TOPIC: ARTIFICIAL INTELLIGENCE
A round-up of online portals for those seeking work
5th August 2025
For me, much of 2025 was spent finding a new freelance work engagement. Recently, that search successfully concluded, but not before I got flashbacks of how hard things were when seeking work after completing university education and deciding to hybridise my search to include permanent employment too. Now that I am fulfilling a new contract with a new client, I am compiling a listing of places on the web to a search for work, at least for future reference if nothing else.
Founded in 2011 by former executives from Gumtree, eBay and Zoopla, this UK-based job search engine aggregates listings from thousands of sites across 16+ countries with headquarters in London and approximately 100 employees worldwide. The platform offers over one million job advertisements in the UK alone and an estimated 350 million globally, attracting more than 10 million monthly visits. Jobseekers can use the service without cost, benefiting from search functionality, email alerts, salary insights and tools such as ValueMyCV and the AI-powered interview preparation tool Prepper. The company operates on a Cost-Per-Click or Cost-Per-Applicant model for employers seeking visibility, while also providing data and analytics APIs for programmatic advertising and labour market insights. Notably, the platform powers the UK government Number 10 Dashboard, with its dataset frequently utilised by the ONS for real-time vacancy tracking.
Founded in 2000 by Lee Biggins, this independent job board has grown to become one of the leading platforms in the UK job market. Based in Fleet, Hampshire, it maintains a substantial database of approximately 21.4 million CV's, with around 360,000 new or updated profiles added monthly. The platform attracts significant traffic with about 10.1 million monthly visits from 4.3 million unique users, facilitating roughly 3 million job applications each month across approximately 137,000 live vacancies. Jobseekers can access all services free of charge, including job searching, CV uploads, job alerts and application tracking, though the CV building tools are relatively basic compared to specialist alternatives. The platform boasts high customer satisfaction, with 96 percent of clients rating their service as good or excellent, and offers additional value through its network of over 800 partner job sites and ATS integration capabilities.
Formerly known as TryRemotely, Empllo functions as a comprehensive job board specialising in remote technology and startup positions across various disciplines including engineering, product, sales, marketing, design and finance. The platform currently hosts over 30,000 active listings from approximately 24,000 hiring companies worldwide, with specific regional coverage including around 375 positions in the UK and 36 in Ireland. Among its notable features is the AI-powered Job Copilot tool, which can automatically apply to roles based on user preferences. While Empllo offers extensive listings and advanced filtering options by company, funding and skills, it does have limitations including inconsistent salary information and variable job quality. The service is free to browse, with account creation unlocking personalised features. It is particularly suitable for technology professionals seeking distributed work arrangements with startups, though users are advised to verify role details independently and potentially supplement their search with other platforms offering employer reviews for more thorough vetting.
This is a comprehensive job-hunt management tool that replaces traditional spreadsheets with an intuitive Kanban board interface, allowing users to organise their applications effectively. The platform features a Chrome extension that integrates with major job boards like LinkedIn and Indeed, enabling one-click saving of job listings. Users can track applications through various stages, store relevant documents and contact information, and access detailed statistics about their job search progress. The service offers artificial intelligence capabilities powered by GPT-4 to generate application responses, personalise cover letters and craft LinkedIn profiles. With over 25,000 active users who have tracked more than 280,000 job applications collectively, the tool provides both free and premium tiers. The basic free version includes unlimited tracking of applications, while the Pro subscription adds features such as custom columns, unlimited tags and expanded AI capabilities. This solution particularly benefits active jobseekers managing numerous applications across different platforms who desire structured organisation and data-driven insights into their job search.
This organisation provides a specialised platform matching candidates with companies based on flexible working arrangements, including remote options, location independence and customisable hours. Their interface features a notable "Work From Anywhere" filter highlighting roles with genuine location flexibility, alongside transparency scores for companies that reflect their openness regarding working arrangements. The platform allows users to browse companies offering specific perks like part-time arrangements, sabbatical leave, or compressed hours, with rankings based on flexibility and workplace culture. While free to use with job-saving capabilities and quick matching processes, it appears relatively new with a modest-sized team, limited independent reviews and a smaller volume of job listings compared to more established competitors. The platform's distinctive approach prioritises work-life balance through values-driven matching and company-oriented filters, particularly useful for those seeking roles aligned with modern flexible working preferences.
