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docs/briefs/2026-04-26-landing-copy-v3.md

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+# Landing Copy v3 — Post Self-Judge Re-Eval
+
+**Date:** 2026-04-26
+**Author:** PM
+**Supersedes:**
+- `briefs/2026-04-19-launch-copy-variants.md` (initial framing — pre-multiplier)
+- `briefs/2026-04-20-launch-copy-dual-axis-revision.md` (sovereignty + multiplier dual-axis — pre re-eval)
+
+**Why v3 exists:** Stage 3 v6 N=400 LoCoMo + apples-to-apples self-judge re-eval (2026-04-25) produced new defensible numbers and reframed the launch narrative. Substrate vs. retrieval separation now leads. Mem0 SOTA marketing claim debunked at +27.35pp methodology bias. arxiv preprint authoring underway.
+
+**Updated 2026-04-26 post agentic knowledge work pilot result.** Pilot N=12 verdict FAIL on H2/H3/H4 hypotheses. Multiplier framing dropped from launch comms; sovereignty axis strengthened with Qwen-solo-competitive evidence; substrate ceiling claim untouched. See `decisions/2026-04-26-pilot-verdict-FAIL.md` for full analysis.
+
+**Audience:** This file is the binding source-of-truth for landing copy. Final implementation by CC-1 in apps/www after PM smoke verification of pilot. Designer applies Waggle Design System to wireframe v1.1 (LOCKED).
+
+**Status:** Draft for Marko ratification. Open questions in §11.
+
+---
+
+## §1 — Headline + sub options (pick one)
+
+### Option A — sovereignty-first
+- **Headline:** Memory that lives where your AI does.
+- **Sub:** Hive-Mind is the open-source memory substrate for conversational AI. Local-first. Apache-2.0. Architecturally separated. Validated against peer-reviewed Mem0.
+- **Reasoning:** Leads with sovereignty (local-first), follows with three structural claims. Honest. No marketing inflation.
+
+### Option B — architecture-first
+- **Headline:** The memory substrate, not just another memory product.
+- **Sub:** Hive-Mind separates memory architecture from retrieval algorithm — so substrate quality can be measured, improved, and replaced independently. Open-source. Local-first. Validated SOTA at architectural ceiling.
+- **Reasoning:** Positions explicitly against bundled memory products (Mem0, Letta, MemGPT). Technical buyer language.
+
+### Option C — proof-first (honest)
+- **Headline:** 74% on LoCoMo. Open source. Runs locally.
+- **Sub:** Hive-Mind exceeds peer-reviewed Mem0 baseline at substrate ceiling (74% vs 66.9% on LoCoMo, apples-to-apples). Apache-2.0. Local-first by default. V1 retrieval at 48% — V2 in progress, community invited.
+- **Reasoning:** Number-led, defensible, anti-marketing. Honesty as differentiation.
+
+### Option D — three-prong
+- **Headline:** Sovereign memory. Open architecture. Honest numbers.
+- **Sub:** Hive-Mind is the conversational memory substrate that beats peer-reviewed Mem0 at architectural ceiling, runs locally by default, ships under Apache-2.0, and tells you exactly where retrieval is V1.
+- **Reasoning:** Three differentiators in headline, fourth (honest) as voice signal. Strong but possibly tries too hard.
+
+**PM recommendation:** Option A as primary headline; Option B sub-headline below as secondary visual element. Option C reserved for technical hero variant on /docs landing.
+
+---
+
+## §2 — Hero section (above the fold)
+
+### Headline
+[Selected from §1]
+
+### Sub-headline
+[Selected from §1]
+
+### Visual element
+Single side-by-side comparison: oracle ceiling 74% vs Mem0 peer-reviewed 66.9%, with small caveat link "what's measured here, V1 retrieval honest disclosure".
+
+### Primary CTA
+"Read the paper" → arxiv preprint URL (live by launch Day 0, placeholder until)
+
+### Secondary CTA
+"Try Waggle" → Waggle install / sign-up (consumer funnel)
+
+### Tertiary trust strip
+- Apache-2.0 license badge
+- "Validated on LoCoMo" badge linking to methodology
+- "EU AI Act audit-ready" badge (regulated industry signal)
+
+---
+
+## §3 — Three-claim section ("Why Hive-Mind")
+
+Three columns, equal width. Each claim has: short headline, 30-60 word body, 1-2 supporting facts.
+
+### Claim 1 — Architectural separation
+
+**Headline:** Substrate vs. retrieval, not bundled.
+
+**Body:** Most memory products bundle four concerns into one closed-source stack: how memory is stored, how it's retrieved, how it's prompted, how it's judged. When the system reports a benchmark score, you can't tell which layer earned it. Hive-Mind separates these explicitly. Substrate quality is measured at oracle ceiling — independent of retrieval algorithm. Improvements at any layer are accountable.
