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Thoughts on AI context, token optimization, and building tools for developers.
Code Analysis MCP: The Infrastructure Layer AI Coding Needs
The Model Context Protocol started as Anthropic's open standard in November 2024. Eighteen months later, it has become the plumbing that connects AI…
Introducing Pyckle Signals: Tell Us What to Find
Most market research tools have the same problem. They hand you volume. Thousands of mentions, posts, and listings, and then they leave the reading to…
Your Reading Library Just Got a Knowledge Graph
Most read-later apps give you a list. A knowledge graph makes the invisible connections between your saved articles visible — clusters, threads, relationships the list never showed you.
We Just Launched a Live Embeddings API. Here's the Whole Story.
The origin story, what makes PyckLM different, pricing, billing architecture, and what's next.
Why Generic Embedding Models Fail at Code Search (And What to Do About It)
The vocabulary gap, L0/L1/L2 query framework, why fine-tuning alone doesn't fix it, and the compounding advantage of owning your retrieval model.
Your Codebase Has Its Own Language—And Your AI Doesn't Speak It
Every development team invents a dialect. Your AI needs to understand it.
Configuration Should Travel With You
You spend time configuring your AI tools on one machine. Then you switch to another and start over. That gap is the problem.
We Trained Our Own Code Embedding Model From Scratch. Here's What Happened.
Code search has a specific problem that generic embedding models aren't built to solve. So we built one that is.
Your Team's Knowledge Lives in Multiple Places, and Your AI Only Sees One.
You're debugging a production issue. The AI helps you trace it to a module. You ask why it was built that way. It doesn't know.
Search Is Commoditized. Memory Is the Moat.
Semantic search over a codebase felt like a differentiator 18 months ago. Today it's table stakes. Memory is what comes next.
Why Some Tools Age and Others Compound
Day one with a new AI assistant is rough. You spend more time explaining context than getting help. Some tools fix this. Most don't.
The Agent Loop: How Router Searches Until It Finds
Most AI tools make one retrieval attempt per question. Router loops — searching, reasoning, searching again.
Automated PR Reviews That Actually Know Your Codebase
Generic AI PR review tools flag style issues. Pyckle's diff review uses your actual codebase context.
Session Memory: Why Your AI Should Remember What You Worked On
Every new Claude session starts with amnesia. Session memory fixes that.
Notion Meets Code: Indexing Your Wiki Alongside Your Codebase
Your architecture decisions live in Notion. Your code lives in Git. Pyckle bridges the gap.
Why Your AI Can't Parse Your Mobile Code (And How Tree-sitter Fixes It)
AI coding tools excel at Python and TypeScript but fail on Java, Kotlin, and Swift. Tree-sitter AST parsing delivers accurate code search for mobile developers.
Your Codebase Has Its Own Language
Embedding models are trained on massive datasets — books, articles, documentation. Your codebase speaks a different dialect entirely.
One Index, Every Editor
Developer teams use multiple editors and AI tools. Each tool builds its own context from scratch. MCP changes this by separating the context layer from the editor.
The Flat Index Problem Nobody Talks About
Flat codebase indexing treats auth/login.py and tests/test_auth.py as equal neighbors. Code has structural relationships that embeddings cannot capture.
The Developer Reading List Problem
Developers accumulate technical articles, tutorials, GitHub issues, and documentation while solving problems. Finding what you saved requires remembering why you saved it.
AI Code Context Without the Lock-In
Don't leave your IDE. Just make it smarter. Pyckle gives any AI editor deep codebase context via MCP — model-agnostic, local-first. No vendor lock-in.
Browse all blog posts
- 1 Million Tokens on a Budget GPU Changes Nothing. And Everything.
- 190x Fewer Tokens: The Math Behind Pyckle's Context Compression
- Apple Brings Agentic Coding to Xcode. The Real Question Is What Happens Next.
