MCP Server

Hologram Cognitive Pro

Self-learning context routing for AI coding assistants.

Hologram Cognitive Pro is an MCP server that sits between your AI assistant and your codebase. Works with any MCP-compatible client — Claude Code, Cursor, Cline, Windsurf, Zed, and more. It learns which files you need before you ask — and gets dramatically smarter with every interaction.

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↓ See how it works

The Problem

The Solution

Hologram replaces guesswork with learned, physics-based context routing:

Pressure Dynamics

Files carry continuous “attention pressure” that activates, propagates along the code graph, and decays naturally — exactly like real human focus.

Basin Dynamics

Stay deep in one area and it becomes “sticky.” Files you’ve worked in for 5+ turns decay 2.5× slower.

Code-Level DAG

Real AST parsing builds a live symbol graph of function calls, imports, inheritance, and documentation bridges — so vague queries instantly resolve to exact source files.

T3v4-R Neural Reranker

A tiny 1.08M-parameter set-attention transformer with online learning (F1: 0.972). Pure NumPy, runs on CPU, learns your project’s unique patterns in just ~32 KB of state.

T3 Adaptive Dynamics

Per-file learned decay/boost parameters that evolve with your actual usage.

Dream Engine

While idle, it quietly walks the code graph, pre-warms clusters, and discovers connections you haven’t made yet.

Memory Index

Pulls relevant past decisions and discoveries from your session history with 100% Recall@5.

Mesh Network

Multi-machine coordination. Gaps? It auto-dispatches research to the best node (right GPU, right codebase) and returns findings in ~50 seconds — zero human intervention.

Auto-Crystallization

When you finish a task, it automatically captures what mattered and links it into long-term memory.

How It Works

One tool call per turn. That’s it.

  1. 1
    hologram_route(query)

    Returns ranked files + bridged code + relevant memories

  2. 2
    You and your AI do the work

    Use the routed context to build, fix, or explore

  3. 3
    hologram_observe(files_used)

    Learning loop closes — the system gets smarter

By turn 50 the routing is noticeably better.
By turn 100 the right files appear before you ask.

Key Numbers

Metric Value
Reranker F10.972
Reranker inference~135 ms CPU / 130 files
Memory query54 ms
Online learning state32 KB per project
Dependenciesnumpy (req), sentence-transformers (opt)

What It’s Not

Early Access
$15/month

Limited beta — join the waitlist for access.

  • Full MCP server (11 tools + 2 resources)
  • T3v4-R neural reranker (frozen weights)
  • Dream engine + auto-crystallization
  • Online learning (per-project adaptive state)
  • Priority support via Discord/email

You’re on the list.

We’ll review your application and send a checkout link to your email when a spot opens.

Beta

Private, paid access. Shipping today as a full MCP server (11 tools + 2 resources).

Installable package and one-click setup coming soon.

Proudly beta tested by Anima Lux Labs (Sweden) — builders of local-first, privacy-native AI infrastructure.