MindsEye Ecosystem — High-Level Overview
This diagram presents MindsEye as an ecosystem, not a single application.
At a glance, what you’re seeing is a ledger-first AI system designed to turn unstructured data (documents, browser activity, external datasets, automation events) into time-labeled, auditable, and queryable records that AI agents can reason over safely.
Instead of treating data as something that is overwritten or forgotten, MindsEye treats every action, transformation, and decision as an append-only event recorded in a central ledger.
How to Read This Diagram
The ecosystem is organized vertically and by flow:
Top / Upper Layers represent inputs and intelligence
(unstructured data, user actions, AI orchestration).
The Center represents the Ledger Core
— the single source of truth where everything is time-stamped and written immutably.
Lower Layers represent automation, indexing, querying, and scheduling
— how recorded knowledge is reused, observed, and acted upon.
Data flows inward toward the Ledger Core, then outward into automations, queries, and dashboards.
Key Labeled Components (What Each Part Is)
Unstructured Data from Web
Raw inputs such as documents, emails, web pages, datasets, or browser activity.
Google Connectors / Browser Actions / Automation Shell
Entry points that ingest data from Google Workspace, Chrome activity, scheduled jobs, or scripts.
Data Splitter
Breaks large or messy inputs into context-preserving chunks that are easier to process.
Gemini Orchestrator
Uses Google’s Gemini models to reason over incoming data, decide next actions, and coordinate agents.
Agentic Layer
AI agents that operate on recorded state instead of raw prompts — enabling reproducible behavior.
MCP Server / State Engine
Maintains system state and controls how agents interact with tools and data.
Ledger Core
The heart of MindsEye.
Every event is:
time-labeled
append-only
never overwritten
This makes the system auditable, explainable, and reversible.
Dashboard / Queries / Automation
Interfaces and workflows that read from the ledger to trigger actions, generate insights, or visualize system memory.
The Data Flow Key (Right Side Legend)
The legend on the right explains what the colored paths mean:
Ingests – Raw data entering the system
Writes To – Events being committed to the ledger
Indexes – Making records searchable
Queries – Reading structured memory
Orchestrates – AI-driven decision making
Secures – Identity, access control, and isolation
Observes – Monitoring system behavior over time
Triggers – Automated reactions to ledger events
The different paths and colors show that MindsEye supports multiple entry points, but all roads lead to the same normalization and ledger pipeline.
Core Principle
Time-label everything. Append-only truth.
This principle allows MindsEye to move beyond prompt-based AI into memory-driven AI systems where:
decisions can be explained
history can be replayed
and automation can be trusted
Section 1 — Ingestion Layer
This layer is the “front door” of MindsEye. Its job is to capture raw, unstructured inputs from multiple sources (Google Workspace, browser activity, external datasets), attach minimal context (timestamps, source metadata, identifiers), and push that material into a consistent pipeline so downstream components can process it safely.
The key idea in the image is multiple entry points: MindsEye does not rely on a single start point. Different sources produce different kinds of raw data, but all of them are routed into the same ingestion and preparation path.
What is happening in this layer (simple, end-to-end)
Raw data enters from real-world sources
Examples: Google Docs/Drive/Gmail/Calendar, browser sessions and page content, Kaggle datasets/competition files, and other web inputs.
Each entry point captures data plus context
The ingestion step is not trying to “understand” everything yet. It captures:
the artifact (document, email, page content, dataset file)
where it came from (source system)
when it was seen (timestamp)
minimal identifiers (file IDs, URLs, user/session references)
Data is routed through a splitter/pre-processor
Large inputs are segmented into smaller units (“chunks”) that preserve context boundaries, so later extraction and labeling can happen reliably.
Output becomes “ingestion payloads”
These are raw artifacts and prepared chunks ready for normalization, labeling, and ledger writing in the next layers.
Component responsibilities and the repos that implement them
1) Google Workspace Connectors
What it does:
Captures raw artifacts and metadata from Google Workspace surfaces (Docs, Drive, Gmail, Calendar). This is where “workspace data becomes ingestible events.”
Responsibilities:
Authenticate and access workspace APIs
Detect changes (documents updated, emails received, calendar events created)
Extract raw content + key metadata (IDs, timestamps, authors, folders, event fields)
Produce raw artifacts / ingestion events
Relevant repos:
https://github.com/PEACEBINFLOW/minds-eye-gworkspace-connectors/tree/main
https://github.com/PEACEBINFLOW/mindseye-google-workflows/tree/main
https://github.com/PEACEBINFLOW/mindseye-google-gateway/tree/main
https://github.com/PEACEBINFLOW/mindseye-google-auth/tree/main
(Gateway/Auth are included here because ingestion from Workspace requires identity + access scaffolding to reliably pull data.)
2) Workspace Automation
What it does:
Runs scheduled or event-driven harvesting jobs so ingestion is continuous. Instead of a one-time import, this component ensures MindsEye keeps collecting data over time.
Responsibilities:
Scheduled ingestion (cron-like polling or timed jobs)
Event-based ingestion (webhooks or triggers where applicable)
Packaging results into “batch ingestion payloads”
Managing retries and routine collection cycles
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-workspace-automation/tree/main
https://github.com/PEACEBINFLOW/minds-eye-automations/tree/main
https://github.com/PEACEBINFLOW/mindseye-google-workflows/tree/main
3) Chrome Agent Shell (Browser Entry Point)
What it does:
Captures unstructured web context and user-driven browser activity as ingestible artifacts. This is the “interaction surface” entry point.
