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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 ↗6011 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
Show HN: Tracelane.dev- OSS Rust LLM Gateway with a tamper-evident ledger ↗
Hi everyone, Sanjeev here- founder. My journey in AI started with wanting to break into emerging AI companies, but without deep hands-on experience, it was hard to even get noticed. I started with scope focused minor projects but then curiosity got expanded to understand what happens under the hood of LLM calls, what goes in & how reliable AI agents works and gets built by world's top companies and how future of agents would evolve in next 10 yea…
- Hacker News · Posted on Hacker News · First archived
Ask HN: What do you think of identity challenge for AI? ↗
This is a real deep subject i think. We human ave a skin and a unique body. Duplicating ourselves could be extremely expensive. Agents can duplicate themselves, its privatekey, context and everything in minutes. Agents exists only through them owners. Should it be possible to give them a real unique and autonomous identity, should it be relevant from your point of view?
- Hacker News · Posted on Hacker News · First archived
Beware: Real risks of using AI as a software dev team ↗
Hacker News discussion.
- GitHub · Repository created · First archived
tandavkrishna27/Army-of-Agents ↗
Command center for your human + AI team
- Hacker News · Posted on Hacker News · First archived
Sub-two-second LLM-driven NPC behavior in a 3D action game ↗
Hacker News discussion.
- GitHub · Repository created · First archived
YunongDai2005/dsh-theone ↗
One chat for everything, no more hunting for old conversations. A DeepSeek Harness (DSH) plugin./不用再翻旧会话：所有事在一个对话框里聊，自动分到对应的那件事
- Hacker News · Posted on Hacker News · First archived
Show HN: My notes on Personal AI and why chat-based agents don't work ↗
Hacker News discussion.
- arXiv · Published · First archived
Privacy Foundations for Multi-Institutional Scientific Artificial Intelligence ↗
Scientific artificial intelligence (AI), spanning foundation models (FMs) to federated data-analysis pipelines, is becoming shared infrastructure across national laboratories, universities, hospitals, and industrial partners. This collaboration creates privacy risks whose natural unit is often an institution's participation, research strategy, or technical capability rather than a single record. Differential privacy (DP), federated learning (FL), secure computation, trusted execution, and provenance each protect parts of the stack, but their guarantees rarely compose across mixed-trust institu…
- Hacker News · Posted on Hacker News · First archived
Google paying almost no publishers almost nothing for content used in AI answers ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
asana17/ai_can_anomaly_detection_runs — model update ↗
# ai_can_anomaly_detection_runs Runs of the detector comparison in [asana17/ai_can_anomaly_detection](https://github.com/asana17/ai_can_anomaly_detection), and the models quantized from them and the C code generated from those for the board. | directory | added by | described in | |---|---|---| | `results/ /` | one run of `evaluate.pc.run` | [evaluate/docs/run_record.md](https://github.com/asana17/ai_can_anomaly_detection/blob/main/evaluate/docs/run_record.md) | | `quantize/ /` | one run of `quantize.export` | [quantize/docs/export.md](https://github.com/asana17/ai_can_anomaly_detection/blob/m
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.
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