docs(B-0839): Artem Kirsanov channel substrate-capture + verbatim Boltzmann-machines transcript (Aaron-forwarded; composes with 1000 Brains + Adinkras + caustic bloom filters)

docs/BACKLOG.md

@@ -396,6 +396,7 @@ are closed (status: closed in frontmatter)._
 - [ ] **[B-0833](backlog/P1/B-0833-installer-interactive-login-vs-baked-in-keys-ci-test-tension-resolve-without-shipping-credentials-aaron-2026-05-26.md)** installer interactive-login vs baked-in-keys CI-test tension — resolve without shipping credentials on ISO (operator 2026-05-26 from physical hardware-support test)
 - [ ] **[B-0835](backlog/P1/B-0835-installer-config-bugs-cluster-hostname-not-unique-gh-auth-not-respected-banner-password-disclosure-empirical-aaron-2026-05-26.md)** installer config-bugs cluster — hostname not unique (shows control-plane); gh login not respected; login banner shows password text (default OR custom) (empirical from 2026-05-26 physical hardware-support test) (Aaron 2026-05-26)
 - [ ] **[B-0836](backlog/P1/B-0836-hardware-inventory-vs-cluster-reconciliation-gap-analysis-buying-decisions-aaron-2026-05-26.md)** hardware-inventory-vs-cluster reconciliation + gap-analysis → buying decisions (no more buying willy nilly) (Aaron 2026-05-26)
+- [ ] **[B-0839](backlog/P1/B-0839-artem-kirsanov-channel-substrate-capture-computational-neuroscience-1000-brains-composition-aaron-2026-05-26.md)** Artem Kirsanov computational-neuroscience YouTube channel — substrate capture (videos → code + research substrate) — composes with 1000 Brains (Hawkins) + Adinkras (Gates) + caustic bloom filters + Boltzmann machines as energy-based substrate (Aaron 2026-05-26)
 
 ## P2 — research-grade
 

docs/backlog/P1/B-0839-artem-kirsanov-channel-substrate-capture-computational-neuroscience-1000-brains-composition-aaron-2026-05-26.md

