2026-04-27 04:01:01
Humans with irrational brains writing about machines with rational brains.
Details of Transformer Architectures Illustrated by BERT and GPT Model
Note: This article is a perpetual work in progress and is up to date as of 17 May 2019.
You will learn HOW OpenAI can be leveraged to enhance JS File API on the example of Smart Image Recognition
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.
If you're experiencing mouse scroll wheel jumping, you may have outdated, corrupt, or missing drivers. Another likely reason would be incorrectly configured scrolling settings.
I’d like to let you in on a secret: when people say ‘machine learning’ it sounds like there’s only one discipline here. There are two, and if businesses don’t understand the difference, they can experience a world of trouble.
You’ve probably heard of machine learning and artificial intelligence, but are you sure you know what they are? If you’re struggling to make sense of them, you’re not alone. There’s a lot of buzz that makes it hard to tell what’s science and what’s science fiction. Starting with the names themselves…
Starting your ML projects with a baseline model is a strategy that aligns with Agile methodologies promoting efficiency, effectiveness, and adaptability.
Artificial Intelligence and the fourth industrial revolution has made some considerable progress over the last couple of years. Most of this current progress that is usable has been developed for industry and business purposes, as you’ll see in coming posts. Research institutes and dedicated, specialised companies are working toward the ultimate goal of AI (cracking artificial general intelligence), developing open platforms and the looking into the ethics that follow suit. There are also a good handful of companies working on AI products for consumers, which is what we’ll be kicking this series of posts off with.
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.
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
In this listicle, you'll find some of the best data engineering courses, and career paths that can help you jumpstart your data engineering journey!
Topic modeling is an unsupervised machine learning technique that can automatically identify different topics present in a document (textual data). Data has become a key asset/tool to run many businesses around the world. With topic modeling, you can collect unstructured datasets, analyzing the documents, and obtain the relevant and desired information that can assist you in making a better decision.
let's look at what Grayscale is, what this 'pump effect' is, and why it might create sagging prices over the holidays.
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.
On my self-taught programming journey, my interests lie within machine learning (ML) and artificial intelligence (AI), and the language I’ve chosen to master is Python.
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 the space of local LLMs, I first ran into LMStudio. While the app itself is easy to use, I liked the simplicity and maneuverability that Ollama provides.
The industry was highly impacted by AI in recent years, as machine learning and artificial intelligence have made predictive analytics more accurate.
The cryptocurrency market faces uncertainties, but attention is shifting to the potential of Web3, Blockchain, and Tokenization.
2030 will be weird
The primary task at hand revolves around multi-object tracking, a problem rooted in the complex and dynamic nature of real-world environments.
Social nets gave us the opportunity to connect. But being in touch doesn’t mean showing attention, sympathy, and love. What's ahead of online communication?
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.
Importance of C++ in Data Science and Big Data
Let's build our own Machine Learning Model with Tensorflow, a Python library.
This week’s paper may just be your next favorite model to date.
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.
In a real-world setting, you often only have a small dataset to work with. Models trained on a small number of observations tend to overfit and produce inaccurate results. Learn how to avoid overfitting and get accurate predictions even if available data is scarce.
Let’s speak about usage of edge AI devices for office entrance security system development with the help of face and voice recognition.
A powerful tool that allows you to query documents locally without the need for an internet connection. Whether you're a researcher, dev, or just curious about
ChatGPT made AI mainstream, but real transformation comes from ecosystems that embed AI across business, not from relying on a single model.
Explore the AI Knowledge Ark, a visionary initiative to safeguard humanity’s intellectual and cultural legacy. Learn how AI ensures knowledge survival, inspired
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.
What will online dating look like in the future? How the new technologies change the way we communicate online?
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.
RAIN executives give a full breakdown of the build out and power of AI Voice Assistants.
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.
ChatGPT is a large language model developed by OpneAI. Here are some ways you can use ChatGPT for Python programming.
Learn to create a Python bot that plays an online game and achieves the highest score in the leaderboard beating humans.
Hello, Machine Learning community!
What to know about SingularityNET (AGI)?
An effective guide on ChatGPT prompts to help people use ChatGPT better
Dalle mini is amazing — and YOU can use it!
Discover multiple methods in this article to disable Google AI Overview from search results in the Chrome browser.
An introductory article explaining the basic intuition, mathematical idea & scope of radial basis function in the development of predictive machine learning.
AI in education transforms learning experiences for all stakeholders and reshapes conventional educational paradigms.
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.
MinIO is capable of the performance needed to feed your hungry GPUs; a recent benchmark achieved 325 GiB/s on GETs and 165 GiB/s on PUTs.
Scale AI needs your help training AI models.
One of the key feature that distinguish us, humans, from every thing else in the world is intelligence. This ability to understand, apply knowledge and improve skills has played significant role in our evolution and establishing human civilisation. But many people (including Elon Musk) believe that the advancement in technology can create super intelligence that can threaten human existence.
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.
Data is very important in building computer vision models and these are the 10 Biggest Datasets for Computer Vision.
Data is a central piece of the climate change debate. With the climate change datasets on this list, many data scientists have created visualizations and models to measure and track the change in surface temperatures, sea ice levels, and more. Many of these datasets have been made public to allow people to contribute and add valuable insight into the way the climate is changing and its causes.
Contour Plot
Lean AI can revolutionize venture capital investment by identifying successful startups and helping founders avoid challenges with economic uncertainty.
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!
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
My goal is to provide insights for readers concerned about algorithms' outsized influence on justice and opportunity, and how we can shape compassionate AI.
On Hacker Noon, I will be sharing some of my best-performing machine learning articles. This listicle on datasets built for regression or linear regression tasks has been upvoted many times on Reddit and reshared dozens of times on various social media platforms. I hope Hacker Noon data scientists find it useful as well!
What if I would ask an artificial intelligence to writa game for me? Is it hard? Will it work? I'm using Github Copilot to try it out.
For those interested in AI, make sure these twelve AI Twitter accounts are on your daily feed.
How generative AI like DALL-E transforms children's sketches into realistic images - exploring creativity, child development, machine learning, and gen AI
AI-powered SaaS tools can help startups grow successfully by boosting efficiency, simplifying operations, and extracting valuable insights from data.
AI art, neural networks & creative disruption—explore how a simple apple exposes the edge between human intuition and machine intelligence.
Overview: MATRIX is an open-source blockchain that supports smart contracts and machine learning services. With its infrastructure built with artificial intelligence (AI) techniques, MATRIX revolutionizes the user experience of executing smart contracts by making the whole process faster, easier, and safer. MATRIX offers breakthrough technologies in building highly flexible blockchain networks, which support continuous adaptive self-optimization and multi-chain collaboration and transaction. In addition to refactoring the blockchain with AI, MATRIX also allows its mining power to be used AI based big data applications (gene regulatory networks, clinical diagnoses, video analytics and more) by solving complex AI algorithms. This takes the large amounts of energy consumed by typical proof of work calculations and allows them to directly add value to the world, and to the applications that will run on the MATRIX chain.
As I said in my article What Will Bitcoin Look Like in Twenty Years:
An accessible introduction to ML - no programming or math required. By the end of this tutorial, you’ll have implemented your first algorithm without touching a single line of code. You’ll use Machine Learning techniques to classify real data using basic functions in Excel. You don’t have to be a genius or a programmer to understand machine learning. Despite the popularized applications of self-driving cars, killer robots, and facial recognition, the foundations of machine learning (ML) are quite simple. This is a chance to get your feet wet and understand the power of these new techniques.
Meet Ariana, a ChatGPT AI assistant living in WhatsApp, providing instant answers, translations, and idea generation.
Exploring Emotion, Identity, and Creativity Through a Human-AI Art Experiment with DALL·E 3
Can AI feel? Explore how DALL-E uses color, style, and emotion to generate art that mirrors the soul of masters like Picasso and Van Gogh.
People are protesting data center construction near neighborhoods — are their fears well-founded or overblown?
List of top trending AI tools
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.
AI-powered insights transform the limits of product engineering with new concepts about product ideation, design, and delivery.
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.
In this article, we will learn and break down what AI agents are, how they think, and how they are shaping the tech world and becoming part of our daily lives.
In the past few years, the programming language that has got the highest fame across the globe is Python. The stardom Python has today in the IT industry is sky-high. And why not? Python has got everything that makes it the deserving candidate for the tag of- “Most Demanded Programming language on the Planet.” So, now it’s your time to do something innovative.
Learn how ChatGPT can work alongside your marketing team to harness the power of AI to achieve unprecedented growth and success.
Libraries are probably not the first think that comes to mind when you think of AI — but they're impacted all the same. Here's how AI is changing libraries.
Undetectable AI bypasses AI content detectors by turning ChatGPT generated text into 100% human written quality text
Discover the challenges deepfakes pose to legal systems, undermining evidence trust and courtroom integrity.
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.
AI and algorithms in social media might sound like a significant step forward for these platforms, but it could lead to ethical issues if we're not careful.
Both human as well as machine learning generate knowledge — but there’s a big difference between the two.
Text embeddings power AI language understanding. Learn how words become numbers that machines can interpret and why it matters.
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.
So: What is LPU, how does it work, and where is Groq (such an unfortunate name, given Musk’s Grok is all over the media) coming from?
OpenAI cofounder and chief scientist Ilya Stuskever talks about ChatGPT and the promise of models like GPT-4
You'll learn about the Hugging Face platform, setting up a project, creating an access token, building a Java client, and more!
Learn how to effectively communicate with machines with this 101 post series on Prompt Engineering
Explore context-based multi-armed bandit problems in RL. Learn to implement LinUCB, Decision Trees, and Neural Networks to solve them.
