Resources
Insights on AI-assisted development, code intelligence, and developer productivity from the Jishu Labs team.
Most AI coding tools generate suggestions from general training data. But your codebase has its own patterns, decisions, and conventions. We built Kiku to bridge this gap - here's the story behind our architecture-aware approach to code completion.
January 24, 2026
After months of private testing with hundreds of engineering teams, we're excited to open Kiku to the public. Here's what you can expect and what we've learned.
January 28, 2026
A deep dive into how Kiku integrates with Kioku's institutional memory to ensure code suggestions respect your team's past decisions and architectural choices.
January 18, 2026
How we built a pattern detection system that can identify naming conventions, module structures, and API patterns across codebases of any size in under 2 minutes.
January 10, 2026
Your code is your intellectual property. We designed Kiku's architecture to ensure your source code never leaves your machine. Here's how our privacy-first model works.
December 20, 2025
Acceptance rate is just the beginning. We explore the metrics that actually matter for AI code completion quality, including context relevance, pattern compliance, and time saved.
December 12, 2025
Lessons learned from building and maintaining code completion extensions across three major IDE platforms. Each has its quirks, strengths, and unique integration patterns.
November 28, 2025