Models - OpenAI API
GPT models are fast, versatile, cost-efficient, and customizable.
Reasoning models use chain-of-thought reasoning to excel at complex tasks.
Structured Outputs - OpenAI API
Friday, January 31, 2025
AI: OpenAI o3-mini model released
the newest, most cost-efficient model in our reasoning series, available in both ChatGPT and the API today. Previewed in December 2024, this powerful and fast model advances the boundaries of what small models can achieve, delivering exceptional STEM capabilities—with particular strength in science, math, and coding—all while maintaining the low cost and reduced latency of OpenAI o1-mini
Models - OpenAI API
GPT models are fast, versatile, cost-efficient, and customizable.
Models - OpenAI API
GPT models are fast, versatile, cost-efficient, and customizable.
Hyperview: mobile apps from server XML metadata
App is a generic React Native client, driven by server-side metadata files.
Why XML metadata now, 20 years later?
Maybe because it can enforce schema, and more visible start/end tags, like in JSX.
Or because it looks like HTML, in fact proper HTML is XML (XHTML)
GitHub - Instawork/hyperview: Server-driven mobile apps with React Native @GitHub (MIT license)
Hyperview is a new hypermedia format and React Native client for developing server-driven mobile apps.
Thursday, January 30, 2025
AI: Tony Robbins virtual coach app
demo of AI app @ 3:11:30
Time to Rise Summit Day 1: Break Through in 2025! - YouTube
alternative: custom prompts with ChatGPT
Make Tony Robbins Your Personal AI Business Coach With ChatGPT
$495 package includes this app, listed with $149 price.
Tony Robbins AI (free) : r/TonyRobbins
Streets GL: 3D OpenStreetMap
Streets GL (streets.gl)
GitHub - StrandedKitty/streets-gl: 🗺 OpenStreetMap 3D renderer powered by WebGL2
Streets GL is a real-time 3D map renderer built for visualizing OpenStreetMap data with a heavy focus on eye-candy features.The whole project is written in Typescript. For rendering it uses a custom low-level library that wraps WebGL2 API
Wednesday, January 29, 2025
Efficient AI? DeepSeek vs NVIDIA, OpenAI
Andrej Karpathy on X: "DeepSeek (Chinese AI co) making it look easy today with an open weights release of a frontier-grade LLM trained on a joke of a budget (2048 GPUs for 2 months, $6M). For reference, this level of capability is supposed to require clusters of closer to 16K GPUs, the ones being" / X
"DeepSeek (Chinese AI co) making it look easy today with an open weights release of a frontier-grade LLM trained on a joke of a budget (2048 GPUs for 2 months, $6M).
...
Does this mean you don't need large GPU clusters for frontier LLMs? No but you have to ensure that you're not wasteful with what you have, and this looks like a nice demonstration that there's still a lot to get through with both data and algorithms."
Andrej Karpathy - Wikipedia
"DeepSeek (Chinese AI co) making it look easy today with an open weights release of a frontier-grade LLM trained on a joke of a budget (2048 GPUs for 2 months, $6M).
...
Does this mean you don't need large GPU clusters for frontier LLMs? No but you have to ensure that you're not wasteful with what you have, and this looks like a nice demonstration that there's still a lot to get through with both data and algorithms."
Andrej Karpathy - Wikipedia
Andrej Karpathy ... computer scientist who served as the director of artificial intelligence and Autopilot Vision at Tesla. He co-founded and formerly worked at OpenAI, where he specialized in deep learning and computer vision.
Python Concurrency: Threads, Processes, asyncio
good explanation and code examples
Python Concurrency: Threads, Processes, and asyncio Explained
- threads are like having many workers share one computer
- processes are like having many workers, each with their own computer
- asyncio is like having one well organized worker who knows when to switch between different tasks
David Beazley - Python Concurrency From the Ground Up: LIVE! - PyCon 2015 - YouTube
Tuesday, January 28, 2025
book: How Countries Go Broke by Ray Dalio
Ray Dalio | The All-In Interview - YouTube
How Countries Go Broke: Questions & Answers | LinkedIn
- How Countries Go Broke: Introduction & Chapter One
- How Countries Go Broke: Chapter Two & Chapter Three
- How Countries Go Broke: Chapter Four & Chapter Five
- How Countries Go Broke: Chapter Six & Chapter Seven
Ray Dalio on X: "How Countries Go Broke: Introduction & Chapter One " / X
Reverse proxy in Go
Caddy is a powerful, extensible platform to serve your sites, services, and apps, written in Go. If you're new to Caddy, the way you serve the Web is about to change.
