Tutorials

Design AI agent memory without a data swamp: session state, durable facts, retrieval, provenance, conflicts, privacy, deletion, and production evaluation.

Secure remote and local MCP servers with OAuth audience binding, least-privilege tools, prompt injection defenses, sandboxing, audit logs, and tests.

Test MCP servers beyond the Inspector: schemas, protocol versions, auth, timeouts, cancellation, retries, failure injection, conformance, and agent behavior.

A practical production LLM stack: request contracts, model routing, tools, validation, observability, evals, cost controls, fallbacks, and safe rollouts.

Build an AI meeting notes workflow that turns explicit commitments into reviewed tasks without treating suggestions or invented details as facts.

The architecture of a self-updating competitive intelligence document: automation with n8n/Make + AI, prompts for section updates, and competitor monitoring.

Complete seed/pre-seed data room checklist. AI prompts for every document, folder structure, and day-by-day schedule. From pitch deck to due diligence package.

20 Midjourney prompts for startup ads: hero banners, product visuals, social proof, lifestyle, and abstract concepts. Structure and parameters.

Replace 2-hour brainstorms with an AI creative brief: field structure, idea-generation prompts, and channel adaptation for email, social, landing, and video.

Complete AI fundraising pipeline: fund research, qualification, warm intro mapping, personalized outreach. Prompts, tools, CRM setup, and target benchmarks.

AI pipeline for competitive analysis: screenshots → Vision API → feature matrix → insights. Prompts for Claude Vision, matrix template, automation scripts.

How to use AI to generate usability testing scripts: session structure, prompts for tasks and questions, results analysis. Ready-to-use templates and examples.

A prompt library for design systems: design tokens, component prompts, and consistency rules that make LLM-generated UI predictable.

How to generate Terraform modules using AI: prompts for AWS and GCP, state management, project structure, best practices. Production-ready examples.

Self-hosted product analytics (PostHog) and BI dashboards (Metabase) via Docker. AI prompts for dashboards, integration, and a production checklist.

Prompts, agenda templates, and Slack + n8n automation for async standups, retrospectives, and 1-on-1s in distributed teams. Ready-to-use workflows, no fluff.

Mechanics of SAFE and priced rounds, an AI simulation of both scenarios with synchronized option-pool timing, plus dilution tables and cap-table prompts.

A four-stage pipeline: audit → categorize → restructure → migrate. Prompts, tools (Outline, Notion, Confluence), and a worked example of the transformation.

A hands-on Langfuse guide for current Python SDK tracing, prompt management, evaluations, cost tracking, privacy, and self-hosted or cloud setup.

A step-by-step guide to building an MCP server in Python: tools, resources, prompts, authentication, testing, and publishing. Complete working code.

An advanced guide to Claude Code: building skills, subagents, hooks, memory, and dynamic workflows. 5 ready-made skill templates, 3 agents, 2 hooks to copy.

A step-by-step guide: from a 20-line minimal agent to a multi-agent system. Claude Agent SDK, MCP, CrewAI, LangGraph, Google ADK. Full working Python code.

A complete guide to MCP (Model Context Protocol): architecture, connecting it to Claude Code and Cursor, the top 20 servers, and writing your own server in TypeScript and Python.

A complete Claude API tutorial: Messages API, tool use, adaptive thinking, prompt caching, vision, streaming, batches, MCP connector, structured outputs. Examples in Python and TypeScript.

A complete guide to RAG: architecture, chunking, embeddings, vector stores, retrieval, reranking. A hands-on tutorial with pgvector and Python.

A systematic approach to prompt engineering: prompt anatomy, 7 principles, production system prompts, CoT and few-shot techniques, prompt management. With code examples.

A systematic approach to vibe coding for professional developers: architecture, rules-driven development, enforcement, team workflows. Not for beginners.

An exhaustive guide to Supabase: architecture, authentication, RLS, Edge Functions, pgvector, branching. Comparison with Firebase, pricing, and best practices for startups as of mid-2026.

A practical guide to work sample tests: job analysis, realistic scope, scoring rubrics, calibration, accommodations, AI rules, and bias monitoring.

Select a North Star Metric with AI: criteria by business model, analysis prompts, unit economics validation. SaaS, marketplace, and e-commerce examples.

Identify PMF from retention curves: SQL for cohort analysis, statistical flattening detection, and AI interpretation prompts. Tutorial with ready-to-use code.

