Free GitHub repos, templates, and open-source tools
Repos, starter templates, and open-source tooling you can copy, fork, and put on your portfolio today.
54 resources filed here — every one is completely free. Open these in the Resource Hub to filter by collection, type, or price.
What you'll find on GitHub
GitHub isn't just for engineers. Templates, checklists, and open datasets live here too — and anything you fork becomes portfolio evidence you can link to in an application.
Starter templates and repos you can fork and make your own
Open-source tools that replace paid software
Public work that doubles as portfolio proof for employers
54 of 54 free · Curated and reviewed personally by Justin Smith
21 structured lessons on prompt engineering and building generative AI applications, with practical exercises in Python and TypeScript.
Why I recommend it: The best free course for actually building something. Work one lesson at a time and keep the code you write — that is your proof of skill.
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.
Why I recommend it: Free and open source, but an individual developer's project — check the commit history and issue activity before relying on it for anything important.
A Jev-like family of small decision models built on Qwen that you can train and run on your own hardware, from Jared Palmer.
Why I recommend it: Free and open source. Running it yourself means paying for your own compute; the models are small enough for a laptop or a cheap server.
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.
Why I recommend it: A research tool, not a beginner resource. Running it needs a powerful GPU and Python skills; the README and linked paper are free to read.
Public proof-of-concept for a Windows Defender update denial-of-service vulnerability. Fills the disk by triggering repeated definition updates.
Why I recommend it: For security researchers and IT people who need to test their own systems. Do not run this on a machine that is not yours; misuse against someone else's system is a crime in most places. Read the README before touching it.
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.
Why I recommend it: useful for saving your own recorded talks, webinars and openly licensed lectures so you can watch or transcribe them offline. Downloading material you have no right to is a different matter and often breaks the site's terms or the law where you live, so keep it to your own content and openly licensed work.
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.
Why I recommend it: Worth it if you need to prove a model is good enough for a specific job rather than good in general — write your own scenario with your own examples and run it. Two practical warnings: the published leaderboard runs are large and expensive to reproduce in full, so start with a single scenario and a small instance count, and if you evaluate a paid API model the token costs are yours, not Stanford's.
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.
From the site: Follow your curiosity. Build across disciplines. Open-source software and hardware for polymaths in the making. - autonomous-ai/openharness
Why I recommend it: The software is free and open source. The agents you run inside it may cost money, and the companion hardware is a separate purchase — the repo itself is the free part.
Open source framework for building AI agents, free to clone and run yourself.
From the site: Open source agentic operating system. Contribute to elizaOS/eliza development by creating an account on GitHub.
Why I recommend it: Free and open source. Clone it and run one agent locally if you want to understand what an agent framework actually does rather than take a vendor's word for it.
Microsoft's open-source toolkit for red-teaming AI systems: automated attack prompts, scoring of the responses, and repeatable runs. Free.
From the site: The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI system...
Why I recommend it: Built by the team that red-teams Microsoft's own AI products, and released as-is. Best paired with a written idea of what you are testing for.
An open-source scanner that probes a language model for weaknesses — prompt injection, data leakage, jailbreaks, toxic output — and reports what it found. Free.
From the site: the LLM vulnerability scanner. Contribute to NVIDIA/garak development by creating an account on GitHub.
Why I recommend it: Point it at a model you are about to rely on and see how it fails before your users do.
An open-source library of metrics and algorithms for finding and reducing unwanted bias in datasets and models, in Python and R. Free.
From the site: A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. - Trusted-AI/AIF360
Why I recommend it: For the harm that shows up in ordinary systems long before anything dramatic does — hiring screens, lending, scoring. Measuring bias is the easy half; deciding what fair means is yours.
An open-source, self-hosted search engine that queries other engines without tracking you or building a profile. AGPL licensed.
From the site: SearXNG is a free internet metasearch engine which aggregates results from various search services and databases. Users are neither tracked nor profiled. - searxng/searxng
Why I recommend it: Useful if you research employers a lot and would rather not have that history tied to an account.
