AIAD - AI Generated Code, Reviews and Testing

AIAD - AI Generated Code, Reviews and Testing

Code Reviews

Agent pull requests are everywhere. Here's how to review them. - The GitHub Blog

Applied ML - genai in domains

Architecture (buildings)

Image generation

mid journey (best)

stable diffusion 

dall-e

controlnet

- https://github.com/lllyasviel/ControlNet

 - ControlNet is a neural network structure to control diffusion models by adding extra conditions.

Code Generation (Programming)

- Cursor IDE

- Kiro IDE (source code analysis)

3D modelling (structure)

rhino 3d modelling tool

https://www.rhino3d.com

grasshopper model scripting 

- part of rhino 3d

- scripting tool, DSL for Rhino3D

- scripts help enable genai

What is Grasshopper and Why Use it

- https://www.youtube.com/watch?v=AKpqK23Q5pM

what is lacking:

- AI models with physics 

- AI models with structural understanding

- AI architecture model that also uses or is integrated with AI material modelling

- an integrated genai workflow

- AI currently seems to fill in at a high-level (e.g. from a drawing with colored 'areas', but has poor quality on details. Also issues with counting (number of floors, windows...).

project - Deep Himmelb(l)au

- https://www.researchgate.net/figure/Deep-Himmelblau-by-Daniel-Bolojan-Bolojan-also-conducted-the-Gaudi-Hallucinations_fig2_350740286

Art and Design

Lovart | The World’s First Design Agent

Web Site Generation

https://gamma.app/

General Business

(AI in Business) - Applied Artificial Intelligence

https://www.bookdepository.com/Applied-Artificial-Intelligence-Where-AI-Can-Be-Used-Business-Francesco-Corea/9783319772516?ref=grid-view&qid=1592465308969&sr=1-1 

Geoff Woods - The AI Driven Leader

AI and Agents - Conferences and Organisations

Multi-Agent Organizations and Conferences

IFAAMAS = International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

AAMAS 2025 Detroit = International Conference on Autonomous Agents and Multiagent Systems

Exploring Multi-Agent Frameworks, Books and Papers

In the rapidly evolving field of artificial intelligence (AI), open-source libraries and frameworks have emerged as valuable resources for developers and researchers. This blog post aims to provide an overview of some notable open-source AI agent libraries, their features, and potential use cases.

LLMs: Lessons Learned and Evaluation

Applied LLMs: What we've learned from a year of building with LLMs
https://applied-llms.org/ 

Lessons Learned Building Products Powered by Generative AI

https://tech.buzzfeed.com/lessons-learned-building-products-powered-by-generative-ai-7f6c23bff376

EvalGen - Who validates the validators - https://arxiv.org/abs/2404.12272

Your AI Product Needs Evals

https://hamel.dev/blog/posts/evals/

LLM evaluation metrics

https://www.confident-ai.com/blog/llm-evaluation-metrics-everything-you-need-for-llm-evaluation

Your AI Product Needs Evals – Hamel's Blog

AI - Reinforcement Learning and Robotics

AI - Reinforcement Learning and Robotics

# Robotics

- Majko Physics Engine - https://github.com/google-deepmind/mujoco

- book: Sutton & Barto - "Reinforcement Learning: An Introduction"

- free eBook

- Robot Operating System (ROS) = https://github.com/ros-infrastructure/www.ros.org

AI: algorithms

K-Means clustering [can be expensive]

- elbow method - calc how many clusters, but can be expsensive

alt: DBSCAN which calculates how many clusters to use (density-based clustering non-parametric algorithm)

https://en.wikipedia.org/wiki/DBSCAN

AI: LLMs, prompting, platforms (hardware) and projects

AI Papers, Books and Datasets

Learning Transferable Visual Models From Natural Language Supervision = 
https://arxiv.org/abs/2103.00020

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Datasets

from AI Engineering:

Resources for Publicly Available Datasets

Here are a few resources where you can look for publicly available datasets. While you should take advantage of available data, you should never fully trust it. Data needs to be thoroughly inspected and validated.

Always check a dataset's license before using it. Try your best to understand where the data comes from. Even if a dataset has a license that allows commercial use, it's possible that part of it comes from a source that doesn't:

1. Hugging Face (https://oreil.ly/tlt5h) and Kaggle (https://oreil.ly/g8A4a) each host
   hundreds of thousands of datasets.
2. Google has a wonderful and underrated Dataset Search (https://oreil.ly/TgOaR).
3. Governments are often great providers of open data. Data.gov (https://data.gov) hosts hundreds of thousands of datasets, and data.gov.in (https://data.gov.in) hosts tens of thousands.
4. University of Michigan's Institute for Social Research (https://oreil.ly/VhVzp)
   ICPSR has data from tens of thousands of social studies.
5. UC Irvine's Machine Learning Repository (https://oreil.ly/jAR9e) and OpenML (https://oreil.ly/d-Yty) are two older dataset repositories, each hosting several thousand datasets.
6. The Open Data Network (https://oreil.ly/_tW6P) lets you search among tens of thousands of datasets.
7. Cloud service providers often host a small collection of open datasets; the most notable one is AWS's Open Data (https://oreil.ly/DZ5uV).
8. ML frameworks often have small pre-built datasets that you can load while using the framework, such as TensorFlow datasets (https://oreil.ly/HMJX_).
9. Some evaluation harness tools host evaluation benchmark datasets that are suff-ciently large for PEFT finetuning. For example, Eleuther AI's Im-evaluation-harness (https://github.com/EleutherAl/m-evaluation-harness) hosts 400+ benchmark datasets, averaging 2,000+ examples per dataset.

10. The Stanford Large Network Dataset Collection (hts://oreilye_B) is a great

repository for graph datasets.

Free Datasets

https://www.v7labs.com/blog/best-free-datasets-for-machine-learning

UI Modelling - annotated UIs

https://uimodeling.github.io/

Datasets - Roboflow (like HuggingFace for data)

https://public.roboflow.com/

Dataset - Anthropic use of AI by job category 

https://huggingface.co/datasets/Anthropic/EconomicIndex

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MoE (Mixture of Experts)

LLM Mixture of Experts Explained (tensorops.ai)

An Introduction to Vision-Language Modeling

Vision AI - VLMs and CNNs
- Vision Language Models
- Convolutional Neural Networks

https://arxiv.org/pdf/2405.17247

Multi-modal LLMs

Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs (cambrian-mllm.github.io)

Movies: rated [for collaborative filtering]

MovieLens Latest Datasets | GroupLens

The Society of Mind

Book by Marvin Minsky

AI Engineering[Book] [O'Reilly] [Zeki recommends]

same author: Designing Machine Learning Systems- DMLS focuses on building applications on top of traditional ML models, which involves more tabular data annotations, feature engineering, and model training

LLM Engineer's Handbook [O'Reilly] by Paul Iusztin, Maxime Labonne

AI and ML for Coders in PyTorch: A Coder’s Guide to Generative AI and Machine Learning by Laurence Moroney is a practical, code-first resource for programmers eager to master modern machine learning and generative AI

Artificial Intelligence: A Modern Approach

gen-ai news and updates

 

LocalLLaMA - gen-ai on reddit

https://www.reddit.com/r/LocalLLaMA/

LLM Leaderboard - https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard

Superintelligence

the beginning of infinity

The Business Case for AI: A Leader's Guide to AI Strategies, Best Practices & Real-World Applications

Book by Kavita Ganesan

- but neglects challenge of culture change and ethics

- profit vs ethics

'Bullshit' Detection rates

- BullshitBench: V2 (New) Viewer

.