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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 ↗5967 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
Show HN: Puppet Master – Manage coding agents at scale ↗
As I started to use `claude` and `codex` more it became too hard to manage all the sessions across all my tmux panes and sessions, so I built Puppet Master. I see now there is quite a lot out there now for managing agents to write software, but a) I started working on this before a lot of them, and b) there are things in Puppet Master that just don't exist all in one product. For me I'd say the highlights are: 1) Web instead of terminal -- it's j…
- Hacker News · Posted on Hacker News · First archived
The Hardest Part of Using AI ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Researchers Backdoor Open AI Model to Steal Credentials in Coding Agents ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
qualcomm/CVT — model update ↗
 # CVT: Optimized for Qualcomm Devices Cross-View Transformer generates real-time bird's-eye view maps from multiple vehicle cameras for autonomous driving. This is based on the implementation of CVT found [here](https://github.com/bradyz/cross_view_transformers). This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.64.0/src/qai_hub_models/models/cvt) library to e
- Hugging Face · Model / dataset date · First archived
Mr-J-369/Fancy-AI — model update ↗
> 📱 **Now on Google Play — [Fancy AI](https://play.google.com/store/apps/details?id=com.mrj.fancyai)** > On-device AI image generation, optimized for Qualcomm NPU phones. --- license: other language: - en tags: - gguf - llama.cpp - android - fancy-ai - on-device pretty_name: Fancy AI — Add-on Catalog --- # Fancy AI — Add-on Catalog Add-on catalog for the **Fancy AI** Android app — an on-device AI companion app powered by `llama.cpp`. The app ships small and streams optional models on demand from this catalog. - App & issues: https://github.com/Mr-J-369/Fancy-Ai - This repo hosts **`manifest.j
- Hugging Face · Model / dataset date · First archived
qualcomm/Llama-v3.2-3B-Instruct-SSD — model update ↗
 # Llama-v3.2-3B-Instruct-SSD: Optimized for Qualcomm Devices Llama 3 is a family of LLMs. The model is quantized to w4a16 (4-bit weights and 16-bit activations) and part of the model is quantized to w8a16 (8-bit weights and 16-bit activations) making it suitable for on-device deployment. This variant uses Self Speculative Decoding (SSD), inference acceleration solution that achieves on-target speed up with guaranteed output accuracy identical to the base model
- Hugging Face · Model / dataset date · First archived
qualcomm/Conditional-DETR-ResNet50 — model update ↗
 # Conditional-DETR-ResNet50: Optimized for Qualcomm Devices DETR is a machine learning model that can detect objects (trained on COCO dataset). This is based on the implementation of Conditional-DETR-ResNet50 found [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models/conditional_detr). This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qu
- OpenAlex · Published · First archived
Algorithmic blind spot: (In)visible racism, multimodality and the limits of AI hate speech detection ↗
Automated hate-speech detection has advanced rapidly, yet significant blind spots remain in how artificial intelligence recognises online abuse and processes its contextual and culturally situated meanings. This article examines why anti-Roma hate often evade moderation on social media. Drawing on insights from multimodal discourse analysis, pragmatics, and critical race theory, this article shows two mechanisms through which anti-Roma hate escapes detection on YouTube. Some expressions are semiotically invisible to commercial AI systems, which cannot recognise humour, irony, euphemism, metaph…
- OpenAlex · Published · First archived
MLATE V3: An Open-Source Multi-Tissue AI Framework for Data-Driven Optimization of 3D-Printed and Bioprinted Scaffolds ↗
3D (bio)printing is increasingly used for scaffold fabrication in tissue engineering; however, identifying suitable biomaterial compositions, cell types, cell densities, and printing conditions remains largely empirical, time-consuming, and costly. Practical application of machine learning in this field is further limited by the scarcity of comprehensive datasets based on pre-fabrication variables, the lack of integrated frameworks for prioritizing candidate scaffold formulations, and the limited availability of accessible tools for non-technical users. To address these challenges, we develope…
- OpenAlex · Published · First archived
Mind the gap: capability, policy and uncertainty in the adoption of generative AI within post-compulsory education ↗
This qualitative, interpretivist study explores perceptions of Generative AI (GenAI) adoption among academic staff and Level 7 senior leader apprentices within a UK business school. Drawing on semi-structured interviews with sixteen participants, inductive thematic analysis revealed three interrelated themes: engagement under uncertainty, the capability gap, and the policy gap. Findings indicate that whilst participants recognise the potential utility associated with embracing GenAI, adoption is constrained by ethical sensitivity, limited application literacy, and the opacity of governance fra…
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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.
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