Tutorial

A hands-on TypeScript tutorial that builds a tolerant partial-JSON parser so an LLM's streamed structured output renders field by field instead of stalling on the closing brace. Tokenizer, auto-closing parser, and 49 offline tests.

A hands-on TypeScript tutorial on Anthropic prompt caching. Build a cache-aware request planner, audit for silent invalidators, and measure real savings from the usage fields, offline and fully tested.

A hands-on TypeScript tutorial: validate LLM tool-call arguments with Zod, auto-repair malformed calls in a bounded loop, and dispatch fully-typed args to your handlers — provider-agnostic, tested, and offline.

A hands-on TypeScript tutorial for making an autonomous, tool-using AI agent observable. You build a small, dependency-light agent loop and wrap it in OpenTelemetry traces — a root span per invocation, child spans for every model call and every MCP tool call, using the gen_ai.* and MCP semantic conventions — then prove the span tree with deterministic, in-memory tests. Runs offline with zero API keys.

A hands-on TypeScript tutorial for proving which agent, model, prompt, and supervisor produced a code changeset — and detecting any later tampering. You build canonical changeset hashing, ed25519-signed attestations, and an append-only chained ledger you can verify offline, with zero runtime dependencies and zero API keys.

A hands-on TypeScript tutorial for building a production LLM client that survives slow upstreams, rate limits, transient 5xx errors, and whole-provider outages — a per-attempt timeout, exponential backoff with jitter, a per-provider circuit breaker, and automatic failover, in about 250 lines of typed, fully tested code.

A hands-on TypeScript tutorial for building an LLM evaluation harness: deterministic scorers, an LLM-as-judge rubric scorer, a concurrent runner with retries, and a CI regression gate — provider-agnostic, fully tested.

Build production Retrieval-Augmented Generation for React Native with one engine that runs both on-device and behind a Claude Opus 4.8 cloud service.

A hands-on TypeScript tutorial for building a parallel subagent orchestrator: bounded-concurrency fan-out, automatic retries, schema-validated structured output, and barrier-free pipelines — provider-agnostic, with tests.

A hands-on TypeScript tutorial for building a durable agent memory layer: vector recall, hybrid recency-and-salience ranking, scoped retrieval, rolling summarization, and eviction policies — with tests.

A hands-on TypeScript tutorial for a CI-integrated eval harness that gates LLM releases on capability, safety, and regression checks — the discipline CAISI now requires from labs.

A complete hands-on tutorial for routing prompts to the cheapest capable LLM in TypeScript. Build a classifier, a model registry, a fallback ladder, and per-request cost telemetry that survives the May 2026 price war.

An end-to-end tutorial for engineers shipping long-running coding agents in production. We build a TypeScript queue on Bun that submits agent jobs to Mistral Le Chat Work mode and OpenAI background mode, polls or receives webhooks for completion, enforces per-job cost ceilings, and exposes a small status dashboard — the operating model behind the new async-coding paradigm.

A practical, end-to-end migration guide for engineers running production coding agents on GPT-5 who want to evaluate or move to DeepSeek V4 — the open-source frontier model that landed Friday claiming the strongest agentic coding scores in the open ecosystem. Covers API differences, tool-calling adaptation, streaming, the agent loop, evaluation, and the real cost math.

A complete hands-on tutorial for building a production-viable AI evaluation harness in TypeScript, covering endpoint abstraction, ground-truth management, scoring pipeline design, variance analysis, and the reporting layer. Includes working code and a GitHub companion repository.

Learn how to call foundation models like Claude, Llama, and Nova through AWS Bedrock using Python and boto3. Beginner-friendly tutorial covering single prompts, multi-turn conversations, and streaming responses.

Build a production-ready tech layoff tracking dashboard using Next.js 15, server components, and public WARN Act data. Aggregate layoff notices, visualize trends by company and sector, and deploy to Vercel — a timely project as Oracle cuts 30,000 jobs while spending $50 billion on AI.

A complete hands-on tutorial for building and deploying a remote Model Context Protocol server using the new Streamable HTTP transport on Cloudflare Workers. Go beyond local stdio servers and make your AI tools accessible from anywhere.

Step-by-step tutorial on building, securing, and deploying autonomous AI agents using NVIDIA NemoClaw and the NeMo Agent Toolkit. Covers installation, agent workflows, sandbox policies, inference profiles, multi-agent orchestration, and production deployment on the NVIDIA AI stack.

Step-by-step tutorial for building an AI-powered code review agent using the Claude Agent SDK in Python. From basic diff analysis to custom MCP tools, severity classification, and GitHub integration. Includes a working project inspired by CodeSentri.

