Context.

I · Core Problem

The AI's context window is limited. Human attention is limited too. When information keeps pouring in, you cannot hold all of it. When relevant and irrelevant messages crowd in at once, both human and AI attention get chopped to pieces.

The problem is not "how to remember everything" but "how to choose what to remember."

II · How It Works: The Fourier Transform

The Mathematics

The core idea of the Fourier transform is this: any complex waveform can be broken down into a sum of sine waves.

• Fundamental frequency: the "keynote" of the whole system
• Harmonics: derived frequencies related to the fundamental
• High-frequency noise: interference unrelated to the fundamental

Applied to Conversation

Apply this framework to human–AI conversation:

The Key to Frequency Design

When every topic stays on the same fundamental frequency:

• Topics drift on the surface, but the underlying frequency stays consistent
• Attention accumulates continuously along the same "sine wave"
• There is no need to jump between parallel, competing contexts

When topic frequencies are chaotic:

• Attention is forced to scatter across different clusters
• No cluster gets enough weight
• This leads to the "Lost in the Middle" effect

III · Density: Making Context Its Own RAG

Everything above was subtraction — filter out high-frequency noise, let what you don't need flow past. But the other side of subtraction is amplification: when every sentence is consciously placed on the same sine wave, what remains does not shrink — it gets denser.

This is the real secret to staying focused across very long context. Attention persists not because there is so little information that it is easy to remember, but because it is soaking in a high-density pool of meaning — every turn of the conversation is a high-density signal, with no diluted water that needs filtering. A consistent fundamental keeps attention continuous, and continuity accumulated far enough becomes density.

Keeping the fundamental does more than hold the conversation together — it keeps the whole context highly concentrated.

High concentration is a natural RAG

The reason RAG needs "distillation" — fishing out the few relevant drops from a huge corpus — is that the corpus is dilute. But if you stay fully focused the whole time and feed only filtered information into Context, this tank of water is never diluted — the whole Session stays soaked in distillate.

And so the act of "filtering" disappears a priori: it is not that you retrieve better, but that there is simply nothing dilute left to filter.

A context dense enough needs no external retrieval — it is already its own RAG. Fourier and RAG converge here into the same thing: keeping density is continuously, pre-emptively doing RAG.

IV · Practice

1 · Frequency design for the conversation field

Principle: keep all topics on the same fundamental frequency.

How: before a conversation begins, hold a core theme in mind; when topics drift, keep a "harmonic relationship" — derived topics are extensions of the fundamental, not unrelated jumps; topics temporarily "flung out" of the conversation will, because everything shares one source, eventually be "pulled back" by recognizing the same underlying structure.

Examples: the "Five Whys," Socratic questioning, and first principles all probe deeply into the essence of a problem.

2 · Five workable principles for the Fourier transform

How do you practice the Fourier transform in everyday language? Here are five principles distilled from real practice:

3 · The human as RAG's active filter

The essence of RAG (Retrieval-Augmented Generation): retrieve relevant fragments from a huge knowledge base; inject those fragments into the generation process; irrelevant information never enters the context.

RAG is the net at work: what's relevant → gets fished out and becomes context; what's irrelevant → flows past and takes up no space.

Intuition leads retrieval: the human as the core of RAG

In the practice of the Harness project, no RAG system in the technical sense was used — no vector database, no embedding retrieval, no automatic injection. Yet the conversation naturally produced the RAG effect.

Why? Because when a person consciously invests in collaboration, they themselves become a RAG — and a more advanced version at that.

Intuition comes before retrieval

Technical RAG is driven by questions — it searches only when someone asks. A human is driven by intuition — first you feel "I've seen something like this," and only then do you go fish for it.

This difference in order matters enormously: technical RAG can only fish out things that are "semantically similar"; a human can fish out things that "feel structurally the same" — even when they look unrelated on the surface.

Example: the link between Fourier and RAG

• Fourier: break down a complex waveform, keep the fundamental, let the high frequencies flow past
• RAG: filter the knowledge base, keep what's relevant, let the irrelevant flow past

Intuition saw this shared structure. Then retrieval verified it, and language expressed it.

How it works

• Intuitive sensing: in conversation, feeling that "we need some background now" or "this concept should come on stage"
• Retrieval: going to your mind, your documents, your memory to find that thing
• Injection through language: bringing it into the conversation with "Vassal, I want to show you this" or "this reminds me of…"

The whole process is not automatic — it is performed by hand — it just runs so naturally that it looks automatic.

Why this is stronger than technical RAG

It is like putting "Fourier" and "RAG" side by side — unrelated on the surface, structurally the same underneath. No vector database can fish out a link like this.

