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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 ↗5222 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
A Claude Code skill that takes an iOS app from code to the App Store ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
More on Anthropic's Scion ripping off Mononoke ↗
Redesigning source control at Anthropic - https://news.ycombinator.com/item?id=50073633 (996 comments) Anthropic's Scion is heavily inspired by Mononoke - https://news.ycombinator.com/item?id=50094694 (149 comments)
- Hugging Face · Model / dataset date · First archived
francesca9805/swa-100mb-after-wc-zipf-newlex-after-ckpt500-packed-bfdiso_seed455 — model update ↗
# Model Card for swa-100mb-after-wc-zipf-newlex-after-ckpt500-packed-bfdiso_seed455 This model is a fine-tuned version of [francesca9805/ppt-wc-zipf-newlex-swa-after-100mb-packed-bfdiso_seed455](https://huggingface.co/francesca9805/ppt-wc-zipf-newlex-swa-after-100mb-packed-bfdiso_seed455). 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", mo
- Hugging Face · Model / dataset date · First archived
francesca9805/hin-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso_seed3407 — model update ↗
# Model Card for hin-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso_seed3407 This model is a fine-tuned version of [francesca9805/ppt-wc-loglinear-newlex-hin-before-100mb-packed-bfdiso_seed3407](https://huggingface.co/francesca9805/ppt-wc-loglinear-newlex-hin-before-100mb-packed-bfdiso_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(
- Hugging Face · Model / dataset date · First archived
unsloth/Muse-Spark-Open-816B-A42B-GGUF — model update ↗
## Muse Spark Open Model Card **Authors**: Meta Superintelligence Labs **Model Release Date:** October 2026 **License**: [Muse Model License](https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B/blob/main/LICENSE.md) Muse Spark Open is an 816B total/42B active parameter open weights model with a context window of 1M tokens. Built for coding and agentic work, Muse Spark Open is natively multimodal. This open weights version has additional safety post-training to make it more robust against misuse as a standalone model without system guardrails. We recommend deploying Muse Spark Open as
- Hugging Face · Model / dataset date · First archived
francesca9805/eus-100mb-after-wc-zipf-newlex-after-ckpt500-packed-bfdiso_seed455 — model update ↗
# Model Card for eus-100mb-after-wc-zipf-newlex-after-ckpt500-packed-bfdiso_seed455 This model is a fine-tuned version of [francesca9805/ppt-wc-zipf-newlex-eus-after-100mb-packed-bfdiso_seed455](https://huggingface.co/francesca9805/ppt-wc-zipf-newlex-eus-after-100mb-packed-bfdiso_seed455). 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", mo
- Hacker News · Posted on Hacker News · First archived
List of email domains used by AI agents ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Chinese Users Find Little Money in 'Selling' Their Faces to AI ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
TR-Core – A non-neural symbolic AI architecture built in pure Python ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Anthropic: The Agony and the Ecstasy ↗
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.