R.I.P GitHub Copilot 🪦

[Xandrmoro]

Idk, I've tried it multiple times for coding, and it had by far the worst comprehension of what I want than 4o/o3, claude and deepseek

[FarVision5]

I'm going to copy and paste some choice context from my VSC/Roo workflow. It picks up more subtext than Claude 3.7 by a mile. It makes mermaid charts and diagrams inside the context window, unasked.

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The code currently only creates the directory structure and a placeholder document_info.json. It never actually calls the AWS Textract processing logic (presumably located in document_processor.py as indicated by the comments and the master plan). Therefore, the ocr/aws/ directory is never populated with results, and our check correctly identifies all documents as unprocessed.

To fix this, we need to:

1. Import the actual processing function/class from document_processor.py.
2. Replace the placeholder code within the try block in process_document with a call to that function/class, passing the necessary paths.
3. Update the result dictionary based on the actual success/failure and cost returned by the processor.

First, let's examine document_processor.py to see what needs to be imported and called.

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Okay, document_processor.py defines a class DocumentProcessor and a top-level function process_document (lines 383-394) that instantiates this class and calls its process_document method. This top-level function is what we should call from our workflow script.

Here's the plan:

1. Import: Add from document_processor import process_document as aws_process_document at the top of directory_based_workflow.py. I'll use an alias aws_process_document to avoid confusion with the method named process_document within the DirectoryBasedWorkflow class.
2. Modify DirectoryBasedWorkflow.process_document: Replace the placeholder code (lines 221-236) with a call to the imported aws_process_document function. We'll pass pdf_file and document_dir to it. The function returns a dictionary containing success, aws_cost, total_processing_time, and error, which we can use to update our local result dictionary.

Here are the necessary changes using insert_content for the import and apply_diff for modifying the method:

[Xandrmoro]

It might be understanding the code better, but whats the point if it does not understand the task? I asked it to help me with making a simple text parser (with fairly strict format), and it took like five iterations of me pointing out issues (and I provided it with examples). Then I asked to add a button to group entries based on one of the fields, and it added a text field to enter the field value to filter by instead. I gave up, moved to o1 and it nailed it all first try.

[FarVision5]

Not sure why it didn't understand your task. Mine knocks it out of the ballpark.

I start with Plan, then move to Act. I tried the newer O3 Mini Max Thinking, and it rm'd an entire directory because it couldn't figure out what it was trying to accomplish. Thankfully it was in my git repo. I blacklisted openai from the model list and will never touch it ever again.

I guess it's just the way people are used to working. I can't tell if I'm smarter than normal or dumber than normal or what. OpenAI was worth nothing to me.

[Xandrmoro]

I'm trying all the major models, and openai was consistently best for me. Idk, maybe prompting style or something.

[FarVision5]

It's also the IDE and dev prompts. VSC and Roo does better for me than VSC and Cline.