Learning Day - Algorithms and Automation
Algorithm Deep Dive: Bellman-Ford
Had a fascinating morning diving into graph theory, specifically the Bellman-Ford algorithm. The concept is quite elegant – it’s essentially a layer-by-layer approach to finding shortest paths in graphs, even when dealing with negative edge weights.
The algorithm starts by setting the source node to 0 and all other nodes to infinity. Then comes the interesting part: the “relaxation” process. For each iteration, you compare the current shortest distance with potential new shorter paths. If you find a shorter route, you update it. What makes this particularly clever is that it builds on results from the previous iteration, so you don’t need to recalculate everything from scratch.
This connects beautifully to dynamic programming principles – it’s like breadth-first search but with memory optimization. The algorithm can handle negative edge weights, which sets it apart from Dijkstra’s algorithm. Even more impressive, it can detect negative cycles: if you run the algorithm for n-1 iterations (where n is the number of vertices) and then run one more iteration, any further updates indicate the presence of a negative cycle.
The mathematical elegance of this “relaxation” concept really struck me – there’s something poetic about gradually refining your understanding until you reach the optimal solution.
AI Automation Inspiration
Had an interesting conversation with a friend about automation processes. He’s working on a system that takes pictures of financial reports and uses AI models to extract numerical data automatically. The idea is to streamline financial report processing by eliminating manual data entry.
This sparked some ideas for my own projects. The concept of using AI to process and structure information could be applied beyond financial reports. In fact, what I’m doing right now with this voice-to-diary workflow is a similar concept – using AI to transform raw audio transcripts into structured, meaningful content.
There’s definitely potential to explore this automation approach further, perhaps in different domains or with different types of data processing.
Blog Setup Progress
Made progress setting up Hugo for this blog project. The voice-to-diary processing workflow is starting to take shape. The idea is to use voice recordings as the raw input, then leverage AI models to clean up the transcripts and transform them into polished diary entries.
This approach feels natural and sustainable – I can capture thoughts spontaneously through voice, then have them processed into well-structured content that could eventually be compiled into blog posts. It’s like having a personal content pipeline that works with how I naturally think and communicate.
Reflections
Today highlighted the beauty of algorithmic thinking and the practical potential of AI automation. The Bellman-Ford algorithm’s approach to gradually refining solutions mirrors how we often approach complex problems in life – starting with rough estimates and iteratively improving them.
The automation discussion reinforced my belief that AI’s real value lies in augmenting human capabilities rather than replacing them. Whether it’s processing financial reports or transforming voice notes into structured content, the goal is to handle the tedious parts so we can focus on the creative and analytical aspects.
The blog setup feels like the beginning of something meaningful – a way to capture and share the learning journey in a more structured way.
Note: This entry represents the start of a new voice-to-diary workflow experiment, combining spontaneous voice capture with AI-assisted content structuring.