Tutorials

How prompt caching works on Claude, OpenAI, Gemini and DeepSeek: price multipliers, TTL, a break-even formula, OpenRouter measurements and why cache misses happen.

Set up LiteLLM Proxy: config.yaml, virtual keys with budgets, Ollama-to-cloud fallback, Claude Code and OpenAI SDK clients, 2026 CVEs and hardening.

What llms.txt is, who actually reads it (Ahrefs data, Google's position), how to generate it and serve .md pages, and what it does and does not do for GEO.

How OpenRouter works: one API for 400+ models, the 5.5% credit fee, provider routing, fallbacks, ZDR, prompt caching, BYOK and billing traps.

What an LLM context window is, what fills it, window sizes for Claude, GPT-6, Gemini and DeepSeek, what happens on overflow, and why long context costs more.

When is a Claude Code subagent worth it? Startup cost and result size from the author's runs, what cuts startup context, model choice, and when subagents hurt.

LM Studio vs Ollama: licenses, MLX on Mac, OpenAI and Anthropic APIs, Claude Code and Codex, headless mode, an M4 Max benchmark, and which to pick.

What a token is in an LLM, how many tokens a word or page takes in English and Russian, how to count them before and after a call, and what they cost.

Working Claude Code hooks recipes: events and matchers, exit 2 vs JSON output, PreToolUse guards, Stop gates, notifications, and securing the hooks themselves.

How to run an LLM locally: size models by memory with the weights plus KV cache formula, compare Qwen, Gemma 4 and gpt-oss, licenses, M4 Max tok/s.

What an embedding is: a vector of meaning, cosine similarity, dimensions and Matryoshka. Python code with Ollama, a cross-lingual test and model picks for Nov 2026.

What AGENTS.md is, which coding agents read it, and how to pair it with CLAUDE.md: native reading, @import, symlinks, nested files, and size limits.

Install Ollama on macOS, Windows and Linux, pick a model that fits your memory, use the CLI, REST and OpenAI-compatible APIs, and build a Modelfile.

How to design the MCP server set for an AI agent: classes of capability, what tools really cost in context, security, and where the standard stands in 2026.

What CLAUDE.md is, where it lives, how it loads and what to put in it: hierarchy, @ imports, the 200-line guideline, .claude/rules, hooks and a template.

A tested Python pattern for Langfuse agent traces with explicit tool parentage, separate retries, child errors, redaction, and exporter isolation.

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 AI lead scoring that ranks B2B leads with evidence, holdout metrics, human review, and safe CRM writes - without treating scores as probabilities.

Prepare a buyer-specific product demo from verified discovery notes, rehearse the environment, handle failures, and record a concrete next step.

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.

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 and 3 agents 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 to Claude Code and Cursor, the top 20 servers, and writing your own in TypeScript or Python.

Claude API tutorial for the 5.5 models: Messages API, tool use, adaptive thinking, prompt caching, vision, streaming, batches, structured outputs. Python and TS.

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.

A complete guide to Supabase: architecture, auth, RLS, Edge Functions, pgvector, branching. Firebase comparison, pricing, and startup best practices 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.

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

AI keyword research pipeline: seed generation with Claude, clustering with prompts, validation in Ahrefs/SEMrush. Step by step from zero to a ready content plan.

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

B2B sales objection classification, LLM prompts for generating responses, and a ready-to-use playbook template with examples. A system for handling 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 calculation prompts, comparison tables, and a decision framework with 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 startup dashboard design: executive, product, and engineering. Concrete metrics, SQL queries, and prompts for 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 choosing between bootstrapping and fundraising: financial models for both paths, Claude prompts, comparison tables, 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.

15 common production code vulnerabilities with before/after examples and prompts for AI-assisted audits: SQL injection, SSRF, path traversal, JWT, race conditions.

Sensitivity analysis for SaaS startups with AI: formulas, modeling prompts, scenario tables, tornado diagrams. Churn vs CAC: which 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.

Magic number methodology: SQL cohort analysis, AI correlation of user actions with 30-day retention, and 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, good and bad Key Results. The 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.

Run prompt experiments on a fixed dataset first, then canary two labeled versions in production. Includes sticky assignment, guardrails, and tested code.

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.

Build a Langfuse prompt workflow in Python: get_prompt(), compile(), version labels, dataset evals, trace linking, canaries, and safe rollback.

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

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 multi-provider LLM routing with tested admission gates, task contracts, bounded retries, 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 each model call.

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

Build a custom MCP server from scratch and run it in production: error handling, fallback chains, circuit breakers, monitoring, and security patterns from practice.