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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
Amex introduces a framework to prevent AI agents from unauthorized purchases ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Pulse Local, a Mac budgeting app whose AI models run on your machine ↗
Hi HN, I'm Niz. Pulse Local is a budgeting app for Mac (Apple Silicon, beta) that answers questions about your spending with models that run on your machine. Local mode needs no account. The ledger and your questions stay on the machine, and the app sends no analytics or crash logs. I felt there are not enough consumer apps that run locally with no calls to the outside world that help us manage sensitive corners of our lives. This app in its loca…
- Hugging Face · Model / dataset date · First archived
Netuoso/qwepus-dataset — dataset update ↗
 **Hand-written engineering conversations. Real problems, real constraints, code that builds.**     , produced by the [Perturb](https://perturbai.io) network. Each row is one clean image together with all of its verified adversarial versions: images that are imperceptibly different from the original (L∞ ≤ 0.03 in [0,1] pixel scale) yet change the model's top-1 prediction. **This dataset grows continuously.** New rows are appended as the network produces them and uploaded in batches; re-run `load_dataset` (or pass `download_mode="force_redownload"`) to pi
- Hugging Face · Model / dataset date · First archived
woog/open-text-detector-qwen36-35b-a3b-oct10 — model update ↗
# Open Text Detector: Qwen3.6-35B-A3B LoRA, Oct 10 (the model on opentextdetector.com) **Website:** [opentextdetector.com](https://opentextdetector.com): browse every public ICLR 2027 submission scored by this model, passage by passage. A detector of AI-written text in English research papers. It scores every token, and from those every sentence, with the probability that the text is AI-written. It is built to catch small AI edits (one or a few sentences) inside otherwise human papers while keeping false positives on human writing low. This is the model that scores every paper on [opentextdete
- Hugging Face · Model / dataset date · First archived
MIUProject/VNCCS_v3.0 — model update ↗
[GitHub VNCCS](https://github.com/AHEKOT/ComfyUI_VNCCS) # VNCCS V3.0 Model Pack This repository contains model files and model metadata used by [AHEKOT/ComfyUI_VNCCS](https://github.com/AHEKOT/ComfyUI_VNCCS), the Visual Novel Character Creation Suite for ComfyUI. VNCCS is a ComfyUI custom-node project focused on building consistent visual novel character sprites. Instead of generating a single isolated image, it provides a full character workflow: base character creation, character cloning, clothing sets, emotion sets, pose workflows, sprite generation, and optional dataset creation for LoRA t
- Hugging Face · Model / dataset date · First archived
SZLHOLDINGS/szl-invariants — model update ↗
# szl-invariants has moved to the kernel repository This model repository holds no loadable build. The `kernels` library (0.16 and later) loads kernels only from the kernel repository: **[https://huggingface.co/kernels/SZLHOLDINGS/szl-invariants](https://huggingface.co/kernels/SZLHOLDINGS/szl-invariants)** ```python # pip install "kernels>=0.17.2" torch from kernels import get_kernel kernel = get_kernel("SZLHOLDINGS/szl-invariants", version=1, trust_remote_code=True) ``` The source of truth is [szl-holdings/szl-invariants](https://github.com/szl-holdings/szl-invariants). Its `hf-kernel-publish
- Hacker News · Posted on Hacker News · First archived
Personal Agent Protocol – AI Agents ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
mradermacher/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF — model update ↗
## About static quants of https://huggingface.co/llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF).*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/G4-MeroMero-26B-A4B-it-uncensored-heretic-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part f
- Hugging Face · Model / dataset date · First archived
mradermacher/Goetia-26B-A4B-v1.3-GGUF — model update ↗
## About static quants of https://huggingface.co/OccultAI/Goetia-26B-A4B-v1.3 ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Goetia-26B-A4B-v1.3-GGUF).*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Goetia-26B-A4B-v1.3-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality.
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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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