wasmCloud with K8S, at CNCF

behind this "alphabet soup" is great tech that could be "next generation" of "apps deployment" infrastructure, much faster to deploy and start than containers.

 The Kubernetes of Lambda with Bailey Hayes & Taylor Thomas (Ship It! #110) podcast

Bailey Hayes & Taylor Thomas from Cosmonic join the show for a look at WebAssembly Standard Interfaces (WASI) and trade-offs for portable interfaces.

wasmCloud | CNCF

Homepage | wasmCloud

wasmcloud @GitHub

wasmCloud is a universal application platform that helps you build and run globally distributed WebAssembly applications on any cloud or edge.

wasmCloud/wasmCloud: wasmCloud allows for simple, secure, distributed application development using WebAssembly components and capability providers. @GitHub

Rust, Apache

Cosmonic - Distributed Compute Mesh built on CNCF wasmCloud | Cosmonic

Cosmonic is the Universal Application Platform, powered by wasmCloud.

• Distribute your app in cross-cloud, edge, on-prem, on-device deployments with wasmCloud
• Write shared code once and re-use in any language that compiles to Wasm
• Zero downtime upgrades without recompiling through runtime-configuration

wasmCloud ❤️ Kubernetes - Distributed Compute Mesh built on CNCF wasmCloud | Cosmonic | Cosmonic

wasmCloud is designed to fit seamlessly into existing cloud native ecosystems, integrating with Kubernetes using an operator to take full advantage of components and WASI APIs without re-platforming.

 

WebAssembly.org

WASM = WebAssembly - Wikipedia

Wasm code (binary code, i.e. bytecode) is intended to be run on a portable virtual stack machine (VM).[94] The VM is designed to be faster to parse and execute than JavaScript and to have a compact code representation.[52] Any external functionality (like syscalls) that may be expected by Wasm binary code is not stipulated by the standard. It rather provides a way to deliver interfacing via modules by the host environment that the VM implementation runs in.[95][9]

WASI: WebAssembly - Wikipedia

WebAssembly System Interface (WASI) is a simple interface (ABI and API) designed by Mozilla intended to be portable to any platform.[88] It provides POSIX-like features like file I/O constrained by capability-based security.[89][90]

Request For Comments (RFC)

• About RFCs
• Common network RFCs

AI cloud tool: Amazon Bedrock

Build Generative AI Applications with Foundation Models - Amazon Bedrock - AWS

"Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI."

 Build Generative AI Applications with Foundation Models - Amazon Bedrock Pricing - AWS

Data protection - Amazon Bedrock

"Amazon Bedrock doesn't store or log your prompts and completions. Amazon Bedrock doesn't use your prompts and completions to train any AWS models and doesn't distribute them to third parties."

Architecture: Sandcastle-Shaped Beach House

 Inside A Stunning Sandcastle-Shaped Beach House | On The Market | Architectural Digest - YouTube

rugged shores of Malibu, California to tour Sandcastle House, the remarkable family home of architect Harry Gesner. This stunning property was born from a promise to Gesner’s wife to build her dream house on the shores of Malibu. This one-of-a-kind home is made almost entirely from reclaimed materials salvaged from surrounding areas and inspired by the structural design of a sandcastle. But what makes Sandcastle House so special is Gesner built it with his own two hands for his family, making it a true labor of love.

Harry Gesner’s Malibu Sandcastle House Lists for $27.5 Million - Mansion Global

33604 Pacific Coast Highway, Malibu, CA, 90265 | Chris Cortazzo

Is Microsoft Teams new Internet Explorer?

Decades ago, Microsoft "bundled" IE with Windows to "kill" Netscape.
It kind-off worked, so well that it required anti-monopoly measures,
and major competition for more than a decade to "unstuck" web browsers progress.

Now by "bundling" MS Teams for "free" in MS Office, Microsoft has triggered the same reaction from EU. But this time seem to have pro-actively slightly adjust the practice.

