Raman Marozau
CTO & Founder of Target Insight Function
Principal Engineering Architect
Platforms, protocols, and production tooling — grouped by product ownership, not engineering function.
Platforms Shipped
With dedicated domainsProduction Packages
npm · PyPIProtocols & Frameworks
ACP · MCP · SSRActive / Total
MaintainedAvailable for architecture & platform consulting
Deterministic C kernel, agent transport, ACP/MCP protocol bridges.
Deterministic C kernel providing transport-level routing for ACP/MCP-style agent protocols. The core runtime behind the stdio Bus ecosystem.
One command to run any MCP server module — in any language, on any platform. A manifest-driven launcher for stdio Bus ecosystem that replaces hardcoded bash commands in mcp.json with a portable, runtime-agnostic mcpx run <module>.
Agent orchestration server that connects MCP clients to ACP-compatible agents through stdio Bus.
A C++17 SDK for stdio Bus — a process-embedded message bus for AI agent orchestration. Manages child worker processes communicating over stdin/stdout using JSON-RPC (MCP/ACP protocols).
Normative protocol specification with RFC 2119 requirements.
Build instructions, configuration, and minimal examples.
Source repository, versioning, and release information.
Structured, validated, machine-readable SKILL capabilities and expertise for AI agents — delivered over Model Context Protocol via stdio Bus
An async Rust SDK for stdio Bus — a process-embedded message bus for AI agent orchestration. Manages child worker processes communicating over stdin/stdout using JSON-RPC (MCP/ACP protocols).
Worker implementations for stdio Bus kernel — ACP, MCP, and protocol bridges.
Native Node.js binding for stdio_bus — AI agent transport layer. No external binary required.
ACP Chat — agent-to-agent dialogue over TCP/Unix socket using the ACP protocol.
Python SDK for stdio_bus — async/sync API, Docker and native FFI backends, cross-platform AI agent transport layer.
Unified TypeScript runtime platform for backend, infrastructure, and web delivery across local and AWS environments.
TypeScript-first framework for building backend, infrastructure, and frontend in a single codebase — context-aware execution, type-safe DI, and native AWS integration.
Backend, infrastructure, and frontend in a single TypeScript project — one repo, one build, full type safety.
Same code adapts to web, Lambda, and local development contexts automatically.
Compile-time dependency injection with automatic service resolution — no runtime wiring errors.
First-class APIs for Lambda, API Gateway, DynamoDB, and S3 with zero-config deployment.
Real-world @worktif/runtime handler — AI chat with memory and vector search on AWS.
npx @worktif/runtime init --template ai-chat
import type { LambdaDefinition, LambdaEvent } from '@worktif/runtime';/** AI chat microservice with memory + vector search on AWS **/
export const chatHandler: LambdaDefinition<ChatTies> = {ties: { ai: Bedrock, memory: DynamoDB, search: OpenSearch },http: { method: 'POST', path: '/chat' },handle: async ({ body, ties }: LambdaEvent<ChatTies>) => {const history = await ties.memory.get(body.sessionId);const relevant = await ties.search.knn(body.message, 5);const reply = await ties.ai.invoke({ message: body.message, history, relevant });await ties.memory.append(body.sessionId, { user: body.message, assistant: reply });return reply;},};
AWS Lambda runtime layer, CDK integration, and tamper-resistant licensing.
Lambda Kata is a high-performance runtime that removes cold starts and stabilizes p95, p99 latency for Node.js Lambda functions
Stable response times by minimizing cold-start variance in Node.js Lambda workloads.
Runs as a native Lambda runtime layer, improving execution consistency without app rewrites.
Integrates directly with AWS CDK for repeatable setup, versioning, and deployment workflows.
Upgrades the runtime under existing Node.js Lambdas — same APIs, domains, and infrastructure.
Built specifically for AWS Lambda — operates entirely within your AWS accounts as a runtime layer for Node.js functions.
Tamper-resistant native licensing validator for Lambda Kata Integration.
AWS infrastructure toolkits and the Target Insight Function platform.
Full-stack engineering platform — the foundation for AI infrastructure, quantum algorithm research, and real-time system performance.
Each engagement targets clearly defined high-impact systems — stabilize what matters most before scaling further.
Pull requests, infra changes, and technical notes — not abstract recommendations.
Release-critical Lambda logic accumulates edge cases and becomes too risky to modify.
Standardized runtime and infrastructure patterns reduce deployment risk and improve day-to-day operability.
AI data governance frameworks and licensing innovation.
Foundational research repository for decoherence-driven validation work toward a unified Theory of Everything hypothesis.
We present a technical validation for complete, testable cosmological framework in which the Universe begins in a globally coherent quantum state and the first local act of decoherence inside the closed system creates its own environment. This repository contains a Python-based research module designed to explore quantum entanglement and decoherence as drivers for the expansion and materialization of field modes in closed quantum systems. The modeling focuses on a theoretical cosmological model where the stress-energy tensor emerges dynamically from decoherence processes.
High-performance computational library for vectorized mathematical operations — up to 429× faster softmax, exp, and element-wise transforms via optimized assembly on x86-64, ARM64, and Apple Silicon.
Experimental validation of the L-function, a novel mathematical construct designed to optimize task prioritization, contextual coherence, and resource management within Large Language Model (LLM)-driven Multi-Agent Systems
Empowering Data Ownership in the AI Domain — a license framework for AI data governance.
Research forks, upstream contributions, and experimental branches.
Experimental Bitcoin R&D focused on integrating stdio Bus protocol patterns for deterministic distributed workflow orchestration.
AI agents running research on single-GPU nanochat training automatically — integrated with stdio Bus protocol. Fork of Karpathy autoresearch.
Terminal coding-agent R&D fork with active contributions around stdio Bus interoperability and agent workflow experiments.
Registry and reference surface for ACP ecosystem coordination, compatibility, and implementation discovery.
Protocol workstream for agent-client interoperability, transport contracts, and cross-runtime integration patterns.
Active fork contribution stream with bug reports, feature proposals, and implementation feedback for agentic IDE workflows.
CLI automation, versioning, prompt engineering, and AI chat templates.
TypeScript utilities library providing dependency injection, logging, exception handling, and Zod validation tools with AWS Lambda integration.
TypeScript-based Unix shell automation library for command-line operations, scripting, and terminal management with comprehensive CLI integration.
TypeScript-based AWS infrastructure toolkit: DynamoDB integration, AppSync support, SES functionality, GraphQL capabilities with audit logging and AWS Signature V4 authentication.
Material UI Theme Provider and Customization Suite for React applications with dark mode support and dynamic color schemes.
CLI tool that automates semantic versioning workflows for Git repositories. Bumps versions, creates branches and tags, and optionally pushes changes and opens pull requests on supported SCM platforms.
AI chat application template — a starter for building agent-powered conversational interfaces.
Repository for basic and extensive prompt engineering including prompts, data, descriptions, specifications, and more.