AI will empower individuals to perform tasks beyond their previous scope by shifting work left, prompting reconsideration of user personas and role evolution.
Innovative solutions can enhance workflows, like blending AI copilots with support agents to streamline tech support and improve developer productivity.
Building products with a focus on entire workflows, rather than individual users, can uncover root problems and provide opportunities for improvement and differentiation.
Knowledge management is crucial for large enterprises to maintain a competitive edge and prevent knowledge debt.
Traditional chatbots face challenges like slow time-to-market, lack of domain knowledge, and difficulty in managing multilingual and international content.
The KODA stack addresses issues like time to market, internationalization, domain knowledge modeling, and scalability for large enterprises seeking efficient knowledge management solutions.
Open Science Hardware focuses on creating accessible tools for research and experimentation, promoting collaboration and knowledge sharing.
Gathering for Open Science Hardware (GOSH) events bring together diverse professionals to discuss open hardware issues and solutions, aiming for global impact.
GOSH 2018 resulted in action items like organizing regional open hardware movements, creating maps of makerspaces, and offering programs with local universities, all to support the open science community.
Built a domain-specific language (AXL) to help domain experts write logic, reducing codebase size by 80% and time taken to write code from several days to 2-3 hours.
Focused on creating a DSL with simple, English-like syntax, built-in domain knowledge, and extensibility to allow importing libraries and reusing components.
Implemented the language using Python, with modules for Lexer, Parser, and Interpreter, and developed a UI called 'The AXL Playground' for easier usage by non-technical users.
Building a news scraper involved challenges like writing crawlers, applying machine learning concepts, and using Natural Language Processing.
Collaborating with others and seeking help when needed led to valuable insights and the discovery of useful resources and libraries like NLTK and Naive Bayes Classifier.
The project's outcome included the development of a Smart News Scraper, with room for improvement in accuracy, filters, multithreading, and expansion to cover news relevant to more colleges.