Responsibilities:
Collect browser actions (navigation, selection, captured text, page context)
Package web artifacts (URLs, extracted content, session context)
Provide a controlled interface for “browser-sourced ingestion”
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-chrome-agent-shell/tree/main
https://github.com/PEACEBINFLOW/mindseye-workspace-automation/tree/main
(if used to schedule or coordinate pulls from browser tasks)
4) Data Splitter (Ingestion Preparation)
What it does:
Transforms large raw inputs into smaller context-preserving segments. This is not deep extraction yet; it is preparation so later engines can work deterministically.
Responsibilities:
Segment long documents into coherent chunks
Preserve boundaries (sections, headings, blocks, message threads)
Attach chunk metadata (source, offsets, timestamps, lineage)
Produce “ready-for-processing” units
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-data-splitter/tree/main
5) Kaggle Binary Ledger (External Dataset Entry Point)
What it does:
Ingests structured and semi-structured datasets (CSV, binary files, competition artifacts) from Kaggle and converts them into experiment-ready records.
Responsibilities:
Pull/import datasets and competition files
Record dataset identity, versions, and acquisition context
Produce structured experiment records for downstream normalization and ledger writing
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-kaggle-binary-ledger/tree/main
Summary of the ingestion contract
At the end of this layer, MindsEye has not “solved” or “understood” the data yet. It has done something more foundational:
gathered raw artifacts from multiple real-world entry points
attached time and source context
segmented large inputs into processable chunks
produced standardized ingestion payloads that downstream layers can normalize, label, and write to the ledger
Section 2 — Extraction and Normalization
This layer is where MindsEye converts raw, messy inputs into records that can be safely stored, searched, and reasoned over. The ingestion layer brings data in. This layer makes that data usable.
In simple terms, the goal here is:
Split large unstructured artifacts into stable chunks
Extract structure from those chunks (entities, fields, labels)
Normalize formats so different sources look consistent
Preserve reversibility so the original can be reconstructed from processed output
Produce structured records ready to be written into the ledger core
The diagram shows this as a three-step flow:
Ingests (raw data arrives), 2) Extracts (meaning is pulled out), 3) Writes To (structured output is prepared for the ledger).
What is happening in this layer (simple, end-to-end)
Raw unstructured data arrives from ingestion
Inputs can be long documents, emails, PDFs, browser captures, or dataset files.
Data is segmented with context preserved
The system splits content into chunks that preserve boundaries (sections, topics, message threads) so extraction does not lose meaning.
Binary Engine performs extraction and labeling
The system applies deterministic labeling and structured extraction, producing records with explicit fields and tags.
Moving Library supplies transformation utilities
Conversions and standardization operations are applied (data types, formats, schemas) to ensure outputs are consistent.
Output becomes structured records
Each processed unit now contains standardized format, extracted entities, metadata tags, and timestamps, ready for ledger storage.
Component responsibilities and the repos that implement them
1) Data Splitter (Detailed)
What it does:
Transforms large or messy inputs into context-preserving chunks. This step reduces ambiguity and makes downstream extraction repeatable.
Responsibilities:
Semantic boundary detection (identify natural breakpoints)
Context-preserving segmentation (avoid splitting meaning mid-stream)
Chunk size optimization (consistent processing units)
Metadata attachment (source, offsets, timestamps, lineage)
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-data-splitter/tree/main
2) Binary Engine
What it does:
Performs structured extraction and labeling on the chunks produced by the splitter. This is where unstructured text becomes explicit records.
Responsibilities:
Binary labeling (0/1 classification-style tagging)
Entity extraction (people, projects, events, objects, references)
Reversible transformations (retain enough information to reconstruct original context)
Field normalization (map extracted content into consistent structured fields)
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-binary-engine/tree/main
Supporting repos (system-level definitions and shared schemas):
https://github.com/PEACEBINFLOW/mindseye-protocol/tree/main
https://github.com/PEACEBINFLOW/minds-eye-core/tree/main
(Protocol and core are included here because extraction and labeling require shared schemas for what constitutes a record, a label set, and a normalized field map.)
3) Moving Library
What it does:
Provides the transformation operators used throughout normalization. This is the utility layer that keeps normalization consistent across many sources.
Responsibilities:
Data type conversions (text, numbers, timestamps, identifiers)
Format standardization (consistent record shapes)
Motion transformations (the reusable transformation operators used across pipelines)
Utility operators (helpers used by Binary Engine and later ledger writing)
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-moving-library/tree/main
4) Structured Output Preparation (Write-Ready Records)
What it does:
Packages normalized records so the next layer (ledger storage) can write them as append-only events.
This is not yet the ledger core itself. It is the output contract: a consistent shape that ledger writers and ledger HTTP interfaces can accept.
Responsibilities:
Ensure every record includes mandatory metadata (time, source, lineage)
Maintain stable IDs for linking and future querying
Prepare records for append-only commit
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-ledger-core/tree/main
https://github.com/PEACEBINFLOW/mindseye-ledger-http/tree/main
https://github.com/PEACEBINFLOW/mindseye-google-ledger
(These are placed here because the output of normalization must match the ledger writing contract. Ledger Core defines the write model; Ledger HTTP exposes the write surface; Google Ledger represents the Google-integrated ledger implementation in your ecosystem.)
Normalization goal (what this layer guarantees)
By the end of this layer, every piece of input data becomes a consistent structured record with:
A standardized format (same field layout across sources)
Extracted entities (names, events, projects, references)
Metadata tags (source, type, lineage)
Timestamps (when seen, when processed)
Reversible context (ability to trace back to the original chunk)
This is what makes MindsEye “ledger-first”: the ledger does not store raw noise. It stores normalized, time-labeled truth.
Section 3 — Ledger Core (Time-Labeled, Append-Only Truth)
This layer is the heart of MindsEye.
Everything before this point prepares data.