Most people use Caddy as a web server or proxy, but at its core, Caddy is a server of servers. With the requisite modules, it can take on the role of any long-running process!Caddy is an extensible server platform that uses TLS by default.
Automatic HTTPS by default
- ZeroSSL and Let's Encrypt for public names
Monday, January 27, 2025
Marc Andreessen: podcasts, manifesto
The Techno-Optimist Manifesto | Andreessen Horowitz
Lies, Truth, Technology, Markets, "Machine", Intelligence, Energy, Abundance, Values, Meaning, Enemies, Future, People
Podcasts
Superintelligence is Upon Us | Marc Andreessen | EP 515 - YouTube at Jordan B Peterson podcast
Dr. Jordan B. Peterson sits down with entrepreneur and software pioneer, Marc Andreessen. They discuss the timeline of the woke institutional takeover, the ruinous effects it has had on Western ideology and business, the ways in which AI will shape society, and the immense responsibility we have to instill the future with an ethos and morality that serves human flourishing.
#458 – Marc Andreessen: Trump, Power, Tech, AI, Immigration & Future of America | Lex Fridman Podcast
Marc Andreessen: Trump, Power, Tech, AI, Immigration & Future of America | Lex Fridman Podcast #458 - YouTube
Elon Musk, The Changing World Order & America’s Future - Marc Andreessen - YouTube
at Chris Williamson podcast
Trump is About to Change Everything For Tech Startups - YouTube at podcast
node.js += TypeScript
Node.js Now Supports TypeScript By Default | Total TypeScript
Node 23.6 will soon be able to run TypeScript files without any extra configuration.
Practically, this means a few things:
Node 23.6 will soon be able to run TypeScript files without any extra configuration.
Practically, this means a few things:
- You can create an index.ts file containing TS syntax, like type annotations.
- You can run node index.ts with no further flags
- Node will strip out the types using a version of swc, then run the resulting code.
Sunday, January 26, 2025
AI: terminology: Amazon Bedrock
a good overview of key terms used in AI/LLMs:
Build Generative AI Applications with Foundation Models - Amazon Bedrock - AWS
Key terminology - Amazon Bedrock
Foundation model (FM) – An AI model with a large number of parameters and trained on a massive amount of diverse data. A foundation model can generate a variety of responses for a wide range of use cases. Foundation models can generate text or image, and can also convert input into embeddings. Before you can use an Amazon Bedrock foundation model, you must request access. For more information about foundation models, see Supported foundation models in Amazon Bedrock.
Base model – A foundation model that is packaged by a provider and ready to use. Amazon Bedrock offers a variety of industry-leading foundation models from leading providers. For more information, see Supported foundation models in Amazon Bedrock.
Model inference – The process of a foundation model generating an output (response) from a given input (prompt). For more information, see Submit prompts and generate responses with model inference.
Prompt – An input provided to a model to guide it to generate an appropriate response or output for the input. For example, a text prompt can consist of a single line for the model to respond to, or it can detail instructions or a task for the model to perform. The prompt can contain the context of the task, examples of outputs, or text for a model to use in its response. Prompts can be used to carry out tasks such as classification, question answering, code generation, creative writing, and more. For more information, see Prompt engineering concepts.
Token – A sequence of characters that a model can interpret or predict as a single unit of meaning. For example, with text models, a token could correspond not just to a word, but also to a part of a word with grammatical meaning (such as "-ed"), a punctuation mark (such as "?"), or a common phrase (such as "a lot").
Model parameters – Values that define a model and its behavior in interpreting input and generating responses. Model parameters are controlled and updated by providers. You can also update model parameters to create a new model through the process of model customization.
