{"slug": "show-hn-core-1-a-distributed-c-agi-framework-for-trillion-parameter-scale", "title": "Show HN: Core-1 – A distributed C++ AGI framework for trillion-parameter scale", "summary": "TitanCore released Core-1, a distributed C++ AGI framework supporting up to 1 trillion parameters, featuring a 120-layer Mixture-of-Experts Transformer with full cognitive architecture including persistent memory, reasoning, planning, and online learning across multi-node GPU clusters.", "body_md": "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.\n\n| Property | Detail |\n|---|---|\n| Version | 1.0.0 |\n| Release Date | February 2026 |\n| Status | AGI Framework — Inference-Ready |\n| Tokenization | Custom BPE — 400,000 token vocabulary |\n| Weight Format | GGUF (`titancore.gguf` ) |\n| Parameters | Up to 1 Trillion |\n\nTitanCore Core-1 implements the full **Perceive → Remember → Reason → Plan → Act → Learn** cognitive loop:\n\n```\n┌──────────────────────────────────────────────────────────────┐\n│                    TITANCORE AGI COGNITIVE LOOP              │\n│                                                              │\n│   Input                                                      │\n│     │                                                        │\n│     ▼                                                        │\n│  ┌──────────────┐    ┌───────────────┐    ┌───────────────┐  │\n│  │   Perceive   │───▶│   Remember    │───▶│    Reason     │  │\n│  │ Working Mem  │    │ Episodic Mem  │    │ Chain-of-Thought│ │\n│  │ Safety Gate  │    │ Semantic Mem  │    │ Tree-of-Thought│  │\n│  └──────────────┘    └───────────────┘    └───────┬───────┘  │\n│                                                   │          │\n│  ┌──────────────┐    ┌───────────────┐    ┌───────▼───────┐  │\n│  │    Learn     │◀───│      Act      │◀───│     Plan      │  │\n│  │ Online GD    │    │  Tool Use     │    │  MCTS Planner │  │\n│  │ EWC + MAML   │    │  API Calls    │    │  Goal Stack   │  │\n│  └──────┬───────┘    └───────────────┘    └───────────────┘  │\n│         │                                                     │\n│         ▼                                                     │\n│  ┌──────────────┐                                            │\n│  │ World Model  │  Predict future states, detect novelty     │\n│  │ VAE+Dynamics │                                            │\n│  └──────────────┘                                            │\n└──────────────────────────────────────────────────────────────┘\n```\n\n**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\n\nLearns from every new interaction without forgetting prior knowledge:\n\n**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\n\n| System | File | Description |\n|---|---|---|\nEpisodic Memory |\n`core/memory/episodic.cpp` |\nStores 50K past episodes; cosine-similarity + temporal-decay retrieval |\nSemantic Memory |\n`core/memory/semantic.cpp` |\nLong-term factual knowledge graph; confidence-scored, conflict-resolved |\nWorking Memory |\n`core/memory/working.cpp` |\nActive context window; attention-weighted importance-based eviction |\n\nFour structured reasoning modes:\n\n| Mode | Description |\n|---|---|\nStandard CoT |\nLinear step-by-step reasoning with confidence gating |\nSelf-Consistency |\nSample N reasoning paths, majority-vote the answer |\nTree-of-Thought |\nBFS branching + value-guided pruning of the reasoning tree |\nReflection |\nDraft → Critique → Revise loop for high-accuracy answers |\n\n**Monte Carlo Tree Search (MCTS)** with UCB1 selection- Neural-guided rollout policy for state evaluation\n- Hierarchical goal decomposition into ordered subgoals\n- Configurable depth, breadth, and exploration constant\n\nLearn to learn — adapt to any new task in a few gradient steps:\n\n**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\n\nInternal predictive model of the environment:\n\n**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\n\nAllows the AGI to call external systems:\n\n| Built-in Tool | Description |\n|---|---|\n`calculator` |\nSafe mathematical expression evaluator |\n`web_search` |\nReal-time web search via search API |\n`code_interpreter` |\nSandboxed Python execution environment |\n`read_file` |\nSecure file system access |\n`db_query` |\nRead-only SQL against the knowledge database |\n\nCustom tools can be registered at runtime with a schema and handler function.