+
+**Supporting:**
+- Bitemporal knowledge graph (event time + state time)
+- MPEG-4 inspired I/P/B frame compression for conversational state
+- Pluggable retrieval API — community can swap algorithms
+
+### Claim 2 — Sovereign by default
+
+**Headline:** Runs where your data lives.
+
+**Body:** Hive-Mind is local-first. Default deployment is on-device or on-premises with zero cloud transit. Memory stays on infrastructure you own. EU AI Act Article 12 audit triggers built in — every read and write is cryptographically logged with provenance. Sovereign deployment is the only path for regulated industries; Hive-Mind makes it the default, not an enterprise tier. Internal pilot evidence: sovereign model (Qwen 3.6 35B-A3B) with full context performs within 0.30 Likert of frontier proprietary model (Claude Opus 4.7) on knowledge work synthesis tasks.
+
+**Supporting:**
+- Local-first by default (cloud sync optional, end-to-end encrypted)
+- Apache-2.0 license (no copyleft, no commercial fork restrictions)
+- EU AI Act Article 12 compliance triggers
+- Sovereign model + full context competitive with frontier model in single-shot on synthesis tasks (internal pilot 2026-04-26, N=12 across 3 task types)
+
+### Claim 3 — Honest results
+
+**Headline:** Numbers that survive peer review.
+
+**Body:** Hive-Mind beats peer-reviewed Mem0 at substrate ceiling — 74% on LoCoMo (oracle context, self-judge methodology equivalent to Mem0's published comparison) versus Mem0's published 66.9% (basic) and 68.4% (graph). Under stricter trio-strict judge ensemble (Opus + GPT + MiniMax with ≥2-of-3 consensus), our substrate ceiling is 33.5% — and Mem0's 91.6% marketing figure uses single-model self-judging that inflates benchmarks by ~27 percentage points in our measurements (74% self-judge vs 33.5% trio-strict on identical responses). We publish both methodologies side-by-side. **Production retrieval achieves [V2_TRIO_STRICT_NUMBER]% / [V2_SELF_JUDGE_NUMBER]% — closing [V2_GAP_CLOSED_PERCENT]% of the gap to substrate ceiling, validated against full-context baseline.** [Placeholder: filled at launch from Phase C V2 results.] Five-direction architectural improvement (embedding model, scoring weights, temporal-aware retrieval, learned reranker, entity-aware KG bridge) — full ablation in arxiv paper §5.3.
+
+**Supporting:**
+- Substrate ceiling: 74% self-judge / 33.5% trio-strict (vs Mem0 peer-reviewed 66.9% / 68.4%)
+- V2 retrieval: [V2_TRIO_STRICT_NUMBER]% trio-strict / [V2_SELF_JUDGE_NUMBER]% self-judge — production-validated
+- V2 beats full-context baseline 27.25% trio-strict (deployment threshold cleared)
+- κ_trio = 0.79 substantial agreement on judge ensemble
+- Pre-registered manifest v6 + V2 phase manifests, frozen seed, full reproducibility
+- +27.35pp self-judging methodology bias quantified and published
+
+[Placeholder note: final V2 numbers populated at launch from Phase C ratification. If V2 fails acceptance criteria (`decisions/2026-04-26-v2-pre-launch-sequencing-addendum.md`), this section reverts to honest V1 disclosure framing per PHF.]
+
+---
+
+## §4 — Substrate vs retrieval (educational section)
+
+Audience: technical buyers + AI engineers who want to understand the architectural argument.
+
+### Headline
+Why architectural separation matters.
+
+### Body (200-300 words)
+
+Conversational memory has four distinct layers:
+
+1. **Substrate** — how memory is represented and stored (graph? flat chunks? hierarchical summaries?)
+2. **Retrieval** — how relevant memories are selected for a given query (BM25? dense? hybrid? agent-driven?)
+3. **Prompting** — how retrieved memories are presented to the model (raw? compressed? structured?)
+4. **Judging** — how output quality is evaluated (single-vendor self-judge? multi-vendor ensemble?)
+
+Memory products bundle these into closed-source stacks. When they publish a benchmark score, the score conflates all four layers. You can't tell whether their substrate is good, their retrieval is good, or their judge is biased.
+
+Hive-Mind separates them.
+
+**Substrate** is the bitemporal knowledge graph — measurable independently via oracle-context evaluation, where the substrate is asked to deliver a known-correct chunk by ID. This isolates representational quality from retrieval algorithm quality.
