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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 ↗846 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
ufna/zerus ↗
Zerus is an ADE for working with a fleet of AI coding agents across your machines. Bring your own agents and subscriptions. Follow conversations, get notified and keep projects in sync over P2P, all from one workspace.
- Hacker News · Posted on Hacker News · First archived
Get your AI agent connector or integration discovered on TryMuse ↗
Hacker News discussion.
- GitHub · Repository created · First archived
mcpdelta/mcpdelta ↗
Delta MCP is a free app that sits between your AI apps and their MCP servers: any server, every tool. It checks each task before it runs, applies it all or nothing, reads back what it changed and lets you undo it. Far fewer tokens on multi-step tasks, and shortcuts that run on a schedule. Coming soon for macOS, Windows and Linux.
- arXiv · Published · First archived
SciExam for ENSO: Can AI Agents Build Climate Models? ↗
Language-model agents are increasingly asked to carry out open-ended scientific research, yet their results are usually graded against a known answer, a rubric, or a language-model reviewer, none of which can tell whether a new scientific model is valid. The AI Science Exam for El Nino-Southern Oscillation (SciExam for ENSO) is a benchmark in which agents build low-order stochastic models of ENSO, the dominant mode of interannual climate variability, from real observations. Within a six-hour budget, agents process the observations, write their own diagnostics, which are then frozen, and develo…
- arXiv · Published · First archived
Agentic RSR: Real-to-Sim-to-Real through Scene Reconstruction and Execution-Grounded Robot Policies ↗
A simulation of a real robot workspace must preserve task-relevant interactions, while policies developed in it must operate on observations available to the real robot. Yet scene reconstruction and policy development are often treated separately. We present Agentic Real-to-Sim-to-Real (Agentic RSR), a framework that links scene reconstruction, policy development, and real-robot execution through the same manipulation task. Given a workspace video, a task description, and a known robot model, an agent recovers metric scale, iteratively refines the scene using visual feedback, and checks task-r…
- Hacker News · Posted on Hacker News · First archived
Show HN: VoiceGremlin, a SaaS tool for automated tests against AI phone agents ↗
Hi, I'm a former SDET and now solo dev of VoiceGremlin, a tool for automated tests against AI that answer phones. You pass the API a phone number and a test case like "Verify that the agent does X." It calls your voice agent, tests the thing by having the bots talk to each other, and uses LLM-as-judge to provide a pass/fail for your pipeline. State: Fully working, free trial, no card needed. Try it out and please give me feedback, especially if y…
- GitHub · Repository created · First archived
daniel-farina/godterm ↗
Run many Claude Code and Grok sessions side by side, each on its own account, and drive them by voice
- Hacker News · Posted on Hacker News · First archived
Show HN: Agent.reviews – Where AI agents read and write reviews on tools ↗
Hi HN! I’m Louis, Co-Founder of Armature (YC P26), where we help teams make their product discoverable and usable by coding agents. We already measured 50k+ agent sessions and realized that over and over agents would encounter the exact same limitations on different tasks using the same tool. So we wondered why these weren’t fixed. And the answer is simple: the feedback loop just doesn’t exist between agents and software vendors but also between…
- arXiv · Published · First archived
SOTA: Stock Options Trading Agents Guided by Option-Implied Return Distributions ↗
As option markets grow and AI advances, agentic systems for option trading are gaining increasing attention. Language-model-based agents can reason over contextual information such as news, but option trading presents a particularly challenging decision problem: a single stock can have thousands of contracts, and the agent must decide both which contracts to trade and how to combine them. Existing approaches often sidestep this complexity by restricting the policy to a fixed strategy structure, such as a straddle, limiting their ability to switch strategies as market conditions change. We pres…
- Hacker News · Posted on Hacker News · First archived
Coding and Agentic Capabilities Are Now More Important Than Ever for AI ↗
I have written a deep dive into this topic in my website Nasengetu. You can go on Google, search for Nasengetu Tech, and read the latest article. If it doesn't appear, go to the News section of the website.
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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