Principles by Ray Dalio

this is available free, it would be useful if politicians would at least glance...
dangerous times to not know what they are doing...

Ray Dalio Explains Debt Cycles - YouTube

How Countries Go Broke - YouTube

Principles by Ray Dalio - YouTube

Principles for Dealing with the Changing World Order by Ray Dalio - YouTube

brains/Zen-Of-Capital/Ray Dalio - How the Economic Machine Works - Leveragings and Deleveragings(Full).pdf at master · aalhour/brains · GitHub

Principles: Life And Work : Ray Dalio : Free Download, Borrow, and Streaming : Internet Archive

Principles by Ray Dalio

How Countries Go Broke: The Big Cycle (Principles): Dalio, Ray: 9781501124068: Amazon.com: Books

AI app tool: Vibes.diy & Fireproof

When vibe coding goes viral with J. Chris Anderson (Changelog Interviews #647)

good "party trick" apps, don't seem to be very useful... but who knows... 

Vibes DIY - AI App Builder

Visual Sorting Algorithms Simulator - made on Vibes DIY

prompt: "visual sorting algorithms"! 

simple as that, and in seconds generated (almost) functional React app. magic?

quick sort needs some work... good, not perfect yet

example apps  App grand-platypus-4140 - made on Vibes DIY

There U Glow – Technical Knowledge Nook


2 Vibe Coders inside you - YouTube

book about AI: Vibe Coding

 Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond , Kim, Gene, Yegge, Steve, eBook - Amazon.com (pre-order)

interesting discussion, about future of (AI supported) coding

Adventures in babysitting coding agents with Steve Yegge, co-author of Vibe Coding (Changelog & Friends #96)

Claude Code

Amp

OpenAI Codex

Revenge of the junior developer

similar

Beyond Vibe Coding: Leveraging Your Experience in the Age of AI-Assisted Coding: Osmani, Addy: 9798341634756: Amazon.com: Books (also pre-order)

Roc func-prog-lang, vs Elm, Go, Rust, Zig

interesting new functional prog. lang

The Roc programming language with Richard Feldman, creator of Roc (Changelog Interviews #645)
The Roc programming language with Richard Feldman - YouTube

chat with Richard Feldman about Roc – his fast, friendly, functional language
inspired by Richard’s love of Elm.

Roc takes many of Elm’s ideas beyond the frontend and introduces some great ideas of its own.
Get ready to learn about static dispatch, platforms vs applications, opportunistic mutation, purity inference, and a whole lot more.

using LLVM, compiles to WASM (to run on web), can be used for scripting, 
functional, no OO, internally using reference counting, no GC

The Roc Programming Language     //www.roc-lang.org/

Roc · GitHub repos

GitHub - roc-lang/roc: A fast, friendly, functional language.

written in Rust
GitHub - roc-lang/roc-compiler-fuzz: Continual fuzzing of the roc compiler in CI jobs

and Zig

Roc Zulip Chat

Software Unscripted

Elm   //elm-lang.org/

Elm crash course - Building unbreakable webapps fast - YouTube

Richard Feldman – GitHub, X

Introduction to Roc Programming Language by Richard Feldman - YouTube

Roc - A Functional Language looking for those Software Sweetspots (with Richard Feldman) - YouTube

Welcome to Elm - 1.1 Primitives - YouTube

Autonomous Tesla Car Delivery

World's First Autonomous Delivery of a Car | Tesla - YouTube

Autonomous Tesla Delivery | Long Version - YouTube

This Tesla drove itself from Gigafactory Texas to its new owner's home ~30min away — crossing parking lots, highways & the city to reach it's new owner. The first autonomous vehicle delivery of it's kind in the world

Tesla says it delivered its first car autonomously from factory to customer. It was a significant milestone, but still highlights shortcomings in the company’s tech. | The Verge

Tesla completes first fully autonomous Model Y delivery ahead of schedule | Reuters

Tesla drives autonomously from plant to buyer, Elon Musk says - Automotive News

LLM AI = "people spirits", SW 3.0

very interesting an potentially influential view on AI and future

Andrej Karpathy on X: "Nice - my AI startup school talk is now up! Chapters: 0:00 Imo fair to say that software is changing quite fundamentally again. LLMs are a new kind of computer, and you program them *in English*. Hence I think they are well deserving of a major version upgrade in terms of" / X

"14:39 LLM psychology: LLMs = "people spirits", stochastic simulations of people,
where the simulator is an autoregressive Transformer.

