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Notes

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Investigating challenging problems and bugs is an exciting experience that often leads to remarkable discoveries and personal growth. As a engineer, I’ve always found joy in decoding complex issues and diving deep into the unknown. The thrill of the hunt, the satisfaction of piecing together clues, and the ahá moment when everything falls into place are unique.

One of the most rewarding aspects of problem-solving is the opportunity to learn new things. Every bug, every system failure, and every unexpected behavior is a chance to expand your knowledge and skillset. It’s like being a detective in the digital world, where each case brings its own unique challenges and lessons.

The skills developed through problem-solving and investigation are not only valuable in professional settings but can also be applied to personal life. Embracing the challenge of investigating problems and bugs not only makes us better professionals but also equips us with valuable life skills. It teaches us to be persistent, adaptable, and open to learning.

While the truths we uncover can be difficult or painful, they ultimately lead us to growth and a deeper understanding of ourselves and the world around us.

Indeed, these problem-solving skills can sometimes lead to unexpected and challenging discoveries in our personal relationships.

Slightly inspired by this thread: https://twitter.com/rosapolis/status/1828014752112545924

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Zed screenshot

I’ve never been enthusiastic about the AI revolution.

However, this week, I started using Zed.dev together with their AI assistant integration. I’m surprised by the experience and efficiency gains. I still have reservations, but it can be valuable allies in tackling programming challenges and routine tasks.

For example, a few days ago, I was working on a tool that needed to parse ClickHouse explain output1, an easy task requiring recursively transverse all the properties looking for some specific properties.

With a simple prompt, the model wrote a working version.

Write code to extract all primary key granules from explain_result. An example of explain_result here:

{…}

def extract_primary_key_granules(explain_result):
    def traverse(node):
        granules = []
        if isinstance(node, dict):
            if 'Indexes' in node:
                for index in node['Indexes']:
                    if index.get('Type') == 'PrimaryKey':
                        granules.append({
                            'Keys': index.get('Keys', []),
                            'Initial Granules': index.get('Initial Granules', 0),
                            'Selected Granules': index.get('Selected Granules', 0)
                        })
            for value in node.values():
                granules.extend(traverse(value))
        elif isinstance(node, list):
            for item in node:
                granules.extend(traverse(item))
        return granules

    return traverse(explain_result)

# Example usage:
result = extract_primary_key_granules(explain_result)
for granule in result:
    print(f"Keys: {granule['Keys']}")
    print(f"Initial Granules: {granule['Initial Granules']}")
    print(f"Selected Granules: {granule['Selected Granules']}")
    print()

The task was relatively easy and I had to make changes and improve the initial code. Despite that, this saved me at least an hour of back-and-forth writing the function and running some tests to see if it worked as expected!

I started using Zed AI assistant with ChatGPT integration. A day after they released their own integration that I’m now testing together with Anthropic’s Claude model. I’m not even using Google for simple searches anymore.

Footnotes

  1. This is how a ClickHouse explain output looks like ↩

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