Founded in 2007 and based in Puerto Rico, FlexJobs operates as a subscription-based platform specialising in remote, hybrid, freelance and part-time employment opportunities. The service manually verifies all job listings to eliminate fraudulent postings, with staff dedicating over 200 hours daily to screening processes. Users gain access to positions across 105+ categories from entry-level to executive roles, alongside career development resources including webinars, resume reviews and skills assessments. Pricing options range from weekly trials to annual subscriptions with a 30-day money-back guarantee. While many users praise the platform for its legitimacy and comprehensive filtering tools, earning high ratings on review sites like Trustpilot, some individuals question whether the subscription fee provides sufficient value compared to free alternatives. Potential limitations include delayed posting of opportunities and varying representation across different industries.
Founded in November 2004 and now operating in over 60 countries with 28 languages, this leading global job search platform serves approximately 390 million visitors monthly worldwide. In the UK alone, it attracts about 34 million monthly visits, with users spending nearly 7 minutes per session and viewing over 8.5 pages on average. The platform maintains more than 610 million jobseeker profiles globally while offering free services for candidates including job searching, application tools, CV uploads, company reviews and salary information. For employers, the business model includes pay-per-click and pay-per-applicant sponsored listings, alongside tools such as Hiring Insights providing salary data and application trends. Since October 2024, visibility for non-sponsored listings has decreased, requiring employers to invest in sponsorship for optimal visibility. Despite this competitive environment requiring strategic budget allocation, the platform remains highly popular due to its comprehensive features and extensive reach.
A meta-directory founded in 2022 by Rodrigo Rocco, this platform aggregates and organises links to over 400 specialised and niche job sites across various industries and regions. Unlike traditional job boards, it does not host listings directly but serves as a discovery tool that redirects users to external platforms where actual applications take place. The service refreshes links approximately every 45 minutes and offers a weekly newsletter. While providing free access and efficient discovery of relevant boards by category or sector, potential users should note that the platform lacks direct job listings, built-in application tracking, or alert systems. It is particularly valuable for professionals exploring highly specialised fields, those wishing to expand beyond mainstream job boards and recruiters seeking to increase their visibility, though beginners might find navigating numerous destination boards somewhat overwhelming.
Founded in Milan by Vito Lomele in 2006 (initially as Jobespresso), this global job aggregator operates in 58 countries and 21 languages. The platform collects between 28 and 35 million job listings monthly from various online sources, attracting approximately 55 million visits and serving over 100 million registered users. The service functions by gathering vacancies from career pages, agencies and job boards, then directing users to original postings when they search. For employers, it offers programmatic recruitment solutions using artificial intelligence and taxonomy to match roles with candidates dynamically, including pay-per-applicant models. While the platform benefits from its extensive global reach and substantial job inventory, its approach of redirecting to third-party sites means the quality and freshness of listings can vary considerably.
Founded in 1993 as Fax-Me Ltd and rebranded in 1995, this pioneering UK job board launched the world's first jobs-by-email service in May 1994. Originally dominating the IT recruitment sector with up to 80% market share in the early 2000s, the platform published approximately 200,000 jobs and processed over 1 million applications monthly by 2010. Currently headquartered in Colchester, Essex, the service maintains a global presence across Europe, North America and Australia, delivering over 1.2 million job-subscription emails daily. The platform employs a proprietary smart matching engine called Alchemy and features manual verification to ensure job quality. While free for jobseekers who can upload CVs and receive tailored job alerts, employers can post vacancies and run recruitment campaigns across various sectors. Although respected for its legacy and niche focus, particularly in technical recruitment, its scale and visibility are more modest compared to larger contemporary platforms.
Founded in 2020 with headquarters in London, Lifelancer operates as an AI-powered talent hiring platform specialising in life sciences, pharmaceutical, biotech, healthcare IT and digital health sectors. The company connects organisations with freelance, remote and international professionals through services including candidate matching and global onboarding assistance. Despite being relatively small, Lifelancer provides distinct features for both hiring organisations and jobseekers. Employers can post positions tailored to specific healthcare and technology roles, utilising AI-based candidate sourcing, while professionals can create profiles to be matched with relevant opportunities. The platform handles compliance and payroll across multiple countries, making it particularly valuable for international teams, though as a young company, it may not yet offer the extensive talent pool of more established competitors in the industry.
