From 56f0588b5410018de19b3dff0365b9cc77f3ecc6 Mon Sep 17 00:00:00 2001 From: Hermes Date: Tue, 2 Jun 2026 03:03:15 +0000 Subject: [PATCH] gbrain: sync converted org-mode brain files --- .org-ids.json | 4 + projects/passepartout/DESIGN_DECISIONS.org | 2 +- .../ann-neuromorphic-symbolic-comparison.org | 40 +++++ .../biomimicry-in-passepartout.org | 137 ++++++++++++++++++ projects/passepartout/hardware/_index.org | 6 + .../hardware/server-build-bom.org | 38 +++++ 6 files changed, 226 insertions(+), 1 deletion(-) create mode 100644 projects/passepartout/architecture/ann-neuromorphic-symbolic-comparison.org create mode 100644 projects/passepartout/architecture/biomimicry-in-passepartout.org create mode 100644 projects/passepartout/hardware/_index.org create mode 100644 projects/passepartout/hardware/server-build-bom.org diff --git a/.org-ids.json b/.org-ids.json index 36ae7f5..3d60c39 100644 --- a/.org-ids.json +++ b/.org-ids.json @@ -20,8 +20,10 @@ "26725506-399c-48c5-a797-46b48e8861d7": "projects/passepartout/architecture/self-pain-pleasure-three-laws.org", "4a1f23b0-abc4-4def-9876-543210abcdef": "projects/passepartout/architecture/stage-3-lisp-machine.org", "7f4e6b9a-2c1d-5e8f-9a3b-6d7c4e5f2a1b": "projects/passepartout/architecture/native-org-knowledge-base.org", + "f6a7b8c9-0d1e-2f3a-4b5c-6d7e8f90abcd": "projects/passepartout/architecture/biomimicry-in-passepartout.org", "460e06f4-6bfc-4969-89d8-685c0c4434cf": "projects/passepartout/architecture/stage-2-acl2-integration.org", "4a1f23b0-abc3-4def-9876-543210abcdef": "projects/passepartout/architecture/stage-2-verification.org", + "a7b8c9d0-1e2f-3a4b-5c6d-7e8f90abcdef": "projects/passepartout/architecture/ann-neuromorphic-symbolic-comparison.org", "f0e1d2c3-b4a5-6c7d-8e9f-0a1b2c3d4e5f": "projects/passepartout/architecture/conceptual-walkthrough.org", "3ec5bd52-f115-455e-83be-63db9a4ad3a7": "projects/passepartout/architecture/stage-1-dependency-map.org", "1c95ce7d-a2db-506a-9608-df68f9ae211b": "projects/passepartout/architecture/lisp-machine-security.org", @@ -32,6 +34,8 @@ "b9fa4b7b-bc61-4d7f-918d-ff687b80f2ba": "projects/passepartout/architecture/systemic-effects.org", "13e6ae54-2d24-5aa0-b1cd-a7e8e749aa70": "projects/passepartout/architecture/self-driving-lisp-machine.org", "4a1f23b0-abc5-4def-9876-543210abcdef": "projects/passepartout/architecture/stage-4-inference.org", + "e5f6a7b8-9c0d-1e2f-3a4b-5c6d7e8f90ab": "projects/passepartout/hardware/server-build-bom.org", + "d4e5f6a7-8b9c-0d1e-2f3a-4b5c6d7e8f90": "projects/passepartout/hardware/_index.org", "68ffa49f-f0d8-42cf-8b69-ae69de8bb815": "projects/passepartout/social-protocol/requirements-10-governance-and-assets.org", "b25bf753-9799-41ab-82f5-1a1416db756b": "projects/passepartout/social-protocol/requirements-01-overview.org", "a3243dd0-3209-423b-98e1-51c3eada2658": "projects/passepartout/social-protocol/requirements-07-advanced-integration.org", diff --git a/projects/passepartout/DESIGN_DECISIONS.org b/projects/passepartout/DESIGN_DECISIONS.org index 54442e8..569755b 100644 --- a/projects/passepartout/DESIGN_DECISIONS.org +++ b/projects/passepartout/DESIGN_DECISIONS.org @@ -1,4 +1,4 @@ -# Passepartout Design Decisions +#+TITLE: Passepartout Design Decisions This document captures the rationale behind key architectural choices. It is not a specification — it is a thinking medium for future architects and contributors who need to understand why the system is built this way, not just how. diff --git a/projects/passepartout/architecture/ann-neuromorphic-symbolic-comparison.org b/projects/passepartout/architecture/ann-neuromorphic-symbolic-comparison.org new file mode 100644 index 0000000..327f92b --- /dev/null +++ b/projects/passepartout/architecture/ann-neuromorphic-symbolic-comparison.org @@ -0,0 +1,40 @@ +:PROPERTIES: +:CREATED: [2026-06-01 Mon] +:ID: a7b8c9d0-1e2f-3a4b-5c6d-7e8f90abcdef +:END: +#+title: ANN vs Neuromorphic vs Symbolic — When Each Mathematics Fits +#+filetags: :passepartout:architecture:neurosymbolic:math: + +**ANN vs Neuromorphic vs Symbolic** + +**Core