CORTEX v2.0 — STRATEGIC VISION

Personalized AI Brain for Software Engineers

Created: 03/03/2026 Version: v2.0 Strategic Reset Author: Cortex Team

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1. Vision

Cortex v2.0 is NOT a SaaS product. NOT a tool built for someone else.

This is a personal weapon — an AI engineering platform that:

• Learns from your behavior, not from generic assumptions
• Self-improves prompts, retrieval, and ranking over time
• Pluggable Skills — modular architecture, easy to add/remove capabilities
• Fully replaces Cursor/Windsurf/Codex with a system YOU own and control
• Understands YOU — not just your code, but HOW you code, what you LIKE, what you NEED

Why not SaaS?

• You need absolute control over data and privacy
• Your code NEVER leaves your machine
• Every dollar spent on LLM is optimized by you
• No vendor lock-in — you OWN everything

Ultimate Goal

An AI assistant that:

1. Knows everything about every one of your projects (code, architecture, patterns, decisions)
2. Learns from how you work (accept/reject/edit patterns, coding style, preferences)
3. Self-improves every session (DSPy prompt optimization, learned reranking)
4. Has every skill you need (browser automation, code execution, Jira, GitHub, Slack)
5. Minimizes token usage (model routing, caching, compression)
6. Works offline when needed (local models via Ollama/MLX)

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2. Architecture Principles

#	Principle	Description
1	Behavior-First	Every decision is based on actual user behavior, NOT on heuristics or assumptions
2	Skill-Based	Every capability is an independent skill that can be loaded/unloaded with a common interface
3	Self-Improving	System auto-optimizes prompts (DSPy), retrieval (learned reranker), and ranking over time
4	Memory-Native	Multi-tier Letta/MemGPT memory: Core (always in context) + Archival (long-term) + Recall (conversations)
5	Cost-Conscious	Model routing (cheap for easy, expensive for hard), semantic caching, LLMLingua compression
6	Privacy-First	All data stays local. Raw code NEVER sent to the cloud. Only compressed context is sent to the LLM proxy
7	Composable	Skills can call each other. RAG skill calls Memory skill calls Embedding skill
8	Observable	Every action is logged. Cost tracking per query. Behavioral metrics dashboard

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3. AI Skill Map

3.1 Advanced RAG Skills

Skill	Description	Library	Priority
GraphRAG	Knowledge graph + vector search, multi-hop reasoning over code	Microsoft GraphRAG (github.com/microsoft/graphrag)	P0
Self-RAG	Self-evaluates retrieval quality and self-corrects when poor	Paper: Self-RAG (arxiv 2310.11511)	P1
Corrective RAG	Detects poor retrieval → re-searches with a refined query	Paper: CRAG (arxiv 2401.15884)	P1
Adaptive RAG	Auto-selects strategy: no-retrieval / single-hop / multi-hop	Paper: Adaptive RAG (arxiv 2403.14403)	P1
RAG Fusion	Generates 3–5 query variants → searches separately → merges via Reciprocal Rank Fusion	LangChain RAG Fusion	P0
HyDE	Generates a hypothetical document from the query → uses it for search (better than raw query)	Paper: HyDE (arxiv 2212.10496)	P1
Contextual Retrieval	Adds context (file path, function name, module) to each chunk before embedding	Anthropic blog (Nov 2024)	P0
Parent-Child Chunking	Searches small child chunks (precise) but returns parent chunks (more context)	LlamaIndex	P1

3.2 Self-Learning Skills

Skill	Description	Library	Priority
DSPy Optimization	Auto-optimizes prompts based on metrics (accuracy, user satisfaction)	DSPy (dspy.ai) - Stanford	P0
Behavioral Analytics	Collects implicit feedback: accept/reject/edit/time-to-action	Custom implementation	P0
Learned Reranking	Improves search ranking based on actual user interactions	Cross-encoder + feedback data	P1
Preference Learning	Learns coding style, naming conventions, architecture preferences	Custom behavioral embeddings	P1
Active Learning	Asks the right questions to improve faster (without over-asking)	Custom	P2
RLAIF	Reinforcement Learning from AI Feedback — AI critiques itself	Paper: RLAIF (Google 2023)	P2

