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An open GitHub repository from Jane Street with agent skills for AI coding assistants, including a Socratic tutor skill, published with a license and contribution guide.
DeepSeek's open-source research prototype of a proactive coding agent for a single GitHub repository, packaged for DeepSeek Harness. DeepSeek labels it reference research code, not an official or production-ready product.
A daily.dev roundup of reactions to Entire, a Git forge for agent-human collaboration launched by former GitHub CEO Thomas Dohmke, including investor Gergely Orosz's stated reasons for backing it.
GitHub's recap of announcements from its Universe 2026 conference, including stacked pull requests that split large changes into smaller linked reviews, plus updates across the GitHub platform and Copilot.
An open-source design skill for Claude Code, Cursor, and Codex that aims to steer AI coding agents away from generic-looking interface designs.
An open-source project for AI teammates that each get their own computer and browser, built on Cloudflare.
OpenAI's open-source PHP version checker that warns when a website's PHP headers or a local installation advertise a version exposed to the PHP2Shell vulnerability, with Codex skills and a script to run directly.
An open-source managed agent platform on GitHub, with an API for running AI agents.
Short GitHub repository tutorial explaining how to clone and access GitHub repositories over HTTPS on Linux and Windows, aimed at beginners. Last updated November 2022.
Open-source AI-powered answering engine that searches the web and answers questions with cited sources; can be self-hosted.
A GitHub blog post describing Project HydraFusion, a research preview in GitHub Copilot that orchestrates multiple AI models for coding workflows. According to the post, in controlled offline evaluations HydraFusion's selective coding workflows matched or exceeded the evaluated Opus 5 baseline while reducing estimated workflow cost. Published September 4, 2026.
An open-source agent skill on GitHub by nicobailon that generates rich HTML pages or slide decks for diagrams, diff reviews, plan audits, data tables, and project recaps.
OpenAI's official guide to configuring Codex code and security reviews for GitHub pull requests, including automatic reviews and custom repository instructions.
Bespoke Labs' open-source toolkit for local typed decisions, contrastive data curation and model evaluation.
Why I recommend it: Free and open source, from a company that also sells services — the repo is usable on its own.
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.
Searchable directory of more than 10,000 open-source projects on GitHub, grouped by category and sortable by stars and community activity, built around finding free alternatives to paid software.
Why I recommend it: Good starting point when a subscription is eating your budget. Stars mean popularity, not maintenance, so before you commit check the last commit date and whether anyone answers issues.
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.
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.
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.
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.
Video walkthrough of open-source repositories founders can use for content quality, distribution, and monetization experiments.
Why I recommend it: Watch for the ideas, not the tools. The repos change monthly; the distribution thinking lasts.
A free 12-page playbook from Tiffany Teasley (Data Sistah) with six portfolio projects built on real business problems, a free browser-based coding setup, a GitHub README template, a resume rewrite prompt, and the exact referral messages that turn a 15-minute chat into an introduction.
Why I recommend it: I like this one because it refuses to let you hide behind another certificate. Pick one project this week, finish it, then use the referral scripts at the back - that pairing is what moves people from studying to hired.
Created by Tiffany Teasley - Data Sistah
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.
India-based 4-week open-source engineering internship where you solve real GitHub issues, earn MSME-recognized certificates, and build verified proof of work.
From the site: India's 4-week open-source engineering internship. Solve real GitHub issues, earn MSME-recognized certificates, and build verified proof of work.
Why I recommend it: If you need real, verifiable engineering experience on a resume, shipping merged pull requests beats another certificate. Great for students and career switchers building a public track record.
Free AI skill that converts GitHub Actions workflows into GitLab CI/CD pipelines, usable from Cursor, VS Code, Claude, or any MCP-compatible client.
From the site: Convert GitHub Actions workflows to GitLab CI/CD in seconds. Add a free AI skill to Cursor, VS Code, Claude, or any MCP-compatible client.
Why I recommend it: A practical way to show DevOps range: migrate a pipeline, document what changed, and add it to your portfolio. Also a clean example of how MCP skills plug into everyday tooling.
Open-source job search system with skill modes, a dashboard, PDF generation, and batch processing.
Why I recommend it: Read the repo even if you never run it — the workflow design is a good model for your own search system.
Open-source guide to designing and scaling large web systems.
Why I recommend it: The standard prep for senior engineering interviews.
Visual learning paths and skill maps for modern engineering and tech roles.
Why I recommend it: Pick one track and finish it — the roadmaps are for focus, not collecting.
Curated coding challenges pulled from real big-tech technical interviews.
Why I recommend it: Work a few problems a day rather than cramming the week before.
The web's largest open-source collection of algorithms across many languages.
Why I recommend it: Read the implementations in the language you interview in.
Structured, free computer science study plan for landing engineering roles.
Why I recommend it: A full self-taught CS curriculum, written by someone who did it himself.
Open learning library with a 10-week applied LLM curriculum, 90+ free courses, and 60 AI interview questions.
Why I recommend it: One repo replaces a paid AI bootcamp if you follow the weekly plan.
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.
Two pitch decks compared side by side: Airbnb's 10-slide deck that raised $600K, and Fyre Festival's deck, whose company later faced fraud charges, plus sample GitHub work to use as a model for your own portfolio.
In plain terms: This guide compares successful and failed pitch decks from Airbnb and Fyre Festival alongside real GitHub profiles. You can use these examples to learn how to present business ideas clearly and structure your own technical portfolio.
Why I recommend it: One idea per slide. Problem, solution, evidence. Decoration never closed a round.
Created by Justin Smith — Launchpad Library
Step-by-step toolkit for curating a recruiter-ready GitHub: profile README, three pinned public projects, clean repo naming, and the six-section README map every project should follow. Includes a quality checklist.
In plain terms: This guide shows you how to organize and clean up your GitHub profile for job applications. You can follow the quality checklist and project outline to present your best work clearly to recruiters.
Why I recommend it: Three well-documented projects beat ten messy ones. Test your profile in an incognito window.
Created by Justin Smith — Launchpad Library
