Dev Lab#

1. ServiceScope — AI Dependency Mapper#

Focus: AI-Assisted Dependency Analysis & Blast-Radius Modeling Stack: FastAPI, Celery, PostgreSQL, LLM Integration

Summary: An AI-native platform designed to model the “Blast Radius” of code changes. It replaces manual reasoning with machine-assisted dependency intelligence.

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The Problem: The “Implicit Dependency” Trap#

In large microservice ecosystems, dependencies are often implicit (dynamic calls, shared config), undocumented, and tribal. This makes impact analysis slow and error-prone. Engineers cannot answer the fundamental question: “If I change this component, what breaks?”

The Solution#

ServiceScope is a multi-tenant, AI-native system designed to model the “Blast Radius” of code changes. It replaces manual reasoning with machine-assisted dependency intelligence.

System Architecture#

The platform utilizes a fully asynchronous, event-driven pipeline:

ServiceScope Architecture Diagram

Key Design Decisions:

  • Ingestion Plane: Incremental indexing to avoid reprocessing unchanged code.
  • Intelligence Engine: Hybrid approach combining static analysis (AST) with LLM inference for semantic understanding.
  • Real-Time Graphing: Continuous graph updates for instant upstream/downstream analysis.

2. Adaptive Compute Orchestrator (ACO)#

Focus: Predictive Scheduling for Heterogeneous Compute Stack: Python, GPU/CUDA, Distributed Systems

Summary: A mission-critical scheduler for heterogeneous environments (CPU, GPU, ARM64) that shifts from reactive allocation to predictive, intent-aware orchestration.

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The Concept#

Traditional schedulers treat workloads as opaque units. ACO treats workloads as profiles with intent.

Core Architecture#

ACO Architecture Diagram

1. Intelligent Control Plane#

  • Predictive Modeling: Time-series models forecast workload spikes using historical telemetry.
  • Cost-Aware logic: Balances performance targets (SLA) against power and spot-instance pricing.

2. High-Throughput Data Plane#

  • Sub-millisecond Decisions: Optimized for extreme concurrency.
  • Lightweight Agents: No heavy hypervisors; raw execution on bare metal or lightweight containers.
  • Dynamic Bin-Packing: Maximizes hardware utilization across NVIDIA GPUs and x86_64 nodes.

Performance & Impact#

  • <10ms end-to-end scheduling latency.
  • 28% improvement in resource efficiency via predictive scaling.
  • 95%+ SLA adherence during burst workloads.
Backward Experience