SynaWeave-ce

🛣️ Master Planning

🧩 Purpose

This document is the source of truth for the program-level build plan of the rebuild. It defines the planning hierarchy, naming rules, sprint map, deliverable sequencing, and the standards that every sprint plan must follow.

This file is intentionally broader than any individual sprint file. It answers:

This file does not replace sprint planning files. It governs them.

🧭 Planning hierarchy

The planning hierarchy is fixed:

```text id=”vq8sgy” 🛣️ Sprint └─ 🚚 Deliverable └─ 🎟️ Task


### 🛣️ Sprint

A sprint is a major time-boxed build stage with a clear program objective.

### 🚚 Deliverable

A deliverable is a concrete, shippable outcome inside a sprint.

### 🎟️ Task

A task is an implementation item required to complete a deliverable.

No other planning hierarchy should be introduced unless this file is updated first.

---

## 📜 Planning file layout

All planning files live under:

```text id="nyl49z"
docs/planning/

Each sprint gets its own folder:

```text id=”g8c1h1” docs/planning/sprint-001/ docs/planning/sprint-002/ docs/planning/sprint-003/ …


Each sprint folder must contain:

* `overview.md`
* one file per deliverable

### 📜 Deliverable file naming

Deliverable files must use this pattern:

```text id="f4xsmz"
d1-name.md
d2-name.md
d3-name.md

Example:

```text id=”gh09rn” docs/planning/sprint-001/d1-foundation.md docs/planning/sprint-001/d2-runtime.md docs/planning/sprint-001/d3-quality.md


### 📜 ADR relationship

Each sprint must also have one ADR file:

```text id="vh8dgv"
docs/adrs/sprint-001.md
docs/adrs/sprint-002.md
docs/adrs/sprint-003.md

The sprint ADR records the architectural decisions that shaped that sprint. The sprint planning files record how those decisions are executed.

📦 Planning scope rules

This rebuild is governed by the following scope rules:

🧾 Template relationship

Recurring planning, ADR, spec, and verification artifacts must reuse docs/templates/.

That means:

Owner docs still define the durable rules. Templates standardize the recurring artifact shape only.

🧠 Product thesis

The product is being rebuilt as an AI and ML study intelligence platform focused on actively weaving knowledge for users so they can learn more, deeper, faster, and retain that knowledge better.

The product direction is shaped by:

The platform is not just a flashcard extension. It is a learning system with:

🏗️ Program architecture summary

The platform is built around four main surfaces:

🪟 Extension

A thin MV3 browser client for capture, quick study, side-panel tutoring, and in-context workflows.

🌐 Web

A control plane for dashboards, settings, deck/source management, analytics, admin tooling, labeling, and evaluation views.

⚙️ Backend

A Python platform for APIs, ingestion, retrieval, graph logic, recommendations, NLP, ML, evaluation, and MCP.

👀 Observability

A first-class quality and telemetry plane for traces, logs, metrics, evals, latency, cost, and operational insight.

☁️ Locked platform choices

The current implementation choices are:

These are implementation choices, not domain concepts. The core architecture must remain provider-agnostic.

🧭 1. Overview

This part defines the permanent product direction of SynaWeave.

It answers five questions:

This part does not define:

Those belong in later parts.

📌 2. Emoji system

Use this emoji system consistently across the packet for fast skimming and stable meaning.

🎯 3. Product contract

🎯 3.1 Product class

SynaWeave is a knowledge-weaving learning operating system.

It is not defined primarily as:

Those are possible feature surfaces, but they are not the product class.

The product class is defined by one core promise:

SynaWeave should help a learner transform raw source material into structured knowledge, convert that knowledge into adaptive practice, and improve long-term retention through grounded, measurable learning loops.

🎯 3.2 Product objective

The product objective is to let a learner move from:

inside one coherent system.

🎯 3.3 User classes

The product must serve all serious learners, but it is specifically optimized for:

These users are not a side segment. They are part of the core design target.

🎯 3.4 Product outcome definition

A successful user outcome is not just “used the app.”