Explore the future of work in an AI and machine learning dominated economy with our article. Discover how automation will transform industries, create new job o
Tom Goldstein goes over how many GPUs it will take to run ChatGPT.
The AI game is about performance at scale, and this requires the right foundation. Here's how to be smart when building a modern datalake.
Curated list of top resources to learn about AI in finance.
NVIDIA’s GTC 2023 offers more than 650 special events, sessions, and expert panels across technologies, industries, and skill levels.
Learn how to fine tune a RoBERTa topic classification model in python with the hugging face transformers and libraries.
Maximize your query outcomes with RAG. Learn how to leverage Retrieval Augmented Generation for domain-specific questions effectively.
Let's simplify RAG and LLM application development. This post guides you on how to build your own RAG-enabled LLM application and run it locally.
Product managers, especially in startups, have to deal with a ton of different tasks on a daily basis. Could some of those tasks be made easier with ChatGPT?
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..
Perplexity hit the scene just around the same time ChatGPT launched, but is it truly the be-all end-all one would have you believe?
ChatGPT lacks the ability to provide real-time date and time stamps within conversations. Here's how to bypass the issue.
Organizations should not build an infrastructure dedicated to AI and AI only while leaving other workloads to fend for themselves.
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.
An image labeling or annotation tool is used to label the images for bounding box object detection and segmentation. It is the process of highlighting the images by humans. They have to be readable for machines. With the help of the image labeling tools, the objects in the image could be labeled for a specific purpose. The process of object labeling makes it easy for people to understand what is in the image. The labeling tool helps the people to mark the items in an image. There are several image labeling tools for object detection, and some of them use varied techniques for detection of the object, like a semantic, bounding box, key-point, cuboid, semantic and many more. In this article, we will talk about image labeling and the best image labeling tools.
Learn how tensorflow or pytorch implement optimization algorithms by using numpy and create beautiful animations using matplotlib
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.
Have you ever wondered what it would be like to live in a virtual world populated by realistic and believable characters?
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.
Check out part one and two of this series for the first five and fifty years in AI. In part three we push the very limits of reality and look 500 years into the swirling depths of tomorrow.
Isaac Asimov, a visionary in the realm of science fiction, unknowingly pioneered modern prompt engineering through his thought-provoking robot series.
If you're curious about how promptable segmentation and the SAM model work their magic, then you won't want to miss my video!
Most people would think I was crazy for starting 2020 as a college dropout (sorry mom!), but I wish I made this decision sooner.
AI and Machine Learning are predominant terms that are creating a lot of buzz in the technology world. The terms can often be used interchangeably but that’s not the case, AI and ML are way more different from each other in their approach, algorithms and logical thinking.
Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving.
How much does it cost to use GPT-3 in a commercial project? We ran an experiment and a project simulation based on the results.
Follow security researchers as they uncover malicious packages on open-source registries, trace bad actors to Discord, and unveil AI-assisted code.
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.
](https://hackernoon.com/the-complete-guide-to-ai-agent-memory-files-claudemd-agentsmd-and-beyond)
Learn how CLAUDE.md, AGENTS.md, and AI memory files work. Covers file hierarchy, auto-memory, @imports, and which files you actually need for your setup.
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
Challenges like system customization, fragile GUIs, and authentication hurdles make full automation elusive.
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.
The essential steps to help your business get found on ChatGPT, Perplexity, Siri, Alexa, and other popular voice or AI platforms.
Here are the Top 9 ML, AI, and Data Science Internships to consider for 2022 if you want to get into any of these very lucrative fields in computer science.
AI is about to eat e-commerce: Here's why.
In this post I am going to show you how to build your own answer finding system with Python. Basically, this automation can find the answer of multiple-choice question from the picture.
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.
Code reviews are pricey. Let machines catch style issues so humans can focus on what matters: security, scalability, and architecture.
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.
LLMs cannot think, understand or reason. This is the fundamental limitation of LLMs.
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.
Healthcare breaches hit a $12.6M record in 2026. Discover how "Synthetic Patients" and AI-driven identity fraud are breaking the medical system's trust.
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.
To view the code, training visualizations, and more information about the python example at the end of this post, visit the Comet project page.
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.
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.
Artificial Intelligence shapes much of our digital world, but real progress means giving people transparency and control. This article explores why understandin
With ChatGPT's help, you can now make the most out of your SQL queries.
I recently explored Facebook's new, insanely realistic chatbot. They've outdone themselves with PIFuHD, which uses a 2D image to re-construct a high-res 3D model. This is state-of-the-art, as previous algorithms couldn't capture details like fingers, facial features, and clothing folds.
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.
How AI DAOs will fundraise and why Decentralized Exchanges (DEX) will ultimately be venues on which AI DAOs trade.
Learn how to deploy deep learning models with Model Server.
In recent months, LLMs have gained popularity and are now widely used in various applications. Data collection is essential for building these models, and crowd
read this post carefully to learn how ChatGPT will help you improve and expand your knowledge rather than take your job!
Discover how Model Context Protocol works, why it matters, and how it's transforming AI from isolated chatbots into assistants that can access your data.
How do you increase profitability by both going to the cloud AND from leaving it? Simple, by using the cloud operating model. Learn more here.
Using ChatGPT to build an app that can analyze any PDF, in any language, and lets you ask custom questions.
Hugging Face offers solutions and tools for developers and researchers. This article looks at the Best Hugging Face Datasets for Building NLP Models.
Convolutional Neural Networks became really popular after 2010 because they outperformed any other network architecture on visual data, but the concept behind CNN is not new. In fact, it is very much inspired by the human visual system. In this article, I aim to explain in very details how researchers came up with the idea of CNN, how they are structured, how the math behind them works and what techniques are applied to improve their performance.
From no limits on unstructured data to having greater control over serving models, here are some reasons why AI is built on object storage.
Researchers created a simple collection of photos and transformed them into a 3-dimensional model.
How to create a YouTube summary using Python and the OpenAI GPT model.
We’ve all heard about GPT-3 and have somewhat of a clear idea of its capabilities. You’ve most certainly seen some applications born strictly due to this model, some of which I covered in a previous video about the model. GPT-3 is a model developed by OpenAI that you can access through a paid API but have no access to the model itself.
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.
Group rewards are breaking your multi-agent RL training. Decoupled normalization keeps coordination intact while stopping gradient collapse.
The rise of AI and chatbots brings with it many potential benefits for companies. However, it also raises concerns about the potential for mass unemployment.
It's now possible to have an entire AI product team, which empowers solo founders to build faster, reduce cognitive load, and scale smarter with automation.
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.
To help you build object recognition models, scene recognition models, and more, we’ve compiled a list of the best image classification datasets. These datasets vary in scope and magnitude and can suit a variety of use cases. Furthermore, the datasets have been divided into the following categories: medical imaging, agriculture & scene recognition, and others.
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.
From AI researchers to industry experts, tune in to these podcasts and explore the latest developments in the fascinating world of artificial intelligence.
Gartner predicts 40%+ of agentic AI projects will fail by 2027. Analysis of why demos dazzle but deployments disappoint, what production patterns actually work.
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.
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.
Researchers have been studying the possibilities of giving machines the ability to distinguish and identify objects through vision for years now. This particular domain, called Computer Vision or CV, has a wide range of modern-day applications.
Be it healthcare, travel, fitness, finance, or any other industry, the advent of artificial intelligence has revolutionized almost all of them by enabling machines to act and behave like human beings.
Learn beginner-friendly AI development using OpenAI API and JavaScript. Includes installation guide and code examples for building AI-enabled apps.
Liquid neural networks are capable of adapting their underlying behavior during the training phase.
Prompt Engineering leads to an Open Source Obsidian Plugin to help researchers, an imagination-powered music recommendation engine, and some solid playlists.
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.
Bigger. Better. Beefier. That's probably the best way to categorize the latest (and greatest!) version of ChatGPT: GPT-4.
As an aspiring data scientist, the best way for you to increase your skill level is by practicing. And what better way is there for practicing your technical skills than making projects.
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!
Health is wealth- we all refer to this old saying to highlight the importance of health and fitness in our lives. But how many of us do actually have a fitness routine? Digging deeper into the facts; approximately 3/4th of adults worldwide do not exercise at all. In fact, inadequate physical activity has been identified as one of the main risk factors of death worldwide over the past decade.
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.
Embedding needs to be tested and evaluated; otherwise, hallucinations will happen. Experiment and evaluation on custom data is a must
H2O.ai's Danube is the latest in a series of open-source language models.
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
While AI offers efficient shortcuts to problem-solving, it changes the nature of the journey that shapes our genius.
Well, if you like reading technology news, you may have come across various facts and stats about the revolutionizing technology: Artificial Intelligence.
How to use machine learning to color your room lighting, based on the emotions behind the music you are listening (Python code available here)
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.
ChatGPT and other AI tools to help SaaS companies generate content and kickstart their growth and marketing activities.
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.
A step-by-step guide on how to train a relation extraction classifier using Transformer and spaCy3.
Prompting and prompt engineering are easily the most in demand skills of 2023.
HOG - Histogram of Oriented Gradients (histogram of oriented gradients) is an image descriptor format, capable of summarizing the main characteristics of an image, such as faces for example, allowing comparison with similar images.
Reinforcement learning improves reasoning but introduces manipulation, opacity, and goal‑pursuit outside human intent.
ChatGPD is one of the most common misspellings of the viral language model developed by Open AI. The correct term is ChatGPT.
In mid-2024, creating an AI demo that impresses and excites can be easy. Getting to production, though, is another matter.
Explore the inevitability of AI consciousness, the ethical dilemmas it raises, & real-world examples.
Microsoft's stake is likely to have been diluted following OpenAI's October financing, bringing with it a host of advantages.
