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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 ↗5200 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.
- GitHub · Repository created · First archived
feder-cr/git-ai ↗
Git designed for AI agents.
- Hacker News · Posted on Hacker News · First archived
Deobfuscating Luraph: Lifting Lua to LLVM, Then Letting AI Try ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Cloudflare and OpenAI ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
Oriskany/coco2017-mds-512px — dataset update ↗
# COCO 2017 MosaicML MDS Dataset (512px Shortest-Edge) This dataset contains pre-sharded **MosaicML Streaming (`.mds`)** shards for monocular depth estimation pre-training: - **Image Resolution**: Scaled with aspect-ratio preservation to min(H, W) = 512px (JPEG 95). - **Depth Labels**: 16-bit unsigned PNG (`uint16`, $[0, 65535]$) generated via `Depth-Anything-V2-Small-hf` teacher inference. - **Format**: Zero-copy chunked binary shards (`.mds`) with `index.json` manifests. - **Total Processed Samples**: 287,360 ### Partition Layout: Each partition under `parts/` contains autonomous `.mds` bina
- Hugging Face · Model / dataset date · First archived
juliensimon/starlink-fleet-data — dataset update ↗
# Starlink Constellation Fleet Data Credit: NASA *Part of a [dataset collection](https://huggingface.co/collections/juliensimon/orbital-mechanics-datasets-69c24caca4ab3934c9856994) on Hugging Face.* ## Dataset description Daily health snapshots of the SpaceX Starlink constellation, derived from CelesTrak GP (General Perturbations) data. Tracks satellite count, orbital shells, operational status, and ISL capability across five inclination-based shells. Starlink is the largest satellite constellation ever built, representing a fundamental shift in how broadband internet is delivered globally. Sp
- Hugging Face · Model / dataset date · First archived
cleyesode/GLM-4.7-Flash-Coder-MTP-NVFP4 — model update ↗
# Model Card Source Model: [whitecircle/GLM-4.7-Flash-Coder](https://huggingface.co/whitecircle/GLM-4.7-Flash-Coder) THIS REPO: NVFP4 quantization for use on Blackwell gpus sm100 sm120 sm121. MTP is present (transplanted from pre-finetune zai-org/GLM-4.7-Flash) and re-trained across short and long context chat and terminal samples using AdamW and Muon optimizers. Ready to serve on latest vLLM branch. Please take note of the server configuration below. Quantization via NVIDIA/Model-Optimizer custom workflow: | Component | Precision | |---|---| | Routed experts | NVFP4 W4A4 (group 16, FP8 block
- Hugging Face · Model / dataset date · First archived
Elpriser/nordic-price-forecast — model update ↗
# Nordic + NL Electricity Price Forecast — 13 bidding zones Day-ahead electricity price forecasts 2 to 9 days out, hourly, in EUR/MWh, for every bidding zone in Denmark, Norway, Sweden, Finland and the Netherlands: **DK1, DK2 · NO1, NO2, NO3, NO4, NO5 · SE1, SE2, SE3, SE4 · FI · NL** Retrained and re-uploaded daily, so the weights here are what production runs. A companion model specialised to Denmark alone lives at [Elpriser/denmark-price-forecast](https://huggingface.co/Elpriser/denmark-price-forecast). ## Design: one pooled model, not thirteen A single LightGBM quantile model covers all 13
- Hacker News · Posted on Hacker News · First archived
Show HN: Netlumi – Cloud and AI agent security with auto-remediation(Live demo) ↗
Hi HN, I'm Prem. I worked in cloud security for years and decided to build Netlumi. The mission is to take the burden of cloud, infrastructure and AI security off teams. We auto remediate. We find threats and attack paths in your cloud and open a pull request with the fix in your IaC code. All you need to do is review and merge. Each PR says what was checked, and we validate our fixes before we send them. Nothing is ever applied for you. We also…
- Hacker News · Posted on Hacker News · First archived
Show HN: Face, a browser window over agents that keeps the plan in files ↗
Hi HN, I'm the author. FACE is a single Go binary. You run `face` in the folder that holds your projects and it serves a page on 127.0.0.1. It speaks ACP (the Agent Client Protocol that Zed published) to the official CLI of the agent you already use: Claude Code, Codex, Copilot, Cursor, OpenCode, Qwen Code or Antigravity, signed in with your own account. FACE has no model, no key and no agent loop of its own. Why I built it: a coding agent ships…
- Hacker News · Posted on Hacker News · First archived
RIAA Flags Anna's Archive and AI Voice Cloner to U.S. Government ↗
Hacker News discussion.
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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