Technology Tales

The notion that AI will decimate the job market for developers is greatly exaggerated. In fact, AI represents a platform shift that's changing what it looks like to build software and ushering in a period of enormous demand for ambitious, innovative and highly specialised code. This demand is driven by the imagination engine of the human mind, which constantly comes up with better ways of doing things. Each imagined future requires software to become reality, leading to new jobs and new approaches to existing ones. The changing nature of development work means that developers are shifting from writing every line of code by hand to orchestrating AI agents that generate code. New roles are emerging, such as AI orchestrators, prompt engineers and human-AI collaboration architects, which require an in-depth understanding of both traditional computer science fundamentals and how to work effectively with AI tools.

Data Hub is an AI-native data platform developed by Datopian that allows users to discover, publish and interact with datasets through agentic workflows. It offers a curated selection of regularly maintained core datasets covering areas such as country codes, S&P 500 companies, airport codes and geographic boundaries, alongside enterprise-grade data solutions including postal codes, logistics data and country reference data.

For those unable to find what they need independently, a Premium Data Service connects users with data experts who will source, verify and integrate customised datasets on their behalf. The platform also supports easy publishing of data in a Markdown-based format with optional GitHub synchronisation and a free entry tier. A companion blog covers practical topics such as decoupling frontend architecture from CKAN-based portals and modernising open data infrastructure for organisations ranging from government bodies to private enterprises.

Claude has been integrated into Microsoft Excel as a beta feature available to Pro, Max, Team and Enterprise subscribers. The AI assistant can analyse entire workbooks, including complex formulas and dependencies across multiple tabs, whilst providing explanations with specific cell references for verification. Users can test different scenarios by updating assumptions throughout their models without disrupting existing formulas, with all changes clearly highlighted and explained. The tool can identify and help resolve common spreadsheet errors such as reference errors, value errors and circular dependencies by tracing them back to their origin. Additionally, Claude can generate draft financial models based on user requirements or populate existing templates with new data whilst preserving all formulas and structural elements.

Following user feedback that Claude Code was being applied to non-coding tasks, Anthropic has introduced Cowork, a simplified version designed for general productivity work rather than software development. Available initially as a research preview for Claude Max subscribers on macOS, Cowork allows users to grant Claude access to specific folders on their computers where it can read, edit and create files autonomously. The system can handle tasks such as reorganising downloads, generating spreadsheets from screenshots or drafting reports from notes, operating with greater independence than standard conversational interactions by making plans and executing them whilst keeping users informed of progress. Users can enhance Cowork's capabilities through existing connectors and newly added skills for document creation, and can combine it with Claude in Chrome for browser-based tasks.

Whilst users maintain control by selecting which folders Claude can access and receive prompts before significant actions occur, the system carries risks including potential file deletion through misinterpreted instructions and vulnerability to prompt injection attacks where malicious content might alter Claude's behaviour. The company plans to expand availability to other subscription tiers, add cross-device synchronisation and Windows support, and continue developing safety features based on feedback from this early release.

After open weight language models made it cheaper and more practical to run capable systems outside proprietary platforms, many teams have found that hosting them locally still demands extreme hardware, so attention has shifted to specialist API providers that charge by tokens and remove most of the infrastructure burden. The piece compares several providers using benchmark and live performance observations, focusing on speed, latency, cost, accuracy and reliability: Cerebras stands out for very high throughput on large models, Fireworks AI and Groq emphasise very low latency suited to interactive and real-time agent use, Together.ai aims for broadly strong, steady production performance on conventional GPU infrastructure and Clarifai targets enterprise needs with hybrid deployment control and cost management. A lower cost option, DeepInfra, is presented as suitable for batch or non-critical workloads, with the trade-off of weaker reliability than the leading services.

As software systems grow more complex and delivery cycles shorten, requirements engineering is under pressure to stay rigorous while moving faster, and a recent article argues that artificial intelligence is increasingly being used to support that shift. It outlines how AI can help teams capture and refine requirements earlier through meeting transcription and summarisation, automatic drafting of user stories and acceptance criteria, clustering and sentiment analysis to surface disagreement and themes and live translation to improve collaboration in distributed teams. It also describes tools that generate diagrams, detect duplicates, suggest tests, support traceability and predict change impacts, alongside general purpose assistants that help analysts brainstorm, rephrase and review specifications. Alongside these potential gains in efficiency and consistency, it stresses the need for careful governance around bias, privacy and transparency, with human oversight, clear accountability and compliance measures such as GDPR remaining essential.

A recent study led by researchers at University College Cork used literary stylometry to compare hundreds of human written short stories with creative prose generated by large language models such as GPT-3.5, GPT 4 and Llama 70B, finding that while AI can produce fluent and polished writing it still shows a detectable stylistic fingerprint. The analysis identified clear differences in subtle linguistic markers, with AI outputs clustering into narrow, uniform patterns specific to each model, whereas human writing displayed far greater variation shaped by individual voice and creative intent, and GPT 4 was found to be even more consistent than GPT-3.5. The researchers caution that stylometry is not suitable for judging student authorship in education due to ethical and reliability concerns, but argue it can help clarify what distinguishes human expression from algorithmic generation, and they call for broader datasets and further testing as generative AI raises wider questions about authenticity and authorship in creative work.

CoCalc is a collaborative platform designed for computational work, offering real-time editing of Jupyter Notebooks, LaTeX documents and Linux terminals alongside access to powerful compute servers with optional GPU support. It provides pre-installed software such as Python, R, Julia and SageMath, along with tools for course management, automated grading and integration with generative AI models to assist with coding and documentation. Users can deploy projects on-premises or via cloud services, utilising scalable resources and flexible licensing options.

The platform supports extensive collaboration features, including shared workspaces, version control and the ability to synchronise files across multiple servers, making it suitable for academic, research and professional environments. Testimonials highlight its ease of use, robust support and effectiveness in facilitating both individual and group computational tasks.

JuliaHub offers two AI-powered platforms designed to modernise engineering and scientific computing workflows. Dyad combines traditional physics modelling with artificial intelligence to support digital twin development, system optimisation and embedded software deployment, while JuliaHub provides cloud-native infrastructure for high-performance scientific computing and AI workloads. Both platforms are built on Julia, a programming language that delivers performance claimed to be fifty times faster than Python, MATLAB and R, and which has attracted over one million users, more than one hundred million downloads and upwards of twelve thousand packages across fifteen hundred universities. The company serves clients in aerospace, pharmaceutical and technology sectors, and publishes regular technical updates, product releases and case studies through its blog.

Web scraping has evolved into an essential skill in today's data-driven landscape, particularly as large language models require high-quality information from the internet to function effectively. Modern AI-powered scraping tools have transformed this once technical process into something accessible to users without coding experience. Platforms like Firecrawl and ScrapeGraphAI offer API-based solutions that convert web content into clean, structured formats ready for use with language models, whilst handling challenges such as anti-bot systems and dynamic content automatically. Open-source options like Crawl4AI provide fast, efficient crawling capabilities with intelligent algorithms that optimise data collection without requiring API keys. User-friendly platforms such as Octoparse and Browse.AI feature drag-and-drop interfaces and pre-built templates that allow non-technical users to extract data through simple point-and-click operations. More advanced solutions like ScrapingBee and Apify handle proxy rotation, JavaScript rendering and large-scale extraction whilst offering integration with popular tools and frameworks. These modern platforms have democratised web scraping by combining artificial intelligence capabilities with intuitive interfaces, making it possible for anyone to build data pipelines and extract valuable information regardless of their technical background.