 Microsoft splits Teams from Office as antitrust pressure ramps up - The Verge

Microsoft, possibly hoping to deflect the blow of an ongoing antitrust investigation in the EU, is spinning Teams off from Office 365 to sell as its own separate app globally,

good "All-in Podcast" discussion here

Presidential Debate Reaction, Biden Hot Swap?, Tech unemployment, OpenAI considers for-profit & more - YouTube

JavaScript logs, in color: chalk.js

(AI answer)

node.js: process.stdout.write() vs. console.log() 

In node.js, process.stdout.write() and console.log() are both used to write to the standard output (stdout), but there are some differences between them:

Formatting: console.log() supports formatted output, similar to printf() in C. For example, you can use placeholders like %s for strings, %d for numbers, and %j for JSON. process.stdout.write() does not support this kind of formatting.

Newline character: console.log() automatically appends a newline character (\n) at the end of the output, while process.stdout.write() does not. If you want a newline at the end of the output with process.stdout.write(), you have to add it manually.

Synchronous vs Asynchronous: console.log() is synchronous on Windows and asynchronous on POSIX (Unix, Linux, macOS). process.stdout.write() is synchronous if the destination is a terminal or a file (to prevent lost data), and asynchronous if it's a pipe or a socket.

Performance: process.stdout.write() can be faster than console.log() because it doesn't involve as much overhead (like string formatting and adding a newline character).

In general, you can use console.log() when you need formatted output or when you're debugging, and process.stdout.write() when you need to write raw output or when performance is a concern.

console.log('Hello, %s!', 'world'); // Hello, world!

process.stdout.write('Hello, world!\n'); // Hello, world!

color logs in node.js console / terminal

To change colors with console.log(), you can use ANSI escape codes. These are sequences of characters that can be used to change the color of the text in the terminal. Here's an example:

console.log('\x1b[36m%s\x1b[0m', 'I am cyan');
 // Outputs "I am cyan" in cyan.

In this code, \x1b[36m is the ANSI escape code for cyan, and \x1b[0m resets the color back to the default color. The %s is a placeholder that gets replaced with the string 'I am cyan'.

Here are the ANSI color codes for 8 different colors:

Black:   \x1b[30m

Red:     \x1b[31m
Green:   \x1b[32m
Yellow:  \x1b[33m
Blue:    \x1b[34m
Magenta: \x1b[35m
Cyan:    \x1b[36m
White:   \x1b[37m

Remember to reset the color back to the default after you're done, or else the color change will affect all subsequent text that gets printed to the console. You can do this with \x1b[0m.

Note: These color codes might not work in all terminals or operating systems. If you're writing a Node.js application that needs to be portable across different systems, you might want to use a library like chalk that handles these differences for you.

const chalk = require('chalk');
console.log(chalk.blue('Hello, world!'));

chalk - npm 311 million weekly downloads!

chalk/chalk: 🖍 Terminal string styling done right @GitHub, MIT license

color logs in web browser console

Since Chrome 69, ANSI escape codes are natively supported in the developer console.

you can also use colors in console.log() in web browsers, but the method is different from Node.js. In web browsers, you can use CSS to style your console output. Here's an example:

console.log('%cHello, world!', 'color: blue; font-size: 20px');

In this code, %c is a placeholder for CSS styles. The second argument to console.log() is a string of CSS properties that will be applied to the text. In this case, the text will be blue and 20 pixels in size.

Note that not all CSS properties are supported, and the exact support may vary between different browsers. Commonly used properties like color and font-size are generally well-supported.

New JavaScript Set methods

 New JavaScript Set methods | MDN Blog

• intersection() returns a new set with elements in both this set and the given set.
• union() returns a new set with all elements in this set and the given set.
• difference() returns a new set with elements in this set but not in the given set.
• symmetricDifference() returns a new set with elements in either set, but not in both.
• isSubsetOf() returns a boolean indicating if all elements of a set are in a specific set.
• isSupersetOf() returns a boolean indicating if all elements of a set are in a specific set.
• isDisjointFrom() returns a boolean indicating if this set has no elements in common with a specific set.