Since they are trained on human data, they have a kind of emergent psychology, a
nd are simultaneously superhuman in some ways, but also fallible in many others.

Given this, how do we productively work with them hand in hand?"

Andrej Karpathy: Software Is Changing (Again) - YouTube

Karpathy vs. McKinsey: The Truth About AI Agents (Software 3.0) - YouTube

1. Software 3.0 Paradigm Shift: Andrej Karpathy argues the next “language” of coding is English, forcing teams to rethink every layer of software design—from data pipelines to deployment. 

2. LLMs as “People Spirits”: Large language models are stochastic simulations with jagged intelligence; they feel human but still need tight human supervision and constrained output. 

3. Human-in-the-Loop by Design: Success hinges on making validation frictionless and deliberately limiting AI generation so reviewers can keep up. 

4. Builder Honesty vs. Boardroom Hype: Karpathy names CI/CD gaps and edge-model limits, while McKinsey’s “agentic mesh” offers a seductive yet unbuildable fairy tale. 

5. Enterprise AI Reality Check: Plug-and-play agents and tiny edge models don’t exist; incremental crawl-walk-run adoption with clear culture change is mandatory. 

6. Risk of Consultant Oversimplification: CEO faith in word-salad frameworks stalls projects and wastes budgets—tech leaders must push for empirically grounded plans. 

7. Edge Computing Debate: Despite bets from Apple and others, large centralized models still outperform small edge deployments in 2025; prudence beats hype.

McKinsey Agentic Mesh - YouTube

"enterprise consulting speak" (useless)

🚀 McKinsey’s 2025 AI Report: Are Leaders Underestimating AI Readiness? #AI #Leadership #mckinsey - YouTube

The State of AI: Global survey | McKinsey

JS Charting Libraries

GitHub - zingchart/awesome-charting: A curated list of the best charting and dataviz resources that developers may find useful, including the best JavaScript charting libraries

Top 7 JavaScript Charting Libraries

15 JavaScript Libraries for Creating Beautiful Charts — SitePoint

• D3.js – Popular, well supported but a steep learning curve.
• Plot.ly  – Great for scientific charting. Built off D3.js.
• Chart.js – Simple and elegant

• Google Charts
• Chartist.js
• Recharts
• n3-charts
• ZingChart
• Highcharts
• Fusion Charts
• Flot
• amCharts 
• CanvasJS
• ToastUI
• AnyChart

A Very Exciting New Chart Library - YouTube

shadcn/ui

AI tool: Gemini CLI: open-source AI agent

Google announces Gemini CLI: your open-source AI agent

Free and open source, Gemini CLI brings Gemini directly into developers’ terminals — with unmatched access for individuals.

GitHub - google-gemini/gemini-cli: An open-source AI agent that brings the power of Gemini directly into your terminal.

npx https://github.com/google-gemini/gemini-cli

or

npm install -g @google/gemini-cli
gemini

1. Authenticate: When prompted, sign in with your personal Google account. This will grant you up to 60 model requests per minute and 1,000 model requests per day using Gemini.

Gemini Code Assist | AI coding assistant

https://codeassist.google/

Gemini Code Assist: AI-first coding in your natural language

Gemini Code Assist - Visual Studio Marketplace

• Coding assistance powered by Gemini.
• Contextualized responses to your prompts to help guide you on what you're trying to do with your code.
• ...

