“Bayesian Modeling and Computation in #Python” available at http://
amzn.to/3K15NQo
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#Statistics #Mathematics #Probability #MachineLearning #DataScience #Inference #ComputationalScience #Simulation #coding #DataScientist
SOFTWARE
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Bayesian Modeling and Computation in Python Book
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HTMX and JavaScript Integration for Client-Side Development
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Not at all! HTMX and JS get along great 😀 HTMX lets you focus your JS on the purely client side stuff where it's really useful.
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Hyperscript to jQuery: Seamless Framework Migration Success
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(It was actually _hyperscript that got me over the line. We wrote something in _hyperscript, and I thought it looked great. Then I thought it looked a bit like jQuery — so I rewrote it with that. And yup, it was a direct line-by-line easy migration! Sorry @htmx_org
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Developer Returns to jQuery After Decade Away
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Anyway, it took me a decade or so, but I've finally decided that I'm gonna use jQuery again anyway, because I like it!
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jQuery 4.0.0 Beta: Slim 30K Library for Web Development
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Specifically, I'm now using jquery-4.0.0-beta.2.slim.min.js. It's so great! Only 30k compressed, and everything works so well together. LLMs know it really well too.
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Claude Code Dev Server Features Improve Development Workflow
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Claude Code keeps on adding features that I didn't realize I needed – being able to start a dev server in the background and occasionally dip into the logs is super useful https://t.co/z6XaOKUpDC
— Simon Willison (@simonw) 9 août 2025Claude Code keeps on adding features that I didn't realize I needed – being able to start a dev server in the background and occasionally dip into the logs is super useful
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Databricks Adds Recursive CTEs for SQL Hierarchies
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Recursive Common Table Expressions are now supported in Databricks! This brings a native way to express loops and traversals in SQL when working with hierarchical and graph-like structures. Plus, support for recursive CTEs simplifies migrations from legacy database systems. See
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API Router Gives Complete Model Control Over Reasoning Effort
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The router only affects ChatGPT – when you use the API you get complete control over which model you are calling and the reasoning effort it uses
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Adopting SLMs without rearchitecting your entire stack
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Adopt SLMs without rearchitecting your entire stack: → Start by auditing where LLMs are used and which tasks can be offloaded → Use distillation and fine-tuning to train SLMs on those subtasks → Gradually replace LLMs with SLMs in your pipeline, monitor, and optimize
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SLMs Over LLMs: Better Choice for Agentic AI Systems
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Here’s why SLMs are a better choice for agentic AI: • High task accuracy on focused, repetitive tasks • Lower memory usage and faster inference • Huge cost savings over time • Easy to slot into a hybrid setup with LLMs for general reasoning