A successful user outcome means the learner can do at least one of the following better than before:

🎯 3.5 Product requirements

The product must support all of the following as permanent capabilities:

📥 Source handling

📚 Knowledge work

🃏 Practice

🎓 Tutoring

🕸️ Connected knowledge

👀 Proof

💼 3.6 Investor-facing product requirement

The product must be understandable as a business in progress, not just an engineering experiment.

That means every major product milestone must make visible progress on at least one of these:

🧠 4. Learning contract

🧠 4.1 Learning model

SynaWeave is built on the assumption that durable learning requires more than passive exposure. Educational psychology literature has repeatedly found that practice testing and distributed practice are among the highest-utility broadly applicable learning techniques, while self-explanation and interleaving remain valuable supporting methods. ([Sage Journals][1])

Therefore, SynaWeave must treat:

🧠 4.2 Retrieval contract

The product must support retrieval practice as a core behavior.

This means:

🧠 4.3 Spacing contract

The product must support distributed review over time.

This means:

Spacing and retrieval should work together rather than exist as separate decorative features, because the combination is one of the most robust ways to support long-term retention. ([PMC][2])

🧠 4.4 Self-explanation contract

The product must support explain-back behavior.

This means:

🧠 4.5 Interleaving contract

The product must support mixed practice where appropriate.

This means:

🎓 4.6 Tutor contract

The tutor must behave like an adaptive instructional system.

It must be able to:

The tutor must not be treated as:

🎓 4.7 Supported tutoring modes

The tutor must support a family of deliberate instructional modes, including:

These modes must exist because different knowledge types and learner states require different instructional forms.

🧪 4.8 Technical learning contract

For SWE, ML, and AI learners, SynaWeave must support programming-specific and systems-specific practice structures rather than forcing all learning into generic question types.

Research on Parsons problems and related programming-learning methods continues to support their use for engagement, learning efficiency, and programming pattern recognition. ([Falmouth University Research Repository][3])

That means SynaWeave must support:

🧠 4.9 Learner-state contract

The product must maintain a meaningful learner model.

This model must be capable of representing:

This requirement is also consistent with recent programming-education knowledge-tracing work showing that learner questions and skill signals can materially improve prediction of later performance and support adaptive learning behavior. ([ACL Anthology][4])

🛡️ 4.10 Human-agency contract

The product must preserve learner agency.

Current AI-in-education guidance increasingly emphasizes that large language models should be integrated with intelligent tutoring systems and knowledge-tracing methods rather than replacing them, and that human agency should be preserved rather than undermined by automation.

That means:

🏗️ 5. System contract

🏗️ 5.1 Permanent system layers

The platform has five permanent layers:

This layering is mandatory at the conceptual level even if some layers are colocated early in the roadmap.

🪟 5.2 Client contract

The client layer owns all user-facing control surfaces.

It must:

It must not:

⚙️ 5.3 Runtime contract

The runtime layer owns:

It must:

🗃️ 5.4 Data contract

The data layer must separate:

It must preserve these truths:

🤖 5.5 Intelligence contract

The intelligence layer owns:

It must:

👀 5.6 Proof contract

The proof layer owns:

It must:

📏 6. Invariants

These are non-negotiable truths unless explicitly changed by a later architecture decision.

📏 6.1 Product invariants

📏 6.2 Learning invariants

📏 6.3 System invariants

📏 6.4 Business invariants

📏 6.5 Roadmap invariants

🔄 7. Loop diagrams

🔄 7.1 Learning loop

flowchart TD
    A[Source] --> B[Clean]
    B --> C[Note]
    C --> D[Link]
    D --> E[Quiz]
    E --> F[Explain]
    F --> G[Score]
    G --> H[Schedule]
    H --> I[Revisit]
    I --> D

Interpretation:

🔄 7.2 Tutor loop

flowchart TD
    A[Request] --> B[Context]
    B --> C[Learner State]
    C --> D[Mode Pick]
    D --> E[Tool Plan]
    E --> F[Retrieve]
    F --> G[Teach]
    G --> H[Grade]
    H --> I[State Update]
    I --> J[Schedule Update]
    J --> K[Trace and Eval]
    K --> C

Interpretation:

🔄 7.3 Product improvement loop

flowchart TD
    A[Use] --> B[Trace]
    B --> C[Measure]
    C --> D[Evaluate]
    D --> E[Improve]
    E --> A

Interpretation:

✅ 8. Part 1 acceptance standard

Part 1 is correct only if a reader can answer all of these without consulting lower-level docs:

If those answers are not clear after reading this section, this part is incomplete.

