How to maintain character consistency, style consistency, etc in an AI video. Prosumers can use Google Veo 3’s "High-Quality Chaining" for fast social media content. Indie filmmakers can achieve narrative consistency by combining Midjourney V7 for style, Kling for lip-synced dialogue, and Runway Gen-4 for camera control, while professional studios gain full control with a layered ComfyUI pipeline to output multi-layer EXR files for standard VFX compositing.

S-Tier: Google Veo

The market leader due to superior visual quality, physics simulation, 4K resolution, and integrated audio generation, which removes post-production steps. It accurately interprets cinematic prompts ("timelapse," "aerial shots"). Its primary advantage is its integration with Google products, using YouTube's vast video library for rapid model improvement. The professional focus is clear with its filmmaking tool, "Flow."

A-Tier: Sora & Kling

• OpenAI Sora: Excels at interpreting complex narrative prompts and has wide distribution through ChatGPT. Features include in-video editing tools like "Remix" and a "Storyboard" function for multi-shot scenes. Its main limits are 1080p resolution and no native audio.
• Kuaishou Kling: A leader in image-to-video quality and realistic high-speed motion. It maintains character consistency and has proven commercial viability (RMB 150M in Q1 2025). Its text-to-video interface is less intuitive than Sora's.
• Summary: Sora is best for storytellers starting with a narrative idea; Kling is best for artists animating a specific image.

Control and Customization: Runway & Stable Diffusion

• Runway: An integrated creative suite with a full video editor and "AI Magic Tools" like Motion Brush and Director Mode. Its value is in generating, editing, and finishing in one platform, offering precise control over stylization and in-shot object alteration.
• Stable Diffusion: An open-source ecosystem (SVD, AnimateDiff) offering maximum control through technical interfaces like ComfyUI. Its strength is a large community developing custom models, LoRAs, and ControlNets for specific tasks like VFX integration. It has a steep learning curve.

Niche Tools: Midjourney & More

• Midjourney Video: The best tool for animating static Midjourney images (image-to-video only), preserving their unique aesthetic.
• Avatar Platforms (HeyGen, Synthesia): Built for scalable corporate and marketing videos, featuring realistic talking avatars, voice cloning, and multi-language translation with accurate lip-sync.

Head-to-Head Comparison

Feature	Google Veo (S-Tier)	OpenAI Sora (A-Tier)	Kuaishou Kling (A-Tier)	Runway (Power-User Tier)
Photorealism	Winner. Best 4K detail and physics.	Excellent, but can have a stylistic "AI" look.	Very strong, especially with human subjects.	Good, but a step below the top tier.
Consistency	Strong, especially with Flow's scene-building.	Co-Winner. Storyboard feature is built for this.	Co-Winner. Excels in image-to-video consistency.	Good, with character reference tools.
Prompt Adherence	Winner (Language). Best understanding of cinematic terms.	Best for imaginative/narrative prompts.	Strong on motion, less on camera specifics.	Good, but relies more on UI tools.
Directorial Control	Strong via prompt.	Moderate, via prompt and storyboard.	Moderate, focused on motion.	Winner (Interface). Motion Brush & Director Mode offer direct control.
Integrated Audio	Winner. Native dialogue, SFX, and music. Major workflow advantage.	No. Requires post-production.	No. Requires post-production.	No. Requires post-production.

Advanced Multi-Tool Workflows

• High-Quality Animation: Combine Midjourney (for key-frame art) with Kling or Runway (for motion), then use an AI upscaler like Topaz for 4K finishing.
• VFX Compositing: Use Stable Diffusion (AnimateDiff/ControlNets) to generate specific elements for integration into live-action footage using professional software like Nuke or After Effects. All-in-one models lack the required layer-based control.
• High-Volume Marketing: Use Veo for the main concept, Runway for creating dozens of variations, and HeyGen for personalized avatar messaging to achieve speed and scale.

Decision Matrix: Who Should Use What?

