2pk03 over AI, ML, BigData and data processing

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

Searching and filtering large IP address datasets within PostgreSQL can be challenging. Why? Databases excel at data storage and structured queries, but often struggle with full-text search and complex analysis. Apache Solr, a high-performance search engine built on top of Lucene, is designed to handle these tasks with remarkable speed and flexibility. What do we need? A running PostgreSQL database with a table containing IP address information (named "ip_loc" in our example). A basic installation of Apache Solr. Setting up Apache Solr Create a Solr Core: Bash solr create -c ip_data -d /path/to/solr/configsets/ Define the Schema ( schema.xml ) XML < field name = "start_ip" type = "ip" indexed = "true" stored = "true" /> < field name = "end_ip" type = "ip" indexed = "true" stored = "true" /> < field name = "iso2" type = "string" indexed = "true&q

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 ( https://wayang.apache.org ) 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