The first JetBrains x Codex Hackathon turned the IDE into a laboratory for AI-native development. Over a single weekend, roughly forty teams explored what happens when a capable coding model lives inside the developer's primary workspace—not as an add-on but as the core of the experience. The result? The IDE transforms from a code editor into a command center where you guide an agent, observe its reasoning, manage its focus, and decide when to ship its output. Six finalists emerged with the most innovative answers to that challenge. Below, we break down their approaches through a series of questions and answers.
What was the overarching goal of the JetBrains x Codex Hackathon?
The hackathon aimed to push beyond superficial AI integrations—where a chatbot sits in a sidebar—and instead embed intelligent agents natively into the development environment. Participants were challenged to build systems that treat the IDE as a place to direct an agent, watch how it reasons, manage its attention, and decide when its output is ready to ship. With about forty submissions in one weekend, the projects tackled everything from code generation to hardware testing. The finalists demonstrated that when AI is woven into the fabric of the IDE, developers gain not just speed but also visibility into the agent's thinking process, making the IDE a cockpit for human-AI collaboration rather than a passive editor.
How did hyperreasoning, the first-place winner, improve on standard coding agents?
Typical coding agents call a language model once and hope the result is correct. Aditya Mangalampalli's hyperreasoning replaces that single shot with a search-like process. The system generates several possible approaches to a task, then a learned controller decides which paths to expand, which to prune, and which to verify against compiler errors or test failures. Inside the IDE, a live tool window renders the search tree, letting developers watch which routes the controller explored before settling on one. The key insight: a smaller local model wrapped in this verified search loop can rival much larger frontier models at a fraction of the cost. The IDE becomes a place where reasoning is visible and adjustable—not a black box that spits out code.
What makes Scopecreep's hardware bring-up approach unique?
Hardware testing usually requires juggling multiple tools: a schematic viewer, vendor apps for oscilloscopes and power supplies, a terminal, and a spreadsheet. Scopecreep, created by Bhavik Sheoran, Kenneth Ross, Roman Javadyan, and Joon Im, collapses that entire workflow into a single JetBrains tool window. You hand it a circuit schematic, and the agent systematically tests the board—selecting signals, capturing readings, and generating a report. The clever design decision: when the agent decides a probe needs to be placed, it pauses the session and shows the engineer exactly where. The engineer physically places the probe and clicks Resume. This hybrid approach respects the reality of working with real instruments: full autonomy for digital tasks, but a human-in-the-loop for anything that touches the physical world.
How does mesh-code solve the problem of agent continuity across machines?
Switch laptops mid-task, and most coding agents lose all context and start over. mesh-code, built by Ayush Ojha, Coco Cao, Kush Ise, and AL DRAM, gives agents shared memory of an in-progress project. It tracks what's been tried, what decisions have been made, and what tasks are still pending. A session that starts on one laptop can seamlessly continue on another, using whichever agent happens to be available—Codex is among the supported agents. This persistent memory means developer teams or individuals working across multiple machines don't lose momentum. The agent's state follows the project, not the device, making collaborative development more fluid and efficient.
What role did the IDE play in these projects?
Across all finalists, the IDE became more than a code editor—it emerged as a control panel for AI agents. Hyperreasoning embeds a live search tree window so developers can inspect the agent's reasoning step by step. Scopecreep integrates oscilloscope readings and schematic views directly into the IDE, avoiding context switches. mesh-code uses the IDE's project model as the foundation for persistent agent memory. The common thread is that the IDE provides the context—files, errors, test results, user inputs—that the agent needs to act intelligently, while also giving the developer the controls to pause, redirect, and validate the agent's work. It's a shift from writing code to actively managing an autonomous process within a familiar environment.
What lessons can developers take from the hackathon's finalists?
Three key lessons stand out. First, visibility matters: agents that show their reasoning (like hyperreasoning's search tree) build trust and allow developers to steer them effectively. Second, human-in-the-loop design works for physical tasks, as Scopecreep demonstrated by pausing for probe placement. Third, persistence is critical—mesh-code's shared memory shows that agent sessions must survive machine switches and team handoffs. These projects prove that AI-native IDEs are not about replacing the developer but about enhancing their ability to direct, verify, and iterate with an intelligent partner. The hackathon's winners offer practical blueprints for making that partnership a reality.