User Profile	Primary Goal	Recommendation	Justification
The Indie Filmmaker	Pre-visualization, short films.	OpenAI Sora (Primary), Google Veo (Secondary)	Sora's storyboard feature is best for narrative construction. Veo is best for high-quality final shots.
The VFX Artist	Creating animated elements for live-action.	Stable Diffusion (AnimateDiff/ComfyUI)	Offers the layer-based control and pipeline integration needed for professional VFX.
The Creative Agency	Rapid prototyping, social content.	Runway (Primary Suite), Google Veo (For Hero Shots)	Runway's editing/variation tools are built for agency speed. Veo provides the highest quality for the main asset.
The AI Artist / Animator	Art-directed animated pieces.	Midjourney + Kling	Pairs the best image generator with a top-tier motion engine for maximum aesthetic control.
The Corporate Trainer	Training and personalized marketing videos.	HeyGen / Synthesia	Specialized tools for avatar-based video production at scale (voice cloning, translation).

Future Trajectory

1. Pipeline Collapse: More models will integrate audio and editing, pressuring silent-only video generators.
2. The Control Arms Race: Competition will shift from quality to providing more sophisticated directorial tools.
3. Rise of Aggregators: Platforms like OpenArt that provide access to multiple models through a single interface will become essential.

The 2025 generative AI image market is defined by a split between two types of tools. "Artists" like Midjourney excel at creating beautiful, high-quality images but lack precise control. "Collaborators" like OpenAI's GPT-4o and Google's Imagen 4 are integrated into language models, excelling at following complex instructions and accurately rendering text. Standing apart are the open-source "Sovereign Toolkit" Stable Diffusion, which offers users total control, and Adobe Firefly, a "Professional's Walled Garden" focused on commercial safety.

The Five Main Platforms

The market is dominated by five platforms with distinct strengths and weaknesses.

Tool	Parent Company	Core Strength	Best For
Midjourney v7	Midjourney, Inc.	Artistic Aesthetics & Photorealism	Fine Art, Concept Design, Stylized Visuals
GPT-4o	OpenAI	Conversational Control & Instruction Following	Marketing Materials, UI/UX Mockups, Logos
Google Imagen 4	Google	Ecosystem Integration & Speed	Business Presentations, Educational Content
Stable Diffusion 3	Stability AI	Ultimate Customization & Control	Developers, Power Users, Bespoke Workflows
Adobe Firefly	Adobe	Commercial Safety & Workflow Integration	Professional Designers, Agencies, Enterprise Use

Platform Analysis

• Midjourney v7: Delivers the best aesthetic and photorealistic quality via a new web UI. Its "Draft Mode" allows for rapid, low-cost ideation. However, it cannot reliably render text, struggles to follow precise instructions (like counting objects), makes all images public on cheaper plans, and strictly prohibits API access or automation.
• GPT-4o: Its strength is conversational refinement within ChatGPT, allowing users to edit images through dialogue (e.g., "change the shirt to red"). It has excellent instruction-following and text-rendering capabilities. Weaknesses include being slower than competitors and generating only one image at a time.
• Google Imagen 4: A practical tool integrated directly into Google Workspace and Gemini. It produces high-quality, high-resolution (2K) photorealistic images quickly and renders text well. Its primary advantage is letting users generate images without leaving their documents or presentations.
• Stable Diffusion 3 (SD3): An open-source model that provides users with total control and privacy. The new SD3 architecture significantly improves prompt understanding and text generation. It can run on consumer hardware, and its quality is free after the initial hardware cost. Its power comes from a vast ecosystem of community tools (see below), but it has a steep learning curve.
• Adobe Firefly: Embedded within Adobe Creative Cloud (e.g., Photoshop's Generative Fill). Its key differentiator is commercial safety; it is trained only on licensed Adobe Stock and public domain content to indemnify users from copyright claims. It excels at editing existing images rather than generating from scratch.

