Context is what the AI loads before it answers. The feedback loop writes what you actually did back into the files.
📎 Same 5-layer map as Module 0. Framing follows Anthropic, "Effective context engineering for AI agents," Sep 29, 2025. · source
L1, the index (the CONTEXT file): a short map. Always read first.
L2, the detail: full content, opened on demand.
L3, the archive: finished work, out of the way. Not listed in the index means invisible to the AI.
📎 Load-on-demand mechanics: Anthropic, "Effective context engineering for AI agents," 2025. Layer pattern: one builder's live vault, running since 2025. · source
My network folder: 5 subfolders. People, companies, communities, events, call transcripts.
The AI reads one file: _network-CONTEXT.md, 2.4k tokens. It says what lives where. Everything else opens on demand.
📎 Counts: one builder’s live vault, Jul 2026 · index file 9.6 KB ≈ 2.4k tokens.
6 files load before every single answer: 47 KB, about 12,000 tokens.
That is 1.2 percent of a 1M-token window. The window is not the constraint, the recurring bill is: every always-on line is billed every session.
📎 1 token ≈ 4 chars: OpenAI API docs, "Concepts." ≈ 3.5 chars for Claude: Anthropic glossary. Always-on files load and consume tokens every session: code.claude.com/docs/en/memory. Cache pricing: platform.claude.com, prompt caching. · source
Models load and bill tokens, not KB. 1 KB is roughly 250 to 300 tokens.
My bands: under 2k it barely knows you, 5 to 20k is healthy, past 50k cost rises and answers degrade.
The size rule is written inside the constitution itself.
📎 1 token ≈ 4 chars: OpenAI API docs, "Concepts." ≈ 3.5 chars for Claude: Anthropic glossary. File sizes: my live vault, measured Jul 2026. The bands and the in-file budget are my practice, not vendor guidance. · source
Every week the system reads what actually happened, calendar, email, notes, compares it with the plan, and writes updates back into the files.
📎 Schluntz & Zhang, "Building effective agents," Anthropic, Dec 19, 2024. · source
Cron is the same skill run on a clock. Mine: calendar sync, call transcript ingest, health data, a 05:00 morning brief.
All access is read-only. This is the feedback loop running without me.
📎 calendar.readonly and gmail.readonly are standard read-only OAuth scopes: developers.google.com/workspace/calendar/api/auth · developers.google.com/workspace/gmail/api/auth/scopes. Job list: my live crontab. · source
Monday, a client call: the transcript job writes decisions and promises into that person’s file.
All week, email and calendar scans collect what changed. Sunday it converges into the focus draft. I reply OK.
📎 Pipeline: one builder’s live week, Jul 2026 · scans are read-only, writes wait for approval.
Create a new, separate Gmail account that belongs to the AI. Forward the emails that matter.
It reads them and sends back a weekly digest, 4 emails per week max.
Your main mailbox is never handed over.
📎 Gmail read via official OAuth (gmail.readonly): developers.google.com/workspace/gmail/api/auth/scopes. Caps and categories: my live setup. · source
Every Sunday that Gmail account sends me a draft: my focus file refreshed across 7 life areas, only the changes shown.
I reply from my phone: OK 1 3 5, EDIT 4. It writes only after approval.
📎 Layout and approval grammar: one builder’s live Sunday email, running weekly since 2025.
Every skill is a file with a trigger word. Say the word, the AI opens the file and follows it.
Mine today: 19 triggers. Seven are packaged for you in the library.
📎 Trigger table: my live system, Jul 2026 · the packaged seven: materials/library
One lead agent splits the job. Each subagent gets its own context window, works in parallel, and reports back. Results are checked before merging.
In Claude Code one word triggers it: ultracode, now an official effort setting. OpenAI and Gemini offer SDK equivalents.
📎 Dynamic Workflows + ultracode: claude.com/blog, May 28, 2026 · subagents and agent teams: code.claude.com/docs · Codex subagents (TOML, max_threads 6, Ultra delegation): learn.chatgpt.com/docs/agent-configuration/subagents · Gemini CLI to Antigravity CLI: developers.googleblog.com, May 19, 2026. · source
This deck was inflow and learning. Next deck: the fifteen-minute Sunday routine, two audit prompts, and the growth path into Modules 4 and 5. Module 4 installs all of this as skills.
loai.wisdomblend.com · livingoffai.nexus/accountability
📎 Anthropic, "Effective context engineering for AI agents," 2025 · Schluntz & Zhang, "Building effective agents," 2024.