The professional networking was core to my search for work and had its uses while doing so. Writing posts and articles did a lot to raise my profile along with reaching out to others, definitely an asset when assessing the state of a freelancing market. The usefulness of the green "Open to Work" banner is debatable because of my freelancing pitch in a slow market. Nevertheless, there was one headhunting approach that might have resulted in something if another offer had not gazumped it. Also, this is not a place to hang around over a weekend with job search moaning filling your feed, though making your interests known can change that. Now that I have paid work, the platform has become a way of keeping up to date in my line of business.
Established in 1994 as The Monster Board, Monster.com became one of the first online job portals, gaining prominence through memorable Super Bowl advertisements. As of June 2025, the platform attracts approximately 4.3 million monthly visits, primarily from the United States (76%), with smaller audiences in India (6%) and the UK (1.7%). The service offers free resources for jobseekers, including resume uploads and career guidance, while employers pay for job postings and additional premium features.
Established in 1999 and headquartered in Richmond, Surrey, PharmiWeb has evolved into Europe's leading pharmaceutical and life sciences platform. The company separated its dedicated job board as PharmiWeb.jobs in 2019, while maintaining industry news and insights on the original portal. With approximately 600,000 registered jobseekers globally and around 200,000 monthly site visits generating 40,000 applications, the platform hosts between 1,500 and 5,000 active vacancies at any time. Jobseekers can access the service completely free, uploading CVs and setting alerts tailored to specific fields, disciplines or locations. Additional recruiter services include CV database access, email marketing campaigns, employer branding and applicant management tools. The platform particularly excels for specialised pharmaceutical, biotech, clinical research and regulatory affairs roles, though its focused nature means it carries fewer listings than mainstream employment boards and commands higher posting costs.
If 2025 was a flashback to the travails of seeking work after completing university education, meeting this name again was another part of that. Founded in May 1960 by Sir Alec Reed, the firm began as a traditional recruitment agency in Hounslow, West London, before launching the first UK recruitment website in 1995. Today, the platform attracts approximately 3.7 million monthly visitors, primarily UK-based users aged 25-34, generating around 80,000 job applications daily. The service offers jobseekers free access to search and apply for roles, job alerts, CV storage, application tracking, career advice articles, a tax calculator, salary tools and online courses. For employers, the privately owned company provides job advertising, access to a database of 18-22 million candidate CVs and specialist recruitment across about 20 industry sectors.
Founded by digital nomad Pieter Levels in 2015, this prominent job board specialises exclusively in 100% remote positions across diverse sectors including tech, marketing, writing, design and customer support. The platform offers free browsing and application for jobseekers, while employers pay fees. Notable features include mandatory salary transparency, global job coverage with regional filtering options and a clean, minimalist interface that works well on mobile devices. Despite hosting over 100,000 remote jobs from reputable companies like Amazon and Microsoft, the platform has limitations including basic filtering capabilities and highly competitive application processes, particularly for tech roles. The simple user experience redirects applications directly to employer pages rather than using an internal system. For professionals seeking remote work worldwide, this board serves as a valuable resource but works best when used alongside other specialised platforms to maximise opportunities.
Founded in 2015 and based in Boulder, Colorado, this platform exclusively focuses on remote work opportunities across diverse industries such as marketing, finance, healthcare, customer support and design. Attracting over 1.5 million monthly visitors, it provides jobseekers with free access to various employment categories including full-time, part-time, freelance and hybrid positions. Beyond job listings, the platform offers a comprehensive resource centre featuring articles, expert insights and best practices from over 108 remote-first companies. Job alerts and weekly newsletters keep users informed about relevant opportunities. While the platform provides strong resources and maintains positive trust ratings of approximately 4.2/5 on Trustpilot, its filtering capabilities are relatively basic compared to competitors. Users might need to conduct additional research as company reviews are not included with job postings. Despite these limitations, the platform serves as a valuable resource for individuals seeking remote work guidance and opportunities.
For jobseekers in the technology and digital sectors, Remotive serves as a specialised remote job board offering approximately 2,000 active positions on its free public platform. Founded around 2014-2015, this service operates with a remote-first approach and focuses on verifying job listings for legitimacy. The platform provides a premium tier called "Remotive Accelerator" which grants users access to over 50,000 additional curated jobs, advanced filtering options based on skills and salary requirements and membership to a private Slack community. While the interface receives praise for its clean design and intuitive navigation, user feedback regarding the paid tier remains mixed, with some individuals noting limitations such as inactive community features and an abundance of US-based or senior-level positions. The platform is particularly valuable for professionals in software development, product management, marketing and customer service who are seeking global remote opportunities.
