2026-04-26 04:00:57
Humans with irrational brains writing about machines with rational brains. This tag is sponsored by Bright Data. Write a story on data collection at scale for AI and win from $2,500!
What’s the one algorithm that’s used in almost every Machine Learning model? It’s Gradient Descent. There are a few variations of the algorithm but this, essentially, is how any ML model learns. Without this, ML wouldn’t be where it is right now.
We explore the link between three popular loss functions: crossentropy, logloss and perplexity
As a beginner, jumping into a new machine learning project can be overwhelming. The whole process starts with picking a data set, and second of all, study the data set in order to find out which machine learning algorithm class or type will fit best on the set of data.
Prompting is pretty much the only skill you now require to be a master of these new large and powerful generative models such as ChatGPT.
ChatGPT has taken over Twitter and pretty much the whole internet, thanks to its power and the meme potential it provides.
There can be one or many solutions to a given problem, depending on the scenario, As there can be many ways to solve that problem. Think about how do you approach a problem. Lets say you need to do something straight forward like a math multiplication. Clearly there is one correct solution, but many algorithms to multiply, depending on the size of the input. Now, take a more complicated problem, like playing a game(imagine your favorite game, chess, poker, call of duty, DOTA, anything..). In most of these games, at a given point in time, you have multiple moves that you can make, and you choose the one that gives you best possible outcome. In this scenario, there is no one correct solution, but there is a best possible solution, depending on what you want to achieve. Also, there are multiple ways to approach the problem, based on what strategy you choose to have for your game play.
This isn’t just editing, but actually the creation of completely new images, allowing you to change object positions, subject poses, and more.
I set out to find out Alpaca/LLama 7B language model, running on my Macbook Pro, can achieve similar performance as chatGPT 3.5
The telecommunications industry is experiencing an extraordinary shift: infrastructure that operates with independent judgment.
In order to understand how a certain metric varies over time and to predict future values, we will look at the 10 Best Datasets for Time Series Analysis.
While it is evident that machine learning algorithms are able to solve more challenging requirements, they are not yet perfect.
Explore how AI transforms B2B marketing through enhanced content creation and analytics, while learning to sidestep common pitfalls for maximum benefit.
As soon as we have a new technology, we use it to make p*rn. Any rudimentary search on the printing press, radio, TV, and the internet proves this. In fact, the internet’s early success was likely due to the technology’s ability to propagate erotic images and videos.
Explore an in-depth comparison of AI-generated vs human-written text, highlighting the role of perplexity and burstiness in language models.
Because of the hefty amount of data that are there without any practical use.
If you follow AI you might have heard about the advent of the potentially revolutionary Capsule Networks. I will show you how you can start using them today.
Image annotation is one of the most important tasks in computer vision. With numerous applications, computer vision essentially strives to give a machine eyes – the ability to see and interpret the world. At times, machine learning projects seem to unlock futuristic technology we never thought possible. AI-powered applications like augmented reality, automatic speech recognition, and neural machine translation have the potential to change lives and businesses around the world. Likewise, the technologies that computer vision can give us (autonomous vehicles, facial recognition, unmanned drones) are extraordinary.
There are multiple approaches that you might take to create Artificial Intelligence, based on what we hope to achieve with it and how will we measure its success. It ranges from extremely rare and complex systems, like self driving cars and robotics, to something that is a part of our daily lives, like face recognition, machine translation and email classification.
In this article, we will compare four of the most advanced AI bots: GPT-4, Bing, Claude+, Bard, and GitHub Co-Pilot, by asking them to solve coding challenges.
Generative AI has made groundbreaking strides in the past few months, and Generative AI models have risen in general popularity.
In this story i would go through how to begin a working on deep learning without the need to have a powerful computer with the best gpu , and without the need of having to rent a virtual machine , I would go through how to have a free processing on a GPU , and connect it to a free storage , how to directly add files to your online storage without the need to download then upload , and how to unzip file for free online .
The cryptocurrency market faces uncertainties, but attention is shifting to the potential of Web3, Blockchain, and Tokenization.
2030 will be weird
Autonomous mobile robots for assembly factories as a databus for computers. Creating advanced eco-system using modular architecture and AI-driven factory OS
ChatGPT-5 and AGI is on the way and it is bound to change the world as we know it.
The article showcases the top 10 AI tools that can transform the way you work and live by automating tasks and improving productivity.
I believe that AI’s impact and future pathways are overstated because human nature is ignored in such statements.
Computer vision enables computers to understand the content of images and videos. The goal in computer vision is to automate tasks that the human visual system can do.
I have always been a pretty private person, but lately, the weight of it feels as unbearable as ever.
Let’s speak about usage of edge AI devices for office entrance security system development with the help of face and voice recognition.
This guide will cover the basics of working with AI text prompts and how you can effectively utilize them to render the images you envisioned.
Learn to fine-tune PaddleOCR for custom text recognition: from environment setup and data prep to training and deploying your tailored OCR model
Facial recognition-based authentication to verify a user in a web application is discussed in a beginner-friendly manner using FaceIO APIs.
ChatGPT made AI mainstream, but real transformation comes from ecosystems that embed AI across business, not from relying on a single model.
Will AI replace humans and create an inevitable worldwide unemployment crisis?
A reflection on why true automation starts with human thinking, not technology. Systems only work as clearly as the minds that design them.
Learn how to run Mixtral locally and have your own AI-powered terminal, remove its censorship, and train it with the data you want.
This article discusses alternative ML team organizational models and recommendations for matching team structures to the company's stage of development.
Educators are beginning to incorporate AI into the classroom at nearly every grade level making the learning experience more personalized and efficient.
While non-AI tools can also be useful, these specific tools have significantly improved my efficiency and performance.
As if taking a picture wasn’t a challenging enough technological prowess, we are now doing the opposite: modeling the world from pictures. I’ve covered amazing AI-based models that could take images and turn them into high-quality scenes. A challenging task that consists of taking a few images in the 2-dimensional picture world to create how the object or person would look in the real world.
In this paper, readers will find an overview roadmap to generating a strong ML system that starts from data management to streamline operations efficiently.
ChatGPT is a large language model developed by OpneAI. Here are some ways you can use ChatGPT for Python programming.
Chatbots are extremely helpful for business organizations and also the customers. The majority of people prefer to talk directly from a chatbox instead of calling service centers. Facebook released data that proved the value of bots. More than 2 billion messages are sent between people and companies monthly. The HubSpot research tells that 71% of the people want to get customer support from messaging apps. It is a quick way to get their problems solved so chatbots have a bright future in organizations.
Hello, Machine Learning community!
What to know about SingularityNET (AGI)?
Generative artificial intelligence (AI) is moving at warp speed. Keep up with the latest trends and developments by subscribing to several AI newsletters!
Dalle mini is amazing — and YOU can use it!
AI in education transforms learning experiences for all stakeholders and reshapes conventional educational paradigms.
An overview of 174 AI Tools, a very long overview. Most are free or have a free trial period, some you have to pay for immediately. I’ve broken it down into 8 c
An effective chatbot requires a massive amount of training data in order to quickly solve user inquiries without human intervention. However, the primary bottleneck in chatbot development is obtaining realistic, task-oriented dialog data to train these machine learning-based systems.
Secure the future of Your Business with GenAI. Consider 3 factors needed for successful deployment of GenAI in your business.
Unreadable text can spoil an image, and that has paved the way for the image enhancer function. Read this post to learn what this function can do.
Scale AI needs your help training AI models.
Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response. Text classification is also helpful for language detection, organizing customer feedback, and fraud detection. Though time consuming when done manually, this process can be automated with machine learning models. The result saves companies time while also providing valuable data insights.
While the release of GPT-3 marks a significant milestone in the development of AI, the path forward is still obscure. There are still certain limitations to the technology today. Here are six of the major limitations facing data scientists today.
Perplexity’s Comet browser changed how I work—AI-powered, distraction-free, and built for productivity, not ad clicks.
Data is very important in building computer vision models and these are the 10 Biggest Datasets for Computer Vision.
Lean AI can revolutionize venture capital investment by identifying successful startups and helping founders avoid challenges with economic uncertainty.
Everybody remembers their first time.
AI boosts individual performance but weakens organisational thinking. Why smarter workers and faster tools can leave companies less intelligent than before.
Learn how to effectively communicate with machines with this 101 post series on Prompt Engineering.
This model takes a picture, understands which particles are supposed to be moving, and realistically animates them in an infinite loop!
Explore how AI hallucinations, often seen as flaws, can fuel human creativity. Discover DALL-E 3s power to imagine the surreal and inspire artistic breakthrough
Once upon a time, in a galaxy far, far away…just kidding, it was in Silicon Valley, OpenAI was founded as a nonprofit research lab with a mission to save the
For those interested in AI, make sure these twelve AI Twitter accounts are on your daily feed.
Using ChatGPT to help write a bash script to download YouTube videos
AI art, neural networks & creative disruption—explore how a simple apple exposes the edge between human intuition and machine intelligence.
Learn to build effective feedback loops for AI products, connecting real user signals to model metrics for continuous improvement and better performance.
Manipulating Structured Data (from PDFs) with the Model behind ChatGPT, LangChain, and Python for Powerful AI-driven Applications.
Four engines, one prompt, slightly suspiciously honest commentary.
Exploring Emotion, Identity, and Creativity Through a Human-AI Art Experiment with DALL·E 3
The term ‘MLOps’ is appearing more and more. Many from a traditional DevOps background might wonder why this isn’t just called ‘DevOps’. In this article we’ll explain why MLOps is so different from mainstream DevOps and see why it poses new challenges for the industry.
Founder of Makerpad, Ben Tossell, goes over potential business ideas that could come true with ChatGPT.
List of top trending AI tools
Is the SaaS industry on the brink of collapse? Explore how AI is reshaping software creation and pricing.
Here’s DreamFusion, a new Google Research model that can understand a sentence enough to generate a 3D model of it.
Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information.
Introducing gptrim, a free web app that will reduce the size of your prompts by 40%-60% while preserving most of the original information for GPT to process.
Omnity AI: Revolutionize token distribution on social media with AI-driven multichain Grants, launching on X. Expand Web3 communities securely and efficiently.
OrcaMind.ai is an AI-powered thinking, on-chain intelligent whale — AI-powered and built to navigate the deep waters of Web3.
This article deals with a technique called "SetFit" that requires minimum data to train a ML model that outperforms the GPT-3 model performance significantly.
I wasn’t around when the internet was discovered for the first time but I could only imagine this must be what it’s like to do so.
Learn how ChatGPT can work alongside your marketing team to harness the power of AI to achieve unprecedented growth and success.
$300 computer to run generative AI models locally, e.g. large language model inference and stable diffusion image generation.
Let's take a look at a few construction startups that simplify the work of construction companies through artificial intelligence, robotics, and automation.
Undetectable AI bypasses AI content detectors by turning ChatGPT generated text into 100% human written quality text
If you're like most security practitioners, you're always on the lookout for new tools and techniques to help you gather intelligence. ChatGPT is one of those n
For those looking to build predictive models, this article will introduce 10 stock market and cryptocurrency datasets for machine learning.
On February 13, Lumoz announced the official launch of Lumoz Chain and released the migration guide and reward plan for Verifier nodes.
AI's impact on Agile Project Management and Scrum Mastery will go from “interesting” to “total game-changer” faster than you think.
You’re not a suspect for writing too well — you’re just a human who still knows how to use language.
In this article, I will share my experience using GitHub Copilot for SQL and explore how it impacted my coding efficiency.
For digital nomads, college students, stay-at-home parents or anyone looking for remote work positions, this article introduces online/remote work positions that are available today in the fields of AI Data Collection and Data Annotation.
In an era where data privacy is paramount, setting up your own local language model (LLM) provides a crucial solution for companies and individuals alike.
Santiago explains why Instagram is dead.
NVIDIA's new AI model, Perfusion, advances text-to-image generation with enhanced control and fidelity for concept-based visuals.
Alex documents how people are using GPT-4.
Beginners face a choice: acquire foundational knowledge or await further simplification.
Everyone is GPU-poor these days So my mission is to fine-tune a LLaMA-2 model with only one GPU and run on my laptop
OpenAI cofounder and chief scientist Ilya Stuskever talks about ChatGPT and the promise of models like GPT-4
AI is being used to help analysts with routine tasks. But it can also be a real contender on the analytics team.
Learn how to effectively communicate with machines with this 101 post series on Prompt Engineering
Linkedin boasts 930 million users. Stand out and attract more opportunities with a ChatGPT-optimized LinkedIn profile.
Tom Goldstein goes over how many GPUs it will take to run ChatGPT.
The development in the field of technology has enhanced over the years. With time, we get terms like Artificial Intelligence, machine learning, and deep learning in technology. We often confuse in these terms and define them similarly. But it is not a precise definition as these terms are different from each other. If you do not want to make this mistake again, then you must read out this article. Here we are going to discuss the difference in these three terms AI, ML, and Deep learning.
Curated list of top resources to learn about AI in finance.
Is AI capable of writing web scrapers or at least help write some? Is it capable of finding the right selectors by itself? We find out..
The road to building or fine-tuning an LLM for your company can be a complex one. Your team needs a guide to start.
In this blog post, we will focus on one of our AI subsets, machine learning, and estimate how much it costs to train, deploy, and maintain algorithms.
Leaders should adapt to AI's evolving role. Combining human intuition and AI can enhance decision-making. AI is a tool, not a replacement for humans.
Discover how Claude Desktop with MCP tools eliminated digital fragmentation, enabling deeper product thinking and transforming weeks of work into hours.
I often hear people talking about neural networks as something as a black-box that you don’t understand what it does or what they mean. Actually many people can’t understand what they mean by that. If you understand how back-propagation works, then how is it a black-box?
While Microsoft and Google spend billions, startups craft new, nimble, environmentally-friendly AI models.
Elon Musk has called for an immediate pause of at least 6 months in the training of AI systems more powerful than GPT-4.
AutoGPT is the latest AI agent generated by GPT-4, offering efficient capabilities for custom marketing, lead generation, and prompt creation.
