News and Tech Update

You can’t make this crap up. You just wish you could. Jer Crane, founder of the small vertical software company, PocketOS , reported on X that the AI Cursor coding agent and a Railway backup misconfiguration combined to briefly wipe out the company’s car‑rental customer production data . Not some of the data. All of it. That’s a company killer. Fortunately for PocketOS and its customers, Crane later reported that Railway had managed to “recover the data (thank God!).” Thanks to that miracle save of reconstructing the missing data from earlier backups, PocketOS and its customers are back in business. But how could this happen in the first place? According to Crane, it was a chain of failures from both Cursor , the AI development environment, and Railway , his infrastructure provider. Together, they created a “perfect storm” that turned a routine staging bug fix into a company‑threatening outage. In his post, Crane recounted how an autonomous AI coding agent running inside Cursor, ...

AI coding agents are becoming more capable, but evaluating them is harder than it looks. Most benchmarks focus on a single dimension of agent capabilities; for instance, the popular SWE-Bench benchmark only focuses on fixing issues on open source Python repositories. Real-world software engineering involves fixing bugs of course, but it is a lot more multifaceted: in any single week a software developer may also debug complex issues, building a new greenfield script or app, improving test coverage, fix bugs on a frontend repo, research unfamiliar APIs – the list goes on. The OpenHands Index addresses this by building a much broader benchmark evaluating language models across five distinct categories: Issue Resolution (fixing bugs), Greenfield development (building new applications), Frontend development (UI tasks requiring visual understanding), Testing (generating tests to reproduce bugs), and Information Gathering (research and documentation tasks). This diversity matters because...

It appears that GitHub has its hands full adjusting to the demands of scaling AI workloads. First, the company paused sign-ups for its Copilot subscription tiers in response to a wave of demand from agentic AI projects. Then it shifted to usage-based pricing to, again, better align revenue with the heavy compute demands of AI projects. Now GitHub is confronting still more infrastructure challenges as it deals with the rapid growth in AI-driven software development. Two recent service disruptions have highlighted the pressure, prompting the company to upgrade its platform for higher capacity and resilience. Tenfold Capacity Boost Is Not Enough GitHub had initially planned for a tenfold increase in capacity beginning in late 2025. Within months, even that ambitious projection proved insufficient. The company is now engineering for a thirtyfold expansion, reflecting both the speed and magnitude of demand tied to AI-assisted development workflows. The urgency, as detailed by GitHu...

OpenAI has unveiled Symphony, an open-source specification that shifts how software development teams deploy AI in workflows, moving from interactive coding assistance toward continuous orchestration of autonomous agents. Symphony reframes project management tools as operational hubs for AI-driven coding. Rather than prompting an assistant for individual tasks, developers assign work through issue trackers, allowing agents to execute tasks in parallel and deliver outputs for human review. The change reflects a trend in enterprise AI in which systems are increasingly embedded into production pipelines rather than used as standalone tools. Symphony emerged from internal experimentation at   OpenAI , where engineers attempted to scale the use of   Codex   across multiple concurrent sessions. While the agents proved capable, human operators became the limiting factor. Engineers found they could only manage a handful of sessions before coordination overhead offset pro...

AI has already arrived in the software development lifecycle; not as a pilot program or controlled experiment, but as an everyday reality. Developers are using AI coding assistants to generate functions, refactor modules, review pull requests, and accelerate delivery, often in direct tension with corporate policies meant to limit or control that use.   While it’s tempting to consider this some kind of ‘Shadow AI’ or ‘Governance Failure’, it is a signal of things to come in this brave new world of AI-accelerated software engineering.   Recent industry surveys show that  well over half of developers now rely on AI coding assistants in their daily work, with many using them frequently or constantly. At the same time, more than three-quarters of organisations have formal policies that restrict or prohibit that same usage. From a security perspective, that tension is understandable but may be misplaced, because from the standpoint of application risk, ...

The development community saw this one coming: GitHub will transition its Copilot service to a usage-based billing model on June 1, replacing its existing system of fixed subscriptions supplemented by premium request limits. As reported last week, GitHub suspended new sign-ups for several of its Copilot subscription tiers as it faced a surge in demand from agentic coding workflows. To address that, under GitHub’s new pricing model, customers across individual, business, and enterprise tiers will receive a monthly allocation of AI credits, which are consumed based on token usage. This includes input, output, and cached data processed by underlying models. Once those credits are exhausted, users can purchase additional capacity at published rates. The change leaves base subscription prices intact. Individual plans remain priced at $10 per month for Pro and $39 for Pro+, while business and enterprise tiers continue at $19 and $39 per user per month, respectively. Each plan’s monthly ...