Every open-source project here, with the steps to run it
121 projects in one place: the 50 written up by hand on Free AI Projects (licenses read from their own license files on 22 September 2026), plus the open-source entries from the tools library. Each one shows what it does, the license it carries, and a run-it guide. Where the person who built it has a Who’s Who profile, they are named on the card.
Being straight about the guides: they are the same honest sequence for every project of a shape, and the last step always sends you to the project’s own README or model card. The exact install command belongs to the project and changes — inventing one here would only go stale. Library entries show the license their own page names; the ones marked as read were checked against the license file.
Wanted a tool rather than something to run?
The Tools page is the wider list: every tool in the library, free or paid, grouped by the job it helps with, with what each one costs. This page is the narrower one — only projects whose code is open, each with its license and the steps to run it.
Downloads and runs open models with one command, and serves them to other programs on your machine.
What you need to run it: A normal laptop will run a small model. Around 8GB of memory for a 7–8 billion parameter model; more memory means bigger models, a graphics card means faster ones.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/ollama/ollama.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
A polished browser interface over Ollama or any OpenAI-compatible API, with users, chat history and document search.
What you need to run it: Docker or Python, and a model source such as Ollama running alongside it.
Read the license first: Not a standard open-source license. It is BSD-3 with an added clause about keeping the Open WebUI branding unless you have 50 or fewer users or a separate agreement. Fine for personal and small-team use; read it before you rebrand it for a client.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/open-webui/open-webui.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
A desktop app and web interface for local models, with tool calling and an OpenAI-compatible API.
What you need to run it: A one-click installer, then a model file. A graphics card makes it far more pleasant.
Read the license first: Strong copyleft. Using it yourself is unrestricted, but if you modify it and let other people use it over a network, you have to publish your changes.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/oobabooga/text-generation-webui.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
Meta's large open-weight model, widely used as the baseline for self-hosted work.
What you need to run it: Serious hardware: a multi-GPU machine, or a heavily compressed build with a lot of system memory. Not a laptop model.
Read the license first: Free, but not open source. Meta's license adds an acceptable-use policy, a requirement to credit Llama in products built on it, and a separate license if your product passes 700 million monthly users. You also have to accept the terms on the model page before downloading.
Who’s Who connected to it
Yann LeCunFounded and led FAIR, Meta's AI research lab where the Llama models were built. He has since left Meta to start his own lab, so he is not behind the newest releases.
How to run it yourself
These are model weights: a file you download, plus a program from the first section of the Free AI Projects page to load it.
Install a runner first — Ollama is the shortest route, llama.cpp if you want the engine itself.
Open the model card on this card's link and read the license and the size before downloading anything.
Pick the size that fits your machine: a smaller, more compressed file if you have 8GB of memory, a larger one if you have a graphics card.
Download it the way the model card says, then load it in your runner and ask it something you already know the answer to.
Check the answer quality yourself on your own work — benchmark numbers on a model card are the maker's own.
Read the license before commercial use: some open-weight models restrict it.
Small, efficient models that handle images as well as text — useful on one machine.
What you need to run it: The 4-billion version runs on a laptop; the larger sizes want a GPU.
Read the license first: Free to use commercially, but governed by Google's own terms and a prohibited-use policy rather than an open-source license, and Google can update that policy. You must pass the same restrictions on to anyone you give the model to.
Who’s Who connected to it
Demis HassabisRuns Google DeepMind, the lab that trained and released Gemma.
How to run it yourself
These are model weights: a file you download, plus a program from the first section of the Free AI Projects page to load it.
Install a runner first — Ollama is the shortest route, llama.cpp if you want the engine itself.
Open the model card on this card's link and read the license and the size before downloading anything.
Pick the size that fits your machine: a smaller, more compressed file if you have 8GB of memory, a larger one if you have a graphics card.
Download it the way the model card says, then load it in your runner and ask it something you already know the answer to.
Check the answer quality yourself on your own work — benchmark numbers on a model card are the maker's own.
