Abhishek Jha | Freelancer & ethical hacker

Personal Reflections & Tech Insights

Blogs

Sep 21, 2026 5 min read

Making Distributed Systems Consistent with Raft

Building reliable distributed systems means handling failures while keeping data consistent. Raft consensus helps manage this complexity through a clear approach to agreement.

Sep 20, 2026 6 min read

Estimating at Scale: Probabilistic Data Structures

Real-time AI analytics often needs to handle massive data streams. Probabilistic data structures offer a clever way to scale by trading exact accuracy for speed and memory efficiency.

Sep 17, 2026 5 min read

Semantic Caching for LLMs: Smarter Cache Hits

Traditional caching struggles with LLM prompts. Semantic caching uses embeddings to find similar queries, dramatically improving cache hit rates and reducing costs.

Sep 15, 2026 5 min read

Stateful Serverless for Long AI Workflows

Serverless functions are designed to be stateless and ephemeral, which often clashes with the long-running, multi-step nature of many AI workflows. Here's how to bridge that gap.

Sep 14, 2026 5 min read

Tracing AI Agent Decisions: The Observability Challenge

Building AI agents is one thing; understanding why they do what they do is another. Tracing their internal decisions is becoming a crucial, complex observability problem.

Sep 13, 2026 5 min read

Transactional Outbox: Reliable Events for Microservices

Building reliable microservice communication is hard. The transactional outbox pattern helps ensure events are published consistently with database changes, avoiding common pitfalls.

Sep 10, 2026 5 min read

Time-Series Databases: Beyond Basic Metrics

Time-series databases are purpose-built for the massive amounts of time-stamped data generated by modern systems, offering unique advantages for observability and analytics.

Sep 09, 2026 5 min read

LLM Caching: When Key-Value Stores Fall Short

Caching LLM responses with traditional key-value stores often misses the mark because queries aren't exact. Semantic caching matches similar questions to provide better hit rates and reduce API costs.

Sep 08, 2026 5 min read

Dynamic Batching: More AI Throughput, More Latency?

Dynamic batching can significantly boost AI model inference throughput by grouping requests. But it's a tradeoff: better throughput often means higher latency for individual requests.

Blogs

Abhishek's AI Clone