Technology Tales

5 LLM Tools I Cannot Live Without

Matthew Mayo, Managing Editor at KDnuggets, outlines five large language model tools he considers essential to his current workflow. LlamaIndex is a framework built for data-centric applications, particularly retrieval-augmented generation systems, offering seamless integration with over 40 vector stores and data sources.

For those who prefer to keep models on their own infrastructure, Ollama enables users to run a variety of language models locally on their own hardware with minimal effort, and also supports serving models to external applications via a Python API. Complementing this, Ollama UI is a no-configuration chat interface, available as a Chrome extension, that provides a straightforward way to interact with locally hosted models.

On the productivity side, NotebookLM is a Google AI tool that lets users create notebooks drawing on uploaded reference material to produce summaries, FAQs, study guides and podcast-style audio overviews. Rounding out the list, ControlFlow is a Python framework for building agentic AI workflows by defining discrete tasks, assigning specialised agents and combining them into structured flows, with a syntax designed for rapid prototyping.

ScraperAPI is a web scraping platform designed to help businesses and developers collect data from public pages at scale, without the complexity of managing proxies, headless browsers or CAPTCHA handling. The service offers a plug-and-play API, asynchronous request handling and a no-code data pipeline tool, allowing teams to gather large volumes of information efficiently. It also provides structured data endpoints for popular platforms including Amazon, Google and Walmart, returning clean JSON or CSV output rather than raw HTML. A geotargeting feature grants access to a pool of over 40 million proxies across more than 50 countries, helping users avoid blocks. The platform is positioned as a cost-effective alternative to building in-house scraping infrastructure, with compliance with both CCPA and GDPR and a reported track record of serving over 11 billion requests within a 30-day period.

7 GPTs to Help Improve Your Data Science Workflow

The GPT Store, which features more than three million custom models built by developers, offers several tools that can meaningfully enhance a data science workflow. The Data Analyst GPT, created by the ChatGPT team, can process uploaded data files and perform tasks such as correlation analysis whilst also generating reusable code.

The Machine Learning GPT by Maryam Eskandari and the Machine Learning Engineer GPT by Hustle Playground both serve as assistants for building and comparing predictive models, with the latter placing particular emphasis on deployment and production structuring. For coding support, AutoExpert by llmimagineers.com functions as a pair programming assistant with code generation capabilities and session state management.

ScholarGPT by awesomegpts.ai helps practitioners locate relevant academic research papers based on a given use case or problem. Whimsical Diagrams by whimsical.com enables the creation of flowcharts, mind maps and sequence diagrams to visualise workflows and concepts, whilst the Canva GPT helps practitioners present their findings through professionally designed layouts and slide formats. Together, these tools cover a broad range of data science activities, from technical modelling and research through to communication and visual presentation.

Data Science and the Go Programming Language

Go is a programming language developed by Google engineers Robert Griesemer, Rob Pike and Ken Thompson, introduced in 2009 as a systems programming language that retains the performance advantages of C whilst being simpler and safer to use. Unlike Python, which predates multicore processors and is single-threaded and interpreted, Go is designed for concurrent processing and compiles directly to machine code, making it significantly faster and better suited to the computational demands of modern data science.

Northwestern University's Master of Science in Data Science programme has adopted a trilingual approach, using R as the primary language for analytics and modelling, Python for artificial intelligence and natural language processing, and Go for data engineering. This reflects a recognition that each language has distinct strengths suited to different aspects of the discipline.

Go is already widely used in industry by major organisations including Google, Netflix, Uber and Dropbox, and underpins many prominent computing tools and platforms. It is considered easier to maintain than Python, offers automated memory management, enforces a consistent coding style and has a strong commitment to backward compatibility. These characteristics make it well suited to building scalable, high-performance data science systems including web applications, database servers and distributed computing environments.

Tugan.ai is an AI-powered content generation tool that allows users to produce original written content by simply submitting a URL, removing the need for manual prompts or copywriting expertise. The platform can transform existing online content, including videos and web pages, into a range of formats such as email sequences, social media posts, newsletters, LinkedIn articles and advertising copy. Unlike general-purpose AI tools, it is built on accumulated copywriting knowledge, intended to produce results that read as human and engaging rather than mechanical.