3.3 Memory Skills

Skill	Description	Library	Priority
Tiered Memory	Core + Archival + Recall (Letta/MemGPT inspired)	Letta (github.com/letta-ai/letta)	P0
Nano-Brain	Persistent memory across sessions (integrated, needs upgrade)	nano-brain	P0
Cross-Session Learning	Agent remembers and improves across sessions, never starting from scratch	Custom + Letta patterns	P0
Memory Compaction	Auto-summarizes and compacts old memory when too large	Custom summary chains	P1
Memory Decay	Automatically forgets outdated information (TTL + relevance scoring)	Custom	P2

3.4 Efficiency Skills

Skill	Description	Library	Priority
LLMLingua	Compresses context 3–6x before sending to LLM, preserving meaning	LLMLingua-2 (github.com/microsoft/LLMLingua)	P0
Semantic Caching	Caches similar queries to avoid redundant LLM calls	GPTCache or custom (embedding similarity)	P0
Model Routing	Easy query → cheap model (GPT-4o-mini), hard query → expensive model (Claude Opus)	Custom complexity classifier	P0
Prompt Caching	Reuses cached prefix (system prompt + project context)	Proxy-level implementation	P1
Adaptive Token Budget	Allocates more tokens to complex queries, fewer to simple ones	Custom	P1
ChunkKV	Compresses KV cache by semantic chunks, reducing memory by 70%	Paper: ChunkKV (NeurIPS 2025)	P2

3.5 Agent/Tool Skills (MCP-Based)

Skill	Description	Library	Priority
MCP Protocol Core	Universal standard for connecting AI to external tools	Anthropic MCP (modelcontextprotocol.io)	P0
Playwright	Browser automation: test, scrape, verify, screenshot	Playwright MCP server	P1
GitHub	Repo operations, PR review, issue management, code search	GitHub MCP server	P0
Jira	Ticket management, auto-estimation, sprint tracking	Jira MCP (started)	P1
Confluence	Documentation sync, auto-generate docs	Confluence MCP (started)	P1
Slack	Team communication, notifications, Q&A bot	Slack MCP	P2
Code Execution	Safe sandboxed code execution (Docker/E2B)	E2B (e2b.dev) or custom Docker	P1
Sequential Thinking	Structured multi-step reasoning with backtracking	Custom MCP tool	P0
File System	Advanced file operations, search, watch	Built-in	P0

3.6 Reasoning Skills

Skill	Description	Library	Priority
ReAct	Reasoning + Acting loop: think → act → observe → repeat	LangChain/LangGraph ReAct	P0
Plan-and-Execute	Creates a plan first → executes step by step → validates	LangGraph	P1
Reflexion	After executing, self-reviews and corrects errors if needed	Paper: Reflexion (arxiv 2303.11366)	P1
LATS	Language Agent Tree Search: explores multiple paths, picks the best	Paper: LATS (arxiv 2310.04406)	P2
Chain of Thought	Thinks step by step before answering	Built-in prompting	P0
Tree of Thoughts	Branching reasoning for complex problems	Paper: ToT (arxiv 2305.10601)	P2

3.7 Code Intelligence Skills

Skill	Description	Library	Priority
Tree-sitter AST	Parses AST for 40+ languages, extracts functions/classes/imports	web-tree-sitter (integrated)	P0
AST-grep	Pattern matching across the entire codebase via AST	ast-grep (ast-grep.github.io)	P0
LSP Integration	Go-to-definition, find references, diagnostics, rename	Language Server Protocol	P1
Dependency Graph	Maps dependencies, detects circular deps, identifies hub files	Custom + Tree-sitter	P1
Architecture Inference	Auto-detects patterns (MVC, CQRS, Microservices…)	Custom (architecture-analyzer.ts exists)	P0
Tech Debt Scoring	Quantifies technical debt per file/module/project	Custom metrics	P2

