Why Logging Matters More Than You Think
3 min read
Logs are more than debug prints. They're your visibility layer in ML systems, APIs, and background jobs. Here's why logging matters, and what to log.
- #logging
- #mlops
- #system-design
- #backend
- +1 more
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Hands-on notes on backend services, distributed systems, caching, queues, and solme AI/ML real world engineering work.
How Cron Jobs Work: The Engine Behind Automated Tasks
3 min read
Cron jobs let you automate tasks on a schedule — from backups to reports. Here's a simple guide on how they work, why they're useful, and what to watch out for.
- #cron
- #automation
- #system-design
- #devops
Estimating Prompt Complexity (So You Can Route to the Right LLM)
5 min read
A compact, practical method to estimate prompt complexity—measure structure, semantic load, reasoning demand, and compressibility, then compute a simple score to route prompts to the right LLM.
- #LLM
- #Prompt Engineering
- #Evaluation
- #Routing
- +1 more
Weekend project: Re-implementing consistent hashing from scratch
1 min read
Started as a "quick review" but turned into a 6-hour deep dive. Sometimes the best way to really understand something is to build it yourself. Built a simple hash ring with virtual nodes and tested it with different…
- #Backend
- #ConsistentHashing
- #DistributedSystems
- #SystemDesign
The Rise of Event-Driven Microservices: Why Traditional REST APIs Might Not Be Enough
1 min read
Been following the shift from request-response to event-driven architectures lately, and the trend is fascinating. **What I'm Seeing:** Companies like Netflix, Uber, and LinkedIn are increasingly moving away from…
- #ApacheKafka
- #DistributedSystems
- #EventDrivenArchitecture
- #Microservices
Why I'm Obsessed with Database Query Optimization
1 min read
Spent the weekend diving deep into PostgreSQL query performance — specifically how indexing strategies affect distributed system bottlenecks. **The Experiment:** Built a small Spring Boot app to simulate real-world…
- #Backend
- #Database
- #Performance
- #PostgreSQL
- +1 more
Distributed Caching 101
1 min read
👋 Let’s talk about something that makes systems faster and smoother — **Distributed Caching**. Caching stores data so we don’t have to fetch or compute it again and again.…
- #Backend
- #Caching
- #DistributedSystems
- #MachineLearning
- +3 more
From Notebooks to Production: What Actually Changes
1 min read
👋 We often train machine learning models in notebooks, but getting them to work in the real world is a whole different story. Here’s what changes when you move from a notebook to **production**: 1) **From pandas to…
- #AI
- #APIs
- #MachineLearning
- #MLOps
- +3 more
How Do We Evaluate Language Models?
1 min read
Hey Connections! How do we actually know if a language model is any good? It’s not just about generating text that sounds right — we need ways to measure how well it matches the task. **Common Metrics:** 1)…
- #AI
- #BLEU
- #DeepLearning
- #Evaluation
- +5 more
Multi-Container ML Deployment Lessons
1 min read
Ever tried turning your ML project into a full-blown deployed app? I tested the deployment flow for my upcoming AI project and learned a ton. **What I built:** - Dockerized FastAPI backend, ML model, and React frontend…
- #AI
- #Deployment
- #DevOps
- #Docker
- +7 more
Experiment Tracking & Model Versioning Matter
1 min read
Training a model is easy. Reproducing it later? Not so much. As learning rates, architectures, datasets, and seeds change, things get messy. That’s why experiment tracking and model versioning are critical. **In real…
- #AI
- #MachineLearning
- #MLflow
- #MLOps
From Training to Your Device: Shipping ML Features
1 min read
Ever wonder how ML features actually reach your phone or laptop? The magic is in **deployment**. **Typical steps:** - Export the model (.pt, .onnx, .tflite) - Wrap it in an API (FastAPI, Flask) - Package with Docker -…
- #AI
- #MachineLearning
- #MLOps
- #ModelDeployment
- +1 more
Model Quantization: Making AI Efficient on the Edge
1 min read
How do features like on-device text summarization or photo object removal run without the cloud? One key is **model quantization**. Large models are compute-heavy — not ideal for mobile or edge devices. **Quantization…
- #AI
- #DeepLearning
- #MachineLearning
- #MLDeployment
- +1 more
Learning in Public: Weekly Updates
1 min read
I recently wrapped up my Master’s in Computer Science (Intelligent Systems) at UT Dallas — lots of learning, building, and late-night debugging. Starting today, I’ll share weekly progress: what I’m learning, projects,…
- #AI
- #FullStackDevelopment
- #MachineLearning