Accountability circles
Build in sprints with shared momentum and live check-ins for progress, blockers, and next steps.
Action-oriented builders. Now open.
A community for action-oriented builders interested in AI engineering and AI tools. Get the structure, focus, and accountability you need to ship practical AI products.
Build
Practical AI projects
Ship
With structure & accountability
Grow
Through peer collaboration
Philosophy
Designed for motivated learners who prefer learning by doing. Get clear frameworks, direction, and community support to make consistent progress on your projects.
No passive consumption. Every activity is designed around building, shipping, and getting feedback on real work.
Focus on what actually works in production. Move from prototypes to reliable systems with battle-tested patterns.
Work alongside other practitioners. Hackathons, projects, and group problem-solving instead of isolated learning.
Develop better instincts through peer feedback, expert guidance, and exposure to real-world decision-making patterns.
What members do
AI Shipping Labs turns project ideas into a shared practice: focused work, useful feedback, and a cadence that keeps the next step visible.
Compare activities by tierBuild in sprints with shared momentum and live check-ins for progress, blockers, and next steps.
Turn solo research into shared leverage by publishing findings in a format other builders can reuse.
Join live 1.5–2 hour working sessions once or twice a month on topics requested by members—not passive webinars.
Trace one trending AI idea back to code and primary sources, then evaluate it through an engineering lens.
Work through interviews, offers, salary, LinkedIn, GitHub, and how to present projects to hiring teams.
Main-tier workflow
Community sprints turn good intentions into shipping windows. Members set a concrete goal, work inside a shared sprint cadence, and leave with a project milestone they can show.
Explore sprintsPick one project outcome that matters enough to finish inside a defined window.
Work alongside other builders with a live cadence, check-ins, and visible progress.
End the sprint with a project milestone, demo, or public artifact you can build on.
Live sessions on the calendar
Follow the current schedule of building sessions, office hours, and community conversations. Each event shows the membership level it requires.
View all upcoming eventsWhat learners say
AI Shipping Labs community is new, but here's what practitioners say about the courses that inspired it.
Membership
Each tier is designed for a different type of builder. More investment means more structure, accountability, and support to help you ship your AI projects consistently.
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Get community updates, browse open resources, and register for free and open events. Paid community activities remain clearly labeled in the membership comparison above.
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From the blog
Long-form notes, walkthroughs, and experiments. Stay close to how we build and reason.
View all postsA practical guide to project deep dives, Python exercises, unfamiliar tasks, and the evidence hiring managers look for in AI engineering interviews.
AI Shipping Labs Sprint 2 starts today. Join the six-week sprint to work from your personal plan, or combine it with LLM Zoomcamp in a closed cohort with weekly office hours for AI Shipping Labs members.
See how CRISP-DM still guides AI engineers in 2026, translating each phase into practical workflows for LLM apps, RAG pipelines, and production AI systems.
Project Ideas
Project ideas and real projects from people who've taken courses. End-to-end AI applications and agentic workflows you can learn from and build on.
View all project ideas
Build a research assistant that uses multiple specialized agents — a searcher, a summarizer, and a fact-checker — coordinated by an orchestrator agent. Learn agent communication patterns, tool use, and how to build reliable multi-step workflows.
A reference project from the AI Engineering Buildcamp: an agent that interacts with a simple to-do list application. Built with Lovable for the frontend and FastAPI (Python) for the backend. Uses the backend's OpenAPI spec so the model can create tools to get tasks or mark them complete, with Logfire for monitoring and pytest for testing.
A small, self-contained project idea: take a photo of an everyday object and turn it into a short story with AI. Expand gradually with illustration, audio, a small website, and a podcast feed. Great for experimenting with multimodal pipelines (vision, text, speech) and sharing with kids, family, or friends.
Curated Links
Curated GitHub repos, model hubs, and learning resources. Dev tools, local LLMs, and courses to level up.
View all curated linksCurated list of ML resources. Frameworks, papers, and tools.
Large language model course. From basics to RAG, agents, and fine-tuning.
Open-weight reasoning
AI copilot for data science. One prompt runs the full
Open standard for AI coding
Course from dlthub on building robust ELT pipelines. Includes a holiday lesson (Dec 22) on integrating LLMs into your workflow, with 50 swag packs to compete for.
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