Learn how to deploy your AI PR reviewer as an automated CI/CD pipeline using GitHub Actions and GitLab CI. Part 3 covers platform abstraction with interfaces, the Octokit SDK for posting inline review comments, token chunking for large diffs, rate limiting, unit testing with xUnit, and complete YAML pipeline configurations.

Learn how to build a C# console application that captures and parses git diffs using pure .NET 8. Part 1 of a 3-part YouTube tutorial series covering Process execution, unified diff parsing, and CLI tool development with zero external dependencies.

A complete hands-on tutorial for building a production-ready Model Context Protocol server in TypeScript. Learn to create tools, resources, and prompts, then connect your server to Claude Desktop, VS Code, and Cursor.

A complete beginner-friendly tutorial for Django 6.0. Learn to build your first Python web application with the framework that powers Instagram, Pinterest, and thousands of production apps.

Build a production-ready Chrome extension that tracks AI API costs in real-time across OpenAI, Anthropic, and Google AI. Complete tutorial with working code, network interception, token counting algorithms, and cost visualization dashboards.

A comprehensive deep dive into Django's async capabilities — from async views and ORM queries to ASGI deployment, real-world benchmarks, and production patterns used at companies like Kraken and Instagram.

Complete hands-on tutorial for Google Antigravity, the revolutionary free AI IDE that combines multi-agent orchestration, integrated browser automation, and support for multiple AI models. Learn to build production applications using the tool that dethroned Windsurf in December 2025.

Build production-ready NFT smart contracts from scratch with Solidity, OpenZeppelin, and Web3.js. Complete tutorial covering ERC-721 implementation, IPFS metadata storage, minting functionality, and full-stack integration patterns.

Step-by-step guide to building a production-ready agentic AI system with tool calling, error handling, rate limiting, and observability. Includes complete working code and GitHub repository with TypeScript implementation using Claude API.

Learn exactly how Applicant Tracking Systems work, why 75% of resumes never reach human eyes, and how to optimize your resume with real examples showing what works and what fails.

Learn how to measure enterprise AI productivity with quantifiable metrics, calculate true ROI beyond simple time savings, and build automated tracking systems that prove business value to skeptical stakeholders.

Learn Playwright browser automation from fundamentals through production deployment. Covers installation, test writing, fixtures, parallel execution, visual testing, and enterprise CI/CD integration with practical examples.

Master GitHub Actions from scratch with hands-on examples. Build complete CI/CD pipelines with testing, linting, deployment, and advanced workflow patterns for modern development teams.

Build production-ready voice-first ambient AI interfaces that eliminate traditional screens. This comprehensive tutorial covers OpenAI Whisper integration, GPT-4 natural language understanding, context management, spatial audio, and privacy-preserving deployment patterns. Includes complete source code, architecture patterns, and real-world implementation strategies for the post-screen computing era.

Comprehensive hands-on tutorial for building autonomous AI agents that integrate multiple tools, APIs, and data sources. Learn enterprise-grade architecture, error handling, monitoring, and deployment strategies with complete GitHub repository and production deployment guide.

Build a production-ready agentic meeting transcription system with real-time speech-to-text, speaker diarization, RAG-powered context retrieval, and multi-agent analysis. Complete code, architecture patterns, and Kubernetes deployment included.

Build enterprise-grade vector databases for RAG applications with hybrid search, metadata filtering, and production deployment. Complete implementation with Pinecone, Weaviate, embedding strategies, and Kubernetes scaling patterns.

Build your own AI assistant from scratch using modern LLM APIs, RAG pipelines, and agent frameworks. Complete implementation guide with working code, architecture patterns, and deployment strategies for production-ready personal AI systems.

Learn to build production-grade AI model monitoring with drift detection, performance tracking, and automated alerting. Complete implementation with Prometheus, Grafana, and Kubernetes deployment patterns.

Master app ownership transfers with step-by-step guides for Apple App Store and Google Play Store including legal, technical, and compliance requirements

Learn how to transform your terminal into a powerful development environment with production-ready Zsh configuration, essential plugins, and the CrashBytes zsh-setup automation toolkit.

Learn to build production-scale distributed training systems with Horovod and PyTorch. Complete implementation with gradient aggregation, fault tolerance, and efficient data loading for enterprise ML.

Learn to build enterprise-grade LLM guardrails with content filtering, PII detection, toxicity scoring, and rate limiting. Complete implementation with monitoring, testing, and deployment strategies.

Build enterprise-grade MLOps pipelines on Kubernetes with automated training, validation, deployment, and monitoring. Complete implementation with Kubeflow, model registry, and production patterns.

Master zero-downtime database migrations for mission-critical systems. Complete implementation with blue-green deployments, data synchronization, rollback strategies, and testing patterns for enterprise databases.