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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.
Day 31: Lambda + Bedrock: Sync, Stream, and Batch
Posted inAI AWS

Lambda Bedrock integration: Lambda + Bedrock: Sync, Stream, and Batch

This senior-level guide covers Lambda Bedrock integration with production trade-offs for reliability, security, and cost.
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Day 33: Agents on Step Functions, Not Infinite Loops
Posted inAI AWS System Design

Step Functions agents: Agents on Step Functions, Not Infinite Loops

This senior-level guide covers Step Functions agents with production trade-offs for reliability, security, and cost.
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Day 34: EventBridge as the Agent's Async Backbone
Posted inAI AWS

EventBridge agent backbone: EventBridge as the Agent’s Async Backbone

This senior-level guide covers EventBridge agent backbone with production trade-offs for reliability, security, and cost.
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Day 37: Provisioned Throughput vs On-Demand Cliffs
Posted inAI AWS

Bedrock provisioned throughput: Provisioned Throughput vs On-Demand Cliffs

This senior-level guide covers Bedrock provisioned throughput with production trade-offs for reliability, security, and cost.
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Day 39: Observability: Traces Across Prompt, Retrieval, Tools
Posted inAI AWS System Design

LLM observability: Observability: Traces Across Prompt, Retrieval, Tools

This senior-level guide covers LLM observability with production trade-offs for reliability, security, and cost.
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Day 40: Project: On-Call Copilot That Cannot Mutate Prod
Posted inAI AWS System Design

AI On-Call Copilot: A Project That Cannot Mutate Prod

This senior-level guide covers AI on-call copilot with production trade-offs for reliability, security, and cost.
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Day 42: Preference Data Without Vanity Accept Rates
Posted inAI Machine Learning

LLM preference data: Preference Data Without Vanity Accept Rates

This senior-level guide covers LLM preference data with production trade-offs for reliability, security, and cost.
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Day 43: Distillation for Edge and Lambda
Posted inAI AWS Machine Learning

LLM distillation: Distillation for Edge and Lambda

This senior-level guide covers LLM distillation with production trade-offs for reliability, security, and cost.
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Day 44: Continued Pretrain vs Instruct Tune vs Adapters
Posted inAI Machine Learning

LLM model tuning: Continued Pretrain vs Instruct Tune vs Adapters

This senior-level guide covers LLM model tuning with production trade-offs for reliability, security, and cost.
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Day 45: Catastrophic Forgetting Checks
Posted inAI Machine Learning

Day 45: Catastrophic Forgetting Checks

This senior-level guide covers catastrophic forgetting with production trade-offs for reliability, security, and cost.
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