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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 ↗5194 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.
- Hugging Face · Model / dataset date · First archived
JRamirez-UAB/Ornith-1.5-9B-EXL3-16GB-12GB — model update ↗
# Ornith-1.5-9B — EXL3 for 16 GB and 12 GB GPUs (vision + MTP, served with vLLM) > **Install from this card, not from generic snippets.** This is an EXL3 checkpoint: vLLM loads it only together with exllamav3 and our plugins. Follow [Requirements & install](#requirements--install-vllm-031--exllamav3-151--plugins) and [Quick start](#quick-start); a plain `pip install vllm`, Transformers or SGLang cannot load it. An EXL3 quantization of [ornith-ai/Ornith-1.5-9B](https://huggingface.co/ornith-ai/Ornith-1.5-9B) made with [exllamav3](https://github.com/turboderp-org/exllamav3)'s per-tensor bit-allo
- Hugging Face · Model / dataset date · First archived
AlexKarachun/vlm-data-in-weights — model release ↗
# vlm-data-in-weights Small neural generators that store a vision-language dataset in their weights and replay it without reading the original files. This is an experiment in synthetic data generation for the VLM backbone of a vision-language-action (VLA) model: how faithfully a QA corpus with images can be kept in compact model weights and regenerated offline. ## What the weights contain The generators were trained to **reproduce existing public data**, not to invent new data. **Benchmark part** (`generator/benchmark/`): questions, answer options, correct answers and images of the publicly re
- Hugging Face · Model / dataset date · First archived
JRamirez-UAB/Ornith-1.5-35B-A3B-EXL3-4.00bpw-24GB — model update ↗
# Ornith-1.5-35B-A3B — EXL3 MoE for vLLM, 262k context with vision and MTP on a 24 GB GPU > **Install from this card, not from generic snippets.** This is an EXL3 checkpoint: vLLM loads it only together with exllamav3 and our plugins. Follow [Requirements & install](#requirements--install-vllm-031--exllamav3-151--plugins) and [Quick start](#quick-start-single-rtx-3090-24-gb); a plain `pip install vllm`, Transformers or SGLang cannot load it. An EXL3 quantization of [ornith-ai/Ornith-1.5-35B-A3B](https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B) made with [exllamav3](https://github.com/turbo
- GitHub · Repository created · First archived
sirioberati/Emotion-Control-Open-Source-Workflow ↗
TEIN AI: the final glossy facial-performance mask workflow, with animated examples and reproducible setup.
- Hacker News · Posted on Hacker News · First archived
Nvidia's China AI-chip share falls to 34% as Huawei reaches 28% ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Coding with AI stopped feeling like craft, so I built a tiny desk radio ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Atelier – Watch AI Learn to Paint (Sometimes, Live) ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Anthropic revenue jumps to $16.8B in third quarter, claims profitable ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Reflex, a TUI that compiles Claude Code and Codex transcripts into agent skills ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
Yoshi-Dai/financial-lakehouse — dataset update ↗
# Dataset Card **NOTICE: DO NOT USE THIS DATASET FOR ANY COMMERCIAL OR AI TRAINING PURPOSES. UNAUTHORIZED REDISTRIBUTION IS STRICTLY PROHIBITED.** ## 利用規約 本データセットは、金融庁のEDINETが公開しているXBRLデータを著作者が独自に解析・蓄積したものです。 ### 1. 利用制限 - **二次利用・商用利用の禁止:** 本データセットの再配布、販売、商用目的での使用を一切禁止します。 - **AI学習への利用禁止:** 機械学習モデルの訓練（学習）、ファインチューニング、蒸留への使用を禁止します。 ### 2. アクセス制限 - 本データセットは **Gated Dataset** です。 - データの取得を希望する場合は、Hugging Faceアカウントでログインし、アクセスリクエストを行ってください。 ### 3. 出典 - 元データ：[金融庁 EDINET](https://disclosure2.edinet-fsa.go.jp/) - 本データセットは、上記データを著作者が独自に加工・構造化した二次的著作物です。 --- ## 更新履歴 - 2026年5月2日：ライセンスをMITから独自規約に変更し、アクセス制限
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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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