MuseTrainer: MusicXML library

 GitHub - musetrainer/library: Public domain MusicXML files

MuseTrainer - Library

MuseTrainer: Fur_Elise_-_Beethoven_-_for_beginner_piano.mxl

MuseTrainer: Fur_Elise_fingered.mxl

MuseTrainer · GitHub

MusicXML Piano Trainer app

GitHub - musetrainer/source: Source code of MuseTrainer

alternative format (abc)

abc | Für Elise - trillian.mit.edu/~jc/music/abc/mirror/musicaviva.com/beethoven-ludwig-van/be059/be059-pno2/0000

trillian.mit.edu/~jc/music/abc/mirror/musicaviva.com/beethoven-ludwig-van/be059/be059-pno2.abc

Goolge AlphaGenome: AI for genetics

 AlphaGenome: AI for better understanding the genome - Google DeepMind

Introducing a new, unifying DNA sequence model that advances regulatory variant-effect prediction and promises to shed new light on genome function — now available via API.

AlphaGenome, a new artificial intelligence (AI) tool that more comprehensively and accurately predicts how single variants or mutations in human DNA sequences impact a wide range of biological processes regulating genes. This was enabled, among other factors, by technical advances allowing the model to process long DNA sequences and output high-resolution predictions.

To advance scientific research, AlphaGenome is available in preview via AlphaGenome API for non-commercial research, and planning to release the model in the future.

Google’s new AI will help researchers understand how our genes work | MIT Technology Review
First came AlphaFold. Now comes AlphaGenome for DNA.

Mojo in bioinformatics: ish tool

Community Meeting #17: Mojo in bioinformatics and accelerating particle physics - YouTube

Bioinformatics with Mojo: Seth walked us through ish, a high-performance, index-free alignment tool built in Mojo. He shared insights on SIMD optimizations, GPU acceleration, and benchmarking against C++ libraries like Parasail.

Ish: SIMD and GPU Accelerated Local and Semi-Global Alignment as a CLI Filtering Tool

Ish: a grep-like CLI tool for filtering using alignment - Community Showcase - Modular

A look at the Mojo language for bioinformatics

EV: Rivian Amazon Delivery Van

The Rivian Electric Amazon Delivery Van Is Highly Innovative and Incredibly Cool - YouTube
by Doug DeMuro

no copyright rules for AI?

Anthropic wins key US ruling on AI training in authors' copyright lawsuit | Reuters

"June 24 (Reuters) - A federal judge in San Francisco ruled late on Monday that Anthropic's use of books without permission to train its artificial intelligence system was legal under U.S. copyright law.
Siding with tech companies on a pivotal question for the AI industry, U.S. District Judge William Alsup said Anthropic made "fair use", opens new tab of books by writers Andrea Bartz, Charles Graeber and Kirk Wallace Johnson to train its Claude large language model.

Alsup also said, however, that Anthropic's copying and storage of more than 7 million pirated books in a "central library" infringed the authors' copyrights and was not fair use. The judge has ordered a trial in December to determine how much Anthropic owes for the infringement."

Mojo: meta-prog lang for AI

Of all programming languages available now, Mojo has best chance to make the biggest positive difference. With simple and relatively clean syntax of Python, while with types, it also has performances often better than C++, and even memory safety of Rust.

But the biggest surprise in Mojo meta-programing: the same language syntax can be used for "compile time" as well as for "run time". Meaning, for what is usually macros, templates or generics, Mojo is using the same syntax as regular language! The only other modern language with similar feature is Zig language. Or Lisp.

Mojo (programming language) - Wikipedia

excellent interview with creator and lead of Mojo language and related tools.

Mojo and Building a CUDA Replacement with Chris Lattner - Software Engineering Daily

Mojo is a new programming language designed to combine the simplicity of Python with the performance of C and the safety of Rust. It also aims to provide a vendor-independent approach to GPU programming. Mojo is being developed by Chris Lattner, a renowned systems engineer known for his seminal contributions to computer science, including LLVM, the Clang compiler, and the Swift programming language.

transcript

The cool thing about Mojo is that you can just write code, and you can use normal code, you can use a list or an array, or a string, or like whatever data type that you want at comp time, at the compiler runtime. So now, Mojo has a very clean division between the code that runs runtime, the code that you can use at compile time, and when you're writing these algorithms, it's the same code, and that becomes very powerful.

