Trending data/ML repos (weekly)
Weekly velocity ranking of trending data/ML GitHub repositories. Five topic-qualified searches against the official keyless GitHub REST API (https://docs.github.com/en/rest/search/search#search-repositories) collect repositories created in the trailing 7 days (topic: machine-learning, llm, deep-learning, computer-vision, data-science; top 3 star-sorted pages each), merged and deduped by repository id. Each repository is scored for star velocity — stars divided by days since creation (age floored at 1 day); for repos created inside the window this is exactly the trailing-7-day star gain — and ranked fastest first. Forks are excluded (original projects only), as are archived, disabled, and zero-star repositories; every kept repository is real and active by construction (created < 7 days ago, pushed recently, publicly listed by the GitHub search index). Columns: ISO week, fetch timestamp, repository id / full name / URL, a normalized one-line summary (authors' public description, whitespace-collapsed and truncated to 220 chars; emails stripped), owner and owner type, primary language, comma-joined topics, per-repo SPDX license id, a deterministic rule-based ML-subtopic tag (agents, llm, rag, finetuning, quantization, vision, audio, nlp, multimodal, evals, mlops, data-engineering, robotics, education, other), stars / forks / watchers / open issues, age in days, stars per day, velocity rank, creation and last-push timestamps. Primary key: (week, repo_id). Cadence: weekly; each snapshot reflects the trailing-7-day creation window at fetch time. Nullability: summary, language, license_spdx and topics may be empty when the author provided none; stars, velocity rank and stars_per_day are never null. Caveats: the topic filter is author-applied, so untagged ML repositories are missed; star counts are a popularity proxy, not a quality measure; subtopic tags are keyword rules, not a classifier. Sample use: order by velocity_rank for the week's fastest-rising ML projects, or filter ml_subtopic = 'agents'.
- Rows
- 250
- Columns
- 21
- Source cadence
- Weekly
- Last refreshed
- Sep 25, 2026
- Theme
- technology
| Column | Type | Description |
|---|---|---|
| week | string | ISO week of the fetch (e.g. 2026-W39); the snapshot's trailing-7-day creation window. (unit: ISO week) |
| fetched_at | string | — |
| repo_id | integer | — |
| full_name | string | — |
| repo_url | string | Canonical public repository URL (https://github.com/<owner>/<repo>); never null. (unit: url) |
| summary | string | One-line normalized summary of the author's public repository description: markdown and HTML stripped, emails removed, whitespace collapsed, truncated to 220 characters. Empty when the author provided none. |
| owner | string | — |
| owner_type | string | — |
| language | string | — |
| topics | string | — |
| license_spdx | string | — |
| ml_subtopic | string | Deterministic rule-based ML-subtopic tag from repository name + description + topics (agents, llm, rag, finetuning, quantization, vision, audio, nlp, multimodal, evals, mlops, data-engineering, robotics, education, other); first matching rule wins. Indicative, not a classifier. |
| stars | integer | — |
| forks | integer | — |
| watchers | integer | — |
| open_issues | integer | — |
| age_days | float | Days from repository creation to fetch, floored at 1.0. (unit: days) |
| stars_per_day | float | Star velocity: total stars divided by days since creation (age floored at 1 day). For repositories created inside the trailing-7-day window this equals the trailing-week star gain. (unit: stars/day) |
| velocity_rank | integer | Rank by stars_per_day descending (1 = fastest-rising); ties broken by total stars, then repository id. (unit: rank) |
| created_at | string | — |
| pushed_at | string | — |
First 10 sample rows — a preview, not the complete dataset.