Microsoft Clarity is an analytics tool that uses artificial intelligence to provide insights into user interactions with websites and applications. It offers features such as session recordings to observe user behaviour, heatmaps to visualise engagement patterns and AI-generated summaries to highlight key trends and areas for improvement. The tool also includes AI chat functionality for querying data and brand agents to assist users directly on sites.

Designed for businesses of all sises, it supports mobile app analytics through lightweight integrations and works across multiple platforms, including Android, iOS and web-based applications. Users have reported improved decision-making through data-driven observations, with features helping to identify usability issues, track conversion rates and refine strategies based on real-time user feedback. The service is available free of charge, with no restrictions on traffic or usage, and is compatible with privacy regulations such as GDPR and CCPA.

OWASP Top 10 for Large Language Model Applications

The OWASP GenAI Security Project is a global, open-source initiative focused on identifying and addressing security risks in generative AI technologies, including large language models and AI-driven applications, by providing guidance and tools to support secure development and deployment. Originally launched as the OWASP Top 10 for Large Language Model Applications, the project has expanded into a broader effort involving over 600 contributors from more than 18 countries and nearly 8,000 community members, offering resources, educational materials and opportunities for collaboration through meetings, contributions and community engagement. The initiative remains non-commercial, relying on community support and sponsorships to sustain its work, while continuing to update its Top 10 list as a core reference for critical vulnerabilities in LLM applications.

So You Want to Build Your Own Open Source ChatGPT-Style Chatbot…

Mozilla undertook a week-long hackathon to build an internal, open-source chatbot prototype running entirely on its own cloud infrastructure, using no third-party APIs or proprietary services. The team navigated multiple layers of technical decision-making, choosing the llama.cpp runtime over Hugging Face's tools due to time constraints and configuration difficulties, and ultimately selecting Facebook's LLaMA 2 model after manually evaluating several options for resistance to bias, toxicity and misinformation.

Model selection was further complicated by legal restrictions, as many popular models inherit non-commercial licensing terms from the original LLaMA weights, limiting the number of viable options. To give the chatbot access to a small amount of internal Mozilla knowledge, the team implemented a custom vector search and embedding solution using Python, whilst carefully crafting a system prompt to align the chatbot's behaviour with Mozilla's values and policies around inclusion and factual accuracy.

LangChain was used only minimally for the embedding layer, with most orchestration handled through custom Python code, and an existing internal interface was repurposed as the front end. The project concluded that open-source chatbot development remains complex and inconsistent, and that evaluating models on trustworthiness criteria beyond raw performance is still too difficult. Mozilla signalled its intention to address these challenges by contributing further to the open-source AI community, with prompt engineering identified as remaining critically important throughout the process.

Did ChatGPT Write This? Here's How to Tell.

The rise of AI chatbots such as ChatGPT has introduced new possibilities for generating information, whilst also raising concerns about accuracy and the spread of misinformation. Although these tools can produce detailed responses on a wide range of topics, they occasionally provide incorrect or misleading information, complicating efforts to distinguish between human and AI-generated content.

Detection methods, including tools developed by OpenAI and academic institutions, are available but imperfect, often failing to identify shorter texts or content that has been edited by humans. Experts caution that no detection system is foolproof, as AI models continue to evolve and make it increasingly difficult to reliably identify AI-generated text.

Verifying the accuracy of ChatGPT's responses therefore requires critical evaluation, including checking for inconsistencies, errors or contextually inappropriate information. Organisations such as Mozilla are addressing these broader challenges by promoting responsible AI development and emphasising the need for education on the societal and ethical implications of emerging technologies.

Voiceflow is a platform designed to enable the creation and deployment of AI-driven customer experience solutions across multiple channels, offering tools for building, testing and scaling conversational agents. It supports omnichannel integration, allowing businesses to embed AI agents into websites, mobile applications and call centres, with features such as real-time collaboration, customisable workflows and compatibility with major enterprise systems.

The platform is used by organisations to automate customer support, generate leads and improve user engagement, with case studies highlighting rapid implementation and measurable outcomes such as increased automation rates and user satisfaction. Users and industry professionals describe it as a versatile tool that simplifies the development process, facilitates teamwork and provides flexibility through visual design interfaces, API integrations and code editing capabilities, while adhering to enterprise security standards including SOC-2, ISO 27001 and GDPR compliance.