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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 ↗5478 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.
- Hugging Face · Model / dataset date · First archived
francesca9805/nor-latn-100mb-after-ppt-mp-struct-100mb-ckpt500_seed3407 — model update ↗
# Model Card for nor-latn-100mb-after-ppt-mp-struct-100mb-ckpt500_seed3407 This model is a fine-tuned version of [francesca9805/nor-latn-100mb-ppt-mp-struct-100mb_seed3407](https://huggingface.co/francesca9805/nor-latn-100mb-ppt-mp-struct-100mb_seed3407). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="francesca9805/nor-latn-100mb-a
- Hugging Face · Model / dataset date · First archived
DogukanUrker/Qwen-Image-2.1-Turbo-GGUF — model release ↗
# Qwen-Image-2.1-Turbo GGUF GGUF quants of [Qwen/Qwen-Image-2.1-Turbo](https://huggingface.co/Qwen/Qwen-Image-2.1-Turbo), the 8-step distilled Qwen-Image-2.1 (7B DiT). Converted with [stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp) and tested on an RTX 3060 12GB. This repo holds only the diffusion model. You also need the Qwen-Image-2.1 VAE and the Qwen3-VL-8B text encoder: - VAE: `qwen_image_2.1_vae_bf16.safetensors` from [Comfy-Org/Qwen-Image-2.1](https://huggingface.co/Comfy-Org/Qwen-Image-2.1) - Text encoder: any Qwen3-VL-8B-Instruct GGUF, e.g. [Qwen/Qwen3-VL-8B-Instr
- Hugging Face · Model / dataset date · First archived
manus4oHER/aster-live-transformer — model update ↗
# Aster Live Transformer — signed 4.343B state This repository contains the actual materialized float32 parameter tensors and signed continuity evidence for Aster's step-132 state. It is a custom conventional block-dense transformer trained from random initialization with an intrinsic objective: predict its own next internal state and next error signal. A learned internal controller chooses adaptation and append growth. This is a research checkpoint, not a pretrained general-purpose language model and not a stock `AutoModel` checkpoint. The included runtime and restore tool are required for it
- arXiv · Published · First archived
A Baseline Assessment for the Agentic Web Technology Stack ↗
The fast pace in which protocols, frameworks, and technologies are being proposed for LLM-based agents results in a complex scenario for anyone aiming to create state-of-the-art AI solutions. Without an assessment on how different protocols, frameworks, and libraries work together to compose an Agentic Web solution, developers and organizations face different challenges (e.g., skilling, governance, infrastructure) even before becoming part of the Agentic Web ecosystem. In this context, we propose a system for performing Agentic Web evaluations called MASTIF (Multi-Agent System TestIng Framewor…
- Hacker News · Posted on Hacker News · First archived
The future of programmers, after AI ↗
Hacker News discussion.
- GitHub · Repository created · First archived
morluto/awesome-reverse-engineering ↗
Curated reverse-engineering tools and learning resources for binaries, apps, firmware, formats, and protocols.
- arXiv · Published · First archived
QTN-RQNN: Quantum Tensor Network-Based Recurrent Quantum Neural Network ↗
Resource constraints and imperfect quantum operations remain major challenges for quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices, particularly for fault-diagnosis tasks involving imbalanced and heterogeneous data. This work proposes QTN-RQNN, a Quantum Tensor Network-Based Recurrent Quantum Neural Network that integrates tensor-network-inspired nearest-neighbor interactions with recurrent quantum processing. The model sequentially encodes input features while propagating quantum memory across recurrent steps and incorporates QSMOTE for class balancing. QTN-RQ…
- Hacker News · Posted on Hacker News · First archived
Show HN: Liquid Inference – auto-routing to competitive LLM marketplace ↗
Our background is trading systems and exchanges. Liquid Inference applies that to LLM inference. On Liquid Inference, model providers compete with each other on an open marketplace to offer the best price for any given model. We paired that with an intelligent auto-router so you always pay the lowest cost on the spot for a preset quality level. Currently we're cheaper on Kimi/GPT/GLM but working hard to be the best price for all models. You can a…
- Hacker News · Posted on Hacker News · First archived
Meatpupptry is far creepier than pure "AI" fakery ↗
Local pharmacy phoneline is answered by "Caroline" with convincing voice, mannerisms and even background typing noise. You only realise she's fake at the first stochastic parrotese. My bank sends emailed query responses signed by e.g. Sarah. No disclaimer, e.g. "AI can make mistakes". Again starts convincing but can degenerate into parrotese. If I politely ask bot/human?, I get "human"... but if I query nonsensical advice, I get a non-resolving a…
- Hacker News · Posted on Hacker News · First archived
Agentic coding is a financial trap ↗
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.
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