Techniques & Tools

• In-painting/Out-painting: Core editing functions. In-painting modifies a specific area within an image. Out-painting expands an image beyond its original borders.
• Stable Diffusion Power Tools:
  • LoRAs (Low-Rank Adaptations): Small files that apply a specific style, character, or concept to the main model.
  • ControlNet: A framework that uses a reference image (e.g., a sketch or a stick-figure pose) as a "blueprint" to enforce a specific composition or pose.
• Stable Diffusion Interfaces: Users choose a UI to run the model. Automatic1111 is a beginner-friendly, tab-based dashboard. ComfyUI is a more complex but powerful node-based interface for building custom, automated workflows.

Feature Comparison & Exclusion Rules

The choice of tool often depends on a single required feature.

Model	Text-in-Image Accuracy	Photorealism Quality	Complex Prompt Adherence
Midjourney v7	Poor. A major weakness.	Best-in-Class	Fair
GPT-4o	Excellent. A key strength.	Very Good	Best-in-Class
Google Imagen 4	Excellent	Excellent	Very Good
Stable Diffusion 3	Good to Excellent	Good to Excellent	Good to Excellent

This leads to several hard rules for choosing a tool:

• If you need accurate in-image text: Exclude Midjourney. Use GPT-4o, Google Imagen 4, or specialist tool Ideogram.
• If you require absolute privacy or must run locally: Stable Diffusion is your only option.
• If you require a guarantee of commercial safety: Adobe Firefly is the most prudent choice.
• If you need to automate generation via an API: Use OpenAI or Google's official APIs. Midjourney bans automation and will close your account.

Fundamentals of Autoencoders

• Autoencoders are neural networks designed to reconstruct their input data by passing data through a compressed intermediate representation called a “code.”
• The architecture typically follows an hourglass shape: a wide input and output separated by a narrower bottleneck layer that enforces information compression.
• The encoder compresses input data into the code, while the decoder reconstructs the original input from this code.

Comparison with Supervised Learning

• Unlike traditional supervised learning, where the output differs from the input (e.g., image classification), autoencoders use the same vector for both input and output.

Use Cases: Dimensionality Reduction and Representation

• Autoencoders perform dimensionality reduction by learning compressed forms of high-dimensional data, making it easier to visualize and process data with many features.
• The compressed code can be used for clustering, visualization in 2D or 3D graphs, and input into subsequent machine learning models, saving computational resources and improving scalability.

Feature Learning and Embeddings

• Autoencoders enable feature learning by extracting abstract representations from the input data, similar in concept to learned embeddings in large language models (LLMs).
• While effective for many data types, autoencoder-based encodings are less suited for variable-length text compared to LLM embeddings.

Data Search, Clustering, and Compression

• By reducing dimensionality, autoencoders facilitate vector searches, efficient clustering, and similarity retrieval.
• The compressed codes enable lossy compression analogous to audio codecs like MP3, with the difference that autoencoders lack domain-specific optimizations for preserving perceptually important data.

Reconstruction Fidelity and Loss Types

• Loss functions in autoencoders are defined to compare reconstructed outputs to original inputs, often using different loss types depending on input variable types (e.g., Boolean vs. continuous).
• Compression via autoencoders is typically lossy, meaning some information from the input is lost during reconstruction, and the areas of information lost may not be easily controlled.

Outlier Detection and Noise Reduction

• Since reconstruction errors tend to move data toward the mean, autoencoders can be used to reduce noise and identify data outliers.
• Large reconstruction errors can signal atypical or outlier samples in the dataset.

Denoising Autoencoders

• Denoising autoencoders are trained to reconstruct clean data from noisy inputs, making them valuable for applications in image and audio de-noising as well as signal smoothing.
• Iterative denoising as a principle forms the basis for diffusion models, where repeated application of a denoising autoencoder can gradually turn random noise into structured output.