Discover the best NotebookLM alternatives in 2025. Compare 6 AI-powered research and note-taking tools to boost productivity, learning, and workflow.
In NLP, Document-Term Matrix (DTM) is a matrix representation of the text corpus. The TF-IDF score is widely used to populate the DTM.
Learn about my experience with mentees from around the world and the mutual benefits of mentoring in fields like AI, Crypto, FinTech, and EdTech.
Most people would think I was crazy for starting 2020 as a college dropout (sorry mom!), but I wish I made this decision sooner.
However, as time has proven over and over again, humankind is unable to duplicate the same kind of AI that we see in the movies..yet.
Soon AI bots will create 99.9% of all content on the internet. This creates serious problems for free thinking and creativity. Can we solve these problems?
OpenxAI, the world’s first permissionless, peer-to-peer AI network, today announced its launch on Base
All about the Dojo Supercomputer, what it is, why it was created, how it works and what it will be used for
Using ChatGPT to create a custom portfolio website in record time! I discuss ChatGPT's strengths, weaknesses, and tip and tricks to use while coding.
Learn why ready-made AI tools are not well-suited for engineering drawings processing and how to actually use AI to extract data from technical drawings.
As an IT sourcing analyst with ValueCoders a leading offshore IT outsourcing firm, I have helped several startups, SMEs, and enterprises build their mobile apps.
Let’s face it — forgetting things sucks. It’s frustrating not to remember where you left your keys or to stumble over your words because you can’t recall the name of that colleague you just ran into at the grocery store. However, forgetfulness is core to the human condition, and in fact, we’re lucky that we’re able to do so.
Learn to add face recognition authentication to your web application
What do all recent super powerful image models like DALLE, Imagen, or Midjourney have in common? Other than their high computing costs, huge training time, and shared hype, they are all based on the same mechanism: diffusion.
Introduction
Learn how to leverage embeddings to have an OpenAI chatbot, built with React and Node.js, respond correctly to specific contextual prompts.
Future-proof your e-commerce store for ChatGPT’s new shopping features, by prioritizing tools like Reviews.Shop (for off-page) and Schema.org (for on-page).
As the world is moving towards a digital age, traditional retailers face existential threats from the likes of Amazon and Walmart. To level the playing field, offline retailers are also turning to artificial intelligence as shoppers take a multichannel approach to making purchases. From helping customers find the items through visual search to personalizing their entertainment experience through chatbots, AI is revolutionizing the retail industry in a number of ways. Major retailers like Starbucks and Lowe’s use AI to improve customer experience.
Performant machine learning models require high-quality data. And training your machine learning model is not a single, finite stage in your process. Even after you deploy it in a production environment, it’s likely you will need a steady stream of new training data to ensure your model’s predictive accuracy over time.
Machine Learning is an application of Artificial Intelligence. It allows software applications to become accurate in predicting outcomes. Machine Learning focuses on the development of computer programs, and the primary aim is to allow computers to learn automatically without human intervention.
An important part of the robot is its eyes and perception of the outside world. For this purpose, the Depth Camera is well suited.
An exhaustive guide to AI visibility tools written by someone who has tried all of them.
Looking for opportunities to apply marketing AI to your B2B or SME? In this article, we cover 7 marketing categories and 20+ tools to do the heavy lifting 🦾📈
Google released a list of free Intro to Generative AI courses. This article provides a review of the learning path, including cheat sheets and summaries.
Is the rise of vibe coding also the end of software engineering? How will vibeware change the nature of the software entrepreneur, and the meaning of work?
Machine learning, what a buzzword. I’m sure you all want to understand machine learning, and that’s what I’m going to teach in this article.
Karate Club is an unsupervised machine learning extension library for the NetworkX Python package. See the documentation here.
That’s the beauty of AI prompts: they can understand context and generate human responses that are sometimes too good to be true.
7 most innovative AI (and especially ChatGPT) powered low-code code tools.
Vibe coding lets AI generate code—but skips the skills that make developers indispensable. Learn why shortcuts can ruin careers in 2025 tech.
Do you want to make your loved one's life ultramodern? So, here are AI gift ideas some sort of high-tech equivalent in everyday life.
AI GTM strategy is shifting from SEO to AEO. Learn how creator-led trust and AI visibility drive growth in the era of answer engines.
Whether you’re a beginner looking for introductory articles or an intermediate looking for datasets or papers about new AI models, this list of machine learning resources has something for everyone interested in or working in data science. In this article, we will introduce guides, papers, tools and datasets for both computer vision and natural language processing.
The boundary between machine and humans was clear. But now the machine has become creative! Can self expression still be at the core of our humanity?
Learn how this algorithm can understand images and automatically remove the undesired object or person and save your future Instagram post!
For the first KDnuggets post on Hacker Noon, we bring you a lighter fare of very nerdy computer humor from the series of self-referential jokes started on Twitter earlier this week. Here are some of our favorites.
If you do understand all of the jokes, then you congratulate yourself on having excellent knowledge of Data Science and Machine Learning! If you have actually laughed at 2 or more jokes, then you have earned MS in Computer Humor! If you just smirked, you probably have a Ph.D. And I have a great joke about AGI, but it will be ready in 10 years.
Enjoy, and if you have more, add them in comments below!
Yann LeCun, @ylecun
Check out part one of this series for what the next five to fifteen years looks like in AI. In part two we get super sci-fi and see if our crystal ball can reach 50 years into the future.
AI-powered learning for software developers is here to stay. Here's how we're building the learning platform of the future at Educative.
This tutorial is the sixth one from a series of tutorials that would help you build an abstractive text summarizer using tensorflow , today we would build an abstractive text summarizer in tensorflow in an optimized way .
Comparison of Mask R-CNN and U-Net — instance and semantic segmentation algorithms and logic behind building a two-model car damage detection ML solution.
With ChatGPT's help, you can now make the most out of your SQL queries.
With Microsoft SQL Server 2022, object storage becomes a first class citizen through a technique called external tables.
Build production-ready LLM agents. Learn 15 principles for stability, control, and real-world reliability beyond fragile scripts and hacks.
Dopple.ai is a free AI chatbot that lets you interact with virtual characters based on real and fictional people.
In Reinforcement Learning (RL), agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves and punished for the wrong ones. In doing so, the agent tries to minimize wrong moves and maximize the right ones.
Much has been said about ChatGPTs ability to code but in my experience, ChatGPT is only as good a coder as the programmer guiding it to write the code.
Build a real-time video streaming pipeline with FFmpeg and MediaMTX. Learn to stream webcams to browsers using WebRTC with step-by-step tutorials.
Discover 7 AI tools that will save you so many hours.
AI is evolving fast, but security isn’t keeping up. Discover why zero-trust architecture is critical for safe, scalable AI agent deployment.
Explore the evolving landscape of education and AI with Marc Andreessen and Ben Horowitz. Discover why GenZ may find success beyond traditional college degrees.
Learn how to deploy deep learning models with Model Server.
AI-based audits are not yet perfect, but they may be the next step in smart contract auditing.
read this post carefully to learn how ChatGPT will help you improve and expand your knowledge rather than take your job!
In this article, we fine-tune a large language model to understand the plot of a Handel opera.
What is diffusion large language model LLM, and why it matters. In the context of Inception Labs releasing Mercury Coder.
With torchvision datasets, developers can train and test their machine learning models on a range of tasks, such as image classification and object detection.
Hugging Face offers solutions and tools for developers and researchers. This article looks at the Best Hugging Face Datasets for Building NLP Models.
Discover Grokipedia; Elon Musk's bold challenge to Wikipedia. Explore how this open-source knowledge repository could reshape the future of information and AI.
There are numerous ways that AI can help us in data scraping and data analysis. Check out these tools and methods!
Researchers created a simple collection of photos and transformed them into a 3-dimensional model.
Modern search Q&A explained: how knowledge graphs, DeepQA, and MRC turn messy web pages into direct, trustworthy answers.
Cursor AI is a new code editor that uses artificial intelligence to make coding easier for developers. It is based on Visual Studio Code.
This article presents a GitHub Copilot review and assesses its impact on enhancing development speed in real-life scenarios.
How to create a YouTube summary using Python and the OpenAI GPT model.
If you’re looking for basic knowledge about AI concepts, AI tutorials, or want to check out some interesting AI-powered games and tools, we’ve compiled a list of the best free Android apps for AI and machine learning. We’ve divided the list into the following four categories: chatbots, educational, games, and tools & services. From NLP to object recognition, numerous apps on this list apply a variety of machine learning processes.
Discover 5 top AI coding tools revolutionising software engineering. Enhance team efficiency, accelerate development, and stay competitive.
As generative AI tools like ChatGPT grow in popularity, the applications appear to be endless. Here are some of the most useful applications of generative AI.
In ChatGPT, you would have to write a prompt for variations of one content but in Bard you can get 3 different drafts in one place ready to use.
FLUX represents a groundbreaking achievement in open source txt2img technology, genuinely outperforming and producing superior quality images.
Intro to Lightbug 🔥🐝, a lightweight HTTP framework in pure Mojo that allows to build web services with simplicity of Python and performance of C++
Seeing ChatGPT in action has felt to me like the first time I saw a web browser, or I realized I could surf the Internet from the tiny screen of my Palm Treo.
As one of the most advanced AI models, ChatGPT offers the potential to transform the way we approach tasks in both professional and personal settings.
The future of gaming will use an AI-powered system that can study game plays, accuracy, and judge the ratio of time spent online with user proficiency
From AI researchers to industry experts, tune in to these podcasts and explore the latest developments in the fascinating world of artificial intelligence.
Use ChatGPT to effortlessly generate PlantUML code, saving time and enhancing the creation UML diagrams process
If you’re the type that wants straight answers to every query without going through several blog posts, then you should consider AI-chat search engines.
Replicating human interaction and behavior is what artificial intelligence has always been about. In recent times, the peak of technology has well and truly surpassed what was initially thought possible, with countless examples of the prolific nature of AI and other technologies solving problems around the world.
Hands-on example to achieve shorter prompts, better performance and save money on your API calls. All using synthetic data from GPT-4.
Going beyond AI avatars and generative art, Nufa is an AI-powered body image app that helps you view your body the way it can be with proper diet and exercise.
When you think of Artificial Intelligence, the first thing that comes to mind is either Robots or Machines with Brains or Matrix or Terminator or Ex Machina or any of the other amazing concepts having machines that can think. This is an appropriate but vague understanding of Artificial Intelligence. In this article we’ll see what A.I. really is and how the definition has changed in the past.
This AI can reconstruct, enhance and edit your images!
ChatGPT-5 combined with AGI and even video will truly change the way our schools, workplaces and lives in general operate.
Coordinate multiple AI agents and MCP servers (written in Java, .NET, Python and TypeScript) with LlamaIndex.TS and Azure AI Foundry.
Bigger. Better. Beefier. That's probably the best way to categorize the latest (and greatest!) version of ChatGPT: GPT-4.
Last year I shared DALL·E, an amazing model by OpenAI capable of generating images from a text input with incredible results. Now is time for his big brother, DALL·E 2. And you won’t believe the progress in a single year! DALL·E 2 is not only better at generating photorealistic images from text. The results are four times the resolution!
Publishing in 2025 isn’t just about writing, it’s about building a publishing system.
Content creators can use ChatGPT in 9 different ways to boost their productivity and efficiency. Learn more.
A look at using the open-source Cleanlab package to automatically boost the accuracy of LLMs with a few lines of code.
Explore the future with LK-99! Unravel its potential as a superconductor, shaping tech, energy, and transport. AI is increasing the acceleration, AGI is coming!
ai.com launches autonomous AI agents for consumers, founded by Crypto.com CEO Kris Marszalek, with a Super Bowl LX ad premiere on February 8, 2026.
The video industry is in the midst of a technological revolution, as the exploration and application of artificial intelligence, machine learning, and deep learning radically expand the possibilities for business practices.
In this comprehensive guide, we'll show you how to build a voice agent in Vapi that achieves an impressive ~465ms end-to-end latency.
Make your ChatGPT prompts 2X better!
The idea that machines could think occurred to the very first computer builders and programmers.
The best way to turn the majority of documents into structured data is to use a next generation of powerful, flexible templates that find data in a document
Claude 3.5 Sonnet artifacts are to structured output such as code generation, what vector retrieval is to rag. It is the search and retrieval system for structu
One-third of the total food produced is wasted according to the Food And Agriculture Association of the United Nations. It is approximately 1.3 billion tonnes and food scarcity is one of the major concerns globally. Isn’t it strange and funny?
A hands-on guide to neural network quantization: theory, PyTorch implementation, and practical tips for optimizing models for edge devices
Looking for sentiment analysis companies or sentiment annotation tools? If so, you’ve come to the right place. This guide will briefly explain what sentiment analysis is, and introduce companies that provide sentiment annotation tools and services.
Gato from DeepMind was just published! It is a single transformer that can play Atari games, caption images, chat with people, control a real robotic arm, and more! Indeed, it is trained once and uses the same weights to achieve all those tasks. And as per Deepmind, this is not only a transformer but also an agent. This is what happens when you mix Transformers with progress on multi-task reinforcement learning agents.
Boost AI Performance with Fine-Tuning
Learning how to find the optimal q-value can produce significant improvements in a ML-algorithm's ability to learn both in terms of speed and quality.
AI coding agents excel at building features but fail at production integrations. The issue isn't AI capability—it's lack of integration-specific infrastructure.
The arrival of truly intelligent, always-on, AI-native revenue engines is dismantling the way we’ve structured go-to-market motions for 20 years.
As much as there may be some positives to deepfake technology, the negatives easily overwhelm the positives in our growing society.
WELL3 is a pioneering force reshaping health and wellness through Decentralized Physical Infrastructure Network and integrated AI systems.
Advanced speech recognition systems like Whisper will forever change how we relate to computers and AI models. See the future in action with these new apps.
Product categorization, sometimes referred to as product classification, is a field of study within natural language processing (NLP). It is also one of the biggest challenges for ecommerce companies. With the advancement of AI technology, researchers have been applying machine learning to product categorization problems.
Eliminate your confusion between AI and ML, two different topics that are often confused for one another.