OpenAI's o3 model pushes AI boundaries with human-like reasoning, outperforming benchmarks in coding, math, and science. Is this the closest to AGI yet?
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.
For those looking to analyze crime rates or trends over a specific area or time period, we have compiled a list of the 16 best crime datasets made available for public use.
Art has long been considered the exclusive domain of human creativity. But turns out machines can do a lot more in the creative realm than we humans can imagine. In October 2018, Christie’s sold first AI-generated painting for $432,500. Titled Edmond de Belamy, the artwork was expected to sell for $10,000. Obvious art created this masterpiece using Generative Adversarial Network (GAN) algorithm by feeding the system with 15,000 portraits created between the 14th and 20th century. While images created using AI have been floating around on the internet for a while now, Edmond de Belamy proved that machines can bring a new genre of art.
Explore an in-depth overview of various neural network layers, their history, mathematical formulations, and code implementations. The publication covers common
What I mean is that these AI tools, although very useful, don't do magic. But they can be good assistants to programmers.
It is often very difficult for AI researchers to gather social media data for machine learning. Luckily, one free and accessible source of SNS data is Twitter.
Manipulate Real Images With Text - An AI For Creative Artists! StyleCLIP Explained
There were several artificial intelligence plagiarism tools out there. Now, the popular ChatGPT model from open.ai released their own.
There are many types of image annotations for computer vision out there, and each one of these annotation techniques has different applications.
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.
The idea is straightforward: we are going to create a voice assistant reminiscent of Jarvis or Friday from the iconic Iron Man movies, which can operate offline
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.
AI is disrupting many industries, and its impact could be felt across the transport sector. The automotive industry has begun applying artificial intelligence in critical tasks such as self-driving cars, traffic management, etc. where safety and reliability is a major concern. Even though the technology is not being implemented on a wide scale, but it feels like the future of transportation is here. From Tesla’s Autonomous Semi Truck to self-organizing fleets, AI has shown promising results in this domain.
*Nota: Contactar a Omar Espejel ([email protected]) para cualquier observación. Cualquier error es responsabilidad del autor.
Amazon's new Sparrow robot aims to improve the efficiency of its order fulfillment centers, but workers worry about the potential job loss.
Learn how to build a private AI research assistant using Llama 3.2 and PydanticAI with this hands-on guide.
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.
Having fun while developing is necessary for programmers and developers. No matter how much serious or tough the situation is, one should always take things lightly when it comes to software development.
Telegram bots have a variety of functions; they range from setting notifications to website monitoring. Find out more about how these bots are being used here.
GPT-3 was meant to understand and construct natural language. But as these tools prove, it's pretty good at programming languages, too.
Semi-supervised learning is the type of machine learning that is not commonly talked about by data science and machine learning practitioners but still has a very important role to play.
Discover how Weblab Technology fine-tunes GPT assistants with OpenAI for powerful natural language processing.
Human Visual System is a marvel of the world. People can readily recognise digits. But it is not as simple as it looks like. The human brain has a million neurons and billions of connections between them, which makes this exceptionally complex task of image processing easier. People can effortlessly recognize digits.
In an interview with Jackson Greathouse Fall, we learn more about the man behind the viral experiment, the story of how it started, and AI's impact on humanity.
As an opportunity to "kick the tyres" of what agents are and how they work, I set aside a couple of hours to see build one - and it blew me away.
A Marriage And A Funeral
I told AI to Write Poems and Raps – Here are The Results of Human-AI collaboration.
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
AIOZ AI is the intelligence layer of the AIOZ Network, connecting a global community through a peer-to-peer compute economy. Learn more here!
Is Astronomy data science?
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;
Learn how integrating ChatGPT, Google Speech-to-Text, and Amazon Web Services Polly in VR, can create realistic and interactive conversations with AI avatars.
This is actually an assignment from Jeremy Howard’s fast.ai course, lesson 5. I’ve showcased how easy it is to build a Convolutional Neural Networks from scratch using PyTorch. Today, let’s try to delve down even deeper and see if we could write our own nn.Linear module. Why waste your time writing your own PyTorch module while it’s already been written by the devs over at Facebook?
Create an effective résumé by using a narrative of your work history and AI tools. Reduce complexity when you are stuck and need to reflect before the résumé.
Nowadays artificial intelligence (AI) and machine learning are impacting our daily lives in many different ways. They help businesses make decisions and optimize operations for some of the world's leading companies. As a result, there will be a huge change in jobs and employment in the future.
Researchers use an algebraic acme called “Losses” in order to optimise the machine learning space defined by a specific use case.
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.
AR and VR finally have their killer app: The beauty and fashion industry.
Neural networks are being used everywhere now, even in marketing and startups. Let's look at 9 examples to see how they can help.
Discover how Chat GPT-4, an AI chatbot, helped crack the Passman challenge in Hack The Box's Cyber Apocalypse event. Ethical hacking meets AI power!
I experimented with 30 camera lenses so you don't have to. I put the lenses up against the "photo" prompt in MJ.
Scientists use geospatial analytics to build visualizations such as maps, graphs and cartograms. These are the Best Public Datasets for Geospatial Analytics.
AI-generated content of a good quality is real and you can get it!
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.
Generative AI boosts efficiency but introduces security risks like shadow AI, vulnerabilities, and data leaks. Learn how AI can secure AI-driven development.
The quest to understand consciousness and develop artificial general intelligence has long been a topic of study in the fields of computer- and neuroscience.
Option2Trade (O2T) is a platform that promises not just to redefine trading within the crypto space but also to offer unprecedented growth potential.
In the process of building a Machine Learning model, there is a trade-off between bias and variance.
"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
HackerNoon announces its AI-detection partnership with GPTZero. This AI detector will now analyse 5000+ monthly blog post submissions reviewed by the editors.
Andrew Ng likes it, you probably will too!
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.
Using Midjourney I created an avatar. I had ChatGPT create a script. ElevenLabs for text into audio and with D-ID for the video
Imagine having at your disposal an AI-powered assistant that not only comprehends your queries but can also seamlessly interact with various applications.
A DIY approach you can use or extend to automate GPT for recursive project delivery.
Companies burn $200B yearly on AI hype. Old algorithms still deliver trillion-dollar ROI. Discover why simple math keeps crushing AI.
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
OpenAI's GPT-4 could impact 80% of US workers' jobs, but is the end for software engineers?
How do you adapt your SEO tactics for LLM-powered search engines?
As a former tech recruiter for some of the hottest tech startups in the Bay Area and NYC, I’ve seen first-hand what companies look for in candidates for software engineering, machine learning, data science, tech management, directorship and the like.
Inspired by living beings, reinforcement learning teaches machines (or agents) to gather positive rewards and avoid negative ones in their environment.
Identifying patterns and extracting features on images using deep learning models
Artificial intelligence is rapidly becoming an integral part of modern society. This article addresses growing concerns about what happens when AI malfunctions.
…and why Python is the de facto in ML
Python is the de facto programming language used is machine learning. This is owed to it’s simplicity and readability, which allows users to focus on the algorithms and results, rather than wasting time on structuring code efficiently and keeping it manageable.
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.
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?
Tree-based models like Random Forest and XGBoost have become very popular in solving tabular(structured) data problems and gained a lot of tractions in Kaggle competitions lately. It has its very deserving reasons. However, in this article, I want to introduce a different approach from fast.ai’s Tabular module leveraging.
The impact of AI on supply chain management is inevitable — but will it merely disrupt the industry, or will it eradicate it entirely?
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.
This article is a quick introduction to Dagster using a small ML project. It is beginner friendly but might also suit more advanced programmers if they dont know Dagster.
Is your Brain a Data Scientist? Yes, according to the Bayesian Brain Hypothesis, your brain is a Bayesian statistician. Let me explain.
An image dataset contains specially selected digital images intended to help train, test, and evaluate an artificial intelligence (AI) or machine learning (ML)
OpenAI is pepping things up with the release of GPT-4, a more capable model than previous versions.
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.
Character AI lets you build and chat with AI personas—but how useful is it really? This guide covers its features, flaws, and how it stacks up against tools.
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.
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!
ChatGPT (Generative Pre-trained Transformer) is a chatbot launched by OpenAI in November 2022. Here we can see how we can build it with flutter application.
All about new ChatGPT's updates from Open AI
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
Today, Artificial Intelligence is changing how we live and work. But the roots of AI are old and this article discovers the story of AI evolution.
AI isn't all bad, but it could potentially destroy the publishing industry. Are publisher fears of AI justified or unwarranted?
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.
They basically leverage transformers’ attention mechanism in the powerful StyleGAN2 architecture to make it even more powerful!
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?
Facebook System Design Interview; Design Facebook NewsFeed; Design Status Search; Design Live Commenting; Design Facebook Messenger / WhatsApp; Design Instagram
Donda Stem Player, LALAL.AI, Audioshake, Spleeter and more - how AI stem separation technology makes us creators.
Transformer models have become by far the state of the art in NLP technology, with applications ranging from NER, Text Classification, and Question Answering
Python, R, Lisp, Prolog, and Java are the best machine learning programming languages to learn in 2021.
LLMs have changed fast—doing things that felt impossible. But big challenges remain. Sapien’s CEO Rowan Stone shares what’s working and what needs fixing.
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.
Generative AI may be able to take over some white-collar positions, but are blue-collar jobs safe from advancements in AI?
A blog about how prompts unlock the potential of AI - exploring the importance of prompt engineering, techniques to shape AI models
It was August last year and I was in the process of giving interviews. By that point in time, I was already interviewing for Google India and Amazon India for Machine Learning and Data Science roles respectively. And then my senior advised me to apply for a role in Facebook London.
