novatechflow | Alexander Alten

This article shows founders how to use GenAI tools like OpenAI and Gemini as practical growth levers rather than just curiosities. By priming the model with a clear introduction of yourself, your startup, your product and your market, you can generate sharper mission statements, refine buyer personas, craft USPs, create elevator pitches and even estimate market size through iterative prompting. The key advice is to treat GenAI like a collaborative strategist: feed it context, rephrase prompts when the answers miss the mark, and use each iteration to deepen clarity. Done well, this becomes a fast, low-cost way to validate messaging, explore positioning and improve sales and marketing outputs that would otherwise take days of manual work. OpenAI and Gemini   are the most used GenAI tools today; the first one has a massive Microsoft investment, and the other one is the one from Google. But did you know that you can also use them to optimize and hack your startup?  For startup...

This article explains how to offload heavy IP search and filtering workloads from PostgreSQL to Apache Solr to gain speed, scalability and richer query capabilities. PostgreSQL remains the system of record for IP ranges and geolocation data, while Solr—backed by Lucene’s inverted indexes—handles fast range lookups and flexible text queries over fields like country, state and city. The piece walks through creating a Solr core, defining an IP-focused schema, wiring PostgreSQL to Solr via the DataImportHandler, and running full imports so Solr can be queried for IP ranges and geo filters with simple syntax. The takeaway: combining Postgres for storage and Solr for search yields a robust, scalable IP management stack that outperforms pure SQL approaches for complex, high-volume search use cases. 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 ...