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MLA 022 Code AI Tools

Machine Learning Guide

Release Date: 02/09/2025

MLA 022 Code AI Tools show art MLA 022 Code AI Tools

Machine Learning Guide

Try a walking desk while studying ML or working on your projects! Show notes: Tools discussed: Windsurf: Copilot: Cursor: Cline: Roo Code: Aider: Other: Leaderboards: Video of speed-demon: Reddit: Boost programming productivity by acting as a pair programming partner. Groups these tools into three categories: • Hands-Off Tools: These include solutions that work on fixed monthly fees and require minimal user intervention. GitHub Copilot started with simple tab completions and now offers an agent mode similar to Cursor, which stands out for its advanced codebase indexing and...

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Machine Learning Guide

Try a walking desk while studying ML or working on your projects! Show notes: 3Blue1Brown videos: Background & Motivation: RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware. Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture: Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped in residual connections and layer normalization. Positional...

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Machine Learning Guide

Try a walking desk while studying ML or working on your projects! Discussing Databricks with Ming Chang from  (part of )

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Machine Learning Guide

Try a walking desk while studying ML or working on your projects! Conversation with Dirk-Jan Kubeflow (vs cloud native solutions like SageMaker)  - Data Scientist at Dept Agency . (From the website:) The Machine Learning Toolkit for Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be...

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Machine Learning Guide

Try a walking desk while studying ML or working on your projects! Chatting with co-workers about the role of DevOps in a machine learning engineer's life Expert coworkers at Dept  - Principal Software Developer  - DevOps Lead  (where Matt features often) Devops tools Pictures (funny and serious)

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Machine Learning Guide

Try a walking desk while studying ML or working on your projects! (Optional episode) just showcasing a cool application using machine learning Dept uses Descript for some of their podcasting. I'm using it like a maniac, I think they're surprised at how into it I am. Check out the transcript & see how it performed.  How to ship software, from the front lines. We talk with software developers about their craft, developer tools, developer productivity and what makes software development awesome. Hosted by your friends at Rocket Insights. AKA shipit.io  An agency podcast with...

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Machine Learning Guide

while studying ML or working on your projects! Show notes:  Developing on AWS first (SageMaker or other) Consider developing against AWS as your local development environment, rather than only your cloud deployment environment. Solutions: Stick to AWS Cloud IDEs (, ,  Connect to deployed infrastructure via  Infrastructure as Code

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Machine Learning Guide

while studying ML or working on your projects! Part 2 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See  for an overview of tooling (also generally a great ML educational run-down.)

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Machine Learning Guide

while studying ML or working on your projects! Part 1 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See  for an overview of tooling (also generally a great ML educational run-down.) And I forgot to mention , I'll mention next time.

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Machine Learning Guide

while studying ML or working on your projects! Server-side ML. Training & hosting for inference, with a goal towards serverless. AWS SageMaker, Batch, Lambda, EFS, Cortex.dev

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More Episodes

Try a walking desk while studying ML or working on your projects! https://ocdevel.com/walk

Show notes: https://ocdevel.com/mlg/mla-22

Tools discussed:

  1. Windsurf: https://codeium.com/windsurf
  2. Copilot: https://github.com/features/copilot
  3. Cursor: https://www.cursor.com/
  4. Cline: https://github.com/cline/cline
  5. Roo Code: https://github.com/RooVetGit/Roo-Code
  6. Aider: https://aider.chat/

Other:

  1. Leaderboards: https://aider.chat/docs/leaderboards/
  2. Video of speed-demon: https://www.youtube.com/watch?v=QlUt06XLbJE&feature=youtu.be
  3. Reddit: https://www.reddit.com/r/chatgptcoding/

Boost programming productivity by acting as a pair programming partner. Groups these tools into three categories:

• Hands-Off Tools: These include solutions that work on fixed monthly fees and require minimal user intervention. GitHub Copilot started with simple tab completions and now offers an agent mode similar to Cursor, which stands out for its advanced codebase indexing and intelligent file searching. Windsurf is noted for its simplicity—accepting prompts and performing automated edits—but some users report performance throttling after prolonged use.

• Hands-On Tools: Aider is presented as a command-line utility that demands configuration and user involvement. It allows developers to specify files and settings, and it efficiently manages token usage by sending prompts in diff format. Aider also implements an “architect versus edit” approach: a reasoning model (such as DeepSeek R1) first outlines a sequence of changes, then an editor model (like Claude 3.5 Sonnet) produces precise code edits. This dual-model strategy enhances accuracy and reduces token costs, especially for complex tasks.

• Intermediate Power Tools: Open-source tools such as Cline and its more advanced fork, RooCode, require users to supply their own API keys and pay per token. These tools offer robust, agentic features, including codebase indexing, file editing, and even browser automation. RooCode stands out with its ability to autonomously expand functionality through integrations (for example, managing cloud resources or querying issue trackers), making it particularly attractive for tinkerers and power users.

A decision framework is suggested: for those new to AI coding assistants or with limited budgets, starting with Cursor (or cautiously exploring Copilot’s new features) is recommended. For developers who want to customize their workflow and dive deep into the tooling, RooCode or Cline offer greater control—always paired with Aider for precise and token-efficient code edits.

Also reviews model performance using a coding benchmark leaderboard that updates frequently. The current top-performing combination uses DeepSeek R1 as the architect and Claude 3.5 Sonnet as the editor, with alternatives such as OpenAI’s O1 and O3 Mini available. Tools like Open Router are mentioned as a way to consolidate API key management and reduce token costs.