MLA 022 Code AI: Cursor, Cline, Roo, Aider, Copilot, Windsurf
Release Date: 02/09/2025
MLG 035 Large Language Models 2
Machine Learning Guide
At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as...
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MLG 034 Large Language Models 1
Machine Learning Guide
Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex...
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MLA 024 Code AI MCP Servers, ML Engineering
Machine Learning Guide
Tool use in code AI agents allows for both in-editor code completion and agent-driven file and command actions, while the Model Context Protocol (MCP) standardizes how these agents communicate with external and internal tools. MCP integration broadens the automation capabilities for developers and machine learning engineers by enabling access to a wide variety of local and cloud-based tools directly within their coding environments. Links Notes and resources at stay healthy & sharp while you learn & code Tool Use in Code AI Agents Code AI agents offer two primary modes of...
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MLA 023 Code AI Models & Modes
Machine Learning Guide
Gemini 2.5 Pro currently leads in both accuracy and cost-effectiveness among code-focused large language models, with Claude 3.7 and a DeepSeek R1/Claude 3.5 combination also performing well in specific modes. Using local open source models via tools like Ollama offers enhanced privacy but trades off model performance, and advanced workflows like custom modes and fine-tuning can further optimize development processes. Links Notes and resources at stay healthy & sharp while you learn & code Model Current Leaders According to the (as of April 12, 2025), leading...
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MLA 022 Code AI: Cursor, Cline, Roo, Aider, Copilot, Windsurf
Machine Learning Guide
Vibe coding is using large language models within IDEs or plugins to generate, edit, and review code, and has recently become a prominent and evolving technique in software and machine learning engineering. The episode outlines a comparison of current code AI tools - such as Cursor, Copilot, Windsurf, Cline, Roo Code, and Aider - explaining their architectures, capabilities, agentic features, pricing, and practical recommendations for integrating them into development workflows. Links Notes and resources at stay healthy & sharp while you learn & code Definition and...
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MLG 033 Transformers
Machine Learning Guide
Links: Notes and resources at 3Blue1Brown videos: stay healthy & sharp while you learn & code audio/video editing with AI power-tools 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...
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MLA 021 Databricks: Cloud Analytics and MLOps
Machine Learning Guide
Databricks is a cloud-based platform for data analytics and machine learning operations, integrating features such as a hosted Spark cluster, Python notebook execution, Delta Lake for data management, and seamless IDE connectivity. Raybeam utilizes Databricks and other ML Ops tools according to client infrastructure, scaling needs, and project goals, favoring Databricks for its balanced feature set, ease of use, and support for both startups and enterprises. Links Notes and resources at stay healthy & sharp while you learn & code Raybeam and Databricks Raybeam is a...
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MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes
Machine Learning Guide
Machine learning pipeline orchestration tools, such as SageMaker and Kubeflow, streamline the end-to-end process of data ingestion, model training, deployment, and monitoring, with Kubeflow providing an open-source, cross-cloud platform built atop Kubernetes. Organizations typically choose between cloud-native managed services and open-source solutions based on required flexibility, scalability, integration with existing cloud environments, and vendor lock-in considerations. Links Notes and resources at stay healthy & sharp while you learn & code - Data Scientist...
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MLA 019 Cloud, DevOps & Architecture
Machine Learning Guide
The deployment of machine learning models for real-world use involves a sequence of cloud services and architectural choices, where machine learning expertise must be complemented by DevOps and architecture skills, often requiring collaboration with professionals. Key concepts discussed include infrastructure as code, cloud container orchestration, and the distinction between DevOps and architecture, as well as practical advice for machine learning engineers wanting to deploy products securely and efficiently. Links Notes and resources at stay healthy & sharp while you learn...
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MLA 017 AWS Local Development Environment
Machine Learning Guide
AWS development environments for local and cloud deployment can differ significantly, leading to extra complexity and setup during cloud migration. By developing directly within AWS environments, using tools such as Lambda, Cloud9, SageMaker Studio, client VPN connections, or LocalStack, developers can streamline transitions to production and leverage AWS-managed services from the start. This episode outlines three primary strategies for treating AWS as your development environment, details the benefits and tradeoffs of each, and explains the role of infrastructure-as-code tools such as...
info_outlineVibe coding is using large language models within IDEs or plugins to generate, edit, and review code, and has recently become a prominent and evolving technique in software and machine learning engineering. The episode outlines a comparison of current code AI tools - such as Cursor, Copilot, Windsurf, Cline, Roo Code, and Aider - explaining their architectures, capabilities, agentic features, pricing, and practical recommendations for integrating them into development workflows.
