Database aficionado with a penchant for software development. Fights for the users!

Joined July 2026
My prediction for software engineering over the next 3 to 5 years. AI won't replace software engineers. It'll drive even greater demand for them. A team of 5 experienced engineers using AI will outperform a team of 20 pre-AI. Which will dramatically reduce the cost of building software. I think we'll see Jevons paradox in action. As something becomes cheaper and more efficient to produce, demand for it can increase dramatically. Thousands of software projects that previously weren't economically viable will become worth doing. The winners will be companies, big and small, that behave like Google, Amazon and 3M, with a culture of experimentation, a willingness to try lots of ideas and the discipline to kill their darlings. TL/DR: Smaller engineering teams, more innovation, more software products, and potentially more software engineers than ever. What do you think?
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There is a massive gap between Machine Learning (ML)/AI expertise and Software Engineering. There is a BIG difference between creating an LLM and using one. ML experts in the spotlight trumpeting the end of Software Engineering are currently mistaken.
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ML experts misunderstand the task. ML/Deep Learning experts are smart folks. They work with smart Software Engineers to generate their models. LLMs are amazing. But there is a HUGE difference in creating a model and using a model and code generation is not software engineering.
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The LLM to a Software Engineer is like a scalpel to a surgeon. A surgeon can save your life with a scalpel but how many surgeons know how to make scalpels? They definitely need each other to succeed though. Scalpel's inert w/o a surgeon. Surgeon can't operate w/o one.
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So much goodness here in patterns & best practices for building a knowledge layer for observability. Not everyone should follow the lead of building this on a homegrown triplestore layer on top of key-value or use RDF, LPG works great, but the rest I love netflixtechblog.com/ontology…
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Pete Aven retweeted
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This is the least surprising graph in the world if you understand how gradient descent works.
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This nonsense. I just asked Gemini and it agreed. Recreating something such as photoshop can cost anywhere from $10k in tokens to output a lightweight clone to $250k for enterprise grade. infoworld.com/article/423470…
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Gemini says recreating Neo4j with a frontier model is also a multi-million dollar token endeavor.
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"The biggest financial driver when using frontier models for a project of this scale is Context Window Decay and Reasoning Tokens." So pair the LLM with a graph database such as Neo4j for managing context and you can save money in tokens for these projects.
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Has anyone asked the Super Intelligence how to stop the flow of cocaine into our country?
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