Current AI coding tools assist but don't fully automate software engineering. Better RL environments are what's missing.
Challenges and Paths Towards AI for Software Engineering
View recent discussion. Abstract: AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be addressed before automated software engineering reaches its full potential. It should be possible to reach high levels of automation where humans can focus on the critical decisions of what to build and how to balance difficult tradeoffs while most routine development effort is automated away. Reaching this level of automation will require substantial research and engineering efforts across academia and industry. In this paper, we aim to discuss progress towards this in a threefold manner. First, we provide a structured taxonomy of concrete tasks in AI for software engineering, emphasizing the many other tasks in software engineering beyond code generation and completion. Second, we outline several key bottlenecks that limit current approaches. Finally, we provide an opinionated list of promising research directions toward making progress on these bottlenecks, hoping to inspire future research in this rapidly maturing field.
'AI' Sucks the Joy Out of Programming
I’ve used spicy auto-complete, as well as agents running in my IDE, in my CLI, or on GitHub’s server-side. I’ve been experimenting enough with LLM/AI-driven programming to have an opinion on it. And it kind of sucks.
How to Do AI-Assisted Engineering
15 experienced engineers and engineering leaders share their real-world experiences with AI-assisted engineering.
My Thoughts on AI, Part 2: Agent Setup, Workflow, and Tools
My own personal AI development setup, workflow, and tooling
Environments Hub: A Community Hub To Scale RL To Open AGI
RL environments are the playgrounds where agents learn. Until now, they’ve been fragmented, closed, and hard to share. We are launching the Environments Hub to change that: an open, community-powered platform that gives environments a true home.Environments define the world, rules and feedback loop of state, action and reward. From games to coding tasks to dialogue, they’re the contexts where AI learns, without them, RL is just an algorithm with nothing to act on.
Running an AI-native engineering org | Claude
How the Claude Code engineering team’s processes and structure changed once agentic coding became the default way of working.
Agent Skills
AI coding agents take the shortest path to done, which usually means skipping the specs, tests, and reviews that make software reliable at scale. Agent Skill...
How to harness AI
"Coding agents" are complicated but intelligible
The Real Reason We Still Need Software Developers in the World of AI
The dream of AI churning out perfect production-ready code doesn’t hold up against the reality of modern software development.
AI should help us produce better code - Agentic Engineering Patterns
AI should help us produce better code - Agentic Engineering Patterns
Why AI hasn’t replaced software engineers, and won’t
Coding agents as normal technology
LukeW | Common AI Product Issues
At this point, almost every software domain has launched or explored AI features. Despite the wide range of use cases, most of these implementations have been t...
Git AI - Track AI Code all the way to production
Cross-agent observability from prompt to production. Track AI-generated code from Cursor, Claude Code, GitHub Copilot, Gemini, and more through the entire SDLC.
Meet Foundry: An AI Startup that Builds, Evaluates, and Improves AI Agents
Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl
Notes from my Thoughtworks colleagues on AI-assisted software delivery