🧭 9. Overview

This part defines the technical contract for how SynaWeave will be built and judged.

It answers these questions:

This part does not define:

Those belong in later parts or lower-level technical documents.

🏗️ 10. Stack contract

🎯 10.1 Stack philosophy

The stack is not chosen for trend alignment. It is chosen to satisfy five product needs:

The stack must support:

🪟 10.2 Product shell contract

Locked decision

Why this is locked Bun supports monorepo workspaces directly, which keeps the workspace model simple. Next supports static export for sites that can be pre-rendered to static HTML, CSS, and JavaScript, which fits the public docs surface. Chrome Manifest V3 uses extension service workers and extension-specific runtime constraints, so the browser surface must remain a dedicated extension runtime instead of being collapsed into a generic web application. ([bun.com][1])

Spec requirements

✏️ 10.3 Editor contract

Locked decision

Why this is locked Tiptap is a headless editor framework built on ProseMirror and is designed to support custom editor behavior through extensions, nodes, and marks. It is therefore a better fit for a block-first learning workspace than a generic rich-text field. Its headless model also supports strong product control rather than forcing SynaWeave into a pre-shaped document UI. ([Tiptap][2])

Spec requirements

🌓 10.4 Frontend state contract

Locked decision

Why this is locked The product needs a strict separation between remote synchronized state and transient interface state. TanStack Query is purpose-built for fetching, caching, synchronizing, and updating server state, while Zustand is lightweight and well-suited for local interaction state. This keeps the control plane, browser client, and editor from collapsing into one opaque global state model. ([nextjs.org][3])

Spec requirements

⚙️ 10.5 Runtime contract

Locked decision

Why this is locked FastAPI provides a typed, high-performance application boundary for request-serving work. Cloud Run explicitly supports both services and jobs, which fits the architectural split between public requests and asynchronous or batch work such as ingestion, evaluation, and training. ([Google Cloud Documentation][4])

Spec requirements

🤖 10.6 Orchestration and tool-use contract

Locked decision

Why this is locked LangGraph is designed around stateful, long-running workflows and agentic execution. Model Context Protocol is an open standard for connecting AI applications to tools, prompts, data sources, and workflows. Together, they support a tutor that behaves like a managed system rather than a stateless prompt wrapper. ([LangChain Docs][5])

Spec requirements

🔬 10.7 ML and workflow contract

Locked decision

Why this is locked PyTorch is production-ready, supports distributed training, and has a mature ecosystem. Parameter-efficient fine-tuning methods are designed to adapt models by training a small fraction of parameters, which reduces training and storage costs while preserving useful downstream behavior. Metaflow is explicitly designed for building and operating data-intensive AI and ML applications, while MLflow provides a unified platform for experiment tracking, model management, and AI observability. ([PyTorch][6])

Spec requirements

🗃️ 10.8 Data platform contract

Locked decision

Why this is locked Supabase combines database, authentication, and storage services in one early-stage platform, which reduces platform sprawl. Neo4j supports vector indexes and graph-native relationship structures, which makes it appropriate for concept linking and graph-enhanced retrieval rather than treating the operational relational system as a graph store. ([Supabase][7])

Spec requirements

👀 10.9 Proof-stack contract

Locked decision

Why this is locked OpenTelemetry is a vendor-neutral observability framework for traces, metrics, and logs, and the Collector receives, processes, and exports telemetry. Prometheus is an established time-series monitoring and alerting system, and Grafana provides the dashboard layer on top. Langfuse is specifically built for large-language-model tracing, prompt management, and evaluation, while MLflow spans model and AI lifecycle tracking. ([OpenTelemetry][8])

Spec requirements

🗃️ 11. Data contract

🎯 11.1 Data philosophy

The data layer exists to preserve:

The system must not blur those categories together.