...there's no trade-off. There's no downside. It strictly makes the language more simple, more consistent, more powerful. There's no trade-off. It makes the compiler more different than previous generation languages, right? But if you look at, again, I'm fond of Mojo. Of course, I'll say nice things about other people's systems. If you go look at Zig, Zig's a relatively very simple language, but has very powerful metaprogramming and generics capabilities. So, they made other decisions in other parts of the language, so they decided not to have a bar checker because that was part of what they were going for, but powerful metaprogramming doesn't have to come with complexity.

Modular: Exploring Metaprogramming in Mojo

Mojo provide utilities for directing the compiler to generate and modify code at compile time. If you are already familiar with Rust generics or C++ templates, the Mojo parameter system will likely feel familiar.

2023 LLVM Dev Mtg - Mojo 🔥: A system programming language for heterogenous computing - YouTube

Course: The Complete Python Course 2025 | Udemy Business (Mojo section)

by Luka Anicin and Chris Haroun

Developer Console (Mojo online playground)

A common use case for metaprogramming is creating generic functions and types; Python can do this implicitly via duck typing. The Python interpreter is generally unconcerned with the type of a variable until it comes time to perform some operation on it, so as long as the type you provide to a function satisfies those definitions, everything will move along just fine.

def doubler(a):

    return  a  +  a

b = doubler(2)

c = doubler(1.245)

‍Without using generics, statically typed languages like Mojo need to rely on creating multiple definitions of that function in order to handle different input types:

fn doubler(i: Int) -> Int:
    return i + i
fn doubler(f: Float64) -> Float64:
    return f + f

All parameters must be types or expressions that are known at compile time. Mojo uses traits to inform the compiler about the kinds of types that a type parameter can bind to. Using traits, we can create an Addable trait"This function operates on all types that have the Addable trait and as a result, implement the __add__ method." Compiler then does the work of generating a different function for each data type required.

trait Addable:
    fn __add__(self, other: Self) -> Self:
        ... # this is actual syntax
        
@no_inline
fn doublerx[T: Addable](x: T) -> T:
    return x + x

fn main():
  var a = doubler(2)
  var b = doubler(20.0)
  print(a,b)
  
  var ax = doublerx(2)
  var bx = doublerx(20.0)
  print(ax,bx)

  

EV: Hyundai Ioniq 9

Hyundai Ioniq 9 - A Killer 3-Row with NACS! - YouTube
by Auto Focus

AI: RAG vs CAG

 RAG vs. CAG: Solving Knowledge Gaps in AI Models - YouTube

RAG (Retrieval-Augmented Generation) and CAG (Cache-Augmented Generation) are both methods for augmenting Large Language Models (LLMs) with external knowledge. RAG dynamically retrieves relevant data for each query, while CAG preloads data into a cache for faster access. RAG is better for large, dynamic datasets and situations requiring real-time information, while CAG is suitable for smaller, more stable datasets where speed and simplicity are prioritized.

Cache-Augmented Generation (CAG) vs. Retrieval-Augmented Generation (RAG) | by Hamza Ennaffati | Medium

In contrast to on-demand retrieval, Cache-Augmented Generation (CAG) loads all relevant context into a large model’s extended context window and caches its runtime parameters. During inference, the model references this cache — no additional retrieval required.

• Pick RAG if your knowledge environment is massive, fast-moving, and you frequently need the latest information.
• Pick CAG if your domain is well-defined, stable, and you prioritize speed and simplicity (no retrieval step!).

Don’t Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks

tool: acorn.js: tiny, fast JavaScript/TypeScript parser

acornjs/acorn: A small, fast, JavaScript-based JavaScript parser @GitHub

MIT license

acorn - npm

How to Create Abstract Syntax Tree with Acorn | HackerNoon

Using Acorn to implement a refactoring in Javascript (Part 1: hacking it)

Build a JS Interpreter in JavaScript Using Acorn as a Parser | by Chidume Nnamdi 🔥💻🎵🎮 | Bits and Pieces

acornjs/acorn-jsx: Alternative, faster React.js JSX parser

TyrealHu/acorn-typescript: Alternative, TypeScript parser

Modding Web apps with ASTs. Fixing legacy code or moving to a newer… | by Divya Mamgai | JavaScript in Plain English