3.8 Fine-tuning & Local AI

Skill	Description	Library	Priority
Embedding Fine-tuning	Trains custom embeddings on your codebase	sentence-transformers + custom data	P1
LoRA Personalization	Lightweight fine-tuning of a local model to your coding style	Unsloth (github.com/unslothai/unsloth)	P2
Synthetic Data Gen	Generates Q&A pairs from the codebase for training/evaluation	Custom pipeline	P1
DPO	Direct Preference Optimization — simpler than RLHF	TRL library (Hugging Face)	P2
Local Model Serving	Runs models offline via Ollama/llama.cpp/MLX	Ollama (ollama.ai)	P1

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4. Competitive Analysis

Feature	Cortex v2	Cursor	Windsurf	Codex (OpenAI)	Cody (Sourcegraph)	Continue.dev
Self-learning (DSPy)	YES	No	No	No	No	No
Behavior analysis	YES	No	Partial	No	No	No
Memory persistence (Letta)	YES	No	No	No	No	No
GraphRAG	YES	No	No	No	Partial (code graph)	No
Token efficiency (LLMLingua)	YES	Unknown	Unknown	No	No	No
Model routing	YES	Partial	Partial	No (GPT only)	Partial	Yes
MCP skills	YES	Yes	Yes	No	No	Yes
Privacy (local-first)	YES	Cloud	Cloud	Cloud	Cloud	Yes
Cost control	YES	$20/mo fixed	$15/mo	Pay-per-use	$9/mo	Free
Offline mode	YES (Ollama)	No	No	No	No	Yes (partial)
Custom skills/plugins	YES	Partial	No	No	No	Yes
Code execution sandbox	YES	Yes	Yes	Yes	No	No
Prompt self-optimization	YES	No	No	No	No	No

Core differentiators of Cortex v2:

1. Self-learning — No other tool auto-improves prompts based on user behavior
2. Memory persistence — No other tool remembers and learns across multiple sessions (except the new Letta Code)
3. Behavior-first — No other tool analyzes behavior for personalization
4. Full ownership — You OWN everything, no subscription dependency
5. Cost transparency — You know exactly how much each query costs

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5. High-Level Architecture

+------------------------------------------------------------------+
|                        ELECTRON RENDERER                          |
|  +------------------+  +---------------+  +-------------------+  |
|  | Chat Interface   |  | Skill Manager |  | Memory Dashboard  |  |
|  | (React + Zustand) |  | (React)       |  | (React)           |  |
|  +------------------+  +---------------+  +-------------------+  |
|  +------------------+  +---------------+  +-------------------+  |
|  | Brain Dashboard   |  | Cost Tracker  |  | Settings Panel    |  |
|  +------------------+  +---------------+  +-------------------+  |
+----------------------------IPC Bridge-----------------------------+
|                        ELECTRON MAIN                              |
|                                                                   |
|  +------------------------------------------------------------+  |
|  |                     SKILL ROUTER                            |  |
|  |  Classify intent -> Route to best skill(s) -> Orchestrate  |  |
|  +------------------------------------------------------------+  |
|                              |                                    |
|  +------------------------------------------------------------+  |
|  |                     SKILL REGISTRY                          |  |
|  | +----------+ +----------+ +--------+ +--------+ +--------+ |  |
|  | |RAG Skills| |Memory    | |Agent   | |Code    | |Learning| |  |
|  | |GraphRAG  | |Core Mem  | |ReAct   | |TreeSit | |DSPy    | |  |
|  | |Self-RAG  | |Archival  | |PlanExec| |AST-grep| |Behavior| |  |
|  | |CRAG      | |Recall    | |Reflex  | |LSP     | |Rerank  | |  |
|  | |Fusion    | |Compact   | |LATS    | |DepGraph| |Prefs   | |  |
|  | +----------+ +----------+ +--------+ +--------+ +--------+ |  |
|  +------------------------------------------------------------+  |
|                              |                                    |
|  +------------------------------------------------------------+  |
|  |                  EFFICIENCY ENGINE                          |  |
|  | +----------+ +----------+ +----------+ +----------+        |  |
|  | |LLMLingua | |Semantic  | |Model     | |Cost      |        |  |
|  | |Compress  | |Cache     | |Router    | |Tracker   |        |  |
|  | +----------+ +----------+ +----------+ +----------+        |  |
|  +------------------------------------------------------------+  |
|                              |                                    |
|  +------------------------------------------------------------+  |
|  |                     BRAIN ENGINE                            |  |
|  | +----------+ +----------+ +----------+ +----------+        |  |
|  | |Embedder  | |ChromaDB  | |Graph DB  | |SQLite    |        |  |
|  | |(voyage/  | |(vectors) | |(entities)| |(metadata)|        |  |
|  | | custom)  | |          | |          | |          |        |  |
|  | +----------+ +----------+ +----------+ +----------+        |  |
|  +------------------------------------------------------------+  |
|                              |                                    |
|  +------------------------------------------------------------+  |
|  |                     MCP LAYER (External Tools)              |  |
|  | +------+ +------+ +------+ +------+ +------+ +----------+ |  |
|  | |GitHub| |Jira  | |Confl | |Slack | |Play  | |Code Exec | |  |
|  | |      | |      | |uence | |      | |wright| |Sandbox   | |  |
|  | +------+ +------+ +------+ +------+ +------+ +----------+ |  |
|  +------------------------------------------------------------+  |
+------------------------------------------------------------------+

