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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 ↗6053 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: Clai – featured agentic harness at 10MB, as pkg or CLI ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Claude Mods – Minesweeper ↗
A mod to kill time whilst waiting for the agent response. More on the building process: https://dev.to/reporails/claude-code-mods-minesweeper-and-te...
- Hacker News · Posted on Hacker News · First archived
AI safety is mostly a sex cult in Berkeley, CA [pdf] ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Florida AG seeks injunction to hamper OpenAI development ↗
Hacker News discussion.
- arXiv · Published · First archived
RETRACE: From Entangled Repair Histories to Reusable Experience for CI Repair ↗
Large language model (LLM) agents increasingly reuse prior experience, but most approaches assume that problems and solutions are already aligned. Software histories rarely provide this alignment: a pull request (PR) may contain multiple continuous integration (CI) problems, failed attempts, reverted edits, and unrelated changes, obscuring which changes resolve each problem. We present RETRACE, a framework for reconstructing problem-level repair experience from such histories. RETRACE combines an endpoint view that reasons backward from changes retained in the passing revision with a developme…
- Hacker News · Posted on Hacker News · First archived
AI Agents Need a System of Record, Not Just a Dashboard ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
AI and the Bottom Rung of the Career Ladder ↗
Hacker News discussion.
- arXiv · Published · First archived
Back to the Future: Regressing Readability Features from LLMs ↗
Code readability supports software understanding, review, and maintenance. As AI coding agents become more widely used, readable code helps both humans review agent-generated output and agents maintain code within limited context windows. Yet existing readability models do not consistently agree with human judgements across datasets. We introduce BTTF (Back To The Future), pairing linear regression with information-theoretic features. Rather than using language models as black-box judges, BTTF uses them as measurement instruments. It combines traditional code measurements, embedding-based sema…
- arXiv · Published · First archived
When Talk Isn't Code: Comparing LLM Agents That Simulate and Develop Software ↗
LLMs are used both to simulate network protocol implementations, as honeypots do, and to write them. Prior work evaluates the two uses separately, from a model's answers or conversations in one case and from its generated code in the other. We observe that a knowledge probe (asking the model which security checks an implementation needs) and a conversation can credit security checks that the generated program lacks, but no study has compared them with the code the same model writes. To fill this gap, we present the first such comparison between simulation mode (S-mode), where the model plays t…
- GitHub · Repository created · First archived
lnblfkj-del/Dralo-Studio ↗
Dralo Studio 短剧工坊 | 开源 AI 短剧创作工作台：剧本创作、无限画布、智能体、3D 分镜导演台、角色与场景管理、多模型图像/视频生成、多轨剪辑。本地运行，自备模型。Open-source AI short-drama studio with screenwriting, infinite canvas and storyboard workflows.
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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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.