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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 ↗5437 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
glossia/Rambley-V2-350M-Preview — model update ↗
 | Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr| |-------------|------:|------|-----:|--------|---|-----:|---|-----:| |arc_challenge| 1|none | 0|acc |↑ |0.2295|± |0.0123| | | |none | 0|acc_norm|↑ |0.2637|± |0.0129| |arc_easy | 1|none | 0|acc |↑ |0.5400|± |0.0102| | | |none | 0|acc_norm|↑ |0.4722|± |0.0102| |hellaswag | 1|none | 0|acc |↑ |0.3140|± |0.0046| | | |none | 0|acc_norm|↑ |0.3595|± |0.0048| |piqa | 1|none | 0|acc |↑ |0.6556|± |0.0111| |
- Hugging Face · Model / dataset date · First archived
294-288-pretrain-interaction/160M-rtp — model update ↗
# 160M RTP dose campaign This repository collects Yihan's 24 research runs: six injection doses (`10`, `25`, `50`, `100`, `250`, `500`) and four source allocations (`M`, `C`, `G`, `MCG`). The exact trigger is **`dAtA[iNdEx]`**. Models use the Pythia-160M architecture and train from random initialization, seed 42, for 16,000,090,112 tokens on the frozen Dolma Math/Code/General mixture. The campaign is **in progress**. A run is released only when its checkpoint audit and all scheduled evaluations pass. `releases/ .json` records uploaded file checksums and the verified Hub revision. Missing runs
- Hacker News · Posted on Hacker News · First archived
Why AI doesn't make companies more productive ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
OpenKylin: The AI Agent Linux Desktop ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
The Tetrad Tour, Part III: Why AI Needs Its Linnaeus ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Claude Notify – Desktop Notifications for Claude Code on Windows ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
The CEO's of Broadcom and Intel Say the Quiet Part Out Loud about AI and ROI ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
julien-c/caliceo — dataset update ↗
# Caliceo Time-Series ### Zoom on single day patterns
- Hugging Face · Model / dataset date · First archived
DatKurai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-Uncen-IQ1-332Exp-GGUF — model update ↗
# Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-Uncen · IQ1 · 332 experts/layer (GGUF) **Swift-1.5 Uncensored (`Uncen`)** is an abliterated (refusal-reduced) build of Swift 1.5 — the uncensored line of the Qwen3.8-Flash-Next GSQ-RCO family. This release shrinks the MoE expert pool from **512 → 332 experts/layer** so it serves on consumer hardware (a single 16 GB GPU + 32 GB RAM with the Strata engine), while keeping as much coding, agent and tool-calling capability as possible. - **About the `IQ1` name — read first.** `IQ1` here does **not** mean 1-bit tensor quantization. Every tensor keeps its origin
- Hugging Face · Model / dataset date · First archived
agurung/cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp5-n3nc-groot16-s3 — model update ↗
# seeded_rl_base_ramp25_stoppen_gen4k_ep2_ncp5_n3nc_groot16_s3 — RL checkpoint (global step 54) **Role in this run:** best by pass@8 ## What it is An **OpenRLHF GRPO** reinforcement-learning checkpoint for Qwen3-4B. - Base model: `Qwen/Qwen3-4B-Instruct-2507` - Seeded from: base Qwen3-4B (no SFT seed — RL applied directly to the base model) - Saved at global step 54 of RL run `seeded_rl_base_ramp25_stoppen_gen4k_ep2_ncp5_n3nc_groot16_s3`. ## What it's best at - This is the **best checkpoint by pass@8** so far in this run. Trained and validated on the **cobalt-train ≤2/64 frontier** (canonical
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.