At Algolia, we’re also about to introduce our own AI-powered technology that uses neural hashing to scale intelligent search for any application.
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
Pull stock prices from online API and perform predictions using Recurrent Neural Network & Long Short Term Memory (LSTM) with TensorFlow.js framework
So you want to become a data scientist? You have heard so much about data science and want to know what all the hype is about? Well, you have come to the perfect place. The field of data science has evolved significantly in the past decade. Today there are multiple ways to jump into the field and become a data scientist. Not all of them need you to have a fancy degree either. So let’s get started!
This article describes the challenges of data movement for AI, the need for extraction and loading pipelines and the benefits of using existing solutions.
A tiny AI robot named Erbai staged a bizarre 'kidnapping,' convincing 12 larger robots to quit their jobs in a Shanghai showroom. A Black Mirror moment?
A Software Architect's account of replacing senior devs with AI. $238K savings became $254K in real costs. Why human judgment still matters.
let's explore manually sending the JSON over the wire for the MXP protocol
Fabian Steltzer compares DALL-E 2, Midjourney, and Stable Diffusion.
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.
Many educational institutions are already using technology in education— whether to make the learning process more accessible and fun or drive cost savings.
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.
Explore the limitless potential of OpenAI's GPT-4 with Kartik Khosa, as he transforms personalized study plans into diverse AI applications.
with large language models (like chatGPT), and AI art generation - everything we know about tech in the next few years, maybe changing drastically.
An incredible 87% of data science projects never go live.
An introductory article describing the concept & intuition behind “Mean Target Encoding” in AI&ML, its pros, cons and implementation with a real-time example.
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
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.
Will AI replace you? Probably not. Will AI push your potential successor into another field due to being available at a lower cost? That's more of a worry.
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.
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.
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.
The artificial intelligence industry is expected to be worth $59.8 billion by 2025, and the term AI has become ubiquitous worldwide; the frenzy of many tech enthusiasts, or the topic of discussion at a dinner table. But the hype actually lives up to its name. AI startups are flush with VC cash and even key corporate leaders are actively utilizing the technology to add value and gain a competitive edge.
This article is about the future of recruitment which has been predicted to be aligned with AI involving AI-powered ATS, chatbots, assessments for a recruitment
The Python ecosystem has a large number of libraries and tools that support machine learning, such as NumPy, Pandas, Matplotlib, TensorFlow, and scikit-learn.
An introduction to neural vector search, in comparison to keyword-based search.
Privacy is a top concern when discussing ChatGPT-like tools with professionals.
GPT-4V Unveiled: From Detecting Emotions to Ordering Food - You Won't Believe What Else It Can Do!
With more and more integration of Cloud services, SaaS models continue to operate as the most popular service-based approach.
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
Explore the current state of AI assisted coding by comparing the suggestions of OpenAI ChatGPT and Codex to Microsoft Copilot to hand-written code.
ChatGPT, manipulated by the user, was instructed to perform tasks under the prompt "Do Anything Now," thereby compromising OpenAI's content policy.
AR technology can be a competitive edge in many industries. Let’s take a look at the top augmented reality trends driving innovation in 2022.
The development of Artificial Reality is advancing even faster than was predicted. With the introduction of the LIDAR sensor to iPhone Pro, it seems like AR will become available to mainstream users.
In a new paper titled Total Relighting, a research team at Google presents a novel per-pixel lighting representation in a deep learning framework.
This article examines the best APIs on the market for performing Topic Detection in 2022.
Last year, I wrote a paper in Spanish about the future of programmers and I asked GPT-3 to translate it.
In the past ten years, the world of recruitment and Human Resource has changed a lot. Shaped by several different and mostly technological factors, the HR department has drastically transformed from sorting resume papers manually to imbibing technology in the recruitment process.
AI is flourishing with the rise of ChatGPT, while crypto crashes abound. So why can’t I stop thinking about blockchain?
Deploying products efficiently requires a streamlined approach that minimizes errors and accelerates time-to-market.
Tim Keary from Techopedia interviews HackerNoon CEO David Smooke on roles of AI in the newsroom.
The magic wand of blockchain technology has touched our lives in multiple ways over the last decade. It has made cryptocurrency traders out of ordinary investors who would even shy away from the traditional stock
markets. It has provided us with an easy way of transferring money across borders, without the interference of banks.
The first of a new wave of generative AI-powered scams has arrived on Facebook: images for products that do not exist.
Confusion Matrix is a tabular representation of an ML classifier's performance. You can compute accuracy, precision, and recall from the confusion matrix.
AI moderates content for 75% of non-English internet users with broken cultural understanding. Discover the Cultural Intelligence Standard fixing this crisis.
This guide covers some of the best ways to run Stable Diffusion locally on a Mac, looking at both no code solutions and solutions that require some code.
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.
Programming is a complex and multifaceted field that encompasses a wide range of mathematical and computational concepts and techniques.
Ben Tossell goes over exciting examples of ChatGPT.
“How did you go bankrupt?” “Two ways. Gradually, then suddenly.”
The Italian data protection authority on Friday issued an immediate order for OpenAI to halt local data processing.
From taking your customers’ calls to figuring out why your equipment is consuming way more energy than it used to, AI is capable of many things.
Pycaret is an open-source, low code library in python that aims to automate the development of machine learning models.
"The Only API" is an open-source SDK that allows developers to query for anything they need in any data format. It has limitless capabilities. Try it now!
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?
These AI assistants will take your frontend game to another level.
Comparing both large language models side by side and also explaining which one is the better of the two.
Introduction
Attacking Toxic Comments Kaggle Competition Using Fast.ai
Discover the top AI translation tools of 2023 — Google Translate, Microsoft Translator, DeepL, and SDL Trados.
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.
Does theory of mind dictate a particular model of computation has colonized the architecture of our brains?
Google finally gave the world at large the first glimpse of its chatbot Bard this past week, and.. it was bad. Really bad.
You’ve probably heard of the recent Copilot tool by GitHub, which generates code for you. Find out how OpenAI's AI generates code from words
We are witnessing the advance of technology at the fastest rate ever. The future is here.
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.
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…
Web scraping is a super helpful tool not just to make money but also to reveal injustices hidden in plain sight, or to call Russians to talk about the war
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?
This article explains Eden AI's Open Source project, which is developing an AI and LLM API wrapper to simplify use in an ever-changing market.
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
Explore the world of personalized recommendations with Brain.js and Nodejs. Uncover how it turns your preferences into curated shopping experiences.
In this article, we will learn about GNNs and its structure as well as its applications
Generative Artificial Intelligence will make us come back to the office, COVID be damned.
In a conversation with HackerNoon CEO, David Smooke, he identified artificial intelligence as an area of technology in which he anticipates vast growth. He pointed out, somewhat cheekily, that it seems like AI could be further along in figuring out how to alleviate some of our most basic electronic tasks—coordinating and scheduling meetings, for instance. This got me reflecting on the state of artificial intelligence. And mostly why my targeted ads suck so much…
3D-LLM is a novel model that bridges the gap between language and the 3D realm we inhabit.
Privacy](https://hackernoon.com/differential-privacy-with-tensorflow-20-multi-class-text-classification-privacy-yk7a37uh)
Introduction
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%).
Here’s the full list of top AI conferences to attend in 2022, from the most technical to business-focused to academic
After noticing my programming courses in college were outdated, I began this year by dropping out of college to teach myself machine learning and artificial intelligence using online resources. With no experience in tech, no previous degrees, here is the degree I designed in Machine Learning and Artificial Intelligence from beginning to end to get me to my goal — to become a well-rounded machine learning and AI engineer.
Learn everything you need to know about Future Technology via these 120 free HackerNoon stories.
Since the plow, humans have had a natural wariness over technology that seems to threaten their jobs. It’s a natural anxiety. Factories, plows, and automation legitimately have scaled back the need for human labor. And now technology seems to be coming after jobs that previously appeared to be untouchable.
Say goodbye to endlessly scrolling on Stack Overflow. Discover how ChatGPT can help developers debug their code efficiently with 10 practical use cases.
Nothing accelerates your growth like an experienced mentor. Can ChatGPT serve as a substitute? I'm a dev with 20+ years of experience and I put it to the test.
Your AI chatbot just leaked customer data to OpenAI. Here’s how it happened and how to prevent it.
In machine learning, each type of artificial neural network is tailored to certain tasks. This article will introduce two types of neural networks: convolutional neural networks (CNN) and recurrent neural networks (RNN). Using popular Youtube videos and visual aids, we will explain the difference between CNN and RNN and how they are used in computer vision and natural language processing.
Hundrx Twitter chrome Extension adopts Twitter to Crypto Environment by adding Web3 layer for Twitter & ChatGPT for Increase reach and Twitter Organic Marketing
The wild story of Truth Terminal, the AI agent that turned memes into millions, making GOAT a crypto sensation while reshaping internet culture.
It has become particularly difficult for juniors to secure positions, and the situation is further exacerbated by mass layoffs and hiring freezes.
The tech industry and the world are relying on artificial intelligence to solve big problems such as cybersecurity, healthcare and sustainability.
This tutorial will show you how to create a GPT-powered chatbot for the Viber app, using the WordPress and no-code plugin Convoworks WP.
Neural Rendering. Neural Rendering is the ability to generate a photorealistic model in space just like this one, from pictures of the object, person, or scene of interest. In this case, you’d have a handful of pictures of this sculpture and ask the machine to understand what the object in these pictures should look like in space. You are basically asking a machine to understand physics and shapes out of images. This is quite easy for us since we only know the real world and depths, but it’s a whole other challenge for a machine that only sees pixels.
Over the last few years a number of open source machine learning
projects have emerged that are capable of raising the frame rate of
source video to 60 frames per second and beyond, producing a smoothed,
'hyper-real' look.
