Deep dives into AI, data engineering, cloud architecture, and product development from our engineering teams
A comprehensive guide to technical strategy and architecture decisions at each startup stage - MVP, pre-seed, seed, and Series A. Learn what to build, what to defer, and how to scale intelligently.
Deep dive into vLLM architecture, continuous batching, PagedAttention, tensor parallelism, and advanced techniques for serving large language models at scale with optimal throughput and latency.
Comprehensive exploration of erasure coding techniques, Reed-Solomon codes, storage efficiency, fault tolerance mathematics, and practical implementation in systems like HDFS, Ceph, and S3.
Learn how to architect, deploy, and scale large language models in production using AWS Bedrock, covering cost optimization, security, and performance best practices.
Deep dive into performance tuning Spark clusters on EMR, memory management, partitioning strategies, and cost reduction techniques for processing massive datasets.
Explore distributed systems architecture patterns including MapReduce, actor models, event sourcing, and CQRS with real-world implementation examples.
How to leverage AI tools, no-code platforms, and modern frameworks to ship production-ready MVPs in weeks, not months. Lessons from 50+ successful launches.
Complete guide to architecting a cost-effective, scalable data lake using AWS services with automated ETL pipelines and real-time analytics capabilities.
Production-hardened Kubernetes deployment strategies covering service mesh, observability, auto-scaling, and infrastructure-as-code best practices.
Architecture and implementation of streaming data pipelines for real-time analytics, handling millions of events per second with sub-second latency.
A proven framework for rapid product iteration, continuous deployment, feature flagging, and data-driven decision making used by top tech companies.
End-to-end guide to fine-tuning large language models for domain-specific tasks, including data preparation, evaluation metrics, and deployment strategies.
Best practices for managing infrastructure across AWS, GCP, and Azure using Terraform, including state management, modules, and CI/CD integration.
Practical guide to transitioning from monolithic data warehouses to a decentralized data mesh architecture with domain-driven ownership.
Comprehensive comparison of API design patterns with real-world examples, performance benchmarks, and guidance on choosing the right approach.