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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 ↗5974 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.
- OpenAlex · Published · First archived
ChemAIFlow: a scalable and interpretable applied-AI framework for high-dimensional data analytics in health informatics ↗
Purpose The study addresses operational demands for scalable artificial intelligence frameworks in high-dimensional affinity prediction. Current computational solutions prioritize architectural complexity over interpretability. Architectural complexity creates operational barriers for deployment in decision-support systems. Design/methodology/approach The ChemAIFlow pipeline processed 68,443 bioactive interaction pairs across 500 targets utilizing a hybrid descriptor framework. Methodological implementation encompasses structural deduplication yielding 55,535 unique chemical compounds, hyperpa…
- OpenAlex · Published · First archived
Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles ↗
Abstract We show that ensembles of deep neural networks, called deep ensembles, can be used to perform quantum parameter estimation while also providing a means for quantifying uncertainty in parameter estimates, which is a key advantage of using Bayesian inference for parameter estimation that is lost when using existing machine learning methods. We show that optimizing for both accurate parameter estimates and well calibrated uncertainty estimates does not lead to degradation in the former as opposed to only optimizing for accuracy. We also show that the drift detection capabilities of these…
- Hacker News · Posted on Hacker News · First archived
Linux 7.3-Rc6 Released: Normal for the New "AI Normal" ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: EditUI – Cursor/Claude Design-style UI editing directly in the browser ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
An OpenAI agent reached four Australian government systems. Nobody noticed ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Redactpdf.ai: Open-source AI-based PDF redaction tool ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
mlfoundations/dcvlm_pool_large — dataset update ↗
# DCVLM-Pool (large) The **raw candidate pool** at the `large` scale of our [DataComp-VLM](https://arxiv.org/abs/2606.28551) benchmark: **1,949,321,868 samples / 166.7 TB** across **166 source datasets**, as [WebDataset](https://github.com/webdataset/webdataset) tar shards — **≈4× the medium pool**. > ## 🚚 Upload in progress > > This repo is being populated incrementally and is **not yet complete** — shards are still being > uploaded. Sources already present are final and safe to use; sources with fewer shards than the counts > quoted below have not finished uploading yet. Please bear with us
- Hugging Face · Model / dataset date · First archived
princeton-nlp/queen_hce-4 — model update ↗
# QUEEN (HCE-4) Chess analysis and move selection with a fine-tuned SmolLM3 decoder and LC0 BT5 board encoder. All weights and inference code are included; no separate LC0 or Stockfish installation is needed. Use the runner below, not `AutoModelForCausalLM` alone (which omits board conditioning). ## Run Requires Linux, an NVIDIA GPU with BF16 support, a CUDA-13-compatible driver, and [uv](https://docs.astral.sh/uv/getting-started/installation/). uv downloads Python automatically. Model downloads total about 8.4 GiB; the CUDA environment needs additional disk space. Tested hardware is an H100 8
- Hugging Face · Model / dataset date · First archived
princeton-nlp/queen_pawn-8 — model update ↗
# QUEEN (PAWN-8) Chess analysis and move selection with a fine-tuned SmolLM3 decoder and LC0 BT5 board encoder. All weights and inference code are included; no separate LC0 or Stockfish installation is needed. Use the runner below, not `AutoModelForCausalLM` alone (which omits board conditioning). ## Run Requires Linux, an NVIDIA GPU with BF16 support, a CUDA-13-compatible driver, and [uv](https://docs.astral.sh/uv/getting-started/installation/). uv downloads Python automatically. Model downloads total about 8.4 GiB; the CUDA environment needs additional disk space. Tested hardware is an H100
- GitHub · Repository created · First archived
omarqaterge/hermes-mobile-app ↗
Your agent, on your phone. A native Android app for Hermes Agent, running entirely on-device (Termux).
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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