Software Engineering

When it comes to software development, coding is optional — debugging is inevitable. And yet, most AI code tools today act like overconfident interns: quick to suggest, but clueless when the system breaks. Kodezi Chronos flips that script. Instead of trying to stretch token windows to a million and hoping for the best, Chronos builds an entirely new foundation for debugging: persistent memory, adaptive retrieval, and autonomous iteration. Beyond Token Stuffing: Why Context Windows Miss the Point Large Language Models like GPT-4 and Claude 3 boast massive context windows — 128K, 200K, even a million tokens. But real-world debugging rarely needs to read the whole repository at once. It needs to find the right needle in a messy, multi-decade haystack, then trace its thread through historical commits, CI logs, and edge-case test failures. ...

In the race to build autonomous software engineers, large language model (LLM) agents like Devin and Copilot Chat are lauded for fixing bugs, writing code, and even completing tasks from GitHub issues. But what happens when the code doesn’t even run? That’s the uncomfortable gap SetupBench aims to measure—and the results are sobering. SetupBench introduces a 93-task benchmark evaluating a foundational but under-tested skill: bootstrapping a development environment from scratch. Unlike prior benchmarks that hand agents a fully pre-configured Docker container, SetupBench drops them into a barebones Linux sandbox and challenges them to install dependencies, initialize databases, configure background services, and resolve real-world version conflicts. It sounds simple. It isn’t. ...