Data Imputation

• Autoencoders can aid in data imputation by filling in missing values: training on complete records and reconstructing missing entries for incomplete records using learned code representations.
• This approach leverages the model’s propensity to output ‘plausible’ values learned from overall data structure.

Cryptographic Analogy

• The separation of encoding and decoding can draw parallels to encryption and decryption, though autoencoders are not intended or suitable for secure communication due to their inherent lossiness.

Advanced Architectures: Sparse and Overcomplete Autoencoders

• Sparse autoencoders use constraints to encourage code representations with only a few active values, increasing interpretability and explainability.
• Overcomplete autoencoders have a code size larger than the input, often in applications that require extraction of distinct, interpretable features from complex model states.

Interpretability and Research Example

• Research such as Anthropic’s “Towards Monosemanticity” applies sparse autoencoders to the internal activations of language models to identify interpretable features correlated with concrete linguistic or semantic concepts.
• These models can be used to monitor and potentially control model behaviors (e.g., detecting specific language usage or enforcing safety constraints) by manipulating feature activations.

Variational Autoencoders (VAEs)

• VAEs extend autoencoder architecture by encoding inputs as distributions (means and standard deviations) instead of point values, enforcing a continuous, normalized code space.
• Decoding from sampled points within this space enables synthetic data generation, as any point near the center of the code space corresponds to plausible data according to the model.

VAEs for Synthetic Data and Rare Event Amplification

• VAEs are powerful in domains with sparse data or rare events (e.g., healthcare), allowing generation of synthetic samples representing underrepresented cases.
• They can increase model performance by augmenting datasets without requiring changes to existing model pipelines.

Conditional Generative Techniques

• Conditional autoencoders extend VAEs by allowing controlled generation based on specified conditions (e.g., generating a house with a pool), through additional decoder inputs and conditional loss terms.

Practical Considerations and Limitations

• Training autoencoders and their variants requires computational resources, and their stochastic training can produce differing code representations across runs.
• Lossy reconstruction, lack of domain-specific optimizations, and limited code interpretability restrict some use cases, particularly where exact data preservation or meaningful decompositions are required.

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 chain-of-thought reasoning, structured few-shot prompts, positive instruction framing, and iterative self-correction.

Links

• Notes and resources at ocdevel.com/mlg/mlg35
• Build the future of multi-agent software with AGNTCY
• Try a walking desk stay healthy & sharp while you learn & code

In-Context Learning (ICL)

• Definition: LLMs can perform tasks by learning from examples provided directly in the prompt without updating their parameters.
  • Types:
    • Zero-shot: Direct query, no examples provided.
    • One-shot: Single example provided.
    • Few-shot: Multiple examples, balancing quantity with context window limitations.
  • Mechanism: ICL works through analogy and Bayesian inference, using examples as semantic priors to activate relevant internal representations.
  • Emergent Properties: ICL is an "inference-time training" approach, leveraging the model’s pre-trained knowledge without gradient updates; its effectiveness can be enhanced with diverse, non-redundant examples.

Retrieval Augmented Generation (RAG) and Grounding

• Grounding: Connecting LLMs with external knowledge bases to supplement or update static training data.
  • Motivation: LLMs’ training data becomes outdated or lacks proprietary/specialized knowledge.
  • Benefit: Reduces hallucinations and improves factual accuracy by incorporating current or domain-specific information.
• RAG Workflow:
  1. Embedding: Documents are converted into vector embeddings (using sentence transformers or representation models).
  2. Storage: Vectors are stored in a vector database (e.g., FAISS, ChromaDB, Qdrant).
  3. Retrieval: When a query is made, relevant chunks are extracted based on similarity, possibly with re-ranking or additional query processing.
  4. Augmentation: Retrieved chunks are added to the prompt to provide up-to-date context for generation.
  5. Generation: The LLM generates responses informed by the augmented context.
  • Advanced RAG: Includes agentic approaches—self-correction, aggregation, or multi-agent contribution to source ingestion, and can integrate external document sources (e.g., web search for real-time info, or custom datasets for private knowledge).

