The speculative decoding trick using existing source code is genuinely clever. Most AI coding tools treat your codebase as context - Cursor treats it as a prediction shortcut.
This kind of architectural differentiation is why the 'best model wins' narrative is wrong. Tool engineering matters more than model capability for daily coding work.
I ended up building my workflow around multiple tools for this reason - Cursor for inline edits, Claude Code for autonomous execution, GPT for quick lookups. Different architectures solve different problems.
The 90-minute RL retraining loop is the detail that stood out most. That's not just personalization - that's the tool learning your codebase in real time.
Spot on that it's tool engineering over model capability. The speculative edits trick is a perfect example - it's not about having a smarter model, it's about being clever with what's already there. Your multi-tool split makes sense too, Cursor's architecture is optimized for inline speed while Claude Code's strength is longer autonomous tasks. The 90-minute RL loop is wild though - it's not really learning your codebase, it's learning your accept/reject patterns across all users. Still impressive at 400M requests/day. How stable has your tool split been as each one keeps shipping new capabilities?
That's the move - a meta-layer that owns context across all the tools underneath. Shared memory solves the biggest pain of multi-tool workflows. What's Wiz built on, and how are you handling context sync between the different tools it orchestrates?
Ha, that's the thing right - you can know a tool is technically better at something and still gravitate back to what clicks. Is it Opus's reasoning style, or more about how it handles your specific workflow with Wiz?
The speculative decoding trick using existing source code is genuinely clever. Most AI coding tools treat your codebase as context - Cursor treats it as a prediction shortcut.
This kind of architectural differentiation is why the 'best model wins' narrative is wrong. Tool engineering matters more than model capability for daily coding work.
I ended up building my workflow around multiple tools for this reason - Cursor for inline edits, Claude Code for autonomous execution, GPT for quick lookups. Different architectures solve different problems.
Explored the multi-tool approach: https://thoughts.jock.pl/p/multi-model-ai-workflow-2026-gpt-claude-gemini
The 90-minute RL retraining loop is the detail that stood out most. That's not just personalization - that's the tool learning your codebase in real time.
Spot on that it's tool engineering over model capability. The speculative edits trick is a perfect example - it's not about having a smarter model, it's about being clever with what's already there. Your multi-tool split makes sense too, Cursor's architecture is optimized for inline speed while Claude Code's strength is longer autonomous tasks. The 90-minute RL loop is wild though - it's not really learning your codebase, it's learning your accept/reject patterns across all users. Still impressive at 400M requests/day. How stable has your tool split been as each one keeps shipping new capabilities?
I have my Ai Agent - Wiz, that have everything under it. So shared context / memory and tools. Oversee everything :)
That's the move - a meta-layer that owns context across all the tools underneath. Shared memory solves the biggest pain of multi-tool workflows. What's Wiz built on, and how are you handling context sync between the different tools it orchestrates?
Wiz primary was (and still is) Claude Code. But - I can change models as I wish(for example Codex / Mistral / others).
Ha, that's the thing right - you can know a tool is technically better at something and still gravitate back to what clicks. Is it Opus's reasoning style, or more about how it handles your specific workflow with Wiz?