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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 ↗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
Zhanli-Li/DataWorld-Train-2k-part2 — dataset update ↗
Scaling Data Environments from Scratch Specify the world behind the files. Generate the data. Ground the supervision. # DataWorld-Train-2k-part2 — the training slice (part 2 of 2) **2,135 tasks** in exactly the same **[harbor](https://github.com/Zhanli-Li/Data-World) task format** as [`Zhanli-Li/DataWorld-Bench`](https://huggingface.co/datasets/Zhanli-Li/DataWorld-Bench), and graded by the same harness. One self-contained directory per task, each carrying its own copy of the world's data, nothing mounted from a sibling task. **This repository is part 2: it holds 1,538 of those 2,135 tasks** —
- Hugging Face · Model / dataset date · First archived
kyutai/reki-2b-robocasa365 — model update ↗
# REKI-2B RoboCasa365 [REKI](https://huggingface.co/kyutai/reki-2b) is a 2.4B vision-language-action model with register tokens in its action expert, pretrained on public data only. This model is [`kyutai/reki-2b`](https://huggingface.co/kyutai/reki-2b) finetuned on **RoboCasa365** (Franka Panda arm on an Omron mobile base, simulated kitchens). We evaluate it on the 50 target tasks of the official multitask protocol. The [`kyutai/reki-2b`](https://huggingface.co/kyutai/reki-2b) card describes the architecture and the pretraining. ## Model Details - **Developed by:** Kyutai, Inria, École nation
- Hugging Face · Model / dataset date · First archived
kyutai/reki-2b-libero — model update ↗
# REKI-2B LIBERO [REKI](https://huggingface.co/kyutai/reki-2b) is a 2.4B vision-language-action model with register tokens in its action expert, pretrained on public data only. This model is [`kyutai/reki-2b`](https://huggingface.co/kyutai/reki-2b) finetuned on **LIBERO** (Franka Panda, simulation). One model covers the four suites: Spatial, Object, Goal and Long. We evaluate it on LIBERO and on the seven perturbation axes of **LIBERO-Plus**. The [`kyutai/reki-2b`](https://huggingface.co/kyutai/reki-2b) card describes the architecture and the pretraining. ## Model Details - **Developed by:** K
- Hugging Face · Model / dataset date · First archived
kyutai/reki-2b — model update ↗
# REKI-2B [REKI](https://github.com/kyutai-labs/reki) is a 2.4B vision-language-action model (VLA) with memory, pretrained on public data only. - **Memory at negligible cost.** 32 register tokens in the action expert summarize the output of the VLM at each frame and persist in the expert's cache. Memory adds less than 1% to the GPU time of a chunk. It raises the success rate by 1.5 points on LIBERO-Plus, 5.5 on RoboTwin clean scenes and 5.4 on RoboCasa365, and makes oscillations between poses about four times rarer. - **Fast inference.** With compilation, REKI predicts an action chunk in 21.8
- Hugging Face · Model / dataset date · First archived
lodestones/MOSS-Image-SFT-8-steps-dmd — model release ↗
# Demo https://huggingface.co/spaces/hugging-apps/moss-image-8-step-dmd # MOSS-Image-SFT, 8-step data-space DMD Work-in-progress distillation of [OpenMOSS-Team/MOSS-Image-SFT](https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT) (50 steps, CFG 3) to 8 network evaluations, no CFG. Data-space DMD (arXiv 2610.12102): the generator iterates on its own clean estimate from fixed noise. Trained on compact-JSON `scene_graph` captions. Checkpoint: update 1200 (EMA), float32 weights. `transformer/` is a stock `MOSSImageTransformer` (load it with the release `pipeline_moss_image.py`); the text encoder, t
- Hacker News · Posted on Hacker News · First archived
Overalls: An AI-first developer platform that you own ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: rStation – A shared research lab for your AI agents ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
AI Support Agent That Carries Context Between Email and WhatsApp (Python) ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
MeshXFS: A clustered filesystem built on XFS, written by AI ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Reflection AI Launches Beam, Now on Hugging Face ↗
Hacker News discussion.
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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