Everything after this point trusts data.
The Ledger Core is where processed information becomes immutable fact. Once data enters this layer, it is no longer “data being worked on” — it is recorded history.
The image represents the Ledger Core as a central, glowing structure because it is the single source of truth for the entire ecosystem.
What is happening in this layer (simple, end-to-end)
Normalized records arrive from Extraction & Normalization
Each record already has structure, metadata, timestamps, and lineage.
Records are written as append-only events
Nothing is updated, overwritten, or deleted. New truth is always added.
Events are time-labeled and indexed
Every fact is anchored in time, allowing replay, snapshots, and historical queries.
Multiple ledger surfaces expose the same truth
Different consumers (APIs, agents, templates, dashboards) access the same underlying event stream through controlled interfaces.
Downstream systems observe, query, and reason
Agents, workflows, and dashboards operate on facts, not mutable state.
Core principles of this layer
Time only moves forward
Facts do not change
State is derived, never stored as mutable truth
History is queryable and replayable
Every action leaves a trace
This is what makes MindsEye “ledger-first.”
Component responsibilities and aligned repositories
1) Ledger Core (Foundation)
What it does:
Implements the append-only event storage engine that all other systems rely on.
Responsibilities:
Immutable event entries
Timestamp-indexed storage
Snapshot support (derived state, not mutation)
Queryable historical timeline
Event-sourcing architecture
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-ledger-core/tree/main
https://github.com/PEACEBINFLOW/mindseye-protocol/tree/main
(The protocol defines what an event is. The ledger core enforces how events are stored.)
2) Time-Labeled Storage
What it does:
Ensures every event carries precise temporal context and lineage.
Responsibilities:
High-precision timestamps
Source identification
Event ordering guarantees
Traceability across ingestion, extraction, and execution
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-ledger-core/tree/main
https://github.com/PEACEBINFLOW/mindseye-google-ledger
3) Google Ledger (Workspace-Specific Events)
What it does:
Implements a Google-integrated ledger that captures Workspace activity as first-class events.
Responsibilities:
Document edit events
Email send/receive records
Calendar event changes
Drive file operations
Collaboration actions
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-google-ledger
https://github.com/PEACEBINFLOW/minds-eye-gworkspace-connectors/tree/main
4) Ledger HTTP (Access Surface)
What it does:
Exposes the ledger through a controlled HTTP API without compromising immutability.
Responsibilities:
Event ingestion endpoints
Query APIs
Snapshot retrieval
Controlled read access for agents and dashboards
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-ledger-http/tree/main
https://github.com/PEACEBINFLOW/mindseye-google-gateway/tree/main
5) MindScript Ledger (Execution Records)
What it does:
Stores execution and template-level actions as ledger events.
Responsibilities:
Template execution records
Agent decision traces
Script-level outcomes
Deterministic replay of executions
Relevant repos:
https://github.com/PEACEBINFLOW/mindscript-ledger/tree/main
https://github.com/PEACEBINFLOW/mindscript-core/tree/main
https://github.com/PEACEBINFLOW/mindscript-runtime/tree/main
How data moves through this layer
Incoming structured records are written once
Ledger Core assigns time and ordering
Specialized ledgers (Google, MindScript) tag domain-specific metadata
HTTP and internal gateways expose read access
No consumer can mutate history
All state elsewhere is derived from ledger events
Why this layer matters
Without this layer:
AI systems forget
State becomes inconsistent
Debugging becomes guesswork
Trust erodes when outputs change silently
With this layer:
Every decision is explainable
Every output has provenance
Every system can replay history
Intelligence compounds instead of resetting
Section 4 — Protocol + Identity Layer (The Security Shield)
This layer defines how components communicate and who is allowed to participate in that communication.
Everything below this layer (ledger, data, execution) assumes truth and immutability.
Everything above this layer (agents, workflows, dashboards) assumes safety and trust.
The Protocol + Identity Layer is the boundary that enforces both.
In the image, this layer is represented as a protective dome because nothing enters or exits the MindsEye ecosystem without passing through it.
What is happening in this layer (simple, end-to-end)
A request is initiated
A user, agent, workflow, or external integration attempts to access a MindsEye component.
The request enters through the Gateway
All traffic is routed through a controlled entry point rather than direct service-to-service access.
Identity is verified
Authentication and authorization determine who or what is making the request and what they are allowed to do.
Protocol contracts are enforced
Requests must conform to agreed schemas, versions, and error-handling rules.
The request is forwarded to the target component
Only validated, well-formed, and authorized requests reach internal services.
Core purpose of this layer
Prevent unauthorized access
Enforce consistent communication rules
Provide auditability and traceability
Decouple internal services from direct exposure
Establish trust between distributed components
This layer makes the system safe to scale.
Component responsibilities and aligned repositories
1) MindsEye Protocol (Communication Contracts)
What it does:
Defines the shared language that all MindsEye components use to communicate.
This is not transport-specific. It defines what a valid request or event looks like, regardless of where it flows.
Responsibilities:
API request and response schemas
Data exchange formats
Error handling patterns
Versioning strategy
Service-to-service contracts
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-protocol/tree/main
https://github.com/PEACEBINFLOW/minds-eye-core/tree/main
(Core is included here because protocol definitions rely on shared domain models and event shapes.)
2) Google Gateway (Traffic Control and Routing)
What it does:
Acts as the controlled front door for all Google-integrated and external traffic entering the system.
No internal service is directly exposed.
Responsibilities:
Request routing
Load balancing
Rate limiting
Service discovery
Google API integration
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-google-gateway/tree/main
https://github.com/PEACEBINFLOW/mindseye-ledger-http/tree/main
(Ledger HTTP appears here because it is exposed only through gateway-controlled access.)
