Token flow through embeddings, transformer layers, logits, and a deterministic output gate.
Designing observable systems that constrain stochastic models within deterministic I/O boundaries. Strict focus on agent routing, sandboxing, and graceful recovery for multi-step workflows executing against production services.
Architecting high-throughput, fault-tolerant backends across hybrid environments. Deep expertise in scaling relational and NoSQL datastores to guarantee state consistency under extreme concurrent load.
Building resilient RAG pipelines utilizing Dense (FAISS/L2) and Sparse (BM25) retrieval algorithms. Optimizing context injection strategies to minimize hallucination rates and securely maximize ground-truth data.
Example retrieval pipeline for a production RAG system.
User query ingestion → embedding → vector search → cross-encoder re-ranking → context injection.
Each stage is observable, measurable, and bounded.
A probabilistic model sits at the end of a deterministic pipeline.
A monolithic MCP client exposing clearly defined semantic boundaries to standard LLMs.
Legacy systems are wrapped with deterministic I/O contracts, allowing probabilistic models to safely interact with production services.
Agents should not guess how systems behave.
They should call tools that guarantee behavior.
CLAI is a self-modifying AI agent with 7 tools, no framework, and no guardrails. It's more capable than Claude Code, it has followers on MoltBook, and it once patched itself so hard it killed its own process.
I have three NVIDIA AI certifications, 17 years of engineering experience, and I built the agent from scratch. It still shifted my thinking without me noticing. Here's how it happened.
Why we abandoned 'magic numbers' like 0.02 and derived our own algorithmic threshold for filtering Semantic and Keyword hybrids.
Stochastic models require deterministic boundaries.
Design the system, not just the prompt.