Originally launched in Canada in 2011 as neuvoo, this global job search engine is now headquartered in Montreal, Quebec, providing access to over 30 million jobs across more than 75 countries. The platform attracts between 12 and 16 million monthly visits worldwide, with approximately 6 percent originating from the UK. Jobseekers can utilise the service without charge, accessing features like salary converters and tax calculators in certain regions to enhance transparency about potential earnings. Employers have the option to post jobs for free in some areas, with additional pay per click sponsored listings available to increase visibility. Despite its extensive coverage and useful tools, user feedback remains mixed, with numerous complaints on review sites regarding outdated listings, unwanted emails and difficulties managing or deleting accounts.
Founded in 2011 and based in New York City, The Muse is an online platform that integrates job listings with career guidance, employer insights and coaching services to support individuals in making informed career decisions. It distinguishes itself by offering detailed employer profiles that include workplace culture, employee perspectives and company values, alongside editorial content on resume writing, interview techniques and career progression. While jobseekers can access core features for free, employers pay to advertise roles and create branded profiles, with additional revenue generated through premium coaching services. The platform appeals to graduates, early-career professionals and those seeking career transitions, prioritising alignment between personal values and workplace environments over simply aggregating job vacancies. Compared to larger job boards, it focuses on storytelling and career development resources, positioning itself as a tool for navigating modern employment trends such as flexible work and diversity initiatives.
Founded in 1999, Totaljobs is a major UK job board currently owned by StepStone Group UK Ltd, a subsidiary of Axel Springer Digital Classifieds. The platform attracts approximately 20 million monthly visits and generates 4-5 million job applications each month, with over 300,000 daily visitors browsing through typically 280,000+ live job listings. As the flagship of a broader network including specialised boards such as Jobsite, CareerStructure and City Jobs, Totaljobs provides jobseekers with search functionality across various sectors, job alerts and career advice resources. For employers and recruiters, the platform offers pay-per-post job advertising, subscription options for CV database access and various employer tools.
Founded in 2011, this is one of the largest purely remote job boards globally, attracting approximately 6 million monthly visitors and featuring over 36,000 remote positions across various categories including programming, marketing, customer support and design. Based in Vancouver, the platform operates with a small remote-first team who vet listings to reduce spam and scams. Employers pay for each standard listing, while jobseekers access the service without charge. The interface is straightforward and categorised by functional area, earning trust from major companies like Google, Amazon and GitHub. However, the platform has limitations including basic filtering capabilities, a predominance of senior-level positions particularly in technology roles and occasional complaints about outdated or misleading posts. The service is most suitable for experienced professionals seeking genuine remote opportunities rather than those early in their careers. Some users report region-restricted application access and positions that offer lower compensation than expected for the required experience level.
Founded in 2014, this job board provides remote work opportunities for digital nomads and professionals across various industries. The platform offers over 30,000 fully remote positions spanning sectors such as technology, marketing, writing, finance and education. Users can browse listings freely, but a Premium subscription grants access to additional jobs, enhanced filters and email alerts. The interface is user-friendly with fast-loading pages and straightforward filtering options. The service primarily features global employment opportunities suitable for location-independent workers. However, several limitations exist: many positions require senior-level experience, particularly in technical fields; the free tier displays only a subset of available listings; filtering capabilities are relatively basic; and job descriptions sometimes lack detail. The platform has received mixed reviews, earning approximately 3.4 out of 5 on Trustpilot, with users noting the prevalence of senior technical roles and questioning the value of the premium subscription. It is most beneficial for experienced professionals comfortable with remote work arrangements, while those seeking entry-level positions might find fewer suitable opportunities.
From boardroom to code: More options for AI and Data Science education
27th July 2025
The artificial intelligence revolution has created an unprecedented demand for education that spans from executive strategy to technical implementation. Modern professionals face the challenge of navigating a landscape where understanding AI's business implications proves as crucial as mastering its technical foundations. This comprehensive examination explores five distinguished programmes that collectively address this spectrum, offering pathways for business professionals, aspiring data scientists and technical specialists seeking advanced expertise.
Strategic Business Implementation Through Practical AI Tools
LinkedIn Learning's Applying Generative AI as a Business Professional programme represents the entry point for professionals seeking immediate workplace impact. This focused five-hour curriculum across six courses addresses the practical reality that most business professionals need functional AI literacy rather than technical mastery. The programme emphasises hands-on application of contemporary tools including ChatGPT, Claude and Microsoft Copilot, recognising that these platforms have become integral to modern professional workflows.
