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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 ↗5186 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
Agentic autofix for code scanning alerts is now general availability ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
GitHub AI Scan for pull requests is now generally available ↗
Hacker News discussion.
- arXiv · Published · First archived
WEAVE: Weaving Real-World Code Evolution into Dependency-Structured Tasks for Benchmarking and Training Code Agents ↗
Real-world software engineering rarely consists of isolated changes. Features, migrations, and systematic fixes often unfold across multiple pull requests (PRs), requiring coordinated modifications across a repository. Yet most executable repository-level benchmarks retain a single issue or PR as the task unit, while existing multi-PR settings rely on coarse aggregation and structure-agnostic evaluation. This mismatch obscures dependencies among changes and limits progress measurement and learning signals for long-horizon code agents. We introduce WEAVE, a framework that transforms verified PR…
- Hacker News · Posted on Hacker News · First archived
Show HN: CommonForge – Let clients' AI agents work in your codebase safely ↗
Hacker News discussion.
- GitHub · Repository created · First archived
irraz/vulnerator-ai ↗
Autonomous OODA-loop pentesting agent. Hermes brain, Kali + Playwright tools over MCP, ATT&CK as the Orient language.
- Hacker News · Posted on Hacker News · First archived
Ask HN: In the age of AI does your boss still ask you for time estimates? ↗
See title, just curious.
- Hacker News · Posted on Hacker News · First archived
Anthropic Merges 30k Changes a Day, and It Shows ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Mr. Chatterbox-2.7B: An AI model trained on all-Victorian text ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
agurung/cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp5-base-q3c — model update ↗
# seeded_rl_base_ramp25_stoppen_gen4k_ep2_ncp5_base_q3c — RL checkpoint (global step 92) **Role in this run:** best by pass@8 ## What it is An **OpenRLHF GRPO** reinforcement-learning checkpoint for Qwen3-4B. - Base model: `Qwen/Qwen3-4B-Instruct-2507` - Seeded from: base Qwen3-4B (no SFT seed — RL applied directly to the base model) - Saved at global step 92 of RL run `seeded_rl_base_ramp25_stoppen_gen4k_ep2_ncp5_base_q3c`. ## What it's best at - This is the **best checkpoint by pass@8** so far in this run. Trained and validated on the **cobalt-train ≤2/64 frontier** (canonical clean_eval pro
- Hugging Face · Model / dataset date · First archived
CapitaineJACOB/Stera-AI — model update ↗
# Stera IA Modèle personnel entraîné par Fendrix Studio (fine-tuning LoRA, export GGUF via llama.cpp). ## Fichiers - `model-Stera-Q4_K_M.gguf` : modèle quantifié en Q4_K_M - `mmproj-Stera-f16.gguf` : projecteur de vision, nécessaire pour l'analyse d'images ## Fichiers de vision (mmproj) - `mmproj-Stera-f16.gguf` : meilleure qualité (~800 Mo) ## Utilisation Dans LM Studio (ou llama.cpp), charge le modèle principal. Garde le fichier `mmproj` dans le même dossier pour activer la vision. ## Modèle de base Qwen/Qwen3-VL-4B-Instruct (licence Apache 2.0).
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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