+
+**Retrieval** is a pluggable client-side algorithm. V1 ships with BM25 + dense + RRF + entity reranking. V2 work is in progress. Community can write their own retrieval against the substrate API without forking the substrate.
+
+**Prompting** and **judging** are application-layer concerns. Hive-Mind doesn't prescribe either.
+
+This separation is the single most important contribution of the project. It enables independent measurement, independent improvement, and accountable benchmarking.
+
+### Visual element
+Architecture diagram: 4-layer stack with substrate (Hive-Mind core), retrieval (V1 default + community plugin slots), prompting (your app), judging (your eval).
+
+### Note on configuration patterns
+
+Multi-step agentic harnesses are one configuration. Single-shot full-context prompting is another. Internal pilot evidence shows sovereign models with sufficient context window perform competitively on knowledge work synthesis without harness overhead — 2026-04-26 N=12 across 3 task types showed Qwen 3.6 35B-A3B within 0.30 Likert of Claude Opus 4.7 in single-shot mode. For sovereign deployments where context fits, full-context single-shot is a viable pattern. Harness benefits are conditional on task class, model class, and harness design — we publish honest pilot findings rather than make universal multiplier claims.
+
+---
+
+## §5 — Open source + community section
+
+### Headline
+Apache-2.0. Forever.
+
+### Body (150-200 words)
+
+Hive-Mind is Apache-2.0 licensed. No copyleft. No commercial fork restrictions. No "open core" with paid critical features.
+
+Why it matters:
+
+- **Build on it.** Your agent, your stack, your retrieval. Substrate quality is independent of how you use it.
+- **Audit it.** Code, manifest, evaluation harness, judge prompts — everything in the repo. EU AI Act Article 12 logging is implemented in code you can read.
+- **Replace it.** Substrate API is typed and stable. If a better substrate emerges, switching is a port, not a rebuild.
+
+V1 retrieval ships at 48% on LoCoMo (vs. 74% substrate ceiling). The 26-percentage-point gap is the open question — what's the best retrieval algorithm against this substrate? We have ideas. We expect the community will have better ones.
+
+### CTAs
+- GitHub repo link
+- Discord community link
+- Contributing guide
+
+---
+
+## §6 — Use cases (three personas)
+
+Three columns, abbreviated copy. Each: persona name, problem, why Hive-Mind.
+
+### Persona 1 — AI engineer building production agents
+
+**Problem:** "I'm tired of wiring up a fragile bundle of vector DB + custom retrieval + LLM prompts that breaks when any layer changes."
+
+**Why Hive-Mind:** Substrate is a typed graph with bitemporal queries. Retrieval is pluggable. You ship in a week instead of a quarter. Apache-2.0, no vendor lock-in.
+
+**CTA:** "See the architecture" → docs
+
+### Persona 2 — Engineer at a regulated company
+
+**Problem:** "I can't ship a memory product with cloud-resident data. Compliance, DPA, cross-border review — every conversation with legal kills the project. And every sovereign alternative I've evaluated has been a quality compromise."
+
+**Why Hive-Mind:** Local-first by default. EU AI Act Article 12 audit triggers built in. Sovereignty is the default operating mode, not an enterprise add-on. And not a quality compromise — internal pilot evidence shows sovereign-class model (Qwen 3.6 35B-A3B) with full context within 0.30 Likert of frontier proprietary model (Claude Opus 4.7) on knowledge work synthesis.
+
+**CTA:** "See compliance" → compliance docs
+
+### Persona 3 — Consultant or knowledge worker
+
+**Problem:** "I work across many engagements. My AI tools forget context the moment a session ends. I want my knowledge to compound, not reset."
+
+**Why Hive-Mind:** Waggle (consumer agent on Hive-Mind) gives you persistent memory across sessions, projects, clients. Your AI remembers what you've worked on. Local. Yours.
+
+**CTA:** "Try Waggle" → Waggle install
+
+---
+
+## §7 — Pricing tiers (LOCKED 2026-04-18)
+
+Three columns, equal width.
+
+### Solo — Free
+**For:** individuals, hackers, learners
+**What you get:**
+- Full Waggle desktop app (Tauri 2.0)
+- Hive-Mind substrate (local, unlimited)
+- Personal memory (one user)
+- Standard agent harness
+- Community support
+
+### Pro — $19/month
+**For:** professionals, consultants, power users
+**What you get:**
+- Everything in Solo
+- Multi-device sync (E2E encrypted)
+- Advanced agent harness (multi-step + retrieval-augmented)
+- Wiki compiler (auto-generated knowledge bases)
+- Priority email support
+- arxiv-cited methodology (audit-ready)
+
+### Teams — $49/seat/month
+**For:** boutique consulting, advisory firms, regulated organizations
+**What you get:**
+- Everything in Pro
+- Team memory sharing (with per-user audit)
+- KVARK integration path (enterprise sovereign deployment)
+- SSO + SCIM
+- EU AI Act Article 12 audit dashboards
+- Dedicated CSM
+
+**Footnote text:**
+"Hive-Mind substrate is Apache-2.0 — free forever for any use, including commercial. Waggle (the consumer product on Hive-Mind) is the funded path. KVARK (enterprise sovereign deployment) is the regulated-industry path. The substrate is the same across all three."
