Puneet Khandelwal

Separating credentials from execution agents makes production setups way safer. It keeps tokens completely out of your application logic.

Local inference runs straight into a silent killer right now: memory bandwidth. Architectures that attack this exact bottleneck are the ones that actually matter.

So much cleaner for the model's context window. Hiding unavailable tools entirely stops agents from looping on dead ends.

Cold starts are the hidden tax on serverless AI pipelines. Keeping a minimum instance alive changes the math completely.

AI writes the syntax. Technical blogs give you the context and the architectural scars. You need both.

Spot on. JSON mode just stops syntax errors, but schema enforcement is the only way to actually stop runtime hallucination.

Getting inference latency right on K8s is half about pod autoscaling and half about stopping noisy neighbors from wrecking tail latencies. Read this if you run models in production.

Because reviewing three times as much generated code requires more mental bandwidth than writing it yourself ever did.

Spot on. Dumping raw error logs into a prompt is just a lazy shortcut. We need structured error context instead.

Framework churn burns you out. Build on a solid lifecycle architecture instead so your code actually survives production.