Visualize AI attention mechanisms in competitive programming solutions
AlphaCode lets you explore how an AI model pays attention to different parts of code it generates for programming problems. This interactive visualization helps you understand the model's decision-making process, making AI-generated code more transparent and easier to debug. It's great for learning, research, and improving AI interpretability in real-world scenarios.
See which tokens the model focuses on during code generation
Hover and click on tokens to inspect attention scores dynamically
Access diverse competitive programming problems with solutions
Get insights from world-class programmers on selected solutions
View attention across different model layers for deeper insights
Hover over tokens in the solution to see which tokens the model attended to when generating the code.
Solutions from various competitive programming problems, selected randomly with pass or fail status.
The dataset has limited test cases, so there may be false positives or inefficient solutions.
Researchers, educators, and programmers interested in AI code generation and interpretability.
This tool is best for:
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Recent data shows that AlphaCode has 6.0K monthly visits (369.5% increase from the previous month), 35.0% bounce rate, and average 2.10 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 5 countries worldwide, led by India contributing 26% of total traffic.
Monthly Visits
6.0K
Pages per Visit
2.10
Bounce Rate
35.0%
Average Time on Site
59s
| Source | Traffic Share |
|---|---|
| Direct | 34% |
| Search | 51% |
| Social | 4% |
| Referrals | 10% |
| Paid Referrals | 1% |
| Country Name | Traffic Share |
|---|---|
 | 26% |
 | 21% |
 | 19% |
 | 7% |
 | 7% |
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