3) Google Auth (Identity and Access Control)
What it does:
Verifies identity and enforces permissions for users, services, and automated agents.
Responsibilities:
Authentication (user/service identity)
Authorization (what actions are permitted)
Credential validation
Access scoping
Integration with Google identity systems
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-google-auth/tree/main
4) Security Flow (End-to-End Enforcement)
What it does:
Coordinates protocol validation, identity checks, and routing into a single enforcement pipeline.
Responsibilities:
Validate credentials before execution
Ensure requests conform to protocol contracts
Reject malformed or unauthorized requests
Forward only verified traffic to internal components
Provide an audit trail for access and actions
Relevant repos (supporting the flow):
https://github.com/PEACEBINFLOW/mindseye-google-gateway/tree/main
https://github.com/PEACEBINFLOW/mindseye-protocol/tree/main
https://github.com/PEACEBINFLOW/mindseye-google-auth/tree/main
How data and requests move through this layer
All requests enter through the gateway
Identity is verified before access is granted
Protocol rules enforce consistency and safety
Authorized requests are routed internally
Every interaction can be logged and audited
No component trusts another blindly
Why this layer matters
Without this layer:
Unauthorized access becomes possible
Components cannot trust incoming data
APIs drift without version control
There is no clear audit trail
Scaling introduces risk instead of resilience
With this layer:
Trust is enforced, not assumed
Communication remains stable as the system grows
Security is centralized and observable
Internal services stay isolated and safe
Section 5 — Orchestration & AI Reasoning (The Brain That Decides)
This layer is where MindsEye becomes active.
Everything below this layer records truth.
This layer reads that truth, reasons over it, and decides what happens next.
The image represents this layer as a centralized brain with multiple outgoing connections because orchestration is not about storage or transport. It is about decision-making under context.
What is happening in this layer (simple, end-to-end)
A trigger occurs
A user action, automation, or scheduled event initiates a request.
Context is retrieved from the Ledger
The system reads relevant history, state snapshots, and prior outcomes.
The Orchestrator reasons about next steps
An AI-powered decision engine determines what actions should be taken.
State is tracked and updated
Session progress, task state, and transitions are managed consistently.
Tools and agents are invoked
External tools, APIs, or internal agents execute actions.
Results are written back to the Ledger
Every decision and outcome becomes part of the permanent record.
This loop is continuous: observe → decide → act → record.
Core purpose of this layer
Turn passive data into active intelligence
Coordinate complex multi-step workflows
Enable autonomous and semi-autonomous behavior
Maintain explainability and traceability of decisions
Allow AI systems to operate safely at scale
This is the layer that transforms MindsEye from a memory system into a thinking system.
Component responsibilities and aligned repositories
1) Gemini Orchestrator (Decision Engine)
What it does:
Serves as the central reasoning engine that decides what happens next based on context.
Responsibilities:
Read contextual data from the Ledger
Reason over goals, constraints, and history
Select next actions or workflows
Coordinate execution order
Determine completion or continuation
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-gemini-orchestrator
https://github.com/PEACEBINFLOW/mindscript-google-executor/tree/main
(The executor bridges reasoning outputs into executable actions.)
2) Agentic System (Autonomous Actions)
What it does:
Executes tasks autonomously once decisions are made by the orchestrator.
Responsibilities:
Perform delegated actions
Handle retries and error paths
Operate independently under defined constraints
Report outcomes back to the orchestrator
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-agentic
https://github.com/PEACEBINFLOW/MindsEye-hunting-engine
3) MCP Server (Tool Integration Layer)
What it does:
Provides standardized access to tools and external capabilities.
Responsibilities:
Tool discovery and registration
Context sharing between tools
Plugin-based architecture
Tool call execution and response aggregation
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-mcp-server
4) State Engine Framework (Session and Progress Tracking)
What it does:
Tracks conversational state, task progress, and execution flow across time.
Responsibilities:
Session persistence
Context window management
State transitions
Progress tracking
Resume and replay support
Relevant repos:
https://github.com/PEACEBINFLOW/state-engine-framework-mindseye-addition-
5) Orchestration Flow (End-to-End Execution Loop)
What it does:
Coordinates how reasoning, state, agents, and tools interact in sequence.
Responsibilities:
Receive user or automation triggers
Load ledger context
Invoke reasoning engine
Dispatch actions to agents or tools
Persist outcomes back to the ledger
Relevant repos (supporting flow):
https://github.com/PEACEBINFLOW/minds-eye-core/tree/main
https://github.com/PEACEBINFLOW/minds-eye-automations/tree/main
https://github.com/PEACEBINFLOW/minds-eye-dashboard/tree/main
How intelligence moves through this layer
Triggers initiate orchestration
Ledger provides historical grounding
AI reasoning determines intent and next steps
State engine tracks execution context
Agents and tools execute actions
Results are recorded as immutable events
No decision exists without history.
No action occurs without traceability.
Why this layer matters
Without this layer:
Data remains passive
Automation becomes brittle
AI decisions are opaque
Systems cannot adapt dynamically
With this layer:
Decisions are explainable
Workflows become adaptive
Intelligence compounds over time
Automation remains auditable and safe
Section 6 — Search & Hunting Layer (Finding Signal in Noise)
This layer is how MindsEye sees its own memory.
Everything below this layer records and reasons.
This layer retrieves, filters, and prioritizes information so both humans and agents can find what matters.
The image shows three coordinated towers connected to the Ledger Core because search in MindsEye is not a single index. It is a multi-perspective retrieval system operating over immutable history.
What is happening in this layer (simple, end-to-end)
A query is issued
A human, agent, or automation asks a question or requests information.
