlex-ind-before-100mb-packed-bfdiso_seed10](https://huggingface.co/francesca9805/ppt-wc-loglinear-newlex-ind-before-100mb-packed-bfdiso_seed10). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-
- Hugging Face · Model / dataset date · First archived
Iwas-6127/act-model-mypick-color-20260926_235331 — model update ↗
# Model Card for act [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. / /resolve/main/demo.gif" width="60%"/> --> This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). Learn how to train and run it in the [LeRobot act guide](https://huggingface.co/docs/lerobot/main/en/act), or browse the [full documentation](https://huggingface.co/docs/lerobot/in
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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