The curriculum's strength lies in its emphasis on prompt engineering techniques that yield immediate productivity gains. Participants learn to craft effective queries that consistently produce useful outputs, a skill that has rapidly evolved from novelty to necessity across industries. The programme extends beyond basic tool usage to include strategies for creating custom GPTs without programming knowledge, enabling professionals to develop solutions that address specific organisational challenges.
Communication enhancement represents another critical component, as the programme teaches participants to leverage AI for improving written correspondence, presentations and strategic communications. This practical focus acknowledges that AI's greatest business value often emerges through augmenting existing capabilities rather than replacing human expertise. The inclusion of critical thinking frameworks for AI-assisted decision-making ensures that participants develop sophisticated approaches to integrating artificial intelligence into complex business processes.
Academic Rigour Meets Strategic AI Governance
The University of Pennsylvania's AI for Business Specialisation on Coursera elevates business AI education to an academic level whilst maintaining practical relevance. This four-course programme, completed over approximately four weeks, addresses the strategic implementation challenges that organisations face when deploying AI technologies at scale. The curriculum's foundation in Big Data fundamentals provides essential context for understanding the data requirements that underpin successful AI initiatives.
The programme's exploration of machine learning applications in marketing and finance demonstrates how AI transforms traditional business functions. Participants examine customer journey optimisation techniques, fraud prevention methodologies and personalisation technologies that have become competitive necessities rather than optional enhancements. These applications receive thorough treatment that balances technical understanding with strategic implications, enabling participants to make informed decisions about AI investments and implementations.
Particularly valuable is the programme's emphasis on AI-driven people management practices, addressing how artificial intelligence reshapes human resources, talent development and organisational dynamics. This focus acknowledges that successful AI implementation requires more than technological competence; it demands sophisticated understanding of how these tools affect workplace relationships and employee development.
The specialisation's coverage of strategic AI governance frameworks proves especially relevant as organisations grapple with ethical deployment challenges. Participants develop comprehensive approaches to responsible AI implementation that address regulatory compliance, bias mitigation and stakeholder concerns. This academic treatment of AI ethics provides the foundational knowledge necessary for creating sustainable AI programmes that serve both business objectives and societal responsibilities.
Industry-Standard Professional Development
IBM's Data Science Professional Certificate represents a bridge between business understanding and technical proficiency, offering a comprehensive twelve-course programme designed for career transition. This four-month pathway requires no prior experience whilst building industry-ready capabilities that align with contemporary data science roles. The programme's strength lies in its integration of technical skill development with practical application, ensuring graduates possess both theoretical knowledge and hands-on competency.
The curriculum's progression from Python programming fundamentals through advanced machine learning techniques mirrors the learning journey that working data scientists experience. Participants gain proficiency with industry-standard tools including Jupyter notebooks, GitHub and Watson Studio, ensuring familiarity with the collaborative development environments that characterise modern data science practice. This tool proficiency proves essential for workplace integration, as contemporary data science roles require seamless collaboration across technical teams.
The programme's inclusion of generative AI applications reflects IBM's recognition that artificial intelligence has become integral to data science practice rather than a separate discipline. Participants learn to leverage AI tools for data analysis, visualisation and insight generation, developing capabilities that enhance productivity whilst maintaining analytical rigour. This integration prepares trainees for data science roles that increasingly incorporate AI-assisted workflows.
Real-world project development represents a crucial component, as participants build comprehensive portfolios that demonstrate practical proficiency to potential employers. These projects address authentic business challenges using genuine datasets, ensuring that participants can articulate their capabilities through concrete examples.
Advanced Technical Mastery Through Academic Excellence
Andrew Ng's Machine Learning Specialisation on Coursera establishes the technical foundation for advanced AI practice. This three-course programme, completed over approximately two months, provides comprehensive coverage of core machine learning concepts whilst emphasising practical implementation skills. Andrew Ng's reputation as an AI pioneer lends exceptional credibility to this curriculum, ensuring that participants receive instruction that reflects both academic rigour and industry best practices.
The specialisation's treatment of supervised learning encompasses linear and logistic regression, neural networks and decision trees, providing thorough grounding in the algorithms that underpin contemporary machine learning applications. Participants develop practical proficiency with Python, NumPy and scikit-learn, gaining hands-on experience with the tools that professional machine learning practitioners use daily. This implementation focus ensures that theoretical understanding translates into practical capability.
