Hot Projects

The open-source products that were heating up on July 19, 2026 — ranked by daily star velocity.

hot products

+10086.6

top stars / day

Previously hot projects 44 products

AI & Machine Learning 14 products

Discover model tooling, training systems, inference runtimes, vector databases, data pipelines, and applied AI software used in production.

4,877Stars
15Hotness
55Reliability
5 monthsAge
+8242.4stars / day
+172.47%/ day
+4,779in 1d

Comprehensive production pipeline for quad-modal AI filmmaking with Seedance 2.0

2,523Stars
15Hotness
72Reliability
2 monthsAge
+4783.2stars / day
+191.71%/ day
+2,495in 1d

A股多Agent投研框架 — 适配A股数据源(龙虎榜/游资/解禁等)，7位分析师基于A股规则的辩论决策，基于TradingAgents深度改造，适配大A。A-share multi-agent investment research framework — 7 AI analysts, bull/bear debate, risk assessment。

13,990Stars
87Hotness
40Reliability
3 monthsAge
+624.2stars / day
+4.70%/ day
+450in 1d

Anti-AI-slop design skill for Claude Code, Cursor, and Codex.

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

85 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

85 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

16,757Stars
80Hotness
59Reliability
21 daysAge
+460.1stars / day
+2.80%/ day
+341in 1d

Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

20 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

20 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

18,679Stars
72Hotness
79Reliability
4 monthsAge
+438.6stars / day
+2.39%/ day
+330in 1d

agent multiplexer that lives in your terminal.

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

116 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

116 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

20,052Stars
70Hotness
77Reliability
4 monthsAge
+405.9stars / day
+2.07%/ day
+310in 1d

OfficeCLI is the first and best Office suite purpose-built for AI agents to read, edit, and automate Word, Excel, and PowerPoint files. Free, open-source, single binary, no Office installation required.

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

128 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

128 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

16,205Stars
63Hotness
36Reliability
4 monthsAge
+305.5stars / day
+1.91%/ day
+226in 1d

1,324-exercise fitness dataset — animation GIFs, 180×180 thumbnails, muscle-group & equipment data, and step-by-step instructions in 6 languages. The exercise data layer behind the LogPress app.

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

125 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

125 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

8,782Stars
53Hotness
71Reliability
about 1 monthAge
+226.7stars / day
+2.63%/ day
+153in 1d

Practical patterns, starters & CLI tools for loop engineering with AI coding agents. Design systems that prompt and orchestrate agents (inspired by Addy Osmani and Boris Cherny). Includes loop-audit, loop-init, loop-cost.

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

42 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

42 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

9,356Stars
59Hotness
51Reliability
3 monthsAge
+146.7stars / day
+1.59%/ day
+100in 1d

Give Claude the ability to watch any video. /watch downloads, extracts frames, transcribes, hands it all to Claude.

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

88 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

88 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

727Stars
40Hotness
1Reliability
+71.2stars / day
+10.30%/ day
+62in 1d

MuScriptor is a multi-instrument music transcription model developed by Kyutai and Mirelo.

2,452Stars
51Hotness
86Reliability
about 1 monthAge
+66.9stars / day
+2.79%/ day
+35in 1d

Where people and agents build in tune.

2,440Stars
55Hotness
25Reliability
6 monthsAge
+41.7stars / day
+1.73%/ day
+24in 1d

Agent skills for designers and builders using Codex, Claude, Cursor, and other AI coding agents

1,239Stars
61Hotness
1Reliability
2 monthsAge
+20.9stars / day
+1.79%/ day
+17in 1d

DojoAgents: Full-Market AI Copilot for Personal Investment

629Stars
38Hotness
1Reliability
+11.3stars / day
+1.84%/ day
+13in 1d

「說人話」：繁體中文的去 AI 味改寫 skill。抓 38 種 AI 寫作痕跡，順手校正中國用語與半形標點，給 Claude Code / Codex / Cursor 用。

Media & Creative Tools 6 products

Organize video, audio, imaging, 3D modeling, CAD, animation, and content-production software used by creators and product teams.

4,265Stars
14Hotness
1Reliability
16 daysAge
+6548.3stars / day
+172.60%/ day
+3,794in 1d

A skill that turn any brand into a scrollable 3D world

3,057Stars
15Hotness
1Reliability
almost 9 yearsAge
+5235.6stars / day
+173.08%/ day
+3,025in 1d

Source code for Unturned, a free open-world zombie survival sandbox game.

