/ topics / tutorial
tutorial
18 stories we've shipped under this tag. Each is the actual link from the original issue — no rehashing.
AI Engineer Notebooks – free, framework-free RAG/agents/evals on Colab
Useful as a low-friction reference for testing RAG and agent patterns without committing to a framework or local setup.
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How to Build a Simple AI Web Scraper with Python
Useful for prototyping small web-QA systems, but production scrapers still need crawling controls, extraction tests, caching, and prompt-injection defenses.
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AI At Home Part 1: A Box Of Scraps
Useful for builders evaluating the practical trade-offs of running AI workloads at home rather than relying entirely on cloud APIs.
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Process a Million Documents Overnight: Batch Inference End-to-End
Useful for teams designing large offline workloads; covers the operational path from data preparation to monitoring rather than just model calls.
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How to Run Claude Code Agents for 24+ Hours
As context windows expand, managing state for multi-hour agentic loops is a practical hurdle; this addresses the reliability gap in current LLM-based IDE tools.
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The Eval Flywheel: Turning Every Production AI Failure Into a Regression Test
Moving beyond vibes-based development requires a systematic pipeline where edge cases found in production logs are automatically integrated into CI/CD eval suites.
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Design Loops, Not Prompts
Relevant for engineers building production AI: self-checking loops often fail due to model hallucination and circular reasoning. Structured feedback loops with external validation are more reliable.
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Have your agent record video demos of its work with shot-scraper video
Useful for agents that need to demonstrate their work programmatically. Reduces manual demo creation overhead when building agent evaluation or showcase pipelines.
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My Homelab AI Dev Platform
Relevant for engineers optimizing inference costs and latency; shows practical setup for running open models locally vs. API calls, but limited to single-machine constraints.
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Claude Code Guide 2026: 25 Features with Examples + Demo
Useful reference for engineers integrating Claude into agentic workflows; covers practical patterns (subagents, MCP) that affect architecture decisions.
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4 Lines You Should Include in Your Claude Skill
Practical guidance for engineers integrating Claude into production systems; addresses hallucination mitigation which directly impacts reliability of shipped AI features.
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Slightly reducing the sloppiness of AI generated front end
Practical post-processing strategies for code-gen output matter if you're shipping AI-assisted dev tools; addresses real friction point between model output and production-ready code.
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How to Refactor Code with Claude Code
Relevant for engineers integrating Claude into development workflows, but lacks concrete metrics on refactoring quality or productivity gains vs. manual review.
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The Speed of Prototyping in the Age of AI
Relevant for engineers evaluating AI-assisted development workflows; practical perspective on productivity gains vs. hype claims, though lacks quantitative benchmarks.
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Going full AI engineer, not touching code anymore
Relevant for engineers evaluating AI-assisted development trade-offs: productivity gains vs. code quality, debugging overhead, and long-term maintainability when outsourcing all implementation.
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How I Continually Improve My Claude Code
Practical patterns for engineers shipping with Claude; covers feedback loops and prompt refinement but lacks benchmarking rigor or comparison to other code-gen models.
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From Vibe Coding to Spec-Driven Development
Practical case study on agent-based development patterns; relevant for engineers evaluating LLM-assisted workflows and spec-to-code tooling maturity.
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How to Build a Claude Code-Powered Knowledge Base
Relevant for engineers integrating Claude into RAG pipelines; demonstrates practical use of Claude's code execution for structured knowledge queries vs. traditional vector search.
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