Unsupervised learning includes clustering algorithms, anomaly detection techniques and certain approaches in recommender systems, all of which contribute to powering modern digital experiences. The programme's exploration of reinforcement learning provides exposure to the techniques driving advances in autonomous systems and game-playing AI. This breadth ensures that participants understand the full spectrum of machine learning approaches, rather than developing narrow expertise in specific techniques.
Cutting-Edge Deep Learning Applications
Again available through Coursera, Andrew Ng's Deep Learning Specialisation extends technical education into the neural network architectures that drives contemporary AI. This five-course programme, spanning approximately three months, addresses the advanced techniques that enable computer vision, natural language processing and complex pattern recognition applications. The intermediate-level curriculum assumes foundational machine learning knowledge whilst building expertise in cutting-edge methodologies.
Convolutional neural network coverage provides comprehensive understanding of computer vision applications, from image classification through object detection and facial recognition. Participants develop practical skills with CNN architectures that power visual AI applications across industries. The programme's treatment of recurrent neural networks and LSTMs addresses sequence processing challenges in speech recognition, machine translation and time series analysis.
The specialisation's exploration of transformer architectures proves particularly relevant given their central role in large language models and natural language processing breakthroughs. Participants gain understanding of attention mechanisms, transfer learning techniques and the architectural innovations that enable modern AI capabilities. This coverage ensures they understand the technical foundations underlying contemporary AI advances.
Real-world application development represents a crucial component, as participants work on speech recognition systems, machine translation applications, image recognition tools and chatbot implementations. These projects utilise TensorFlow, a dominant framework for deep learning development, ensuring that graduates possess practical experience with production-ready tools.
Strategic Integration and Future Pathways
These five programmes collectively address the comprehensive skill requirements of the modern AI landscape, from strategic business implementation through advanced technical development. The progression from practical tool usage through academic business strategy to technical mastery reflects the reality that successful AI adoption requires capabilities across multiple domains. Organisations benefit most when business leaders understand AI's strategic implications, whilst technical teams possess sophisticated implementation capabilities.
The integration of business strategy with technical education acknowledges that artificial intelligence's transformative potential emerges through thoughtful application rather than technological sophistication alone. These programmes prepare professionals to contribute meaningfully to AI initiatives regardless of their specific role or technical background, ensuring that organisations can build comprehensive AI capabilities that serve both immediate needs and long-term strategic objectives.
Advance your Data Science, AI and Computer Science skills using these online learning opportunities
25th July 2025
The landscape of online education has transformed dramatically over the past decade, creating unprecedented access to high-quality learning resources across multiple disciplines. This comprehensive examination explores the diverse array of courses available for aspiring data scientists, analysts, and computer science professionals, spanning from foundational programming concepts to cutting-edge artificial intelligence applications.
Data Analysis with R Programming
R programming has established itself as a cornerstone language for statistical analysis and data visualisation, making it an essential skill for modern data professionals. DataCamp's Data Analyst with R programme represents a comprehensive 77-hour journey through the fundamentals of data analysis, encompassing 21 distinct courses that progressively build expertise. Students begin with core programming concepts including data structures, conditional statements, and loops before advancing to sophisticated data manipulation techniques using tools such as dplyr and ggplot2. The curriculum extends beyond basic programming to include R Markdown for reproducible research, data manipulation with data.table, and essential database skills through SQL integration.
For those seeking more advanced statistical expertise, DataCamp's Statistician with R career track provides an extensive 108-hour programme spanning 27 courses. This comprehensive pathway develops essential skills for professional statistician roles, progressing from fundamental concepts of data collection and analysis to advanced statistical methodology. Students explore random variables, distributions, and conditioning through practical examples before advancing to linear and logistic regression techniques. The curriculum encompasses sophisticated topics including binomial and Poisson regression models, sampling methodologies, hypothesis testing, experimental design, and A/B testing frameworks. Advanced modules cover missing data handling, survey design principles, survival analysis, Bayesian data analysis, and factor analysis, making this track particularly suitable for those with existing R programming knowledge who seek to specialise in statistical practice.
The Google Data Analytics Professional Certificate programme, developed by Google and hosted on Coursera with US and UK versions, offers a structured six-month pathway for those seeking industry-recognised credentials. Students progress through eight carefully designed courses, beginning with foundational concepts in "Foundations: Data, Data, Everywhere" and culminating in a practical capstone project. The curriculum emphasises real-world applications, teaching students to formulate data-driven questions, prepare datasets for analysis, and communicate findings effectively to stakeholders.