Links
- Notes and resources at ocdevel.com/mlg/mla-22
- Try a walking desk stay healthy & sharp while you learn & code
Definition and Context of Vibe Coding
- Vibe coding refers to using large language models (LLMs) to generate or edit code directly within IDEs or through plugins.
- Developers interface with AI models in their coding environment by entering requests or commands in chat-like dialogues, enabling streamlined workflows for feature additions, debugging, and other tasks.
Industry Reception and Concerns
- Industry skepticism about vibe coding centers on three issues: concerns that excessive reliance on AI can degrade code quality, skepticism over aggressive marketing reminiscent of early cryptocurrency promotions, and anxieties about job security among experienced developers.
- Maintaining human oversight and reviewing AI-generated changes is emphasized, with both senior engineers and newcomers encouraged to engage critically with outputs rather than use them blindly.
Turnkey Web App Generators vs. Developer-Focused Tools
- Some AI-powered platforms function as turnkey website and app generators (for example, Lovable, Rept, and Bolt), which reduce development to prompting but limit customizability and resemble content management systems.
- The focus of this episode is on developer-oriented tools that operate within professional environments, distinguishing them from these all-in-one generators.
Evolution of Code AI Tools and IDE Integration
- Most contemporary AI code assistants either fork Visual Studio Code (Cursor, Windsurf), or offer plugins/extensions for it, capitalizing on the popularity and adaptability of VS Code.
- Tools such as Copilot, Cline, Roo Code, and Aider present varied approaches ranging from command-line interfaces to customizable, open-source integrations.
Functional Capabilities: Inline Edits and Agentic Features
- Early iterations of AI coding tools mainly provided inline code suggestions or autocompletions within active files.
- Modern tools now offer “agentic” features, such as analyzing file dependencies, editing across multiple files, installing packages, executing commands, interacting with web browsers, and performing broader codebase actions.
Detailed Overview of Leading Tools
- Cursor is a popular standalone fork of VS Code, focused on integrating new models with stability and offering a flat-fee pricing model.
- Windsurf offers similar agentic and inline features with tiered pricing and a “just works” usability orientation.
- Copilot, integrated with VS Code and GitHub Code Spaces, provides agentic coding with periodic performance fluctuations and tiered pricing.
- Cline is open-source and model-agnostic, pioneering features like “bring your own model” (BYOM) and operating on a per-request billing structure.
- Roo Code, derived from Cline, prioritizes rapid feature development and customization, serving users interested in experimental capabilities.
- Aider is command-line only, focusing on token efficiency and precise, targeted code modifications, making it useful for surgical edits or as a fallback tool.
Community and Resource Ecosystem
- Resources such as leaderboards enable developers to monitor progress and compare tool effectiveness.
- Aiding community support and updates, the Reddit community discusses use cases, troubleshooting, and rapid feature rollouts.
- Demonstrations such as the video of speed-demon illustrate tool capabilities in practical scenarios.
Models, Pricing, and Cost Management
- Subscription tools like Cursor, Copilot, and Windsurf have flat or tiered pricing, with extra fees for exceeding standard quotas.
- Open-source solutions require API keys for model providers (OpenAI, Anthropic, Google Gemini), incurring per-request charges dependent on usage.
- OpenRouter is recommended for consolidating credits and accessing multiple AI models, streamlining administration and reducing fragmented expenses.
Model Advancements and Recommendations
- The landscape of model performance changes rapidly, with leaders shifting from Claude 3.5, to DeepSeek, Claude 3.7, and currently to Gemini 2.5 Pro Experimental, which is temporarily free and offers extended capabilities.
- Developers should periodically review available models, utilizing OpenRouter to select up-to-date and efficient options.
Practical Usage Strategies
- For routine development, begin with Cursor and explore alternatives like Copilot and Windsurf for additional features.
- Advanced users can install Cline or Roo Code as plugins within preferred IDEs, and maintain Aider for precise code changes or fallback needs.
- Balancing subscription-based and open-source tools can increase cost-efficiency; thoughtful review of AI-generated edits remains essential before codebase integration.
Conclusion
- Vibe coding, defined as using LLMs for software and machine learning development, is transforming professional workflows with new tooling and shifting best practices.
- Developers are encouraged to experiment with a range of tools, monitor ongoing advancements, and integrate AI responsibly into their coding routines.