Read JavaScript Source Code, Using an AST | DigitalOcean

person: Isaac Asimov: 500 books

The Scientists Ep. 3: Isaac Asimov -- Atomic Habits - YouTube
by Dr Brian Keating

Isaac Asimov - Wikipediawas an American writer and professor of biochemistry at Boston University. During his lifetime, Asimov was considered one of the "Big Three" science fiction writers, along with Robert A. Heinlein and Arthur C. Clarke.[2] A prolific writer, he wrote or edited more than 500 books. He also wrote an estimated 90,000 letters and postcards

Apparently he was an excellent writer, 

and a book about chemistry is strongly recommended
as an example of how to explain complex things in a simple and understandable way

Asimov On Chemistry : Isaac Asimov : Free Download, Borrow, and Streaming : Internet Archive

here he predicted exactly what happened just a few days ago, targeted gene engineering to save life
Isaac Asimov's Vision Of The Future | Letterman - YouTube

Bond crisis looming? GOP abandons DOGE, Google disrupts Search with AI, OpenAI buys Jony Ive's IO - YouTube Science Corner: CRISPR breakthrough!

Isaac Asimov's Vision Of The Future | Letterman - YouTube

Best Asimov Books (86 books)

​In 1983, Isaac Asimov predicted the world of 2019. Here's what he got right (and wrong). - Big Think

• “Computerization will undoubtedly continue onward inevitably.”
• The “mobile computerized object” will “penetrate the home,” and the increasing complexity of society will make it impossible to live without this technology.
• Computers will disrupt work habits and replace old jobs with ones that are radically different.
• Robotics will kill “routine clerical and assembly-line jobs.”

Isaac Asimov talks about superstition, religion and why he teaches rationality - YouTube

Windows & Linux

Linux is already available on Windows with WSL2 

Microsoft Azure is already a very significant user or Linux, even with own distribution.

Now with creators of Linux and Windows talking, there may be even more integration coming!

Long-time rivals Bill Gates and Linus Torvalds meet for the first time, have dinner — 'No major kernel decisions were made, but maybe next dinner' | Tom's Hardware

Bill Gates stands next to Linus Torvalds, alongside Microsoft Fellows
Mark Russinovich (left) and David Cutler (right). (Image credit: Mark Russinovich on LinkedIn)

AI Startup School @ Y Combinator

Sam Altman: The Future of OpenAI, ChatGPT's Origins, and Building AI Hardware - YouTube

Elon Musk: Digital Superintelligence, Multiplanetary Life, How to Be Useful - YouTube

Andrej Karpathy: Software Is Changing (Again) - YouTube

Satya Nadella: Microsoft's AI Bets, Hyperscaling, Quantum Computing Breakthroughs - YouTube

Y Combinator - YouTube

AI Startup School | Y Combinator

Y Combinator (@ycombinator) / X

Y Combinator - Wikipedia

AI tool: Gradio

Gradio

 Quickstart

Postgres DB: VS Code extension (POSETTE: An Event for Postgres 2025 )

Introducing Microsoft's VS Code Extension for PostgreSQL | POSETTE: An Event for Postgres 2025 - YouTube

Announcing a new IDE for PostgreSQL in VS Code from Microsoft | Microsoft Community Hub

POSETTE: An Event for Postgres 2025 - POSETTE

Schedule | POSETTE: An Event for Postgres 2025 - POSETTE

POSETTE: An Event for Postgres 2025 - YouTube

PostgreSQL - Visual Studio Marketplace

AI tool: FastMCP: OpenAPI => MCP

Welcome to FastMCP 2.0! - FastMCP
The fast, Pythonic way to build MCP servers and clients.

The Model Context Protocol (MCP) is a new, standardized way to provide context and tools to your LLMs, and FastMCP makes building MCP servers and clients simple and intuitive. Create tools, expose resources, define prompts, and more with clean, Pythonic code:

from fastmcp import FastMCPmcp = FastMCP("Demo 🚀") @mcp.tool()def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b if __name__ == "__main__":
mcp.run()