THE INTERNET IS THINKING
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 ↗5135 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.
- Hugging Face · Model / dataset date · First archived
DedeProGames/lm-tetris-arena-results — dataset update ↗
# SLM Tetris Arena: results Public results of the [SLM Tetris Arena](https://huggingface.co/spaces/DedeProGames/LM-Tetris-Arena) Space, where small decoder-only language models play Tetris zero-shot. - `leaderboard.json`: current Elo per protocol (`guided`, `blind`) and per model. - `matches/YYYY-MM-DD/ .json`: one file per ranked match: seed, protocol, and for each player the model commit SHA, parameter count, score, lines, pieces, rank and Elo before/after. Matches are reproducible: the same seed, model commits and app version replay the same game (up to rare floating-point differences betwe
- Hugging Face · Model / dataset date · First archived
francesca9805/jpn-100mb-after-wc-from-eng-uniform-newlex-before-ckpt500-packed-bfdiso_seed455 — model release ↗
# Model Card for jpn-100mb-after-wc-from-eng-uniform-newlex-before-ckpt500-packed-bfdiso_seed455 This model is a fine-tuned version of [francesca9805/ppt-wc-uniform-newlex-eng-before-100mb-packed-bfdiso_seed455](https://huggingface.co/francesca9805/ppt-wc-uniform-newlex-eng-before-100mb-packed-bfdiso_seed455). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline(
- Hugging Face · Model / dataset date · First archived
Bluefish2333/benchmark — dataset update ↗
# Benchmark datasets Preprocessed, canonically-laid-out copies of the datasets used by this repo's `BenchmarkEvaluator` (see `docs/benchmark/evaluator_quickstart.md`), packaged for direct download instead of re-running every `scripts/download/` + `scripts/preprocess/` step yourself. **Layout:** each dataset scene is stored as one uncompressed ` / / .tar` (the raw tree is ~900k files). After downloading, extract everything in place with the bundled script: ```bash huggingface-cli download Bluefish2333/benchmark --repo-type dataset --local-dir /path/to/benchmark_root python /path/to/benchmark_ro
- Hugging Face · Model / dataset date · First archived
EQTYLab/Llama-3.1-Nemotron-Nano-4B-v1.1-banking77 — model update ↗
[](https://huggingface.co/spaces/EQTYLab/lineage-explorer?model=EQTYLab/Llama-3.1-Nemotron-Nano-4B-v1.1-banking77) # Llama-3.1-Nemotron-Nano-4B-v1.1-banking77 [nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1) fine-tuned on [banking77](https://huggingface.co/datasets/PolyAI/banking77) intent classification with LoRA, then merged into standalone weights. Built with Llama. | Model | banking77 test accuracy | |---|---| | base | 30.8% | | t
- Hugging Face · Model / dataset date · First archived
addisai/addis-scribe-streaming — model update ↗
# Addis Scribe Streaming **Realtime Amharic speech recognition by Addis AI.** Text appears while people speak, in 0.32-second steps, and is final about 30 ms after they stop. Built on NVIDIA Nemotron 3.5 ASR Streaming (0.6B), trained on 5,000+ hours of quality-checked Amharic speech. | | Addis Scribe Streaming | [Google Chirp 3](https://cloud.google.com/speech-to-text/v2/docs/chirp_3-model) (streaming) | |---|---|---| | Word error rate, natural speech | **28.4%** | 32.5% | | Character error rate, natural speech | **13.1%** | 16.5% | | Final text after the speaker stops | **30 ms** | 1,411 ms |
- Hacker News · Posted on Hacker News · First archived
Show HN: Monitoring my 'dumb' boiler with AI ↗
I have this boiler at a rental property I own. It has 3 smart thermostats (Ecobee). They're great and give the individual tenants zone control, but they only notify me something is wrong when it's already too late as they rely on observing the 'call for heat' and a ongoing decline in room temperature to trigger the alert. The boiler is pretty smart, it has all kinds of cool error codes, but the issue is I have no way of knowing theres an error fl…
- Hacker News · Posted on Hacker News · First archived
The evolution of LLM tool-use from API calls to agentic applications (2025) ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: SlopGrid – Create your own oneshot AI model benchmarks ↗
This is something I've been playing with for the last couple days - an app I vibecoded to arrange oneshot LLM visual benchmarks without the need to write any code. I do it form my phone so the excellence of mobile UI is high in the priority list. It is inspired by Simon Willison's benchmark writeups[0], basically you can recreate them in one go. Besides text and images, it supports SVG outputs and whole static websites, which is where benchmarks…
- Hacker News · Posted on Hacker News · First archived
Your LLM is billing you for whitespace ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Heldkeys, see which credentials your AI coding agent can reach ↗
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 ↗
Follow a thread
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.