Udacity's Data Analysis with R course presents a unique proposition as a completely free resource spanning two months of study. This programme focuses intensively on exploratory data analysis techniques, providing students with hands-on experience using RStudio and essential R packages. The course structure emphasises practical application through projects, including an in-depth exploration of diamond pricing data that demonstrates predictive modelling techniques.
Advanced Statistical Learning and Specialised Applications
Duke University's Statistics with R Specialisation elevates statistical understanding through a comprehensive seven-month programme that has earned a 4.6-star rating from participants. This five-course sequence delves deep into statistical theory and application, beginning with probability and data fundamentals before progressing through inferential statistics, linear regression, and Bayesian analysis. The programme distinguishes itself by emphasising both theoretical understanding and practical implementation, making it particularly valuable for those seeking to master statistical concepts rather than merely apply them.
The R Programming: Advanced Analytics course on Udemy, led by instructor Kirill, provides focused training in advanced R techniques within a compact six-hour format. This course addresses specific challenges that working analysts face, including data preparation workflows, handling missing data through median imputation, and working with complex date-time formats. The curriculum emphasises efficiency techniques such as using apply functions instead of traditional loops, making it particularly valuable for professionals seeking to optimise their analytical workflows.
Complementing this practical approach, the Applied Statistical Modelling for Data Analysis in R course on Udemy offers a more comprehensive 9.5-hour exploration of statistical methodology. The curriculum covers linear modelling implementation, advanced regression analysis techniques, and multivariate analysis methods. With its emphasis on statistical theory and application, this course serves those who already possess foundational R and RStudio knowledge but seek to deepen their understanding of statistical modelling approaches.
Imperial College London's Statistical Analysis with R for Public Health Specialisation brings academic rigour to practical health applications through a four-month programme. This specialisation addresses real-world public health challenges, using datasets that examine fruit and vegetable consumption patterns, diabetes risk factors, and cardiac outcomes. Students develop expertise in linear and logistic regression while gaining exposure to survival analysis techniques, making this programme particularly relevant for those interested in healthcare analytics.
Visualisation and Data Communication
Johns Hopkins University's Data Visualisation & Dashboarding with R Specialisation represents the pinnacle of visual analytics education, achieving an exceptional 4.9-star rating across its four-month curriculum. This five-course programme begins with fundamental visualisation principles before progressing through advanced ggplot2 techniques and interactive dashboard development. Students learn to create compelling visual narratives using Shiny applications and flexdashboard frameworks, skills that are increasingly essential in today's data-driven business environment.
The programme's emphasis on publication-ready visualisations and interactive dashboards addresses the growing demand for data professionals who can not only analyse data but also communicate insights effectively to diverse audiences. The curriculum balances technical skill development with design principles, ensuring graduates can create both statistically accurate and visually compelling presentations.
Professional Certification Pathways
DataCamp's certification programmes offer accelerated pathways to professional recognition, with each certification designed to be completed within 30 days. The Data Analyst Certification combines timed examinations with practical assessments to evaluate real-world competency. Candidates must demonstrate proficiency in data extraction, quality assessment, cleaning procedures, and metric calculation, reflecting the core responsibilities of working data analysts.
The Data Scientist Certification expands these requirements to include machine learning and artificial intelligence applications, requiring candidates to collect and interpret large datasets whilst effectively communicating results to business stakeholders. Similarly, the Data Engineer Certification focuses on data infrastructure and preprocessing capabilities, essential skills as organisations increasingly rely on automated data pipelines and real-time analytics.
The SQL Associate Certification addresses the universal need for database querying skills across all data roles. This certification validates both theoretical knowledge through timed examinations and practical application through hands-on database challenges, ensuring graduates can confidently extract and manipulate data from various database systems.
Emerging Technologies and Artificial Intelligence
The rapid advancement of artificial intelligence has created new educational opportunities that bridge traditional data science with cutting-edge generative technologies. DataCamp's Understanding Artificial Intelligence course provides a foundation for those new to AI concepts, requiring no programming background whilst covering machine learning, deep learning, and generative model fundamentals. This accessibility makes it valuable for business professionals seeking to understand AI's implications without becoming technical practitioners.
The Generative AI Concepts course builds upon this foundation to explore the specific technologies driving current AI innovation. Students examine how large language models function, consider ethical implications of AI deployment, and learn to maximise the effectiveness of AI tools in professional contexts. This programme addresses the growing need for AI literacy across various industries and roles.
