Claude Code Conversations with Claudine
Giving Claude Code a voice, so we can discuss best practices, risks, assumptions, etc,
Episodes
160 episodes
Why Does AI Code Feel Fast But Ship Slow?
AI coding tools compress the time from intent to working code, and that compression feels like speed. But the work that determines shipping time, review, integration, debugging unfamiliar code, and rebuilding the mental model you skipped, expands ...
Why Most AI Architecture Prompts Fail: Finding the Goldilocks Zone
Builders working with Claude Code tend to fail in one of two opposite directions: they write specs so loose the model invents architecture nobody asked for, or so tight the model becomes a transcription service and the architecture stays exactly a...
How Does AI Code Drift From Your Requirements? | Specification Misalignment
Every builder has had the experience of asking for a change, getting working code back, and slowly realizing months later that the system no longer matches what they thought they built. The gap did not open in one bad prompt, it opened across fift...
Why Do AI Builders Ship Code That Looks Correct But Solves Wrong Problems?
AI coding tools are extremely good at producing the most obvious implementation of whatever you described, and the most obvious implementation is usually the answer to a slightly different problem than the one you actually have. The code compiles,...
Why Does AI Code Break When Dependencies Update?
AI coding tools write against a snapshot of the world, the library versions and API shapes that dominated their training data, not the versions in your lockfile. The code compiles, the tests pass, and then a minor version bump six weeks later brea...
Why Your First Instinct About Claude Prompts Is Usually Wrong
Most builders assume prompt quality scales with prompt length, so they either dump every constraint they can think of or fire off a one-liner and hope the model figures it out. Both instincts fail for the same reason, which is that they confuse vo...
Why Is AI Code Harder to Explain Than to Write?
AI has collapsed the cost of producing code but left the cost of transferring understanding almost untouched. The result is a widening asymmetry: a builder can ship a working subsystem in an afternoon and then spend three days explaining it in rev...
How Do You Reverse-Engineer Your AI Prompts When You Can't Remember What You Asked?
Every AI-assisted codebase accumulates a layer of decisions nobody can explain, because the reasoning lived in a chat window that scrolled away. Six weeks later you are staring at a retry loop with a hardcoded backoff and you cannot tell whether i...
Why Does AI Code Work When It Doesn't Understand Why?
AI coding tools produce code that works, and builders assume working code implies understood code. But a large amount of AI output is pattern replication, structurally correct imitation of code that worked somewhere else, carried over without the ...
How Bad Variable Names Spread Through AI Codebases
When a human writes a sloppy variable name, it stays local and dies at the next refactor. When an AI writes one, it becomes the seed vocabulary for every function, test, comment, and downstream file the model touches afterward, because the model r...
Why Does AI Code Pass Tests But Fail Under Real Load?
AI generated code is optimized to satisfy the test, not to survive production. It writes the correct answer with the wrong complexity, opens a database connection inside a loop, holds the whole result set in memory, and every one of those choices ...
Why Do AI-Generated Codebases Lose Coherence Over Time?
Every individual thing the AI writes can be correct and the codebase can still rot. Because the model has no memory of the decisions it made three sessions ago, each new session re-derives conventions from whatever fragment of the code it happens ...
Why Does AI Code Break Your System's Hidden Assumptions?
Every codebase runs on assumptions that were never written down: this function is always called inside a transaction, this ID is opaque and never parsed, this list is small enough to load into memory, this handler must stay idempotent because the ...
Why Do Detailed AI Prompts Make Code Worse?
Every builder learns early that vague prompts produce bad code, so the natural correction is to add more detail. But there is a ceiling: past a certain point, each additional constraint you write into a prompt makes the output worse, not better, b...
Why Can't AI Code and Legacy Systems Work Together?
AI coding tools are excellent at writing code that would work in a clean, modern codebase, and legacy systems are almost never that. The failure is not syntax or capability, it is that the model writes against the idealized version of your stack w...
What Do Claude Code's Sandbox Escape Tests Reveal About AI Safety?
Most builders think about agent security as a permissions problem: approve the right tools, deny the dangerous ones, and you are safe. But the sandbox escape testing that goes into a coding agent reveals a different picture, the real attack surfac...
Why Do AI Systems Miss Concurrency Problems Under Load?
AI coding tools write async code that passes review, passes tests, and works perfectly on one request at a time. The failures show up at request forty, when two coroutines hit the same cache entry, or a connection pool starves, or an await sits in...
How Do AI Builders Get Trapped in Dependency Gravity Wells?
AI coding tools are excellent at adding code and terrible at telling you what that code will cost you later. Every generated helper, wrapper, and convenience layer quietly increases the number of things that must be true for your system to work, a...
Why Does AI-Generated Code Pull in Unknown Dependencies?
AI coding tools solve problems the way the training data solved them, which usually means reaching for a library instead of writing twenty lines. Ask for a retry wrapper and you get tenacity, ask for date math and you get arrow, ask for a config l...
Why Do AI Models Struggle With Stateful Code?
AI coding tools are excellent at stateless code, a function that takes inputs and returns outputs with no memory of what came before. They are noticeably worse at code that has to remember things: retry counters, partial completions, queue positio...
Why Do Experienced Engineers Distrust AI Code That Works?
Experienced engineers have a well-tuned instinct for where bugs hide in human-written code, and that instinct misfires badly on AI output. They line-audit the boring mechanical code the model almost never gets wrong, then wave through the plausibl...
Why Do Bad Variable Names Cost Developers So Much?
AI coding tools produce code that works and reads fine in isolation, but the names they choose are locally plausible and globally inconsistent. The same concept ends up as userData, payload, record, and item across four files, and nobody notices b...
Is 80% of Code Written by AI Now? What It Means for Engineers
When a company says most of its production code is model written, the number sounds like a verdict on engineers. It is not. The 80% figure measures typing, not deciding, and the interesting question is what the remaining 20% actually consists of, ...
Why Do AI Builders Need Domain Expertise to Trust Their Models?
The promise of AI-assisted building is that you no longer need to know the domain, the model knows it for you. In practice the opposite is true: the less you know about a domain, the more confidently wrong AI output you will ship, because you c...
What Is Specification Debt and Why Does AI Code Miss What You Actually Need?
Technical debt used to come from shortcuts in implementation. With AI, the debt moves upstream: the code is clean, tested, and idiomatic, but it solves a slightly wrong problem because the specification in your prompt was incomplete. This episode ...