Trending AI/ML Projects
FinRL®: Financial Reinforcement Learning. 🔥
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A framework for building agentic apps
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Contains everything related to Stiver's A2Z sheet along with question, approach and code.
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Project NOMAD is an offline-first knowledge and education server. Wikipedia, thousands of books, courses, maps, and optional local AI, all running on hardware you own with no internet required.
FreeToken brings datacenter-scale model serving to your desktop. Run massive models locally, fast and efficiently.
Dotted thought-orb loading indicators for AI & agent UIs, 9 tuned types, two sizes, auto dark/light
Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop
Train, inspect, edit, automate, and export 3D Gaussian Splatting scenes from a single native application.
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OpenStock is an open-source alternative to expensive market platforms. Track real-time prices, set personalized alerts, and explore detailed company insights — built openly, for everyone, forever free.
MiniCPM5: SOTA on-device LLMs, small yet powerful.
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Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
A unified AI model hub for aggregation & distribution. It supports cross-converting various LLMs into OpenAI-compatible, Claude-compatible, or Gemini-compatible formats. A centralized gateway for personal and enterprise model management.
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Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click
盯盘侠 PanWatch · 自托管 AI 盯盘助手，集成 TradingAgents 多 Agent 投资决策 | A股/港股/美股实时监控、持仓管理、智能分析、全渠道推送
Let AI agents use your real, logged-in browser without interrupting your work. CLI + extension for browser automation across any shell-capable AI agent.
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, and cost-free scheduled runs.
Production-grade engineering skills for AI coding agents.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
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The open-source AI research workbench for scientific research and agent workflows. Local-first, model-agnostic desktop app with extensible skills, MCP tools and connectors, Python/R execution and traceable artifacts for reproducible research on macOS, Windows and Linux.
Solution for long term memory for agent coding CLIs and to facilitate handoff between different agent vendors
Secure, fast, efficient, battle-tested at Alibaba's scale. Hybrid architecture code review tool: deterministic pipelines + LLM Agent, precise line-level comments, built-in multi-language ruleset (NPE, thread-safety, XSS, SQL injection), OpenAI & Anthropic compatible.
Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.
Community plugin marketplace for Claude Cowork and Claude Code. Read-only mirror — submit plugins at clau.de/plugin-directory-submission.
Open source repository of plugins primarily intended for knowledge workers to use in Claude Cowork
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A skill to stop your coding agent from burying the answer. ADHD-friendly output.
Agent skill that removes signs of AI-generated writing from text
Your personal intelligence agent. Watches the world from multiple data sources and pings you when something changes.
Autonomous coding agent as an SDK, IDE extension, or CLI assistant.
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A coding-agent skill for multi-phase security audits with independently verified, machine-readable findings
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Secure environments for developers and their agents
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Enhanced ChatGPT Clone: Features Agents, MCP, Skills, DeepSeek, Anthropic, AWS, OpenAI, Responses API, Azure, Groq, o1, GPT-5, Mistral, OpenRouter, Vertex AI, Gemini, Artifacts, AI model switching, message search, Code Interpreter, langchain, DALL-E-3, OpenAPI Actions, Functions, Secure Multi-User Auth, Presets, open-source for self-hosting. Active
CLI tool for configuring and monitoring Claude Code
AI agent toolkit: unified LLM API, agent loop, TUI, coding agent CLI
Repo for the Complete Agentic AI Engineering Course
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DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability — leave it running.
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Машинное обучение на ФКН ВШЭ
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SDR Rx/Tx software for Airspy, Airspy HF+, BladeRF, HackRF, LimeSDR, PlutoSDR, RTL-SDR, SDRplay and FunCube
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Prompt as Code | GPT Image 2 / 2.5 提示词与案例库，530+ 个案例、20+ 套工业级模板与可复用 Skills，新增 2.5 同提示词对比专区，附完整提示词与生成记录，持续更新。
Optimize prompts, code, and more with AI-powered Reflective Optimization
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LLM inference in C/C++
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TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
Flax is a neural network library for JAX that is designed for flexibility.
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
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Grab your own sweet-looking '.is-a.dev' subdomain.
🧠 Train a 64M-parameter LLM from scratch in just 2h!
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
Formerly KrillinAI. Open-source AI workspace for creators, powered by Codex. Create videos, images, voice, avatars, video translation, and edits with Agents in one place.
Build an email assistant with human-in-the-loop and memory
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The official Lark/飞书 CLI tool, maintained by the larksuite team — built for humans and AI Agents. Covers core business domains including Messenger, Docs, Base, Sheets, Calendar, Mail, Tasks, Meetings, and more, with 200+ commands and 20+ AI Agent Skills.
