Library
Books & Guides
Free resources on code intelligence, semantic search, and AI-assisted development. No sign-up required.
The Developer's Guide to Semantic Code Search
Embeddings, Chunking, Vector DBs, Hybrid Search, and Production Indexing - End to End
A 7-part comprehensive guide covering the full semantic code search stack: how embeddings work, chunking strategy, vector DB selection, hybrid search (BM25+semantic, RRF), indexing pipeline, query pipeline, and production considerations.
Building RAG Systems for Codebases
Architecture, Tradeoffs, and Implementation - Why Standard RAG Fails on Code and What to Do Instead
Explains why standard RAG fails on code (vocabulary, granularity, context completeness), then walks through the full production architecture: query processing, semantic + keyword + graph traversal retrieval, context assembly, and generation with token budgeting.
The Complete Guide to AI-Assisted Development with Pyckle
Solving the Context Problem — Semantic Search, Intelligent Routing, and Persistent Memory for AI Coding Tools
For developers who have used AI coding tools and hit the context wall — Copilot suggesting functions that don't exist, Claude hallucinating APIs, ChatGPT losing track of decisions made three messages ago.
Agentic Coding: AI Agents for Software Development
Building, Evaluating, and Running AI Agents That Write, Review, and Modify Code
Senior engineers and tech leads evaluating or building AI coding agents — familiar with LLMs and tool use, moving beyond copilot-style suggestions
AI-Assisted Code Review
Using Semantic Search and AI to Find What Human Reviewers Miss
Senior engineers and tech leads who run or participate in code review — looking to make review faster, more consistent, and higher signal
AI-Generated Code: Quality, Risk, and Review
What Changes When AI Writes the Code and Humans Review It
Engineering managers, senior engineers, and tech leads managing AI-assisted development workflows — responsible for code quality and review processes
API Design Consistency at Scale
Using AI and Semantic Search to Enforce Conventions Across Hundreds of Services
Architects and senior engineers responsible for API design standards across large engineering organizations — managing consistency without blocking teams
Building a Semantic Search Pipeline
From Raw Code to Intelligent Retrieval — Architecture, Embeddings, and Search at Scale
Senior developers, ML engineers, and architects who want to understand the internals of semantic code search — embeddings, chunking strategies, indexing, retrieval, and ranking
The Code Intelligence Buyer's Guide
How to Evaluate AI Code Search for Your Team
Engineering managers, team leads, and VPs of Engineering evaluating whether to adopt AI-powered code search and context routing tools for teams of 10-100 developers
Code Migration Playbook
Using AI to Plan, Execute, and Verify Large-Scale Framework and Language Migrations
Senior engineers and tech leads leading major migrations — familiar with large codebases and the operational complexity of rewriting or porting significant systems
Code Retrieval from Scratch
Chunking, Embeddings, and Hybrid Search for Code
Senior/staff engineers who want to understand or build code retrieval systems — comfortable with Python, familiar with basic ML concepts, interested in information retrieval
Code Search Patterns
50 Query Recipes for Debugging, Reviews, Onboarding, and Architecture
All Pyckle users — developers who want a ready-made library of semantic search queries for everyday tasks across debugging, code review, onboarding, refactoring, architecture analysis, performance, and security
Context Engineering for Developers
How to Give AI the Right Information at the Right Time
Software developers using AI coding assistants daily — comfortable with LLMs, frustrated with context failures, ready to be intentional about what goes in the window
The CTO's Guide to AI Developer Tooling
Portfolio Decisions, Vendor Risk, and the Build-vs-Buy Question for AI-Assisted Development
CTOs, VPEs, and senior engineering leaders making tooling decisions for organizations of 50+ engineers — past the experimentation phase, making bets that affect the whole org
Custom Embedding Models: Fine-Tuning, Evaluation, and Deployment
When Off-the-Shelf Isn't Enough and What to Do About It
This book started as a collection of notes from a production failure.
The Developer's Complete Guide to Code Embeddings
From Theory to Production: How to Build Search Systems That Actually Understand Your Code
This book exists because most of the writing on embeddings is either too abstract to act on or too narrow to generalize from. You get either the gradient-descent-from-scratch lecture or a vendor tutorial that assumes you...