Architecture: Akshardham Hindu temple in NJ, 2nd largest in the World

Largest Hindu temple outside Asia opens in New Jersey, built by 12,500 volunteers

Swaminarayan Akshardham (New Jersey) - Wikipedia
The BAPS Swaminarayan Akshardham is a Hindu mandir (temple) built by the BAPS Swaminarayan Sanstha in Robbinsville, New Jersey. It is the largest Hindu mandir in the United States and the second-largest Hindu mandir in the world, rising 213 ft (65 m) above ground.[1]

Bochasanwasi Akshar Purushottam Swaminarayan Sanstha - Wikipedia
is a Hindu denomination within the Swaminarayan Sampradaya

Parts of Akshardham: Akshardham Mahamandir - YouTube

It Took 12 Years and $96 Million to Build a Temple in New Jersey | The Free Press

The BAPS Shri Swaminarayan Mandir—set on a 180-acre lot in Robbinsville, New Jersey
It’s made of more than 1.9 million cubic feet of off-white stone, and its spire rises 191 feet.
It’s stunning.

BAPS Swaminarayan Akshardham Temple | Globetrotters

A $96 Million Hindu Temple Opens Amid Accusations of Forced Labor - The New York Times

xkcd: Earth Temperature Timeline

xkcd: Earth Temperature Timeline

Ice Ages | The New York State Museum

The Ice that Covered New York

Just 24,000 years ago, the spot where the New York State Museum (housed in the Cultural Eduation Center) is located was under more than 1.5 miles (2.4 km) of ice. At that time, the last ice sheets had reached their maximum size. A huge glacier covered nearly all of New York State.

How Cold Was the Ice Age? Researchers Now Know | University of Arizona News

the average global temperature of the ice age was 6 degrees Celsius (11 F) cooler than today.

VW => $5B => Rivian

 Rivian: Volkswagen takes $1 billion stake in EV maker

Volkswagen Group plans to invest up to $5 billion in electric vehicle startup Rivian,
starting with an initial investment of $1 billion.

Shares of Rivian soared more than 50% during after-hours trading

Rivian teases five new vehicles, and I have no idea what they are - The Verge

Why VW is giving US EV maker Rivian $5 Billion dollars - YouTube
by The Electric Viking

Volkswagen And Rivian Team Up To Rival Tesla - YouTube Forbes

R. J. Scaringe - Wikipedia Rivian founder and CEO

AI chat web sites / apps

The best AI chatbots of 2024: ChatGPT, Copilot and worthy alternatives | ZDNET

13 Best AI Chatbots in 2024: ChatGPT, Gemini & More Tested

Perplexity.ai

Claude,ai

ChatGPT

Microsoft Copilot in Bing = Microsoft Copilot

Gemini.google.com

Jasper | AI copilot for enterprise marketing teams

HuggingChat app : GitHub - huggingface/chat-ui: Open source codebase powering the HuggingChat app

A chat interface using open source models, eg OpenAssistant or Llama.
It is a SvelteKit app and it powers the HuggingChat app on hf.co/chat.

Eliza + OpenAI chatbot

1960s chatbot ELIZA beat OpenAI’s GPT-3.5 in a recent Turing test study | Ars Technica

Eliza, Computer Therapist

ELIZA is a computer program that emulates a Rogerian psychotherapist. Just type your questions and concerns and hit return. Eliza will answer you.

When the original ELIZA first appeared in the 60's, some people actually mistook her for human. The illusion of intelligence works best, however, if you limit your conversation to talking about yourself and your life.

miguelgrinberg/Eliza-GPT: The classic chatbot from the 1960s running on OpenAI's Chat Completions API. @ GitHub, Python, no license

Eliza, the classic chatbot from the 1960s, running on OpenAI's Chat Completions API.

