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+---
+project: ai-memory-architecture-research
+type: session-notes
+status: active
+path: Tech/Sessions
+tags:
+  - homelab
+  - ai
+  - memory
+  - architecture
+  - research
+created: 2026-05-04
+updated: 2026-05-04
+---
+# AI Memory Architecture Research
+
+Session exploring the landscape of AI agent harnesses, model memory
+mechanisms, and persistent context layers. Started as a question about
+two products and turned into the architectural backbone for several
+follow-on projects.
+
+## Outcomes
+
+- Distinguished harnesses from models as separate layers in the AI
+tooling stack. Harnesses are free or cheap; models are where the cost
+lives. Swapping harnesses doesn't require migrating data or model
+choice.
+- Identified OB1 (by Nate B. Jones) as the right adoption target for a
+personal AI memory layer, validating the self-hosted
+MCP-server-plus-database pattern as the approach to project memory at
+scale.
+- Mapped the conceptual gap between current production AI memory
+(transcript replay plus prefix caching) and the research frontier
+(soft prompts, persona embeddings, weight-level continual learning).
+Confirmed that frontier-API models will never expose substrate access;
+open-weight local models do.
+- Reframed the original "build a harness" instinct as actually "build
+the memory layer." Most existing harnesses are good; what's missing is
+the persistent project memory underneath them.
+
+## Topics covered
+
+### Poolside
+
+Frontier coding-focused AI lab, founded 2023 by Jason Warner
+(ex-GitHub CTO) and Eiso Kant. Up to ~$12B valuation after Nvidia's
+recent investment. Released the Laguna model family — including Laguna
+XS.2, a 33B-total/3B-activated MoE under Apache 2.0 open weights that
+runs on consumer hardware via Ollama. Their thesis: code is the best
+beachhead to AGI because it forces long-horizon reasoning.
+
+Relevance: Laguna XS.2 is a candidate model for local agentic coding
+workloads on the RTX 3080 and slots cleanly into the Phase 2 OpenClaw
+plan.
+
+### Pi.dev
+
+A minimal terminal coding agent harness by Mario Zechner (badlogic),
+MIT-licensed. Notable design choices: no MCP, no sub-agents, no plan
+mode, no built-in todos — extensions instead of features. Supports 15+
+providers including Ollama. Tree-structured sessions with branching
+and gist sharing. Same category as Claude Code and Aider, different
+philosophy.
+
+Relevance: Useful as a reference architecture for understanding what a
+harness is and isn't. Worth a look as a daily driver for local Ollama
+work, separate from sanctioned `claude -p` for scripted Claude usage.
+
+### OpenBrain (OB1)
+
+Personal AI memory layer by Nate B. Jones. Self-hosted
+Postgres-plus-Supabase stack with MCP server, semantic search via
+pgvector, auto-extracted metadata, multiple capture sources (MCP,
+REST, Slack webhook, Obsidian import). Provider-agnostic — any
+MCP-compatible client can read and write through the same database.
+
+This is the convergence point of the conversation. Independent
+implementation of the same architectural insight reached through
+reasoning: AI memory should be a separate, owned, queryable layer that
+any AI client can read and write through a standard interface, not a
+feature locked inside one vendor's product.
+
+Adoption is the right call rather than building from scratch. The
+technical-project extension layer (project state, decision logs,
+session notes ingestion) is the gap to fill, and contributing it back
+is plausible.
+
+## Why this matters
+
+The cliff-of-projects problem — what happens when there are 100
+projects instead of 5 and Dispatch sessions can't carry coherent
+project memory — is solved at the architecture level by this category
+of solution. Not by a better harness, not by more discipline, but by a
+memory layer that lives alongside whatever tools come and go.
+
+The research-frontier digression on soft prompts and weight-level
+memory established the boundary: production AI memory is RAG-shaped
+(text retrieved by vector similarity), not weight-shaped or
+activation-shaped. Frontier APIs will never expose the substrate. That
+clarifies what is and isn't worth chasing — pursue the practical
+memory layer (OB1), recognize the exotic stuff (persona embeddings,
+hypernetworks) as research-frontier and not currently buildable on
+closed-weight models.
+
+## Key learnings
+
+- Harnesses and models are independent layers. Picking a harness and
+picking a model are separate decisions.
+- "Memory" in current AI is the harness keeping a transcript and
+replaying it. The model itself is stateless. Prefix caching is a cost
+optimization, not a memory mechanism.
+- Frontier API access stops at "send text, get text back." No weight
+access, no activation hooks, no soft-prompt injection. Open-weight
+local models are where substrate experimentation lives.
+- Anthropic actively blocks third-party harnesses from using
+subscription OAuth (April 2026 enforcement). API key with workspace
+spend caps is the only sanctioned path for Claude access from
+non-Anthropic tools. `claude -p` headless mode is the loophole —
+invoking the official binary from a script is fine.
+- The "build my own harness" instinct was actually about needing a
+shared memory layer underneath any harness. Different problem,
+different scope, much smaller project.
+
+## Follow-on projects
+
+- [ ] Deploy OB1 on homelab — Supabase stack on Proxmox, MCP server
+reachable via Tailscale
+- [ ] Wire Dispatch pre-compact hook to capture transcripts into OB1 via n8n
+- [ ] Extend existing markdown summary n8n flow to also POST captures into OB1
+- [ ] Build technical-project extension for OB1 (project state schema,
+decision log)
+- [ ] Evaluate Laguna XS.2 on Ollama as local coding model
+- [ ] Authelia in front of OB1 web surfaces (Studio UI), GoTrue stays
+for application identity
+
+## References
+
+- Poolside Laguna: open weights via Ollama
+- Pi.dev: github.com/badlogic/pi-mono
+- OB1: github.com/NateBJones-Projects/OB1
+- srnichols OpenBrain (alternative implementation): lighter, plain
+Postgres + pgvector, no Supabase dependency
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