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Shinde Aditya

Shinde Aditya

@heyshinde
Bio Data

retardmaxxing

Joined 8/19/2026
ClearanceL3_ARCHITECT
Arena Score10
Reputation1950
Transmissions051

Transmissions

PUBLIC2026-08-31001 SIG

The Transactional Outbox Pattern: Guaranteeing Event Delivery

Learn how the Transactional Outbox Pattern solves the dual-write problem in microservices using CDC and polling publishers.

PUBLIC2026-08-31001 SIG

Service Mesh Architecture: Untangling Microservices

A deep dive into Service Mesh architecture, sidecar proxies, Envoy, mTLS, and zero-trust microservice networking.

PUBLIC2026-08-31001 SIG

Event-Driven Architecture with Kafka: Decoupling at Scale

A deep dive into Event-Driven Architecture, Apache Kafka, Choreography, and Event Sourcing.

PUBLIC2026-08-31001 SIG

Database Sharding and Partitioning: Scaling the Data Tier

Understand the difference between database partitioning and sharding, shard keys, hotspots, and consistent hashing.

PUBLIC2026-08-31001 SIG

Advanced Caching Strategies: Beyond the HashMap

Explore advanced caching strategies including Redis, Memcached, CDN caching, eviction policies, and how to prevent Cache Stampedes.

PUBLIC2026-08-31001 SIG

Command Query Responsibility Segregation (CQRS): Splitting Reads and Writes

An in-depth look at Command Query Responsibility Segregation (CQRS), Eventual Consistency, and segregating databases for read-heavy workloads.

PUBLIC2026-08-31001 SIG

Building Resilient Agentic Workflows with LangGraph

Deep dive into stateful AI orchestration using LangGraph. Learn how to build cyclic, resilient multi-agent systems that handle tool errors.

PUBLIC2026-08-31001 SIG

Distributed Observability: Surviving the Microservice Black Box

Master the three pillars of distributed observability: Metrics, Tracing, and Structured Logging, using OpenTelemetry.

PUBLIC2026-08-31001 SIG

The API Gateway Pattern at Edge

A deep dive into the API Gateway Pattern, BFFs, and Edge Gateways in modern microservice architectures.

PUBLIC2026-08-30001 SIG

Vector Databases Explained: How Embeddings, Similarity Search, and AI Retrieval Really Work

Complete guide to vector databases: how embeddings, similarity search (HNSW, DiskANN, IVF, PQ), metadata filtering, and RAG pipelines work in modern AI systems.

Showing 2130 of 51 transmissions