Clarify operating model and project corpus state
This commit is contained in:
@@ -86,6 +86,24 @@ file replication of the live machine store.
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- OpenClaw must continue to work if AtoCore is unavailable
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- write-back from OpenClaw into AtoCore is deferred until later phases
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## Intended Daily Operating Model
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The target workflow is:
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- the human continues to work primarily in PKM project notes, Git/Gitea repos,
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Discord, and normal OpenClaw sessions
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- OpenClaw keeps its own runtime behavior and memory system
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- AtoCore acts as the durable external context layer that compiles trusted
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project state, retrieval, and long-lived machine-readable context
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- AtoCore improves prompt quality and robustness without replacing direct repo
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work, direct file reads, or OpenClaw's own memory
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In other words:
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- PKM and repos remain the human-authoritative project sources
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- OpenClaw remains the active operating environment
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- AtoCore remains the compiled context engine and machine-memory host
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## Current Status
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As of the current implementation pass:
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@@ -95,6 +113,8 @@ As of the current implementation pass:
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- the service is running from Dalidou
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- the T420/OpenClaw machine can reach AtoCore over network
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- a first read-only OpenClaw-side helper exists
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- the live corpus now includes initial AtoCore self-knowledge and a first
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curated batch for active projects
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- the long-term content corpus still needs broader project and vault ingestion
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This means the platform is hosted on Dalidou now, the first cross-machine
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@@ -5,7 +5,8 @@
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AtoCore is no longer just a proof of concept. The local engine exists, the
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correctness pass is complete, Dalidou now hosts the canonical runtime and
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machine-storage location, and the T420/OpenClaw side now has a safe read-only
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path to consume AtoCore.
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path to consume AtoCore. The live corpus is no longer just self-knowledge: it
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now includes a first curated ingestion batch for the active projects.
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## Phase Assessment
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@@ -41,6 +42,10 @@ path to consume AtoCore.
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- Dalidou Docker deployment foundation
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- initial AtoCore self-knowledge corpus ingested on Dalidou
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- T420/OpenClaw read-only AtoCore helper skill
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- first curated active-project corpus batch for:
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- `p04-gigabit`
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- `p05-interferometer`
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- `p06-polisher`
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## What Is True On Dalidou
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@@ -67,10 +72,23 @@ The Dalidou instance already contains:
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- Master Plan V3
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- Build Spec V1
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- trusted project-state entries for `atocore`
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- curated staged project docs for:
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- `p04-gigabit`
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- `p05-interferometer`
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- `p06-polisher`
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- curated repo-context docs for:
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- `p05`: `Fullum-Interferometer`
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- `p06`: `polisher-sim`
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Current live stats after the first active-project ingest pass:
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- `source_documents`: 32
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- `source_chunks`: 523
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- `vectors`: 523
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The broader long-term corpus is still not fully populated yet. Wider project and
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vault ingestion remains a deliberate next step rather than something already
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completed.
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completed, but the corpus is now meaningfully seeded beyond AtoCore's own docs.
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## What Is True On The T420
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@@ -81,14 +99,41 @@ completed.
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- `/home/papa/clawd/skills/atocore-context/`
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- the T420 can successfully reach Dalidou AtoCore over network/Tailscale
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- fail-open behavior has been verified for the helper path
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- OpenClaw can now seed AtoCore in two distinct ways:
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- project-scoped memory entries
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- staged document ingestion into the retrieval corpus
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## What Exists In Memory vs Corpus
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These remain separate and that is intentional.
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In `/memory`:
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- project-scoped curated memories now exist for:
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- `p04-gigabit`: 5 memories
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- `p05-interferometer`: 6 memories
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- `p06-polisher`: 8 memories
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These are curated summaries and extracted stable project signals.
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In `source_documents` / retrieval corpus:
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- real project documents are now present for the same active project set
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- retrieval is no longer limited to AtoCore self-knowledge only
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This separation is healthy:
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- memory stores distilled project facts
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- corpus stores the underlying retrievable documents
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## Immediate Next Focus
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1. Use the new T420-side AtoCore skill in real OpenClaw workflows
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2. Ingest selected active project sources in a controlled way
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3. Define the first broader AtoVault/AtoDrive ingestion batches
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4. Add backup/export strategy for Dalidou machine state
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5. Only later consider deeper automatic OpenClaw integration or write-back
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2. Tighten retrieval quality for the newly seeded active projects
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3. Promote only the most stable active-project facts into trusted project state
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4. Define the first broader AtoVault/AtoDrive ingestion batches
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5. Add backup/export strategy for Dalidou machine state
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6. Only later consider deeper automatic OpenClaw integration or write-back
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## Guiding Constraints
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@@ -9,38 +9,67 @@ AtoCore now has:
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- initial self-knowledge ingested into the live instance
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- trusted project-state entries for AtoCore itself
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- a first read-only OpenClaw integration path on the T420
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- a first real active-project corpus batch for:
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- `p04-gigabit`
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- `p05-interferometer`
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- `p06-polisher`
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## Immediate Next Steps
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1. Use the T420 `atocore-context` skill in real OpenClaw workflows
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- confirm the ergonomics are good
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- confirm the fail-open behavior remains acceptable in practice
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2. Ingest selected active projects only
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- start with the current active project set
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- prefer trusted operational/project sources first
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- ingest broader PKM sources only after the trusted layer is loaded
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3. Review retrieval quality after the first real project ingestion batch
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2. Review retrieval quality after the first real project ingestion batch
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- check whether the top hits are useful
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- check whether trusted project state remains dominant
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4. Define backup and export procedures for Dalidou
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- reduce cross-project competition and prompt ambiguity where needed
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3. Promote a small number of stable active-project facts into trusted project
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state
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- active architecture
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- current selected path
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- key constraints
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- current next step
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4. Continue controlled project ingestion only where the current corpus is still
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thin
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- a few additional anchor docs per active project
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5. Define backup and export procedures for Dalidou
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- SQLite snapshot/backup strategy
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- Chroma backup or rebuild policy
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5. Keep deeper automatic runtime integration deferred until the read-only model
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6. Keep deeper automatic runtime integration deferred until the read-only model
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has proven value
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## Recommended Active Project Ingestion Order
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## Recommended Near-Term Project Work
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The first curated batch is already in.