The Ledger Core is queried
All search systems pull from the same append-only event history.
Multiple retrieval strategies are applied
Keyword search, semantic search, intent-based ranking, and template-aware lookup run in parallel.
Results are ranked and filtered
Context, relevance, time, and intent determine what surfaces first.
Results are returned or fed back into orchestration
Output is either shown to users or passed upstream to the Orchestration Layer for decision-making.
Core purpose of this layer
Make large volumes of recorded history usable
Surface relevant context quickly
Support both human exploration and machine reasoning
Enable explainable retrieval from immutable data
Reduce cognitive load by prioritizing signal over noise
This layer turns memory into insight.
Component responsibilities and aligned repositories
1) MindsEye Search Engine (Core Search Infrastructure)
What it does:
Provides the foundational search capability across the entire ecosystem.
Responsibilities:
Full-text search across ledger data
Semantic similarity search
Time-range filtering
Source-based filtering
Entity-based queries
Hybrid search (keyword + semantic)
Indexes:
Documents
Emails
Events
Entities
Templates
Any structured ledger record
Relevant repos:
https://github.com/PEACEBINFLOW/minds-eye-search-engine/tree/main
https://github.com/PEACEBINFLOW/minds-eye-core/tree/main
2) MindsEye Hunting Engine (Intent-Aware Retrieval)
What it does:
Adds intelligence on top of search by understanding what the query is really asking.
Responsibilities:
Query intent detection
Context-aware re-ranking
Relevance scoring
Result diversity management
Precision targeting
Capabilities:
“Find all decisions related to X”
“Show me patterns over time”
“Surface anomalies or rare events”
Relevant repos:
https://github.com/PEACEBINFLOW/MindsEye-hunting-engine
3) MindScript Search (Template and Execution Search)
What it does:
Provides specialized search over MindScript artifacts and execution history.
Responsibilities:
Template library search
Historical template execution lookup
Prompt pattern retrieval
Cognitive action record discovery
Enables queries such as:
“Show all times this template was executed”
“Find similar prompts used last quarter”
“Which workflows used this action?”
Relevant repos:
https://github.com/PEACEBINFLOW/mindscript-search/tree/main
https://github.com/PEACEBINFLOW/mindscript-ledger/tree/main
4) Search Integration with Orchestration
What it does:
Feeds retrieved context directly into reasoning and decision-making workflows.
Responsibilities:
Provide ranked results to the Orchestrator
Support context expansion during reasoning
Enable dynamic retrieval during execution
Maintain traceability between queries and decisions
Relevant repos (supporting integration):
https://github.com/PEACEBINFLOW/mindseye-gemini-orchestrator
https://github.com/PEACEBINFLOW/mindseye-agentic
How search intelligence moves through this layer
Queries enter from users, agents, or automations
Core search retrieves candidate results
Hunting engine refines based on intent and context
Specialized search handles domain-specific artifacts
Results flow back to users or orchestration
Every query and result remains auditable
Why this layer matters
Without this layer:
Recorded history becomes unusable at scale
Context is lost in volume
Agents reason blindly
Humans cannot explore system memory effectively
With this layer:
Knowledge becomes accessible
Decisions are grounded in evidence
Exploration and reasoning converge
Memory becomes a strategic asset
Section 7 — MindScript Runtime Ecosystem (Templates → Execution)
This layer is where intention becomes action.
Everything above this layer decides what should happen.
Everything below this layer records what did happen.
This layer is the bridge that executes cognition in a deterministic, repeatable way.
The image shows MindScript as a runtime ecosystem, not just a DSL. It is a full execution pipeline that turns structured cognitive intent into real, traceable outcomes.
What is happening in this layer (simple, end-to-end)
A cognitive action is defined as a template
Reusable intent is encoded once instead of rewritten repeatedly.
Templates are parsed using a deterministic grammar
No free-form prompting. No ambiguity.
The runtime loads and binds context
Inputs, state, variables, and constraints are attached.
Execution happens in a controlled engine
Actions are executed step-by-step, not guessed.
Results are emitted and recorded
Outputs are sent to external systems and written back to the Ledger.
This is how MindsEye avoids “prompt chaos” and achieves structured cognition at scale.
Core purpose of this layer
Encode repeatable cognitive actions
Make AI behavior deterministic and auditable
Separate intent definition from execution
Enable high-performance and portable runtimes
Turn workflows into first-class, versioned assets
This layer turns thinking into infrastructure.
Component responsibilities and aligned repositories
1) MindScript Core (Language & Grammar)
What it does:
Defines the formal language used to describe cognitive actions.
Responsibilities:
Grammar rules
Syntax validation
Semantic constraints
Deterministic structure
Think of it as:
The programming language for structured AI interactions.
Relevant repos:
https://github.com/PEACEBINFLOW/mindscript-core/tree/main
2) MindScript Templates (Reusable Cognitive Actions)
What it does:
Stores reusable, composable cognitive templates.
Responsibilities:
Summarization templates
Analysis templates
Generation templates
Decision templates
Characteristics:
Parameterized
Versioned
Reusable across workflows
Executable, not just descriptive
Relevant repos:
https://github.com/PEACEBINFLOW/mindscript-templates/tree/main
https://github.com/PEACEBINFLOW/mindscript-demos/tree/main
3) MindScript Runtime (Execution Engine)
What it does:
Executes templates deterministically.
Execution flow:
Load → Bind → Execute → Result
Responsibilities:
Template loading
Context binding
Execution lifecycle
Output handling
Relevant repos:
https://github.com/PEACEBINFLOW/mindscript-runtime/tree/main
4) MindScript Runtime C (High-Performance Variant)
What it does:
Provides a high-speed runtime for performance-critical execution.