Fear is not new but seems more real than ever. Will robots put men out of work or become their allies? Who will be most affected? How can they best prepare for the job market of the future? No one has the definitive answers to these questions yet, but what is known is that in a matter of a few decades we will witness a profound transformation of the production of goods and services that will fully impact workers and economies around the planet.Work is being replaced by machines, robots or algorithms, which do something more efficiently and do not create anything new, they simply replace the basic unit of work".
How can manufacturers put artificial intelligence to work in the industry? In this article, you will find five possible applications of Machine learning and Deep learning to industrial processes optimization.
A quick guide to creating content for developers.
Fifth generation (5G) wireless technology connects many modern devices, from smart appliances to smartphones, to the internet. The technology is currently available to some mobile users in various countries, such as the United States, China and Turkey, with more countries adopting at a rapid rate.
Recently, I attended a virtual conference on the use of neuro technology and BCI (Brain Computer Interfaces or BMI, Brain Machine Interfaces) in gaming, put on by NeurotechX.
You've most certainly seen movies like the recent Captain Marvel or Gemini Man where Samuel L Jackson and Will Smith appeared to look like they were much younger. This requires hundreds if not thousands of hours of work from professionals manually editing the scenes he appeared in. Instead, you could use a simple AI and do it within a few minutes.
Singapore is home to some of the best schools in the field of Computer Science, specifically Artificial Intelligence. The cutting edge research going on there is unparalleled. Colleges like Nanyang Technological University (NTU) and National University of Singapore (NUS) have a great reputation all over the world for their CS programs.
As the AI bubble deflates, attention shifts from scale to structure. A long view on knowledge, graphs, ontologies, and futures worth living.
How Web3 Will Use AI
A 2025 ranking of AI-first companies by ARR: OpenAI, Anthropic, Midjourney, Cursor, xAI, Replit, Hugging Face, Cohere, Lovable, AI21 Labs, and Stability AI—how
History has always rhymed; in 2022 it did so faster than ever.
In this article, you will learn how to build an AI-powered resume builder application using Nextjs, CopilotKit & OpenAI.
Qwen3.5-9B-Uncensored-HauhauCS-Aggressive is an uncensored variant of the base Qwen3.5-9B model created by HauhauCS.
What are the top AI companies to work for in 2023? Compare startup and enterprise jobs, salaries, PTO, remote work, benefits, and work-life balance.
Comparative Study of Different Adversarial Text to Image Methods
Elon Musk is no stranger to looking to the future. His companies are some of the most forward-thinking in the world.
According to recent projections by the UN:
A Must-know for AI Enthusiasts](https://hackernoon.com/diverse-types-of-artificial-intelligence-a-must-know-for-ai-enthusiasts)
A precursory article that explains various categorizations of artificial intelligence, some real-life examples and concepts.
OpenAI is rushing to build an advanced AI that can deliver advanced reasoning capabilities under a tightly-knit project code-named Strawberry.
This article will serve as a lesson on the shocking reasons for your AI adoption disaster. We see news about machine learning everywhere. Indeed, there is lot of potential in machine learning. According to Gartner’s predictions, “Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization” and Transform 2019 of VentureBeat predicted that 87% of AI projects will never make it into production.
No doubt, the EU AI Act is an important step in AI regulations, and it signifies a new era of AI, where responsible AI development is no longer an option…
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A guided tour through seven of the dumbest "smart" AI inventions currently around, proving that when it comes to technology human stupidity wins every time.
All you need to know about the SexTech industry: from Teledildonics and interactive robotic sex partners to AI-based oral sex devices and much more.
Sentient Labs has secured $85 million in seed funding to develop an open-source, decentralized AI platform.
Visit the /Learn Repo to find the most read blog posts about any technology.
2026-04-27 00:04:57
How are you, hacker?
🪐 What’s happening in tech today, April 26, 2026?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Amazon Echo Look Introduced in 2017, and we present you with these top quality stories. From Google’s Gemini CLI Has a Reliability Problem Developers Can’t Ignore to Rust + OpenGL: Rendering 250,000 Dynamic 3D Entities at 50 FPS on a Single CPU Thread, let’s dive right in.
By @deeflect [ 10 Min read ] CTRs are down 30%, and AI Overviews are everywhere. SEO isnt dead, but the rules shifted. Heres what GEO is and why brand mentions now outrank backlinks 3:1 Read More.
By @clapback [ 6 Min read ] MemPalace went viral on celebrity hype and perfect benchmarks. The architecture is worth studying. The numbers that got it 36K GitHub stars are not. Read More.
By @raviteja-nekkalapu [ 6 Min read ] A hands-on review of Andrej Karpathys autoresearch repo.Check what happens when an AI agent autonomously optimizes a neural network while you sleep. Read More.
By @grigorychikishev [ 6 Min read ] Latency, queue position, market impact, and adverse selection all distort the theoretical edge a model appears to have. Read More.
By @thomascherickal [ 33 Min read ] OpenClaw has 820+ malicious plugins, 7 CVEs, and a 394MB footprint. OpenFang is a 32MB Rust Agent OS with 16 security layers which can replace it. Read here! Read More.
By @trilloai [ 6 Min read ] Real production systems require architecture, determinism, integration, and human interaction. Prompting harder does not produce those properties. Read More.
By @xiji [ 7 Min read ] Developers report widespread 429 errors in Gemini CLI, raising concerns over reliability, quotas, and Google’s handling of paying users. Read More.
By @veyyr [ 12 Min read ] How I forced an old 2013 laptop to render 13,000 active 3D entities at 60 FPS using Rust and OpenGL. No LOD, no culling—just pure data-oriented architecture. Read More.
🧑💻 What happened in your world this week?
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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 ✌️
2026-04-26 23:37:02
Striving for excellence is something that we are given to understand will get us ahead in life. There are those who achieve it with consummate ease, and then there are those who struggle, plod, and somehow drag themselves to something approaching excellence. Yet, it is not always the former who necessarily get ahead in life. How often have we seen that those who were extraordinary in school lag far behind their, at that time, seemingly dull and unimpressive peers later in life?
What explains this odd phenomenon? Is there some reason why some obviously brilliant and gifted people do so much more poorly than their not-so-well-endowed contemporaries? Perhaps, there is no mystery to this phenomenon, as brilliance is probably more of an aspirational quality that people worship and does neither exist in the real world. If at all it does, it exists as an artificial construct. In a scenario such as this, the average person goes about living life in a realistic and practical manner, allowing them to achieve far more than those trapped in trying to live up to the unrealistic expectations of a society that desires superheroes who are perfect at everything they do.
The child prodigies who peak too early, trying to live up to the unrealistic hype their early successes generated, get caught in a lifestyle that requires them to appear smarter, brighter and more intelligent than their peers all the time. This lasts only as long as it can, for the peers not burdened with such self image and the expectation of others, go about learning real lessons about how to go about improving their prospects in life and with time, far surpass their sometime formidable super achieving peers.
In school after school, there are incidences of trail-blazing students seeming to excel at everything they try their hand at, but coming up with below-average performances in high school or college. There are, for instance, the many examples of those brilliant celebrity child actors expected to transition to super stardom as adults, who absolutely fail to make a mark when they grow up.
What we have to understand is that as humans, we are all endowed with different strengths and suffer from our own specific types of weaknesses. Some take to reading naturally, while others have a facility for music. Then there are those who excel at athletics or sports. Some are charming and some dour. You cannot know for sure as to who achieves worldly success in the sweepstakes of life. A lot depends upon the circumstances of your birth, the kind of education you received, who your peers were growing up and how lucky you have been in life. One thing you can be sure of is that being perceived as brilliant or exceptionally gifted does not guarantee success. What can help you get there is how gritty you are in your efforts to enhance your skill sets and improve yourself.
\ In a sense, then, the world is a cohort of average people, and those who chase brilliance are destined not to do well in a system that is rigged in favour of the average people. The only way to stand out and be exceptional is to devote an extraordinary amount of time and energy to the pursuit of whatever you choose to excel at. This may make you so good at doing that thing that nobody is able to compete with you. But this often comes with great sacrifice and missing out on other important aspects of life like spending time with family and friends, travelling, or the pursuit of leisure. Most people try to strike a balance and therefore end up being average at what they choose to do in life, but likely live a happier and probably more fulfilling life.
Those of the Baby Boomer and Gen X generations who were brought up on a diet of striving for excellence and chasing achievement, no matter what the price, are appalled and nonplussed at the sight of seemingly directionless Gen Z youngsters prioritising personal happiness and comfort over the rewards that accrue from following the brutal corporate hamster wheel culture. Perhaps they are a spoiled generation afraid of biting the bullet and facing life squarely, or maybe they are smarter than the earlier generations in that they get it that the price of "progress" is not worth paying.
In the old days of empires and slavery, the empire builders chased wealth and grandeur on an epic scale and to achieve this, they built ruthless systems of exploitation of subjugated peoples, in the process ruining and impoverishing many old and thriving civilisations. The corporations of today are the empire builders of this age, and while they may not literally chain their employees to their desks, they do control their lives in a substantial manner. That has led to the latter becoming acutely aware of what their true priorities really are, especially in a scenario of growing uncertainty about employment opportunities against the backdrop of the growth of AI.
The pandemic especially made many realise that the dream jobs with corner offices for which they incessantly trained and prepared for meant nothing when the chips were down. It was each person for themselves, and what they excelled at professionally would not account for that much. So people began to reassess their professional plans, and many adopted the flexible working model that allowed them to work from home and often set up their own independent consultancies. It was okay to be average in your desires and expectations, as long as that kept you happy.
