Articles & Updates
Engineering insights, product updates, and deep dives from the CodeKarma team.
AI Will Write More Code. Who Will Understand It?
AI will write the code. Production will tell you what it actually built.
Your Architecture Diagram Is Probably Lying
The most useful architecture view is the one the system writes itself.
From Signal-Based Observability to Behaviour-Based Engineering
A look at the shift from traditional observability toward behaviour-based engineering — where production behaviour becomes the foundation for development, architecture, migrations, and AI-assisted coding. The next generation of engineering tools will not just surface signals, but help teams understand how their systems actually work.
The Future of Developer Productivity Is Production-Aware
A look at why the next leap in developer productivity won’t come from writing more code faster, but from understanding production systems better. As AI copilots automate syntax, the real bottleneck becomes context, confidence, and visibility into what code is actually doing in production.
The Hidden Cost of Understanding Software
Production never lies. The cost of not listening to it is on every engineering invoice, just not labelled that way.
Engineering Decisions Should Be Based on Behaviour, Not Assumptions
CodeKarma is making behaviour cheap enough to consult that no serious change has to be argued from memory.
Why Production Context Belongs Inside the IDE
A developer opens a file. The cursor lands on a method. The first question, almost always, is the same: Is this thing actually used?
Why Change Feels Risky in Distributed Systems
CodeKarma brings real production dependencies into the spot where changes are decided.
Dead Code Is Not Just a Cleanup Problem
The point is not to delete more code. The point is to read the system you actually have, not the one the repository remembers.
The Cost of Misdiagnosis in Software Systems
Most expensive incidents are not expensive because of the fix. They are expensive because of the wrong theories that came first.
Why Better Dashboards Don't Always Create Better Decisions
Dashboards centralise data. They do not centralise understanding.
From Incident Response to Incident Understanding
You cannot fix a system faster than you can agree on what it is doing.
The Missing Layer Between Code and Observability
Observability shows what the system emitted. Engineering needs to know what the system did.
Why AI SRE Needs Ground Truth, Not Just More Telemetry
Faster interpretation is cheap. Better understanding is the work.
Observability Shows Signals. Engineering Needs Behaviour.
Observability tells teams when systems are unhealthy. Behavioural understanding tells them how systems actually work. The next engineering layer is not more dashboards — it is production-grounded context.
AI Readiness Is Context Readiness
A look at why most enterprise AI initiatives fail to move beyond demos. The missing layer is not better models, but reliable production context — the live system behaviour AI agents need to reason accurately about code, infrastructure, and operational decisions.
Why Enterprise Software Decisions Stall
A look at why enterprise software deals often stall despite strong technical interest. The real challenge is not convincing the first buyer — it is preserving the product’s value as it moves through finance, procurement, risk, and executive layers without losing clarity or urgency.
Why Technical Debt Becomes Invisible in Production Systems
The teams that move fastest in the next decade will not be the ones with the cleanest code. They will be the ones who know which parts of their system are still earning their keep.
The Real Problem With Microservices Is Interpretation
Every few years, someone declares micro-services a mistake. The argument is familiar. Too many services. Too many queues. Too many async hops. Too much coordination. Too much overhead. Bring back the monolith. It is a satisfying argument. It is also slightly off.
Production Never Lies
Modern engineering teams are drowning in signals but starving for clarity. The next shift is from interpreting fragmented telemetry to understanding live production behaviour directly.
Rethinking Observability
How at CodeKarma re-imagining observability in the era of LLMs?
The Migration Nightmare
Every enterprise has legacy software that grows unmanageable over time. Re-architecture doesn't have to be a desperate move — with the right insights, it becomes a strategic choice.