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Tutorials & guides

Step-by-step playbooks for AI workflows · 20 articles

Hands-on walkthroughs, cheatsheets, prompt recipes and step-by-step playbooks for AI dev workflows.

Subtopics:Prompt recipesCheatsheetsRAGFine-tuning
Tutorials & guidesAug 30, 2026 2 min read

Build Custom Batched Ensemble Weather Forecasting with NVIDIA Earth2Studio

Learn how to install NVIDIA Earth2Studio in Google Colab while preserving your CUDA PyTorch environment, load the FCN prognostic model, and implement custom wind-power diagnostics. The guide walks through building a coordinate-aware Zarr data backend and executing batched ensemble forecasts.

Why it matters

You can now construct your own end-to-end meteorological pipelines and run spatial perturbation ensembles locally with production-grade tooling.

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Tutorials & guidesAug 29, 2026 2 min read

Using Gemini Notebook for Grounded Study Guides, Quizzes, and Note Synthesis

Google's rebranded Gemini Notebook (formerly NotebookLM) allows developers to ingest markdown notes, drive files, and links to generate grounded quizzes, flashcards, and briefing docs. By anchoring outputs strictly in uploaded sources, it eliminates hallucinations during research and learning.

Why it matters

You can import Obsidian markdown notes directly into Gemini Notebook to auto-generate active recall quizzes and structured briefs for fast technical upskilling.

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Tutorials & guidesAug 25, 2026 2 min read

JetBrains and UPenn Studies Identify Negative Expertise in AI-Assisted Coding

Empirical studies from JetBrains and UPenn reveal that unconstrained AI assistance causes novices to skip planning and score up to 17% worse. The most successful developers cultivate negative expertise—the ability to actively reject unhelpful AI suggestions.

Why it matters

You can improve code quality by practicing negative expertise: explicitly planning system architecture before prompting and filtering out low-quality AI suggestions.

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Tutorials & guidesAug 23, 2026 2 min read

Building 5-Microsecond JIT Compilers with AI and Copy-and-Patch Stencils

Implementing custom JIT compilers historically required complex assembly skills, forcing engines to rely on heavy frameworks like LLVM. By leveraging AI assistance and copy-and-patch assembly stencils, developers can build lightweight ARM64 JIT compilers that execute in 5 microseconds.

Why it matters

You can replace slow AST interpreters or heavy LLVM compilation pipelines in custom engines with sub-5-microsecond copy-and-patch JIT execution.

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Tutorials & guidesAug 16, 2026 2 min read

Structuring Agent Workflows with Deterministic Feedback and Test-Driven Development

AI coding agents excel at tool-calling and instruction following but lack genuine reasoning, necessitating strict human architecture and deterministic validation. Implementing red-green test-driven development prompts and automated natural language feedback loops significantly improves code maintainability.

Why it matters

Enforce test-driven prompts and deterministic validation tools to prevent agentic coding assistants from generating unmaintainable spaghetti code.

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Tutorials & guidesAug 16, 2026 2 min read

Structuring Software Seams and Deterministic Validation for Coding Agents

Effective AI coding workflows rely on clean architectural boundaries and deterministic validation loops rather than raw LLM reasoning. Combining red-green test-driven prompts with automated runtime feedback allows models to self-correct and produce maintainable code.

Why it matters

Enforce red-green test-driven development prompts and automated test feedback to prevent LLM hallucination cascades in agentic harnesses.

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