Show HN: Core-1 – A distributed C++ AGI framework for trillion-parameter scale

TitanCore Core-1 is a full-stack AGI engine built in C++17 and CUDA. It combines a 120-layer Mixture-of-Experts Transformer with a complete cognitive architecture: persistent memory, structured reasoning, goal-directed planning, meta-learning, world modelling, and continuous online learning — all running across multi-node GPU clusters.

TitanCore Core-1 implements the full Perceive → Remember → Reason → Plan → Act → Learn cognitive loop:

┌──────────────────────────────────────────────────────────────┐
│                    TITANCORE AGI COGNITIVE LOOP              │
│                                                              │
│   Input                                                      │
│     │                                                        │
│     ▼                                                        │
│  ┌──────────────┐    ┌───────────────┐    ┌───────────────┐  │
│  │   Perceive   │───▶│   Remember    │───▶│    Reason     │  │
│  │ Working Mem  │    │ Episodic Mem  │    │ Chain-of-Thought│ │
│  │ Safety Gate  │    │ Semantic Mem  │    │ Tree-of-Thought│  │
│  └──────────────┘    └───────────────┘    └───────┬───────┘  │
│                                                   │          │
│  ┌──────────────┐    ┌───────────────┐    ┌───────▼───────┐  │
│  │    Learn     │◀───│      Act      │◀───│     Plan      │  │
│  │ Online GD    │    │  Tool Use     │    │  MCTS Planner │  │
│  │ EWC + MAML   │    │  API Calls    │    │  Goal Stack   │  │
│  └──────┬───────┘    └───────────────┘    └───────────────┘  │
│         │                                                     │
│         ▼                                                     │
│  ┌──────────────┐                                            │
│  │ World Model  │  Predict future states, detect novelty     │
│  │ VAE+Dynamics │                                            │
│  └──────────────┘                                            │
└──────────────────────────────────────────────────────────────┘

120-layer MoE Transformer— 8 experts per layer, top-2 routing** FlashAttention v2**— custom CUDA tiled kernel, 128k+ context** Paged KV Cache**— logical-to-physical block mapping, zero fragmentation** RoPE embeddings**, SwiGLU MLP, Pre-LayerNorm** Parallelism**— Tensor ×4, Pipeline ×2, Data ×4, Expert ×8

Learns from every new interaction without forgetting prior knowledge:

Online Gradient Descent— real-time weight updates from live data streams** Elastic Weight Consolidation (EWC)— Fisher Information diagonal protects prior knowledge Experience Replay Buffer**— 100K capacity, reservoir sampling** EMA Weight Snapshots**— stable inference weights via exponential moving average** Adaptive per-parameter learning rate**via AdamW

Four structured reasoning modes:

Monte Carlo Tree Search (MCTS) with UCB1 selection- Neural-guided rollout policy for state evaluation

• Hierarchical goal decomposition into ordered subgoals
• Configurable depth, breadth, and exploration constant

Learn to learn — adapt to any new task in a few gradient steps:

MAML(Model-Agnostic Meta-Learning) — full second-order** FOMAML**— first-order approximation (faster, production default)** Reptile**— scalable alternative with simple moving-average updates- Fast inference-time adaptation with only a handful of examples

Internal predictive model of the environment:

VAE Encoder— maps observations to compact latent state z** Dynamics Model**— predicts next latent z' given z + action** Reward Predictor**— estimates expected reward from any state** Novelty Detection**— z-score anomaly flag for unexplored states** Imagination**— simulate N-step future trajectories for planning

Allows the AGI to call external systems:

Custom tools can be registered at runtime with a schema and handler function.

Core-1/
├── main.cpp                          # AGI master orchestrator
├── CMakeLists.txt
│
└── core/
    ├── configs/
    │   ├── gpt4o.yaml                # Model & runtime config
    │   ├── cluster.yaml              # Cluster topology
    │   ├── safety.yaml               # Safety policy
    │   └── agi.yaml                  # AGI subsystem config
    │
    ├── model/                        # Transformer backbone
    ├── distributed/                  # NCCL, FSDP, MPI
    ├── optimizer/                    # ZeRO-3 AdamW
    ├── data/                   # Memory-mapped dataset
    ├── safety/                       # Moderation, jailbreak, rate limit
    ├── logging/                      # Audit trail
    │
    ├── learning/
    │   └── online_learning.cpp       # Online GD + EWC + Replay + EMA
    │
    ├── memory/
    │   ├── episodic.cpp              # Past episode store + retrieval
    │   ├── semantic.cpp              # Long-term knowledge graph
    │   └── working.cpp               # Active context window
    │
    ├── reasoning/
    │   ├── chain_of_thought.cpp      # CoT / Self-Consistency / ToT / Reflection
    │   └── planner.cpp               # MCTS goal-directed planner
    │
    ├── meta/
    │   └── maml.cpp                  # MAML / FOMAML / Reptile
    │
    ├── world_model/
    │   └── world_model.cpp           # VAE encoder + dynamics + reward + novelty
    │
    ├── tools/
    │   └── tool_executor.cpp         # Function calling + built-in tools
    │
    └── agi/
        └── agi_core.cpp              # Unified AGI cognitive loop controller
git clone https://github.com/litonsarkar3988-max/Core-1
cd Core-1
mkdir build && cd build

cmake .. \
  -DCMAKE_BUILD_TYPE=Release \
  -DTorch_DIR=/path/to/libtorch/share/cmake/Torch \
  -DCMAKE_CUDA_ARCHITECTURES="80;86;90"

make -j$(nproc)
./titancore \
  --model  core/weights/titancore.gguf \
  --config core/configs/gpt4o.yaml \
  --agi    core/configs/agi.yaml
mpirun -np 32 -hostfile hosts.txt \
  ./titancore \
  --config   core/configs/gpt4o.yaml \
  --cluster  core/configs/cluster.yaml \
  --agi      core/configs/agi.yaml

All input passes through a mandatory safety pipeline before any model computation:

Jailbreak Detection— regex + semantic scan** Rate Limiting**— sliding-window per user/session** Multi-Vector Moderation**— embedding-based classifier** EWC Knowledge Protection**— prevents unsafe fine-tuning from corrupting core knowledge

Rahul Sarkar — India GitHub: github.com/Sarkar-AGI

Disclaimer:TitanCore Core-1 is an independent research project. NVIDIA GPU hardware is required. CPU execution is not supported.