Eliza-GPT: The Classic ELIZA Chatbot Running On OpenAI's Chat Completions API - miguelgrinberg.com

related examples

eliza-chatbot · GitHub Topics GitHub topics search, can select language

IUseDebianBtw/Neo-ELIZA: Neo-ELIZA is a modern implementation of Eliza as Chatgpt 
@GitHub, python, GPL3 

rafazv/i2a2-eliza: This is a repo for the Eliza challenge of i2a2 course 
@GitHub, node.js + openai, no license 
"Dr. Eliza - Chatbot"

cs124/pa7-chatbot 

In the early 1960s, the well-known early artificial intelligence (AI) program ELIZA made a big impact after showing its ability to process input and produce output expressed in natural language. ELIZA was one of the first conversational agents in AI, designed to simulate intelligent conversations, also known as chatbots. Developed by Joseph Weizenbaum at MIT, ELIZA had several modules to interact in English, German, and Welsh, and different modes of operation. The most famous was a module that simulated the interaction with a Rogerian psychotherapist

cs124 @GitHub (CS class, Canada?)

IA Strategy Course | I2A2 

"Building a future where AI improves and advances society"

brackendev/ELIZA-Smalltalk: A Smalltalk implementation of ELIZA, an early natural language processing computer program

Smalltalk (Pharo) implementation of ELIZA, an early natural language processing computer program created from 1964 to 1966 at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum.

This implementation is based on this Python implementation and elizabot.js (for the welcome messages). There are similar implementations for Go and Swift.


The History and Mystery Of Eliza With Jeff Shrager - CoRecursive Podcast

ELIZAGEN - Original ELIZA
Most people who know anything about Joseph Weizenbaum's ELIZA at the level of the program code think that it was written in Lisp and that it has been floating around since the publication of Weizenbaum's 1966 CACM paper. However, surprisingly, the ELIZA program itself was never published, and it wasn't written in Lisp, but in a now-obscure language called MAD-SLIP running on an IBM 7094 computer at MIT. Until now, the ELIZA source code has been missing, presumed by many to be lost, and because many alternate versions have been created over the years, the original source code remained undiscovered.

ELIZA - Wikipedia

ELIZA is an early natural language processing computer program developed from 1964 to 1967[1] at MIT by Joseph Weizenbaum.[2][3] Created to explore communication between humans and machines, ELIZA simulated conversation by using a pattern matching and substitution methodology that gave users an illusion of understanding on the part of the program, but had no representation that could be considered really understanding what was being said by either party

Perplexity.AI search engine alternative

What is Perplexity?

Perplexity is an alternative to traditional search engines, where you can directly pose your questions and receive concise, accurate answers backed up by a curated set of sources. It has a conversational interface, contextual awareness and personalization to learn your interests and preferences over time.

Perplexity’s mission is to make searching for information online feel like you have a knowledgeable assistant guiding you, it is a powerful productivity and knowledge tool that can help you save time and energy with mundane tasks for a multitude of use cases.
How does Perplexity accomplish this?

With the help of our advanced answer engine, it processes your questions and tasks It then uses predictive text capabilities to generate useful responses, choosing the best one from multiple sources, and summarizes the results in a concise way.

Perplexity

Perplexity.ai - Wikipedia

Perplexity AI is an AI chatbot-powered research and conversational search engine that answers queries using natural language predictive text.[2][3] Launched in 2022, Perplexity generates answers using sources from the web and cites links within the text response.[4] Perplexity works on a freemium model; the free product uses its Perplexity model based on OpenAI's GPT-3.5 model combined with the company's standalone large language model (LLM) that incorporates natural language processing (NLP) capabilities, while the paid version Perplexity Pro has access to GPT-4, Claude 3, Mistral Large, Llama 3 and an Experimental Perplexity Model.[3][4][1] As of early 2024, it has about 10 million monthly users.[5]

podcasts:

#434 – Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet | Lex Fridman Podcast

The perplexities of information retrieval with Denis Yarats of Perplexity (Practical AI #274)

AI vs SW Security (cURL)

 The I in LLM stands for intelligence | daniel.haxx.se

"Having a bug bounty means that we offer real money in rewards to hackers who report security problems. The chance of money attracts a certain amount of “luck seekers”. People who basically just grep for patterns in the source code or maybe at best run some basic security scanners, and then report their findings without any further analysis in the hope that they can get a few bucks in reward money.