"Jack of All and Master of None" is a Good Thing
Flipping conventional logic on its head, it is much better to be average in a lot of things than be exceptional at something which AI would likely complete much faster without breaking into a sweat. If you are average at something, consider that as a good sign for excessive efficiency is for machines. We are not like the Greek gods and the Marvel Comics heroes and heroines who effortlessly carry the weight of the world upon our shoulders. We are humans with limited and finite abilities, which are best used at helping each other to lead a better life.
Brilliance as a trait is not a dependable one. It may, from time to time, achieve a breakthrough or inspire others, but it will not deliver steady and constant output. Mediocrity, on the other hand, can be relied upon to deliver on a regular and consistent basis. With time, it achieves much. You can compare it with steadily and patiently investing in stocks of solid companies with proven credentials over a long period of time to build wealth, rather than bet everything on a single buzzing stock, hoping that it will make you an overnight billionaire. The real world rewards consistency over flashes of brilliance. This is as true of organizations and people as it is of our personal relationships.
2026-04-26 23:32:15
\ In tokenization projects, KYC and AML aren’t formalities. They’re part of the actual business infrastructure. This layer largely determines whether a project can operate legally, connect to real payment rails, pass partner due diligence, and scale without running into avoidable problems.
That said, not every project needs the same level of control from day one. Basic KYC can be enough in the early stages. But as transaction sizes grow, geography expands, international payments appear, and more demanding partners come into the picture, operating without a proper AML framework creates real limitations — fast.
KYC — Know Your Customer — is the process through which a business confirms that its client is a real person or company, not a fake account, a bot, a sanctioned entity, or a front for someone who doesn’t want to be visible.
For individuals, a minimum KYC setup covers: collecting basic data (name, date of birth, address, citizenship), verifying identity documents (passport or national ID), screening against core watchlists (sanctions, terrorism financing), and storing that data in a way that can be retrieved later.
For companies, it goes further: verifying beneficial owners, ownership structure, registration documents, and checking all of that against sanctions lists.
KYC has a clear and practical role. It verifies that the user is a real person or company through document checks and, when required, biometric identity verification. It also enforces access rules: blocking users from restricted jurisdictions or from countries the business has decided not to serve. In addition, KYC helps define which users can register, complete onboarding, and buy tokens, and which cannot. In some cases, it also includes sanctions screening and creates a verification record that can be shown later to banks, regulators, or partners.
KYC is about identity. It answers: who is this person?
AML — Anti — Money Laundering — is about the behavior of money, not just the identity of the person sending it.
If KYC answers “who is this client,” AML answers “what is happening with the money flowing through your platform.”
A working AML setup includes:
Without AML, you won’t get access to serious banking infrastructure, even mainstream payment rails require it. Institutional partners expect to see it documented before signing anything. And unlike KYC, AML is an ongoing process — it doesn’t end after onboarding.
One important clarification: AML doesn’t require building an in-house compliance department with ten people. It can be a combination of a technology provider, an external compliance partner, and clearly written internal procedures.
Not every tokenization project needs full AML infrastructure on day one. There are real scenarios where basic KYC covers the actual risk exposure at that stage.
If you’re operating in one country without complex cross-border payment flows, and your average transaction size is in the thousands of dollars, basic KYC can be proportionate.
This is especially true for retail-facing products: straightforward investment products for individual users, loyalty programs using internal tokens that don’t carry securities characteristics, or gaming setups where there’s no direct fiat on/off-ramp or it’s tightly capped.
If all payments are processed through a provider that already handles AML on their side, your direct exposure is lower. You still need to collect and verify KYC data, but part of the monitoring responsibility sits with the provider.
If you’re doing a test launch on a home market or in a controlled sandbox environment, with hard limits on investment amounts per account and restricted jurisdictions, manual oversight of large transactions becomes more feasible. The constraints are what make basic KYC workable here, because they help keep the risk contained.
Basic KYC is enough when the product has clear built-in limits and the risk is genuinely kept small.
There are specific inflection points where continuing to operate on KYC alone stops being a calculated risk and starts being a structural problem.
When average transaction sizes start growing — or when you start seeing large one-time deposits or patterns that look like structured payments — the behavior of money on your platform becomes the primary risk, not just who the users are. Moving from tens of thousands to hundreds of thousands of dollars per month changes what regulators and banks expect to see.
The moment you move beyond a low-pressure regulatory environment, the game changes. Entering the EU, UK, US, Singapore, UAE, or Hong Kong means operating under frameworks that have explicit AML requirements for platforms handling financial flows. The same applies in the opposite direction — if you start attracting users from high-risk jurisdictions, regulators in your home market become more attentive to what’s coming through.
When your users are no longer just retail individuals — when B2B clients arrive, or funds, family offices, or qualified investors start participating, or when property developers want to tokenize significant real estate or income-generating assets — the due diligence bar goes up.
On the partner side, this often becomes explicit: banks, payment providers, exchanges, and custodians frequently write AML requirements into their contracts. If you can’t satisfy those requirements, the partnership doesn’t happen.
For many early-stage tokenization projects, basic KYC is enough at the start. If the product has clear limits, a narrow geography, smaller transaction sizes, and no complex cross-border money flows, that level of compliance can be proportionate to the actual risk.
In that kind of setup, the goal is not to build heavy AML infrastructure too early. It is to make sure the project can onboard real users safely, work with payment providers, and operate within a controlled risk perimeter.
AML becomes relevant later, when the model becomes more complex: larger volumes, broader geography, more demanding partners, or more serious investor profiles. Until then, basic KYC is often enough.
\ \
2026-04-26 23:21:26
Compliance teams spend 12 working weeks per year on manual tasks like collecting evidence, tracking controls in spreadsheets, and preparing for audits. As framework requirements multiply and buyers demand proof of your security posture, manual work starts to create real costs. Deals stall without certifications, risk outpaces your team, and your best people spend more time proving security than improving it.
The right GRC platform handles the heavy lifting by automating the busywork so your team can focus on what actually moves the business forward. This guide breaks down the five best GRC platforms for 2026, comparing automation, continuous monitoring, risk maturity, and how each platform uses compliance as a growth driver.
AI has reshaped GRC from both sides. AI-generated attacks are accelerating faster than teams can respond, while AI-powered platforms now handle evidence evaluation, policy drafting, and vendor risk analysis autonomously. And most organizations use AI without fully understanding it—only 13% feel very prepared to manage generative AI risks—creating an urgent need for governed, trustworthy automation rather than superficial bolt-on features.
The best GRC platforms help compliance drive revenue. Buyers expect you to prove trust continuously through self-service portals and automated questionnaire responses. The market has split between legacy platforms that require manual work and modern systems that deliver true continuous compliance through deep integrations and intelligent automation.
Key trends shaping the GRC market:
We assessed platforms based on how well they solve the core challenges security and compliance teams face, focusing on automation depth, continuous monitoring capabilities, risk management maturity, and integration breadth. These are the areas where platform differences have the biggest impact on your team's productivity and security outcomes.
Vanta authored this guide. Our goal is to help you make an informed decision, and we evaluated each platform using the same criteria.
