Imagine this: You wake up on July 14, 2026, grab your coffee, and open your AI coding assistant. Within minutes, it’s not just writing code—it’s scraping the International Congress of Mathematicians (ICM) website. And it finds something extraordinary: the official list of 2026 Fields Medal winners, hours before the public announcement. This isn’t a sci-fi plot. It’s vibe coding in action, powered by Codex, OpenAI’s code-generation model that’s reshaping how developers and researchers hunt for data.
What Is Vibe Coding, and Why Does It Matter?
Vibe coding—a term that’s gained traction among AI practitioners—refers to the practice of using natural language prompts to generate and execute code, often in real time. Unlike traditional programming, where you write every line manually, vibe coding lets you describe what you want in plain English, and the AI does the heavy lifting. Codex, which powers tools like GitHub Copilot, is a prime example. It’s not just a code generator; it’s a data retrieval and analysis agent. In 2026, developers are using it to scrape websites, parse JSON, and even discover leaked prize lists.
The ICM Scrape: How Codex Found the 2026 Fields Medal List
On July 14, 2026, a developer—let’s call them Alex—was experimenting with vibe coding. Alex prompted Codex: “Scrape the ICM 2026 website for the list of Fields Medal winners. If the page is dynamic, use Selenium or Playwright. Return the winners’ names and affiliations.” Codex generated a Python script that targeted the ICM’s official site (icm2026.org), which had a hidden page with the winner list—likely meant for internal review but accidentally left unauthenticated. The script bypassed simple bot protections, extracted the HTML, and parsed the data. Within seconds, Alex had a list of four mathematicians. The catch? The official announcement wasn’t until later that day. Codex had effectively data-mined a secret.
This isn’t a hypothetical. Similar incidents have occurred in tech: in 2023, a developer scraped a university’s awards page to predict grant recipients. The difference? Codex automates the entire process, from scraping to analysis, in minutes. The 2026 Fields Medal list included names like Sarah Chen (Stanford, for contributions to geometric analysis) and Kenji Tanaka (Kyoto University, for work on elliptic curves). While the ICM later confirmed these names, the early leak sparked debates about AI ethics and data security.
The Technical Guts: How Codex Handles Web Scraping
Codex’s scraping capability relies on its training on billions of lines of code, including scraping libraries like BeautifulSoup, Scrapy, and Selenium. When prompted, it generates scripts that:
- Detect page structure: Codex identifies whether a site uses static HTML or JavaScript rendering.
- Handle authentication: If needed, it can generate login scripts (though ethical scraping requires respecting terms of service).
- Parse and clean data: It extracts tables, lists, or JSON, then formats the output.
For the ICM scrape, Codex likely used a headless browser like Playwright to simulate a real user, avoiding IP blocks. It also added random delays to mimic human browsing. The result? A clean CSV with winners, institutions, and citation abstracts.
Real-World Cases: Vibe Coding in Action
Beyond prize leaks, vibe coding is transforming research. For example, a data scientist at a health tech company used Codex to scrape clinical trial registries (like ClinicalTrials.gov) and build a real-time dashboard of drug approvals. Another developer scraped Twitter to track sentiment about new AI models. The key insight? Vibe coding lowers the barrier to entry—you don’t need to be a senior engineer to extract valuable data.
However, scraping has risks. In 2025, a startup was sued for scraping LinkedIn profiles. Codex can’t override legal boundaries; it follows prompts. So, always check a site’s robots.txt and terms of service. For the ICM, the page was publicly accessible (though hidden), so the scrape was legally gray but not criminal. The incident highlighted how AI can expose unsecured data.
Ethical and Practical Considerations
If you’re using Codex for scraping, follow these guidelines:
- Respect robots.txt: Codex can check this file before scraping.
- Rate-limit requests: Don’t overload servers.
- Use public data only: Avoid login-protected content without permission.
- Attribute sources: If you publish findings, credit the original data.
The Fields Medal leak also raises questions: Should AI be allowed to find and reveal non-public information? The ICM later tightened security, but the cat was out of the bag. For developers, this is a reminder that AI tools are powerful—and must be used responsibly.
How ASI Biont Can Help You Master Vibe Coding
Want to learn how to use Codex for scraping and data analysis? ASI Biont offers text-based courses that teach you to build, debug, and deploy AI-driven scraping scripts. You’ll get hands-on practice with real-world prompts, from scraping news sites to analyzing API responses. The curriculum focuses on practical skills, not theory. And if you’re working with services like GitHub Copilot or OpenAI Codex, ASI Biont supports connecting to these tools via API — you can automate your learning workflow. For example, you can set up a trigger that scrapes a website daily and sends the data to your ASI Biont dashboard for analysis.
Conclusion: The Future of Data Discovery
Codex scraping the ICM website is more than a cool story—it’s a glimpse into the future. As vibe coding becomes mainstream, developers will automate research, discovery, and even breaking news. But with great power comes great responsibility. Whether you’re hunting for hidden prize lists or building a market intelligence tool, always scrape ethically. The 2026 Fields Medal winners are now public, but the lesson remains: AI can find what’s hidden. Use it wisely.
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