An open-source drop-in replacement for the OpenAI API that runs models on your own hardware, including CPU-only machines. MIT licensed.
From the site: LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required. - mudler/LocalAI
Why I recommend it: Point existing code at your own server instead of a paid API — no code changes beyond the address.
An open-source desktop and self-hosted app that lets you chat with your own documents using local or hosted models. MIT licensed.
From the site: Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience - Mintplex-Labs/anything-llm
Why I recommend it: Point it at your own files — job descriptions, notes, contracts — and ask questions of them without uploading anything to a company.
An open-source platform for building and running AI apps and agents, with a hosted option and a full self-hosted Docker install.
From the site: Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without ...
Why I recommend it: More structure than a chat window: prompts, data and logs kept in one place. Start with their Docker install and one small app.
A visual workflow tool for connecting apps and AI models — source-available, and free to run on your own server. Their hosted plans are paid.
From the site: Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations. - n8n-io/n8n
Why I recommend it: This is how you automate the boring parts of a job hunt or a small business without writing code. Note the license is source-available, not fully open source.
A self-hosted, open-source chat interface for local or hosted models — chat history, documents, multiple users. Works on top of Ollama or any OpenAI-compatible API.
From the site: User-friendly AI Interface (Supports Ollama, OpenAI API, ...) - open-webui/open-webui
Why I recommend it: If you like the ChatGPT window but not the subscription, this is that window running on your own machine.
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.
Why I recommend it: A rare look at how an AI product feature gets designed in public. Useful if you want to see how technical feedback is actually written.
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.
Why I recommend it: If you install agent skills, scan them first. This is the free tool to do it with.
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.
Why I recommend it: A practical fix if your AI-assisted writing keeps sounding generic.
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.
Why I recommend it: Handy for explaining how something works in an interview or a proposal without hand-drawing diagrams.
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.
Why I recommend it: A striking free build to study or fork if you want a portfolio project that people actually stop and look at.
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.
Why I recommend it: Powerful and blunt. Auto-submitting applications will get you volume, not fit, so if you run this, keep the confidence threshold high and still write your own answers for the roles you actually want. Read the code before you hand it your resume.
A free browser-based practice aid that walks through a recruiter phone screen question by question, so you can rehearse the opening conversation most candidates wing.
Why I recommend it: The recruiter screen is the round people prepare for least and get cut in most. Run through this once out loud before your next call.
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.
Why I recommend it: Listed as evidence, not advice. It shows why AI-detection claims about your writing are shaky, and why disclosure beats concealment.
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.
In plain terms: This open-source tool scans job boards daily to find openings that fit your criteria. You can connect your LinkedIn network to spot contacts at hiring companies and receive daily email updates with active job matches.
From the site: AI agent that scans job boards daily, cross-references your LinkedIn for warm intros, and emails you curated matches - evanzsolomon/job-search-agent
Why I recommend it: For the technically comfortable. Read the code before you point it at your accounts, and never let an agent send applications unreviewed.
GitHub's free article library explaining version control, DevOps, CI/CD, security practices and AI-assisted development in plain terms.
From the site: GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.
Why I recommend it: Good plain-English explainers for the terms that show up in job descriptions — read the DevOps and CI/CD ones before an interview.
Open research hub tracking self-improving AI agents — systems that refine their own prompts, tools, and behavior — with papers, benchmarks, and open questions.
Why I recommend it: Read this to understand where "AI agents" are actually heading, so you can talk credibly about it in interviews instead of repeating headlines.
Owain Evans is an AI alignment researcher leading Truthful AI, a non-profit for AI safety research.
From the site: Owain Evans is an AI Alignment researcher leading Truthful AI, a non-profit for AI Safety research. Discover his publications, blog posts, and collaborative opportunities on AI alignment, AGI risk, and related topics.
