SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
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.
Building and evaluating AI Agents — Sayash Kapoor, AI Snake Oil
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...
Meet Foundry: An AI Startup that Builds, Evaluates, and Improves AI Agents
Agentic Engineering Management
To what extent AI is OK to use in software development might be debated, but in general, the idea is not a controversial one anymore. The debate rather moved on from code completion and simple PR summarizations to Agentic Engineering, where an execution loop allows an AI Agent to function
Project Think: building the next generation of AI agents on Cloudflare
Announcing a preview of the next edition of the Agents SDK — from lightweight primitives to a batteries-included platform for AI agents that think, act, and persist.
No Vibes Allowed: Solving Hard Problems in Complex Codebases – Dex Horthy, HumanLayer
AI Coding Agent Benchmarks & Leaderboard | Artificial Analysis
We measure real-world performance of coding agents on software engineering tasks, including cost, token usage, and execution time. We compare how performance changes across agents, models, and execution settings.
The Eternal Sloptember
I’m calling it now, the adoption of AI agents into software development will be one of the most costly mistakes in the field’s history. Agents cannot program, and it’s taking longer and longer to realize that they can’t. They are a highly sophisticated statistical model designed to mimic the distribution of programming. The output is broken, but in a way that’s getting harder and harder to detect. Which is exactly what you’d expect from an increasingly accurate statistical model.
The Eternal Sloptember
I’m calling it now, the adoption of AI agents into software development will be one of the most costly mistakes in the field’s history. Agents cannot program, and it’s taking longer and longer to realize that they can’t. They are a highly sophisticated statistical model designed to mimic the distribution of programming. The output is broken, but in a way that’s getting harder and harder to detect. Which is exactly what you’d expect from an increasingly accurate statistical model.
The Open-Source Toolkit for Building AI Agents v2
An opinionated, developer-first guide to building AI agents with real-world impact
Why AI hasn’t replaced software engineers, and won’t
Coding agents as normal technology
TERMINAL-BENCH-SCIENCE
A benchmark for evaluating AI agents on research workflows across scientific domains
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...
bytedance/UI-TARS-desktop
The Open-Source Multimodal AI Agent Stack: Connecting Cutting-Edge AI Models and Agent Infra
Cloudflare Workers AI | Open-source AI inference
Workers AI facilitates the scalable development & deployment of AI applications at the edge.
