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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.

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5540 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.

  1. Hugging Face · Model / dataset date · First archived

    dragonlimited/DragonData-Finance-Corpus — dataset update ↗

    ![DragonData — World Finance Database](figures/brand_banner.png) [![License: CDLA-Permissive-2.0](https://img.shields.io/badge/License-CDLA--Permissive--2.0-00E5FF)](metadata/source_registry.json) [![Status: Building](https://img.shields.io/badge/Status-Building-00E5FF)](PROGRESS.md) [![Bilingual: EN + 中文](https://img.shields.io/badge/Bilingual-EN%20%2B%20中文-00E5FF)](DOMAINS.json) [![Live dashboard](https://img.shields.io/badge/Dashboard-PROGRESS.md-00E5FF)](PROGRESS.md) # DragonData World Finance Database > *Priced like infrastructure. Free like air.* > The institutional-grade bilingual (EN/中

  2. Hugging Face · Model / dataset date · First archived

    Arc53/arc-decide-0.6b-v0.1 — model update ↗

    # arc-decide-0.6b-v0.1 **One 0.6B model for the small decisions a RAG or agent app makes on every request.** Give it a state (text or JSON) and a few typed questions, and it returns calibrated probabilities in one forward pass, with no generated text. It covers relevance, prompt injection, grounding, routing, intent, conversation analytics, custom tags and tool use, in English, Spanish, Chinese and Russian. ![Same questions, same held-out sets](assets/vs-models.png) | | **arc-decide-0.6b-v0.1** (0.6B) | Laya (421M) | Qwen3-4B-Instruct (4B) | |---|---|---|---| | Conversation analytics: answer s

  3. Hacker News · Posted on Hacker News · First archived

    AI companies are starting to do phones ↗

    Hacker News discussion.

  4. Hacker News · Posted on Hacker News · First archived

    Tensordyne is making AI compute more efficient using logarithms ↗

    Hacker News discussion.

  5. Hacker News · Posted on Hacker News · First archived

    I just build Claude Dev Tools ↗

    Hacker News discussion.

  6. Hacker News · Posted on Hacker News · First archived

    AI does math. Should you get a PhD? ↗

    Hacker News discussion.

  7. Hacker News · Posted on Hacker News · First archived

    Artex AI, Claude agents used in cyberattacks on South Korean banks ↗

    Hacker News discussion.

  8. GitHub · Repository created · First archived

    SakanaAI/AI-Scientist-v3 ↗

    The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery

  9. Hugging Face · Model / dataset date · First archived

    mondk/Gemma-1B-Jev-Decision-Classification-SystemOne-Safetensors — model release ↗

    # mondk/Gemma-1B-Jev-Decision-Classification-SystemOne-Safetensors It is Gemma, but fine-tuned to resemble TypeSafe/Jev. GGuf: ... Dataset: mondk/Jev-Decisions-EN-Vi-Vietnamese Note regarding the license: If you publish a model derived from this one, you must use the Gemma license (as required by Google). ## Usage ```python pip install transformers accelerate import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer REPO = "mondk/Gemma-1B-Jev-Decision-Classification-SystemOne-Safetensors" tok = AutoTokenizer.from_pretrained(REPO) model = AutoModelForCausalLM.from_pretrain

  10. Hugging Face · Model / dataset date · First archived

    francesca9805/urd-arab-10mb-after-ppt-Dp-10mb-packed-bfdiso-ckpt500_seed10 — model update ↗

    # Model Card for urd-arab-10mb-after-ppt-Dp-10mb-packed-bfdiso-ckpt500_seed10 This model is a fine-tuned version of [francesca9805/urd-arab-10mb-ppt-Dp-10mb-packed-bfdiso_seed10](https://huggingface.co/francesca9805/urd-arab-10mb-ppt-Dp-10mb-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-generation", model="francesca9805/urd-ara

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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