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Writing dynamic Ecto queries with Composite (dev.to)

Ecto Queries Coding Elixir

This article introduces the Composite library for simplifying the writing of dynamic Ecto queries. It covers the basic principles of dynamic queries, demonstrates an alternative approach using Composite, and explains how Composite can improve query efficiency through features like dependencies. Links to the Ecto docs and Composite’s source code are provided for further reading.
Main Points
- Need for an alternative approach to dynamic queriesAnother article is needed despite the existing one in Ecto docs because it suggests a different organization of the final code for dynamic queries.
- Principles of dynamic Ecto query writingWriting dynamic Ecto queries is about the optional application of functions, aiming for query composition based on given input parameters.
- Introduction to the Composite libraryThe Composite library is a result of refactoring various approaches to writing dynamic Ecto queries, showcasing an alternative implementation that is simpler and more efficient.
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Writing dynamic Ecto queries with Composite (dev.to)

This article demonstrates the use of the Composite library to optimize and simplify writing dynamic Ecto queries, highlighting an alternative implementation to conventional methods. The Composite library allows for easy management of query parameters and efficient composition of queries.
Main Points
- Simplified code with CompositeThis implementation places all used parameters at the top level, resembling a router.
- Optimization with CompositeComposite automatically optimizes joining the :authors association based on input parameters.
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TinyML: Machine Learning on ESP32 with MicroPython (dev.to)

The article details a project exploring TinyML on ESP32 microcontrollers using MicroPython for real-time gesture detection. It outlines the project’s goals, architectural choices, experiments, challenges, and optimizations, concluding with potential future improvements.
Main Points
- Definition and purpose of TinyML.TinyML is a fairly new concept that combines Machine Learning with IoT devices, aiming to execute ML algorithms on low-power devices.
- Project focus on standard ML algorithms for gesture detection on ESP32.The project explored using standard machine learning algorithms for real-time gesture detection on ESP32, bypassing the use of neural networks for initial experimentation.
- Requirement for pure Python ML models due to ESP32 and MicroPython constraints.Machine learning models for the project needed to be in pure Python due to constraints of running on ESP32 and MicroPython, which excludes some libraries like scikit-learn.
- Optimization challenges for ML model.Optimization challenges included keeping model size under 20kB, inference time shorter than sampling period, and adapting sampling rates for application needs.
- Suggestions for project improvements.Future tweaks and improvements were suggested to achieve faster classification and more accurate results, including adjustments in model evaluation, feature extraction, and data handling.
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