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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 ↗5203 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
Works on iPhone 18 Pro: AI helps create untethered jailbreak for iOS 27.0.1 ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Camera Controls and Motion for AI Video Prompts ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
LLM says something, my tool track if it followed its promise on tools ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Anthropic assigned highest junk credit rating in June ↗
https://finance.yahoo.com/technology/ai/articles/anthropic-s...
- Hacker News · Posted on Hacker News · First archived
American Medical Association releases governance framework for agentic AI ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
mmpr/open_model_evolution_data — dataset update ↗
# Economies of Open Intelligence: Tracing Power & Participation in the Model Ecosystem This dataset, released in conjunction with the paper [Economies of Open Intelligence: Tracing Power & Participation in the Model Ecosystem](https://huggingface.co/papers/2512.03073), provides a rigorous examination of concentration dynamics and evolving characteristics in the open model economy. It compiles a history of weekly model downloads (February 2025-Present) alongside detailed model metadata from the Hugging Face Model Hub. The dataset encompasses 851,000 models, over 200 aggregated attributes per mo
- Hugging Face · Model / dataset date · First archived
nineninesix/tyger — model update ↗
TYGER Speech recognition and duplex turn control in one model. **TYGER** is NVIDIA's Nemotron speech recognizer with a GRU on top. For every 80 ms of audio, the GRU tells a voice agent whether to **answer** (the user has finished), **stop** (the user is cutting in) or **say "mm-hm"** (the user goes on talking). It hears both sides of the call: the user's mic and the agent's own voice. Both parts load from one `model.safetensors`. It makes no speech itself: your app brings the LLM and the voice. ## Example ## Use it [**tyger-s2s**](https://github.com/nineninesix-ai/tyger-s2s) is our open-source
- Hacker News · Posted on Hacker News · First archived
Designing more efficient quantum circuits with Claude ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
hesamation/crusader-arena-runs — dataset update ↗
# Crusader Arena runs Runs of [Crusader Arena](https://github.com/hesamsheikh/CrusaderArena), a benchmark in which AI models play Stronghold Crusader: Definitive Edition through screenshots and mouse and keyboard tools. Each run keeps the model's full trace, telemetry, a video and the final state of the settlement. > **Research use only.** Crusader Arena studies AI agents in single-player games. It is an > independent project, not affiliated with or endorsed by Firefly Studios. See > [Responsible use](#responsible-use) and [License](#license). ## The benchmark The [benchmark page](https://gith
- Hugging Face · Model / dataset date · First archived
dmis-lab/UltraBERT-150M — model update ↗
# UltraBERT-150M UltraBERT is a family of English bidirectional encoders with a native 32K context that reach state of the art on both classification and retrieval, two task families on which previous encoders had to choose a side. | Model | Parameters | Hidden | Context | Description | |---|---|---|---|---| | **UltraBERT-150M** *(this model)* | 150M | 768 | 32K | Pre-trained encoder | | **[UltraBERT-380M](https://huggingface.co/dmis-lab/UltraBERT-380M)** | 380M | 1,024 | 32K | Pre-trained encoder | | **[UltraBERT-Embed-150M](https://huggingface.co/dmis-lab/UltraBERT-Embed-150M)** | 150M | 768
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.