Logs

Jev, Decision Model, calibration আর specialized inference থেকে শেখা — classification বা scoring - এর জন্য আসলে কতটুকু AI capability দরকার?

Lessons from building an AI sales agent that takes real orders: the model can suggest, but the server decides, especially when money and stock are involved.

Agentic frameworks for Claude Code install once and govern every session. A three-line bug fix shouldn't get the same ceremony as a new subsystem, so I built a per-session dial.

A practical Claude Code and Codex workflow for software engineers: discuss the idea first, lock the implementation plan, break work into epics, ship task by task, and write strong unit tests.

In the current AI era, model quality depends less on clever wording and more on the context you assemble around the task.

AI agents did not remove engineering work. They shifted the bottleneck from typing code to defining scope, guardrails, and review loops.

If your product depends on model output, you need more than anecdotes and vibes. You need evals that tell you when quality is improving or regressing.

I stopped using AI as autocomplete and started using it as an implementation partner. Here's how Claude Code reshaped my entire development workflow.

Not the advanced wizardry — just the practical patterns that show up in real codebases and make the type system work for you.