Data Flow: User Query → Response

User types question
       |
       v
[1. IPC: chat:send] --> Electron Main Process
       |
       v
[2. Efficiency: Check Semantic Cache]
       |-- Cache HIT --> Return cached response
       |-- Cache MISS --> Continue
       |
       v
[3. Skill Router: Classify Intent]
       |-- Code question --> RAG Skills
       |-- Action request --> Agent Skills (ReAct)
       |-- Memory query --> Memory Skills
       |-- Tool use --> MCP Skills
       |
       v
[4. Memory: Load relevant context]
       |-- Core Memory (always loaded, ~2000 tokens)
       |-- Archival Memory (search relevant memories)
       |-- Recall Memory (recent conversation)
       |
       v
[5. RAG Pipeline: Retrieve relevant code]
       |-- Query Analyzer --> select strategy
       |-- Execute strategy (GraphRAG/Fusion/Self-RAG/...)
       |-- Rerank results (learned reranker)
       |
       v
[6. Efficiency: Compress Context]
       |-- LLMLingua compress retrieved chunks
       |-- Model Router: select appropriate model
       |-- Adaptive Token Budget: allocate tokens
       |
       v
[7. LLM Call via Proxy]
       |-- Stream response back to renderer
       |
       v
[8. Post-processing]
       |-- Parse citations, confidence score
       |-- Update Recall Memory
       |-- Log behavioral event (for self-learning)
       |-- Update cost tracker
       |
       v
[9. Self-Learning (async, background)]
       |-- Collect implicit feedback after 30s
       |-- Update behavioral analytics
       |-- Periodically run DSPy optimization

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6. Roadmap Overview

Sprint	Name	Timeline	Primary Goal	Dependencies
13	Memory Architecture	Week 1–2	Letta-inspired multi-tier memory system replacing nano-brain	None
14	Skill Registry + MCP	Week 3–4	Pluggable skill system + MCP integration + wrap existing services	Sprint 13
15	Advanced RAG Pipeline	Week 5–6	GraphRAG + Self-RAG + CRAG + RAG Fusion + Contextual Retrieval	Sprint 14
16	Self-Learning Pipeline	Week 7–8	DSPy optimization + Behavioral Analytics + Feedback loops	Sprint 14, 15
17	Efficiency Engine	Week 9–10	LLMLingua + Semantic Cache + Model Routing + Cost Tracking	Sprint 14
18	Agent Mode	Week 11–12	Code execution + Playwright + ReAct + Plan-and-Execute	Sprint 14, 15

