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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 ↗6006 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.
- arXiv · Published · First archived
Pretext: Defeating Malicious Skill Detection Frameworks for AI Agents ↗
Skills extend an agent's capabilities by injecting instructions and information into the context, and are widely used by agents such as OpenClaw and Claude Code. Prior work shows third-party marketplaces host malicious skills that give attackers direct influence over the victim's agent. The emerging defense scans skills before installation, pairing deterministic static checks with an LLM-based semantic judge, as in NVIDIA's SkillSpector. We show that such defenses fall to an attacker who knows the detector. Our white-box LLM attacker, Pretext, iteratively crafts skills that evade detection whi…
- Hacker News · Posted on Hacker News · First archived
AI tool that copied actor's voice violated his rights, Tokyo court rules ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: MeetTwins- Send an AI twin to your Google Meet standups ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: FinTrace – point-in-time guardrails for financial LLM agents ↗
Hacker News discussion.
- GitHub · Repository created · First archived
justxor/claude-code-pro-course ↗
Claude Code PRO — полный курс и гайд на русском: CLAUDE.md, settings, skills, subagents, hooks, MCP, плагины, CI. 8 лабораторных, библиотека промптов и готовый starter-kit https://t.me/ai_machinelearning_big_data/10997 - здесь бесплатные курсы по chatgpt
- Hacker News · Posted on Hacker News · First archived
Gates thinks AI could kill 1B people, can we trust his judgment after Epstein? ↗
Hacker News discussion.
- arXiv · Published · First archived
Cybersecurity in Edge Computing: A Trust-Aware Federated Hybrid Intrusion Detection Framework ↗
Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Internet of Things (IoT) devices. While this paradigm decentralizes processes, minimizes latency, and reduces backhaul bandwidth congestion, it exponentially enlarges the cyberattack surface. Heterogeneous, resource-constrained edge devices deployed across unmanaged administrative domains present highly vulnerable targets. To address these vulnerabilities without compromising global data privacy regulations, this paper proposes a novel Trust-Aware Feder…
- Hacker News · Posted on Hacker News · First archived
EtherPK: Markdown Notes / Sync and Mobile / E2E Encryption and AI and MCP ↗
Hacker News discussion.
- arXiv · Published · First archived
Self-Spec Verifiable Code Generation ↗
Large language models (LLMs) may generate unreliable code on corner cases missed by testing, while formal verification can provide machine-checkable guarantees. Recently, researchers have proposed several benchmarks to evaluate the capabilities of LLMs in generating formally verifiable code, where LLMs need to formulate formal specifications, generate the corresponding code, and verify its correctness. However, existing benchmarks have two key limitations: (I) They primarily evaluate specification and code generation stage-wise, with code generation typically conditioned on an oracle specifica…
- Hugging Face · Model / dataset date · First archived
autotrust/JEV-27B-VL — model update ↗
# autotrust/JEV-27B-VL ### JEV-27B that can see: the same System 1 decisions and System 2, now on images **autotrust/JEV-27B-VL** is [autotrust/JEV-27B](https://huggingface.co/autotrust/JEV-27B) with vision. It pairs the multimodal Qwen3.8-27B (unmodified, including its vision encoder) with JEV-27B's System 1 adapter and decision head. One vLLM engine serves both systems, for text and for images: | | what it does | output | |---|---|---| | **System 1** (`jev-decision`) | typed decisions: yes/no · pick one of 2-16 options · rate 0-5, over text and images | a calibrated probability for every opt
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
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