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The near-term work is now:
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1. strengthen retrieval quality
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2. promote the most stable facts into trusted project state
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3. only then add a few more anchor docs where still needed
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## Recommended Additional Anchor Docs
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1. `p04-gigabit`
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2. `p05-interferometer`
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3. `p06-polisher`
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For each project:
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P04:
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1. identify the matching AtoDrive/project-operational sources
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2. identify the matching PKM project folder(s)
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3. ingest the trusted/operational material first
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4. ingest broader notes second
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5. review retrieval quality before moving on
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- 1 to 2 more strong study summaries
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- 1 to 2 more meeting notes with actual decisions
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P05:
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- a couple more architecture docs
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- selected vendor-response notes
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- possibly one or two NX/WAVE consumer docs
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P06:
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- more explicit interface/schema docs if needed
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- selected operations or UI docs
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- a distilled non-empty operational context doc to replace an empty `_context.md`
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## Deferred On Purpose
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@@ -56,5 +85,18 @@ The next batch is successful if:
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- OpenClaw can use AtoCore naturally when context is needed
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- AtoCore answers correctly for the active project set
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- retrieval surfaces the seeded project docs instead of mostly AtoCore meta-docs
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- project ingestion remains controlled rather than noisy
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- the canonical Dalidou instance stays stable
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## Long-Run Goal
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The long-run target is:
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- continue working normally inside PKM project stacks and Gitea repos
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- let OpenClaw keep its own memory and runtime behavior
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- let AtoCore supplement LLM work with stronger trusted context, retrieval, and
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context assembly
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That means AtoCore should behave like a durable external context engine and
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machine-memory layer, not a replacement for normal repo work or OpenClaw memory.
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93
docs/operating-model.md
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93
docs/operating-model.md
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@@ -0,0 +1,93 @@
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# AtoCore Operating Model
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## Purpose
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This document makes the intended day-to-day operating model explicit.
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The goal is not to replace how work already happens. The goal is to make that
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existing workflow stronger by adding a durable context engine.
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## Core Idea
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Normal work continues in:
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- PKM project notes
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- Gitea repositories
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- Discord and OpenClaw workflows
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OpenClaw keeps:
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- its own memory
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- its own runtime and orchestration behavior
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- its own workspace and direct file/repo tooling
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AtoCore adds:
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- trusted project state
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- retrievable cross-source context
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- durable machine memory
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- context assembly that improves prompt quality and robustness
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## Layer Responsibilities
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- PKM and repos
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- human-authoritative project sources
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- where knowledge is created, edited, reviewed, and maintained
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- OpenClaw
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- active operating environment
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- orchestration, direct repo work, messaging, agent workflows, local memory
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- AtoCore
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- compiled context engine
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- durable machine-memory host
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- retrieval and context assembly layer
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## Why This Architecture Works
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Each layer has different strengths and weaknesses.
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- PKM and repos are rich but noisy and manual to search
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- OpenClaw memory is useful but session-shaped and not the whole project record
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- raw LLM repo work is powerful but can miss trusted broader context
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- AtoCore can compile context across sources and provide a better prompt input
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The result should be:
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- stronger prompts
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- more robust outputs
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- less manual reconstruction
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- better continuity across sessions and models
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## What AtoCore Should Not Replace
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AtoCore should not replace:
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- normal file reads
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- direct repo search
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- direct PKM work
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- OpenClaw's own memory
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- OpenClaw's runtime and tool behavior
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It should supplement those systems.
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## What Healthy Usage Looks Like
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When working on a project:
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1. OpenClaw still uses local workspace/repo context
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2. OpenClaw still uses its own memory
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3. AtoCore adds:
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- trusted current project state
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- retrieved project documents
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- cross-source project context
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- context assembly for more robust model prompts
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## Practical Rule
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Think of AtoCore as the durable external context hard drive for LLM work:
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- fast machine-readable context
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- persistent project understanding
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- stronger prompt inputs
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- no need to replace the normal project workflow
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That is the architecture target.
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