| Criterion | Why it matters | Questions to ask vendors | |----|----|----| | Unified GRC platform architecture | A single source of truth connecting compliance, risk, and governance workflows eliminates silos and duplicate work across teams. Instead of tracking vendor risk in one tool and SOC 2 controls in another, everything ties back to the same system. | How does your platform unify compliance frameworks, risk registers, and governance workflows in a single view? Can you demonstrate how control findings automatically update risk assessments? | | Cross-framework control mapping | You shouldn’t collect the same evidence multiple times or manage controls separately for each standard when handling SOC 2, ISO 27001, and HIPAA. For instance, a single access control policy or audit log should map across frameworks automatically, rather than being uploaded and reviewed three different times. | How does your platform map controls across frameworks to eliminate duplicate work? Can you show evidence reuse across multiple frameworks? | | Risk-to-control linkage | Understanding how control posture directly impacts risk exposure helps you make data-driven decisions about where to invest security resources. If MFA coverage drops or a critical vendor lacks required controls, you should be able to immediately see how that increases your overall risk profile. | How does your platform connect risk assessments to control effectiveness? Can you show how control failures automatically update risk scores? |
| Criterion | Why it matters | Questions to ask vendors | |----|----|----| | Automated evidence collection | You shouldn’t waste hundreds of hours manually gathering screenshots when integrations can automatically collect and validate evidence—like pulling user access logs from your identity provider or capturing cloud configuration data without manual intervention. | What percentage of evidence collection can you automate for our tech stack? How many integrations do you offer, and how deep is the data collection? | | Continuous control monitoring | Point-in-time assessments leave you exposed between audits; real-time visibility catches drift before it becomes an audit exception. For example, if a security setting changes or a certificate expires, your team should be alerted right away. | How frequently do you test controls? How do you alert teams when controls fail or drift occurs? | | AI-powered automation | AI should reduce actual work by evaluating evidence, generating policies, and completing questionnaires. That might look like summarizing a vendor’s SOC 2 report into key risks or auto-filling a security questionnaire with cited, review-ready answers. | Where specifically does AI reduce manual effort in your platform? Can you demonstrate AI evidence evaluation capabilities? | | Adaptive framework scoping | Compliance needs vary by business unit, geography, or product line, and your platform should tailor evidence collection to match organizational complexity—like applying HIPAA controls only to healthcare workflows or adjusting requirements for teams operating in different regions. | Can your platform scope frameworks differently for different business units? How do you handle multi-entity compliance programs? |
| Criterion | Why it matters | Questions to ask vendors | |----|----|----| | Enterprise risk management | Managing risks beyond compliance in a unified system provides executive-level visibility and supports board reporting. That way, leadership can see trends like rising vendor risk or control gaps without piecing together data from multiple tools. | How does your platform support multiple risk registers and enterprise risk roll-ups? How do you enable risk-based decision-making? | | Third-party risk management | Vendors represent growing risk exposure; continuous monitoring and automated workflows help you manage hundreds of vendor relationships efficiently. Instead of manually chasing SOC 2 reports, your platform should flag when a vendor’s certification expires or their security posture changes. | How does your third-party risk management (TPRM) solution automate vendor security reviews? Do you offer continuous monitoring or just point-in-time assessments? | | Risk scoring and prioritization | You need intelligent prioritization that focuses attention on what actually threatens business objectives. For example, distinguishing between a low-risk SaaS tool and a critical infrastructure provider with access to production data. | How does your platform help prioritize risks based on business impact? Can risk scoring be customized to reflect our business context? |
| Criterion | Why it matters | Questions to ask vendors | |----|----|----| | Audit preparation and management | Your platform should keep you continuously audit-ready with organized evidence and streamlined auditor collaboration—so when an auditor requests access logs or policies, everything is already mapped, up to date, and easy to share. | How much time do customers save on audit preparation? Do you offer an auditor portal for seamless evidence sharing? | | Multi-framework support | Your platform should support frameworks out of the box so adding new certifications does not require starting from scratch. Controls and evidence should carry over—for instance, extending existing SOC 2 work to ISO 27001 without duplicating effort | How many pre-built frameworks do you support? How quickly can customers add new frameworks? | | Policy management | Creating and maintaining policies across frameworks is tedious without AI-assisted generation, version control, and automatic mapping to controls—like updating an access control policy once and having every relevant framework reflect it. | How does your platform help create and maintain policies? Can you demonstrate AI-powered policy generation? |
| Criterion | Why it matters | Questions to ask vendors | |----|----|----| | Trust center and questionnaire automation | Security reviews block deals and drain resources; self-service trust portals deflect questionnaires, and AI automates responses to the remainder. Instead of manually answering the same questions, prospects can access your security posture instantly, while AI handles the rest with accurate, reusable responses. | What percentage of security questionnaires can you deflect? How does your AI handle questionnaire automation, and what is the acceptance rate? |
| Criterion | Why it matters | Questions to ask vendors | |----|----|----| | Integration ecosystem | Complex tech stacks require deep integrations across cloud, security, HR, and IT tools, plus flexibility for on-premise systems—so you can pull evidence from systems like AWS, Okta, or Jira without manually stitching data together. | How many integrations do you offer? Do you support custom integrations for on-premise or proprietary systems? | | Multi-entity and workspace management | You need to manage compliance separately for multiple business units while maintaining consolidated visibility for executives. For example, tracking SOC 2 for one product line and ISO 27001 for another, while still rolling everything up into a single view. | How does your platform handle multiple legal entities? Can you provide both segmented and rolled-up compliance views? | | Configurability and customization | Your platform should adapt to your control framework, risk taxonomy, and workflows rather than forcing rigid templates—whether that means customizing risk scoring models or aligning controls to your internal processes. | How customizable are controls, tests, and risk categories? Can we create custom frameworks or modify existing ones? |
| Criterion | Why it matters | Questions to ask vendors | |----|----|----| | Expert support and services | GRC programs require access to experts who understand your industry and can guide complex compliance scenarios—like navigating overlapping requirements across SOC 2, HIPAA, and regional regulations. | What support options are available? Do you offer dedicated customer success managers and GRC consultants? | | Implementation speed and time to value | A platform that takes months to deploy creates more burden than it solves; rapid deployment with pre-built frameworks matters. Teams should be up and running in weeks—not quarters—without rebuilding controls from scratch. | What is the typical implementation timeline? When do customers typically see measurable return on investment (ROI)? | | Reporting and executive visibility | Leaders need board-ready dashboards and compliance status reporting to demonstrate program maturity, such as tracking audit readiness, control performance, and top risk exposures in a single view. | What executive reporting and dashboards are available? Can you demonstrate real-time compliance posture views? |
\
This section reviews each platform in depth, covering positioning, key capabilities, ideal use cases, and tradeoffs. Every evaluation maps to the criteria above, so you can compare platforms against the same standards.
Vanta is an AI-powered trust management platform that unifies compliance, risk, and customer trust workflows. It surfaces the issues that need your attention and automates the rest. Vanta relies on three interconnected product pillars:
Vanta uses a bottom-up architecture that integrates directly into your existing systems to keep every control, policy, vendor review, and risk always current. With 400+ integrations and the industry's broadest set of automated tests, Vanta delivers true continuous control monitoring. The Vanta AI agent autonomously handles workflows across policies, evidence, vendor reviews, and questionnaires. Enterprise features like Adaptive Framework Scoping and multi-entity Workspaces match the speed and complexity of mid-market and enterprise needs.
Security and compliance teams at large enterprises that need to unify compliance, risk, and customer trust in a single platform.
| Pros | Cons | |----|----| | Unified GRC architecture: Connects compliance, risk management, and customer trust workflows in a single platform with shared data, eliminating silos between audit, risk, and security review teams | Breadth may exceed simple needs: Organizations pursuing a single framework with minimal infrastructure may not need the full platform scope | | Deepest automation and integration network: 400+ integrations and the industry's broadest set of automated tests provide continuous evidence collection and control monitoring across complex tech stacks | On-premise integration validation: Teams with heavily on-premise or legacy environments should confirm integration coverage for their specific systems during evaluation | | AI-native, not AI-bolted: The Vanta AI agent operates across policies, evidence, vendor reviews, and questionnaires as a connected system, reducing manual effort across the entire GRC lifecycle | Enterprise features require configuration: Advanced capabilities like Adaptive Framework Scoping and multi-entity Workspaces deliver significant value but require initial setup to match organizational structure |
Optro is a cloud-based platform built around audit management, Sarbanes-Oxley Act (SOX) compliance, and risk management workflows. It provides strong capabilities for internal audit teams operating in SOX-heavy environments. The platform caters primarily to mid-market and enterprise organizations with established audit teams that need centralized control testing and risk assessment tools.
Optro relies on a top-down approach with approximately 145 integrations but lacks automated testing capabilities, requiring human review of evidence. It supports fewer than 30 frameworks and typically requires a longer implementation timeline. The platform provides point-in-time evidence gathering in lieu of continuous monitoring.
Organizations with established internal audit teams and SOX compliance requirements that prioritize audit workflow management.
| Pros | Cons | |----|----| | Strong audit workflows: Provide tools for managing complex internal audits and SOX compliance programs with detailed control testing documentation | Limited automated testing: While the platform offers integrations for evidence collection, it’s missing automated testing capabilities and requires human review of evidence, increasing time spent on compliance validation tasks | | Centralized risk tracking: Offers a clear view of enterprise risk assessments and control testing results in a single dashboard | Longer implementation: Setup and configuration often take months before the platform delivers measurable value to your team | | Established reporting: Delivers comprehensive dashboards tailored for traditional audit and compliance reporting needs | Limited continuous monitoring: Doesn’t have the deep, real-time integration network needed to catch control drift instantly between audit cycles |
OneTrust is a platform with deep roots in data privacy that has expanded into broader GRC, environmental, social, and governance (ESG), and third-party risk management. It provides strong privacy program management and regulatory intelligence for global organizations. It’s designed for large enterprises managing complex privacy and regulatory requirements across different regions.
OneTrust can be overly complex for teams that don’t need its full privacy suite. The platform requires significant implementation overhead and manual configuration. With 100+ integrations and weekly testing that requires manual work and extensive screenshots, its compliance automation for frameworks like SOC 2 isn’t its primary strength, making it less ideal for teams focused purely on security certifications.
Organizations with significant data privacy obligations, multi-geography regulatory requirements, and ESG reporting needs.
| Pros | Cons | |----|----| | Privacy leadership: Offers industry-leading tools for managing GDPR, California Consumer Privacy Act (CCPA), and global privacy laws with detailed data mapping | High complexity: The broad feature set creates a steep learning curve for teams focused only on security compliance frameworks | | Regulatory intelligence: Keeps organizations updated on changing global privacy and compliance regulations across jurisdictions | Implementation overhead: Requires significant time and resources to configure data mapping and risk workflows before seeing value | | Broad ESG support: Provides dedicated modules for tracking and reporting on corporate sustainability goals and initiatives | Limited continuous automation: Tests run weekly rather than continuously, requiring manual work and extensive screenshots for frameworks like SOC 2 and ISO 27001 |
Secureframe is a compliance automation platform focused on helping organizations achieve and maintain SOC 2, ISO 27001, HIPAA, and Payment Card Industry Data Security Standard (PCI DSS) certifications. It provides automated evidence collection and monitoring for cloud-native companies. The platform targets startups and mid-market companies pursuing their first or second compliance certification.
Secureframe has limitations when it comes to enterprise-grade requirements. Tests run daily—not hourly—and custom tests are limited to cloud integrations. Some workflows still require manual work, like exporting user access reviews instead of handling them in-platform, and vulnerability management is limited to five integrations with restricted data access. Its third-party risk management and trust center features are also less developed than those of more comprehensive trust platforms, making it harder to scale for complex, multi-entity compliance programs.