- Automated PR Reviews That Actually Know Your Codebase
- Chunk Overlap Is Quietly Wrecking Your RAG Results
- Chunking Is Where RAG Goes Wrong
- Claude Opus 4.6: Same Token Price, Five Times the Cost
- Code Analysis MCP: The Infrastructure Layer AI Coding Needs
- Configuration Should Travel With You
- Context Compression: Sending Less, Getting More
- Context Windows Are Not Context Engineering
- Educational Deep Dive - 2026-06-01
- Embeddings as a Service: What You're Actually Buying
- Fine-Tuned vs. General: The Embedding Model Decision You're Getting Wrong
- Five Commercial Models, Zero Recall Cliffs
- Introducing Pyckle Signals: Tell Us What to Find
- Long Context Windows Don't Replace Retrieval. They Replace Excuses.
- Memory vs Retrieval: Your AI Is Only as Good as What You Save
- More Context Is Not Better Context
- Notion Meets Code: Indexing Your Wiki Alongside Your Codebase
- One Index, Every Editor
- Precision vs. Recall — The Last Mile of Code Search
- Prompt Compression Is Going to Production. The Benchmarks Still Aren't Ready.
- Pyckle: Persistent Memory for Your AI Coding Tools
- RAG for Code Is Not RAG for Documents
- RAG for Code: Chunking, Reranking, and Graph Context
- RAG Ops Is the Hard Part Nobody Warned You About
- Search Is Commoditized. Memory Is the Moat.
- Semantic Chunking Will Not Save Your RAG System
- Semantic Code Search: Why Intent Finds What Keywords Miss
- Semantic Routing: The Decision Layer AI Coding Tools Actually Need
- Session Memory: Why Your AI Should Remember What You Worked On
- Sixty-Eight Percent of That LLM Bill Was Optional
- Switching Embedding Providers: Threshold Recalibration Guide
- The 1M Context Window Trap
- The Agent Loop: How Router Searches Until It Finds
- The AI Memory Problem in Dev Tools
- The Architecture Determines the Cliff: A 7-Model NIAH Study
- The Budget Line Nobody Budgets For
- The Context Cliff: What Happens When Your AI Runs Out of Memory
- The Developer Reading List Problem
- The Embedding Dimension Tradeoff Nobody Talks About
- The Flat Index Problem Nobody Talks About
- The Hidden Tax on Every Inference: Why KV Cache Compression Is Having a Moment
- The Quiet Cost That's Reshaping How Teams Build with LLMs
- The Search Result Isn't the Answer
- The Token Tax Is Real—And Developers Are Finally Doing Something About It
- The Vector Database Decision Nobody Actually Makes
- The Vocabulary Problem Generic Code Embeddings Can't Solve
- Token Waste Is a Solvable Problem
- Vector Database Selection: Why the Choice Matters Less Than You Think (And More Than Vendors Admit)
- Vector Databases Are Not the Problem You Think They Are
- Vector Databases Are Not Your RAG Bottleneck
- We Just Launched a Live Embeddings API. Here's the Whole Story.
- We Trained Our Own Code Embedding Model From Scratch. Here's What Happened.
- What Cosine Similarity Actually Tells You (And What It Doesn't)
- When AI Writes Itself - What 100 Percent AI-Generated Code Actually Means
- When Everything Is Flat, Everything Gets Lost
- Which Vector Database You Pick Isn't the Real Question
- Why Chunking Breaks Your AI's Retrieval
- Why Chunking Your Code Breaks Your AI
- Why Code Search Is Harder Than It Looks
- Why General-Purpose Embedding Models Fail on Code
- Why Generic Embeddings Fail at Code Search
- Why Naive Retrieval Breaks at Scale (And What We Built Instead)
- Why Some Tools Age and Others Compound
- Why Your AI Can't Parse Your Mobile Code (And How Tree-sitter Fixes It)
- Why Your Bookmark Tool is Creating More Friction Than It Saves: How to Switch to a Frictionless, Clutter-Free Reading Experience in 2026
- Why Your Browser Bookmarks Are a Graveyard
- Your AI Coding Tool Doesn't Know Your Architecture
- Your Codebase Has Its Own Language—And Your AI Doesn't Speak It
- Your Embeddings Are a Snapshot of Code That No Longer Exists
- Your Reading Library Just Got a Knowledge Graph
- Your Reading List Isn't a Library. It's a Landfill.
- Your Team's Knowledge Lives in Multiple Places, and Your AI Only Sees One.