Life hacks for your startup with OpenAI and Bard prompts

OpenAI and Bard   are the most used GenAI tools today; the first one has a massive Microsoft investment, and the other one is an experiment from Google. But did you know that you can also use them to optimize and hack your startup? Even creating pitch scripts, sales emails, and elevator pitches with one (or both) of them helps you not only save time but also validate your marketing and wording. Curios? Here a few prompt hacks for startups to create / improve / validate buyer personas, your startups mission / vision statements, and USP definitions. Introduce yourself and your startup Introduce yourself, your startup, your website, your idea, your position, and in a few words what you are doing to the chatbot: Prompt : I'm NAME and our startup NAME, with website URL, is doing WHATEVER. With PRODUCT NAME, we aim to change or disrupt INDUSTRY. Bard is able to pull information from your website. I'm not sure if ChatGPT can do that, though. But nevertheless, now you have laid a grea

Can Generative AI profit from using Federated Data Processing?

OpenAI and Bard are all over the place, and I asked Bard:  Can Generative AI profit from using Federated Data Processing? Generative AI can profit from federated data processing by accessing and processing data without having to transfer it to a centralized location. This can be beneficial for a number of reasons, including:   Privacy: Federated data processing can help to protect user privacy by keeping data on the devices where it was generated. This can be important for users who are concerned about their privacy, or for applications that require sensitive data, such as medical records. Security: Federated data processing can help to improve security by making it more difficult for attackers to access data. This is because the data is never stored in a centralized location, where it could be more vulnerable to attack. Scalability: Federated data processing can be scaled more easily than traditional centralized data processing. This is because the data does not have to be transferre

How is Disrupting the IoT Industry with its Cloud Native Platform

The Internet of Things (IoT) is a term that refers to the network of physical devices, sensors, and machines that can communicate and exchange data over the internet. IoT has many applications in various domains such as smart homes, smart cities, healthcare, agriculture, manufacturing, and more. However, building and managing IoT solutions is not an easy task. It requires connecting different types of hardware and software components, ensuring security and privacy of data and devices, collecting and analyzing large volumes of data, and integrating with existing business systems and web services. This is where IoT platforms come in handy. An IoT platform is a software solution that helps developers and businesses to create, deploy, and manage IoT applications. An IoT platform typically provides features such as: Device management: To register, monitor, control, update, and troubleshoot devices remotely Connectivity: To support different communication protocols such as MQTT, CoAP, HTTP e

Some fun with Apache Wayang and Spark / Tensorflow

Apache Wayang is an open-source Federated Learning (FL) framework developed by the Apache Software Foundation. It provides a platform for distributed machine learning, with a focus on ease of use and flexibility. It supports multiple FL scenarios and provides a variety of tools and components for building FL systems. It also includes support for various communication protocols and data formats, as well as integration with other Apache projects such as Apache Kafka and Apache Pulsar for data streaming. The project aims to make it easier to develop and deploy machine learning models in decentralized environments. It's important to note that this are just examples and they may not be the way for your project to interact with Apache Wayang, you may need to check the documentation of the Apache Wayang project ( ) to see how to interact with it. I just point out how easy it is to use different languages to interact between Wayang and Spark. Also, you need to mak

The A3 Home Decor

One of my new angel investments is an e-commerce retailer, so nothing new. But the founder has some real drive; she wants to deliver sustainable and reusable tableware, or more table couture. Yes, there are a few out there, but the typical ones have mostly plastic, paper, and other non-sustainable decor or tableware. The A3 - - has a better idea, as Alexandra, founder and CEO explains:  A3 is an eco lifestyle home decor and tablecloth brand where design and quality are always top of mind.  With A3, you make your home truly a home.  Okay - that was the pitch. So why now and how to get customers? How to build a chain of trust in the crowded home interior and decor market? Here some brain food: In 2021, the size of the world market for home decor was estimated to be USD 665 billion. By 2030, it is anticipated to reach USD 940 billion, expanding at a CAGR of 3.9%. (2022-2030). Various environments, including spas, offices, clean rooms, restaurants, camping, bedrooms, th

Get Apache Wayang ready to test within 5 minutes

Hey followers, I often get ask how to get Apache Wayang ( ) up and running without having a full big data processing system behind. We heard you, we built a full fledged docker container, called BDE (Blossom Development Environment), which is basically Wayang. Here's the repo: I made a short screencast how to get it running with Docker on OSX, and we also have made two hands-on videos to explain the first steps. Let's start with the basics - Docker. Get the whole platform with: docker pull At the end the Jupyter notebook address is shown, control-click on it (OS X); the browser should open and login you automatically: Voila - done. You have now a full working Wayang environment, we prepared three notebooks to make it more easy to dive into. Watch our development tutorial video (part 1) to get a better understanding what Wayang can do, and what not. Click the video below: 

Combined Federated Data Services with Blossom and Flower

When it comes to Federated Learning frameworks we typically find two leading open source projects - Apache Wayang [2] (maintained by  databloom ) and Flower [3] (maintained by  Adap ). And at the first view both frameworks seem to do the same. But, as usual, the 2nd view tells another story. How does Flower differ from Wayang? Flower is a federated learning system, written in Python and supports a large number of training and AI frameworks. The beauty of Flower is the strategy concept [4]; the data scientist can define which and how a dedicated framework is used. Flower delivers the model to the desired framework and watches the execution, gets the calculations back and starts the next cycle. That makes Federated Learning in Python easy, but also limits the use at the same time to platforms supported by Python.  Flower has, as far as I could see, no data query optimizer; an optimizer understands the code and splits the model into smaller pieces to use multiple frameworks at the same ti