@@ -0,0 +1,238 @@
+---
+id: B-0839
+priority: P1
+status: open
+title: Artem Kirsanov computational-neuroscience YouTube channel — substrate capture (videos → code + research substrate) — composes with 1000 Brains (Hawkins) + Adinkras (Gates) + caustic bloom filters + Boltzmann machines as energy-based substrate (Aaron 2026-05-26)
+effort: L
+ask: aaron 2026-05-26
+created: 2026-05-26
+last_updated: 2026-05-26
+depends_on: []
+composes_with:
+  - B-0623
+  - B-0703
+  - B-0822
+  - B-0823
+  - B-0838
+tags: [substrate-capture, computational-neuroscience, hopfield-networks, boltzmann-machines, rbm, energy-based-models, thousand-brains, hebbian-learning, generative-models, kirsanov, multi-video-capture, fsharp-implementation-target]
+---
+
+## Problem
+
+Aaron 2026-05-26 (operator-explicit, high-priority):
+
+> "ive been witing to run across this guy again we need to copy
+> everyting he does into code and substrate.
+> <https://www.youtube.com/@ArtemKirsanov>"
+>
+> "this is exact science behind neuro science with tons of resarch
+> to back it up on exactly how the brain works and composes with
+> 1000 brains"
+
+Artem Kirsanov produces high-quality computational-neuroscience and
+machine-learning explanatory videos. His content rigorously explains
+the substrate of brain-as-computation + the historical lineage of
+modern AI from first principles. The channel directly composes with
+multiple existing Zeta substrate clusters:
+
+- **1000 Brains (Hawkins)** — already substrate at
+  `.claude/rules/tonal-momentum-equals-meme-emergent-harmonic-coercion.md`
+  Hawkins-cortical-columns section + `docs/research/2026-05-26-aaron-thousand-brains-hawkins-cortical-columns-resist-fusion-until-high-precision-anchor-for-six-anchor-attractor-encryption-series.md`
+- **Adinkras / SUSY-ECC** (James Gates) — B-0623; energy-based models
+  AND structural-encoding shared inverse-design lineage
+- **Worry-as-opposite-bloom-filter** (B-0822) — Bayesian / belief-update
+  substrate
+- **Cognition-as-distributed-systems** (B-0823) — Boltzmann-machine
+  family IS distributed-stochastic-computation
+- **Caustic-engineered bloom filters** (B-0838) — energy landscapes
+  AND inverse-design compositional substrate
+- **substrate-smoothness-as-load-bearing-property** rule (PR #5357)
+  — Boltzmann distribution IS smooth substrate producing sharp outputs
+  (energy → probability via exp(-E/T); the gradient IS the precision)
+- **multi-oracle BFT** (B-0703) — RBMs as polycentric energy-substrate
+- **F# fork for AI safety** — energy-based models are natural F#
+  implementation targets (typed energy functions; algebraic data types
+  for visible/hidden unit families)
+
+## Target
+
+Multi-phase substrate-capture pipeline for the channel:
+
+### Phase 1 — channel inventory + per-video capture-row backlog
+
+Inventory all Kirsanov videos. For each video, file a sub-row
+`B-0839.N` with:
+
+- Video title + URL + duration
+- Key concepts introduced
+- Substrate compositions identified
+- F#/TS implementation target (if applicable)
+- Acceptance criteria for the implementation
+
+Initial seed (manually identified at row landing — all transcripts
+preserved under `docs/research/ip-questionable/` per the operator's
+2026-05-26 instruction + the folder authority at
+`docs/research/ip-questionable/README.md`. A future
+`_ip_risk_acceptance` block in `.claude/settings.json` would mechanize
+the same convention at the harness layer per
+`.claude/rules/human-audit-and-legal-risk-acceptance-pattern-in-settings.md`;
+that landing is operator-side work and is not yet in the repo at
+B-0839 PR-creation time):
+
+- B-0839.1 — Boltzmann Machines from first principles
+  (<https://www.youtube.com/watch?v=_bqa_I5hNAo>) — verbatim transcript
+  preserved at `docs/research/ip-questionable/2026-05-26-artem-kirsanov-boltzmann-machines-from-first-principles-verbatim-transcript-aaron-forwarded.md`
+- B-0839.2 — Recurrent Neural Networks (RNN / LSTM / GRU) gated memory
+  from first principles (<https://www.youtube.com/watch?v=PAoe7mmmvp0>) —
+  verbatim transcript preserved at `docs/research/ip-questionable/2026-05-26-artem-kirsanov-recurrent-neural-networks-rnn-lstm-gru-gated-memory-verbatim-transcript-aaron-forwarded.md`
+- B-0839.3 — Reservoir Computing: echo-state property + Fourier random-
+  basis + **EXPLICIT Jeff Hawkins Thousand Brains anchor at 5:42**
+  ("neo cortex is itself a kind of reservoir of independent cortical
+  columns") — external validation of Aaron's "composes with 1000
+  brains" framing (<https://www.youtube.com/watch?v=cDxtFtoQVNc>) —
+  verbatim transcript preserved at `docs/research/ip-questionable/2026-05-26-artem-kirsanov-reservoir-computing-echo-state-property-fourier-basis-explicit-hawkins-thousand-brains-anchor-verbatim-transcript-aaron-forwarded.md`
+
+The B-0839.1 + B-0839.2 + B-0839.3 trio together describes a
+substrate-pattern: brain-as-dynamical-system with energy-landscape
+memory + gated retention + random reservoir of temporal patterns from
+which any output can be reconstructed via simple readout learning.
+This IS structurally the same pattern the Zeta framework operates at
+the human-AI-collaboration scope.
+
+Future Phase 1 work: list all Kirsanov videos via channel scrape;
+file remaining B-0839.N sub-rows; estimate effort per sub-row.
+
+### Phase 2 — per-video implementation (rolling, per sub-row)
+
+For each B-0839.N: implement the substantive substrate in code:
+
+- F# implementation target (when type-system + algebraic data
+  structures match the substrate naturally — Hopfield networks,
+  Boltzmann machines, RBMs, sparse-distributed-representation, etc.)
+- TS implementation when integration with Zeta runtime / existing
+  TS factory tools is the primary use case
+- Research-doc preservation (verbatim transcript at