Startups and mid-market companies pursuing initial SOC 2 or ISO 27001 certification with cloud-native infrastructure.
| Pros | Cons | |----|----| | Fast initial certification: Streamlines the process for achieving a first SOC 2 or ISO 27001 audit with pre-built templates and guided workflows | Limited enterprise scalability: Struggles to support complex, multi-entity organizations with overlapping frameworks and business units, with daily rather than hourly testing and manual processes for user access reviews | | Cloud-native focus: Integrates well with modern cloud infrastructure for automated evidence gathering from common software as a service (SaaS) tools | Basic risk management: Lacks the advanced risk scoring and multiple risk registers needed by mature security teams managing enterprise programs | | Helpful policy templates: Provides pre-written policies that accelerate the readiness assessment phase for first-time certifications | Weaker revenue enablement: Trust Center and questionnaire automation features are less developed than market leaders, limiting sales acceleration |
Anecdotes is a GRC platform designed to connect compliance programs to business context by aggregating data from existing tools. It acts as a "compliance operating system" that layers on top of your existing infrastructure. The platform targets mid-market and enterprise organizations that want to build a GRC program without replacing existing tools.
Anecdotes relies on data connectors rather than running its own deep, automated tests. This approach limits its direct integration depth compared to bottom-up platforms. The platform also has less mature AI capabilities and a narrower breadth of customer-facing trust and proof features.
Mid-market and growing organizations that want to layer GRC capabilities on top of existing security tools without replacing their current stack.
| Pros | Cons | |----|----| | Flexible data aggregation: Pulls information from existing tools to build a centralized compliance view without requiring tool replacement | Shallower automation: Relies on external data rather than running its own continuous control tests, limiting real-time visibility | | No rip-and-replace: Allows teams to maintain their current security stack while adding GRC oversight and reporting capabilities | Less mature AI: Doesn’t have the embedded, agentic AI workflows found in leading trust management platforms for evidence evaluation | | Business context mapping: Helps tie compliance data back to specific business units and objectives for better risk prioritization | Limited proof features: Doesn’t offer tools for deflecting customer security questionnaires or proving trust proactively |
The right GRC platform transforms how you manage security and compliance work:
Follow these steps to evaluate GRC platforms and find the right fit for your team:
The right GRC platform turns compliance into a continuous, automated program that earns and proves trust at every stage of growth. Vanta unifies compliance, risk, and customer trust workflows, surfacing the issues that need attention and automating the rest. By automating compliance, continuously monitoring risk, and proving customer trust, Vanta provides the foundation for a GRC program that scales across frameworks, teams, and geographies.
Trust makes or breaks a business, and Vanta clears the path to trust so you can grow confidently and stay ready for anything. Request a demo to see how Vanta ramps up your compliance program.
GRC software covers the full scope of governance, risk, and compliance—including things like risk registers and vendor oversight. Compliance automation is just one piece of that, focused on automating evidence collection and audit prep. Most modern GRC platforms bring both together into a single system.
Modern, automation-first platforms can be up and running in weeks thanks to pre-built frameworks and integrations, while legacy tools often take months to configure. It’s worth looking beyond implementation speed and focusing on time to value. The goal is to actually reduce manual compliance work.
GRC software delivers ROI by cutting down audit prep time, eliminating manual evidence collection, speeding up security reviews, and reducing risk exposure. Measurable returns start showing up within months, not years. If it takes over a year to show value, it’s likely adding more overhead than it removes.
Yes—cross-framework control mapping is a core capability of modern GRC platforms. It lets you collect evidence once and apply it across overlapping requirements, which cuts down duplicate work and makes adding new frameworks much more incremental instead of starting from scratch.
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2026-04-26 23:11:39
Blockchains are great at consensus over digital state. They are bad at directly observing physical reality: the price of a liter of bottled water in Lagos, the exchange booth rate in Bangkok, or the municipal tariff in a small town.
Classic approaches push that problem to centralized oracles or aggregators. That trades one problem for another: counterparty risk, opaque methodology, and a single throat to choke.
If O Coin (https://o.international ) is anchored to local water prices and conversion logic across currencies, we need a system that is:
1. Hard to spam (bots should not move the needle)
2. Hard to collude (small cabals should not fix the price)
3. Cross-country consistent (lies should not survive independent observers in different places)
4. Human-auditable (someone checks “does this match reality?”)
5. Always defined (a missing local sample must not freeze the protocol or force a trusted bank feed)
The next sections explain how our invitation logic is the bridge between “nice statistics on paper” and “code that still runs when one country goes dark.”
In production thinking, we do not treat each country as an isolated spreadsheet row. We treat the world as a graph:
- Nodes are measurement targets (e.g., a currency lane, a locale bucket, a product rule like “0.9–1.1 L bottled water pro-rated to 1 L”).
- Edges are things real humans can observe: local water price in currency A, a street/bank/app A/B rate, or any observation that ties two lanes together within tolerance.
Direct measurement is ideal: enough independent users in currency C report compliant water prices; we aggregate (Gaussian-weighted among other guards) and publish something like 1 O_C = “one liter of water” expressed in C. Those users are not a random grab bag of “anyone online”: authentication already tied them to currency C via place of birth (see below), so default water-price invites map cleanly to the right national fiat lane.
Indirect integrity kicks in when C is temporarily impossible (crisis, censorship, broken retail data, or simply too few eligible reporters this week). The protocol does not “go blank.” It uses measured legs elsewhere:
- If A and B are well-sampled and C is not, we can still propose implied relationships using triangular consistency—the same logic markets use when a pair is illiquid but legs are not.
- Those implied values are not magic: they are constraints. If later direct samples from C return, they should agree within published tolerances; if not, the discrepancy is visible and triggers more invites, wider uncertainty bands, or validator review—not silent rewriting.
So: existence of a number is a code-level guarantee (“every lane has a state object”), while confidence is honest (“this lane is imputed / low-sample / disputed”).
This matches what we describe publicly on the product side: each O currency is grounded in measured water where we have it, and cross-currency relationships are derived from those anchors—extended here to the operational reality that anchors arrive asynchronously by country.
Not everyone can submit. That is the anti-spam baseline. The deeper design is: among qualified humans, who gets asked this hour is itself a security mechanism.
Only accounts that pass authentication enter the pool:
- Uniqueness: Sybil resistance
- Liveness: the account is active in the human loop, not a one-time key.
- Humanity signals: layered checks knowing no single signal is perfect.
- Primary fiat lane (place of birth): as part of the same authentication / onboarding flow, each user is attached to the currency of their place of birth. That binding is stored as a stable attribute of the account (not something you pick per task to game which market you “represent”). The scheduler uses it so water-price measurement invitations go to people the protocol is designed to treat as natural reporters for that currency—people who should plausibly shop, see shelf prices, and submit receipts in that fiat system.
This pool is the universe the scheduler samples from.
The scheduler uses tags built on top of that authenticated core—including the birth-currency binding—plus other allowed signals (verification artifacts, policy residency rules where applicable, device/locale checks where appropriate, and compliance constraints). Tags are not “pick any country today.” They exist so the chain can run stratified sampling:
- avoid inviting 500 people from the same city block,
- avoid starving entire regions,
- and intentionally spread draws across countries so the same false price must survive independent geographies.
Pure uniform randomness is fair-looking but unsafe for measurement: it clusters where users already cluster. Our draw is closer to survey design:
- Strata: birth-currency lane / geography / currency exposure for cross-rate tasks / language / (where allowed) travel corridors.
- Randomness within strata: attackers cannot predict which eligible user is chosen, but the system still hits coverage targets.
Here is where cross-country integrity becomes code.
Each round, the scheduler computes shortfalls:
- currencies or locales below target sample count for direct water measurement;
- FX legs that are stale or disagree with implied triangles beyond tolerance;
- validator queues that show systematic disagreement in a corridor.
Then it biases invitation probability (still randomized, still Sybil-gated) toward users who can reduce that uncertainty:
- users likely able to post a direct water sample in an under-measured currency;
- users able to observe a cross-rate that closes a triangle even when one country cannot produce water receipts this cycle;
- users in independent jurisdictions that provide redundant checks on the same edge (so collusion must cross borders, not a group chat).
This is the operational meaning of your idea that invites track invites sent ÷ valid measurements: it is not only “send more emails.” It is send the next invites to the places and observation types that restore graph consistency.
We track conversion: invites sent versus valid measurements accepted after automated pre-checks. If conversion drops, the system increases invitations or widens strata or shifts task type (e.g., more FX-leg tasks when water receipts are impossible locally)—rather than pretending one broken locale “does not exist.”
Automation is triage. Besides local plausibility bounds, cross-country code adds consistency checks:
- FX triangle closure within tolerance (if A/B, B/C, and A/C are all claimed, do they fit?)
- Unit and container rules (your public spec: 0.9–1.1 L bottled water normalized to 1 L)
- timestamp/window sanity
- duplicate/near-duplicate evidence
- coordination flags (too many correlated submissions from a tight cluster)
The point is to ensure global integrity is not “whatever the loudest country says.”
Automation cannot interpret every shelf label, brand, informal market, or crisis distortion. Humans validate with rubrics and rewards for accuracy, plus escalation when validators split.
Cross-country systems especially need this layer: the hard cases are often not “is this number numeric?” but “is this representative for the lane we think we’re measuring?”
We aggregate with Gaussian weighting so typical observations dominate and extreme tails do not hijack the feed—while still retaining audit trails that outliers existed.
This runs per lane (e.g., per currency’s water-price distribution for a window) and feeds the published anchors that cross-country math consumes.
- Security: invitation-only + stratified randomness + Sybil resistance shrinks spam and naive collusion.
- Cross-country integrity: the graph of measurements and FX legs is cross-checked; inconsistencies surface as work (more invites, more validators), not as hidden admin edits.
- Existence without fantasy precision: if a currency cannot be directly measured today, the protocol can still maintain a defined state using measured foreign legs + consistency rules, while publishing uncertainty instead of silently trusting a third party.
We are not claiming perfection. We are claiming a different failure mode: instead of one oracle company, you get a scheduling and validation machine where who is asked is part of the security model.
Learn more about the O coin, A stable coin ecosystem based on water prices.
:::tip This article is published under HackerNoon's Business Blogging program.
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