Navattic VS PostgresML

• Ease of Use
  Navattic is designed to be user-friendly, enabling users to create interactive product demos without needing extensive technical knowledge.
• Customizability
  The platform offers a range of customization options, allowing businesses to tailor demos to their branding and specific needs.
• Analytics and Insights
  Navattic provides analytics features that help businesses understand user engagement with the demos, offering insights into customer behavior.
• Integration Capabilities
  It integrates well with CRM and marketing platforms, making it easier to align demos with broader marketing and sales strategies.

Possible disadvantages

• Pricing
  Depending on the features and scale needed, Navattic can be a costly option for smaller businesses or startups.
• Learning Curve for Advanced Features
  While basic features are straightforward, mastering some of the more advanced custom options can require time and effort.
• Limited Offline Access
  Navattic is primarily a cloud-based solution, which can limit access or functionality when offline.
• Dependency on Internet Connection
  As a web-based tool, it requires a stable internet connection to use, which may be a consideration for teams with remote or variable connectivity.

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PLG 123 | Navattic Brings PLG to the Masses

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Tracking Navattic since Mar 2021.

• AI-pipe: Pipeline for generating/storing embeddings from AI models to DB with data scraped from sites using custom scripts

  The web service supports generating embeddings from OpenAI and Ollama AI models. It also provides a fallback for users without access to AI models running on a remote server through PostgresML. - Source: dev.to / almost 2 years ago

• Better RAG Results with Reciprocal Rank Fusion and Hybrid Search

  That's outside of the database, though. This is more like what I had in mind -- I just found it: https://postgresml.org/. - Source: Hacker News / over 2 years ago

• How Modern SQL Databases Are Changing Web Development - #4 Into the AI Era

  Some excellent tools were created to represent these tasks "naturally" in SQL and even let most of the computation happen inside the database. PostgresML is a great example. It's built above PostgreSQL and provides a set of functions... - Source: dev.to / almost 3 years ago

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