Preseason - Track What Tools LLMs Pick for Vibe Coding

TL;DR

TL;DR: Preseason is an open-source benchmark that monitors which tools LLMs recommend across a panel of vibe-coding prompts, letting you see real-world AI tooling trends as they evolve.

Source and Accuracy Notes

• Official site: https://www.preseason.ai
• GitHub: https://github.com/preseasonai (if available)
• Data reflects LLM recommendations on a fixed set of vibe-coding prompts at multiple skill levels

What Is Preseason?

Preseason fills a gap that vibe-coding enthusiasts and AI tooling researchers have felt for a while: which tools are LLMs actually recommending in practice, not just which ones are popular on GitHub or Hacker News?

The platform runs a frozen panel of vibe-coding prompts — ranging from beginner-friendly tasks to expert-engineer complexity — and tracks what tools models like GPT-4o, Claude, Gemini, and others suggest. It is not a static survey. The rankings update as models evolve and new tools enter the ecosystem.

Key differentiators:

• Real LLM behavior, not self-reported preferences — Preseason observes what models actually output when given a prompt
• Skill-level stratification — beginner, intermediate, and expert prompts are evaluated separately
• Open-source — benchmarks and methodology are publicly available

Setup Workflow

Preseason is a web-based benchmark. No installation required.

Step 1: Browse the Benchmark

Visit https://www.preseason.ai and explore the current rankings. Each tool shows:

• Recommendation frequency across the prompt panel
• Performance breakdown by skill level
• Trend lines over recent model updates

Step 2: Filter by Prompt Type

Use the category filters to narrow results to your use case:

• Fitness tracking app
• Social media platform
• AI Revenue Ops copilot
• Online learning platform
• And more

Step 3: Submit a New Prompt

If your tool or use case is not covered, submit a prompt through the site to expand the benchmark panel.

Deeper Analysis

How the Benchmark Works

Preseason maintains a set of “frozen” prompts — fixed prompts that do not change between runs. When a new model version or tool update is evaluated, the same prompts are used, ensuring fair comparisons.

Each model is given the full prompt panel and asked to produce a solution or recommend tools. The output is parsed and cross-referenced against the tool registry.

What the Rankings Mean

A high recommendation frequency means the model consistently suggests that tool when solving the prompt panel. This is a proxy for trust and perceived capability — the model has “learned” that tool is a good fit for that class of problem.

Limitations

• Recommendations reflect model knowledge up to its training cutoff
• New tools released after model training may be under-represented
• Prompt panel is not exhaustive — bias toward common vibe-coding patterns

Practical Evaluation Checklist

• Does the tool appear in high-frequency recommendations across skill levels?
• Is the tool recommended for your specific use case category?
• Are there recent trend changes that suggest the tool is gaining or losing mindshare?
• Is the methodology for the benchmark transparent and reproducible?

Security Notes

• Preseason is a read-only benchmark site — no code execution or data collection from users
• No authentication required to browse rankings
• All benchmark data is publicly visible

FAQ

Q: Is Preseason free to use? A: Yes. The benchmark rankings and prompt panel are publicly accessible at preseason.ai with no account required.

Q: How often are the rankings updated? A: Rankings update when new model versions are evaluated or when new tools are added to the registry. The exact cadence depends on the model release schedule.

Q: Can I contribute a new prompt to the panel? A: Yes, the site accepts prompt submissions to expand coverage across new use cases and tool categories.

Q: How is this different from GitHub star counts or HN trending? A: GitHub stars and HN trending reflect human attention. Preseason reflects what tools LLMs actually recommend when solving problems — a different signal that matters for AI-first workflows.

Conclusion

Preseason is a useful signal layer for anyone building with AI coding tools. Rather than guessing which tools are “hot,” you can see what models actually reach for across a fixed benchmark. For tool builders, it is a reality check; for AI-assisted developers, it is a shortcut to finding tools that actually pass the LLM litmus test.

Bookmark https://www.preseason.ai and check it when evaluating new AI tooling decisions.