...When reports are made to look better and to appear to have a point, it takes a longer time for us to research and eventually discard it. Every security report has to have a human spend time to look at it and assess what it means.

...Right now, users seem keen at using the current set of LLMs, throwing some curl code at them and then passing on the output as a security vulnerability report. What makes it a little harder to detect is of course that users copy and paste and include their own language as well. The entire thing is not exactly what the AI said, but the report is nonetheless crap."

Daniel Stenberg - daniel.haxx.se

excellent podcast with cURL author

Where DOESN’T curl run with curl BDFL Daniel Stenberg (Changelog & Friends #49)

• curl turns 26 today
• My BDFL guiding principles
• The I in LLM stands for intelligence
• tiny-curl
• Verified curl
• How to verify a curl release
• github.com/curl/trurl

AI API deployment: NVIDIA NIM

 NVIDIA NIM Offers Optimized Inference Microservices for Deploying AI Models at Scale | NVIDIA Technical Blog

NVIDIA NIM, part of NVIDIA AI Enterprise, provides a streamlined path for developing AI-powered enterprise applications and deploying AI models in production.

NIM is a set of optimized cloud-native microservices designed to shorten time-to-market and simplify deployment of generative AI models anywhere, across cloud, data center, and GPU-accelerated workstations. It expands the developer pool by abstracting away the complexities of AI model development and packaging for production ‌using industry-standard APIs.

NVIDIA NIM is designed to bridge the gap between the complex world of AI development and the operational needs of enterprise environments, enabling 10-100X more enterprise application developers to contribute to AI transformations of their companies.

htmx 2.0

 </> htmx ~ htmx 2.0.0 has been released!

ends support for Internet Explorer and tightens up some defaults, but does not change most of the core functionality or the core API of the library.

All extensions have been moved out of the core repository to their own repo and website: https://extensions.htmx.org. They are now all versioned individually and can be developed outside of the normal (slow) htmx release cadence.
...

course

HTMX - The Practical Guide | Udemy

Created by Academind by Maximilian Schwarzmüller

Course: HTMX - The Practical Guide | Udemy Business (Gale)

bigskysoftware/htmx: </> htmx - high power tools for HTML @GitHub

htmx allows you to access AJAX, CSS Transitions, WebSockets and Server Sent Events directly in HTML, using attributes, so you can build modern user interfaces with the simplicity and power of hypertext

htmx is small (~14k min.gz'd), dependency-free & extendable

Raspberry Pi Zero 2 W

 Amazon.com: Raspberry Pi Zero 2 W (with Quad-core CPU,Bluetooth 4.2,BLE,onboard Antenna,etc.) : Electronics

$21 (not a best price)

• 802.11 b/g/n wireless LAN (2.4 GHz only)c
• Bluetooth 4.2 / Bluetooth Low Energy (BLE)
• Small form factor, suitable for various DIY projects
• Expansion – Unpopulated 40-pin HAT-compatible I/O header
• Footprint-compatible with earlier members of the Raspberry Pi Zero family
• Includes 512MB LPDDR2 SDRAM

Raspberry Pi Zero – Raspberry Pi

Raspberry Pi Zero is half the size of a Model A+, with twice the utility.

A tiny Raspberry Pi that’s affordable enough for any project!

Raspberry Pi, Pi Zero, Pi Zero v1.3 Products Category on Adafruit Industries

$10

Raspberry Pi Zero W – Raspberry Pi

The Raspberry Pi Zero W extends the Pi Zero family
and comes with added wireless LAN and Bluetooth connectivity.

