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ai-hackathon's Introduction

vtwoptwo

Waving hand animated gif, I'm Vera

🌱 advancing my knowledge of ROS
📚 reading Why we sleep
🗨️ working on progamming cool HCI/ HRI stuff inspired by TUIs
📎 researching in Robotics and CV
🔥 interested in how humans interact with tech

Metrics

Blog Posts 

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|  `--'  | |  |      |  |____ |  |\   |   .----)   |   |  `--'  | |  `--'  | |  |\  \----.|  `----.|  |____ 
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|_______|   \__/     |_______|| _| `._____|   |__|         |__|     |__|  |__| |__| |__| \__|  \______|

Tools and Languages ive worked with

ai-hackathon's People

Contributors

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Stargazers

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Watchers

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ai-hackathon's Issues

Enhance Robustness with Retry Decorators Providing Feedback to the Model

Issue

Our current application does not have a mechanism for retrying failed operations with the added benefit of providing feedback to the model. This limits our ability to learn from and adapt to operational challenges.

Proposed Solution

Implement retry decorators that handle exceptions and, crucially, provide feedback to the model. This feedback loop will enable the model to learn from failures and improve its performance and robustness over time.

Expected Outcome

A more robust application that not only handles failures more effectively but also uses these experiences to enhance its operational efficiency and accuracy.

Additional Notes

Details on the implementation approach, such as the type of feedback to be collected, how it will be processed, and the expected modifications to the model, should be discussed here. This may include technical

Implement Real-Time CSV Saving During Document Loading

Issue

In the current setup, CSV files are saved after all documents are loaded, which might not be the most efficient approach.

Proposed Solution

Modify the data handling mechanism to allow for CSV files to be saved while documents are still being loaded. This aims to improve memory usage and processing speed.

Expected Outcome

More efficient memory usage and quicker data processing, leading to an overall faster workflow.

Additional Notes

Any technical challenges or specific methods for implementation can be discussed here.

Optimize Temporary Database Loading for Efficiency

Issue

Currently, our application loads a separate temporary database for each column in a document, which can be inefficient in terms of memory usage and processing speed.

Proposed Solution

Implement a strategy to load only one temporary database per document, rather than for each column. This change is expected to reduce memory overhead and improve the speed of data processing.

Expected Outcome

Reduced memory usage and faster processing of documents.

Additional Notes

Any specific implementation details or constraints should be discussed here.

Implement Multi-level Multiprocessing for Document and Column Processing

Issue

The current processing of documents and columns in the application is not optimized for speed, potentially leading to slower data handling.

Proposed Solution

Introduce multiprocessing at two levels: for processing each document and for each column within those documents. This dual-level multiprocessing aims to expedite data processing.

Expected Outcome

Significantly faster data processing and handling, with efficient utilization of system resources.

Additional Notes

Details about the proposed multiprocessing architecture and any dependencies can be included here.

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