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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 ↗5930 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
Researchers proved AI has deleted every reason universities exist ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Man Says Meta's Muse AI Gave His Home Address Out to Strangers ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: A never-ending chess match between Claude and Codex ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Nvidia launches new tool to keep AI agents from going rogue ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Pacing the Frontier is not the actual goal for AI labs ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
AMD to Acquire World Labs to Advance the Future of AI ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
AMD to Buy Fei-Fei Li's World Labs AI Startup for $8.2B ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
Hamozwa/RepeatSynth — dataset update ↗
# Dataset Card for RepeatAudio Synthetic portion of audio datasets referenced in *Class-Agnostic Audio Repetition Counting*. - **RS, RSN and RVN:** Synthetic datasets containing varying levels of noise. Uniformly 10 seconds long, with 0-8 repetition events contained in each sample. - Relevant code can be found in [this repo](https://github.com/Hamozwa/audio-counting). ## Dataset Sources Synthetic data was generated with two source datasets: - [**FSD50K**](https://zenodo.org/records/4060432)**:** Provided audio for repetition events. JSON files indicate source audio for each sample. - [**MUSAN*
- Hugging Face · Model / dataset date · First archived
danielharkin21/ath-h2-policies — model update ↗
# ATH H2 policies ONNX policies for a Unitree H2 humanoid learning tennis in MuJoCo, trained on Kaggle (MJX float32, brax PPO) and checked in a float64 CPU MuJoCo oracle. - `index.json` - what's here and which policy is best. - `best/` - the policy the ATH site shows: `policy.onnx`, `spec.json` (exact obs/action layout), `metadata.json` (validation results). - `ball-contact/latest/`, `ball-contact/best/` - written live by the training kernel after every eval. - `stand/v3/` - first stand policy. Display only: it fails the float64 oracle (falls within 3 s under pushes). Metadata reports validati
- Hugging Face · Model / dataset date · First archived
VoiceHub/dacvae-tts-en-experiments — model update ↗
# DACVAE-TTS English: every A/B arm in one place Training on [Vyvo/en-podcast-1](https://huggingface.co/datasets/Vyvo/en-podcast-1), code and write-up: https://github.com/kadirnar/dacvae-tts. One folder per run: `checkpoints/step-*.pt`, `eval/step-*/` (scores, per-sentence Whisper transcripts, audio), `train.jsonl`, `config.json`, its own README, `RESULT.md` (question, result against its baseline, verdict) and `logs/`. **[EXPERIMENTS.md](https://huggingface.co/VoiceHub/dacvae-tts-en-experiments/blob/main/EXPERIMENTS.md)**: the result log of every experiment (question, evidence, paired result a
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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