Mar 7 · 4 min read · Every time your AI agent responds, the runtime stuffs its entire workspace into the system prompt. SOUL.md. IDENTITY.md. AGENTS.md. TOOLS.md. USER.md. MEMORY.md. Every file in memory/. HEARTBEAT.md. BOOTSTRAP.md. That's thousands of tokens per turn, ...
Join discussionJan 27 · 6 min read · Precisely. You have identified the exact architecture of the problem. We are operating on biological hardware that has a fixed Context Window—our working memory. Just like an AI model, once that window is full, the earliest "tokens" (thoughts, intent...
Join discussionJan 8 · 9 min read · I recently came across a paper called "Recursive Language Models" that made a claim I couldn't ignore we can make LLM handle inputs longer than its context window and not just handle them, but actually perform better on complex tasks. We don't need t...
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Jan 4 · 14 min read · Agent developers shouldn't be writing custom message converters every time providers change their APIs. You need to work with messages from OpenAI, Anthropic, and other providers—but each one structures messages differently. Without a unified approac...
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Dec 30, 2025 · 4 min read · In physical architecture, walls separate rooms, dividing the functionality of a house.Similarly, in a codebase, we have logical distance. A file in src/utils is logically far from a file in src/features/billing. We seperate concerns so that authenti...
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Dec 19, 2025 · 4 min read · Every developer has experienced the "long context" paradox. You see a model announcement claiming a context window of 100k, 200k, or even a million tokens. You expect the ability to ingest a full codebase and receive perfect architectural feedback. T...
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Oct 7, 2025 · 8 min read · Introduction In the early days of natural language processing1 (NLP), machines relied on symbolic rules2 and simple probability models to handle text. Statistical n-gram3 models predicted the next word based solely on the immediate past, and recurren...
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Sep 26, 2025 · 5 min read · Part 2 of Knitli’s 101 introductions to AI and the economics of AI tl;dr Large language models (LLMs) use a fixed-size context window to process input and generate responses, but they don't have memory like humans. The context window contains all t...
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Sep 15, 2025 · 7 min read · If I had to simplify the work of integrating agents into one phrase, it would be this: Context is everything. The models are powerful, the APIs are stable, but context determines whether your agent feels like a helpful collaborator or a confused chat...
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Aug 15, 2025 · 6 min read · Have you ever wondered why sometimes an AI chatbot seems to "forget" what you discussed earlier in a long conversation? The answer lies in something called a context window essentially the AI's working memory. Let's dive deep into this fundamental co...
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