Investor-ready model structure: revenue model, P&L, cash flow, unit economics. AI prompts for Google Sheets formulas, formatting standards, and common mistakes.

A 5-component prompt template for building a reusable library. Structure, examples for different tasks, plus organization and versioning of a prompt library.

Anomaly detection, AI prompts for configuring alerts, and tools (Grafana, PostHog, custom). Practical rules and configs for product metrics.

Template for monthly investor updates: prompts to generate them from raw metrics, good vs bad email examples, send timing, and automation with Claude.

Use AI to prepare an NDA without treating generated text as legal advice: secure intake, approved templates, clause review, redlines, and counsel escalation.

Build a competitive intelligence workflow that separates evidence from inference, tracks meaningful changes, and uses AI without inventing competitor strategy.

Build a PQL scoring system without arbitrary weights: define the decision, prevent label leakage, score accounts, calibrate thresholds, and test sales lift.

A practical Docker multi-stage build for Node.js, with cache mounts, minimal runtime files, non-root execution, secret handling, CI checks, SBOM, and provenance.

A practical impact–frequency matrix for product backlog triage: define evidence, score consistently, expose uncertainty, and use AI without fake precision.

Generate safer GitHub Actions workflows with structured prompts, current Node.js and Python matrices, deploy examples, and a practical review checklist.

Run 10 A/B tests monthly in 3.5 hours: LLM hypothesis generation, experiment templates, PostHog and Statsig setup. Full framework inside.

Designing REST APIs with AI: naming conventions, versioning, error handling, and OpenAPI spec generation. Ready-to-use prompts for every stage.

GDPR checklist for startups with AI audit prompts, common violations and fixes, plus a privacy policy template covering all Article 13 requirements.

AI toolkit for performance marketing on a $500/mo budget: prompts for copy and creatives, Meta vs. Google Ads allocation, metrics thresholds, and a weekly optimization cycle.

AI-powered keyword research pipeline: seed generation via Claude, clustering with prompts, validation in Ahrefs/SEMrush. Prompts, tables, step-by-step process from zero to a ready content plan.

Step-by-step method for crafting an elevator pitch with AI: a 6-block structure, prompts for each block, before/after examples, and adaptations for investors, clients, and partners.

B2B sales objection classification, prompts for generating responses via LLM, a ready-to-use playbook template with examples. A system for preparing to handle any objection.

A template for a 90-day new employee onboarding plan generated with an LLM. Prompts, week-by-week checklists, and probationary period success criteria.

Rubric-based resume screening with LLMs: prompts, calibration, bias mitigation, and a step-by-step process from 100 resumes to a shortlist in 30 minutes.

A step-by-step pipeline from exporting Figma mockups to a working prototype using v0, Bolt, and Claude Artifacts. Concrete steps, prompts, and common mistakes.

A TCO model for three startup architectures: monolith, microservices, serverless. AI-assisted calculation prompts, comparison tables, and a decision framework with concrete numbers.

RICE framework with AI automation: prompts for scoring Reach, Impact, Confidence, and Effort. A sample backlog with 10 features, templates, and LLM-based automation.

A step-by-step process for turning a PRD into a PostgreSQL schema in 30 minutes with AI. Extract entities, build relationships, generate migrations.

Build product positioning from real alternatives, differentiated capabilities, customer value, best-fit segments, category context, and verifiable proof.

A practical pricing workflow that uses AI for evidence synthesis and scenario modeling without inventing willingness to pay or replacing customer research.

A practical guide to dashboard design for startups: executive, product, and engineering. Concrete metrics, SQL queries, and prompts for generating dashboards in Metabase and Grafana.

7 SQL queries for cohort analysis: retention, revenue cohorts, activation, churn prediction. Ready-made prompts for AI generation. Copy and run.

Build an auditable TAM, SAM, and SOM model from customer counts, price, product constraints, and sales capacity. Includes formulas, a worked example, and AI prompts.

A framework for deciding between bootstrapping and fundraising: financial models for both scenarios, Claude prompts, comparison tables, sensitivity analysis, and clear selection criteria.

How to test each of the 9 Business Model Canvas blocks with AI prompts. Common weak spots, analysis examples, and ready-to-use prompts for Claude.

A checklist of 15 common production code vulnerabilities with before/after examples and prompts for AI-assisted auditing. SQL injection, SSRF, path traversal, JWT, race conditions, and more.

