AI

Uncategorized (yet)

https://towardsdatascience.com/langgraph-101-lets-build-a-deep-research-agent/ : Learn LangGraph fundamentals from Google’s open-source full-stack implementation

General

The 10+1 Commandments of Human AI Co-Existence: "The 10+1 is a standard. Not everyone will adopt it - but those who do will lead the next era. If you're a policymaker, don't wait for regulation to catch up. If you're a CEO, don't wait for PR fallout to act with integrity. If you're building AI, don't just ask if it works - ask if it's worthy. We don't need more frameworks. We need a philosophical backbone. This is it."
AI Defeats the Purpose of a Humanities Education (The Harvard Crimson)
"The Open-Source Toolkit for Building AI agents"
Is Complexity an Illusion?: "Simplicity is held by many to be the key to general intelligence. Simpler models tend to “generalise”, identifying the cause or generator of data with greater sample efficiency. The implications of the correlation between simplicity and generalisation extend far beyond computer science, addressing questions of physics and even biology. Yet simplicity is a property of form, while generalisation is of function. In interactive settings, any correlation between the two depends on interpretation. In theory there could be no correlation and yet in practice, there is. Previous theoretical work showed generalisation to be a consequence of “weak” constraints implied by function, not form. Experiments demonstrated choosing weak constraints over simple forms yielded a 110 − 500% improvement in generalisation rate. Here we show that all constraints can take equally simple forms, regardless of weakness. However if forms are spatially extended, then function is represented using a finite subset of forms. If function is represented using a finite subset of forms, then we can force a correlation between simplicity and generalisation by making weak constraints take simple forms. If function is determined by a goal directed process that favours versatility (e.g. natural selection), then efficiency demands weak constraints take simple forms. Complexity has no causal influence on generalisation, but appears to due to confounding."
Rob Conery's posts exploring using AI in a dev setting:
"You're Building AI Apps Backwards: The Model-Product Problem"

Criticism

Tools

Lovable: Create apps and websites by chatting with AI

Tutorials

LangGraph 101: Let's build a deep research agent

Reading

Himanshu:
1. Natural Language Processing with Transformers: Start with a thorough understanding of Transformers architecture and it's ecosystem through HuggingFace with Lewis Tunstall Leandro von Werra Thomas Wolf . Personally, There cannot be a better starting point. MUST follow their blogs.
2. Build a Large Language Model (From Scratch): Build your own Decoder Transformer(Architecture for "almost" all modern LLMs) from scratch with Sebastian Raschka, PhD . MUST follow his blogs too for the latest updates. Also, lucky he keeps his GitHub repo updated.
3. Get acquainted with the GenerativeAI ecosystem with Jay Alammar Maarten Grootendorst building LLM pipelines for various tasks and learning nitty gritty details of Fine-Tuning and PEFT. Hands-On Language Models
4. Explore Generative AI on AWS with Chris Fregly Antje Barth Shelbee Eigenbrode for an enterprise level challenges.
5. Dive into the beauty of engineering a GenAI Application with AI Engineering by Chip Huyen . It will level you up to think from the whole system perspective.
6. Build Generative AI Services with FastAPI with Ali Parandeh, CEng . A very practical book to knot all you have learnt and will help you in building a GenAI system.

Definitions

Expert Systems/Rules Engines

Fuzzy Logic

Java:

Natural Language Processing

You Don't Need Backpropagation To Train Neural Networks Anymore

Large Language Models (LLMs)

Small Language Models (SLMs)

Retrieval Augmented Generation (RAG)

Coding Assistants

Create a Coding Assistant with StarCoder

Generative AI

https://www.kdnuggets.com/generative-ai-a-self-study-roadmap
"Godot isn't making it": "What if what we're seeing today isn't a glimpse of the future, but the new terms of the present? What if artificial intelligence isn't actually capable of doing much more than what we're seeing today, and what if there's no clear timeline when it'll be able to do more? What if this entire hype cycle has been built, goosed by a compliant media ready and willing to take career-embellishers at their word? (March 2024) The reason I'm repeating myself is that it's important to note how obvious the problems with generative AI have been, and for how long."
Generative AI exists because of the transformer
"Stable Diffusion in Java (SD4J) Enables Generating Images with Deep Learning"
Stable Diffusion in C# (mentioned in the SD4J article)

Machine Learning

10 Github Repositories to Master Reinforcement Learning
Machine Learning for Software Engineering: A curated list of papers, theses, datasets, and tools related to the application of Machine Learning for Software Engineering.
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations

Detail Pages:

Agent Development Kit (ADK) An open-source, code-first toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
Agentic AI A collection of topics and notes on the subject.
Agent-to-Agent (A2A) protocol An open standard designed to enable seamless communication and collaboration between AI agents.
AIAC (Artificial Intelligence Infrastructure-as-Code generator) A library and command line tool to generate IaC (Infrastructure as Code) templates, configurations, utilities, queries and more via LLM providers such as OpenAI, Amazon Bedrock and Ollama.
AIScript A language designed specifically for web development in the AI era, with AI capabilities as first-class language features, and an intuitive route DSL and directive design.
Arcee Own your own small language models.
Barfi A Flow-Based Programming framework that offers a graphical programming interface.
Claude Code The AI agent.
ComfyUI A node-based Gradio GUI designed for generative AI models to generate AI images, video, and audio locally on your own hardware.
Faiss A library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.
Fuzzy logic Readings and links on fuzzy logic.
Goblin.tools A collection of small, simple, single-task tools, mostly designed to help neurodivergent people with tasks they find overwhelming or difficult.
gptlang A new programming language implemented by GPT-4.
Husky Research language aimed at next generation AI and software.
Jlama A modern Java inference engine for LLMs.
LangChain An AI library. (More description needed!)
LangStream Combines data streaming with generative AI.
Large Language Model (LLM) Collection of links, notes, and models.
LittleHorse A high-performance microservice orchestration engine that allows developers to build scalable, maintainable, and observable applications.
Llama-3 Practical Llama 3 inference in Java.
LMQL A programming language for LLMs; Robust and modular LLM prompting using types, templates, constraints and an optimizing runtime.
Loglan A language which was originally devised to test the Sapir-Whorf hypothesis that the structure of language determines the boundaries of human thought.
Manifest An Open Source, portable backend that fits into 1 YAML file. Easy for both humans and LLMs to generate and validate.
Model Control Protocol (MCP) An open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools.
Ollama Host (?) for multiple open-source LLMs.
Outerbase An AI-powered database platform.
Paradigms of Artificial Intelligence Programming Norvig's classic, on the Web.
PocketFlow A 100-line minimalist LLM framework.
Priml A new programming language aimed at facilitating systems and AI infrastructure development.
Prompt Orchestration Markup Language (POML) An open-source framework designed to bring order, modularity, and extensibility to prompt engineering for LLMs.
Reading in Artificial Intelligence A collection of language-agnostic links on AI and Machine Learning.
Retrival Augmented Generation (RAG) Concepts, notes, links, reading
Small Language Models (SLM) Collection of links and notes.
SmartMock An AI-powered API mock server built with Spring Boot, Ollama, and LangChain4j that, instead of serving hardcoded responses, uses an LLM to generate realistic, context-aware mock data directly from your OpenAPI specifications.
Structured and Unstructured Query Language (SUQL) Conversational Search over Structured and Unstructured Data with LLMs
Suno Generate music from prompts.
TabbyML Opensource, self-hosted AI coding assistant.
The Edge of Sentience Notes on (and links to) the book.

Last modified 15 September 2025