\n\n| Component | Minimum | Recommended |\n|---|---|---|\n| GPU | NVIDIA A100 80GB ×8 | NVIDIA H100 SXM5 80GB ×8 per node |\n| Nodes | 1 | 4 (32 GPUs total) |\n| System RAM | 512 GB | 1 TB per node |\n| Interconnect | NVLink | NVLink + InfiniBand 400 Gbps |\n| Storage | 10 TB NVMe | 100 TB NVMe RAID |\n\n| Dependency | Version |\n|---|---|\n| OS | Ubuntu 22.04 LTS |\n| CUDA Toolkit | 12.2+ |\n| CMake | 3.20+ |\n| C++ Compiler | GCC 11+ / Clang 14+ |\n| LibTorch | 2.2+ |\n| NCCL | 2.18+ |\n| OpenMPI | 4.1+ |\n\n```\nCore-1/\n├── main.cpp                          # AGI master orchestrator\n├── CMakeLists.txt\n│\n└── core/\n    ├── configs/\n    │   ├── gpt4o.yaml                # Model & runtime config\n    │   ├── cluster.yaml              # Cluster topology\n    │   ├── safety.yaml               # Safety policy\n    │   └── agi.yaml                  # AGI subsystem config\n    │\n    ├── model/                        # Transformer backbone\n    ├── distributed/                  # NCCL, FSDP, MPI\n    ├── optimizer/                    # ZeRO-3 AdamW\n    ├── dataloader/                   # Memory-mapped dataset\n    ├── safety/                       # Moderation, jailbreak, rate limit\n    ├── logging/                      # Audit trail\n    │\n    ├── learning/\n    │   └── online_learning.cpp       # Online GD + EWC + Replay + EMA\n    │\n    ├── memory/\n    │   ├── episodic.cpp              # Past episode store + retrieval\n    │   ├── semantic.cpp              # Long-term knowledge graph\n    │   └── working.cpp               # Active context window\n    │\n    ├── reasoning/\n    │   ├── chain_of_thought.cpp      # CoT / Self-Consistency / ToT / Reflection\n    │   └── planner.cpp               # MCTS goal-directed planner\n    │\n    ├── meta/\n    │   └── maml.cpp                  # MAML / FOMAML / Reptile\n    │\n    ├── world_model/\n    │   └── world_model.cpp           # VAE encoder + dynamics + reward + novelty\n    │\n    ├── tools/\n    │   └── tool_executor.cpp         # Function calling + built-in tools\n    │\n    └── agi/\n        └── agi_core.cpp              # Unified AGI cognitive loop controller\ngit clone https://github.com/litonsarkar3988-max/Core-1\ncd Core-1\nmkdir build && cd build\n\ncmake .. \\\n  -DCMAKE_BUILD_TYPE=Release \\\n  -DTorch_DIR=/path/to/libtorch/share/cmake/Torch \\\n  -DCMAKE_CUDA_ARCHITECTURES=\"80;86;90\"\n\nmake -j$(nproc)\n./titancore \\\n  --model  core/weights/titancore.gguf \\\n  --config core/configs/gpt4o.yaml \\\n  --agi    core/configs/agi.yaml\nmpirun -np 32 -hostfile hosts.txt \\\n  ./titancore \\\n  --config   core/configs/gpt4o.yaml \\\n  --cluster  core/configs/cluster.yaml \\\n  --agi      core/configs/agi.yaml\n```\n\n| File | Purpose |\n|---|---|\n`core/configs/gpt4o.yaml` |\nModel architecture, quantization, runtime |\n`core/configs/cluster.yaml` |\nMulti-node topology, network, fault tolerance |\n`core/configs/safety.yaml` |\nContent policy, rate limits, PII redaction |\n`core/configs/agi.yaml` |\nAll AGI subsystem parameters |\n\nAll input passes through a mandatory safety pipeline before any model computation:\n\n**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\n\n| Phase | Milestone | Status |\n|---|---|---|\n| 1 | Core Transformer + CUDA kernels | Complete |\n| 2 | ZeRO-3 distributed training | Complete |\n| 3 | Safety & moderation engine | Complete |\n| 4 | Paged KV cache & inference | Complete |\n| 5 | Continuous learning (Online GD + EWC) | Complete |\n| 6 | Episodic, semantic & working memory | Complete |\n| 7 | Chain-of-Thought & Tree-of-Thought reasoning | Complete |\n| 8 | MCTS goal-directed planner | Complete |\n| 9 | Meta-learning (MAML / Reptile) | Complete |\n| 10 | World model (VAE + dynamics) | Complete |\n| 11 | Tool use & function calling | Complete |\n| 12 | Full YAML config parser (yaml-cpp) | In Progress |\n| 13 | GGUF weight loader & quantized inference | In Progress |\n| 14 | 13T token pre-training run | Planned |\n| 15 | RLHF alignment pipeline | Planned |\n| 16 | Public API release | Planned |\n\n**Rahul Sarkar** — India\nGitHub: [github.com/Sarkar-AGI](https://github.com/Sarkar-AGI)\n\nDisclaimer:TitanCore Core-1 is an independent research project. NVIDIA GPU hardware is required. CPU execution is not supported.", "url": "https://wpnews.pro/news/show-hn-core-1-a-distributed-c-agi-framework-for-trillion-parameter-scale", "canonical_source": "https://github.com/Sarkar-AGI/Core-1", "published_at": "2026-06-28 07:01:48+00:00", "updated_at": "2026-06-28 07:34:35.867350+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-infrastructure", "ai-agents", "ai-research"], "entities": ["TitanCore", "Core-1", "CUDA", "FlashAttention v2", "AdamW", "MCTS", "EWC", "MAML"], "alternates": {"html": "https://wpnews.pro/news/show-hn-core-1-a-distributed-c-agi-framework-for-trillion-parameter-scale", "markdown": "https://wpnews.pro/news/show-hn-core-1-a-distributed-c-agi-framework-for-trillion-parameter-scale.md", "text": "https://wpnews.pro/news/show-hn-core-1-a-distributed-c-agi-framework-for-trillion-parameter-scale.txt", "jsonld": "https://wpnews.pro/news/show-hn-core-1-a-distributed-c-agi-framework-for-trillion-parameter-scale.jsonld"}}