Inference parameters – Values that can be adjusted during model inference to influence a response. Inference parameters can affect how varied responses are and can also limit the length of a response or the occurrence of specified sequences. For more information and definitions of specific inference parameters, see Influence response generation with inference parameters.
Playground – A user-friendly graphical interface in the AWS Management Console in which you can experiment with running model inference to familiarize yourself with Amazon Bedrock. Use the playground to test out the effects of different models, configurations, and inference parameters on the responses generated for different prompts that you enter. For more information, see Generate responses in the console using playgrounds.
Embedding – The process of condensing information by transforming input into a vector of numerical values, known as the embeddings, in order to compare the similarity between different objects by using a shared numerical representation. For example, sentences can be compared to determine the similarity in meaning, images can be compared to determine visual similarity, or text and image can be compared to see if they're relevant to each other. You can also combine text and image inputs into an averaged embeddings vector if it's relevant to your use case. For more information, see Submit prompts and generate responses with model inference and Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases.
Orchestration – The process of coordinating between foundation models and enterprise data and applications in order to carry out a task. For more information, see Automate tasks in your application using AI agents.
Agent – An application that carry out orchestrations through cyclically interpreting inputs and producing outputs by using a foundation model. An agent can be used to carry out customer requests. For more information, see Automate tasks in your application using AI agents.
Retrieval augmented generation (RAG) – The process of querying and retrieving information from a data source in order to augment a generated response to a prompt. For more information, see Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases.
Model customization – The process of using training data to adjust the model parameter values in a base model in order to create a custom model. Examples of model customization include Fine-tuning, which uses labeled data (inputs and corresponding outputs), and Continued Pre-training, which uses unlabeled data (inputs only) to adjust model parameters. For more information about model customization techniques available in Amazon Bedrock, see Customize your model to improve its performance for your use case.
Hyperparameters – Values that can be adjusted for model customization to control the training process and, consequently, the output custom model. For more information and definitions of specific hyperparameters, see Custom model hyperparameters.
Model evaluation – The process of evaluating and comparing model outputs in order to determine the model that is best suited for a use case. For more information, see Evaluate the performance of Amazon Bedrock resources.
Provisioned Throughput – A level of throughput that you purchase for a base or custom model in order to increase the amount and/or rate of tokens processed during model inference. When you purchase Provisioned Throughput for a model, a provisioned model is created that can be used to carry out model inference. For more information, see Increase model invocation capacity with Provisioned Throughput in Amazon Bedrock.
Saturday, January 25, 2025
BOXABL $20K house "Baby Box"
NEW $19,999 BOXABL - YouTube
the $19,999 Baby Box, a 120-square-foot turnkey studio home
designed for affordability, flexibility, and versatility.
designed for affordability, flexibility, and versatility.
install in 1 hour
React Charts - MUI X
React Charts - MUI X
The @mui/x-charts is an MIT (license) library for rendering charts relying on D3.js for data manipulation and SVG for rendering.
The @mui/x-charts is an MIT (license) library for rendering charts relying on D3.js for data manipulation and SVG for rendering.
Friday, January 24, 2025
EV: Honda Prologue
Honda Prologue Electric Car sales skyrocket in America - YouTube
3rd best selling EV in USA!
based on GM platform
2024 Honda Prologue – All-Electric SUV | Honda
AI: speech-to-speech translation in 101 languages, from Meta
Translate @ai.meta.com
The system can do real-time speech-to-speech translation in 101 languages
Universal translators in science fiction, such as the Babel fish in The Hitchhiker’s Guide to the Galaxy, have long offered the dream of instantaneous translation from one spoken language to another. Now, in what may be a key step toward making this fantasy a reality, scientists at Facebook’s parent company Meta have developed an AI system that can instantly translate speech and text, including direct speech-to-speech translations, for up to 101 languages.