LLM Agents

• Overview: Agents extend LLMs by providing goal-oriented, iterative problem-solving through interaction, memory, planning, and tool usage.
• Key Components:
  • Reasoning Engine (LLM Core): Interprets goals, states, and makes decisions.
  • Planning Module: Breaks down complex tasks using strategies such as Chain of Thought or ReAct; can incorporate reflection and adjustment.
  • Memory: Short-term via context window; long-term via persistent storage like RAG-integrated databases or special memory systems.
  • Tools and APIs: Agents select and use external functions—file manipulation, browser control, code execution, database queries, or invoking smaller/fine-tuned models.
• Capabilities: Support self-evaluation, correction, and multi-step planning; allow integration with other agents (multi-agent systems); face limitations in memory continuity, adaptivity, and controllability.
• Current Trends: Research and development are shifting toward these agentic paradigms as LLM core scaling saturates.

Multimodal Large Language Models (MLLMs)

• Definition: Models capable of ingesting and generating across different modalities (text, image, audio, video).
• Architecture:
  • Modality-Specific Encoders: Convert raw modalities (text, image, audio) into numeric embeddings (e.g., vision transformers for images).
  • Fusion/Alignment Layer: Embeddings from different modalities are projected into a shared space, often via cross-attention or concatenation, allowing the model to jointly reason about their content.
  • Unified Transformer Backbone: Processes fused embeddings to allow cross-modal reasoning and generates outputs in the required format.
• Recent Advances: Unified architectures (e.g., GPT-4o) use a single model for all modalities rather than switching between separate sub-models.
• Functionality: Enables actions such as image analysis via text prompts, visual Q&A, and integrated speech recognition/generation.

Advanced LLM Architectures and Training Directions

• Predictive Abstract Representation: Incorporating latent concept prediction alongside token prediction (e.g., via autoencoders).
• Patch-Level Training: Predicting larger “patches” of tokens to reduce sequence lengths and computation.
• Concept-Centric Modeling: Moving from next-token prediction to predicting sequences of semantic concepts (e.g., Meta’s Large Concept Model).
• Multi-Token Prediction: Training models to predict multiple future tokens for broader context capture.

Evaluation Benchmarks (as of 2025)

• Key Benchmarks Used for LLM Evaluation:
  • GPQA (Diamond): Graduate-level STEM reasoning.
  • SWE Bench Verified: Real-world software engineering, verifying agentic code abilities.
  • MMMU: Multimodal, college-level cross-disciplinary reasoning.
  • HumanEval: Python coding correctness.
  • HLE (Human’s Last Exam): Extremely challenging, multimodal knowledge assessment.
  • LiveCodeBench: Coding with contamination-free, up-to-date problems.
  • MLPerf Inference v5.0 Long Context: Throughput/latency for processing long contexts.
  • MultiChallenge Conversational AI: Multiturn dialogue, in-context reasoning.
  • TAUBench/PFCL: Tool utilization in agentic tasks.
  • TruthfulnessQA: Measures tendency toward factual accuracy/robustness against misinformation.

Prompt Engineering: High-Impact Techniques

• Foundational Approaches:
  • Few-Shot Prompting: Provide pairs of inputs and desired outputs to steer the LLM.
  • Chain of Thought: Instructing the LLM to think step-by-step, either explicitly or through internal self-reprompting, enhances reasoning and output quality.
  • Clarity and Structure: Use clear, detailed, and structured instructions—task definition, context, constraints, output format, use of delimiters or markdown structuring.
  • Affirmative Directives: Phrase instructions positively (“write a concise summary” instead of “don’t write a long summary”).
  • Iterative Self-Refinement: Prompt the LLM to review and improve its prior response for better completeness, clarity, and factuality.
  • System Prompt/Role Assignment: Assign a persona or role to the LLM for tailored behavior (e.g., “You are an expert Python programmer”).
• Guideline: Regularly consult official prompting guides from model developers as model capabilities evolve.