Use cases:
Edge execution
Embedded systems
High-throughput automation
Low-latency environments
Relevant repos:
https://github.com/PEACEBINFLOW/mindscript-runtime-c/tree/main
5) MindScript Ledger (Execution Recording)
What it does:
Records every template execution as an immutable ledger event.
Responsibilities:
Execution trace storage
Input/output capture
Version tracking
Auditability
Why it matters:
Every action is explainable
Every result is reproducible
Every decision has provenance
Relevant repos:
https://github.com/PEACEBINFLOW/mindscript-ledger/tree/main
6) MindScript Google Executor (External Delivery)
What it does:
Delivers execution results into Google Workspace and external services.
Responsibilities:
Workspace integrations
Result delivery
External system interaction
Relevant repos:
https://github.com/PEACEBINFLOW/mindscript-google-executor/tree/main
How execution flows through this layer
Orchestrator selects a template
Template is validated by MindScript Core
Runtime binds live context
Execution engine runs deterministically
Results are delivered externally
Execution is written to the Ledger
No black boxes. No silent failures. No lost intent.
Why this layer matters
Without this layer:
AI behavior is inconsistent
Prompts become unmanageable
Execution cannot be audited
Scaling breaks reliability
With this layer:
Cognition is modular
Actions are reusable
Execution is deterministic
Systems remain explainable
Big picture takeaway
MindScript is not “prompt engineering.”
It is cognitive infrastructure.
It turns AI from a conversational tool into an operational system—one that can be reasoned about, audited, optimized, and trusted.
Section 8 — Automation & Workflows Layer (“If X happens, do Y”)
This layer is where decisions turn into recurring behavior.
Everything above this layer reasons, plans, and decides.
This layer ensures those decisions execute automatically, repeatedly, and on time—without human intervention.
The image presents this layer as a mechanical clockwork: triggers feed conditions, conditions select actions, and actions flow back into the system and the Ledger.
What is happening in this layer (end-to-end)
A trigger fires
Time-based, event-based, or system-generated.
Conditions are evaluated
IF / ELSE IF / ELSE logic determines the path.
Actions are executed
Data collection, template execution, notifications, or orchestration.
Results are recorded
Outcomes are written back into the Ledger for traceability.
The cycle continues
Automations become living system behaviors, not one-off scripts.
This layer turns MindsEye from a reactive system into a self-running one.
Core purpose of this layer
Enable scheduled and event-driven execution
Encode “if-this-then-that” logic at system scale
Remove manual repetition
Maintain data freshness automatically
Create continuous feedback loops
This is where time becomes a first-class input.
Component responsibilities and aligned repositories
1) Automation Engine
What it does:
Acts as the execution core for automation logic.
Responsibilities:
Event-driven execution
Trigger evaluation
Conditional branching
Action dispatch
Characteristics:
Deterministic
Repeatable
Ledger-aware
Relevant repos:
https://github.com/PEACEBINFLOW/minds-eye-automations/tree/main
https://github.com/PEACEBINFLOW/state-engine-framework-mindseye-addition-
2) Triggers System
What it does:
Defines when automation runs.
Trigger types:
Time-based (schedules, intervals)
Event-based (data changes, signals)
Webhook-based (external systems)
Manual initiation (user actions)
Relevant repos:
https://github.com/PEACEBINFLOW/minds-eye-automations/tree/main
https://github.com/PEACEBINFLOW/mindseye-workspace-automation/tree/main
3) Schedule Entry Points
What it does:
Provides deterministic temporal entry points into the system.
Examples:
Every Monday at 09:00
Hourly priority checks
Daily backups
Monthly report generation
Why it matters:
Time becomes programmable
No missed tasks
Predictable execution
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-workspace-automation/tree/main
4) Actions Layer
What it does:
Defines what happens when conditions are met.
Action types:
Data harvesting
MindScript template execution
Report generation
Notifications
Workflow orchestration
Relevant repos:
https://github.com/PEACEBINFLOW/mindscript-runtime/tree/main
https://github.com/PEACEBINFLOW/mindscript-templates/tree/main
https://github.com/PEACEBINFLOW/mindseye-google-workflows/tree/main
5) Sources Integration
What it does:
Feeds automations with live system inputs.
Source examples:
Google Workspace events
Ledger updates
Search & Hunting results
External system signals
Relevant repos:
https://github.com/PEACEBINFLOW/minds-eye-gworkspace-connectors/tree/main
https://github.com/PEACEBINFLOW/mindseye-google-ledger
6) MindsEye Playground (Safe Testing Environment)
What it does:
Allows automation logic to be tested without risk.
Use cases:
Prototype new workflows
Debug automation flows
Validate triggers and actions
Experiment safely
Relevant repos:
https://github.com/PEACEBINFLOW/minds-eye-playground/tree/main
Automation flow (as depicted in the image)
Trigger fires (time or event)
Automation engine evaluates IF conditions
ELSE IF / ELSE paths resolved
Actions executed
Outputs delivered
Results written to Ledger
System waits for next trigger
This is closed-loop automation, not fire-and-forget scripting.
Why this layer matters
Without this layer:
Intelligence stays passive
Systems rely on manual execution
Context decays over time
Repetition causes error and fatigue
With this layer:
The system runs continuously
Knowledge stays fresh
Behavior compounds over time
Humans stay focused on intent, not execution
Big picture takeaway
This layer makes MindsEye alive in time.
It does not just think or remember—it acts, repeatedly, reliably, and audibly.
Automation here is not about convenience.
It is about turning cognition into sustained behavior.
Section 9 — UI & Presentation Layer (“Where Humans See It All”)
This layer is where everything the system knows, does, and remembers becomes visible.