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AirLLM 70B inference with single 4GB GPU
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Open source inference engine optimized for consumer hardware. Profiles your machine, recommends the best models for it, then downloads, tunes, and runs them. Works on Apple Silicon, NVIDIA, AMD, or nothing but a CPU.
Worktrunk is a CLI for Git worktree management, designed for parallel AI agent workflows
Official inference framework for 1-bit LLMs
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12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
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A simple screen parsing tool towards pure vision based GUI agent
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.
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Context window optimization for AI coding agents. Sandboxes tool output (98% reduction), persists session memory, and enforces routing across 17 platforms via MCP + hooks.
List of Permanent Free LLM API (API Keys)
Make humans and AI agents work as one team — open-source and self-hostable.
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Customer-run client for Secure MCP Tunnel: connect private or localhost MCP servers to ChatGPT, Codex, the Responses API, and AgentKit without exposing them to the public internet.
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A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
Orca is the ADE for working with a fleet of parallel agents. Run any coding agent with your own subscription. Available on desktop, mobile and remote runtime.
The Postgres development platform. Supabase gives you a dedicated Postgres database to build your web, mobile, and AI applications.
The secure, validated skill registry for professional AI coding agents. Extend Antigravity, Claude Code, Cursor, Copilot and more with absolute confidence.
Scale computer-use 2.0 with open-source drivers, cross-OS fleets, and benchmarks for training, evaluation, and data generation.
Agent skill for beautiful, verifiable architecture, workflow, sequence, data-flow, and lifecycle diagrams—self-contained HTML with motion and crisp export.
Local-first AI coding agent desktop: Electron + Rust host core + pi Agent Harness + user-installable plugins
Turn any technical book PDF into a Claude Code skill — ready to study, reference, and use while you work.
Community maintained hardware plugin for vLLM on Huawei Ascend
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Free and open-source macOS menu bar toolkit.
Create beautiful applications using Go
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OpenAI Codex 桌面端/CLI 的可视化管理工具，具有Provider/API 切换、会话同步、提示词注入、Skills/MCP 管理、TOML 配置可视化的跨平台工具。
嘉立创EDA专业版(EasyEDA Pro)自动化：给 AI harness 装上画板的「手」—— 一套 typed 原理图/PCB 动作，CLI / Agent Skill / stdio MCP 三形态融合接入。承接嘉立创「不以卖板赚钱，以培养中国工程师为己任」 | EasyEDA Pro automation: the hands of your AI harness — typed schematic/PCB actions via CLI, Agent Skill and stdio MCP.
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AutoClip : AI-powered video clipping and highlight generation · 一款智能高光提取与剪辑的二创工具
Latest AI Research Articles
Summary
The scariest diff passed every test — and would've lost a customer money. The scariest diff AI ever...
Core Contributions
- The scariest diff passed every test — and would've lost a customer money.
- The scariest diff AI ever....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
I have a habit of starting projects because I want one very specific thing. Then somewhere along the...
Core Contributions
- I have a habit of starting projects because I want one very specific thing.
- Then somewhere along the....
Summary
One of the things I've always enjoyed about coding is getting completely locked into a problem. You...
Core Contributions
- One of the things I've always enjoyed about coding is getting completely locked into a problem.
Summary
This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content ...
Core Contributions
- This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content ....
Summary
Summary
There's a tempting fix for the moment your team stops trusting its AI code review...add a second AI...
Core Contributions
- There's a tempting fix for the moment your team stops trusting its AI code review...add a second AI....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
TL;DR: I shipped the second project in my AWS nanodegree, an AI support agent on Amazon Bedrock....
Core Contributions
- TL;DR: I shipped the second project in my AWS nanodegree, an AI support agent on Amazon Bedrock.....
Summary
I haven't written anything lately because, honestly, I just didn't have the headspace for it. There...
Core Contributions
- I haven't written anything lately because, honestly, I just didn't have the headspace for it.
Summary
Hi, glad you found your way here. I'm Yash, a developer who contributes to open source, mostly p2p...
Core Contributions
- Hi, glad you found your way here.
- I'm Yash, a developer who contributes to open source, mostly p2p....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
A four-stage DevSecOps CI/CD architecture for securing enterprise AI agents with GitHub Actions, secret scanning, AI-assisted review, Veracode SCA, and Pipeline SAST.
Core Contributions
- A four-stage DevSecOps CI/CD architecture for securing enterprise AI agents with GitHub Actions, secret scanning, AI-assisted review, Veracode SCA, and Pipeline SAST..
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
Jev returns a probability, not prose. That made it possible to model-check the consensus around it, then try to break it. Two of the bugs were mine.
Core Contributions
- Jev returns a probability, not prose.
- That made it possible to model-check the consensus around it, then try to break it.
- Two of the bugs were mine..
Summary
Last month a teammate pasted a Playwright test into our PR channel and wrote "AI generated this in 4...
