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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 ↗5972 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
Show HN: We Westernized Qwen for US enterprise use ↗
Hacker News discussion.
- GitHub · Repository created · First archived
postzen-dev/instagram-agent-skill ↗
Fourteen Claude skills that run an Instagram account, with publishing, comments, DMs and analytics through PostZen
- GitHub · Repository created · First archived
gustavocanepa/gt4-decomp ↗
Matching decompilation of the Gran Turismo 4 (PS2) engine, AI-assisted. No game data included.
- Hacker News · Posted on Hacker News · First archived
Show HN: Vibeconferencing, Bring Claude Code or Codex into Your Google Meet ↗
A friend and I built this for our own calls and are sharing it as is. The itch: we kept copy-pasting Granola transcripts into Claude mid-call, and wondered what would happen if the agent just joined. Two-minute demo: https://youtu.be/XhCSRiiL67A How it works: a local Electron app joins the Meet in its own browser as a participant. Your agent drives it over MCP (or HTTP): tools to listen, speak, and write to a shared whiteboard. Any MCP agent work…
- GitHub · Repository created · First archived
VecSzn/PolyStrata ↗
Run large AI models on your own PC: Qwen3.8-Flash-Next, GLM 5.3 Flash, Qwen3.6, Ornith 1.5 and Gemma 4. One-click install for Windows and Linux, OpenAI/Anthropic API on localhost, image input. Built on Strata.
- Hugging Face · Model / dataset date · First archived
LafouCC/PG-Personalization-ckpt — model update ↗
# PG-Personalization — checkpoints and eval artifacts A **parameter generator (PG)**: one shared hypernetwork that maps a user's history to a **per-user LoRA** on a **frozen** Qwen2.5-7B-Instruct in a single forward pass. No per-user fine-tuning, no test-time optimization. Code: . Everything here is produced by, and consumed by, the scripts in that repo. ## What is here | path | what | size | |---|---|---| | `p2p/t9_v5o2/` | **the settled arm.** PG trained on the 10-task P2P corpus, CE + gap loss | 3.1 GB | | `p2p/t9_v5o3/` | same arm with the gap loss off (`--gap_lambda 0`) — the loss ablatio
- Hugging Face · Model / dataset date · First archived
AutomatosX/AX-DeepSeek-OCR-2-CUDA-AXQ-NVFP4-W4A4 — model update ↗
## Runtime format audit (2026-10-06) No quantization-container correction was needed. This family has no n-gram tensors; no n-gram file or declaration was added. This CUDA pack is outside MLX/oMLX/MTPLX export scope. See [runtime_audit.json](runtime_audit.json) for pinned config/index/header bindings, architecture, physical-format findings, and applied corrections. This is development evidence; no quality, MTP exactness, speed, or certification claim is added. Historical evidence stays bound to its original revision. # AX-DeepSeek-OCR-2-CUDA-AXQ-NVFP4-W4A4 **Development preview with native FP4
- Hugging Face · Model / dataset date · First archived
AutomatosX/AX-DeepSeek-OCR-2-CUDA-AXQ-NVFP4-W4A16 — model update ↗
## Runtime format audit (2026-10-06) No quantization-container correction was needed. This family has no n-gram tensors; no n-gram file or declaration was added. This CUDA pack is outside MLX/oMLX/MTPLX export scope. See [runtime_audit.json](runtime_audit.json) for pinned config/index/header bindings, architecture, physical-format findings, and applied corrections. This is development evidence; no quality, MTP exactness, speed, or certification claim is added. Historical evidence stays bound to its original revision. # AX-DeepSeek-OCR-2-CUDA-AXQ-NVFP4-W4A16 **Development preview.** This traine
- Hugging Face · Model / dataset date · First archived
AutomatosX/AX-Cyber-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP8-MTP — model update ↗
## Runtime format audit (2026-10-06) The quantization container mode was corrected from `affine` to `mxfp8` in both config blocks after inspecting every quantized module header. Weight bytes and per-module precision assignments are unchanged. This family has no n-gram tensors; no n-gram file or declaration was added. This remote format audit does not grant an oMLX/MTPLX runtime profile or a successful load/generation claim. See [runtime_audit.json](runtime_audit.json) for pinned config/index/header bindings, architecture, physical-format findings, and applied corrections. This is development e
- GitHub · Repository created · First archived
iamlukethedev/Herald-OS ↗
An agent-native operating system, with Hermes Agent as the interface. Independent project, not affiliated with Nous Research.
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.