Sensitivity analysis for SaaS startups using AI: formulas, prompts for modeling, scenario tables, tornado diagrams. Churn vs CAC - which parameter has a bigger impact on runway.

A practical red-green-refactor workflow for Claude Code and other coding agents, with contract-first prompts, proof of red, test review, mutation checks, and CI.

How to generate production-ready PRDs with LLMs: document structure, prompts for each section, real output examples, and a ready-to-use template.

Methodology for finding a product's magic number: cohort analysis in SQL, AI correlation of user actions with 30-day retention, A/B test validation. Ready-to-use queries and prompts.

How to build a quarterly content plan with an LLM: topic clusters, format distribution, prompts for topic generation, and a ready-made calendar template.

How to use an LLM to generate OKRs: prompts, templates, examples of good and bad Key Results. A full process from strategic priorities to a finished OKR set in one session.

Design human oversight for AI actions with risk tiers, approval state machines, calibrated routing signals, reviewer UX, queue SLAs, audit logs, and release metrics.

Methodology for A/B testing prompts: quality metrics, statistical significance, tools (Langfuse, DeepEval). Step-by-step guide from hypothesis to production decision.

Generate Architecture Decision Records with AI: template, prompt library, CI automation, full Claude Code workflow from decision to docs.

A complete AI-powered content production pipeline: from keyword research to publication. Prompts, tools, quality metrics, and exact cost figures for each stage.

Build a 13-week cash flow forecast: table structure, formulas, scenario analysis, and AI prompts for automation. Free template included.

A step-by-step process for extracting Jobs-to-be-Done from user reviews using an LLM. Analysis prompts, clustering, output examples, and a ready-to-use workflow.

Model a cap table with AI: dilution formulas, Pre-Seed to Series A calculations, prompts, and founder dilution analysis across 3 rounds.

Design an event taxonomy from business questions to tested payloads: naming, properties, identity, privacy, versioning, QA, and evidence-bound AI prompts.

Battle-tested checklist for AI code review: Correctness → Security → Performance → Readability. Prompts per stage, real findings, CI/CD template.

Pitch deck narrative framework: AI prompts for every slide, transition logic, and sample copy. Build a presentation that survives 60 seconds.

Generate high-converting ad copy with AI using PAS, AIDA, BAB, FAB, and 4U frameworks. Ready-made prompts and before/after examples included.

How to define your Ideal Customer Profile using AI: analysis prompts, ICP card templates, scoring models, and a step-by-step pipeline from data to segments.

How to build a structured interview kit with AI: competency-based questions, candidate scorecards, and debrief agendas. Prompts, templates, and a step-by-step process.

Use AI to turn verified hiring-manager input into a clear job advert without invented requirements, biased proxies, fake metrics, or hidden working conditions.

A production prompt-management workflow for versioning, dataset evals, canary rollouts, trace-linked metrics, caching, and safe rollback with Langfuse.

Multi-agent system architecture patterns for production: Sequential Pipeline, Parallel Fan-Out, Classifier+Router orchestration, task routing, agent specialization with code examples.

Step-by-step SaaS unit economics calculation using Claude: LTV, CAC, Payback Period formulas, benchmarks, sensitivity analysis, and ready-to-use prompts.

How to turn one article into 5 platform-adapted formats in 40 minutes using LLMs. Prompts, n8n + Claude API automation pipeline, and repurposing economics.

Design an LLM judge with narrow rubrics, human calibration, deterministic checks, bias tests, CI gates, production sampling, and safe handling of untrusted outputs.

Use AI to draft SOPs without inventing commands, contacts, or recovery steps. Includes an evidence pack, prompt, review checklist, and validation workflow.

4 patterns of AI slop with real production examples, a 10-point checklist, and automation tools for reviewing AI-generated code.

Set up Maestro E2E tests for Flutter with stable Semantics selectors, reusable YAML flows, iOS workarounds, CI sharding, and production failure patterns.

Design reliable LLM routing across providers with task contracts, bounded retries, circuit breakers, validation, and safe rollout controls.

Build reliable context for LLM agents: select evidence, separate instructions from data, manage tools and memory, compact safely, and test what the model receives.

A production-focused guide to LLM observability: trace model calls, track cost and latency, version prompts, score output quality, and alert on regressions.

Build your own MCP server from scratch and run it in production. Error handling, fallback chains, circuit breakers, monitoring, and security patterns from real-world experience.