AI Build-Alongs - Q2 2026 Playbooks

Stop treating Claude as a chat tab and start treating it as an execution environment. This stage builds the judgment for when tool use and agent loops are real leverage versus just expensive theater.

1. 1Tool Use, Function Calling, Agents — The Maturity Laddermanual12 min readRead the maturity ladder before you wire anything - this is the fastest way to avoid building an "agent" that is really just a brittle prompt chain.
2. 2Anthropic — Research Lab to Product Companycase7 min readStudy where Anthropic drew the line between model lab and product surface, then steal the parts that make trust and execution coexist.
3. 3build-along-claude-code-skills-q2-2026coming soonexternalRecorded build-along coming soon - follow this when you want the exact repo-to-shipping workflow with Claude Code and Skills.optional

Cursor demos are easy; repeatable agent loops are hard. The move here is to design loops that preserve velocity without quietly destroying code quality and decision ownership.

1. 1Tool Use, Function Calling, Agents — The Maturity Laddermanual12 min readUse this chapter as your rubric for when to stay single-step versus when an agent loop is justified by the task shape.
2. 2Cursor — The AI Code Editor That Competed with GitHubcase6 min readCursor's moat is workflow, not raw model novelty - read this to see how product packaging turns commodity intelligence into daily habit.
3. 3build-along-cursor-agent-loops-q2-2026coming soonexternalRecorded build-along coming soon - a tactical walkthrough for setting up, observing, and tightening a Cursor loop in production-like conditions.optional

This stage is about practical shipping, not purity. You will learn where AI-built websites are the right answer, and where human product judgment still has to step in before launch.

1. 1When AI Is the Right Answer (and When It Isn't)manual13 min readThis chapter stops you from building with AI just because you can - decide first if the website problem is unstructured enough to benefit.
2. 2AI UX Patterns That Workmanual13 min readUse these patterns so your AI-built site behaves like a product, not a demo - especially around trust cues, edits, and recovery.
3. 3Notion AI — Adding Intelligence Without Breaking Trustcase7 min readNotion shows how to introduce AI into real user workflows without breaking confidence - essential reading before you "ship fast" on Replit.
4. 4build-along-replit-agent-website-q2-2026coming soonexternalRecorded build-along coming soon - from blank canvas to a live site with agent assistance, including the guardrails non-engineers usually miss.optional

Copilot value compounds when it is connected to the systems your team already runs. The capability here is orchestration: moving from autocomplete novelty to integrated daily execution.

1. 1Tool Use, Function Calling, Agents — The Maturity Laddermanual12 min readTreat connectors as tool-use design, not plugin shopping - this chapter gives you the operating logic before you add integration surface area.
2. 2GitHub Copilot — The First Real AI Product, and What Five Years Taught Uscase14 min readRead the five-year arc to understand why durable Copilot adoption was operational, not magical - and why most rollouts stall in month two.
3. 3build-along-copilot-connectors-q2-2026coming soonexternalRecorded build-along coming soon - a focused session on stitching Copilot into real team workflows instead of isolated solo usage.optional

Multi-agent is not "more agents equals more output." The skill is decomposing work so each agent has a bounded role, explicit handoffs, and measurable failure modes.

1. 1Tool Use, Function Calling, Agents — The Maturity Laddermanual12 min readRevisit the ladder here - most teams jump to multi-agent before they have evidence that single-agent plus tools has hit a ceiling.
2. 2RAG, Fine-Tune, or Context Window?manual16 min readRetrieval-augmented generation (RAG) means fetching external context at runtime; this chapter tells you when that beats bigger context windows or fine-tuning.
3. 3Harvey — Vertical AI for a High-Stakes Professioncase8 min readHarvey is the clean example of constrained AI workflows in a high-accountability domain - use it to reason about role boundaries and auditability.
4. 4build-along-multi-agent-patterns-q2-2026coming soonexternalRecorded build-along coming soon - a tactical build where each agent has one job, one interface, and one evaluation lens.optional

Model Context Protocol (MCP) is the interface layer that lets models safely discover and use external tools and context. This stage is about choosing the MCP surface deliberately, so capability growth does not become operational chaos.

1. 1Tool Use, Function Calling, Agents — The Maturity Laddermanual12 min readUse the chapter's tool-use framing to decide which MCP connections earn their keep and which are just new failure paths.
2. 2Linear — AI as a Quiet Utility, Not a Chat Assistantcase14 min readLinear's restrained rollout is the right counterweight to integration hype - it shows how to add AI utility without turning the product into a noisy assistant.
3. 3build-along-mcp-surface-q2-2026coming soonexternalRecorded build-along coming soon - a tight implementation session on defining, testing, and governing an MCP surface.optional

Pick one capability from this slate, ship a real artifact, and document what held up under pressure. Completion means a working deliverable plus a crisp written retro your peers can challenge.

1. 1Course 1: Model Selection and EvalscourseUse this structured drill to pressure-test your model and eval decisions before you call the capstone done.optional
2. 2Course 2: Prompt as SpeccourseUse this course to tighten your prompt contract so the build is reproducible instead of one lucky run.optional
3. 3Publish a Q2 build-along retroforumPost a 300-word retro: what you shipped, what broke, which pattern felt durable, and what you would cut next quarter.optional