Read the license before commercial use: some open-weight models restrict it.
A very large, strong reasoning model, released with open weights.
What you need to run it: Server-class hardware to run yourself. Most people use it through a hosted provider.
Read the license first: Two different licenses in one repository. The code is MIT; the weights carry DeepSeek's own agreement with use restrictions attached. If you are shipping something built on the weights, read that second file, not the first.
Who’s Who connected to it
Liang WenfengFounded DeepSeek, which trained and released this model.
How to run it yourself
These are model weights: a file you download, plus a program from the first section of the Free AI Projects page to load it.
Install a runner first — Ollama is the shortest route, llama.cpp if you want the engine itself.
Open the model card on this card's link and read the license and the size before downloading anything.
Pick the size that fits your machine: a smaller, more compressed file if you have 8GB of memory, a larger one if you have a graphics card.
Download it the way the model card says, then load it in your runner and ask it something you already know the answer to.
Check the answer quality yourself on your own work — benchmark numbers on a model card are the maker's own.
Read the license before commercial use: some open-weight models restrict it.
A fast, high-quality image model whose weights are open, unlike its bigger siblings.
What you need to run it: A graphics card with around 12GB, or a compressed build on less.
Read the license first: Only the [schnell] version is Apache-2.0. The [dev] version is non-commercial and [pro] is API-only — check which one you downloaded before using an image for paid work.
How to run it yourself
These are model weights: a file you download, plus a program from the first section of the Free AI Projects page to load it.
Install a runner first — Ollama is the shortest route, llama.cpp if you want the engine itself.
Open the model card on this card's link and read the license and the size before downloading anything.
Pick the size that fits your machine: a smaller, more compressed file if you have 8GB of memory, a larger one if you have a graphics card.
Download it the way the model card says, then load it in your runner and ask it something you already know the answer to.
Check the answer quality yourself on your own work — benchmark numbers on a model card are the maker's own.
Read the license before commercial use: some open-weight models restrict it.
The widely supported image model that most free tools and tutorials are built around.
What you need to run it: A graphics card with 8GB or more. Runs in ComfyUI, InvokeAI or diffusers.
Read the license first: A responsible-AI license, not a standard open-source one: it lists uses you agree not to put the model to, and you must pass those restrictions on to anyone you share it with.
How to run it yourself
These are model weights: a file you download, plus a program from the first section of the Free AI Projects page to load it.
Install a runner first — Ollama is the shortest route, llama.cpp if you want the engine itself.
Open the model card on this card's link and read the license and the size before downloading anything.
Pick the size that fits your machine: a smaller, more compressed file if you have 8GB of memory, a larger one if you have a graphics card.
Download it the way the model card says, then load it in your runner and ask it something you already know the answer to.
Check the answer quality yourself on your own work — benchmark numbers on a model card are the maker's own.
Read the license before commercial use: some open-weight models restrict it.
A text-to-speech toolkit with many voices and languages, plus voice cloning.
What you need to run it: Python. A GPU for training your own voice; not needed just to speak text.
Read the license first: Weak copyleft — changes to their files must be shared, but your surrounding code stays yours. The company shut down, so the project is community-maintained now.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/coqui-ai/TTS.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
One consistent way to call a hundred different model providers, so swapping model means changing one line.
What you need to run it: Python, or run it as a proxy server.
Read the license first: Everything outside the enterprise/ directory is MIT. That directory has its own commercial license, so check which features you are relying on before building a product around it.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/BerriAI/litellm.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
Fine-tunes open models on your own examples through a web interface instead of training scripts.
What you need to run it: A GPU. You can rent one by the hour; a consumer card handles smaller models with efficient methods.
Read the license first: The tool is Apache-2.0, but the model you fine-tune keeps its own license — fine-tuning Llama does not free you from Meta's terms.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/hiyouga/LLaMA-Factory.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
A search database built for AI, with hybrid keyword-and-meaning search out of the box.
What you need to run it: Docker or Kubernetes.
