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About Logeshwaran
I am an experienced AWS Full Stack Developer with expertise in building scalable, cloud-native applications using a wide range of technologies, including AWS, Angular, Java, Node.js, Python, Terraform, Docker, and TypeScript. With a strong focus on delivering high-performance solutions, I specialize in leveraging AWS services for cloud architecture, creating dynamic front-end interfaces, and building efficient back-end systems. I have hands-on experience in automating infrastructure with Terraform, containerizing applications using Docker, and implementing DevOps best practices.
AppSync With Bedrock: GraphQL Resolvers That Call Tools Safely
Posted inAI AWS

Appsync Bedrock: GraphQL Resolvers That Call Tools Safely

A senior-engineer guide to AppSync Bedrock, with production-ready patterns, trade-offs, and practical examples.
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Agentic Git Workflows: Atomic Commits From Noisy LLM Diffs
Posted inAI

Agentic Git Workflows: Atomic Commits From Noisy LLM Diffs

A senior-engineer guide to agentic Git workflows, with production-ready patterns, trade-offs, and practical examples.
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Bedrock Batch Inference: Overnight Refactor Diffs Across Large Repos
Posted inAI AWS

Bedrock Batch Inference for Overnight Refactors: Shard, Run, Validate and Apply in Daytime PRs

Overnight repo refactors with Bedrock batch inference: package shards, Converse-format JSONL, job IAM, result parsing, offline validation and daytime stacked PRs.
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Copilot Workspace Tasks: Spec-Driven Changes Across Microservice Boundaries
Posted inAI

Copilot Workspace Tasks: Spec-Driven Changes Across Microservice Boundaries

A senior-engineer guide to Copilot Workspace, with production-ready patterns, trade-offs, and practical examples.
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Claude Projects vs Cursor: Context Hygiene for Regulated Codebases
Posted inAI

Claude Projects vs Cursor: Context Hygiene for Regulated Codebases

A senior-engineer guide to Claude Projects vs Cursor, with production-ready patterns, trade-offs, and practical examples.
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AI PR Reviewers: Diff-Scoped Context Windows That Catch Real Bugs
Posted inAI

Llm Context Window Engineering: Diff-Scoped Context Windows That Catch Real Bugs

A senior-engineer guide to LLM context window engineering, with production-ready patterns, trade-offs, and practical examples.
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SageMaker JumpStart vs Bedrock: When Fine-Tunes Beat Prompting
Posted inAI AWS Machine Learning

SageMaker JumpStart vs Bedrock: When Fine-Tuning Beats Prompting and RAG for Coding Assistants

SageMaker JumpStart vs Amazon Bedrock fine-tuning for coding assistants: decision gate, SFT vs RAG, Bedrock-conversation-2024 data, Nova hyperparameters, on-demand deployment and evaluation.
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Bedrock Guardrails: Block Prompt Injection Inside Internal Dev Tools
Posted inAI AWS

Amazon Bedrock Guardrails: Block Prompt Injection Inside Internal Dev Tools

A senior-engineer guide to Amazon Bedrock Guardrails, with production-ready patterns, trade-offs, and practical examples.
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Bedrock Knowledge Bases: Chunking Strategies That Fit Code RAG
Posted inAI AWS Machine Learning

Bedrock Knowledge Bases Chunking Strategies That Fit Code RAG

Bedrock Knowledge Bases chunking for code RAG: prefer AST-aware symbol chunks, metadata filters, and hit-rate@k evals over fixed 300-token windows that shred functions mid-signature.
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AI Test Generation: Property Tests That Catch Race Conditions
Posted inAI Node.js

AI Test Generation: Property Tests That Catch Race Conditions

A senior-engineer guide to AI test generation, with production-ready patterns, trade-offs, and practical examples.
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