Context Engineering on AIBussin — AI applications, systems and books

What Context Means

Wed, 23 Sep 2026 05:00:01 +0100

Consider a concrete evening. A developer asks a coding agent to fix a failing test. The typed message is eleven words long: “the checkout test fails after my last commit, please fix it.”

Behind those eleven words sits an organisation of information enormously larger than the request. The repository holds hundreds of thousands of lines of code. The version control history holds the last commit and every commit before it. The continuous integration log holds the actual failure output. The open editor holds the file the developer was viewing. The agent’s working directory holds shell history, environment variables, and configuration. Earlier in the session, the developer pasted an error trace, the agent ran two searches, and a linter produced three warnings nobody has read yet.

When the Context Window Fills

Wed, 02 Sep 2026 15:15:00 +0100

A stateful session eventually encounters a physical limit.

Prompts, initial instructions, examples, conversation history and model outputs all compete for a finite context budget. Once the budget is exhausted, the application cannot solve the problem by pretending the next message is independent.

The difficult question is not how to delete text.

It is how to preserve the right state.

1. The context window is a budget

When a session exposes usage and quota, context pressure becomes observable:

Retrieval Is a Policy

Mon, 07 Sep 2026 11:30:00 +0000

Part III — Retrieval Is an Experiment

“Just retrieve the relevant documents”

There is no such operation. Chapter 11 closed on a single insight: a score never travels alone — it arrives with a negative-selection rule that is part of the measurement. This chapter widens the frame. A retrieval result never travels alone either. It arrives wrapped in a query construction, a representation, a candidate-eligibility rule, a scoring and fusion policy, a selection rule, an execution strategy, and a context-assembly step that decides what a downstream model actually sees.

Delta Memory: Cargo-Culting Human Memory with Search

Mon, 18 May 2026 11:14:15 +0100

AI systems today have no idea why their own memory changes, that’s the problem we are trying to solve in this post.

Summary

Most AI memory systems start from a practical place: retrieval. Retrieval is useful, scalable, and often the right tool for the job. But if we want systems that interact with humans in more human‑like ways, we need a different analogy, not storage, but thinking.

Humans don’t store perfect records. We don’t retrieve exact text or replay video files. What we call “memory” is a shifting landscape of associations, impressions, weights, and patterns. When you recall something, you’re not pulling a file from disk, you’re running a search across your internal world, shaped by everything you’ve lived through.

Codex Manager: Building a Prompt-State Runtime for Hackathon-Grade Code Optimization

Sat, 16 May 2026 20:15:59 +0100

TL;DR

Codex Manager uses AI to generate code as an artifact, then tests that artifact, diagnoses what happened, and repairs the prompt state that produced it. The code is not the thing being optimized directly. The prompt state is.

Summary

Humanity’s Last Hackathon framed the challenge as a test of context, not code: the task was hard enough that the real question was not whether someone could hand-write one clever kernel, but whether they could build a system that used AI effectively under changing constraints.