Raspberry Pi, Pi Zero, Pi Zero W Products Category on Adafruit Industries

$15, in stock

from
.NET Rocks! The Nomadic AI Developer with Aaron Erickson podcast

• Jon Skeet Catches the Raspberry Pi Bug (TWEET)
  John Skeet's Digimixer
• 
  

AI: pgvector + RAG

The missing pieces to your AI app (pgvector + RAG in prod) - YouTube

with Suprabase 

A step-by-step guide to going from pgvector to prod using Supabase. We'll discuss best practices across the board so that you can be confident deploying your application in the real world. Learn more about pgvector: https://supabase.com/docs/guides/data...

Workshop GitHub repo: https://github.com/supabase-community... It's easy to build an AI proof-of-concept (POC), but how do you turn that into a real production-ready application? What are the best practices when implementing: - Retrieval augmented generation (RAG) - Authorization (row level security) - Embedding generation (open source models) - pgvector indexes - Similarity calculations - REST APIs - File storage

Large language model - Wikipedia (LLM)

Retrieval Augmented Generation (RAG) and Semantic Search for GPTs | OpenAI Help Center

What Is Retrieval Augmented Generation (RAG)? | Google Cloud

RAGs operate with a few main steps to help enhance generative AI outputs: 

• Retrieval and Pre-processing: RAGs leverage powerful search algorithms to query external data, such as web pages, knowledge bases, and databases. Once retrieved, the relevant information undergoes pre-processing, including tokenization, stemming, and removal of stop words.
• Generation: The pre-processed retrieved information is then seamlessly incorporated into the pre-trained LLM. This integration enhances the LLM's context, providing it with a more comprehensive understanding of the topic. This augmented context enables the LLM to generate more precise, informative, and engaging responses. 

RAG operates by first retrieving relevant information from a database using a query generated by the LLM. This retrieved information is then integrated into the LLM's query input, enabling it to generate more accurate and contextually relevant text. RAG leverages vector databases, which store data in a way that facilitates efficient search and retrieval.

What is RAG? - Retrieval-Augmented Generation Explained - AWS

pgvector/pgvector: Open-source vector similarity search for Postgres @GitHub

Open-source vector similarity search for Postgres

Store your vectors with the rest of your data. Supports:

• exact and approximate nearest neighbor search
• single-precision, half-precision, binary, and sparse vectors
• L2 distance, inner product, cosine distance, L1 distance, Hamming distance, and Jaccard distance
• any language with a Postgres client

Plus ACID compliance, point-in-time recovery, JOINs, and all of the other great features of Postgres

docker pull pgvector/pgvector:pg16

pgvector/pgvector-node: pgvector support for Node.js and Bun (and TypeScript) @GitHub

How to enable and use pgvector - Azure Cosmos DB for PostgreSQL | Microsoft Learn

Perplexity.AI: Seach, Answering

Perplexity.ai

"Where knowledge begins"

Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet | Lex Fridman Podcast #434 - YouTube

Arvind Srinivas is CEO of Perplexity, a company that aims to revolutionize how we humans find answers to questions on the Internet

#434 – Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet | Lex Fridman Podcast

Perplexity.ai - Wikipedia

Perplexity AI is an AI chatbot-powered research and conversational search engine that answers queries using natural language predictive text.[2][3] Launched in 2022, Perplexity generates answers using sources from the web and cites links within the text response.[4] Perplexity works on a freemium model; the free product uses Anthropic's Claude 3 Haiku model combined with the company's standalone large language model (LLM) that incorporates natural language processing (NLP) capabilities, while the paid version Perplexity Pro has access to GPT-4, Claude 3, Mistral Large, Llama 3 and an Experimental Perplexity Model.[3][4][1] As of early 2024, it has about 10 million monthly users.

