Decentralised Neural Architecture

The sovereign AI substrate for the Humanscode ecosystem.

D.N.A. is an autonomous neural platform powering 36 products. 72 specialised agents compete, learn and self-improve through a broadcast competition mesh — owned end-to-end, no third-party API required.

dna.humanscode.siteOpenAI-compatible APISSE streamingLoRA fine-tuningMulti-tenant
0
Cognitive modules
0
Specialised agents
0
Agent clusters
0
Products powered
// Platform

Six pillars of the architecture

01

Self-learning core

Continual learning, DPO self-evaluation and shadow-mode comparison loop the system back into its own training data.

02

Broadcast Competition

Every prompt is auctioned across 72 agents. The best-fit specialist wins, with transparent confidence and quality bidding.

03

Embedding routing

Neural router dispatches each request to the matching cluster — Code, Reasoning, Security, Business, Knowledge.

04

LoRA adapters

Per-agent LoRA fine-tunes train on Vast.AI, merge into the core, and version-roll without downtime.

05

Global Workspace

Dynamic shared context implements a Global Workspace — agents read and write a common cognitive blackboard.

06

OpenAI-compatible

Drop-in replacement: /v1/chat/completions, /v1/models, SSE streaming. Migrate in minutes.

// Stack

From prompt to sovereign answer

L6
Interface Layer
Chat, Console, Multi-tenant Instances
L5
Orchestrator
Routing · Broadcast Competition · Quality Gates
L4
72 Agents · 9 Clusters
Specialised workers with LoRA adapters
L3
Cognitive Modules
Memory · RAG · Reasoning · Self-Eval · DPO
L2
Training Pipeline
Vast.AI · LoRA Merge · Auto-Retrain
L1
DNA Core Model
Sovereign foundation model
// minimal client
const r = await fetch(
  "https://dna.humanscode.site/v1/chat/completions",
  {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      model: "dna-core",
      stream: true,
      messages: [
        { role: "user", content: "Explain D.N.A." }
      ]
    })
  }
);

// SSE: data: {"choices":[{"delta":{"content":"…"}}]}
// 72 agents bid -> winner streams back
// Capabilities

23 cognitive modules

Each module is independently observable, version-rollable and runtime-toggleable.

SL
Self Learning
Самообучение
SH
Shadow Mode
Теневое сравнение
ER
Embedding Router
Нейро-маршрутизация
OR
Orchestrator
Мета-оркестратор
RA
RAG Engine
Семантический поиск
CM
Conversation Memory
Память разговоров
DC
Dynamic Context
Global Workspace
CL
Continual Learning
Непрерывное обучение
SD
Speculative Decoding
Спекулятивная генерация
LM
LoRA Merge
Слияние адаптеров
RC
Reasoning Chain
Цепочка рассуждений
DP
Self-Eval DPO
DPO самооценка
BM
Benchmark
Бенчмарки
AB
A/B Testing
A/B тесты
QG
Quality Gates
Контроль качества
SC
Semantic Cache
Семантический кэш
RL
Rate Limiter
Лимитер запросов
MV
Model Versioning
Версии моделей
AR
Auto Retrain
Авто-ретрейн
MP
Multi Provider
Мульти-провайдер
BC
Broadcast Competition
Конкуренция агентов
LA
LoRA Adapter Manager
LoRA адаптеры
GH
GitHub Dataset Pipeline
GitHub / Датасеты
// Agents

9 specialised clusters · 72 agents

Routing chooses the right expert for each task. Each agent owns a LoRA adapter and a cumulative skill profile.

Code×10
Reasoning×8
Analysis×8
Security×8
Business×10
Knowledge×6
Creative×6
Language×8
Integration×8
// Evolution

14 blocks · 100+ products · 5-year roadmap

Власна екосистема Humanscode: від Foundation до GovTech та Frontier R&D. Послідовна монетизація — кожен блок фінансує наступний.

14
Blocks
67
Products
11
Cases
5
Years
D

Step into the Command Center

Watch 72 agents compete in real time. Inspect modules, training, memory and the global workspace from a single console.