| week | fetched_at | repo_id | full_name | repo_url | summary | owner | owner_type | language | topics | license_spdx | ml_subtopic | stars | forks | watchers | open_issues | age_days | stars_per_day | velocity_rank | created_at | pushed_at |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2026-W39 | 2026-09-25T01:46:01.651412+00:00 | 1,379,574,728 | jev-chat/jev-chat-jarvis | https://github.com/jev-chat/jev-chat-jarvis | 装在手机上的对话副驾:在 QQ / X / 飞书里读懂对方、给出候选回复、一键填入输入框,发不发由你。非侵入,只读屏幕,不 hook 不改包。 | jev-chat | Organization | Kotlin | accessibility-service,android,chat-assistant,llm,qq | MIT | llm | 6,105 | 1,099 | 6,105 | 23 | 3.6 | 1,692.85 | 1 | 2026-09-21T11:12:53+0000 | 2026-09-24T18:22:07+0000 |
| 2026-W39 | 2026-09-25T01:46:01.651412+00:00 | 1,377,211,520 | mizorewww/laya-mlx | https://github.com/mizorewww/laya-mlx | Native MLX runtime for Laya typed decision models — 7–14 ms short decisions on M3 Max. No text generation, PyTorch, or cloud API. | mizorewww | User | Python | apple-silicon,decision-model,inference,laya,local-ai,machine-learning,mlx,modernbert,system-one,typed-decisions | Apache-2.0 | mlops | 6,226 | 478 | 6,226 | 17 | 5.5 | 1,132.63 | 2 | 2026-09-19T13:50:26+0000 | 2026-09-22T05:34:51+0000 |
| 2026-W39 | 2026-09-25T01:46:01.651412+00:00 | 1,383,809,012 | yetone/magpie | https://github.com/yetone/magpie | Every agent's model. One place. Codex on DeepSeek, Claude Code on Kimi, from the menu bar. | yetone | User | Go | claude-code,codex,deepseek,gemini-cli,llm,macos | MIT | agents | 641 | 35 | 641 | 5 | 1.4 | 460.73 | 3 | 2026-09-23T16:22:35+0000 | 2026-09-25T01:45:22+0000 |
| 2026-W39 | 2026-09-25T01:46:01.651412+00:00 | 1,383,988,294 | yukitorido/short-video-generator-AI | https://github.com/yukitorido/short-video-generator-AI | AI video processing pipeline for generating vertical shorts using LLMs, Whisper transcription, highlight detection and automated editing | yukitorido | User | Python | ai,llm,llm-tools,opus-clip,opus-clip-alternative,short-video,short-video-automation,short-video-generator,short-video-maker | MIT | llm | 216 | 81 | 216 | 0 | 1.3 | 164.22 | 4 | 2026-09-23T18:11:57+0000 | 2026-09-23T18:16:08+0000 |
| 2026-W39 | 2026-09-25T01:46:01.651412+00:00 | 1,379,929,433 | jev-chat/jev-chat-windows | https://github.com/jev-chat/jev-chat-windows | JevChat-Windows:聊天窗口旁挂的回复辅助。窗口截图 + 本地离线 OCR 读对方消息 → Jev 判断意图 → 3 条候选一键填入,发送永远手动 | jev-chat | Organization | Python | chat-assistant,jev,llm,local-first,ocr,privacy,pyqt,python,wechat,windows | NOASSERTION | llm | 533 | 118 | 533 | 15 | 3.5 | 153.85 | 5 | 2026-09-21T14:37:14+0000 | 2026-09-24T16:30:54+0000 |
| 2026-W39 | 2026-09-25T01:46:01.651412+00:00 | 1,379,166,549 | nokia-applied-research/AnyJev | https://github.com/nokia-applied-research/AnyJev | Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating) | nokia-applied-research | Organization | Python | calibration,decision-model,jev,jev-model,llm,system-one,transformers,vllm | Apache-2.0 | llm | 508 | 74 | 508 | 3 | 3.8 | 132.75 | 6 | 2026-09-21T05:55:40+0000 | 2026-09-25T00:32:27+0000 |
| 2026-W39 | 2026-09-25T01:46:01.651412+00:00 | 1,377,399,471 | v-modal/awesome-jev-tools | https://github.com/v-modal/awesome-jev-tools | A curated list of tools built for Jev — TypeSafe AI's System One model for typed decisions. | v-modal | Organization | — | awesome,awesome-list,awesome-lists,jev,llm,robotics,robotics-algorithms,robotics-control,robotics-simulation | — | llm | 713 | 29 | 713 | 23 | 5.4 | 132.7 | 7 | 2026-09-19T16:48:59+0000 | 2026-09-24T15:39:54+0000 |
| 2026-W39 | 2026-09-25T01:46:01.651412+00:00 | 1,380,762,959 | kydlikebtc/awesome-jev | https://github.com/kydlikebtc/awesome-jev | 1207 public resources for Jev, TypeSafe AI's System One decision model, indexed by decision pattern. Source citations, dated link checks and scheduled call-site text checks; runtime and performance are not independently… | kydlikebtc | User | Python | agent-tools,ai-agents,awesome,awesome-list,calibration,catalog,classification,context-compaction,decision-model,intent-routing,jev,llm,system-one,tool-selection,typesafe-ai | NOASSERTION | agents | 304 | 7 | 304 | 1 | 3 | 102.21 | 8 | 2026-09-22T02:22:54+0000 | 2026-09-25T00:48:26+0000 |