Perplexity - Wikipedia

In information theory, perplexity is a measure of uncertainty in the value of a sample from a discrete probability distribution. The larger the perplexity, the less likely it is that an observer can guess the value which will be drawn from the distribution.

rust-free concrete: Fiberglas Rebar | Owens Corning

Concrete: A Ticking Time Bomb. Can We Fix It? - YouTube

reinforced concrete has one dark secret, RUST, which we're taking a look at in this video, alongside multiple other problems. Is there a way to use concrete in better ways?


Owens Corning PINKBAR+ 0.5-in x 20-ft Fiberglass #4 Rebar in the Rebar department at Lowes.com

Fiberglas™ Rebar | Owens Corning

Leave the rust and weight of traditional steel behind for a lighter weight, stronger, rustproof concrete reinforcement.

Designed with DOTs (Department of Transportation), engineers and contractors in mind, Fiberglas™ Rebar by Owens Corning Infrastructure Solutions is setting a new bar in concrete reinforcement.

PINKBAR® Fiberglas™ Rebar vs. Steel

Fiberglass Rebar – also known as FRP, GFRP or Composite Rebar – is a more durable, proven and successful reinforcing alternative to steel.

STRONGER

2X the tensile strength compared to steel

LIGHTER

Up to 7x lighter in concrete flatwork applications

4x lighter compared to the same size diameter

RUSTPROOF

Fiberglass rebar will never rust, enabling more durable structures

AI On Your Local Machine: LLM Embeddings

Generate LLM Embeddings On Your Local Machine - YouTube

using

Ollama: https://ollama.ai/

ollama/ollama: Get up and running with Llama 3, Mistral, Gemma, and other large language models. @GitHub

ollama run llama3

4.7 GB download, it works with 32GB RAM

NeuralNine (NeuralNine) @GitHub

NeuralNine (NeuralNine) / Repositories

NeuralNine - Let's Develop Brains

JavaScript AI Chat: NLUX

The Powerful Conversational AI JavaScript Library | NLUX
✔️ AI Chat Component
✔️ React Support
✔️ Next.js Support
✔️ Hugging Face Adapter
✔️ LangChain LangServe Adapters
✔️ Custom Adapters
✔️ Assistant and User Personas
✔️ Markdown Streaming
✔️ Syntax Highlighter
✔️ Event Listeners
✔️ Conversation History
✔️ Context-Aware Conversations
✔️ Conversation Starters
✔️ Advanced Theming

nlkitai/nlux: The 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 Conversational AI JavaScript Library 💬 — UI for any LLM, supporting LangChain / HuggingFace / Vercel AI, and more 🧡 React, Next.js, and plain JavaScript ⭐️

AI DB: Amazon Aurora Postgres + pgvector

Amazon Aurora PostgreSQL now supports pgvector for vector storage and similarity search

Amazon Aurora PostgreSQL-Compatible Edition now supports the pgvector extension to store embeddings from machine learning (ML) models in your database and to perform efficient similarity searches. Embeddings are numerical representations (vectors) created from generative AI that capture the semantic meaning of text input into a large language model (LLM). pgvector can store and search embeddings from Amazon Bedrock, Amazon SageMaker, and more.

pgvector/pgvector: Open-source vector similarity search for Postgres @GitHub

Open-source vector similarity search for Postgres

Store your vectors with the rest of your data. Supports:exact and approximate nearest neighbor search
single-precision, half-precision, binary, and sparse vectors
L2 distance, inner product, cosine distance, L1 distance, Hamming distance, and Jaccard distance
any language with a Postgres client

Plus ACID compliance, point-in-time recovery, JOINs, and all of the other great features of Postgres

AI tool: text => SQL: Vanna.AI

Vanna.AI - Personalized AI SQL Agent

"Let Vanna.AI write your SQL for you
The fastest way to get actionable insights from your database just by asking questions"

The Vanna Python package and the various frontend integrations are all open-source.
You can run Vanna on your own infrastructure.

vanna-ai/vanna: 🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄.