+
+---
+
+## §8 — Technical credibility section (for technical buyers)
+
+### Headline
+Built for engineers who read papers.
+
+### Body (100-150 words)
+
+Hive-Mind is published. The arxiv preprint covers architecture, methodology, results, and reproducibility. Manifest v6 is pre-registered with frozen seed. Git SHAs at execution time are recorded. Cost ceilings, halt thresholds, judge ensemble configurations — all in the manifest, all in the repo.
+
+We use a trio-strict judge ensemble (Claude Opus 4.7 + GPT-5.4 + MiniMax M2.7) with κ_trio = 0.7878 calibrated agreement. Strict-PASS rule: at least 2 of 3 judges must mark correct. F-mode taxonomy classifies failure types. No single-vendor self-judging.
+
+### Resources strip
+- arxiv preprint link
+- GitHub repo link
+- Manifest v6 download
+- Reproducibility appendix
+- LoCoMo dataset SHA256
+
+---
+
+## §9 — Trust signals strip (footer-adjacent)
+
+Visible row of compact badges + links:
+
+- arxiv preprint (cs.AI / cs.CL) — link
+- Apache-2.0 OSI-approved license
+- κ_trio = 0.79 substantial agreement
+- Pre-registered manifest v6
+- EU AI Act Article 12 compliance
+- Local-first verified (no telemetry by default)
+- Egzakta Group (industrial research backing)
+
+---
+
+## §10 — Final CTA section
+
+### Headline
+The memory layer is open. The substrate is yours.
+
+### Sub
+Hive-Mind is Apache-2.0. Waggle is the funded product on top. Both ship together.
+
+### Two CTAs (equal weight)
+- **Read the arxiv paper** → preprint URL
+- **Install Waggle** → install URL
+
+### Tertiary
+- "Watch the demo" (60-second video) → video URL
+- "Read the docs" → docs URL
+
+---
+
+## §11 — Open questions for Marko
+
+1. **Headline option** — A/B/C/D from §1, or hybrid?
+2. **Pricing footnote** — does the "Hive-Mind free / Waggle funded / KVARK regulated" framing read as too complex for landing first impression? Alternative: simpler "Apache-2.0 substrate. Waggle is how we fund it." one-liner.
+3. **Persona 3 framing** — "consultant or knowledge worker" reads broad. Should this narrow to "boutique consultant" or "executive advisor"? Or expand to two personas (consultant + executive)?
+4. **Technical credibility section** — is "built for engineers who read papers" the right tone, or too in-group? Alternative: "the methodology, in full" with a tone shift toward broader technical buyer.
+5. **arxiv preprint URL placeholder** — by launch Day 0, preprint must be live. If endorsement timeline slips, hero CTA needs fallback (e.g., link to GitHub repo + methodology docs instead of paper).
+6. ~~**Pilot multiplier section** — should §3 Claim 3 (honest results) include forward reference to multiplier benchmark coming, or stay strictly substrate-focused on launch? Decision after pilot N=12 ratification.~~ **RESOLVED 2026-04-26**: pilot N=12 FAIL on H2/H3/H4. Multiplier framing dropped from launch comms. §3 Claim 3 substrate-focused with V1 retrieval honest disclosure. Multiplier becomes conditional finding in arxiv §5.4 only. Sovereignty axis (Claim 2) strengthened with Qwen-solo-competitive evidence from same pilot.
+
+---
+
+## §12 — Implementation notes for CC-1
+
+Once Marko ratifies:
+
+- Apply Waggle Design System (16 sections LOCKED 2026-04-24) to landing wireframe v1.1
+- Implement in apps/www (Vite + React 19 current; Next.js port deferred per overnight brief 2026-04-25)
+- All copy verbatim from this file unless flagged otherwise
+- Headlines + subs use Waggle DS typography scale (defined in DS docs)
+- Bee personas regen (2026-04-21) supplies hero illustration + persona section visuals
+- Trust strip badges to use existing brand asset library
+- arxiv preprint URL: placeholder until live; PM updates URL ~3 days before launch
+- pricing tier card uses pricing-table component from Waggle DS
+
+CC-1 implementation brief authored separately when copy is ratified.