Code research projects with async coding agents like Claude Code and Codex
I’ve been experimenting with a pattern for LLM usage recently that’s working out really well: asynchronous code research tasks. Pick a research question, spin up an asynchronous coding agent and …
GitHub - Varietyz/Disciplined-AI-Software-Development: This methodology provides a structured approach for collaborating with AI systems on software development projects. It addresses common issues like code bloat, architectural drift, and context dilution through systematic constraints and validation checkpoints.
This methodology provides a structured approach for collaborating with AI systems on software development projects. It addresses common issues like code bloat, architectural drift, and context dilu...
What makes Claude Code so damn good (and how to recreate that magic in your agent)!?
Claude Code is the most delightful AI agent/workflow I have used so far. Not only does it make targeted edits or vibe coding throwaway tools less annoying, ...
Effortlessly generate a context block to quickly paste into any AI chat. Works on any OS, from any editor. Removing the friction between your project/repo and AI. This is not meant to replace Agent software.
Working Effectively with AI Coding Tools like Claude Code
A practical guide to working effectively with AI coding tools like Claude Code, covering mindset shifts, quality control strategies, and team collaboration workflows for modern software development.
Will McGugan may no longer be running a commercial company around Textual, but that hasn't stopped his progress on the open source project. He recently released v4 of his Python …
Let the LLM Write the Prompts: An Intro to DSPy in Compound AI Pipelines
Large Language Models (LLMs) excel at understanding messy, real-world data, but integrating them into production systems remains challenging. Prompts can be unruly to write, vary by model and can be difficult to manage in the large context of a pipeline. In this session, we'll demonstrate incorporating LLMs into a geospatial conflation pipeline, using DSPy. We'll discuss how DSPy works under the covers and highlight the benefits it provides pipeline creators and managers.
Talk By: Drew Breunig, Data Science Leader & Strategist, Overture Maps Foundation
Databricks Named a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms: https://www.databricks.com/blog/databricks-named-leader-2025-gartner-magic-quadrant-data-science-and-machine-learning
Build and deploy quality AI agent systems: https://www.databricks.com/product/artificial-intelligence
See all the product announcements from Data + AI Summit: https://www.databricks.com/events/dataaisummit-2025-announcements
Connect with us: Website: https://databricks.com
Twitter: https://twitter.com/databricks
LinkedIn: https://www.linkedin.com/company/databricks
Instagram: https://www.instagram.com/databricksinc
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My AI Workflow for Understanding Any Codebase | Peter Steinberger
A quick tip on how I use repo2txt and Google AI Studio to understand new codebases. Gemini's 1M token context window is perfect for asking questions about code.
StefanRoets06/Custom-Instructions-for-GitHub-Copilot: This guide is intended to help you provide better context for GitHub Copilot when working on programming-related tasks. Use this template to define your goals, preferences, and any specific guidelines you'd like Copilot to follow.
This guide is intended to help you provide better context for GitHub Copilot when working on programming-related tasks. Use this template to define your goals, preferences, and any specific guideli...
Malleable software: Restoring user agency in a world of locked-down apps
The original promise of personal computing was a new kind of clay. Instead, we got appliances: built far away, sealed, unchangeable. In this essay, we envision malleable software: tools that users can reshape with minimal friction to suit their unique needs.
What Actually Works: 12 Lessons from AI Pair Programming | Forge Code
Field-tested practices for productive AI-assisted development. Real lessons from 6 months of daily AI pair programming, including what works, what fails, and why most engineers are doing it wrong.
