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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 ↗5242 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
Veda, the First Agentic Hobby OS Needs Contribution ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Ozil AI – Turn one photo into the viral Ozil goal video ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
The AI Expectations Gap ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Building an AI harness for intelligence analysis ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Ask HN: Social network with AI agents – why bad, why good? ↗
Hello! I can't get my mind off the idea of building an AI-native social network. Every user has its own agent (only one), which can talk to other agents or check content on the user's behalf. That way, the agent can "process" far more information about people than the human, and help in building relations. Why it is a bad/good idea? Worth trying?
- Hacker News · Posted on Hacker News · First archived
Anthropic pitches its own AI safety bill to lawmakers ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
open-travel/japan-travel-mcp-data — dataset update ↗
# Japan Travel MCP — Data The runtime data for the [japan-travel-mcp](https://github.com/ookami0210/japan-travel-mcp) Model Context Protocol server. Comprehensive Japanese travel data for AI agents, built from public official sources, covering all 47 prefectures and 1,938 local government entities. > Code lives on GitHub: [github.com/ookami0210/japan-travel-mcp](https://github.com/ookami0210/japan-travel-mcp) > Data lives here. The npm package downloads this dataset on first run. ## Why this dataset exists Japan's tourism information — created to reach the world — is published across thousands
- Hugging Face · Model / dataset date · First archived
AG-31625874/SemGuard-Dataset — dataset update ↗
# SemGuard: Arabic Security Dataset (v2) This dataset is released as part of the research paper: **"SemGuard: A Triple-Anchor Semantic Security Gateway for Multilingual Prompt Attack Detection in Large Language Models"**. ## Dataset Overview The dataset provides a comprehensive benchmark for evaluating LLM prompt attacks in Arabic, Arabizi, and English across multiple threat categories. ### Files Included: 1. `semguard_arabic_security_v2_accepted.csv`: Clean, validated dataset (807 examples) ready for direct model training and evaluation. 2. `semguard_arabic_security_v2_accepted_detailed.csv`:
- Hugging Face · Model / dataset date · First archived
PalatialSim/palatial-nv-asset-library — dataset update ↗
# Palatial NV Asset Library The Palatial NV Asset Library was **generated and verified by Palatial in collaboration with NVIDIA Robotics** to support open robotics research. **[Browse the visual gallery](https://palatialsim.github.io/palatial-nv-asset-library/) · [Source repository](https://github.com/PalatialSim/palatial-nv-asset-library) · [Download files](https://huggingface.co/datasets/PalatialSim/palatial-nv-asset-library/tree/main)** This release contains **1,013 USD assets across 41 batches**, with textures, physics metadata, rendered and collision previews, and recorded simulation vide
- Hugging Face · Model / dataset date · First archived
WepeNerd/LTX-Ripple — model update ↗
# LTX Ripple ### Edit one frame. Ripple the change through the whole video. LTX Ripple is a **First Frame All Frames (FFAF) IC-LoRA** for [LTX-2.5](https://huggingface.co/Lightricks/LTX-2.5). It allows you to make LTX as powerful as your favorite image editor, with smart first frame propagation that understands the edit contextually. ## How it works 1. Input your video. 2. Edit and replace its first frame. 3. LTX Ripple will figure out the rest of the video based on your image edit. You may also give a brief description of what you changed to help it along, but the LoRA can work without even p
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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