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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 ↗5165 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
Management in the Age of AI ↗
Hacker News discussion.
- GitHub · Repository created · First archived
lemonteaau/smartisan-ui-skill ↗
用于构建Smartisan OS风格UI的Agent Skill
- Hacker News · Posted on Hacker News · First archived
How Researchers Made Contact with a Chinese AI Agent Fleet ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
How are you centralising AI for development at your company? ↗
I'm asking this because I'm seeing some frustrating patterns and if I may be bold, I think my answer is the correct one. Here's one I'm seeing: its the SDLC agent pattern and I think its wrong. Step 1: you have good AGENTS.md documentation for your code and other principles. (this is good) Step 2: There's a somewhat strict workflow and steps enforced like planner agent, reviewer agent, critique agent and directly create a PR from a ticket. Every…
- Hugging Face · Model / dataset date · First archived
psdn-ai/CROWS-multilingual — dataset update ↗
# CROWS Multilingual CROWS Multilingual contains 821,361 WAV recordings across 16 languages: 812,464 in main data, 2,260 in the breadth risk partition, and 6,637 in the predicted proportion risk partition. The original release was selected by the `passing_files.csv` inclusion list. The publisher is [psdn-ai](https://huggingface.co/psdn-ai). **Upload status:** Complete. All 821,361 recordings have been packaged, decode-checked, and uploaded. See `release_manifest.json` for artifact checksums. ## Main and synthetic risk partitions Main `data`, `archives`, `checksums`, and root `metadata.csv` con
- Hugging Face · Model / dataset date · First archived
bellahug3/kin-sentiment-transformer — model update ↗
# Model Card for Model ID ## Model Details ### Model Description This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] - **Repository:**
- Hugging Face · Model / dataset date · First archived
kojira/aozoraquest-cognitive-small — model update ↗
## wgsl/ruri-v3-30m.aqw Ruri-v3-30m の 文章埋め込みモデルを、Aozora Quest のブラウザ内 WebGPU 推論 (手書き WGSL) 向けに変換した重み。 - 元モデル: [cl-nagoya/ruri-v3-30m](https://huggingface.co/cl-nagoya/ruri-v3-30m) (Apache License 2.0) - 変換元: [sirasagi62/ruri-v3-30m-ONNX](https://huggingface.co/sirasagi62/ruri-v3-30m-ONNX) の `onnx/model.onnx` (fp32) - 変換: `scripts/convert-cognitive-wgsl.py` (github.com/kojira/aozoraquest)。 - 埋め込み: int8 の 行ごと 対称量子化。 - 線形層: fp16。 - LayerNorm: fp32。 - head: mean-pool (attention mask 上の平均 → L2 正規化)。 - 変更点: 量子化と 格納形式の 変換のみ (重みの 再学習は していない)。 - sha256: `d0e9bcb2774b5b9ea6f55b1ea8036933516d0595fcb79064d
- arXiv · Published · First archived
Decoding What iOS Location Services Send to Apple: An LLM Is an Unreliable Analyst but a Useful Toolmaker ↗
Reverse engineering the data formats used by mobile software is skilled and slow, and using AI to assist with it is attractive. We evaluate this for the protobuf messages that an iPhone sends to Apple's location services. Working from Apple's binaries, we use two frontier models (Claude Opus 4.8, Codex GPT Sol 5.6) to reconstruct the schemas of protobufs observed in network traffic. The great majority of the schemas contain substantial errors, and these are hard to detect because the schemas are internally consistent. Direct AI use is therefore unsuitable for forensic and privacy analysis. AI…
- Hacker News · Posted on Hacker News · First archived
Show HN: I built a rogue AI game, then let five AI models play it ↗
Hacker News discussion.
- arXiv · Published · First archived
Independent Data-Integrity Verification for LLM-Driven Functional Coverage Closure in Hardware Design ↗
LLM agents can write directed tests that close functional coverage, but coverage only shows that a state was reached, not that the data on the way there was correct. This is a long-known point in verification and we do not claim it as new. We describe a small coverage-closure agent that applies it to an LLM loop: coverage is read from the report that the simulation itself exports (cocotb-coverage YAML) and never taken from the model's account, and data integrity is checked by scoreboards that are separate from the model. On three architecturally distinct RTL designs (an asynchronous FIFO, a UA…
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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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