All prior layers operate below the surface: ingesting, normalizing, reasoning, automating, and recording.
This layer translates that complexity into clear, navigable, human-facing interfaces.
If the Ledger is the memory and the Orchestrator is the brain, this layer is the eyes, hands, and control room.
What is happening in this layer (end-to-end)
User issues a query or action
Through dashboards, search, or direct interaction.
Query propagates inward
Routed through gateway → search → ledger → reasoning layers.
System resolves results
Data is fetched, ranked, contextualized, and prepared.
UI renders results back
Visuals, timelines, graphs, documents, and flows are displayed.
Interaction is logged
Every user action becomes an auditable system event.
This layer closes the loop between human intent and system intelligence.
Core purpose of this layer
Make system state observable
Provide explainability and transparency
Enable exploration, not just answers
Give humans confidence and control
Turn intelligence into insight
This is where MindsEye stops being infrastructure and becomes a product.
Component responsibilities and aligned repositories
1) MindsEye Dashboard (Primary Control Surface)
What it does:
Acts as the main interface into the entire ecosystem.
Capabilities:
Search across all data
Browse ledger history
Inspect AI decisions
Monitor automations
Explore documents and templates
Visualize data flows
Why it matters:
This is where users understand what happened, why it happened, and what is happening now.
Relevant repos:
https://github.com/PEACEBINFLOW/minds-eye-dashboard/tree/main
2) Core UI Platform
What it does:
Provides shared UI foundations used across all interfaces.
Responsibilities:
Reusable UI components
Design system enforcement
State management
Navigation infrastructure
UI consistency across surfaces
Relevant repos:
https://github.com/PEACEBINFLOW/minds-eye-core/tree/main
3) User Query Entry Point
What it does:
Defines how humans initiate interaction with the system.
Entry types:
Search queries
Natural language prompts
Dashboard actions
Automation inspection
Document exploration
Flow:
User input → search / ledger → results → render
Relevant repos:
https://github.com/PEACEBINFLOW/minds-eye-search-engine/tree/main
https://github.com/PEACEBINFLOW/mindscript-search/tree/main
4) Rendering & Visualization Engine
What it does:
Turns structured system outputs into human-readable visuals.
Displays:
Charts and metrics
Timelines
Execution graphs
Automation flows
Document diffs
Decision traces
Why it matters:
Understanding emerges from structure + visualization, not raw data.
Relevant repos:
https://github.com/PEACEBINFLOW/minds-eye-dashboard/tree/main
5) Google Analytics Integration
What it does:
Observes how humans interact with the system itself.
Tracks:
Query patterns
API usage
Search behavior
Template execution frequency
Interaction paths
Purpose:
Improve UX
Optimize performance
Detect friction points
Close the feedback loop
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-google-analytics/tree/main
6) Google Devlog & Interaction Logging
What it does:
Records every meaningful UI interaction as a system event.
Captured events:
Queries issued
Results rendered
Dashboard views
State transitions
Debug and inspection flows
Why it matters:
The UI itself becomes part of the auditable memory.
Relevant repos:
https://github.com/PEACEBINFLOW/mindseye-google-devlog/tree/main
Interaction flow (as depicted in the image)
User enters query via UI
Query routed inward to search and ledger
Results resolved and ranked
UI renders insights back
User explores or refines query
All interactions logged
System state remains transparent
This is bidirectional visibility: humans see the system, and the system sees human intent.
Why this layer matters
Without this layer:
Intelligence is hidden
Decisions feel opaque
Trust erodes
Systems feel “black-boxed”
With this layer:
Every action is explainable
Every result is traceable
Every decision is inspectable
Humans stay in the loop
This is where trust is earned.
Big picture takeaway
This layer is not “just a frontend.”
It is:
A transparency layer
A cognition mirror
An accountability surface
A human–AI collaboration space
MindsEye does not replace human understanding.
It amplifies it, visually, interactively, and responsibly.
Section 10 — SQL & Cloud Layer (“The Foundation Below”)
This layer is the structural bedrock of the entire MindsEye ecosystem.
Everything above it reasons, orchestrates, visualizes, and automates — but everything rests on this layer’s guarantees: durability, consistency, scalability, and availability.
If the Ledger is truth and the UI is visibility, this layer is infrastructure reality.
What is happening in this layer (end-to-end)
Ledger events and structured data flow downward
Append-only events, normalized records, and derived views are persisted.
SQL views are materialized
Ledger data is transformed into relational structures for fast querying.
Cross-system synchronization occurs
Event streams, SQL stores, and cloud services remain consistent.
Cloud infrastructure scales transparently
Storage, compute, replication, and networking are handled by managed services.
Higher layers query upward
Search, orchestration, dashboards, and analytics rely on this foundation.
This layer ensures the system does not just think — it endures.
Core purpose of this layer
Durable data storage
High-performance querying
Multi-tier storage strategy
Cloud-native scalability
Infrastructure abstraction
This is where time survives failure.
Component responsibilities and aligned repositories
1) MindsEye SQL Core (Relational Query Engine)
What it does:
Provides structured, relational access to system data.
Capabilities:
SQL-based querying
Relational data modeling
ACID-compliant transactions
Indexed access paths
Aggregation and analytics queries
How it complements the Ledger:
Ledger = immutable event history
SQL Core = queryable relational views
Together, they allow both truth preservation and fast insight.
Relevant repo:
https://github.com/PEACEBINFLOW/mindseye-sql-core/tree/main
2) SQL Bridges (Ledger ↔ SQL Synchronization)
What it does:
Keeps multiple storage systems in sync.