Thursday, January 23, 2025
CPU.fm: Changelog Podcast Universe
A new era for the Changelog Podcast Universe
However, this is where our vision for CPU.fm comes into play. Spin-offs are being planned and new podcasts will form from this change (and CPU.fm will be there to support them). Here’s what we know so far:
- JS Party gets a spin-off! dysfunctional.fm with Nick, KBall, and Amy
- Go Time gets a spin-off! fallthrough.fm with Kris, Ian, and more
- Ship It gets a spin-off! fafo.fm with Justin and Autumn (Soon)
- Practical AI moves to practicalai.fm under Daniel and Chris’ leadership (Soon)
EV: Tesla vs ...
Four EVs that take on the Tesla Model Y - Autoblog
- Price: $44,990 starting
- Range: Up to 337 miles
- Horsepower & Torque: 295 hp and 375 pound-feet
- Battery Size: 75 kWh
- Battery Efficiency: Up to 125 MPGe
2025 Kia EV6
- Price: $44,000 (estimated)
- Range: Up to 361 miles
- Horsepower & Torque: 330 hp (AWD) and 258 pound-feet
- Battery Size: 63 and 85 kWh
- Battery Efficiency: Up to 117 MPGe
2025 Ford Mustang Mach-E
2025 Chevrolet Equinox EV
2025 Hyundai Ioniq 5
EXCLUSIVE: Former GM Exec Warns Tesla and China’s EV Domination Is Unstoppable - YouTube
Wednesday, January 22, 2025
AI: Stargate Project, OpenAI, $500B
Announcing The Stargate Project | OpenAI
"The Stargate Project is a new company which intends to invest $500 billion over the next four years building new AI infrastructure for OpenAI in the United States. We will begin deploying $100 billion immediately. This infrastructure will secure American leadership in AI, create hundreds of thousands of American jobs, and generate massive economic benefit for the entire world. This project will not only support the re-industrialization of the United States but also provide a strategic capability to protect the national security of America and its allies.
The initial equity funders in Stargate are SoftBank, OpenAI, Oracle, and MGX. SoftBank and OpenAI are the lead partners for Stargate, with SoftBank having financial responsibility and OpenAI having operational responsibility. Masayoshi Son will be the chairman.
Arm, Microsoft, NVIDIA, Oracle, and OpenAI are the key initial technology partners. The buildout is currently underway, starting in Texas, and we are evaluating potential sites across the country for more campuses as we finalize definitive agreements."
President Trump makes announcement on AI infrastructure - YouTube
OpenAI Unveils “Project Stargate” - $500 BILLION AI Mega Factories! - YouTube
OpenAI teams up with SoftBank and Oracle on $500B data center project | TechCrunch
"OpenAI says that it will team up with Japanese conglomerate SoftBank and with Oracle, among others, to build multiple data centers for AI in the U.S.The joint venture, called the Stargate Project, will begin with a large data center project in Texas and eventually expand to other states. The companies expect to commit $100 billion to Stargate initially and pour up to $500 billion into the venture over the next four years.
The Stargate Project is a new company which intends to [build] new AI infrastructure for OpenAI in the United States,” OpenAI, Oracle, and SoftBank said in a joint statement. “
AI school: Alpha School
AI-only private school, fully personalized for each student
2hr Learning: How Our Schools Work - YouTube
Private School in Texas, Florida & Arizona | Alpha School
Alpha School uses AI to teach students academics for just two hours a day | FOX 7 Austinbut they are doing it in only 2 hours a day
Students have guides instead of teachers who are there to support students
through a self-driven learning model covering the core subjects.
As for the rest of the school day, it's spent developing life skills
like public speaking, leadership, teamwork, and entrepreneurship through workshops.
Is AI taking over education? Dr. Phil discusses the impact advancements in technology are having on schools across the nation. Will AI technologies improve America’s education system as we know it? Or is AI minimizing students’ ability to think and problem-solve independently?
Tuesday, January 21, 2025
AI from China: DeepSeek; Open Source
DeepSeek claims its 'reasoning' model beats OpenAI's o1 on certain benchmarks | TechCrunch
Chinese AI lab DeepSeek has released an open version of DeepSeek-R1, its so-called reasoning model, that it claims performs as well as OpenAI’s o1 on certain AI benchmarks.R1 is available from the AI dev platform Hugging Face under an MIT license, meaning it can be used commercially without restrictions. According to DeepSeek, R1 beats o1 on the benchmarks AIME, MATH-500, and SWE-bench Verified.