Vanna.ai and Azure SQL Database - Azure SQL Devs’ Corner

Ultra-Processed Content / Food for Mind

On Ultra-Processed Content - Cal Newport

Ultra-processed foods, at their most damaging extreme, are made by breaking down core stock ingredients such as corn or soy into their basic organic building blocks, then recombining these elements into hyper-palatable combinations, rich in salt, sugar, and fat, soaked with unpronounceable chemical emulsifiers and preservatives.

...analogizing food to media content. ...passive text-based media, such as books and articles, to minimally processed whole foods. Linguistic encoding was the first information-bearing media our species developed; something we’ve been working with for over 5,000 years. ...culturally adapted to this format. As with whole foods, consuming writing tends to make us feel better, and we rarely hear concerns about reading too much.

 ...social media content draws on vast databases of user-generated information — posts, reactions, videos, quips, and memes. Recommendation algorithms then sift through this monumental collection of proto-content to find new, hard to resist combinations that will appeal to users...

Slow Productivity: The Lost Art of Accomplishment Without Burnout: Newport, Cal: 9780593544853: Amazon.com: Books

Cody: AI coding assistant from Sourcegraph

Cody | AI coding assistant

"The only AI coding assistant that knows your entire codebase"

Cody uses AI and deep understanding of your codebase to help you write and understand code faster

Sourcegraph | Code Search

Code Search makes it easy to find code, make large-scale changes, and track insights across codebases of any scale and with any number of code hosts.

Summer solstice 2024: the longest day

Summer solstice 2024 marks the longest day in the Northern Hemisphere | Space

Summer officially begins in the Northern Hemisphere today (June 20), marking the longest day of the year.

During the summer solstice, also known as the June solstice, the sun reaches its highest and northernmost point in the sky. It marks the beginning of the summer season in the Northern Hemisphere and winter in the Southern Hemisphere, with the Northern Hemisphere receiving the most daylight hours of the year, and the Southern Hemisphere receiving the least.

Solstice - Wikipedia

Nvidia: the most valuable company! (AI)

Nvidia tops Microsoft as the most valuable public company - CBS News

Nvidia on Tuesday vaulted past Microsoft to become the most valuable publicly listed company in the world, highlighting its place at the forefront of Big Tech.

Nvidia's stock price rose nearly $5, or 3.7%, to $135.77, valuing the AI chip maker at $3.33 trillion, compared with $3.31 trillion for Microsoft and $3.29 trillion for Apple, which boasted the largest market capitalization until being surpassed by Microsoft earlier this year. A year ago, Nvidia's market capitalization had just crossed the $1 trillion threshold.

nvda stock - Google Search

Mkt cap	3.34T
P/E ratio	79.35

AI:Ilya Sutskever: new AI company: Safe Superintelligence Inc. (SSI)

Ilya Sutskever, OpenAI's former chief scientist, launches new AI company | TechCrunch

Ilya Sutskever, one of OpenAI’s co-founders, has launched a new company, Safe Superintelligence Inc. (SSI), just one month after formally leaving OpenAI.

Sutskever, who was OpenAI’s longtime chief scientist, founded SSI with former Y Combinator partner Daniel Gross and ex-OpenAI engineer Daniel Levy.

Amazon OpenSearch Ingestion

 Amazon OpenSearch Ingestion - Amazon OpenSearch Service

s3 source - OpenSearch Documentation

csv codec

The csv codec parses objects in comma-separated value (CSV) format, with each row producing a Data Prepper log event. Use the following options to configure the csv codec.

Option	Required	Type	Description
delimiter	Yes	Integer	The delimiter separating columns. Default is ,.
quote_character	Yes	String	The character used as a text qualifier for CSV data. Default is ".
header	No	String list	The header containing the column names used to parse CSV data.
detect_header	No	Boolean	Whether the first line of the Amazon S3 object should be interpreted as a header. Default is true.