3,594Stars
77Hotness
1Reliability
4 monthsAge
+133.0stars / day
+3.80%/ day
+77in 1d

An experimental PlayStation 5 emulator for Windows, Linux and macOS.

134Stars
+112.0stars / day
+83.58%/ day
+28in 1d

multiple roblox instances multi account launcher github download bloxstrap roblox alt manager ram multi client singletons mutex tool alt account farming concurrent instances pc gaming gameplay background runner open source desktop utility multitaskingsingle client

686Stars
54Hotness
1Reliability
+105.7stars / day
+16.02%/ day
+86in 1d

面向 NAS 用户的全平台、聚焦观影与自动化的核心需求，打造一站式Jav观影中枢。

1,319Stars
51Hotness
1Reliability
15 daysAge
+16.1stars / day
+1.23%/ day
+13in 1d

Infinite Worlds with Versatile Interactions

Databases & Storage 1 product

Organize relational, document, graph, key-value, time-series, and embedded database engines plus replication and migration tooling.

3,608Stars
15Hotness
1Reliability
3 monthsAge
+6111.7stars / day
+173.23%/ day
+3,528in 1d

Postgres rewritten in Rust, now passing 100% of the Postgres regression tests

Security & Privacy 1 product

Group authentication, authorization, secrets management, encryption, vulnerability scanning, and application security software.

3,031Stars
15Hotness
1Reliability
23 daysAge
+5185.7stars / day
+173.14%/ day
+2,995in 1d

Open-source auth gateway connecting 1000+ SaaS providers to AI agents through SDK, CLI, MCP, HTTP, and OpenAPI.

Web Development 2 products

Explore frameworks, runtimes, APIs, CMS platforms, GraphQL servers, and frontends used to build modern software for the browser and the web edge.

18,731Stars
77Hotness
45Reliability
4 monthsAge
+896.2stars / day
+4.94%/ day
+674in 1d

Skills is a curated collection of prompts and instructions that help design engineers use AI coding assistants more effectively. It packages reusable guidance rather than a code library, distinguishing it from typical developer tools. It suits design engineers and frontend developers who want to apply consistent, well-tested practices when working with AI-assisted coding.

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

127 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

127 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

10,103Stars
69Hotness
67Reliability
over 1 yearAge
+633.1stars / day
+6.46%/ day
+434in 1d

Advanced UX and interoperability extension for Wand (WeMod) app

Finance & Business Applications 3 products

Cover ERP, CRM, accounting, blockchain, e-commerce, and business-process software used to run organizations and financial workflows.

25,598Stars
71Hotness
83Reliability
4 monthsAge
+348.9stars / day
+1.38%/ day
+281in 1d

"Vibe-Trading: Your Personal Trading Agent"

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

111 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

111 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

343Stars
+119.1stars / day
+34.71%/ day
+166in 1d

polymarket sports arbitrage trading bot: polymarket trading bot polymarket trading bot polymarket trading bot polymarket trading bot polymarket trading bot polymarket trading bot polymarket trading bot polymarket trading bot polymarket trading bot polymarket trading bot polymarket trading bot polymarket trading bot polymarket trading bot polymarket

1,085Stars
63Hotness
1Reliability
about 1 monthAge
+20.9stars / day
+1.98%/ day
+22in 1d

The trust layer for agent-to-agent commerce — natural-language mandates, ERC-7710 delegated permissions, x402 payments, escrow, and dispute resolution as one open, catch-all Agent Skill / Claude Code plugin.

Networking & Communications 1 product

Capture servers, proxies, API gateways, service meshes, messaging stacks, email protocols, and realtime communication infrastructure.

5,825Stars
72Hotness
52Reliability
29 daysAge
+291.1stars / day
+5.07%/ day
+170in 1d

修改 Apple 网络定位（gs-loc）返回坐标 · 支持 Surge / Quantumult X / Loon / Stash · 快捷指令一键设置/恢复定位

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

27 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

27 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

Operating Systems & System Software 2 products

Catalog OS components, kernels, compilers, bootloaders, virtualization layers, filesystems, and low-level runtime software.

