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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.
- Hacker News · Posted on Hacker News · First archived
AI agents are flooding public services with new requests ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: The Last SaaS – An open source headless SaaS for agents and assistants ↗
This kind of started as a joke, and it still kind of is. But it's also kind of fun to think about what the minimum number of primitives would cover all possible typical SaaS applications. And then drive that from an agent/assistant. There is obvious stuff like CRUD, and then permissions, users, notifications etc. So basically I made a start of a "headless SaaS" that has no UI and is just a CLI/API/MCP that you can connect your Claude etc to and i…
- arXiv · Published · First archived
QuSema: Detecting Silent Bugs in Quantum Libraries via Quantum-knowledge-enhanced Agents ↗
Quantum libraries are now critical infrastructure for quantum algorithm development, yet their correctness remains difficult to test. Existing testing techniques mainly rely on failure-based or comparison-based oracles, exposing bugs only when executions fail, violate runtime checks, or disagree with another implementation. Their applicability is limited when suitable execution-based oracles are unavailable, leaving some silent bugs undetected. Such missed bugs can produce incorrect results that propagate into experimental conclusions, simulation studies, and algorithmic designs. Here we prese…
- arXiv · Published · First archived
OOM-RL II: Reality Is an Oracle, Not a Debugger Provenance-Constrained Diagnosis in Continually Evolving Agent-Engineered Systems ↗
Reality may establish that an outcome occurred without identifying which evolving procedure produced it or why. This distinction matters in production ML systems whose code, configuration, and artifacts change while external feedback accumulates. We examine it in a human-directed, agent-engineered quantitative trading system, using oracle to mean an external source of realized outcomes rather than a complete correctness specification. Across one year, the account gained and outperformed a broad market index, while annual alpha was not statistically distinguishable from zero under the main retr…
- arXiv · Published · First archived
TestGRAD: Evolving Test Suites via Failure Pattern Momentum for SWE-Agent Ensemble ↗
SWE-agent ensembles improve issue resolution by combining candidate patches from different agents with complementary strengths. The central problem is therefore test-based selection: generate tests, execute candidate patches, and identify the best patch. We formulate this process as test-space optimization: evolving an executable repository test suite until it distinguishes competing patches. Existing test-generation methods are limited optimizers. They usually lack an explicit loss for ensemble selection, optimize through incomplete directions that mostly create new tests or delete old ones,…
- Hacker News · Posted on Hacker News · First archived
We Built an Alternative to Vector RAG for AI Agent Memory ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Planlock – Approve an AI agent's plan once, code enforces every call ↗
Hacker News discussion.
- arXiv · Published · First archived
Agentic AI-Assisted Modeling for Production Scheduling: Assessment in Constraint Programming ↗
Developing optimization models for production scheduling requires substantial expert effort. Research on large language models (LLMs) has followed two directions: specialized approaches for automated modeling, mostly for mixed-integer linear programming, which often rely on dedicated training or problem-specific architectures that limit industrial deployment; and agentic artificial intelligence for operational decision support, which generally assumes that the optimization model already exists. This study bridges both directions by assessing whether general-purpose LLMs, orchestrated as agents…
- GitHub · Repository created · First archived
hue913/agentd ↗
Self-hosted multi-model agent with JitRL memory, auditable council runs, SSH operations, live server screen, and macOS/Windows desktop client
- Hacker News · Posted on Hacker News · First archived
Human Friction Makes Agentic AI Safer and Smarter ↗
Hacker News discussion.
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.