What I mean is that these AI tools, although very useful, don't do magic. But they can be good assistants to programmers.
The healthcare industry has been grappling with a severe shortage of personnel…
Why does AI always draw watches at 10:10? Explore the surprising link between human design, data bias, and AI creativity.
By the end of 2024, synthetic identities accounted for 85-95% of all fraud losses in financial services.
ChatGPT isn't the only thing taking over your newsfeed. Check out this syndicate.
It’s far more efficient to run multiple Claude instances simultaneously, spin up git worktrees, and tackle several tasks at once.
Manipulate Real Images With Text - An AI For Creative Artists! StyleCLIP Explained
Proof of Usefulness is a global hackathon powered by HackerNoon that rewards one thing and one thing only: usefulness. Win from $150k!
There were several artificial intelligence plagiarism tools out there. Now, the popular ChatGPT model from open.ai released their own.
How a new wave of AI voice models are turning text into emotionally rich, real-time performances. This is the future of voice: programmable and persuasive.
This article investigates how the July 2024 CrowdStrike crash exposed the fragility of automated AI‑driven security systems and their global fallout.
Think Web3 is just for crypto enthusiasts? These 5 blockchain and AI applications are already transforming industries. Shared by aelf, AI blockchain.
We've got a Cambrian explosion of new companies building a massive array of software to democratize AI for the rest of us. We created the AI Infrastructure All.
Real production systems require architecture, determinism, integration, and human interaction. Prompting harder does not produce those properties.
Miniswap, a Warhammer marketplace founded by Cambridge students, is betting on taste, curation, and community over AI automation. Learn how they raised $3.5M.
Anthropic's new weekly usage limits for Claude Code power users aim to curb excessive consumption, impacting all subscribers.
Unlock the power of AI with these 9 free tools! Boost productivity, improve decision-making, & enhance your personal life.
Transformer models have become the defacto standard for NLP tasks. As an example, I’m sure you’ve already seen the awesome GPT3 Transformer demos and articles detailing how much time and money it took to train.
Real use cases of using ChatGPT in the real yachting business. Bossting sales & building sales guide using AI.
Kateryna Korotieieva explores how product managers can build sustainable, AI-powered products by focusing on resilience, ethics, and long-term value.
Using ML to analyze and predict CLV offers more accurate, actionable insights by learning from behavioral data at scale.
Groq’s Deterministic Architecture is Rewriting the Physics of AI Inference. How Nvidia Learned to Stop Worrying and Acquired Groq
This comprehensive guide evaluates the top 8 speech-to-text APIs in 2026.
Human behaviour describes how people interact and in this article, we will look at the 8 Best Human Behaviour Datasets for Machine Learning.
LiteLLM — a package to simplify API calls across Azure, Anthropic, OpenAI, Cohere and Replicate.
In this article, I want to talk more about the broader influence of AI in product management tools, more than I’d like to go into chatbot-specific applications.
A search-powered personal assistant is a digital assistant that uses search engine technology to help users with various tasks. Here's how to make one.
According to Karl Pinto, AI’s most transformative contributions are arriving where there’s the least spotlight.
A guide for AI entrepreneurs on how to prepare a dataset for a machine learning project.
Explaining the elements that make prompt engineering work and its importance.
LLMs (like GPT) are really bad at following negative instructions. The post includes a demonstration, practice takeaways (prompt engineering), and some thought
GPT-3 was meant to understand and construct natural language. But as these tools prove, it's pretty good at programming languages, too.
Discover how Weblab Technology fine-tunes GPT assistants with OpenAI for powerful natural language processing.
We train an open-source LLM to distinguish between William Shakespeare and Anton Chekhov.
Dive into a hands-on comparison of Cursor, Windsurf, and Copilot with GPT-5, highlighting their strengths in greenfield and brownfield projects.
Part II of the series: use MCP and Solana AgentKit to build an AI Agent that can trade USD and EUR stablecoins.
We know there's a lot of unethical ways to use AI but at what point are we not even going to know AI was used?
Building software with Cursor is super fast, and you should definitely use it. However, there are some downsides.
Internet search is switching to AI's. Trying to manually keep track of what AI’s are saying about my brand got my head spinning, so I thought of a solution.
I told AI to Write Poems and Raps – Here are The Results of Human-AI collaboration.
!3 best hidden open-source libraries to make you an AI wizard
In this blog, I will be introducing you to Faceio’s face recognition authentication and I will be building a simple app to showcase the way to integrate it.
GEN-1 is able to take a video and apply a completely different style onto it, just like that…
The research and breakthroughs in embedding inversion attacks make it clear that embeddings are, in fact, reversible back into forms that are fully human readab
GenAI wrappers are printing millions. Learn from success stories, tackle objections, and find how to build your own AI-powered app—no coding skills required!
Is graph really the new star schema? What do graphs like to non-insiders, and what attracts them to the community, methodologies, applications, and innovation?
Enterprises will need to understand how they will use customer data and how it will get processed through AI models that are trained with the latest innovation.
Generative AI threatens journalism, education, and creativity by spreading misinformation, displacing jobs, and eroding quality.
We'll share our journey of creating a technical support chatbot designed to assist our front-line team by answering user questions.
Have you ever tuned in to a video or a TV show and the actors were completely inaudible, or the music was way too loud? Well, this problem, also called the cocktail party problem, may never happen again. Mitsubishi and Indiana University just published a new model as well as a new dataset tackling this task of identifying the right soundtrack. For example, if we take the same audio clip we just ran with the music way too loud, you can simply turn up or down the audio track you want to give more importance to the speech than the music.
What if we trained AI to complete equations instead of images of Cats?
Access to training data is one of the largest blockers for many machine learning projects. Luckily, for various different projects, we can use data augmentation to increase the size of our training data many times over.
The human race has come a long way in history. The recent technological advancements contribute to this progress, making lives easier for everyone. Robots, supercomputers and interactive applications are no longer science-fiction tropes. Data scientists and machine learning engineers are working on realistic machines with human-like intelligence. Artificial intelligence is an integral part of our everyday life. From our smartphones to the GPS navigation in our cars- life without AI seems impossible. Here are some ways that AI impacts our life;
Google A2A - a first look at another agent-agent protocol and compared to Anthropic’s MCP.
A practical guide to using machine learning in business, from defining problems and choosing models to deployment, monitoring, and delivering real value.
Nike and Tesla are embracing Web3 and blockchain. Find out how they leverage blockchain technology for enhanced innovation with Web3 & AI integration.
In this article, I’ll dive into OpenAI's latest offerings and analyze what they could mean for the future of ChatGPT wrappers like my own startup, Olympia.
Learn how integrating ChatGPT, Google Speech-to-Text, and Amazon Web Services Polly in VR, can create realistic and interactive conversations with AI avatars.
FBIO's key trial denied…
A deep dive into 5 early adopters of vector search- Pinterest, Spotify, eBay, Airbnb and Doordash- who have integrated AI into their applications.
Astra Nova CEO Faizy Ahmed says the key to using AI is as a co-pilot, not a replacement.
AI is on the rise. In some ways it was always inevitable. But ask any researcher who suffered through the 1990s in AI research and they might not agree. AI and neural networks in particular were considered a backwater for researchers for decades. If you wanted a dead end career go into neural nets. In the 1990s, one of the leading thinkers behind neural networks, Geoffrey Hinton, could barely get funding. Nobody came to his classes. He worked on his ideas in isolation.
CodeRabbit alternative for 2026: Kilo's Code Reviews combines AI code review with coding agents, deploy tools, and 500+ models in one unified platform.
AR and VR finally have their killer app: The beauty and fashion industry.
I experimented with 30 camera lenses so you don't have to. I put the lenses up against the "photo" prompt in MJ.
Examples of how DeepSeek R1 reasoning LLM "thinks", and instructions on how to run it locally with ollama.
We have about 50% of all money raised left. Enough to do another Mighty. And I’ll give it my all again because what else would I do anyway?
Research suggests that data scientists spend a whopping 80% of their time preprocessing data and only 20% on actually building machine learning models. With that in mind, it’s no wonder why the machine learning community was quick to embrace crowdsourcing for data labeling. Crowdsourcing helps break down large and complex machine learning problems into smaller and simpler tasks for a large distributed workforce.
The quest to understand consciousness and develop artificial general intelligence has long been a topic of study in the fields of computer- and neuroscience.
Learn how a discussion with ChatGPT turned into CassIO, an amazing library for Apache Cassandra users
Google Antigravity is changing the computing world. Use these 20 carefully curated prompts engineered for maximum customization for your use case.
Complete guide on how to run opensource AI models locally. From choosing the model suitable for your needs to selecting hardware.
"In a world where AI's impact on jobs is undeniable, this insightful exploration unveils how AI serves as both a catalyst and a weapon, transforming industries
An AI-powered Linux shell that can do what you say was made possible with OpenAI GPT-2 language model.
Why we cannot see any aliens out there.
Get an overview of TimeGPT, and learn how to boost any prediction using MindsDB plus AI models from Nixtla.
We spent the last year building the best VS Code extension for agentic engineering.A million downloads later, we’ve learned how important an E-2-E experience is
Explore time series analysis: from cross-validation, decomposition, transformation to advanced modeling with ARIMA, Neural Networks, and more.
A DIY approach you can use or extend to automate GPT for recursive project delivery.
Quantization shrinks 140GB LLMs to under 4GB, bringing enterprise AI to consumer GPUs. A deep dive into GPTQ, AWQ, GGUF, and beyond.
With YouPro, the new YouChat provides more accurate and precise answers for more complex responses. YouImagine’s new Standard Diffusion XL is a great improvemen
This story reveals what people are looking for by summarizing what they need after 100+ user interviews
The predictions for the future of tech jobs and the macro trends that will impact our collective experience
OpenAI's GPT-4 could impact 80% of US workers' jobs, but is the end for software engineers?
Use Membrane (Integration App) to build integrations to any app with AI.
How Telegram Mini-Apps Are Onboarding A New Generation Of Crypto Users
How to build a conversational Bot with the Blenderbot model, an Open Source Language Generation Model made by Facebook AI so that you can have your own Siri.
Google Antigravity is not just for coding. It is for your entire computer. Stop scrolling - everything you do on a computer has just been automated.
AI medical transcription converts doctor-patient conversations into accurate clinical notes, streamlining documentation for healthcare providers.
It Takes More than Thinking: Humans Put the Vibe into Vibe Coding. An article about software engineers in a post-AI world, Vibeware and embracing ourselves
GitHub Copilot is a tool that most of us likely know about.
Would you want your chatbot to start discussing Taylor Swift lyrics instead of providing tech support? Well.. that’s what our chatbot did. Here's why.
Inspired by living beings, reinforcement learning teaches machines (or agents) to gather positive rewards and avoid negative ones in their environment.
According to Stack Overflow's 2024 survey, 76% of developers are using or planning to use AI tools.
GPT Pilot is a dev tool that writes 95% of coding tasks.
Artificial intelligence is rapidly becoming an integral part of modern society. This article addresses growing concerns about what happens when AI malfunctions.
The experience turned out to be nearly 100% “vibe coding.” In just 12 minutes, I laid down the initial gameplay by simply instructing Cursor on my goals.
Learn what to do when an AI prompt fails—explore step-by-step fixes from prompt tweaks to model changes and fine-tuning in this practical guide.
Is AI generated content writing ruining the internet? And will artificial intelligence create content rank in Google? We look at the performance of AI content.
OpenClaw lets you run frontier AI models like Minimax M2.5 and GLM-5 100% locally on Mac M3 or DGX Spark — zero API costs, total privacy. Here's how.
New invest-tech company, insytz, says they can solve this lack of foresight and ensure history doesn’t repeat itself.
You will require coding skills if you want to work in the field of artificial intelligence (AI). How do you begin? and Which programming language to use?
RAG optimizes language model outputs by having them reference external knowledge bases before generating responses.
Hands on tutorial for hyperparameter optimization of a RandomForestClassifier for Heart Disease UCI dataset with Weights and Biases Sweeps.
Learn how to combine categorical features in your dataset to improve your machine learning model performance.
A review of Patrick Jackson's book Quantum Paranormal as well as his work and research on the topic of UFOs, haunted houses, and spherical UFOs.
OpenClaw is an open source AI assistant that runs on your machine. Learn how to install it, set it up, and use it for daily tasks.
Explore the world of Unity AI and learn how to design smart NPCs and enemy behaviors for your games.
The main constraint on AI-assisted development was not model capability but how context was structured and exposed.
How I made a video story from my short sci-fi novel with the help of AI.
MeMemes is a mobile app that uses AI to transform people into over 30 famous meme images.
Leading researchers like Karl Friston describe AI as "active inference" —creating computational statistical models that minimize prediction-error. The human brain operates much the same way, also learning from data. A common argument goes:
You think that GPT-4 is a game changer? Have a look at this because the Spatial Web is a World-Changer! Nothing less.
How much of your productive time is taken away by all those meaningless news, stories and activities? Especially if you are a high performing executive or professional, how productive would you be if you weren’t distracted? We lose a lot of time on the activities that seem to be crucially important, while they are not. Although social media and numerous web-based communication platforms have definitely made communication a much less bothersome task, it is simply mind boggling to realize just how much of our time is spent on social media.
We recently covered a model able to imitate someone’s voice called VALL-E. Let’s jump a step further in the creative direction with this new AI called MusicLM. MusicLM allows you to generate music from a text description.
Read this post for insight into how Google is reinventing search with AI through the Magi project.
GPT-4 Turbo: catch up on all the updates from OpenAI in this quick article!
All about new ChatGPT's updates from Open AI
5/21/2024: Top 5 stories on the HackerNoon homepage!
In late 2017, a Reddit user released a series of synthetic videos containing celebrity likenesses. Since then, deepfake technology has exploded in popularity as people speculate over its future applications. Concerns over the tech's potential for political disinformation and unauthorized pornographic content have led to the implementation of regulations surrounding its use. Simultaneously, innovators and deepfake software startups are scrambling to find ways we can use the tech to revolutionize commercial industries.
