Projects
A complete project archive spanning distributed AI systems, developer tools, data-intensive applications, and full-stack products.
AI / ML Systems
4 projects
Conductor: Distributed Multi-Agent Workflow RuntimeDistributed AI SystemsApr 2026 - Jun 2026A Kubernetes-native Go runtime for crash-resumable multi-agent workflows, distributed scheduling, and event-sourced observability.
Tech stack
Go, Kubernetes, Kafka, Event Sourcing, DAG Orchestration, Multi-Agent Systems
Description
A Kubernetes-native Go runtime for crash-resumable, multi-agent LLM workflows with durable execution, distributed scheduling, and event-sourced observability.
Achievements
- Added per-step checkpoints so long-running workflows can resume after crashes without duplicating tool calls or model spend.
- Scheduled DAG-based workflows across a scalable Kafka-backed worker pool with per-task retries.
- Captured every model and tool call through event-sourced tracing, audit logs, and real-time workflow monitoring.
Engram: Cross-Session Memory MCP for AI Coding AgentsAgent Memory and EvaluationJan 2026 - Mar 2026A Python MCP memory harness for carrying decisions, project facts, and preferences across AI coding sessions while detecting stale context.
Tech stack
Python, MCP, Event-Driven Architecture, Agent Memory, BM25, Evaluation
Description
A Python MCP memory harness that carries decisions, project facts, and preferences across AI coding sessions while detecting stale or conflicting context.
Achievements
- Reduced repeated context tokens by approximately 40% through cross-session state handoffs.
- Consolidated raw sessions into durable short- and long-term memory with stale-memory cleanup and conflict detection.
- Validated retrieval reliability with Recall@5, MRR, and traces for ranking regressions and injected context.
Metrics
- ~40% fewer repeated context tokens
- Recall@5 and MRR evaluation
FigBrain: Figma Multi-Agent PluginMulti-Agent PluginApr 2025 - May 2025An MCP-powered Figma agent that uses a LangGraph supervisor and ReAct tool loops to turn natural-language prompts into FigJam components.
Tech stack
Python, TypeScript, LangGraph, MCP, Figma API, PostgreSQL
Description
An MCP-powered Figma agent built for the Microsoft AI Hackathon, using a LangGraph supervisor and ReAct tool loops to turn natural-language prompts into FigJam components.
Achievements
- Converted natural-language prompts into FigJam components at approximately 85% first-try accuracy.
- Reduced API costs by approximately 28% and lowered latency with PostgreSQL-backed memory, caching, and model routing.
Metrics
- ~85% first-try accuracy
- ~28% lower API costs
AI Recognition in Edge Computing & Cloud NativeAI/ML & Edge ComputingMay 2023 - July 2023A KubeEdge and TensorFlow vegetable-recognition system with Django and MQTT management plus a Kubernetes CI/CD pipeline.
Tech stack
KubeEdge, TensorFlow, CNN, Django, MQTT, Kubernetes, CI/CD, Edge Computing, Cloud Native
Description
Optimized edge computing AI recognition using KubeEdge, streamlining local computations and decision-making. Devised a Convolutional Neural Networks model in TensorFlow for vegetable recognition, achieving 98.6% accuracy. Built a centralized management system using Django with MQTT, enhancing live oversight in multiple edge modules. Fostered system resilience and prompt updates by integrating a Kubernetes orchestrated CI/CD pipeline.