🗃️ 11.2 Operational truth

Operational truth is the product state that powers the learning experience.

It must cover at least:

Specification

🪣 11.3 Artifact truth

Artifact truth is the raw or transformed source material used to support learning.

It includes:

Specification

🕸️ 11.4 Relationship truth

Relationship truth is the structure that connects concepts, notes, cards, and sources.

It includes:

Specification

📎 11.5 Retrieval truth

Retrieval truth is the system’s ability to assemble relevant evidence for a learner task.

It must support:

Specification

🧼 11.6 Data quality contract

Data quality is a first-class system concern.

The data layer must support:

Specification

📏 11.7 Data quality metrics

These metric families must exist by the time data flows are production-facing:

These metrics are not optional because ingestion quality is one of the easiest ways for an AI learning product to quietly fail.

🤖 12. AI and ML contract

🎯 12.1 Intelligence philosophy

AI and ML in SynaWeave exist to improve:

They do not exist merely to increase product surface area.

Every AI or ML feature must improve at least one of:

📎 12.2 Retrieval contract

The product must support hybrid retrieval rather than a single retrieval strategy.

It must be able to combine:

Specification

Industry-standard RAG evaluation guidance now commonly distinguishes retrieve-only and retrieve-and-generate evaluation and includes metrics such as context relevance, context coverage, correctness, completeness, faithfulness, citation precision, and citation coverage. SynaWeave should align its retrieval and answer evaluation with that separation. ([AWS Documentation][9])

🤖 12.3 Tutor contract

The tutor must be stateful and adaptive.

It must:

Specification

📈 12.4 Classical ML contract

Classical ML is a first-class part of the platform.

It should support:

Specification

🧬 12.5 Adaptation contract

Model adaptation is part of the roadmap, but it is not the first move.

Specification

Parameter-efficient adaptation is the default posture because it is designed to update a small subset of parameters and thereby reduce adaptation cost and storage requirements compared with broad full-model retraining. ([Hugging Face][10])

🧠 12.6 Learner-model contract

The learner model is a product system, not a hidden analytics feature.

It must be able to represent:

Specification

🧪 12.7 Evaluation contract

Evaluation is mandatory for all meaningful AI-facing behavior.

The AI layer must support:

OpenAI’s evaluation guidance is explicit that evaluation should be part of the development lifecycle and that teams should compare system variants against known target behavior rather than relying on anecdotal prompts alone. ([OpenAI Developers][11])

Specification

📊 12.8 Required AI metric families

The following metric families must exist by the time the corresponding AI surfaces are user-facing:

📎 Retrieval metrics

🤖 Generation metrics

📚 Grounding metrics

📈 Recommendation and adaptation metrics

These metric families are aligned with current RAG evaluation practice and general LLM evaluation practice rather than ad hoc product-only scoring. ([AWS Documentation][9])

👀 13. Observability contract

🎯 13.1 Proof philosophy

SynaWeave must be able to answer, from evidence rather than opinion:

This is why proof is a permanent system layer rather than an implementation concern.

👀 13.2 Observability scope

The observability contract must cover:

👀 13.3 Telemetry requirements

The platform must support all three classic telemetry classes:

OpenTelemetry explicitly defines these as the core telemetry signals, and the Collector exists to receive, process, and export telemetry data in a vendor-neutral way. ([OpenTelemetry][8])

Specification

👀 13.4 Dashboard requirements

At minimum, the proof layer must support dashboard families for:

Grafana is the default dashboard surface because it is designed for querying, visualizing, and alerting on operational data across multiple sources. ([Grafana Labs][12])

📊 13.5 SLI and SLO contract

The product must use industry-standard service reliability language.

Google’s SRE guidance defines a service level indicator as a carefully defined quantitative measure of service behavior and a service level objective as a target value or range for that indicator. It also recommends multi-threshold latency objectives rather than a single average. ([Google SRE][13])

Specification The following SLI families are mandatory:

The following SLO families must eventually exist:

💸 13.6 Cost contract

AI-assisted products fail silently if they do not measure cost.