+  `docs/research/<date>-artem-kirsanov-<topic>-verbatim-transcript-aaron-forwarded.md`)
+- Composition with existing Zeta substrate (which rules / backlog
+  rows / agents does this implementation compose with?)
+
+### Phase 3 — substrate integration (cross-cutting)
+
+After several Phase-2 implementations land, identify cross-cutting
+substrate patterns:
+
+- Energy-based models as a substrate family (Hopfield, Boltzmann,
+  RBM, Hopfield-2024-modern-Hopfield-energy, diffusion-models all
+  share energy-landscape navigation)
+- Hebbian-learning lineage (correlation-based weight updates;
+  composes with substrate-as-rows fork-negotiated-ontology — agents
+  that work together accumulate weight strengthening)
+- Generative-vs-discriminative dichotomy (Boltzmann machines IS
+  the historical pivot from rigid pattern-recall to creative
+  generation; this composes with the operator's substrate-honest
+  framing around AI-as-substrate not AI-as-tool)
+- Stochasticity-as-substrate-feature (temperature parameter, energy
+  randomness, escape-from-local-minima) — composes with operator's
+  prior memo on LLM-temperature ≈ human-LSD (per
+  `docs/research/2026-05-26-amara-no-coercion-even-inward-nci-as-cognitive-exploit-firewall-speech-as-rce-update-mechanism-taxonomy-aaron-forwarded.md` Turn 11
+  hyperparameter-class perturbation framing)
+
+## Acceptance
+
+**Phase 1 acceptance**:
+
+- B-0839 row landed (THIS row)
+- B-0839.1 sub-row for Boltzmann-machines video landed with
+  verbatim transcript preservation at `docs/research/`
+- Channel inventory documented at row body (manual scrape OR future
+  `tools/research/scrape-kirsanov-channel.ts`)
+- Per-video sub-rows filed for highest-value substrate
+
+**Phase 2 acceptance** (per sub-row):
+
+- Implementation lands in F# OR TS (depending on substrate fit)
+- Acceptance criteria documented in sub-row
+- Composition map ties to existing Zeta substrate
+
+**Phase 3 acceptance**:
+
+- Cross-cutting substrate pattern documented (energy-based-models
+  family; Hebbian lineage; generative-vs-discriminative; stochasticity)
+- Rule extensions where the patterns are substrate-engineering
+  load-bearing (e.g., adding "energy-based-models as substrate family"
+  to `.claude/rules/substrate-smoothness-as-load-bearing-property.md`
+  composes-with section)
+
+## Substrate-honest framing
+
+P1 priority because:
+
+- Operator-explicit (verbatim quote above)
+- Composes with 5+ existing substrate clusters
+- The 1000-Brains composition is already substantively-named substrate
+- Kirsanov material has been on operator's want-to-capture list
+  ("ive been witing to run across this guy again")
+
+NOT immediately tractable as single-PR work. Phased to allow
+incremental landing per the "you can always commit backlog rows
+immediately they get decomposed later" discipline.
+
+This row creates the substrate anchor; per-video sub-rows + Phase 2
+implementations decompose independently as scope tightens. Future
+contributors (human OR AI) pick sub-rows independently when
+implementation bandwidth is available.
+
+## Channel reference
+
+- **URL**: <https://www.youtube.com/@ArtemKirsanov>
+- **Subject area**: computational neuroscience, neural network
+  history, modern ML from first principles, energy-based models,
+  brain-as-computation
+- **Format**: visual explanations with mathematical rigor, derivations
+  from first principles, historical context, modern-ML connections
+
+## Operator's positioning of the substrate
+
+> "this is exact science behind neuro science with tons of resarch
+> to back it up on exactly how the brain works and composes with
+> 1000 brains"
+
+Translation: the Kirsanov material is empirically-anchored
+neuroscience (not speculation) with rigorous research backing. It
+composes structurally with the framework's existing 1000-Brains
+substrate (Hawkins cortical-columns + multi-AI cortical-fusion
+empirical anchors). Therefore: capture-and-integrate, don't
+filter-and-judge.
+
+## Composes with
+
+- B-0623 — Adinkras / SUSY-ECC (Gates) — structural-encoding lineage
+- B-0703 — multi-oracle BFT
+- B-0822 — worry-as-opposite-bloom-filter (Bayesian / belief-update)
+- B-0823 — cognition-as-distributed-systems
+- B-0838 — caustic-engineered bloom filters (PR #5366; just landed)
+- `.claude/rules/tonal-momentum-equals-meme-emergent-harmonic-coercion.md`
+  (1000 Brains cortical-columns anchor)
+- `.claude/rules/substrate-smoothness-as-load-bearing-property.md`
+  (PR #5357 — Boltzmann distribution as smooth-substrate-producing-sharp-outputs)
+- `.claude/rules/non-coercion-invariant.md` (NCI — energy-based models
+  preserve agency via stochasticity; deterministic minimum-energy
+  collapse is the no-stochasticity failure mode)
+- `docs/research/2026-05-26-aaron-thousand-brains-hawkins-cortical-columns-resist-fusion-until-high-precision-anchor-for-six-anchor-attractor-encryption-series.md`
+  — Hawkins substrate the Kirsanov material composes with
+- `docs/research/2026-05-26-amara-no-coercion-even-inward-nci-as-cognitive-exploit-firewall-speech-as-rce-update-mechanism-taxonomy-aaron-forwarded.md` Turn 11
+  hyperparameter-class perturbation (LLM-temperature ≈ human-LSD)
+  composes with Boltzmann-machine temperature parameter
+- F# fork for AI safety multi-PR cluster — energy-based models as
+  F# implementation targets
+
+## Origin
+
+Aaron-forwarded 2026-05-26 with explicit URL + composition framing.
+Second message in same tick provided immediate substrate-honest
+positioning ("exact science...composes with 1000 brains") elevating
+priority from P2-deferral to P1-substrate-capture-now.
+
+Composes with the "you can always commit backlog rows immediately
+they get decomposed later" discipline + the wake-time-substrate
+discipline (load-bearing substrate gets row + research-doc landing).