Trends and Research Outlook

• Inference-time compute is increasingly important for pushing the boundaries of LLM task performance.
• Agentic LLMs and multimodal reasoning represent the primary frontiers for innovation.
• Prompt engineering and benchmarking remain essential for extracting optimal performance and assessing progress.
• Models are expected to continue evolving with research into new architectures, memory systems, and integration techniques.

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 task performance.

Links

• Notes and resources at ocdevel.com/mlg/mlg34
• Build the future of multi-agent software with AGNTCY
• Try a walking desk stay healthy & sharp while you learn & code

Transformer Foundations and Scaling Laws

• Transformers: Introduced by the 2017 "Attention is All You Need" paper, transformers allow for parallel training and inference of sequences using self-attention, in contrast to the sequential nature of RNNs.
• Scaling Laws:
  • Empirical research revealed that LLM performance improves predictably as model size (parameters), data size (training tokens), and compute are increased together, with diminishing returns if only one variable is scaled disproportionately.
  • The "Chinchilla scaling law" (DeepMind, 2022) established the optimal model/data/compute ratio for efficient model performance: earlier large models like GPT-3 were undertrained relative to their size, whereas right-sized models with more training data (e.g., Chinchilla, LLaMA series) proved more compute and inference efficient.

Emergent Abilities in LLMs

• Emergence: When trained beyond a certain scale, LLMs display abilities not present in smaller models, including:
  • In-Context Learning (ICL): Performing new tasks based solely on prompt examples at inference time.
  • Instruction Following: Executing natural language tasks not seen during training.
  • Multi-Step Reasoning & Chain of Thought (CoT): Solving arithmetic, logic, or symbolic reasoning by generating intermediate reasoning steps.
• Discontinuity & Debate: These abilities appear abruptly in larger models, though recent research suggests that this could result from non-linearities in evaluation metrics rather than innate model properties.

Architectural Evolutions: Mixture of Experts (MoE)

• MoE Layers: Modern LLMs often replace standard feed-forward layers with MoE structures.
  • Composed of many independent "expert" networks specializing in different subdomains or latent structures.
  • A gating network routes tokens to the most relevant experts per input, activating only a subset of parameters—this is called "sparse activation."
  • Enables much larger overall models without proportional increases in compute per inference, but requires the entire model in memory and introduces new challenges like load balancing and communication overhead.
• Specialization & Efficiency: Experts learn different data/knowledge types, boosting model specialization and throughput, though care is needed to avoid overfitting and underutilization of specialists.

The Three-Phase Training Process

• 1. Unsupervised Pre-Training: Next-token prediction on massive datasets—builds a foundation model capturing general language patterns.
• 2. Supervised Fine Tuning (SFT): Training on labeled prompt-response pairs to teach the model how to perform specific tasks (e.g., question answering, summarization, code generation). Overfitting and "catastrophic forgetting" are risks if not carefully managed.
• 3. Reinforcement Learning from Human Feedback (RLHF):
  • Collects human preference data by generating multiple responses to prompts and then having annotators rank them.
  • Builds a reward model (often PPO) based on these rankings, then updates the LLM to maximize alignment with human preferences (helpfulness, harmlessness, truthfulness).
  • Introduces complexity and risk of reward hacking (specification gaming), where the model may exploit the reward system in unanticipated ways.

Advanced Reasoning Techniques

• Prompt Engineering: The art/science of crafting prompts that elicit better model responses, shown to dramatically affect model output quality.
• Chain of Thought (CoT) Prompting: Guides models to elaborate step-by-step reasoning before arriving at final answers—demonstrably improves results on complex tasks.
  • Variants include zero-shot CoT ("let's think step by step"), few-shot CoT with worked examples, self-consistency (voting among multiple reasoning chains), and Tree of Thought (explores multiple reasoning branches in parallel).
• Automated Reasoning Optimization: Frontier models selectively apply these advanced reasoning techniques, balancing compute costs with gains in accuracy and transparency.

