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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 ↗5510 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
dvislobokov/csharp-ml-complation — dataset update ↗
# C# full-line completion dataset (Roslyn) Caret-based full-line completion samples extracted from permissively licensed C# repositories with [csharp-dataset-prepare](https://github.com/dvislobokov/csharp-dataset-prepare). Each sample is an exact editor position: `left_context` ends at the caret, `target_text` is the rest of the physical line (no newline), `right_context` follows it. Original source is reconstructable from offsets; every row carries `repository_id`, `revision`, `relative_path` and `license` for attribution. | Config | Content | |---|---| | `samples` | canonical samples (`flc-s
- Hugging Face · Model / dataset date · First archived
ftajwar/grpo_dppo_qwen3_1p7B_maxrl_polaris1000_knk_rollouts — dataset update ↗
Training rollouts of one verl run, one parquet per trainer step (`data/step- .parquet`), rows in the order the trainer consumed them; the `rollout.n` samples of a prompt are contiguous and share `uid`. `prompt_token_ids` / `response_token_ids` are the exact token ids (special tokens kept, no padding) and `rollout_log_probs` the sampling-time log-prob of every response token. `input` / `output` are decoded with `skip_special_tokens=True` and are for reading only. `score` is the raw reward.
- Hacker News · Posted on Hacker News · First archived
AI Assistants Transform Nuclear Power Plant Work ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Spectra – A drop-in optimizer that cuts LLM training VRAM by 50% ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Fired OpenAI researchers say they were let go for 'prioritising safety' ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Samsung expects 780% quarterly operating profit jump on AI boom ↗
Hacker News discussion.
- arXiv · Published · First archived
AgentSWE: Can Coding Agents Build the Agent You Actually Want? ↗
As the production of software passes into the hands of software itself, coding agents are increasingly commissioned to build agents fitted to their users' own workflows. This delegation carries a quiet assumption, that the delivered agent will faithfully serve the requirement it was built for, rather than merely appear to. No existing benchmark tests that assumption. We introduce AgentSWE, a benchmark for agent software engineering spanning three stages of the agent development lifecycle: Creation of a complete agent from a natural-language requirement, Editing of production agent codebases, a…
- arXiv · Published · First archived
ECGWmark: Deep Watermarking for Electrocardiogram Signals ↗
Secure transmission of electrocardiogram (ECG) signals remains a major challenge in telecardiology. While traditional mathematical watermarking can embed patient metadata directly into ECG signals, it is highly fragile against temporal desynchronization such as circular phase shifts. In contrast, existing deep watermarking approaches for temporal signals, such as audio, typically rely on lossy quantization, which compromises the high-frequency micro-morphological details critical for clinical diagnosis. To bridge this gap, we propose ECGWmark, a fully convolutional and quantization-free deep w…
- Hacker News · Posted on Hacker News · First archived
Community pushed OpenAI FFT proof to over 10^-4 ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Docsy for AI Agents, Oh, and People Too ↗
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.