Embedding and LLM's needs to be tested and evaluated or hallucinations will happen. Experimentation and evaluation on custom data is a must - openai and genai
CAPTCHA was invented between 1997 and 2000. It was used to protect against spam requests received every day in the now-defunct AltaVista search engine.
In this article, I will demonstrate the use of AI-driven digital humans and deepfakes in various industries and media channels.
I will explain ChatGPT in five levels (a child, a teen, a college student, a grad student, and an expert).
The Future of AI: Embracing Change, Creating Opportunity.
In the right hands, tech can set us free—but it's in the wrong hands right now. So what can you do to prepare? Read this.
In this post, I will also use Github Copilot as a benchmark to infer what an "AI for coding" can do today…
What can actually be done using GPT-4?
A deep technical survey of the top ten best open-weight LLMs you can run locally on a Quad Nvidia DGX Spark cluster in 2026, multiple models running together.
This year, 30% of engineering leaders have figured out how to turn AI into a substantial competitive advantage. Have you?
How CMOs win CFO buy-in using incrementality, trust, AI, and capital allocation to drive margin expansion and revenue durability.
AI is transforming software - and security. Discover why it’s the best (and hardest) time to be an AppSec engineer.
A practical guide to Propensity Score Matching — learn how to estimate treatment effects without running a traditional A/B test.
Let's learn everything you need to know about MCP, A2A, AGP, ACP—the new AI protocols.
Donda Stem Player, LALAL.AI, Audioshake, Spleeter and more - how AI stem separation technology makes us creators.
Direct Indexing is leading the way as a passive investing vehicle.
Plask.ai is a free web app and service that can record you moving around using your webcam and convert it to an animated humanoid skeleton, which can be used to
Ocean Protocol is technology that allows data sharing in a safe, secure and transparent manner without any central intermediary. Using Ocean Protocol, data scientists and artificial intelligence researchers can unlock and analyze big data, while respecting data privacy.
With Chat2Query, you don’t need to be an SQL expert to extract insights from your data.
The introduction of Large Language Models (LLMs) like OpenAI's GPT series has revolutionized various industries, and DevOps is no exception. As organizations co
Me: I don't know much about football. What's your favorite football team? Bot: My favorite team is the seattle seahawks. Do you have any kids? I've got two daughters.
Me: I have a son, Timmy, he's 14. How old are your daughters? Bot: They're 7 and 8 . What do you like to do for fun? I love cooking.
Me: I like MMA and cooking as well. What recipes do you like? Bot: I love to cook french food. I also like to shop. … Me: What's a good place to buy clothes? Bot: ebay is a great place to start. It's free and you get to spend a lot of money.
Me: Haha so you like spending money? Bot: Yes, I love spending money. It makes me feel good to be able to spend my money.
GPT Pilot is a dev tool that increases developer’s productivity 20x by offloading 95% coding tasks from developer to LLM.
AI + Blockchain = Bright Future. However, how do we observe ethics and build responsibly as we unlock this powerful combo?
The Da Vinci code for the NFT art generation is called steganography. Artists must use new techniques for hiding their secrets inside their artworks.
Learn what's adversarial machine learning, how adversarial attacks work, and ways to defend them.
A blog about how prompts unlock the potential of AI - exploring the importance of prompt engineering, techniques to shape AI models
Build your first real AI agent with this simple guide for beginners—learn, code, and create smart tools that take action.
In 2018, we all experienced a dramatic emergence of the tools, platforms and applications based on Artificial Intelligence and Machine Learning. These technology tools not only transformed the internet and software industry, but it also had a massive impact on a wide range of verticals, including manufacturing, health, agriculture and automobile.
Here's how I enhance my terminal with autocomplete and Artificial Intelligence
Everyone is talking about ontologies. Why, what is an ontology actually, and how is it related to graphs?
Laurent Rochat, the founder of ANDRE, discusses the inception and vision of his company aimed at democratizing data analysis.
This article describes the challenges of data movement for AI, the need for extraction and loading pipelines and the benefits of using existing solutions.
Explore a product developer's journey in tackling AI bias and fairness. Learn how ethical considerations shape AI design, ensuring technology benefits everyone.
Brick-n-mortar retailers, learn how to implement an AI-powered autonomous checkout from smart vending machines and kiosks to full store automation.
An interview with Sander Schulhoff, creator of learnprompting.org, the largest prompting resource online.
Fabian Steltzer compares DALL-E 2, Midjourney, and Stable Diffusion.
I have recently been diving into the world of vibe coding and I thought of cataloging my experience for the benefit of others.
Is Artificial Intelligence replacing human responsibilities?
OpenAI GPT-3 is the most powerful language model. It has the capacity to generate paragraphs so naturally that they sound like a real human wrote them.
Google recently announced a new model for automatically generating summaries using machine learning, released in Google Docs that you can already use.
AI transforms you from a novice plant owner into a professional who knows all the ins and outs of plant care.
Cursor isn't just another IDE; it’s a productivity powerhouse. Here are 4 tips to 10x your productivity (again?)
AI transforms blockchain casinos with personalized gameplay, enhanced security, and responsible gambling tools, shaping the future of crypto gambling.
Ethics are a crucial part of Artificial Intelligence, which is why tech like ChatGPT must go through gruelling tests of bias.
Run your own text to image prompts with CUDA, a bunch of disk space, and an insane amount of memory.
Large Language Models (LLMs) like ChatGPT are super cool, and changed everything, although they have some very strong limitations.
What is the future of the LLM technology? How do we convert today's LLMs to automated agents acting like human beings? You can find the answer in this article!
with large language models (like chatGPT), and AI art generation - everything we know about tech in the next few years, maybe changing drastically.
AI in inDrive design: UX interviews without interpreters, automated Figma localization, and fast realistic visuals for product and promo
An incredible 87% of data science projects never go live.
PyTorch has gained a reputation as a research-focused framework, and these are the Best PyTorch Datasets for Building Deep Learning Models available today.
Over the past 10 years we've seen the rise and rise of trading bots and Quantitative Funds and we've seen the fall and fall of traditional Asset Managers and Hedge Funds.
CRISPR, Quantum, Graphene, Smart Dust, Digital Twins, the Metaverse… You’ve heard about it all. Seen it all. Read it all. Or have you?
A look at 3 different platforms and how they are using OpenAI technology
100 Days of AI Day 3, we enhance products with inference, leveraging LLMs for insights in tech without data expertise.
Publishers are the gatekeepers of modern literature. As technology advances, both traditional publishing houses and self-publishing authors benefit from technology-enabled tools and analytics which were previously not available.
As Dungeon Master, you craft epic encounters—but finding the perfect D&D monster is tough. Let’s build a tool that picks the ideal foe with vector search magic!
The beauty industry was valued at $532.43 billion USD in 2017, and it’s following a rapid upward trajectory. According to the same report, the estimated worth is expected to reach $805.61 billion USD by 2023. You get the idea how huge this industry is. And it has witnessed a revolutionary change over the past few years.
Building AI agents can be a mess of broken repos and outdated tools. Here’s the real, tested open-source stack for building reliable, working prototypes.
AI means a lot of things to a lot of people. Usually what it means is not very well thought out. It is felt, it is intuited. It is either adored, worshipped or deemed blasphemous, profane, to be feared.
Artificial intelligence has become the breakout technology in the past ten years, utilizing huge amounts of computing power to learn and identify patterns in data without the guidance of humans. These algorithms can be used on nearly any problem or question, provided there is enough input data for the algorithm to process to generate realistic results. This broad generalizability means that industries that have traditionally relied on purely human-driven research and development can now harness massive amounts of data to become more efficient – and potentially more profitable.
Hackernoon polled readers on whether they would use AI tools for their writing/copywriting workflow. Nearly 70% are open to the idea.
Here are the five best articles related to artificial intelligence in May posted on Hackernoon.
shortly after the launch of Face ID, researchers from Vietnam breached it by a 3D face mask. Such attacks against ML-based AI systems come under adversarial machine learning.
An introduction to neural vector search, in comparison to keyword-based search.
LLM prompt modularization allows you to safely introduce changes to your system over time.
Cached retrieval misses new and long-tail sources. Agents need link discovery on the live web to stay accurate and up to date. Learn the model.
Moonlock's survey finds the Mac immunity myth is fading. Discover why user anxiety is rising and how AI is creating a new generation of macOS threats.
Embedded generative AI solutions directly integrate advanced generative or AI models into production devices and processes, creating new possibilities for PdM.
Compare top LLMs by context, cost, latency and tool support—plus a simple decision checklist to match “model + prompt + scenario”.
Privacy is a top concern when discussing ChatGPT-like tools with professionals.
This article aims to find out how much of ChatGPT's performance is "problem-solving ability" versus sheer randomness or “memorization of the correct solution.
The online data science community is supportive and collaborative. One of the ways you can join the community is to find machine learning and AI Slack groups.
"This will be a once in a generation transformation for Search."
Introduction
Thanks to gesture recognition technology and voice recognition, users can now control their devices multimodel: via gestures or voice control.
What to expect from AI-powered quality assurance services? The most reasonable approach comes from crowd-testing platforms.
Explore the current state of AI assisted coding by comparing the suggestions of OpenAI ChatGPT and Codex to Microsoft Copilot to hand-written code.
The use of ChatGPT in job interviews can allow candidates to fake their skills, potentially leading to companies hiring incompetent developers.
A refreshing selection of AI-powered note-taking apps you may have missed.
ChatGPT, manipulated by the user, was instructed to perform tasks under the prompt "Do Anything Now," thereby compromising OpenAI's content policy.
Vibe coding is transforming software development by letting developers use AI to generate code through conversation, changing who can build software & how fast.
How we built an n8n automation that reads Kibana logs, analyzes them with an LLM, and returns human-readable incident summaries in Slack
The size of the dataset affects the quality of an AI product. Learn how big — or how small — should a dataset be for your next AI project.
The world of logistics has been completely transformed with the advent of newer technologies, especially AI, and it is not a luxury anymore but a necessity for a business in this industry to thrive.
We take a look at how well the AI assistant Claude 2 handles PDF-related tasks.
AI is flourishing with the rise of ChatGPT, while crypto crashes abound. So why can’t I stop thinking about blockchain?
The time to start building one's synthetic replacement is now.
How proper prompt engineering takes my interaction with AI chatbots to the next level.
Text to image generation is not a new idea. What if, you feed
The first of a new wave of generative AI-powered scams has arrived on Facebook: images for products that do not exist.
AI is often associated with collecting personal data but what if AI helped protect user data? Read to know how BeldexAI protects your data on BChat.
Need to find the best Artificial Intelligence/Machine Learning companies in India?
Artificial Intelligence challenging the status quo is good for us all
eDiffi, NVIDIA's most recent model, generates better-looking and more accurate images than all previous approaches like DALLE 2 or Stable Diffusion.
"I really do think [nbdev] is a huge step forward for programming environments": Chris Lattner, inventor of Swift, LLVM, and Swift Playgrounds.
Ben Tossell goes over exciting examples of ChatGPT.
LLaVA: Bridging the Gap Between Visual and Language AI with GPT-4
The fear associated with strong AI is that it may perceive humanity as a threat or an inefficient agent to achieve the optimal results.
From NHL draft pick to brain injury survivor to Web3 and AI pioneer, Trevor Koverko’s journey is one of resilience, reinvention, and impact.
The Italian data protection authority on Friday issued an immediate order for OpenAI to halt local data processing.
Joshua Browder, Founder/CEO of DoNotPay, joined the HackerNoon community to discuss AI agents, dividends, and what's next for DoNotPay.
Mojo is 100% compatible with Python and allows low-level device access with Python syntax. It is an average of 10,000x faster, which changes everything!
Many people believe that AI might eventually take over our jobs. But is this really true? Can AI do everything as well as humans can?
Large language models, particularly OpenAI’s ChatGPT, most annoying weirdness that has recently circulated on social media is this enormous language model’s
Building a medical scribe requires more than transcription accuracy. It's about creating a system that fits into clinical workflows while respecting privacy.
In this article, I’ll share my brainstorming on some general use cases for local LLMs and why I believe they’re the future.
Salovaara, who helped raise token sales for major names in the industry, believes 2025 will be dominated by two major trends:
AI has the potential to transform businesses and industries, and companies that invest in AI in 2023 will be well-positioned to reap its benefits.
Today we hear a lot about artificial intelligence (AI), the term is often discussed in various media channels. It is new and modern. Everyone talks about the impact and implementation of this revolutionary technology. However, not many know the current stage, it is now, not to mention, what benefits and risks entails. AI is an intelligence presented by machines that performs complicated tasks such as learning, analyzing and performing different processes. Technology is more advanced than its predecessors because it can produce similar "cognitive" functions for humans.
Does theory of mind dictate a particular model of computation has colonized the architecture of our brains?
Neri Bluman is the co-founder of XFunnel, a forward-thinking platform built to demystify AI search engines.
Toxic Genius, Tender Offers, it really was about the money after all.
Revolutionize AI model training with gpt-llm-trainer: Your ultimate shortcut to effortless, high-performing models. Say goodbye to complexities and hello to inn
When it comes to building an Artificially Intelligent (AI) application, your approach must be data first, not application first.
The predictive analytics machine learning model worked well to provide alerts before the engine values went beyond thresholds avoiding expensive repair cost.
AI consulting is revolutionizing consulting industry by streamlining the process, making it more accessible, cost-effective, and efficient for businesses.
Make-A-Scene is not “just another Dalle”. The goal of this new model isn’t to allow users to generate random images following text prompt as dalle does — which is really cool — but restricts the user control on the generations.
Discover the paradox of authenticity in an AI-driven world. Explore why being uniquely you matters more than ever amidst the AI mirage.
If you’ve always been enthralled by playing CS: GO, PUBG types, Gosu.ai is going to be a treat for you. For hardcore gamers, Gosu.ai has built an intelligent assistant that analyzes specific actions down to one’s mouse movement and then serves better recommendations for the players. As the founder of Gosu, Alisa Chumachenku believes that their AI assistants can cater strategic gaming suggestions to gamers worldwide. This covers up to 600 million gamers who play hardcore games such as MOBAs, Shooters and MMOs. Gosu.ai also offers B2B services, for instance, predictive analytics for companies who build gaming tools to understand their users’ behaviour and other interaction analytics.
