Published:
A bank transfer system. A stock exchange. A physics simulator. These are classic examples of traditional software engineering: deterministic, rule-based instruction sets written by humans. Machine learning extends traditional software engineering by applying statistics to software decision making. With statistics, software can learn from observations of the world, generating the instruction sets autonomously.
Published:
I was a co-founder and sole engineer for UniMate in the second year of my degree. It was an event aggregation and data processing system that integrated distributed proxies for web scraping, GPT-based multi-label classification, modular I/O architecture, CLI interaction, dynamic HTML parsing, and a failure-tolerant data pipeline with partial restart. It ran on a no-code frontend. This was my first real-world system, and it taught me about design, trading off speed and quality, and minimal engineering for scalability.
Published:
I was a co-founder and sole engineer for UniMate in the second year of my degree. It was an event aggregation and data processing system that integrated distributed proxies for web scraping, GPT-based multi-label classification, modular I/O architecture, CLI interaction, dynamic HTML parsing, and a failure-tolerant data pipeline with partial restart. It ran on a no-code frontend. This was my first real-world system, and it taught me about design, trading off speed and quality, and minimal engineering for scalability.
Published:
I was a co-founder and sole engineer for UniMate in the second year of my degree. It was an event aggregation and data processing system that integrated distributed proxies for web scraping, GPT-based multi-label classification, modular I/O architecture, CLI interaction, dynamic HTML parsing, and a failure-tolerant data pipeline with partial restart. It ran on a no-code frontend. This was my first real-world system, and it taught me about design, trading off speed and quality, and minimal engineering for scalability.
Published:
A bank transfer system. A stock exchange. A physics simulator. These are classic examples of traditional software engineering: deterministic, rule-based instruction sets written by humans. Machine learning extends traditional software engineering by applying statistics to software decision making. With statistics, software can learn from observations of the world, generating the instruction sets autonomously.
Published:
I was a co-founder and sole engineer for UniMate in the second year of my degree. It was an event aggregation and data processing system that integrated distributed proxies for web scraping, GPT-based multi-label classification, modular I/O architecture, CLI interaction, dynamic HTML parsing, and a failure-tolerant data pipeline with partial restart. It ran on a no-code frontend. This was my first real-world system, and it taught me about design, trading off speed and quality, and minimal engineering for scalability.