"To people who see the performance of DeepSeek and think:
"China is surpassing the US in AI."
You are reading this wrong.
The correct reading is:
"Open source models are surpassing proprietary ones."
DeepSeek has profited from open research and open source (e.g. PyTorch and Llama from Meta)
They came up with new ideas and built them on top of other people's work.
Because their work is published and open source, everyone can profit from it.
That is the power of open research and open source."
"China is surpassing the US in AI."
You are reading this wrong.
The correct reading is:
"Open source models are surpassing proprietary ones."
DeepSeek has profited from open research and open source (e.g. PyTorch and Llama from Meta)
They came up with new ideas and built them on top of other people's work.
Because their work is published and open source, everyone can profit from it.
That is the power of open research and open source."
run TypeScript: tsx vs ts-node
TypeScript is complex. JavaScript ecosystem is complex.
Most things can be done in may different ways, and things break and stop working all the time.
When ts-node has some issues, often tsx works. That is helpful.
But is it enough to "switch"?
Frequently Asked Questions | tsx
GitHub - privatenumber/ts-runtime-comparison: Comparison of Node.js TypeScript runtimes
- ts runtime comparison
- tsx GitHub tsx - npm (1.7M/week)
- ts-node GitHub ts-node - npm (9.2M/week)
- tsx is zero-config because it has smart detections built in. As a runtime, it detects what's imported to make many options in tsconfig.json redundant—which was designed for compiling matching files regardless of whether they're imported.
- It seamlessly adapts between CommonJS and ESM package types by detecting how modules are loaded (require() or import) to determine how to compile them. It even adds support for require()ing ESM modules from CommonJS so you don't have to worry about your dependencies as the ecosystem migrates to ESM.
- At the core, tsx is powered by esbuild for blazing fast TypeScript compilation, whereas ts-node (by default) uses the TypeScript compiler.
ts-nodeincorporates type checking,tsxdoes nottsxhandles package types automatically,ts-nodedoes not
node --import tsx adds support for both Module and CommonJS contexts. To only import one, you can use node --import tsx/esm or node --require tsx/cjs.
node -r ts-node/register only supports a CommonJS context, node --loader ts-node/esm must be used for projects that are type Module.
"On average tsx was faster, (about twice as fast on medium sized projects), than ts-node. tsx also includes a watch option which automatically reruns when the codebase is changed, which can be useful in certain circumstances.
Overall, it feels that losing type checking for a faster and more flexible runtime is a better choice ... for running tests and small dev scripts.
Monday, January 20, 2025
AI Agents: Google whitepaper
Authors: Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic
"Humans are fantastic at messy pattern recognition tasks. However, they often rely on tools - like books, Google Search, or a calculator - to supplement their prior knowledge before arriving at a conclusion. Just like humans, Generative AI models can be trained to use tools to access real-time information or suggest a real-world action. For example, a model can leverage a database retrieval tool to access specific information, like a customer's purchase history, so it can generate tailored shopping recommendations. Alternatively, based on a user's query, a model can make various API calls to send an email response to a colleague or complete a financial transaction on your behalf. To do so, the model must not only have access to a set of external tools, it needs the ability to plan and execute any task in a self- directed fashion. This combination of reasoning, logic, and access to external information that are all connected to a Generative AI model invokes the concept of an agent, or a program that extends beyond the standalone capabilities of a Generative AI model. This whitepaper dives into all these and associated aspects in more detail."
Google recently published a whitepaper on AI Agents that everyone should read.
It covers everything you need to know about this new wave.
Here's what's included:
- Introduction to AI Agents
- The role of tools in Agents
- Enhancing model performance with targeted learning
- Quick start to Agents with LangChain
- Production applications with Vertex AI Agents
Google recently published a whitepaper on AI Agents that everyone should read.
It covers everything you need to know about this new wave.
Here's what's included:
- Introduction to AI Agents
- The role of tools in Agents
- Enhancing model performance with targeted learning
- Quick start to Agents with LangChain
- Production applications with Vertex AI Agents
Intro to AI agents - YouTube by Google Cloud Tech
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