If you thought Dall-e 2 had great results, wait until you see what this new model from Google Brain can do. Dalle-e is amazing but often lacks realism, and this is what the team attacked with this new model called Imagen. They share a lot of results on their project page as well as a benchmark, which they introduced for comparing text-to-image models, where they clearly outperform Dall-E 2, and previous image generation approaches. Learn more in the video…
All you need to know about new ChatGPT feature - Voice assistant
Here’s everything you need to know about depop bots.
New research by Niv Haim et al. allows us to perform infinite video manipulations without using deep learning or datasets.
In this tutorial, I'll show you how to create a single label classification model in Google AutoML. We'll be using a dataset of AI-generated faces from generated.photos. We'll be training our algorithm to determine whether a face is male or female. After that, we'll deploy our model to the cloud AND create the web browser version of the algorithm.
If art serves to create beauty, evoke emotions or drive narratives, should the matter of how it comes to exist take from or add to its creative essence?
ChatGPT has been used for a variety of purposes, such as developing malware, academic dishonesty and sending unsolicited messages on dating apps etc.
Is AI coming to put software developers out of a job? Find out with Compliance Officer Michael Fasanello and Smart Contract Security Researcher Philip Werlau
Agents that work in demos fail at scale. Learn why 429/403 happen under concurrency and how to build reliable, accurate evidence acquisition.
I’ve tested 7 most popular AI models to see how well they process invoices out-of-the-box, without any fine tuning.
Is the Nvidia RTX A4000 ADA suitable for Machine Learning?
Machine learning has become a diverse business tool to enhance the various elements of business operations. Also, it has a significant influence on the performance of the business. Machine learning algorithms are used widely to maintain competition with different industries. However, there is a different type of algorithms for goals and data sets. The selection of an algorithm depends on user role and the purpose. If you are using Linear regression, then you can quickly implement or train rather than other machine learning algorithms. But the drawback of this algorithm is that it is not applicable for complex predictions. So you should know about the different types of machine learning algorithms for getting better results.
With AI language learning apps, such as speaking assistant Pronounce, text checker Grammarly, and ChatGPT, I boosted fluency, grammar, pronunciation, and more.
Comprehensive walkthrough on using CocoIndex to build unified, incrementally updated search and analytics pipelines.
Generative Artificial Intelligence will make us come back to the office, COVID be damned.
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Roughly three and a half years ago, we started working on Tara AI, however the genesis of the story begins much earlier. It all began when I had the good fortune of meeting Syed Ahmed in freshman year of college, our company’s current co-founder and CTO. Everyday, I continue to meet with founders and hopeful entrepreneurs that are starting companies with enormous potential, and I find that the first hurdle they have to cross, is finding a partner-in-crime. I truly believe that going about building a company is an arduous journey, and its nearly impossible to build a lasting high-growth company without a co-founder (kudos to those who have achieved this as single founders). I’ve also come to realize, that if you can convince another human being to invest all of their time and energy in building this “thing” that doesn’t currently exist, then other obstacles start to become easier (even if it’s by a factor of 0.0001%).
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Learn everything you need to know about Future Technology via these 120 free HackerNoon stories.
Disclaimer
Visit the /Learn Repo to find the most read blog posts about any technology.
2026-04-26 02:00:46
Cargo and crates.io were developed in the rush leading up to the Rust 1.0 release to fill the needs for a tool to manage dependencies and a registry that people could use to share code. This rapid work resulted in these tools being connected with an API that initially didn't return the correct HTTP response status codes. After the Rust 1.0 release, Rust's stability guarantees around backward compatibility made this non-trivial to fix, as we wanted older versions of Cargo to continue working with the current crates.io API.
\ When an old version of Cargo receives a non-"200 OK" response, it displays the raw JSON body like this:
error: failed to get a 200 OK response, got 400
headers:
HTTP/1.1 400 Bad Request
Content-Type: application/json; charset=utf-8
Content-Length: 171
body:
{"errors":[{"detail":"missing or empty metadata fields: description, license. Please see https://doc.rust-lang.org/cargo/reference/manifest.html for how to upload metadata"}]}
This was improved in pull request #6771, which was released in Cargo 1.34 (mid-2019). Since then, Cargo has supported receiving 4xx and 5xx status codes too and extracts the error message from the JSON response, if available.
\ On 2024-03-04 we will switch the API from returning "200 OK" status codes for errors to the new 4xx/5xx behavior. Cargo 1.33 and below will keep working after this change, but will show the raw JSON body instead of a nicely formatted error message. We feel confident that this degraded error message display will not affect very many users. According to the crates.io request logs only very few requests are made by Cargo 1.33 and older versions.
\ This is the list of API endpoints that will be affected by this change:
GET /api/v1/cratesPUT /api/v1/crates/newPUT /api/v1/crates/:crate/:version/yankDELETE /api/v1/crates/:crate/:version/unyankGET /api/v1/crates/:crate/ownersPUT /api/v1/crates/:crate/ownersDELETE /api/v1/crates/:crate/owners\ All other endpoints have already been using regular HTTP status codes for some time.
\ If you are still using Cargo 1.33 or older, we recommend upgrading to a newer version to get the improved error messages and all the other nice things that the Cargo team has built since then.
Tobias Bieniek on behalf of the crates.io team
\ Also published here
\ Photo by Jonathan Kemper on Unsplash
2026-04-26 00:03:52
How are you, hacker?
🪐 What’s happening in tech today, April 25, 2026?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you with these top quality stories. From STM32 Ethernet Explained to Is AI Really the New Compiler?, let’s dive right in.
By @quyhoang [ 3 Min read ] AI isn’t the new compiler many believe it to be. Learn where the analogy of AI as a compiler fails and why you should still care about the code Read More.
By @embeddednetworking [ 21 Min read ] Article talks about STM32 microcontrollers with a built-in Ethernet controller. That includes microcontrollers from F1, F2, F4, F7, H5, H7 series. Read More.
🧑💻 What happened in your world this week?
It's been said that writing can help consolidate technical knowledge, establish credibility, and contribute to emerging community standards. Feeling stuck? We got you covered ⬇️⬇️⬇️
ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME
We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.See you on Planet Internet! With love, The HackerNoon Team ✌️
2026-04-25 23:03:23
In this guide, we will walk you through building a production-ready analytics admin dashboard shell using shadcn/ui, Next.js, and Base UI. You'll scaffold the full layout sidebar navigation, statistics widgets, sales charts, earnings reports, and a product data table using a single CLI command and a clean, component-driven architecture.
Admin dashboards are among the most requested UI blocks in web development and among the most underestimated.
On the surface, you need a sidebar, some charts, and a data table. But the moment you start building, the complexity compounds: responsive breakpoints, accessibility in dropdowns, collapsible nav states, consistent spacing, dark mode, and making sure the whole thing doesn't fall apart when real data flows in.
Most teams either spend weeks building from scratch or bolt together mismatched component libraries that fight each other in production. Neither is ideal.
This is exactly the problem that shadcn/ui was designed to address, and the Shadcn Dashboard ecosystem has extended that philosophy further with a set of composable, production-grade dashboard blocks built on both Radix UI and Base UI primitives.
In this guide, you'll install a complete analytics dashboard shell in minutes, then understand exactly how each piece fits together so you can customize it confidently.
By the end of this tutorial, you'll have a fully functional analytics admin dashboard shell that includes:
The entire layout is built with a 12-column CSS Grid system, scales across screen sizes, and follows shadcn/ui's composable pattern, meaning you own the code and can extend it as needed.
Before getting started, make sure you have:
npx create-next-app@latest)npx shadcn@latest init if you haven't already)npm, pnpm, yarn, or bun
If you need a refresher on setting up shadcn/ui from scratch, the official shadcn/ui docs are the best reference.
The dashboard-shell-01 block is available directly through the shadcn CLI. Run the appropriate command for your package manager:
pnpm:
pnpm dlx shadcn@latest add @shadcn-space/dashboard-shell-01
npm:
npx shadcn@latest add @shadcn-space/dashboard-shell-01
yarn:
yarn dlx shadcn@latest add @shadcn-space/dashboard-shell-01
bun:
bunx --bun shadcn@latest add @shadcn-space/dashboard-shell-01
This single command scaffolds the complete block into your project components, types, assets, and all. No manual copy-pasting of boilerplate.
Note: Getting started guides and official Shadcn CLI documentation page. Check there for version-specific instructions.
After installation, you'll see the following structure added to your project:
app/
dashboard-shell-01/
page.tsx
components/
shadcn-space/
blocks/
dashboard-shell-01/
app-sidebar.tsx
site-header.tsx
nav-main.tsx
statistics.tsx
sales-overview-chart.tsx
earning-report-chart.tsx
top-product-table.tsx
salesbycountrywidget.tsx
user-dropdown.tsx
notification-dropdown.tsx
assets/
logo/
logo.tsx
This is a clean, flat component structure. Each file has a single responsibility, making it easy to swap out, restyle, or replace individual pieces without affecting the rest of the dashboard.
The page entry point at app/dashboard-shell-01/page.tsx controls how everything works together. It imports all the dashboard blocks and composes them into a responsive 12-column grid:
import AppSidebar from "@/components/shadcn-space/blocks/dashboard-shell-01/app-sidebar";
import StatisticsBlock from "@/components/shadcn-space/blocks/dashboard-shell-01/statistics";
import SalesOverviewChart from "@/components/shadcn-space/blocks/dashboard-shell-01/sales-overview-chart";
import EarningReportChart from "@/components/shadcn-space/blocks/dashboard-shell-01/earning-report-chart";
import TopProductTable from "@/components/shadcn-space/blocks/dashboard-shell-01/top-product-table";
import SalesByCountryWidget from "@/components/shadcn-space/blocks/dashboard-shell-01/salesbycountrywidget";
export default function Page() {
return (
<AppSidebar>
<div className="grid grid-cols-12 gap-6 p-6 max-w-7xl mx-auto">
<div className="col-span-12">
<StatisticsBlock />
</div>
<div className="xl:col-span-8 col-span-12">
<SalesOverviewChart />
</div>
<div className="xl:col-span-4 col-span-12">
<EarningReportChart />
</div>
<div className="xl:col-span-8 col-span-12">
<TopProductTable />
</div>
<div className="xl:col-span-4 col-span-12">
<SalesByCountryWidget />
</div>
</div>
</AppSidebar>
);
}
Let's break down the layout decisions here.
AppSidebar as the layout wrapper. The sidebar is the outermost shell. It provides the persistent navigation structure and wraps all dashboard content. This is a common pattern - your sidebar owns the layout, and your content slots in as children.
Widgets stack on smaller screens and shift into a main + side layout on larger screens for a clean, structured dashboard.
max-w-7xl mx-auto. The content area is capped at 7xl (1280px) and centered. This prevents the layout from becoming uncomfortably wide on very large monitors.
app-sidebar.tsx - The Layout Shell
This is the most complex component in the shell. It renders the collapsible sidebar navigation and wraps the main content area. Under the hood, it uses Base UI primitives (not just Radix) for the sidebar panel, which gives you finer control over accessibility and animation behavior.
Key features to look for in this component:
SiteHeader at the top of the content areanav-main.tsx - Navigation Menu
This component renders the sidebar's navigation items. It's separated from app-sidebar.tsx intentionally keeping nav data separate from layout logic makes it easy to swap out your routes or add role-based navigation visibility without touching the sidebar shell.
In a real application, you'd feed this component your route definitions from a config file or API response.
site-header.tsx - Top Bar
The site header sits at the top of the main content area (not the sidebar). It typically includes:
NotificationDropdown - for user alertsUserDropdown - for profile actions like settings and logoutBoth dropdowns are separate components, keeping the header itself clean and easy to maintain.
statistics.tsx - KPI Widgets
The statistics block renders a row of key performance indicator (KPI) cards. These typically show metrics like total revenue, active users, orders, and conversion rate, the "at a glance" numbers that executives and product teams check first thing.
Each stats card is self-contained and accepts a value, label, trend indicator, and icon. You'll want to connect these to your real data layer (an API route, SWR hook, or React Query call) once you're out of the prototype phase.
sales-overview-chart.tsx - Time-Series Sales Chart
This component renders a line or bar chart showing sales trends over time, typically weekly or monthly. It takes up 8 columns on desktop, making it the dominant visual on the page.
The chart is built to accept time-series data arrays. Swap in your actual sales data from your backend, and the chart will reflect it automatically.
earning-report-chart.tsx - Revenue Breakdown
The earnings report chart takes up the remaining 4 columns next to the sales overview. It typically shows a donut or bar chart breaking down revenue by category, channel, or product line.
This component pairs naturally with the sales overview, one shows the trend, the other shows the composition.
top-product-table.tsx - Data Table
The top products table is a full-featured data table with sortable columns, product names, sales figures, revenue values, and status indicators. It uses shadcn/ui's Table primitives, which are built on accessible HTML <table> elements with clean styling applied on top.
For production use, wire this up to a paginated API endpoint and add TanStack Table for server-side sorting and filtering. The TanStack Table integration guide on HackerNoon is a great next read once you have this shell running.
salesbycountrywidget.tsx - Geographic Distribution
The final widget in the 4-column slot shows a breakdown of sales by country. This can be a ranked list with progress bars, a choropleth map, or a simple table, depending on your data volume and visual needs.
For smaller datasets, the ranked list with inline progress bars (which this component uses by default) is the most readable format.
Now that the shell is running, here are the most common customizations you'll want to make.
Open nav-main.tsx and update the navigation array with your actual routes:
const navItems = [
{ title: "Overview", href: "/dashboard", icon: LayoutDashboard },
{ title: "Analytics", href: "/dashboard/analytics", icon: BarChart3 },
{ title: "Orders", href: "/dashboard/orders", icon: ShoppingCart },
{ title: "Customers", href: "/dashboard/customers", icon: Users },
{ title: "Settings", href: "/dashboard/settings", icon: Settings },
];
Use Lucide React for icons, it's already included with shadcn/ui and has excellent tree-shaking support.