Read the license first: The license file says plainly that different directories carry different terms. Most of it is BSD-3; some is under their own license. Check the directory your feature lives in.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/weaviate/weaviate.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
Composer by DXOS — Local-First Collaborative Workspace
License not checked yet — read it in the repository
DXOS
A local-first workspace from DXOS combining documents, spreadsheets, tables, and sketches with real-time peer-to-peer collaboration, custom plugins, custom functions, and AI agent workflows. Data syncs between devices without centralized servers. Composer is in early access; the product site and documentation are free.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
MoltBot
Open-source personal AI assistant that runs locally on your own devices and connects to WhatsApp, Telegram, and Discord. Features persistent memory, proactive notifications, and community-built skill extensions. Formerly known as ClawdBot. Free and open source.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
Nandha Kishor M on GitHub
Open-source "System 1" decision engine: typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/NandhaKishorM/laya.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
Aaron Rose (GitHub)
Free, open-source code and dataset for the paper "Detecting Multi-Agent Collusion Through Multi-Agent Interpretability." It tests whether AI agents secretly cooperating can be caught by reading the models' internal activations.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/aaronrose227/narcbench.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
Powermove
Free open-source video and motion editor for Apple Silicon Macs. Connect a coding agent (Claude Code, Codex) to rewrite panels, effects, and extensions inside the running app.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
Open Measures
Research reports on how harmful content, extremism, and coordinated campaigns spread across social platforms. Public app and API are free (rate-limited to 39 requests per day, data 6 months old).
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
pyLoad
Free, open-source download manager written in Python, managed entirely through a web interface. Lightweight enough to run on a home server, NAS or router, with plugins to automate repetitive downloads.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
yt-dlp
Free, open-source command-line tool for downloading video and audio from thousands of sites. Actively maintained on GitHub, runs on Windows, macOS and Linux, and is the tool most other download apps wrap.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/yt-dlp/yt-dlp.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
Apache-2.0 — named by the project, not yet read here
Stanford CRFM
The Python framework behind Stanford's HELM leaderboards, released under the Apache License 2.0 (licence file read, not copied from a roundup). You install it with pip, describe a run (scenario plus model plus metrics), and it evaluates the model and produces the same structured results the public site displays, including its own local web UI for viewing them. It supports hosted model APIs and locally run open-weight models, and you can add your own scenario to test a model on your own task or data. Free to use, modify and use commercially under Apache 2.0; you pay only for whatever model API calls or compute your own runs consume.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/stanford-crfm/helm.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
Autonomous AI
An open-source platform for running coding agents such as Claude Code and Codex across your own machines and specialising them for domains like CAD, PCBs, robotics and games, with optional open hardware.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/autonomous-ai/openharness.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
NVIDIA
An open-source scanner that probes a language model for weaknesses — prompt injection, data leakage, jailbreaks, toxic output — and reports what it found. Free.
Who’s Who connected to it
Jensen Huangtheir company’s repository, not their own codeCo-founded NVIDIA and runs it; garak is published by its security team, not by him.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/NVIDIA/garak.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
promptfoo
An open-source tool for testing and red-teaming prompts and AI apps — run the same prompts across models, compare answers, and catch regressions. Free and self-hosted.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
Kilo
A free, open-source AI coding agent for VS Code, JetBrains, the command line and the cloud, with support for local models and your own API keys at no markup.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
Herdr
A free, open-source tool for running and monitoring multiple AI coding agents from one place, with a plugin ecosystem and documentation for the agent CLIs it supports.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
Free, MIT-licensed Python library and documentation for applying AI to satellite and geospatial data, with tutorials, notebooks, a QGIS plugin and video walkthroughs.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
Zed Industries
A free, open-source code editor built in Rust for speed, with built-in AI assistance, real-time collaboration and multiplayer editing for pair programming.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
Anthropic
The public GitHub discussion where Anthropic and the community designed function hooks — a way for plugins to extend Claude Code — including the shipped design decisions and community feedback.
