Insights on AI, Cloud, Data Engineering, DevOps, SaaS, Product strategy, and Student perspectives from the Clarieon.ai team.
Large language models and predictive analytics are enabling clinicians to make faster, more accurate diagnoses — and what the next frontier looks like for healthcare AI.
Crafting effective prompts for production AI systems goes beyond trial and error. We cover chain-of-thought, few-shot patterns, and guardrails that keep enterprise LLMs on track.
When should you fine-tune a model versus building a retrieval-augmented generation pipeline? We break down cost, latency, accuracy, and maintenance trade-offs for real projects.
A practical guide to architecting secure, scalable cloud infrastructure for healthcare — covering VPCs, IAM policies, encryption at rest, and audit logging.
Running workloads across AWS, Azure, and GCP simultaneously sounds powerful — but multi-cloud adds real complexity. Here's how to evaluate whether it's right for your organisation.
The anatomy of a production-grade data pipeline — from real-time event streaming with Kafka, through transformation layers, to dashboards your stakeholders actually use.
Data Build Tool (dbt) has become the standard for SQL-first transformations. We share the patterns that work in heavily regulated healthcare data environments — lineage, testing, and docs.
GitOps fundamentally shifts how teams manage infrastructure and deployments. Here's a roadmap for organisations ready to make the transition from traditional CI/CD pipelines.
Cloud infrastructure waste is rampant. We walk through node autoscaling, resource requests vs limits, Spot instances, and tools like Kubecost to cut your K8s bill without sacrificing reliability.
From micro-frontends to serverless back-ends — the architecture decisions that separate scalable SaaS products from those that buckle under growth pressure.
Choosing the wrong multi-tenancy model early on is costly to reverse. We compare silo, pool, and bridge approaches with real trade-offs for security, cost, and performance.
Engineers who understand product management build better software. We explore frameworks from Jobs-to-Be-Done to outcome-driven roadmaps that bridge code and customer value.
Most Product Requirements Documents are ignored the moment they're written. Here's a lean, collaborative template that keeps engineering, design, and business aligned throughout the build.
Okay so I honestly did NOT think this would work but I made a Python chatbot that answers questions about my school timetable. It took me three weekends, a LOT of Stack Overflow, and one very patient older cousin. Here's exactly how I did it.
My mum thought I was gaming. I was actually learning Python. For 30 days straight, every evening after homework, I followed free tutorials online. Week 1 was boring, Week 2 was confusing, Week 3 clicked, Week 4 changed everything. Real talk from a 15-year-old.
I am a 13-year-old girl and for my science fair I used a free AI model to analyse plant growth data from our garden. My teacher gave me full marks and asked me to explain it to the class. Spoiler: it was awkward but amazing. Here's what I used and how you can do it too.
Normal AI tells you what to do. Agentic AI just does it. I used the example of buying a laptop online to explain the biggest difference — and honestly once you get it, you'll see it everywhere.