The proof layer must support:

Langfuse explicitly supports token and cost tracking for large-language-model workflows, which is why it is part of the proof stack rather than a nice-to-have. ([Langfuse][14])

🧪 13.7 Experiment contract

The proof layer must support:

MLflow is part of the proof contract because it supports experiment tracking and broader model lifecycle management, which complements the online and product-facing observability surfaces. ([MLflow AI Platform][15])

🔐 14. Trust and quality contract

🎯 14.1 Trust philosophy

Trust in SynaWeave must be designed, not implied.

The product must be able to explain:

NIST’s AI Risk Management Framework and its generative AI profile make clear that trustworthy AI systems should be valid and reliable, safe, secure and resilient, accountable and transparent, privacy-enhanced, and continuously measured and managed. ([NIST Publications][16])

🛡️ 14.2 Trust requirements

SynaWeave must support:

🛡️ 14.3 Safety contract

The platform must assume that generative and adaptive systems can fail in ways that are:

Specification

📏 14.4 Quality contract

Quality in SynaWeave is multi-dimensional.

It includes:

Specification No capability is “done” unless it can be judged across all relevant quality dimensions.

For example:

📚 14.5 Accessibility and usability contract

The product must be accessible and understandable enough for real learners to use effectively.

Specification

📊 14.6 Required trust and quality metrics

The following metric families must exist by the time the corresponding surfaces are public:

🔐 Trust metrics

🎓 Learning metrics

👀 Product-quality metrics

⚙️ Operational-quality metrics

📏 14.7 Standard for claims

SynaWeave may only claim a quality attribute publicly when the proof layer supports it.

Examples:

No marketing claim should outrun measurement.

🧱 15. Part 2 acceptance standard

Part 2 is correct only if a reader can answer all of these without consulting lower-level documents:

If those answers are not clear after reading this section, Part 2 is incomplete.

🧭 16. Overview

This part defines the roadmap contract for SynaWeave.

It answers these questions:

This part is intentionally deliverable-level only. It does not define task breakdowns. Task decomposition belongs in sprint-level deliverable planning files.

🗺️ 17. Roadmap philosophy

The roadmap is designed around three constraints.

🎯 17.1 Product constraint

Every sprint must produce visible product progress for learners. There are no “infrastructure-only” sprints after the platform shell is established.

💼 17.2 Proof constraint

Every sprint must also produce visible proof for investors and senior technical reviewers. That proof can take the form of:

🔀 17.3 Team constraint

From Sprint 2 onward, the roadmap must support five parallel work lanes so a five-engineer team can execute asynchronously with minimal blocking.

This is mandatory. The roadmap should not assume a single-threaded team.

🔀 18. Parallel delivery model

Starting in Sprint 2, every sprint is organized into five parallel deliverables.

These five deliverables are not random. They are the permanent concurrency lanes of the roadmap.

📥 18.1 Source lane

Owns:

✏️ 18.2 Workspace lane

Owns:

🃏 18.3 Practice lane

Owns:

🤖 18.4 Intelligence lane

Owns:

👀 18.5 Proof lane

Owns:

📏 18.6 Parallel lane rule

These lanes are permanent because they map cleanly to the actual product and technical architecture. A five-engineer team should be able to own one lane each per sprint while still converging on one product story.

This lane structure minimizes blocking because:

🛣️ 19. Sprint sequence

The roadmap is fixed to six sprints.

Sprint 1  →  base and shell
Sprint 2  →  capture and workspace
Sprint 3  →  practice and pedagogy
Sprint 4  →  intelligence and adaptation
Sprint 5  →  hardening and proof
Sprint 6  →  native and browser path

📏 19.1 Sequence invariants

These rules are mandatory:

🛣️ 20. Sprint 1 — Base and shell

🎯 Goal

Establish the platform shell and the permanent execution contract.

Sprint 1 exists to make all later work predictable, measurable, and aligned.