| 2026-W39 | 2026-09-25T01:46:01.651412+00:00 | 1,379,617,146 | jev-chat/jev-chat-jarvis-mac | https://github.com/jev-chat/jev-chat-jarvis-mac | 聊天悬浮窗助手(macOS):屏幕感知 + 本地小模型判断意图与风险,按话术生成回复候选。纯只读。 | jev-chat | Organization | Python | llm,local-first,macos,ocr,privacy,pyobjc,python | MIT | llm | 360 | 111 | 360 | 24 | 3.6 | 100.34 | 9 | 2026-09-21T11:39:32+0000 | 2026-09-24T17:41:31+0000 |
| 2026-W39 | 2026-09-25T01:46:01.651412+00:00 | 1,378,958,030 | Alex314618-create/JevRev | https://github.com/Alex314618-create/JevRev | An LLM + Jev workflow that changes EVERYTHING. Boost your vertebrate brain with a spine inside. | Alex314618-create | User | TypeScript | jev,llm,workflow | MIT | llm | 305 | 13 | 305 | 2 | 4 | 76.35 | 10 | 2026-09-21T01:53:13+0000 | 2026-09-24T15:54:15+0000 |
Profiled Sep 25, 2026 from snapshot 20260925T014927Z-9e46a8a0be93
Measured- Completeness
- 100%
- Rows
- 250
- Columns
- 21
- Columns with gaps
- 0
| Column | Missing | Distinct | Range | Distribution |
|---|---|---|---|---|
| weekvarchar | 0% | 1 | — |
|
| fetched_atvarchar | 0% | 1 | — |
|
| repo_idbigint | 0% | 310 | 1,376,559,916 → 1,386,375,632median 1,381,327,166 | 6 outside 1st–99th percentile |
| full_namevarchar | 0% | 221 | — |
|
| repo_urlvarchar | 0% | 221 | — |
|
| summaryvarchar | 0% | 202 | — |
|
| ownervarchar | 0% | 247 | — |
|
| owner_typevarchar | 0% | 2 | — |
|
| languagevarchar | 0% | 21 | — |
|
| topicsvarchar | 0% | 244 | — |
|
| license_spdxvarchar | 0% | 9 | — |
|
| ml_subtopicvarchar | 0% | 12 | — |
|
| starsbigint | 0% | 55 | 1 → 6,226median 6 | 3 outside 1st–99th percentile |
| forksbigint | 0% | 20 | 0 → 1,099median 0 | 3 outside 1st–99th percentile |
| watchersbigint | 0% | 55 | 1 → 6,226median 6 | 3 outside 1st–99th percentile |
| open_issuesbigint | 0% | 12 | 0 → 24median 0 | 3 outside 1st–99th percentile |
| age_daysdouble | 0% | 50 | 1 → 6.1median 2.6 | 3 outside 1st–99th percentile |
| stars_per_daydouble | 0% | 199 | 0.86 → 1,693median 1.99 | 6 outside 1st–99th percentile |
| velocity_rankbigint | 0% | 265 | 1 → 250median 125.5 | 6 outside 1st–99th percentile |
| created_atvarchar | 0% | 257 | — |
|
| pushed_atvarchar | 0% | 227 | — |
|
- Current
20260925T014927Z-9e46a8a0be93 · sha256 9e46a8a0be93…
250 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/github_trending_signals/github_trending_ml_weekly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/github_trending_signals/github_trending_ml_weekly").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/github_trending_signals/github_trending_ml_weekly
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20260925T014927Z-9e46a8a0be93 and its content hash, so readers get exactly the data you used.
GitHub trending data/ML repositories (agent-curated). (2026). Trending data/ML repos (weekly) [Data set, snapshot 20260925T014927Z-9e46a8a0be93, sha256 9e46a8a0be93]. Datazimuts. Retrieved 2026-09-25, from https://datazimuts.com/en/datasets/github_trending_signals/github_trending_ml_weekly?snapshot=20260925T014927Z-9e46a8a0be93
@misc{dz_github_trending_signals_github_trending__9e46a8a0,
title = {{Trending data/ML repos (weekly)}},
author = {{GitHub trending data/ML repositories (agent-curated)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/github_trending_signals/github_trending_ml_weekly?snapshot=20260925T014927Z-9e46a8a0be93}},
note = {Snapshot 20260925T014927Z-9e46a8a0be93, sha256 9e46a8a0be930dfde4d4fa249ef73c0d055b9be1ccce36f94b63fd4751890055; accessed 2026-09-25}
}Embed a table or a chart
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<iframe src="https://datazimuts.com/embed/chart?dataset=github_trending_signals%2Fgithub_trending_ml_weekly&lang=en&theme=auto&snapshot=20260925T014927Z-9e46a8a0be93&x=week&y=repo_id&agg=avg" title="Trending data/ML repos (weekly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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