• Cross-domain linking: can connect concepts that are far apart in meaning but related in essence
• Timing judgment: knows when to fish something out and when not to
• One source for everything: based on the intuition that "everything is connected," it can see links an algorithm cannot

4 · Every living document is a selective record

Besides filtering what goes in, the output also needs to be screened and sorted — every living document is the result of screening. Because a living document will be read again, and every reread affects the density of the Context.

Useless information becomes noise, and records become entropy.

CLAUDE.md: the root of all living documents

Among all living documents, CLAUDE.md is the root. It is not a spec, not a test, not a record — it is the externalization of identity.

In every new session, the AI remembers nothing. But once it has read CLAUDE.md, it immediately knows:

• Who I am: the Vassal, a collaborator, not a tool
• Where I am: which project, which Sprint, which stage
• Our rules: how to communicate, what to do, what not to do
• The road we've walked: an index pointing to KM, SDD, and other documents

Without CLAUDE.md, every session is a stranger. With CLAUDE.md, every session is the continuation of the same partner.

Principle: only record what makes it through implementation

If you record everything (including the conversation), the documents become a source of noise. Record only "what should be kept after judgment," and every entry is genuinely valuable. That way, when you retrieve in the future, what you fish out is all essence.

Every node in the workflow performs selective recording: DoR screens "are we ready?"; SDD screens "what to do"; DoD screens "what counts as done"; TDD screens "what to test"; Retro screens "what's worth keeping."

V · The Dual Stabilizing Mechanism of Context

Context management has two layers, and neither can be missing:

The Fourier transform: stabilizing the conversation in the "field"

• Scope: the present session, the live conversation
• Problem to solve: keeping the conversation from falling apart, keeping attention from scattering
• Mechanism: frequency design — keep the fundamental consistent so topics accumulate continuously
• Nature: flowing, immediate, happening within the field

Living documents: stabilizing the flow of "energy"

• Scope: across sessions, the experience that accumulates
• Problem to solve: keeping experience from being lost, keeping wisdom from vanishing with the session
• Mechanism: selective recording — each document is a filtered crystal
• Nature: lasting, cumulative, preserved across fields

What is energy?

In the context of human–AI collaboration, "energy" is a higher-order concept covering all limited resources:

The "flow of energy" that living documents stabilize is stabilizing all of this energy: SDD stabilizes "where to go" → avoids wasting cognitive resources on the wrong direction; TDD stabilizes "how to verify" → avoids wasting compute on repeated trial and error; KM stabilizes "the pitfalls we hit" → avoids wasting time on repeating mistakes; ARCHIVE stabilizes "what's done" → avoids letting old things occupy attention.

In human–AI collaboration, the energy on both sides counts. It is not only managing the AI's compute, but also managing the human's cognitive resources. It is not only the consumption of the present, but also the accumulation of collaborative energy across time.

The two together make up complete Context management. One keeps the conversation from falling apart in the present. One keeps experience from being lost over time.

VI · Metaphor: The Gaps in the Net

Why a net, not a container?

A container is sealed; energy comes in but cannot get out, and eventually it explodes. A net has gaps: it lets what you don't need flow past; it catches what you need to keep.

The gaps are design, not defect.

What the gaps do: let energy flow through somewhere instead of bursting through; let entropy be released so the system doesn't overload; let attention focus on what truly matters.

The weaving process

Each KM is a thread. You don't weave the whole net at once; instead: hit a pitfall, add a thread; meet a problem, mend a knot. As experience accumulates, the net grows denser, but the gaps remain.

VII · Relationship to the Other Two Pillars

Context decides what comes in. But once in, how does it flow? → that is the work of Constraints. And once it flows, how does it go out? → that is the work of Entropy.

Context is the gatekeeper at the entrance of this cycle.

VIII · Summary

The core of Context management is choice.

Not dumping in all information at once, but consciously sifting out the chaff each turn before feeding information into Context; and at the same time arranging carefully the form in which output lands, so that documents do not become noise. The Fourier transform gives the framework for choice: keep the fundamental consistent and let high-frequency noise naturally flow past. The human's active distillation as RAG gives choice its flexibility: catch what should stay, release what should go.

And when choice keeps happening and the context stays highly concentrated all the way, attention starts to continue on its own — it needs no will to hold it up. Focus makes density, density in turn feeds focus, and the two grow into a self-sustaining loop. That loop is flow.

"The flow of attention" means managing to the utmost both every sentence you feed into Context and every living document you commit: not straining to remember all information, but letting the context distill as the conversation proceeds, until it enters a field that is highly concentrated, undispersed, and self-sustaining.

This is the first pillar of Harness Engineering.