🚚 Deliverable 1 — Foundation

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🚚 Deliverable 2 — Runtime

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🚚 Deliverable 3 — Quality

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

✅ Sprint 1 exit criteria

Sprint 1 is complete only when:

🛣️ 21. Sprint 2 — Capture and workspace

🎯 Goal

Make the product genuinely useful for real knowledge work.

Sprint 2 is the first sprint in which the product must behave like a real learning tool instead of a platform shell.

🔀 Parallel deliverables

📥 Deliverable 1 — Capture and provenance

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

✏️ Deliverable 2 — Workspace and note structure

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🃏 Deliverable 3 — Practice and review core

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🤖 Deliverable 4 — Guidance and organization intelligence

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

👀 Deliverable 5 — Activation and proof baseline

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

✅ Sprint 2 exit criteria

Sprint 2 is complete only when:

🛣️ 22. Sprint 3 — Practice and pedagogy

🎯 Goal

Turn SynaWeave from a strong workspace into a strong learning experience.

Sprint 3 is the pedagogy sprint. It must make the product visibly better at producing learning, not just organization.

🔀 Parallel deliverables

📥 Deliverable 1 — Practice content expansion

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

✏️ Deliverable 2 — Guided workspace support

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🃏 Deliverable 3 — Quiz engine and memory systems

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🤖 Deliverable 4 — Technical learner tracks

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

👀 Deliverable 5 — Learning and engagement proof

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

✅ Sprint 3 exit criteria

Sprint 3 is complete only when:

🛣️ 23. Sprint 4 — Intelligence and adaptation

🎯 Goal

Make the system adaptive, graph-grounded, and technically differentiated.

Sprint 4 is where the product’s AI and ML depth must become obvious to both users and technical reviewers.

🔀 Parallel deliverables

📥 Deliverable 1 — Multimodal data engine

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

✏️ Deliverable 2 — Knowledge graph and connected workspace

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🃏 Deliverable 3 — Adaptive practice and recommendation

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🤖 Deliverable 4 — Grounded tutor and learner model

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

👀 Deliverable 5 — AI and ML proof depth

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

✅ Sprint 4 exit criteria

Sprint 4 is complete only when:

🛣️ 24. Sprint 5 — Hardening and proof

🎯 Goal

Add no major new breadth. Make the platform credible for live users, investor diligence, and senior-to-staff technical scrutiny.

Sprint 5 is where quality becomes legible and defendable.

🔀 Parallel deliverables

📥 Deliverable 1 — Source and trust hardening

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

✏️ Deliverable 2 — Workspace and user reliability

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🃏 Deliverable 3 — Performance and efficiency

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🤖 Deliverable 4 — Trustworthy intelligence

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

👀 Deliverable 5 — Flagship proof surface

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

✅ Sprint 5 exit criteria

Sprint 5 is complete only when:

🛣️ 25. Sprint 6 — Native and browser path

🎯 Goal

Begin the native and browser-level expansion path only after the platform is already credible.

Sprint 6 is about extension of the moat, not rescue of the core.

🔀 Parallel deliverables

📥 Deliverable 1 — Local source acceleration

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

✏️ Deliverable 2 — Local workspace resilience

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🃏 Deliverable 3 — Local review and study support

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

🤖 Deliverable 4 — Native intelligence seam

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

👀 Deliverable 5 — Expansion proof

Purpose:

Must prove:

User-visible outcome:

Investor-visible outcome:

✅ Sprint 6 exit criteria

Sprint 6 is complete only when:

📏 26. Roadmap-level acceptance standards

The roadmap is only successful if all of the following are true by the end of Sprint 6.

✅ 26.1 Product acceptance

✅ 26.2 Learning acceptance

✅ 26.3 AI acceptance

✅ 26.4 Systems acceptance

✅ 26.5 Investor acceptance

A reasonable investor should be able to understand:

✅ 26.6 Senior-to-staff interview acceptance

A reasonable senior-to-staff technical reviewer should be able to see:

❌ 27. Roadmap failure conditions

The roadmap should be treated as failing if any of these become true:

✅ 28. Part 3 acceptance standard

Part 3 is correct only if a reader can answer all of these without consulting lower-level documents:

If those answers are not clear after reading this section, Part 3 is incomplete.

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