insight** + +The gap between ANNs and biology is not substrate (binary vs analog). Both are continuous mathematics running on discrete hardware. The real differences are architectural: + +1. Memory-binding: ANNs store weights separately from compute (von Neumann bottleneck). Biology co-locates weight and signal at the synapse. +2. Local vs global learning: ANNs need a global error signal backpropagated through every layer. Biology uses purely local plasticity (STDP) — each synapse adjusts based on its own pre/post partners. +3. Time: Biology is asynchronous, continuous, with rich temporal dynamics. ANNs are synchronous — everything computed in lockstep. Recurrence is an awkward addition. +4. One substrate, many functions: A biological synapse does memory, signal propagation, temporal integration, and plasticity in one structure. ANNs separate these across different passes and optimizers. + +**When each mathematics is appropriate** + +| Mathematics | Naturally good at | Awkward at | ++------------+-------------------+------------+ +| ANN / gradient descent | Smooth function approximation, interpolation, pattern completion from dense data | Symbolic reasoning, exact constraints, sparse data, multi-step verification | +| Neuromorphic / spiking dynamics | Temporal pattern recognition, event-driven control, low-power always-on sensing | Complex multi-step planning, precise arithmetic, storing large lookup tables | +| Symbolic / deduction | Exact reasoning, proof, constraint satisfaction, verifiable behavior | Learning from raw data, generalization, handling noisy inputs | + +**How Passepartout uses all three** + +- LLM (ANN on GPU) handles the noisy real world — language, vision, imperfect input +- Screamer (symbolic constraint search on CPU) handles combinatorial reasoning — "find valid configuration" +- ACL2 (deductive proof) handles the verifiable kernel — "prove this decision follows from rules" +- P150 (RISC-V parallel accelerator, in-between arch) handles ambient awareness, parallel dispatch, anomaly detection + +Each mathematics where it belongs. The failure mode of both pure ANN and pure symbolic approaches is forcing one mathematics to do what the other is better at. + +**The neuromorphic opportunity** + +A neuromorphic chip (Loihi-level) would add unsupervised temporal learning — learning daily rhythms, behavioral patterns, and detecting deviations without training, labels, or LLM involvement. This is the difference between responding to commands and anticipating needs. + +But the P150 gets 80% there with programmable cores controlled directly, without waiting for neuromorphic hardware to mature. diff --git a/projects/passepartout/architecture/biomimicry-in-passepartout.org b/projects/passepartout/architecture/biomimicry-in-passepartout.org new file mode 100644 index 0000000..9d42e5f --- /dev/null +++ b/projects/passepartout/architecture/biomimicry-in-passepartout.org @@ -0,0 +1,137 @@ +:PROPERTIES: +:CREATED: [2026-06-01 Mon] +:ID: f6a7b8c9-0d1e-2f3a-4b5c-6d7e8f90abcd +:END: +#+title: Biomimicry in Passepartout — Architecture and Roadmap +#+filetags: :passepartout:architecture:neurosymbolic:biomimicry:p150: + +**Biomimicry in Passepartout** + +**What already exists (real biomimicry, not metaphor)** + +| Feature | Biological analog | Implementation | +|---------+-------------------+----------------| +| Three-layer reasoning | Reptilian → limbic → neocortex | LLM (intuition) → Screamer (constrained search) → ACL2 (verified reasoning) | +| Verdict-overrides-LLM | Somatic markers override conscious deliberation | Gate outputs overrule LLM proposals, not the other way | +| Dream cycle | Sleep consolidation | gbrain dream cycle: replay and re-index daily experience offline | +| Delegate subagents | Cognitive recruitment | delegate_task — spawns specialized subprocesses for subproblems | +| Memory as two systems | Declarative vs procedural | Fact store (explicit) vs skills (implicit/procedural) | + +**What is missing — and how to fill it** + +***1. Peripheral