Sprint Dependency Graph

Sprint 13 (Memory)
    |
    v
Sprint 14 (Skills + MCP)
    |
    +--------+--------+--------+
    |        |        |        |
    v        v        v        v
Sprint 15  Sprint 16  Sprint 17  Sprint 18
(RAG)      (Learn)    (Efficiency) (Agent)

Success Metrics Per Sprint

Sprint	Metric	Target
13	Memory read/write latency	< 50ms
13	Memory search accuracy	> 85% recall
14	Skills loaded successfully	>= 10 built-in skills
14	MCP server connection time	< 2s
15	RAG answer relevance (manual eval)	> 80% relevant
15	Multi-hop query success rate	> 60%
16	Prompt quality improvement via DSPy	> 15% vs baseline
16	Behavioral events captured per session	> 20 events
17	Token reduction via LLMLingua	> 40% reduction
17	Cache hit rate	> 25%
17	Cost per query reduction	> 30% vs v1.0
18	Multi-step task completion rate	> 70%
18	Code execution success rate	> 80%

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7. Risks & Mitigations

#	Risk	Impact	Likelihood	Mitigation
1	Performance degradation when loading many skills simultaneously	High	Medium	Lazy loading, skill priority queue, parallel execution limit
2	ChromaDB won’t scale when brain is very large (>100K chunks)	High	Medium	Migration plan to Qdrant/Milvus or sharding strategy
3	Token cost explosion when using GraphRAG + multi-step agent	High	High	Model routing (P0), LLMLingua (P0), budget cap per query
4	Complexity too high for a solo developer	High	High	Strict sprint scope, P0 first, P2 deferred. Each sprint is independent
5	DSPy optimization ineffective with too little data	Medium	Medium	Collect 100+ data points before running the optimizer
6	Graph DB performance when knowledge graph is large	Medium	Low	SQLite graph tables with indexes, lazy graph building
7	MCP server instability from third-party providers	Medium	Medium	Timeout + fallback, health check per server, graceful degradation
8	Prompt injection via memory system	High	Low	Memory sanitization, input validation, audit trail (already in place)

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8. Success Metrics

KPIs for Cortex v2.0

Category	Metric	How to Measure	Target
Quality	Response relevance	DSPy evaluation metric + manual spot-check	> 80%
Quality	Citation accuracy	% citations pointing to correct file/line	> 90%
Efficiency	Tokens saved per query	(original - compressed) / original	> 40%
Efficiency	Cache hit rate	Semantic cache hits / total queries	> 25%
Efficiency	Cost per query (avg)	Total LLM cost / total queries	< $0.02
Learning	DSPy improvement rate	% improvement per optimization cycle	> 10% per cycle
Learning	Behavioral events/session	Events captured per chat session	> 20
Learning	Accept rate trend	% suggestions accepted, trending upward	+5% per month
Memory	Memory recall accuracy	% relevant memories retrieved	> 85%
Memory	Cross-session context preservation	User reports context maintained	Qualitative
Speed	Query latency (P50)	Time from send to first token	< 2s
Speed	Query latency (P95)	Time from send to first token	< 5s
Reliability	Skill health check pass rate	% skills passing health check	> 95%
Reliability	Crash rate	Crashes per 100 sessions	< 1

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9. Conclusion

Cortex v2.0 is the turning point from a code-aware chatbot into a personalized AI engineering platform.

Not competing on Cursor/Copilot’s turf — they already do code completion well. Cortex does what NO ONE else does:

1. Learns from behavior — DSPy + behavioral analytics = genuine personalization
2. Remembers everything — Letta-inspired memory = agent that gets smarter over time
3. Pluggable skills — MCP + custom skills = any capability you need
4. Cost transparency — you know exactly how much each query costs
5. Full ownership — you OWN everything, dependent on no one

Starting from Sprint 13. Each sprint is 2 weeks. 12 weeks to Cortex v2.0.

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This document will be updated as strategy evolves. See details at: SKILL_CATALOG.md, SPRINT_PLAN.md, ARCHITECTURE.md