Optimization for Training and Inference

• Tradeoffs: The optimal balance between model size, data, and compute is determined not only for pretraining but also for inference efficiency, as lifetime inference costs may exceed initial training costs.
• Current Trends: Efficient scaling, model specialization (MoE), careful fine-tuning, RLHF alignment, and automated reasoning techniques define state-of-the-art LLM development.

Tool Use in Code AI Agents

• Code AI agents offer two primary modes of interaction: in-line code completion within the editor and agent interaction through sidebar prompts.
• Inline code completion has evolved from single-line suggestions to cross-file edits, refactoring, and modification of existing code blocks.
• Tools accessible via agents include read, write, and list file functions, as well as browser automation and command execution; permissions for sensitive actions can be set by developers.
• Agents can intelligently search a project’s codebase and dependencies using search commands and regular expressions to locate relevant files.

Model Context Protocol (MCP)

• MCP, introduced by Anthropic, establishes a standardized protocol for agents to communicate with tools and services, replacing bespoke tool integrations.
• The protocol is analogous to REST for web servers and unifies tool calling for both local and cloud-hosted automation.
• MCP architecture involves three components: the AI agent, MCP client, and MCP server. The agent provides context, the client translates requests and responses, and the server executes and responds with data in a structured format.
• MCP servers can be local (STDIO-based for local tasks like file search or browser actions) or cloud-based (SSE for hosted APIs and SaaS tools).
• Developers can connect code AI agents to directories of MCP servers, accessing an expanding ecosystem of automation tools for both programming and non-programming tasks.

MCP Application Examples

• Local MCP servers include Playwright for browser automation and Postgres MCP for live database schema analysis and data-driven UI suggestions.
• Cloud-based MCP servers integrate APIs such as AWS, enabling infrastructure management directly from coding environments.
• MCP servers are not limited to code automation; they are widely used for pipeline automation in sales, marketing, and other internet-connected workflows.

Retrieval Augmented Generation (RAG) as an MCP Use Case

• RAG, once standard in code AI tools, indexed codebases using embeddings to assist with relevant file retrieval, but many agents now favor literal search for practicality.
• Local RAG MCP servers, such as Chroma or LlamaIndex, can index entire documentation sets to update agent knowledge of recent or project-specific libraries outside of widely-known frameworks.
• Fine-tuning a local LLM with the same documentation is an alternative approach to integrating new knowledge into code AI workflows.

Machine Learning Applications

• Code AI tooling supports feature engineering, data cleansing, pipeline setup, model design, and hyperparameter optimization, based on real dataset distributions and project specifications.
• Agents can recommend advanced data transformations—such as Yeo-Johnson power transformation for skewed features—by directly analyzing example dataset distributions.
• Infrastructure-as-code integration enables rapid deployment of machine learning models and supporting components by chaining coding agents to cloud automation tools.
• Automation concepts from code AI apply to both traditional code file workflows and Jupyter Notebooks, though integration with notebooks remains less seamless.
• An iterative approach using sidecar Python files combined with custom instructions helps agents access necessary background and context for ML projects.

Workflow Strategies for Machine Learning Engineers

• To leverage code AI agents in machine learning tasks, engineers can provide data samples and visualizations to agents through Python files or prompt contexts.
• Agents can guide creation and comparison of multiple model architectures, metrics, and loss functions, improving efficiency and broadening solution exploration.
• While Jupyter Lab plugin integration is currently limited, some success can be achieved by working with notebook files via code AI tools in standard code editors or by moving between notebooks and Python files for maximum flexibility.

 

Model Current Leaders

According to the Aider Leaderboard (as of April 12, 2025), leading models include for vibe-coding:

• Gemini 2.5 Pro Preview 03-25: most accurate and cost-effective option currently.
• Claude 3.7 Sonnet: Performs well in both architect and code modes with enabled reasoning flags.
• DeepSeek R1 with Claude 3.5 Sonnet: A popular combination for its balance of cost and performance between reasoning and non-reasoning tasks.