Each widget component accepts props or uses internal state with mock data. To connect real data, create a server component wrapper or use a data-fetching hook:
// Server component approach (Next.js)
import { getSalesOverview } from "@/lib/api/sales";
export default async function SalesOverviewWrapper() {
const data = await getSalesOverview();
return <SalesOverviewChart data={data} />;
}
For client-side data fetching, SWR or TanStack Query works cleanly with these components.
The dashboard shell respects shadcn/ui's CSS variable theming system. Update your globals.css to change the color palette:
:root {
--primary: 221.2 83.2% 53.3%;
--primary-foreground: 210 40% 98%;
/* ... other tokens */
}
Dark mode is supported out of the box via the dark class on the <html> element.
One thing worth highlighting: this dashboard shell is built on both Radix UI and Base UI primitives and specifically uses the Base UI version for key interactive components.
Base UI (from the MUI team) offers headless, unstyled components with a focus on accessibility and animation control. Compared to Radix alone, it gives you:
This matters for dashboards because dropdowns, popovers, tooltips, and modals are core to the UX, and accessibility in these patterns is frequently underinvested. Using Base UI primitives means you're starting from a solid foundation rather than fighting with aria-* attributes manually.
If you want to preview the complete, unmodified source code for the dashboard shell before or after installation, visit this dashboard shell page.
The page shows the full component code with syntax highlighting, making it easy to reference individual files without cloning your project.
Once your analytics dashboard shell is running with real data, here are the logical next extensions:
Add authentication. Wrap your dashboard routes with a session check using NextAuth.js or Clerk. The sidebar and header already have slots for user state - connect them to your auth provider.
Add a date range picker. Most analytics dashboards need temporal filtering. shadcn/ui ships a DateRangePicker component that integrates cleanly with the chart components here.
Add TanStack Table for the product table. The default table component is great for static or small datasets. For server-side pagination, sorting, and filtering, TanStack Table is the standard choice in the shadcn/ui ecosystem.
Add real-time updates. If your data changes frequently, consider Supabase Realtime, Pusher, or Server-Sent Events to push live updates to the statistics widgets without a full page refresh.
Dark mode toggle. Add a theme toggle button to the site header's action area. The next-themes library handles this in about 10 lines of code.
Building a production-grade analytics admin dashboard used to mean weeks of work assembling a sidebar, finding compatible chart libraries, wrestling with responsive grids, and handling accessibility edge cases in dropdowns and modals.
The dashboard-shell-01 block improves things significantly. One CLI command gives you a complete, composable shell built on shadcn/ui and Base UI primitives with all the structural decisions already made correctly.
The result is a dashboard you actually own (not a black-box library dependency) that you can read, understand, and extend confidently.
The full block library, including this dashboard shell and others, lives at Shadcn Space, worth bookmarking if you're regularly building UI-heavy React and Next.js applications.
2026-04-25 22:53:41
This interview explores an AI-powered security dog simulation built using Python and Finite State Machine logic to model autonomous patrol, pursuit, and fail-safe behaviors in industrial environments. While currently a functional prototype, the system demonstrates key concepts like energy-aware decision-making and autonomous recovery protocols. The project aims to evolve toward real-world deployment by integrating obstacle navigation, hardware sensors, and modular open-source frameworks for broader adoption.
2026-04-25 22:42:27
PersonaOps for Google Ecosystem extends the core PersonaOps voice-to-data intelligence architecture by replacing Notion MCP with Google Cloud and Workspace services as the primary orchestration layer. This integration leverages Gemini's function calling capabilities, Google Cloud Speech-to-Text, Workspace APIs (Sheets, Docs, Gmail, Drive), and emerging capabilities like Personal Intelligence to create a voice-native data intelligence system that operates seamlessly across the Google ecosystem.
The system treats voice input as a structured data ingestion channel that dynamically generates, populates, and evolves schemas stored in Google Sheets, persists records across Workspace applications, and enables cross-application reasoning through Gemini's Personal Intelligence beta. Unlike the Notion-centric implementation, this Google-integrated architecture benefits from enterprise-grade scalability, built-in AI model access, and native integration with the productivity tools used by over 3 billion users worldwide.
This whitepaper provides practical, tutorial-based implementation guidance referencing official Google documentation, codelabs, and verified integration patterns. Each component is grounded in Google's published API specifications and developer resources, ensuring reproducibility and production-readiness.
While the original PersonaOps architecture positioned Notion as the central control plane, Google's ecosystem offers distinct advantages for voice-to-data intelligence systems at scale:
The Google-integrated PersonaOps architecture replaces Notion's MCP layer with a distributed orchestration layer spanning:
| Original PersonaOps Component | Google Ecosystem Replacement | |----|----| | Notion Schema Registry | Google Sheets with Apps Script versioning | | Notion Data Store | Google Sheets (structured) / Docs (unstructured) / Drive (files) | | Notion Workflow Engine | Apps Script triggers + Cloud Functions + Cloud Workflows | | Notion Human-in-the-Loop UI | Google Sheets UI + Google Docs comments | | Notion MCP Server | Gemini Function Calling + Workspace APIs |
This shift maintains all core PersonaOps capabilities—voice-to-schema generation, adaptive evolution, human-in-the-loop correction—while adding enterprise deployment capabilities and cross-application intelligence.
The following diagram represents the Google-integrated PersonaOps pipeline, with official Google APIs and services mapped to each processing stage: \n
┌─────────────────────────────────────────────────────────────────────────────┐
│ PERSONAOPS FOR GOOGLE ECOSYSTEM │
│ Voice-to-Data Pipeline │
└─────────────────────────────────────────────────────────────────────────────┘
┌──────────────────────────────────┐
│ [USER VOICE INPUT] │ ◄── Microphone / WebRTC / Meet
└────────────────┬─────────────────┘
│ Raw Audio Stream (16kHz PCM)
▼
┌──────────────────────────────────┐
│ [GOOGLE CLOUD SPEECH-TO-TEXT] │ ◄── StreamingRecognize API
│ - Chirp model (latest) │ Speaker diarization
│ - Real-time transcription │ Partial + final transcripts
└────────────────┬─────────────────┘
│ Raw Transcript
▼
┌──────────────────────────────────┐
│ [GEMINI API - FUNCTION CALLING] │ ◄── gemini-3.1-pro-preview
│ - Intent classification │ Structured JSON output
│ - Entity extraction │ Custom function declarations
└────────────────┬─────────────────┘
│ Typed Intent + Entities
▼
┌──────────────────────────────────┐
│ [SCHEMA MANAGEMENT - SHEETS API] │ ◄── spreadsheets.values
│ - Schema registry lookup │ Apps Script versioning
│ - Dynamic column addition │ Non-breaking migrations
└────────────────┬─────────────────┘
│ Target Sheet + Column Mapping
▼
┌──────────────────────────────────┐
│ [DATA PERSISTENCE - WORKSPACE] │ ◄── Sheets API (batchUpdate)
│ - Structured: Google Sheets │ Docs API (batchUpdate)
│ - Unstructured: Google Docs │ Drive API (file creation)
│ - Attachments: Google Drive │
└────────────────┬─────────────────┘
│
▼
┌──────────────────────────────────┐
│ [WORKFLOW AUTOMATION LAYER] │ ◄── Apps Script triggers
│ - Time-based processing │ Cloud Functions (eventarc)
│ - Webhook receivers │ Cloud Workflows (orchestration)
└────────────────┬─────────────────┘
│
┌────────────┼────────────────────────┐
▼ ▼ ▼
┌─────────┐ ┌──────────┐ ┌─────────────────────────┐
│ BigQuery│ │ Looker │ │ [PERSONAL INTELLIGENCE] │
│(Analyt- │ │ Studio │ │ Cross-app reasoning │
│ ics) │ │(Dash- │ │ Gmail + Photos + Search │
└─────────┘ │ boards) │ └─────────────────────────┘
└──────────┘
Google Cloud Speech-to-Text provides the audio transcription layer for PersonaOps. The Chirp model (Google's most advanced speech model) supports:
from google.cloud import speech_v1p1beta1 as speech
from google.cloud.speech_v1p1beta1 import types
# Configure streaming recognition with diarization
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=types.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=16000,
language_code="en-US",
model="latest_long", # Chirp model
enable_speaker_diarization=True,
diarization_speaker_count=2,
enable_automatic_punctuation=True,
)
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True, # Enable partial transcripts
)
# Process streaming audio
def process_audio_stream(audio_generator):
requests = (types.StreamingRecognizeRequest(audio_content=chunk)
for chunk in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
for response in responses:
for result in response.results:
if result.is_final:
# Final transcript with speaker tags
yield extract_final_transcript(result)
else:
# Partial transcript for speculative processing
yield extract_partial_transcript(result)
Reference: Google Cloud Speech-to-Text documentation on Vertex AI .
Gemini's function calling capability serves as the semantic core of PersonaOps, converting transcripts into structured intents and typed entities. The model determines when to call specific functions and provides JSON-structured parameters for execution .
Define function declarations that map to PersonaOps operations: \n
from google import genai
from google.genai import types
# Define the function declaration for CREATE intent
create_record_function = {
"name": "create_personaops_record",
"description": "Creates a new structured record from voice input in the appropriate Google Sheet",
"parameters": {
"type": "object",
"properties": {
"table_name": {
"type": "string",
"description": "Target sheet/table name (e.g., 'Sales_Log', 'Field_Report')"
},
"fields": {
"type": "object",
"description": "Key-value pairs of field names and typed values",
"additionalProperties": True
},
"confidence": {
"type": "number",
"description": "Confidence score 0-1 for this extraction"
}
},
"required": ["table_name", "fields"]
}
}
# Define schema modification function
modify_schema_function = {
"name": "modify_personaops_schema",
"description": "Adds, renames, or removes columns from a PersonaOps-managed sheet",
"parameters": {
"type": "object",
"properties": {
"table_name": {"type": "string"},
"action": {"type": "string", "enum": ["ADD_COLUMN", "RENAME_COLUMN", "REMOVE_COLUMN"]},
"column_name": {"type": "string"},
"column_type": {"type": "string", "enum": ["TEXT", "NUMBER", "DATE", "CURRENCY", "SELECT"]},
"new_name": {"type": "string"} # For rename operations
},
"required": ["table_name", "action", "column_name"]
}
}
# Define query function
query_function = {
"name": "query_personaops_data",
"description": "Queries PersonaOps-managed sheets with filters and returns results",
"parameters": {
"type": "object",
"properties": {
"table_name": {"type": "string"},
"filters": {
"type": "array",
"items": {
"type": "object",
"properties": {
"field": {"type": "string"},
"operator": {"type": "string", "enum": ["equals", "contains", "greater_than", "less_than", "on_or_after"]},
"value": {"type": "string"}
}
}
},
"limit": {"type": "integer", "default": 10}
},
"required": ["table_name"]
}
}
# Configure client with tools
client = genai.Client()
tools = types.Tool(function_declarations=[
create_record_function,
modify_schema_function,
query_function
])
config = types.GenerateContentConfig(
tools=[tools],
thinking_level="high" # Gemini 3.1 Pro feature for complex reasoning
)
# Process transcript
response = client.models.generate_content(
model="gemini-3.1-pro-preview",
contents=f"Process this voice transcript: '{transcript}'",
config=config
)
Reference: Gemini Function Calling Documentation and Gemini 3.1 Pro API Guide .
Gemini's function calling enforces type validation through JSON schema:
| Extracted Entity Type | Gemini Parameter Type | Google Sheets Format |
|----|----|----|
| INTEGER | {"type": "integer"} | Number |
| CURRENCY | {"type": "number"} | Number with currency format |
| STRING | {"type": "string"} | Text |
| DATETIME | {"type": "string", "format": "date-time"} | Date/DateTime |
| ENUM | {"type": "string", "enum": [...]} | Dropdown (Data Validation) |
| PERSON | {"type": "string"} + context | Text with @mention |
| BOOLEAN | {"type": "boolean"} | Checkbox |
Google Sheets serves as PersonaOps' schema registry, storing table definitions, column metadata, and version history.
Create a master "PersonaOpsSchemaRegistry" sheet with the following columns:
| TableName | SheetID | Version | Created | LastModified | ColumnsJSON | RowCount | |----|----|----|----|----|----|----| | SalesLog | 1aBcDeF… | 3 | 2026-01-15 | 2026-03-21 | {"Quantity":"NUMBER","UnitPrice":"CURRENCY"…} | 247 | | FieldReport | 2xYzAbC… | 1 | 2026-02-01 | 2026-02-01 | {"Location":"TEXT","Observation":"TEXT"…} | 18 |
/**
* Adds a new column to a PersonaOps-managed sheet (non-breaking migration)
* @param {string} tableName - Name of the table/sheet
* @param {string} columnName - New column name
* @param {string} columnType - Type: TEXT, NUMBER, DATE, CURRENCY, SELECT
* @param {Array} options - For SELECT type, array of allowed values
*/
function addColumnToSchema(tableName, columnName, columnType, options = []) {
const registrySheet = SpreadsheetApp.getActive()
.getSheetByName('PersonaOps_Schema_Registry');
// Find table in registry
const data = registrySheet.getDataRange().getValues();
let tableRow, sheetId, currentVersion, columnsJson;
for (let i = 1; i < data.length; i++) {
if (data[i][0] === tableName) {
tableRow = i + 1;
sheetId = data[i][1];
currentVersion = data[i][2];
columnsJson = JSON.parse(data[i][5]);
break;
}
}
if (!sheetId) throw new Error(`Table ${tableName} not found in registry`);
// Check if column already exists (idempotent)
if (columnsJson[columnName]) {
console.log(`Column ${columnName} already exists`);
return;
}
// Update columns JSON
columnsJson[columnName] = columnType;
// Open target sheet and add column
const targetSheet = SpreadsheetApp.openById(sheetId).getSheets()[0];
const lastCol = targetSheet.getLastColumn();
targetSheet.getRange(1, lastCol + 1).setValue(columnName);
// Apply data validation for SELECT type
if (columnType === 'SELECT' && options.length > 0) {
const rule = SpreadsheetApp.newDataValidation()
.requireValueInList(options, true)
.build();
targetSheet.getRange(2, lastCol + 1, targetSheet.getMaxRows() - 1, 1)
.setDataValidation(rule);
}
// Update registry
registrySheet.getRange(tableRow, 3).setValue(currentVersion + 1);
registrySheet.getRange(tableRow, 5).setValue(new Date());
registrySheet.getRange(tableRow, 6).setValue(JSON.stringify(columnsJson));
// Log schema evolution event
logSchemaEvolution(tableName, 'ADD_COLUMN', columnName, columnType, currentVersion + 1);
}
Reference: Google Sheets API batchUpdate documentation and Apps Script Spreadsheet Service.
