software engineering – Page 2 – Andrew Fairless, Ph.D.

What I Read: string ufuncs, NumPy 2.0

By Andrew Fairless on August 27, 2024June 15, 2024

https://labs.quansight.org/blog/numpy-string-ufuncs Writing fast string ufuncs for NumPy 2.0Lysandros NikolaouPublished May 21, 2024 “After a huge amount of work from many people, NumPy 2.0.0 will soon be released, the first NumPyContinue readingWhat I Read: string ufuncs, NumPy 2.0

What I Read: Cookiecutter Data Science V2

By Andrew Fairless on August 22, 2024June 4, 2024

https://drivendata.co/blog/ccds-v2 Cookiecutter Data Science V2by Peter Bull, Jay Qi, Chris KucharczykWed 22 May 2024 “…in the past 5 years, a lot has changed in data science tooling and MLOps. CookiecutterContinue readingWhat I Read: Cookiecutter Data Science V2

What I Read: LLM, DSPy Assertions and Suggestions

By Andrew Fairless on August 6, 2024May 25, 2024

https://learnbybuilding.ai/tutorials/guiding-llm-output-with-dspy-assertions-and-suggestions Guiding LLM Output with DSPy Assertions and SuggestionsBill Chambers “Assertions in DSPy allow you to define strict rules and constraints that the LLM’s output must (or maybe that youContinue readingWhat I Read: LLM, DSPy Assertions and Suggestions

What I Read: LLMs, Open Source

By Andrew Fairless on July 15, 2024May 6, 2024

https://www.aidancooper.co.uk/how-to-beat-proprietary-llms How to Beat Proprietary LLMs With Smaller Open Source ModelsAidan CooperApr 26, 2024 “…we explore the unique advantages of open source LLMs, and how you can leverage them toContinue readingWhat I Read: LLMs, Open Source

What I Read: anti-patterns, data reuse

By Andrew Fairless on July 10, 2024May 6, 2024

https://www.milesmcbain.com/posts/data-analysis-reuse Patterns and anti-patterns of data analysis reuseMiles McBainMarch 10, 2024 “When I look at that function signature I hear the screams of matrices being ground to a fine powderContinue readingWhat I Read: anti-patterns, data reuse

What I Read: Don’t put notebooks into production

By Andrew Fairless on June 27, 2024April 23, 2024

https://martinfowler.com/articles/productize-data-sci-notebooks.html Don’t put data science notebooks into productionDavid Johnston18 November 2020 “…the discussion of how to productionize data science notebooks is missing the point. The goal should be to empowerContinue readingWhat I Read: Don’t put notebooks into production

What I Read: Structured Generation, Constrained Decoding

By Andrew Fairless on June 20, 2024April 16, 2024

https://www.aidancooper.co.uk/constrained-decoding A Guide to Structured Generation Using Constrained DecodingApr 8, 2024 “…there are techniques that ensure language models only return outputs that conform to your requirements. This article serves asContinue readingWhat I Read: Structured Generation, Constrained Decoding

What I Read: speed up code, Numba

By Andrew Fairless on June 5, 2024April 15, 2024

https://pythonspeed.com/articles/slow-numba The wrong way to speed up your code with Numbaby Itamar Turner-Trauring21 Mar 2024 “If your NumPy-based code is too slow, you can sometimes use Numba to speed itContinue readingWhat I Read: speed up code, Numba

What I Read: Modal’s web infrastructure

By Andrew Fairless on May 27, 2024March 21, 2024

https://modal.com/blog/serverless-http Lambda on hard mode: Inside Modal’s web infrastructureEric ZhangMarch 14, 2024 “At Modal, we built an HTTP and WebSocket stack on our platform. In other words, your serverless functionsContinue readingWhat I Read: Modal’s web infrastructure

What I Read: Compound AI Systems

By Andrew Fairless on April 22, 2024March 1, 2024

https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/ The Shift from Models to Compound AI SystemsMatei Zaharia, Omar Khattab, Lingjiao Chen, Jared Quincy Davis, Heather Miller, Chris Potts, James Zou, Michael Carbin, Jonathan Frankle, Naveen Rao, AliContinue readingWhat I Read: Compound AI Systems

Tag: software engineering