Who’s Who connected to it
Dario Amodeitheir company’s repository, not their own codeCo-founded Anthropic, whose repository hosts this thread. The proposal itself was written by someone outside the company.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/anthropics/claude-code/issues/91870.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
Apache-2.0 — named by the project, not yet read here
Chroma
Apache 2.0 open-source search infrastructure for AI applications, supporting vector, full-text, regex and metadata search, free to run locally with optional hosted cloud.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
Matplotlib
The open-source Python library for static, animated and interactive charts, with free plot-type galleries, tutorials, cheat sheets and a full API reference.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
NVIDIA
A free, open-source security scanner that checks AI agent skills and MCP servers for prompt injection, data exfiltration and supply-chain risks before you install them.
Who’s Who connected to it
Jensen Huangtheir company’s repository, not their own codeCo-founded NVIDIA, which publishes this.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/NVIDIA/SkillSpector.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
ItsssssJack
A free open-source tool that scores AI-written copy for tell-tale "AI voice", rewrites it with a rival model, then re-checks it, so landing pages, READMEs and emails read like a human wrote them.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/ItsssssJack/SlopMonster.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
Nutlope
A free MCP server that gives coding assistants design taste, drawing on thousands of real websites captured with their palettes, fonts and layout structures.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
tt-a1i
A free agent skill that turns a codebase or a plain-English system description into an interactive architecture, workflow, sequence or data-flow diagram you can share as a single file.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/tt-a1i/archify.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
Bilawal Sidhu
An open-source, browser-based "spy satellite simulator" that plots real satellites, planes, vessels and public cameras on a photorealistic 3D globe, and answers questions about the planet in plain language.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/bilawalsidhu/gods-eye-view.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
OpenClaw Foundation
An open-source personal AI assistant, run on your own device, that connects to chat apps like WhatsApp, Telegram, Slack, and Teams to handle email, calendars, and everyday tasks.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
An open-source (MIT) job-hunting automation tool that scans company career pages every 15 minutes, submits applications to applicant tracking systems when it is confident about a match, pulls wider market listings through the Adzuna jobs API, and sends alerts to Telegram.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/mr-ravi26/alpha-hunt.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
GitHub
Open-source project by Guillaume Meyer that strips multi-vendor AI provenance marks, including Unicode text artifacts, statistical rewrite hooks, and C2PA metadata from PNG, JPEG, SVG, PDF, DOCX, HTML, and Markdown files.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/guillaumemeyer/watermarks-remover.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
Double-entry, plain-text accounting you can audit line by line. Web interface for the open-source Beancount ledger format — good for founders and freelancers who want real books without a subscription suite.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.
License not checked yet — read it in the repository
Open-source AI agent that scans job boards daily, cross-references your LinkedIn network for warm intros, and emails you a curated list of matches. Self-hosted — you run it with your own keys.
How to run it yourself
This is source code you copy down and run on your own machine.
Read the README on the project page first: it says what the project needs, and whether any part of it calls a paid service.
Copy the code down: git clone https://github.com/evanzsolomon/job-search-agent.git
Install what it depends on using the command in the README — usually npm install, pip install -r requirements.txt, or uv sync.
Put any keys it asks for in a local .env file, and never commit that file.
Run the start command from the README and use it locally on one small real task before you point it at anything that matters.
Read the license before you use it for paid work — it is linked on this card.
License not checked yet — read it in the repository
Free, open-source resume builder with multiple templates, ATS-friendly exports, custom sections, and no paywall or watermark. Self-hostable if you want full control of your data.
How to run it yourself
This one starts from its own site, so the setup route is theirs, not a clone command.
Open the project site and find its documentation or repository link.
Decide which way you want it: their hosted free tier, or running it yourself from the source.
For the hosted route, make an account and read where the free tier stops before you rely on it.
For running it yourself, follow the install steps in their own docs — they change often enough that copying them here would go stale.
Try it once on a real task of your own before building anything around it.