modularity – Andrew Fairless, Ph.D.

What I Read: BAML

By Andrew Fairless on May 28, 2025February 22, 2025

https://thedataquarry.com/posts/baml-is-building-blocks-for-ai-engineers BAML is like building blocks for AI engineersPrashanth Rao2025-02-10 “I’ll explain more about how BAML, a domain-specific language for helping LLMs generate better structured outputs, provides AI engineers theContinue readingWhat I Read: BAML

What I Read: BAML, agentic

By Andrew Fairless on May 27, 2025February 22, 2025

https://thedataquarry.com/posts/baml-and-future-agentic-workflows Why I’m excited about BAML and the future of agentic workflowsPrashanth Rao2025-01-29 “Although there have been new agentic and AI workflow orchestration frameworks coming out seemingly every month latelyContinue readingWhat I Read: BAML, agentic

What I Read: Building agents

By Andrew Fairless on April 9, 2025December 31, 2024

https://www.anthropic.com/research/building-effective-agents Building effective agentsErik Schluntz and Barry ZhangDec 19, 2024 “Over the past year, we’ve worked with dozens of teams building large language model (LLM) agents across industries. Consistently, theContinue readingWhat I Read: Building agents

What I Read: Neural algorithmic reasoning

By Andrew Fairless on January 9, 2024November 7, 2023

https://thegradient.pub/neural-algorithmic-reasoning/ Neural algorithmic reasoningPetar Veličković14.Oct.2023 “…we will talk about classical computation… Think shortest path-finding, sorting, clever ways to break problems down… we will also investigate how to capture such computationContinue readingWhat I Read: Neural algorithmic reasoning

What I Read: Composable Data Systems

By Andrew Fairless on October 19, 2023September 9, 2023

https://wesmckinney.com/blog/looking-back-15-years/ The Road to Composable Data Systems: Thoughts on the Last 15 Years and the FutureWes McKinneySeptember 1, 2023 “Julien Le Dem… coined the term “deconstructed database” to describe theContinue readingWhat I Read: Composable Data Systems

What I Read: Against LLM

By Andrew Fairless on June 29, 2023May 31, 2023

https://explosion.ai/blog/against-llm-maximalism Against LLM maximalismby Matthew HonnibalMay 18, 2023 “However, LLMs are not a direct solution to most of the NLP use-cases companies have been working on… if you want toContinue readingWhat I Read: Against LLM

What I Read: Multi-task Prediction of Organ Dysfunction

By Andrew Fairless on August 25, 2021August 17, 2021

https://ai.googleblog.com/2021/07/multi-task-prediction-of-organ.html Multi-task Prediction of Organ Dysfunction in ICUsThursday, July 22, 2021 “Applying machine learning (ML) to electronic health records (EHRs) has shown promise in predicting clinical outcomes. However, many ofContinue readingWhat I Read: Multi-task Prediction of Organ Dysfunction

What I Read: I Like Notebooks

By Andrew Fairless on August 17, 2021July 22, 2021

https://youtu.be/9Q6sLbz37gk I Like NotebooksOct 28, 2020Jeremy Howard “I like using Jupyter Notebooks… Particularly when combined with nbdev… In this video, I explain why, and explain why I have a differentContinue readingWhat I Read: I Like Notebooks

What I Read: Object-Oriented Programming Disaster

By Andrew Fairless on April 27, 2021April 10, 2021

https://betterprogramming.pub/object-oriented-programming-the-trillion-dollar-disaster-92a4b666c7c7 Object-Oriented Programming — The Trillion Dollar DisasterWhy it’s time to move on from OOPIlya SuzdalnitskiJul 10, 2019 “Object-Oriented Programming has been created with one goal in mind — toContinue readingWhat I Read: Object-Oriented Programming Disaster

What I Read: Software Engineering Best Practices for Data Scientists

By Andrew Fairless on April 13, 2021April 2, 2021

https://towardsdatascience.com/software-engineering-best-practices-for-data-scientists-4c199ede6e03?gi=f6afc2a55c8a Software Engineering Best Practices for Data ScientistsA crash course on how to bridge the gap between data science and software engineeringMadison HunterMar 12 “…data scientists should be able toContinue readingWhat I Read: Software Engineering Best Practices for Data Scientists

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