Functions:
Ledger → SQL projection
Change data capture
Cross-database replication
Cloud format bridging
Incremental updates
Why it matters:
Prevents data divergence while allowing multiple query paradigms.
Relevant repo:
https://github.com/PEACEBINFLOW/mindseye-sql-bridges/tree/main
3) Cloud Fabric (Infrastructure & Scaling Layer)
What it does:
Abstracts cloud infrastructure complexity.
Integrations:
Google Cloud Platform services
Cloud Storage
Pub/Sub messaging
Cloud Functions
Cloud SQL
Responsibilities:
Horizontal scalability
Managed deployments
Service connectivity
Environment consistency
This layer allows MindsEye to scale without architectural rewrites.
Relevant repo:
https://github.com/PEACEBINFLOW/mindseye-cloud-fabric/tree/main
Storage architecture (as depicted in the image)
Multi-tier storage strategy
Hot Storage
Frequently accessed data
Recent ledger events
Fast-response queries
Warm Storage
Historical data
Less frequent access
Archived records
This approach balances performance, cost, and longevity.
Persistence and durability guarantees
Multi-region replication
Automated backups
Point-in-time recovery
Encryption at rest
Managed failover
The system is designed to outlive individual services, failures, or deployments.
Why SQL + Ledger together matters
Ledger ensures immutability and auditability
SQL ensures flexibility and performance
Cloud ensures scale and reliability
Each solves a different problem. Together, they solve reality.
Big picture takeaway
This layer is not optional infrastructure.
It is:
The persistence contract
The scaling guarantee
The durability promise
The reason the system can grow safely
Everything above it assumes this layer never lies, never forgets, and never collapses under load.
Final Note — The Complete Data Journey
This final image ties everything together.
What you are seeing is not a collection of disconnected services, demos, or experiments. It is a single, continuous data journey — from raw, chaotic input to durable insight, automated action, and long-term memory.
MindsEye is built around one core idea:
Intelligence should not disappear after execution. It should accumulate, remain inspectable, and improve over time.
This image follows one piece of unstructured data as it travels through the entire system.
Reading the image as a story
1. Raw capture (top-left)
The journey begins with unstructured data from the web, Google Workspace, browser actions, or external datasets.
At this stage:
- Data is messy
- Context is implicit
- Nothing is trusted yet
The system does not attempt to “understand” anything here. It captures first, asks questions later.
2. Extraction and normalization
Data flows through splitters, binary engines, and transformation utilities.
Here, the system:
- Breaks large inputs into semantic units
- Preserves contextual boundaries
- Extracts entities and signals
- Applies reversible transformations
This ensures that meaning can be derived without destroying the original source.
3. Ledger write (the center)
Everything converges into the Ledger Core.
This is the moment where data becomes fact.
Once written:
- Nothing is overwritten
- Nothing is deleted
- Every event is time-labeled
- Every decision is auditable
This is the system’s memory, not a cache.
4. Orchestration and reasoning
From the ledger, context flows upward into the orchestration layer.
Here:
- The Gemini Orchestrator reasons over past events
- Agents decide what to do next
- Tools are selected deliberately
- State is tracked across sessions
This is where MindsEye differs from reactive AI systems:
decisions are made with history, not just prompts.
5. Indexing, search, and hunting
Once stored and reasoned upon, data becomes searchable.
The system supports:
- Full-text search
- Semantic similarity
- Intent-aware hunting
- Time-based filtering
Instead of “search results,” the system produces ranked, contextual answers.
6. Multiple destinations
Processed knowledge flows outward:
- Dashboards
- Reports
- Automations
- User interfaces
- External integrations
Insight is not locked inside the system. It is delivered where it is needed.
7. Feedback loop
User interaction creates new events.
Those events:
- Go back into the ledger
- Trigger automations
- Refine search rankings
- Improve orchestration decisions
The system learns not by retraining blindly, but by recording reality.
What this architecture represents
MindsEye is not a traditional portfolio project.
A portfolio usually shows:
- A few polished demos
- Isolated features
- Static screenshots
- One-off problem solutions
This architecture shows something different:
- A complete cognitive system
- A consistent philosophy across layers
- Infrastructure-level thinking
- Long-term intelligence design
It demonstrates how systems behave over time, not just how they look on launch day.
Key differences from typical AI systems
Ledger-first, not prompt-first
Decisions are grounded in recorded history.Event sourcing over mutable state
Nothing disappears; everything compounds.Reversible processing
Raw inputs are never destroyed.Search as a first-class citizen
Knowledge is retrievable, not buried.Automation with accountability
Every automated action has a trace.Cloud-native, but not cloud-dependent
Infrastructure supports the system, not the other way around.
Portfolio summary
MindsEye is a ledger-first cognitive architecture designed to turn AI activity into durable, queryable organizational memory.
It combines:
- Structured ingestion
- Reversible normalization
- Immutable event storage
- AI-driven orchestration
- Semantic search and hunting
- Automation and workflows
- Human-facing dashboards
- Cloud-scale persistence
All connected through a single, time-aware data flow.
Learning outcomes and advancements
Building this system led to several core insights:
- Intelligence without memory is unreliable
- Observability must extend beyond logs
- AI systems need auditability by design
- Language, protocol, and time are inseparable
- Architecture matters more than models alone
This approach opens doors to:
- Trustworthy AI systems
- Explainable automation
- Long-lived agent architectures
- Enterprise-grade cognitive tooling
- Systems that improve by remembering, not guessing
Closing thought
This final image is not a diagram of components.
It is a diagram of intent.
MindsEye is an attempt to treat AI not as a black box, but as a living system with memory, responsibility, and continuity.
That is the journey this architecture represents — from chaos to insight, from action to memory, and from execution to understanding.
Top comments (0)