7,081Stars
50Hotness
80Reliability
9 monthsAge
+197.0stars / day
+2.81%/ day
+124in 1d

Free, open-source Windows optimization tool for performance, privacy, and simplicity.

620Stars
43Hotness
1Reliability
+68.2stars / day
+12.05%/ day
+59in 1d

PlayStation 5 emulator for Windows

Software Development Tools 2 products

Gather editors, CLIs, build systems, package managers, code quality, documentation generators, and the everyday tools used to ship code.

6,138Stars
65Hotness
72Reliability
3 monthsAge
+180.2stars / day
+3.03%/ day
+106in 1d

Any agent Skill: generate beautiful architecture diagrams with dark/light theme toggle and PNG/JPEG/WebP/SVG export

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

97 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

97 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

12,594Stars
72Hotness
69Reliability
30 daysAge
+169.7stars / day
+1.37%/ day
+122in 1d

OpenWiki is a CLI that writes and maintains agent documentation for your codebase.

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

29 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

29 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

DevOps & Infrastructure 1 product

Track deployment platforms, CI/CD systems, containers, configuration management, cloud cost tooling, and platform engineering stacks.

803Stars
44Hotness
27Reliability
13 daysAge
+38.1stars / day
+4.94%/ day
+33in 1d

AI SRE AgenticOps for Kubernetes and cloud infrastructure.

More hot products 11 products

5,382Stars
15Hotness
67Reliability
2 monthsAge
+10086.6stars / day
+188.46%/ day
+5,352in 1d

Box3D is a 3D physics engine for games

5,180Stars
15Hotness
73Reliability
7 monthsAge
+8880.2stars / day
+172.33%/ day
+5,153in 1d

The Destructive Command Guard (dcg) is for blocking dangerous git and shell commands from being executed by agents.

4,629Stars
15Hotness
24Reliability
5 monthsAge
+7959.4stars / day
+172.51%/ day
+4,614in 1d

OpenAI Plugins

1,576Stars
15Hotness
42Reliability
almost 2 yearsAge
+6304.0stars / day
+400.00%/ day
+1,576in 1d

Lightning-fast bulk file downloader for datanodes.to & fuckingfast.co - 253 MB/s on 2.5 Gbps fiber. Playwright extraction + aiohttp async engine.

3,225Stars
15Hotness
1Reliability
5 monthsAge
+6124.4stars / day
+190.67%/ day
+3,212in 1d

26m agentic model for tiny devices

2,080Stars
15Hotness
1Reliability
2 monthsAge
+3609.1stars / day
+175.11%/ day
+2,061in 1d

A blueprint-driven AutoResearch runtime for orchestrating AI research workflows from idea generation and experiments to paper writing and peer review.

92,079Stars
66Hotness
74Reliability
4 monthsAge
+1732.3stars / day
+1.89%/ day
+577in 1d

AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and more). Turn any folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph. App code + database schema + infrastructure in one graph.

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

109 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

109 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

24,303Stars
67Hotness
59Reliability
4 monthsAge
+418.0stars / day
+1.74%/ day
+335in 1d

The job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.

Weekly average

Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

125 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))

Date	Stars	Stars 1d	Stars 7d	Stars 14d	Stars 30d	Stars 90d	Same-day multiplier	Weekly multiplier	Fortnight multiplier	Hot today	Hot week	Breakout	Stored score

Weekly average

Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

125 observed daily rows. Missing days are not fabricated.

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
Support burden = 100 − 2 × open issues per 1,000 stars
Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

Date	Issues opened	Issues closed	PRs opened	PRs merged	Releases	Stars	Open issues	Pushed days ago	Continuity	Closure	Shipping	Liveness	Support burden	License	Observed days	Missing days	Needs healing	Stored score

3,916Stars
61Hotness
1Reliability
about 1 monthAge
+85.2stars / day
+2.20%/ day
+45in 1d

An AI-agent skill that generates browser-editable presentations from multiple visual themes, exportable to HTML, PDF, and PPTX.

1,225Stars
48Hotness
25Reliability
13 daysAge
+79.4stars / day
+6.86%/ day
+70in 1d

Codex plugin that turns attached object images into code-only, animation-ready procedural Three.js models.

1,114Stars
48Hotness
25Reliability
15 daysAge
+35.4stars / day
+3.23%/ day
+30in 1d

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