Core Contributions
- Last month a teammate pasted a Playwright test into our PR channel and wrote "AI generated this in 4....
Summary
It's a few hours before a delivery deadline and the work has piled up. Somewhere in that pile is a...
Core Contributions
- It's a few hours before a delivery deadline and the work has piled up.
- Somewhere in that pile is a....
Summary
A test named test_all_adapters_importable asserted nothing. It would pass forever, even if every...
Core Contributions
- A test named test_all_adapters_importable asserted nothing.
- It would pass forever, even if every....
Summary
Why LLMs fail in production, why "more RLHF" cannot fix it, and how transferring 3 AM pager-duty trauma gives autonomous coding agents real survival instincts.
Core Contributions
- Why LLMs fail in production, why "more RLHF" cannot fix it, and how transferring 3 AM pager-duty trauma gives autonomous coding agents real survival instincts..
Summary
Everyone is arguing about which model plans best. I ran 170 goals and found out the model was never...
Core Contributions
- Everyone is arguing about which model plans best.
- I ran 170 goals and found out the model was never....
Summary
Somewhere in the last few years, "good at your job" and "good at your craft" quietly stopped meaning...
Core Contributions
- Somewhere in the last few years, "good at your job" and "good at your craft" quietly stopped meaning....
Summary
I’m awake typing this at 5 AM on a Sunday morning, plagued by two issues: the modern operating model...
Core Contributions
- I’m awake typing this at 5 AM on a Sunday morning, plagued by two issues: the modern operating model....
Summary
So as some of you know, I had a technical interview with Wasmer today. I was up till midnight last...
Core Contributions
- So as some of you know, I had a technical interview with Wasmer today.
- I was up till midnight last....
Summary
I got the rejection email on a Tuesday. I've been rejected before — everyone has. This one broke...
Core Contributions
- I got the rejection email on a Tuesday.
- I've been rejected before — everyone has.
- This one broke....
Summary
Most developers already know this rule: Don't run code from a repository you don't...
Core Contributions
- Most developers already know this rule: Don't run code from a repository you don't....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
Be honest: what do you actually do while the agent types? I used to just watch. Not read, watch....
Core Contributions
- Be honest: what do you actually do while the agent types? I used to just watch.
- Not read, watch.....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
Debug algorithmic trading backtests with Python: catch look-ahead bias, compare complex models fairly, and test execution costs and order failures.
Core Contributions
- Debug algorithmic trading backtests with Python: catch look-ahead bias, compare complex models fairly, and test execution costs and order failures..
Summary
I stored passwords in plaintext on a floppy disk in the 1980s. Recently, a coding agent made a...
Core Contributions
- I stored passwords in plaintext on a floppy disk in the 1980s.
- Recently, a coding agent made a....
Summary
I did not need Jev to beat Claude or Kimi on a benchmark. I needed to know whether I could trust it...
Core Contributions
- I did not need Jev to beat Claude or Kimi on a benchmark.
- I needed to know whether I could trust it....
Summary
The bottleneck moved, and most teams haven't noticed. It isn't writing anymore. It's proving. I...
Core Contributions
- The bottleneck moved, and most teams haven't noticed.
- It isn't writing anymore.
- It's proving.
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
TL;DR: I entered HackerRank Orchestrate wanting to win, then faced an AI judge asking how Praxi Clew...
Core Contributions
- TL;DR: I entered HackerRank Orchestrate wanting to win, then faced an AI judge asking how Praxi Clew....
Summary
In the previous post, we taught a model to read our documents. It could search a pile of files and...
Core Contributions
- In the previous post, we taught a model to read our documents.
- It could search a pile of files and....
Summary
The number said "-50%." I'd been staring at trade logs long enough to have a rough feel for how far a...
Core Contributions
- The number said "-50%." I'd been staring at trade logs long enough to have a rough feel for how far a....
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```
Summary
An agent proposes watering the crops, a human Cabinet approves in a picture-button Studio, and Sanity Workflows waters and harvests. Plus a stopwatch on three ways to edit the same data.
Core Contributions
- An agent proposes watering the crops, a human Cabinet approves in a picture-button Studio, and Sanity Workflows waters and harvests.
- Plus a stopwatch on three ways to edit the same data..
Summary
Same task, same models, two commits of my own repo. From the pre-migration commit every run hand-rolled a 190-line typeahead with the exact defects the migration removed; from the post-migration commit every run reused the shared component in 41 lines.
Core Contributions
- Same task, same models, two commits of my own repo.
- From the pre-migration commit every run hand-rolled a 190-line typeahead with the exact defects the migration removed; from the post-migration commit every run reused the shared component in 41 lines..
Code Example
```python
# Example implementation
import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
model = Model()
inputs = torch.randn(1, 10)
outputs = model(inputs)
```