Local Models

• Tools for Local Models: Ollama is the standard tool to manage local models, enabling usage without internet connectivity.
• Best Models per VRAM: See this Reddit post, but know that Qwen 3 launched after that; and DeepSeek R1 is coming soon.
• Privacy and Security: Utilizing local models enhances data security, suitable for sensitive projects or corporate environments that require data to remain onsite.
• Performance Trade-offs: Local models, due to distillation and size constraints, often perform slightly worse than cloud-hosted models but offer privacy benefits.

Fine-Tuning Models

• Customization: Developers can fine-tune pre-trained models to specialize them for their specific codebase, enhancing relevance and accuracy.
• Advanced Usage: Suitable for long-term projects, fine-tuning helps models understand unique aspects of a project, resulting in consistent code quality improvements.

Tips and Best Practices

• Judicious Use of the @ Key: Improves model efficiency by specifying the context of commands, reducing the necessity for AI-initiated searches.
  • Examples include specifying file paths, URLs, or git commits to inform AI actions more precisely.
• Concurrent Feature Implementation: Leverage tools like Boomerang mode to manage multiple features simultaneously, acting more as a manager overseeing several tasks at once, enhancing productivity.
• Continued Learning: Staying updated with documentation, particularly Roo Code's, due to its comprehensive feature set and versatility among AI coding tools.

Links:

• Notes and resources at ocdevel.com/mlg/33
• 3Blue1Brown videos: https://3blue1brown.com/
• Try a walking desk stay healthy & sharp while you learn & code
• Try Descript 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 in residual connections and layer normalization.
• Positional Encodings: Since self-attention is permutation invariant, add sinusoidal or learned positional embeddings to inject sequence order.

Self-Attention Mechanism

• Q, K, V Explained:
  • Query (Q): The representation of the token seeking contextual info.
  • Key (K): The representation of tokens being compared against.
  • Value (V): The information to be aggregated based on the attention scores.
• Multi-Head Attention: Splits Q, K, V into multiple “heads” to capture diverse relationships and nuances across different subspaces.
• Dot-Product & Scaling: Computes similarity between Q and K (scaled to avoid large gradients), then applies softmax to weigh V accordingly.

Masking

• Causal Masking: In autoregressive models, prevents a token from “seeing” future tokens, ensuring proper generation.
• Padding Masks: Ignore padded (non-informative) parts of sequences to maintain meaningful attention distributions.

Feed-Forward Networks (MLPs)

• Transformation & Storage: Post-attention MLPs apply non-linear transformations; many argue they’re where the “facts” or learned knowledge really get stored.
• Depth & Expressivity: Their layered nature deepens the model’s capacity to represent complex patterns.

Residual Connections & Normalization

• Residual Links: Crucial for gradient flow in deep architectures, preventing vanishing/exploding gradients.
• Layer Normalization: Stabilizes training by normalizing across features, enhancing convergence.

Scalability & Efficiency Considerations

• Parallelization Advantage: Entire architecture is designed to exploit modern parallel hardware, a huge win over RNNs.
• Complexity Trade-offs: Self-attention’s quadratic complexity with sequence length remains a challenge; spurred innovations like sparse or linearized attention.

Training Paradigms & Emergent Properties

• Pretraining & Fine-Tuning: Massive self-supervised pretraining on diverse data, followed by task-specific fine-tuning, is the norm.
• Emergent Behavior: With scale comes abilities like in-context learning and few-shot adaptation, aspects that are still being unpacked.

Interpretability & Knowledge Distribution

• Distributed Representation: “Facts” aren’t stored in a single layer but are embedded throughout both attention heads and MLP layers.
• Debate on Attention: While some see attention weights as interpretable, a growing view is that real “knowledge” is diffused across the network’s parameters.