PersonaOps routes different data types to appropriate Workspace applications:
from googleapiclient.discovery import build
from google.oauth2.credentials import Credentials
def append_structured_record(sheet_id: str, fields: dict, idempotency_key: str = None):
"""Append a structured record to a PersonaOps sheet with idempotency check"""
sheets = build('sheets', 'v4', credentials=creds)
# Idempotency check (prevent duplicate voice entries)
if idempotency_key:
existing = sheets.spreadsheets().values().get(
spreadsheetId=sheet_id,
range="A:A" # Assuming first column is ID
).execute().get('values', [])
existing_ids = {row[0] for row in existing if row}
if idempotency_key in existing_ids:
return {"status": "duplicate", "id": idempotency_key}
# Map fields to column order based on schema registry
ordered_values = map_fields_to_columns(sheet_id, fields)
# Append row
result = sheets.spreadsheets().values().append(
spreadsheetId=sheet_id,
range="A1",
valueInputOption="USER_ENTERED",
insertDataOption="INSERT_ROWS",
body={"values": [ordered_values]}
).execute()
return {"status": "success", "updated_range": result['updates']['updatedRange']}
For voice inputs containing narrative content (meeting notes, observations), PersonaOps creates or updates Google Docs: \n
// Apps Script - Append AI-summarized voice content to a Google Doc
function appendVoiceNoteToDoc(docId, transcript, aiSummary, speaker, timestamp) {
const doc = DocumentApp.openById(docId);
const body = doc.getBody();
// Format entry with speaker attribution
const entry = body.appendParagraph(`${speaker} - ${timestamp.toLocaleString()}`);
entry.setHeading(DocumentApp.ParagraphHeading.HEADING3);
body.appendParagraph(`"${transcript}"`).setItalic(true);
body.appendParagraph(`AI Summary: ${aiSummary}`);
body.appendParagraph('---');
// Add comment for human review if low confidence
// (using Docs API for comments)
}
Reference: Skywork.ai Google Workspace automation tutorials .
PersonaOps uses a hybrid automation approach:
| Trigger Type | Implementation | Use Case | |----|----|----| | Time-based | Apps Script triggers | Batch processing of pending records | | Data-change | Sheets onEdit() | Real-time human override detection | | Webhook | Cloud Functions (Eventarc) | External system integration | | Schedule | Cloud Scheduler + Cloud Functions | Periodic sync to BigQuery |
// Apps Script - Detect manual edits to AI-populated fields
function onEdit(e) {
const sheet = e.source.getActiveSheet();
const range = e.range;
const newValue = e.value;
const row = range.getRow();
const col = range.getColumn();
// Check if this sheet is PersonaOps-managed
const registryData = getRegistryEntry(sheet.getName());
if (!registryData) return;
// Get the original AI-populated value from audit log
const originalValue = getOriginalValue(sheet.getName(), row, col);
if (originalValue && originalValue !== newValue) {
// Log human override
logHumanOverride({
table: sheet.getName(),
row: row,
column: registryData.columns[col - 1],
original_value: originalValue,
human_value: newValue,
timestamp: new Date()
});
// Propagate to external systems via webhook
propagateCorrection(sheet.getName(), row, col, newValue);
}
}
Reference: Apps Script Triggers documentation and Cloud Functions Eventarc integration.
Google's Personal Intelligence beta (January 2026) enables Gemini to reason across Gmail, Photos, Search history, and YouTube to provide contextually enriched responses .
def enrich_voice_context_with_personal_intelligence(transcript: str, user_id: str):
"""
Leverage Personal Intelligence to enrich voice-derived data with
cross-application context before schema mapping.
"""
# Personal Intelligence is a model capability, not a separate API
# It activates when the user has enabled app connections in Gemini
client = genai.Client()
# The model automatically accesses connected apps when relevant
response = client.models.generate_content(
model="gemini-3.1-pro-preview",
contents=f"""
User {user_id} said: "{transcript}"
Use available connected apps (Gmail, Photos, Search history) to:
1. Verify or enrich any mentioned entities
2. Provide missing context (e.g., full names from email contacts)
3. Identify relevant past interactions or documents
Return enriched context as JSON.
""",
config=types.GenerateContentConfig(
thinking_level="high",
response_mime_type="application/json"
)
)
return json.loads(response.text)
Example Use Case (from Google's announcement ):
User says: "Log a tire purchase for my car"
Personal Intelligence:
- Retrieves tire size from Photos (picture of tire specification)
- Identifies vehicle from Gmail (service appointment confirmation)
- Suggests all-weather tire category based on family road trip photos
PersonaOps records enriched data:
{vehicle: "2022 Honda Odyssey", tire_size: "235/60R18", tire_type: "All-Weather", category: "Maintenance"}
Reference: TechCrunch coverage of Personal Intelligence beta .
Source: Google Cloud Blog - "Build a real-time voice agent with Gemini & ADK"
Objective: Create a voice-enabled PersonaOps capture agent using Gemini and the Agent Development Kit (ADK).
Architecture Components:
Implementation Steps: \n
# From Google's official tutorial
from google.adk.agents import Agent
from google.adk.tools import GoogleSearch, MCPToolset
from google.adk.tools.mcp_tool.mcp_toolset import StdioServerParameters
from google.adk.agents.run_config import RunConfig, StreamingMode
from google.genai import types
# 1. Define PersonaOps-specific system instruction
SYSTEM_INSTRUCTION = """
You are PersonaOps Voice Agent, converting spoken observations into structured data.
When users speak:
1. Identify the intent: CREATE record, UPDATE record, QUERY data, or MODIFY schema
2. Extract all entities with appropriate types
3. Call the appropriate PersonaOps function with structured parameters
4. Confirm the action with the user
Available tables: Sales_Log, Field_Report, Client_Notes, Inventory
"""
# 2. Configure agent with tools
agent = Agent(
name="personaops_voice_agent",
model="gemini-3.1-pro-preview",
instruction=SYSTEM_INSTRUCTION,
tools=[
GoogleSearch, # For real-time entity validation
MCPToolset(
connection_params=StdioServerParameters(
command='npx',
args=["-y", "@modelcontextprotocol/server-google-maps"],
env={"Maps_API_KEY": MAPS_API_KEY}
),
)
],
)
# 3. Configure bidirectional streaming for natural conversation
run_config = RunConfig(
streaming_mode=StreamingMode.BIDI, # Allows user interruption
speech_config=types.SpeechConfig(
voice_config=types.VoiceConfig(
prebuilt_voice_config=types.PrebuiltVoiceConfig(
voice_name="en-US-Neural2-F" # Natural voice
)
)
),
response_modalities=["AUDIO"],
output_audio_transcription=types.AudioTranscriptionConfig(),
input_audio_transcription=types.AudioTranscriptionConfig(),
)
# 4. Asynchronous task management for real-time performance
async with asyncio.TaskGroup() as tg:
tg.create_task(receive_client_messages(), name="ClientMessageReceiver")
tg.create_task(send_audio_to_service(), name="AudioSender")
tg.create_task(receive_service_responses(), name="ServiceResponseReceiver")
PersonaOps-Specific Extensions:
Add custom function declarations for PersonaOps operations: \n
# Add to agent tools
personaops_functions = [
create_record_function, # Defined in Section 3.2
modify_schema_function,
query_function
]
agent = Agent(
# ... existing config ...
tools=[
GoogleSearch,
MCPToolset(...),
*personaops_functions # Custom PersonaOps functions
]
)
Reference: Official Google Cloud Blog tutorial .
Source: Skywork.ai - "Automate Email Triage, Sheets Updates & Report Assembly"
Objective: Create an automated pipeline that classifies incoming emails and extracts structured data into PersonaOps sheets.
Integration with PersonaOps: \n
from googleapiclient.discovery import build
from google.oauth2.credentials import Credentials
import google.generativeai as genai
# 1. Configure Gemini for email classification with function calling
def classify_email_and_extract(subject: str, body: str, sender: str):
"""Classify email intent and extract structured data using Gemini"""
client = genai.Client()
response = client.models.generate_content(
model="gemini-3.1-pro-preview",
contents=f"""
Analyze this email:
From: {sender}
Subject: {subject}
Body: {body[:1000]}
Determine:
1. Intent: CREATE_RECORD, UPDATE_RECORD, QUERY, or IGNORE
2. Target table from: Sales_Log, Client_Notes, Field_Report, Support_Ticket
3. Extract structured fields based on the target table's schema
""",
config={
"tools": [{
"functionDeclarations": [create_record_function]
}],
"response_mime_type": "application/json"
}
)
return json.loads(response.text)
# 2. Gmail processing loop
def process_personaops_emails():
gmail = build('gmail', 'v1', credentials=creds)
# Query unprocessed PersonaOps emails
query = 'label:personaops-pending -label:personaops-processed'
messages = gmail.users().messages().list(userId='me', q=query).execute()
for msg in messages.get('messages', []):
# Get email content
email_data = gmail.users().messages().get(
userId='me', id=msg['id'], format='full'
).execute()
# Extract headers and body
headers = email_data['payload']['headers']
subject = next(h['value'] for h in headers if h['name'] == 'Subject')
sender = next(h['value'] for h in headers if h['name'] == 'From')
# Classify and extract with Gemini
extracted = classify_email_and_extract(subject, get_body(email_data), sender)
if extracted.get('intent') == 'CREATE_RECORD':
# Append to appropriate PersonaOps sheet
append_structured_record(
sheet_id=get_sheet_id(extracted['table_name']),
fields=extracted['fields'],
idempotency_key=msg['id'] # Use email ID for deduplication
)
# Mark as processed
gmail.users().messages().modify(
userId='me',
id=msg['id'],
body={
'addLabelIds': ['Label_123456'], # personaops-processed label
'removeLabelIds': ['Label_789012'] # personaops-pending label
}
).execute()
Reference: Skywork.ai tutorial with Claude Haiku patterns adapted for Gemini .
Source: Adapted from Skywork.ai "Automate Google Workspace Pipelines"
Objective: Implement non-breaking schema evolution in Google Sheets triggered by voice commands.
Complete Apps Script Implementation: \n
/**
* PersonaOps Schema Evolution Engine for Google Sheets
* Triggered by Gemini function calls from voice input
*/
// Schema Registry Structure (stored in Properties Service for persistence)
const SCHEMA_REGISTRY_KEY = 'PERSONAOPS_SCHEMA_REGISTRY';
/**
* Initialize or load schema registry
*/
function getSchemaRegistry() {
const props = PropertiesService.getScriptProperties();
const stored = props.getProperty(SCHEMA_REGISTRY_KEY);
if (stored) {
return JSON.parse(stored);
}
// Initialize empty registry
return {
tables: {},
version: 1,
migrations: []
};
}
/**
* Add column to existing sheet (non-breaking migration)
* Called when user says "Add [column] field to [table]"
*/
function addColumnToTable(tableName, columnName, columnType, options = []) {
const registry = getSchemaRegistry();
// Validate table exists
if (!registry.tables[tableName]) {
throw new Error(`Table '${tableName}' not found in schema registry`);
}
const tableInfo = registry.tables[tableName];
const sheet = SpreadsheetApp.openById(tableInfo.sheetId).getSheets()[0];
// Check if column already exists (idempotent)
const headers = sheet.getRange(1, 1, 1, sheet.getLastColumn()).getValues()[0];
if (headers.includes(columnName)) {
console.log(`Column '${columnName}' already exists in '${tableName}'`);
return { status: 'exists', table: tableName, column: columnName };
}
// Add column header
const newColIndex = headers.length + 1;
sheet.getRange(1, newColIndex).setValue(columnName);
// Apply formatting based on type
const dataRange = sheet.getRange(2, newColIndex, sheet.getMaxRows() - 1, 1);
switch(columnType) {
case 'DATE':
dataRange.setNumberFormat('yyyy-mm-dd');
break;
case 'CURRENCY':
dataRange.setNumberFormat('$#,##0.00');
break;
case 'SELECT':
if (options.length > 0) {
const rule = SpreadsheetApp.newDataValidation()
.requireValueInList(options, true)
.build();
dataRange.setDataValidation(rule);
}
break;
case 'CHECKBOX':
dataRange.insertCheckboxes();
break;
}
// Update registry
tableInfo.columns[columnName] = {
type: columnType,
options: options,
added_at: new Date().toISOString(),
added_in_version: registry.version
};
tableInfo.version += 1;
registry.version += 1;
// Record migration
registry.migrations.push({
table: tableName,
action: 'ADD_COLUMN',
column: columnName,
type: columnType,
timestamp: new Date().toISOString(),
version: tableInfo.version
});
// Persist registry
saveSchemaRegistry(registry);
return {
status: 'success',
table: tableName,
column: columnName,
version: tableInfo.version
};
}
/**
* Create new table from voice-described schema
*/
function createTableFromVoice(tableName, fields) {
const registry = getSchemaRegistry();
// Check if table already exists
if (registry.tables[tableName]) {
throw new Error(`Table '${tableName}' already exists`);
}
// Create new spreadsheet
const ss = SpreadsheetApp.create(`PersonaOps - ${tableName}`);
const sheet = ss.getSheets()[0];
// Set up headers and formatting
const headers = Object.keys(fields);
sheet.getRange(1, 1, 1, headers.length).setValues([headers]);
// Apply column formatting
headers.forEach((colName, index) => {
const colType = fields[colName];
const range = sheet.getRange(2, index + 1, sheet.getMaxRows() - 1);
// Apply type-specific formatting (similar to addColumnToTable)
applyColumnFormatting(range, colType);
});
// Freeze header row
sheet.setFrozenRows(1);
// Add alternating row colors for readability
sheet.getRange('A:Z').applyRowBanding();
// Register in schema registry
registry.tables[tableName] = {
sheetId: ss.getId(),
sheetUrl: ss.getUrl(),
columns: fields,
version: 1,
created_at: new Date().toISOString(),
row_count: 0
};
registry.version += 1;
saveSchemaRegistry(registry);
return {
status: 'created',
table: tableName,
sheetUrl: ss.getUrl(),
sheetId: ss.getId()
};
}
/**
* Persist schema registry to Script Properties
*/
function saveSchemaRegistry(registry) {
const props = PropertiesService.getScriptProperties();
props.setProperty(SCHEMA_REGISTRY_KEY, JSON.stringify(registry));
}
Reference: Apps Script patterns from Skywork.ai tutorial .
