#context-window
45 episodes
#5482: When AI Edits Your Words: The Off Switch Problem
A tool that works reliably — and still gets switched off. What over-editing studies reveal about why AI rewrites more than you asked.
#5480: Writing a Personal Instruction for ChatGPT
Daniel's personal ChatGPT prompt gets a line-by-line critique — and the case for shorter, sharper custom instructions.
#5182: DeepSeek V4.1 Flash: 1M Context, 437x Smaller KV Cache
DeepSeek V4.1 Flash landed with a 1M-token window and a KV cache 437x smaller than V1. Here's what actually changed — and why the middle of your co...
#5056: How AI Writes a 30-Minute Podcast in One Pass
What does it take for a model to write a full episode without edits? The answer involves context windows, repetition, and a surprising architecture...
#4789: How LLMs Actually Know When to Stop
Three layers of stop signals, from EOS tokens to context limits — and why none of them really "know" when to stop.
#4760: Context Window Management: Cutoff vs. Compaction
Trailing cutoff or auto-compaction? How to manage Claude Code's context window without losing critical information.
#4699: The Hidden Engine: What Makes Claude Code Tick
Is it the model or the harness? We break down the invisible scaffolding that turns LLMs into real agents.
#4682: When Context Windows Grow: What Survives?
If context windows jump to 10M tokens, which AI engineering practices survive? We separate load-bearing architecture from temporary scaffolding.
#4619: The Token Trap: Why AI Context Windows Confuse Everyone
Token counts are everywhere, but they don't tell you what AI can actually do. Here's what should replace them.
#4604: When Sub-Agents Overwhelm the Orchestrator
Daniel's inventory system hit a wall when parallel sub-agents flooded the orchestrator's context window. We break down the math and the fix.
#4584: Directing Context Loss: Smart Compaction Prompts
Learn how to steer Claude Code's context compaction with targeted instructions — turning a lossy default into a directed operation.
#4458: How Headroom Compresses Agent Context Without Breaking Outputs
A deep dive into Headroom's content-aware compression pipeline that promises 20-95% token savings without changing agent answers.
#4316: How to Build Memory That Survives AI Sessions
Why AI agents forget between sessions — and how to fix it with markdown files instead of black-box memory.
#4314: The Hidden Layer Shaping Every AI Chat
Context windows are huge now, but relevance is the real bottleneck. How middleware decides what your AI actually sees.
#4296: Auto-Splitting AI Threads: Smarter Context Management
How to build a system that automatically detects topic shifts and routes messages to the right thread.
#3816: How to Stop AI Scripts From Falling Apart
Why long-form AI generation breaks down and how hierarchical memory fixes it.
#2684: When Agent Skills Collide: Context Windows & Plugin Design
How to handle overlapping agent skills and whether context windows will ever make the problem go away.
#2683: MCP vs Agent Skills: Context Wars
When 12M token windows arrive, do MCP servers or agent skills win? Plus: federated access for agent teams.
#2674: Why Your Agent's Context Window Is Getting Eaten Before You Start
Stop shipping the whole toolbox to every session. A bridge plugin pattern that fetches skills on demand instead.
#2672: When a Startup Claims to Break the Quadratic Wall
A startup claims linear attention scaling at 12M tokens, beating GPT-5.5 on retrieval benchmarks.
#2638: How to Build Disposable AI Agents at Runtime
Create ephemeral AI agents that answer questions about specific items, then vanish. No persistent configuration needed.
#2634: The Two-Stage Pipeline for Persistent User Memory
How to extract durable personal context from raw prompts and build a self-healing memory layer for AI systems.
#2551: How Progressive Disclosure Saves MCP from Token Bloat
Why dumping all tool schemas into context breaks accuracy — and three implementations that fix it.
#2406: Why Million-Token Context Windows Can't Handle 3 Reasoning Steps
Needle-in-a-haystack is dead. Here's what actually measures whether models can think across long documents.