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Earlier sources: AI agents
AI agents use models to choose actions, call tools, and work toward a goal. This thread follows agent frameworks, memory, orchestration, evaluation, and safeguards; autonomy and reliability vary between 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 ↗862 classified signals in the fourteen-day window. 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.
- GitHub · Repository created · First archived
Alpha-Park/genpark-agentic-travel-multi-leg-itinerary-synthesizer-skill ↗
Universal Agentic Travel & Multi-Leg Hospitality Itinerary Synthesizer (inspired by Meta Muse, Expedia, Booking). Translates natural language vacation and business travel goals into multi-leg flight segments, lodging selection, layover time buffers, baggage fee optimization, and consolidated reservation manifests.
- GitHub · Repository created · First archived
alphaparkinc/genpark-agentic-travel-multi-leg-itinerary-synthesizer-skill ↗
Universal Agentic Travel & Multi-Leg Hospitality Itinerary Synthesizer (inspired by Meta Muse, Expedia, Booking). Translates natural language vacation and business travel goals into multi-leg flight segments, lodging selection, layover time buffers, baggage fee optimization, and consolidated reservation manifests.
- GitHub · Repository created · First archived
emiralpayar/dashcall ↗
Talk to your Claude Code sessions from your phone or your car's browser — voice & touch, English/Turkish
- arXiv · Published · First archived
PatchHolmes: Agentic Patch Retrieval via Listwise Selection ↗
Patch retrieval, the task of finding the commit that fixes a known vulnerability, is the foundation of vulnerability management workflows, yet 60% to 63% of CVEs in the major advisory databases lack a patch link. We present PatchHolmes, a two-phase patch retrieval system that pairs a hybrid first-stage retriever with an agentic second-stage inspection loop. Unlike pointwise prior work that scores each candidate independently, the Phase 2 agent reads the top-100 listwise: it sees the full candidate list at once and selectively reads 3 to 10 commits through four budgeted tools before submitting…
- Hacker News · Posted on Hacker News · First archived
Show HN: Moching – AI desktop agent with 219 built-in tools (Rust) ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
LabBench: Can AI agents decide what experiment to run next? ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Vibe Coding Discover – A Catalog of AI OSS for Agents, MCP, Skills, RAG ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: InfiniHash App Store – compliance tools built end-to-end by AI agents ↗
Hacker News discussion.
- OpenAlex · Published · First archived
Empathy in the Soil, Agents on the Desktop: Why Plant Pathology Needs a Scientist-in-the-Loop AI Paradigm ↗
Plant pathology faces a cognitive bottleneck caused by the growing volume of biological data. Addressing it requires a shift from passive chatbots to autonomous AI research agents that operate in Goal-Action-State cycles. Researchers can set up a local, desktop-based agentic framework that combines semantic discovery, LLM-Wiki knowledge synthesis, and in silico prevalidation, managed under a human-directed three-tier organizational model. Strict “Scientist-in-the-Loop” boundaries, which means that a human researcher sets the goals, reviews AI-generated output before it is treated as evidence,…
- OpenAlex · Published · First archived
Agentic AI—Physicist Collaboration in Experimental Particle Physics: A Proof-of-Concept Measurement with LEP Open Data ↗
We present an agentic measurement of the thrust distribution in \(e^+e^-\) collisions at \(\sqrt{s}=91.2\) GeV using ALEPH archived data. This work was carried out through an AI agent–physicist collaboration, using interactive natural-language prompting of Anthropic Claude and OpenAI Codex. Under physicist guidance, the agents wrote and executed all analysis code and drafted the majority of the text included in this paper. A fully-corrected thrust spectrum is obtained via Iterative Bayesian Unfolding and Monte Carlo based corrections. The principal contribution of this work is the demonstratio…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about AI agents
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.