Engineering Knowledge Management
Capturing Decisions, Runbooks, and Architectural Intent in a Searchable System
Engineering managers, staff engineers, and architects in growing teams where knowledge silos, documentation gaps, and onboarding friction are real problems
The Engineering Manager's Guide to AI Code Search
Measuring ROI, Driving Adoption, and Scaling Code Intelligence Across Your Team
Engineering managers, directors of engineering, and VP Engineering evaluating AI code search tools for team productivity, onboarding, and code quality
Evaluating LLMs for Code Tasks
Benchmarking Models on Real Workloads, Avoiding Benchmark Gaming, and Making Cost-Quality Decisions
Font pairing : IBM Plex Mono (code) + Inter (body) + Sora (headings)
KV Cache and Inference Optimization
The Infrastructure Layer That Determines Your Real LLM Costs
Platform engineers and ML engineers running inference infrastructure — deploying or evaluating self-hosted LLMs and inference APIs, responsible for latency and cost
Local-First AI
Code Intelligence Without the Cloud Dependency
CTOs, security leads, and architects in regulated industries (healthcare, defense, finance, government) evaluating AI code tools under compliance constraints
Memory Systems for AI Developer Tools
Persistent Context, Knowledge Graphs, and Long-Term Recall for Coding Assistants
Architects and senior engineers building or evaluating AI developer tools — interested in how tools can accumulate and reuse knowledge across sessions
Monorepo Navigation with AI
Search, Ownership, and Discovery in Codebases Too Large to Read
Senior engineers, platform engineers, and architects working in monorepos with 100K+ line codebases, multiple services, or complex dependency graphs
Onboarding Engineers with AI
Cutting Ramp Time, Transferring Knowledge, and Building Codebase Fluency Faster
Engineering managers, senior engineers who mentor, and platform engineers responsible for onboarding — teams where ramp time and knowledge transfer are measurable pain points
Open Source Contribution at Scale with AI
Navigating Unfamiliar Codebases, Finding Entry Points, and Contributing Without Getting Lost
Developers who want to contribute to large open source projects, engineers auditing open source dependencies, and security researchers navigating public codebases
Polyglot and Multi-Language Codebases with AI
Search, Retrieval, and Understanding When Your Stack Speaks Four Languages
Senior engineers and architects working in large multi-language codebases — dealing with Python, TypeScript, Go, Java, SQL, and infrastructure-as-code side by side
Production Code Search
Reranking, Scaling, and Evaluating Code Retrieval Systems
Senior/staff engineers who have built (or understand) basic code retrieval and need to production-harden it — comfortable with Python, familiar with hybrid retrieval fundamentals (embeddings, BM25, RRF)
Prompt Compression in Production
Reducing Token Count Without Losing What the Model Needs
Engineers running LLM systems at scale where context size directly affects latency and cost — familiar with prompt engineering and looking to optimize
RAG for Code: A Complete Guide
Retrieval-Augmented Generation for Software Repositories
A practitioner's guide to building, evaluating, and operating RAG systems over code — from chunking strategy to production deployment
From RAG to Agents: Code-Aware Agentic Pipelines
How Retrieval Feeds Agentic Loops and Why the Combination Changes What's Possible
Architects and senior ML engineers building AI systems that go beyond single-turn generation — designing pipelines where retrieval, planning, and action are interleaved
Refactoring at Scale with AI
Finding, Planning, and Executing Large-Scale Code Changes Safely
Senior engineers and architects leading refactors or migrations in large codebases — dealing with thousands of files, cross-team coordination, and the risk of breaking things
Rolling Out AI Code Search
Adoption, Measurement, and the 90-Day Plan
Engineering managers and team leads who have decided to adopt AI-powered code search and need the implementation playbook — adoption framework, measurement strategy, and operational setup
Security Auditing Your Codebase with AI
Pattern-Based Vulnerability Discovery, Risk Mapping, and Remediation Planning
Security engineers, senior engineers with security responsibility, and engineering managers evaluating security posture — not pen testers, but developers who own the code
Semantic Routing: Design and Implementation
Directing Queries to the Right Handler Based on Meaning, Not Keywords
Senior engineers building multi-tool or multi-model AI systems — familiar with embeddings and classification, building beyond simple single-path pipelines
Testing Strategy with AI Code Search
Finding What's Untested, Understanding What Breaks, and Building Coverage That Lasts
Senior engineers and QA engineers responsible for test coverage and quality — using or evaluating AI tools to improve testing outcomes
Token Economics
Cutting Your LLM Bill Without Cutting Quality
Engineering managers, senior engineers, and platform engineers responsible for LLM-powered systems and their costs — making decisions about inference spend
Vector Database Selection and Architecture
A Practical Guide to Choosing, Configuring, and Scaling Vector Storage
Senior engineers and architects evaluating or building vector storage for production search systems — familiar with databases, evaluating options beyond the hype
The Vibe Coder's Survival Guide
How to Ship, Debug, and Grow When AI Writes Your Code
Junior developers (0-3 years experience), AI-native coders who learned with Copilot, Cursor, or Claude — capable but early in their journey
Vibe Coding, Real Debugging
A Developer's Guide to Debugging What AI Built
Developers who use AI coding tools but struggle when things break — they can vibe-code features but vibe-debugging feels impossible