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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 ↗5996 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
I built a native Apple app for 4 platforms in one afternoon with my AI workflow ↗
Hacker News discussion.
- arXiv · Published · First archived
QC-Stark: A Multi-Task Benchmark Revealing Capability Dissociations in LLMs Evaluated on Quantum Computing Tasks ↗
We introduce QC-Stark, a benchmark for evaluating large language models (LLMs) on 11 quantum computing (QC) tasks, spanning circuit construction, debugging, compilation, error correction, and simulation. Across 2,750 evaluations (10 models $\times$ 11 tasks x 5 difficulty levels x 5 seeds), we find that overall rankings mask substantial per-task variation. The Spearman correlation between overall and per-task rankings is statistically insignificant for 4 out of the 11 tasks included in this benchmark. A 2-parameter Item Response Theory (IRT) model validates measurement quality, and prompt sens…
- arXiv · Published · First archived
Share-Borne AI Virus: Memory-Hopping Attacks Across LLM Agents ↗
Large language models are increasingly deployed as stateful assistants that retain information across interactions and use tools to read, modify, and create persistent artifacts. As these artifacts are shared between users, they form an indirect communication channel between otherwise independent assistants. We study a failure mode in which this channel enables self-propagating attacks. We introduce artifact-mediated propagation, where adversarial content introduced through an artifact (e.g. a report), is stored in an assistant's persistent memory, reproduced in a subsequently created artifact…
- Hacker News · Posted on Hacker News · First archived
Building a physics model with fitted parameters is machine learning, but slower ↗
Hacker News discussion.
- arXiv · Published · First archived
The Compiler May Read It, the Agent May Not: Keeping Part of a Research Code Away from a Coding Agent ↗
The compiler must read modules a physics-based solver cannot build without; the coding agent must not read that intellectual property. The harness does not ship that rule. We classified fifteen read routes against a container, permission rules and a sandbox. None of the three can tell which program is reading.
- Hacker News · Posted on Hacker News · First archived
How to reduce token usage in Claude Code, Codex, and Cursor ↗
Hacker News discussion.
- GitHub · Repository created · First archived
LucasM-Maciel/ticlins-claude-monitor ↗
Ticlin's Claude Monitor: janelinha com as sessões do Claude Code e o uso do limite (Windows e Mac)
- Hacker News · Posted on Hacker News · First archived
The problem is not AI code, but not knowing about system architecture or intent ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
KucLab/kuclab-hertz-0.7f — model update ↗
# KucLab Hertz 0.7F  *Measured on this project's harness — same 240 MMLU-Pro STEM and 206 CZ terminology questions for every model. Scripts: `scripts/bench_mmlu_pro.py`, `scripts/bench_czech_terms.py`.* A Czech/English STEM + programming assistant built by [KucLab](https://kuclab.org) on top of **Qwen/Qwen3.5-9B**. Unlike the Gemma-based Hertz 0.x line, 0.7F is **distilled**: its training data was generated by a 30B teacher model (reasoning strength high, answer-first), then used to fine-tune the 9B student. It beats Hertz 0.
- Hacker News · Posted on Hacker News · First archived
Create AI Workout Plans with Cloudflare Durable Objects ↗
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 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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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.