Code Mode started as a question: what does an AI coding interface look like when the underlying model can actually reason, not just complete?
Most coding tools built on LLMs are good at pattern matching. They’ve seen enough code that they can continue a function, suggest a variable name, or fill in a boilerplate block. That’s useful. But it breaks down the moment you hit a genuinely hard problem — a subtle bug, an architectural decision, a performance issue that requires holding many constraints in mind at once.
Sarvam-105B is different. It was trained with an emphasis on reasoning chains, and it shows. When you describe a bug in Code Mode, Jivam doesn’t guess — it thinks.
The interface problem
The first prototype of Code Mode looked like a chat interface with syntax highlighting bolted on. It was functional but wrong. Developers don’t think about code as a conversation. They think in diffs, in files, in function signatures.
We rebuilt it from scratch around a few principles:
Diffs, not walls of text
When Jivam suggests a change, it shows you exactly what changed — not a block of code you have to compare against your original. One-click copy for the full block, or line-level copy for specific changes.
Files as first-class context
You can drag any file into Code Mode. Jivam reads it, references it, and keeps it in context for the session. No more pasting — just drop in your file and describe your problem.
Focused, not distracted
Code Mode has a different visual register than Conversation Mode — tighter, darker, more like an IDE than a chat interface. The goal is to feel like a natural extension of your existing workflow, not an interruption to it.
What’s next for Code Mode
We’re working on deeper integrations: a VS Code extension that can invoke Jivam from your editor, automatic context from your open files, and a diff-review flow for larger changesets.