Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl

What spec-driven development gets wrong

The most powerful AI software development platform with the industry-leading context engine.

Priivacy-ai/spec-kitty

Spec-Driven Development for serious software developers. Spec Coding with with Claude, Cursor, Gemini, Codex. Kanban dashboard, git worktrees, auto-merge and more.

Exploring Generative AI

Notes from my Thoughtworks colleagues on AI-assisted software delivery

Exploring Generative AI

Notes from my Thoughtworks colleagues on AI-assisted software delivery

The Economic Benefit of Refactoring

Notes from my Thoughtworks colleagues on AI-assisted software delivery

scaleapi/SWE-bench_Pro-os

SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?

Measuring the Impact of Early-2025 AI on Experienced Open-Source...

Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...

From OSS to Open Source AI: an Exploratory Study of Collaborative...

AI development is embracing open-source paradigm, but the fundamental distinction between AI models and traditional software artifacts may lead to a divergent open-source development paradigm with...

Codex | AI Coding Partner from OpenAI

The best way to build with agents. Codex accelerates real engineering work, from planning and building features to refactors, reviews, and releases—working seamlessly across your tools.

SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?

Large Language Model (LLM) agents have been widely adopted in modern software development workflows. SWE-bench [13] and related works [23, 24, 22, 25, 15] establish the task of issue resolution as a de-facto standard for assessing their capability and usefulness. In this setting, an agent is given an entire codebase, a task description (e.g., a bug report or feature request) in natural language and is instructed to produce a code patch that resolves the issue and passes the repository’s test suite. These benchmarks have been instrumental in demonstrating both the substantial potential and the persistent limitations of current models as SWE agents.

Quint (@quint-lang.org)

New podcast with @bugarela.com on @ColorsofWeb3pod talking spec-driven development and how we benefit from precision and executability of specs, especially in the AI era. Why specs can't just be English and markdown↓

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...

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.

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