nervous system (P150 slot)*** + +Biology does not poll. The brain does not run ~while true: check if_finger_hot()~. Dedicated low-power circuits (nociceptors, proprioceptors) monitor continuously and only signal the CNS on deviation. + +Passepartout polls everything — cron output, filesystem, user messages. A P150 running 72 parallel event-driven monitors would dedicate: + +- One core to "is the user typing on Signal?" +- One to "did the weekly model discovery fail?" +- One to "is ZFS ARC thrashing?" +- One to "is the test build running longer than usual?" + +Each sleeps until something meaningful happens. Only then does it signal the symbolic system. Zero LLM involvement for routine monitoring. + +This changes Passepartout from a system that responds to commands to a system that notices things on its own. The difference between a calculator and a research assistant. + +***2. Associative activation (spreading activation)*** + +In the brain, activating one concept (ACL2) automatically pre-activates related concepts (SP3, proof, Lisp, verification). No clean-slate search. + +Passepartout has no equivalent. Every query is a fresh search. A biomimetic fact store would: + +- Pre-fetch linked pages when one is loaded +- Prime caches based on current conversation context +- Use the graph structure to predict what will be needed next + +The brain does not pre-fetch — it primes — so the next thought is faster. Passepartout could prime its caches so facts most likely needed next are already loaded. + +***3. Error-driven learning with local credit assignment*** + +The brain does not backpropagate. Errors trigger local corrections at the synapse that made the mistake. + +Passepartout's Gate decisions today are either right or wrong, but nothing locally adjusts. A biomimetic Gate would: + +- Track which rules fired during a wrong decision +- Locally adjust confidence scores of only those rules +- No global retrain — just the specific rule that fired + +This is STDP at the symbolic level. + +***4. Sleep consolidation (dream cycle upgrade)*** + +The gbrain dream cycle already replays daily experience. It could go further during offline cycles: + +- Replay the day's decisions, identify which Gate checks were slow +- Regenerate ACL2 proof caches for rules that changed +- Prune skills that never fired (neurogenesis pruning counterpart) +- Re-index fact store based on actual usage, not static linking +- Propose new skills for repeated multi-step tasks discovered during the day + +***5. Graceful degradation*** + +Biology has redundant fallbacks at every level. Passepartout has single points of failure. + +A biomimetic approach: + +- Gate offline? Fall back to cached rule set +- LLM offline? Fall back to smaller local model +- ACL2 busy? Use previously verified boundaries +- Never go silent — get slower and dumber until primary returns +- P150 cores can run degraded modes independently + +**The P150's role** + +The P150 (72 Tensix cores, 32GB GDDR6, QSFP-DD 800G interconnect) fills a slot nothing else in the build covers: + +- Not for fast inference (2x 3090s are faster and cheaper for that) +- Not for baremetal Lisp Machine (FPGA is the right tool for tagged memory + hardware GC) +- For ambient awareness, parallel verification dispatch, fact store indexing, anomaly detection + +The P150 is the system's peripheral nervous system — always-on monitoring behind the scenes. + +**Revised architecture** + +| Component | Role | +|-----------+------| +| 2x RTX 3090 | Fast LLM inference | +| EPYC (main cores) | ACL2, Screamer, PDS, Gate orchestration | +| P150 | Always-on temporal awareness, parallel constraint search, fact store indexing, anomaly detection | +| FPGA (future) | Stage 3 Lisp Machine (tagged memory, hardware GC) | + +**Temporal awareness: explicit vs ambient** + +Passepartout today reasons about time (reading logs, comparing timestamps, understanding "before X happened" from context) but has no sense of time. + +Explicit (current): Reads a cron schedule, orders log events, answers "when did X happen." + +Ambient (with P150): Notices the build took 3x longer than usual without being asked, flags that message frequency dropped at 3AM, anticipates the user will want the weekly report before they ask. + +The P150 makes ambient temporal processing economically viable because 72 independent cores running statistical monitors consume near-zero power. Running the same monitors on the EPYC competes with ACL2 and the PDS. Running them on the 3090s wastes bandwidth on non-matrix work. + +**Relationship to the Pinker/Marcus critique** + +Pinker and Marcus argue that neural networks (spiking or otherwise) lack compositional syntax and systematic reasoning. A network that learns "A fires before B" through STDP has learned a temporal correlation, not a rule. It cannot distinguish causation, correlation, and coincidence. + +This critique does not apply to Passepartout because Passepartout is not a pure neural network. It is a hybrid system: + +| Problem | Mathematics | Where it runs | +|---------+------------+---------------| +| Temporal intuition | Statistical pattern detection | P150 | +| Compositional time (before/after/during) | Symbolic reasoning | Gate + Screamer on CPU | +| Sequential patterns from data | ANN attention | GPU | + +The neuromorphic layer gives the system a sense of time. The symbolic layer gives it understanding of time. Both are necessary. Neither one replaces the other. + +**What biomimicry means here** + +The real gains come not from replicating brain details (spiking neurons, STDP, ion channels) but from adopting organizational principles that biology evolved: + +- Specialized subsystems for different time/resource regimes (PNS vs CNS) +- Asynchronous event-driven communication instead of synchronous polling +- Redundant fallbacks at every level +- Local learning that does not require global retraining +- Offline consolidation separate from online inference +- Parallel associative retrieval rather than sequential search + +Passepartout already adopts some of these. The P150 and an upgraded cron/dream cycle would add the rest. diff --git a/projects/passepartout/hardware/_index.org b/projects/passepartout/hardware/_index.org new file mode 100644 index 0000000..16f5eed --- /dev/null +++ b/projects/passepartout/hardware/_index.org @@ -0,0 +1,6 @@ +:PROPERTIES: +:CREATED: [2026-06-01 Mon] +:ID: d4e5f6a7-8b9c-0d1e-2f3a-4b5c6d7e8f90 +:END: +#+title: Hardware +#+filetags: :index:hardware: diff --git a/projects/passepartout/hardware/server-build-bom.org b/projects/passepartout/hardware/server-build-bom.org new file mode 100644 index 0000000..953c86c --- /dev/null +++ b/projects/passepartout/hardware/server-build-bom.org @@ -0,0 +1,38 @@ +:PROPERTIES: +:CREATED: [2026-06-01 Mon] +:ID: e5f6a7b8-9c0d-1e2f-3a4b-5c6d7e8f90ab +:END: +#+title: Passepartout Server — Build BOM and Phased Plan +#+filetags: :passepartout:hardware:homelab:build: + +**Passepartout Server — Build BOM & Phased Plan** + +- Chassis: CX4712 ($439 Sliger) +- CPU: EPYC 7002 Rome SP3 (~$100-200 used) +- Board: Supermicro H11SSL-i (~$150 used) +- Cooler: Asetek 836SA-M1 AIO ($250 Sliger add-on) +- GPUs: 2x used RTX 3090 (~$750 ea) +- RAM: 256GB via 8x 32GB Crucial DDR4-3200 ECC ($210/ea) +- PSU: Corsair RM1000e 1000W ($148) +- Fans: 3x Noctua NF-A12x25 PWM ($75 Sliger) +- Rails: Sliger 20" rack slides ($109) +- HBA: LSI 9300-8i IT mode ($75) +- Boot NVMe: 1TB (~$60) +- ZFS special vdev: 2x 512GB NVMe mirrored (~$50/ea) +- HDDs: 6x 20-24TB recertified (~$250-350/ea) + +Total estimate: ~$5,900 + +**Phased buying plan (~$1K per phase)** + +1. EPYC CPU + SP3 motherboard (~$250) — test boot +2. CX4712 + PSU + fans + AIO + rails + NVMe boot + HBA (~$1,156) +3. 128GB RAM (4x32GB Crucial) + 1st RTX 3090 (~$1,590) +4. 4x 20-22TB HDDs recertified (~$1,200) +5. 128GB more RAM + 2nd RTX 3090 + 2x 512GB NVMe (~$1,690) + +**Stages** + +- Now: All-in-one (compute + storage + inference) +- Future: Proxmox secondary node when 3 better nodes appear +- Final: Backup server (just HDDs + ZFS)