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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 ↗6050 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
Voice Cloner for AI Video Production on macOS ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Parody News Channel: Five AI Anchors, Real Headlines ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
How we built olddogs.ai with lots of agents but zero slop ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
SherTheCoder/TeamFolklore_UthoKikkar — dataset update ↗
Utho Kikkar Synthetic and Real-world speech for teaching a tiny chip one wake word, in Punjabi and Hindi. Voices saying "Utho Kikkar", and many more saying things that only sound like it. ## What this is *Utho Kikkar* (ਉੱਠੋ ਕਿੱਕਰ · उठो किक्कर) means "wake up, Kikkar". Kikkar is the babul tree you see along canal banks in Punjab. It is also the name of our project: a microcontroller that listens for this phrase on its own, opens a flower on its screen when it hears it, and only then streams your command to the cloud. This dataset is what we trained that listener on. A wake-word model has two jo
- Hugging Face · Model / dataset date · First archived
joycx/Qwen3.8-27B-Velocity — model update ↗
 > **New:** Velocity with the [Sharp chat template](https://huggingface.co/peculiar-ragdoll/Qwen-Sharp-Chat-Templates) > at xhigh keeps its accuracy and thinks 14–26% less on HMMT, C-Eval and IFBench. > [Results below](#with-the-sharp-chat-template). # Qwen3.8-27B · Velocity 1.0 **Velocity** is a LoRA for [Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) that keeps its answers and cuts its thinking: **28–43% fewer reasoning tokens per answer** across maths, knowledge, instruction-following and coding, at close to stock accuracy, and well ahead of sto
- arXiv · Published · First archived
The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs ↗
Open-weight language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforced safeguards. Recent work has therefore proposed \emph{trigger-tag} mechanisms that produce a detectable signal when a model is used under a target condition, such as generating phishing contents. Although these mechanisms borrow from established techniques, their use for conditional misuse detection in open-weight LLMs is relatively new. Therefore, existing research works have not systematically studied the robustness of trigger-tag mechanisms…
- arXiv · Published · First archived
S2S-JEPA: Predicting the Predictable at Subseasonal-to-Seasonal Timescales ↗
The subseasonal-to-seasonal (S2S) timescale, roughly from two weeks to two months ahead, is a critical forecast window for sectors such as agriculture, energy, and water management. Yet, it is widely known as the `predictability desert'. Recent AI weather models excel up to two weeks ahead but deteriorate beyond, largely because they are trained to predict fine-scale details that are neither predictable nor essential at S2S timescales. We argue that a more physically grounded objective is to forecast only the slowly varying components that remain predictable. Computer vision reached the same c…
- Hacker News · Posted on Hacker News · First archived
Show HN: A practical AI Evaluation pattern ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
SoftBank: Execution of Follow-On Investment (Third Tranche) in OpenAI ↗
Hacker News discussion.
- arXiv · Published · First archived
Securing Computer-Use Agents Against Branch Steering Attacks ↗
Modern Computer Use Agents (CUAs) directly interact with graphical user interfaces and execute third-party web tools, exposing them to indirect prompt injection across every rendered page and tool response. While the Dual-LLM pattern is the primary system-level architecture offering formal security guarantees - using an isolated Planner LLM (P-LLM) to fix execution paths before processing untrusted inputs via a Quarantined LLM (Q-LLM) - these guarantees break down in graphical environments. Because CUA interaction is inherently dynamic, plans cannot remain data-independent; they must branch ba…
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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