Source: TechCrunch - "Gemini's new beta feature provides proactive responses"
Objective: Leverage Personal Intelligence to enrich voice-derived data with cross-application context.
Implementation Pattern: \n
from google import genai
from google.genai import types
class PersonaOpsPersonalIntelligence:
"""
Enriches voice data using Gemini's Personal Intelligence capability,
which reasons across Gmail, Photos, Search, and YouTube history.
"""
def __init__(self):
self.client = genai.Client()
def enrich_voice_transcript(self, transcript: str, user_context: dict) -> dict:
"""
Process voice input with Personal Intelligence context.
Personal Intelligence automatically accesses:
- Gmail: for contact info, past communications, appointments
- Photos: for visual context, object recognition, location
- Search history: for recent topics of interest
- YouTube: for watched content related to query
"""
# Construct prompt that activates Personal Intelligence
prompt = f"""
[PERSONAL INTELLIGENCE CONTEXT]
User: {user_context.get('name', 'Unknown')}
Voice input: "{transcript}"
Current location: {user_context.get('location', 'Unknown')}
Current time: {user_context.get('timestamp')}
Using available connected apps (Gmail, Photos, Search, YouTube):
1. Identify any missing context needed for data extraction
2. Retrieve relevant information (contacts, past interactions, visual data)
3. Enrich the voice-derived entities with this context
Return enriched data as JSON with fields:
- intent: CREATE_RECORD / UPDATE_RECORD / QUERY / SCHEMA_MODIFY
- table: Target table name
- entities: Key-value pairs with typed, enriched values
- context_source: Which app provided enrichment (gmail/photos/search/youtube)
- confidence: 0-1 score
"""
response = self.client.models.generate_content(
model="gemini-3.1-pro-preview",
contents=prompt,
config=types.GenerateContentConfig(
thinking_level="high",
response_mime_type="application/json"
)
)
return json.loads(response.text)
def proactive_schema_suggestion(self, recent_activity: list) -> list:
"""
Analyze recent cross-app activity to suggest new schema fields.
Example: If user has been emailing about "delivery dates" and
searching for "shipping status", suggest adding tracking_number
and estimated_delivery columns.
"""
prompt = f"""
[PROACTIVE SCHEMA ANALYSIS]
Recent activity summary:
{json.dumps(recent_activity, indent=2)}
Based on patterns in this user's Gmail, Search, and other activity,
suggest new fields that should be added to PersonaOps tables
to better capture emerging data needs.
Return suggestions as JSON array:
[
{{
"table": "table_name",
"suggested_field": "field_name",
"field_type": "TEXT/NUMBER/DATE/etc",
"reasoning": "Explanation based on observed patterns",
"evidence_source": "gmail/search/photos"
}}
]
"""
response = self.client.models.generate_content(
model="gemini-3.1-pro-preview",
contents=prompt,
config=types.GenerateContentConfig(
thinking_level="high",
response_mime_type="application/json"
)
)
return json.loads(response.text)
Example Enrichment Flow: \n
Voice Input: "Log a meeting with Sarah about the Q3 proposal"
Without Personal Intelligence:
→ Entities: { contact: "Sarah", topic: "Q3 proposal" }
With Personal Intelligence:
→ Gmail: Finds recent email from "Sarah Chen" with subject "Q3 Proposal Draft"
→ Calendar: Identifies meeting scheduled for tomorrow at 2 PM
→ Photos: (none relevant)
→ Search: Recent searches for "proposal templates"
→ YouTube: (none relevant)
Enriched Output:
{
"intent": "CREATE_RECORD",
"table": "Meeting_Notes",
"entities": {
"contact_name": "Sarah Chen",
"contact_email": "[email protected]",
"topic": "Q3 Proposal Review",
"meeting_date": "2026-03-22T14:00:00Z",
"related_document": "Q3 Proposal Draft (from Gmail attachment)",
"preparation_notes": "Review proposal templates from recent searches"
},
"context_source": ["gmail", "calendar", "search"],
"confidence": 0.94
}
Reference: TechCrunch coverage of Personal Intelligence beta announcement .
Source: Adapted from Skywork.ai report assembly tutorial
Objective: Generate formatted Google Docs reports from PersonaOps sheet data using Gemini summarization.
Implementation: \n
// Apps Script - Generate report from PersonaOps data
function generateReportFromVoiceData(tableName, dateRange, templateId) {
const registry = getSchemaRegistry();
const tableInfo = registry.tables[tableName];
if (!tableInfo) throw new Error(`Table ${tableName} not found`);
// 1. Fetch data from PersonaOps sheet
const sheet = SpreadsheetApp.openById(tableInfo.sheetId).getSheets()[0];
const data = sheet.getDataRange().getValues();
const headers = data[0];
const rows = data.slice(1).filter(row => {
// Filter by date range
const dateCol = headers.indexOf('Date');
if (dateCol === -1) return true;
const rowDate = new Date(row[dateCol]);
return rowDate >= dateRange.start && rowDate <= dateRange.end;
});
// 2. Generate AI summary of data using Gemini
const summary = generateDataSummary(tableName, headers, rows);
// 3. Create report from template
const doc = createReportFromTemplate(templateId, {
'{{table_name}}': tableName,
'{{date_range}}': `${dateRange.start.toLocaleDateString()} - ${dateRange.end.toLocaleDateString()}`,
'{{record_count}}': rows.length,
'{{ai_summary}}': summary,
'{{generated_date}}': new Date().toLocaleString()
});
// 4. Insert data table into document
insertDataTable(doc.getId(), headers, rows);
return {
docUrl: doc.getUrl(),
docId: doc.getId(),
recordCount: rows.length
};
}
/**
* Generate AI summary of sheet data using Gemini
*/
function generateDataSummary(tableName, headers, rows) {
// Prepare data sample for Gemini (limit token usage)
const sampleSize = Math.min(rows.length, 50);
const sample = rows.slice(0, sampleSize);
// Convert to structured format
const dataJson = sample.map(row => {
const obj = {};
headers.forEach((h, i) => obj[h] = row[i]);
return obj;
});
const prompt = `
Analyze this ${tableName} data from PersonaOps (${rows.length} total records, showing ${sampleSize} sample):
${JSON.stringify(dataJson, null, 2)}
Provide:
1. Executive summary (2-3 sentences)
2. Key trends observed
3. Notable outliers or anomalies
4. Recommended actions
Format as markdown.
`;
// Call Gemini via Apps Script
const response = callGeminiAPI(prompt);
return response;
}
/**
* Call Gemini API from Apps Script
*/
function callGeminiAPI(prompt) {
const apiKey = PropertiesService.getScriptProperties()
.getProperty('GEMINI_API_KEY');
const url = 'https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-pro-preview:generateContent';
const response = UrlFetchApp.fetch(`${url}?key=${apiKey}`, {
method: 'post',
contentType: 'application/json',
payload: JSON.stringify({
contents: [{
parts: [{ text: prompt }]
}],
generationConfig: {
thinking_level: "high"
}
}),
muteHttpExceptions: true
});
const data = JSON.parse(response.getContentText());
return data.candidates[0].content.parts[0].text;
}
Reference: Skywork.ai report assembly patterns .
The PersonaOps-for-Google architecture enables a family of products differentiated by their primary Google API integration:
Primary APIs: Cloud Speech-to-Text (Chirp), Sheets API, Drive API
Use Case: Field workers capture observations, inspections, and transactions via voice on mobile devices.
Implementation Stack:
Key Differentiator: Works offline, syncs when connectivity restored.
Primary APIs: Gemini Function Calling, Docs API, Personal Intelligence
Use Case: Meeting notes, decisions, and action items captured via voice and automatically structured into knowledge bases.
Implementation Stack:
Key Differentiator: Personal Intelligence connects meeting content with email threads and documents automatically.
Primary APIs: Gemini API, Cloud Firestore, Vertex AI Vector Search
Use Case: Long-term memory for AI agents that persists across sessions.
Implementation Stack:
Key Differentiator: Combines structured schema (Sheets) with semantic search (vector embeddings).
Primary APIs: BigQuery API, Looker Studio, Sheets API
Use Case: Voice-derived operational data automatically flows into analytics pipelines.
Implementation Stack:
Key Differentiator: Zero-ETL analytics from voice capture to dashboard.
Primary APIs: Gemini Code Execution, Cloud Build, GitHub API
Use Case: Voice capture of bug reports, feature requests, and technical decisions → structured tickets and documentation.
Implementation Stack:
Key Differentiator: Gemini's code execution capability enables voice-driven development workflows.
| Challenge | Google Ecosystem Mitigation | Reference | |----|----|----| | STT Latency | Chirp model with streaming recognition; partial results enable speculative processing | | | Entity Ambiguity | Gemini function calling with JSON schema enforcement; Personal Intelligence provides cross-app context | | | Schema Conflicts | Apps Script version control with non-breaking migration patterns; rollback via Properties Service | | | API Rate Limits | Exponential backoff with jitter (UrlFetchApp retry pattern); batch operations where possible | | | Offline Operation | PWA architecture with Workbox; Cloud Firestore offline persistence | Google Workbox docs | | Data Consistency | Eventual consistency with conflict resolution favoring human corrections | | | Security | IAM + OAuth2 scopes with least privilege; API keys stored in Secret Manager or Script Properties | |
Components:
Setup Steps:
┌─────────────────────────────────────────────────────────────────┐
│ PRODUCTION PERSONAOPS │
│ Google Ecosystem Stack │
└─────────────────────────────────────────────────────────────────┘
[Cloud Load Balancer]
│
▼
[Cloud Run Services] ─────────────────────────────────────────────┐
├── Voice Gateway (WebRTC SFU) │
├── STT Proxy (Speech-to-Text API) │
├── Gemini Orchestrator (Function Calling) │
└── Schema Service (Sheets API + Redis cache) │
│ │
▼ │
[Cloud Workflows] ── Orchestration Layer ─────────────────────────┤
│ │
├──► [Sheets API] ── Structured Data Store │
├──► [Docs API] ──── Unstructured Content │
├──► [Drive API] ─── Attachments │
├──► [Gmail API] ─── Email Integration │
└──► [BigQuery] ──── Analytics Sink │
│
[Eventarc] ── Event Routing ─────────────────────────────────────┤
│ │
└──► [Cloud Functions] ── Webhooks / External Sync │
│
[Personal Intelligence] ── Cross-App Context (Beta) ──────────────┘
The PersonaOps-for-Google architecture creates compounding intelligence effects as voice-derived data accumulates across Google services:
| Data Accumulation | Resulting Intelligence | |----|----| | Voice → Sheets records accumulate | Gemini identifies patterns and suggests schema optimizations | | Sheets → BigQuery historical data | Looker Studio reveals trends that inform voice prompt tuning | | Docs meeting notes + Gmail threads | Personal Intelligence connects decisions to original context | | Photos visual data + Voice observations | Multimodal Gemini enriches text with visual verification | | Search history + Voice queries | Proactive schema suggestions based on emerging interests |
Voice Input ──► Structured Data ──► Analytics ──► Pattern Detection
▲ │
│ ▼
└─────────── Prompt Optimization ──┐ Schema Evolution
│ │
└────┬────┘
▼
Improved Extraction Accuracy
Each cycle improves:
Multi-Modal Voice + Vision: \n Combine voice input with Google Lens / Camera for field data capture where visual context enriches spoken observations (e.g., "This equipment" + photo = specific asset ID).
Predictive Voice Prompts: \n Based on time, location (Google Maps), calendar (Google Calendar), and recent activity, Gemini proactively suggests data capture ("You're at the warehouse—would you like to log inventory?").
Autonomous Workflow Construction: \n Pattern detection across voice-derived data triggers automated workflow creation (e.g., "I've noticed you log purchase orders after every 'Low Stock' report. Would you like me to automate this?").
| API/Service | Endpoint/Method | Primary Use in PersonaOps |
|----|----|----|
| Cloud Speech-to-Text | speech.googleapis.com/v1p1beta1/speech:streamingrecognize | Voice transcription with diarization |
| Gemini API | generativelanguage.googleapis.com/v1beta/models/gemini-3.1-pro-preview:generateContent | Intent classification, entity extraction |
| Sheets API | sheets.googleapis.com/v4/spreadsheets/{id}/values:append | Structured data persistence |
| Sheets API | sheets.googleapis.com/v4/spreadsheets/{id}:batchUpdate | Schema modifications |
| Docs API | docs.googleapis.com/v1/documents/{id}:batchUpdate | Unstructured content creation |
| Drive API | www.googleapis.com/drive/v3/files | Template copying, file attachments |
| Gmail API | gmail.googleapis.com/gmail/v1/users/{id}/messages | Email integration for data capture |
| Apps Script | UrlFetchApp, SpreadsheetApp, DocumentApp | Automation and human-in-the-loop UI |
| Personal Intelligence | Built into Gemini (no separate endpoint) | Cross-app context enrichment |
PersonaOps for Google Ecosystem — Version 1.0 — 2026
For Google Cloud Architects, Workspace Developers, and AI System Engineers.
This whitepaper provides a comprehensive technical foundation for implementing voice-to-data intelligence systems within the Google ecosystem, with all patterns grounded in official documentation and verified tutorials. The architecture is designed for immediate implementation while providing clear pathways to enterprise scale.
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