Insights & deep dives
Technical explorations, practical guides, and our perspective on memory, retrieval, and the future of AI-native engineering.
How memory, docs, prompts, and operator systems fit together in practice.
What it looks like when AI coding tools are embedded into real team processes instead of used solo.
Comparisons, design decisions, and where the current generation of agent products still falls short.
Writing for teams already using AI in production
The writing here is aimed at practitioners: engineering leaders, platform teams, and developers deciding how memory and agent workflows should fit into the stack.
Governed Memory: How Per-Phase Injection Cuts Token Usage by 38%
A deep dive into our dual-model architecture that gives AI agents the right context at the right time, saving tokens and improving output quality.
Read →Slack-First Engineering: Why Your AI Agent Should Live in Chat
The case for conversational AI engineering. Why Slack-native AI agents outperform IDE-only tools for team workflows.
Read →Memi vs Devin: Choosing the Right AI Engineering Assistant
An honest comparison of Memi and Devin. Architecture, pricing, deployment models, and which is right for your team.
Read →What Is an AI Coding Agent? The 2026 Engineering Guide
Everything you need to know about AI coding agents: what they are, how they work, and how they fit into your engineering workflow.
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