We use cookies to ensure you get the best experience on our website.
by Dave Berner — Published 13 June 2025
Using AI to superpower your software engineering workflow
Learn how to create a lightweight, project-aware AI agent using open-source RAG techniques. Includes an n8n workflow and example integration with your repo and internal docs.
As your team adopts AI-assisted coding, what does the output really look like? Learn what patterns to watch for, how to improve quality, and how to track impact over time.
Vibe coding isn't magic—it's just structured collaboration. This guide breaks down how to convert vague feature ideas into high-context prompts for real-world feature scaffolding.
Explore a tactical breakdown of how experienced teams use AI to triage PRs, spot security flaws, and enforce architectural consistency—without giving up human control.
Get reusable prompt recipes to go from product idea → schema design → CRUD APIs → frontend scaffolding—all within a single chat thread. Perfect for hack days and rapid prototyping.
A practical guide to using LLMs as your first line of support when debugging. Covers prompt strategies, follow-up techniques, and how to refine model answers to match your codebase and tools.
Turn noisy commits and vague PRs into a thing of the past. Learn how to automate concise, meaningful commit messages and pull request summaries using AI workflows.
From async agent-based pair programming to LLM-generated standup summaries, this guide explores how AI reshapes day-to-day team rituals in high-performing engineering orgs.
Explore techniques for embedding LLMs into VS Code, CI/CD pipelines, and internal dev tools—so your team can use AI without leaving the stack or copy-pasting code blocks.
AI hallucinations can be costly. This article shows how to implement confidence checks, test scaffolds, and human-in-the-loop review steps to keep LLM output safe and usable.
Hear from us about product updates, news and cool things in the startup ecosystem.