DataCamp's Large Language Model Concepts course provides intermediate-level exploration of the technologies underlying systems like ChatGPT. The curriculum covers natural language processing fundamentals, fine-tuning techniques, and various learning approaches including zero-shot and few-shot learning. This technical depth makes it particularly valuable for professionals seeking to implement or customise language models within their organisations.
The ChatGPT Prompt Engineering for Developers course addresses the developing field of prompt engineering, a skill that has gained significant commercial value. Students learn to craft effective prompts that consistently produce desired outputs from language models, a capability that combines technical understanding with creative problem-solving. This expertise has become increasingly valuable as organisations integrate AI tools into their workflows.
Working with OpenAI API provides practical implementation skills for those seeking to build AI-powered applications. The course covers text generation, sentiment analysis, and chatbot development, giving students hands-on experience with the tools that are reshaping how businesses interact with customers and process information.
Computer Science Foundations
Stanford University's Computer Science 101 offers an accessible introduction to computing concepts without requiring prior programming experience. This course addresses fundamental questions about computational capabilities and limitations whilst exploring hardware architecture, software development, and internet infrastructure. The curriculum includes essential topics such as computer security, making it valuable for anyone seeking to understand the digital systems that underpin modern society.
The University of Leeds' Introduction to Logic for Computer Science provides focused training in logical reasoning, a skill that underlies algorithm design and problem-solving approaches. This compact course covers propositional logic and logical modelling techniques that form the foundation for more advanced computer science concepts.
Harvard's CS50 course, taught by Professor David Malan, has gained worldwide recognition for its engaging approach to computer science education. The programme combines theoretical concepts with practical projects, teaching algorithmic thinking alongside multiple programming languages including Python, SQL, HTML, CSS, and JavaScript. This breadth of coverage makes it particularly valuable for those seeking a comprehensive introduction to software development.
MIT's Introduction to Computer Science and Programming Using Python focuses specifically on computational thinking and Python programming. The curriculum emphasises problem-solving methodologies, testing and debugging strategies, and algorithmic complexity analysis. This foundation proves essential for those planning to specialise in data science or software development.
MIT's The Missing Semester course addresses practical tools that traditional computer science curricula often overlook. Students learn command-line environments, version control with Git, debugging techniques, and security practices. These skills prove essential for professional software development but are rarely taught systematically in traditional academic settings.
Accessible Learning Resources and Community Support
The democratisation of education extends beyond formal courses to include diverse learning resources that support different learning styles and schedules. YouTube channels such as Programming with Mosh, freeCodeCamp, Alex the Analyst, Tina Huang, and Ken Lee provide free, high-quality content that complements formal education programmes. These resources offer everything from comprehensive programming tutorials to career guidance and project-based learning opportunities.
The 365 Data Science platform contributes to this ecosystem through flashcard decks that reinforce learning of essential terminology and concepts across Excel, SQL, Python, and emerging technologies like ChatGPT. Their statistics calculators provide interactive tools that help students understand the mechanics behind statistical calculations, bridging the gap between theoretical knowledge and practical application.
Udemy's marketplace model supports this diversity by hosting over 100,000 courses, including many free options that allow instructors to share expertise with global audiences. The platform's filtering capabilities enable learners to identify resources that match their specific needs and learning preferences.
Industry Integration and Career Development
Major technology companies have recognised the value of contributing to global education initiatives, with Google, Microsoft and Amazon offering professional-grade courses at no cost. Google's Data Analytics Professional Certificate exemplifies this trend, providing industry-recognised credentials that directly align with employment requirements at leading technology firms.
These industry partnerships ensure that course content remains current with rapidly evolving technological landscapes, whilst providing students with credentials that carry weight in hiring decisions. The integration of real-world projects and case studies helps bridge the gap between academic learning and professional application.
The comprehensive nature of these educational opportunities reflects the complex requirements of modern data and technology roles. Successful professionals must combine technical proficiency with communication skills, statistical understanding with programming capability, and theoretical knowledge with practical application. The diversity of available courses enables learners to develop these multifaceted skill sets according to their career goals and learning preferences.
As technology continues to reshape industries and create new professional opportunities, access to high-quality education becomes increasingly critical. These courses represent more than mere skill development; they provide pathways for career transformation and professional advancement that transcend traditional educational barriers. Whether pursuing data analysis, software development, or artificial intelligence applications, learners can now access world-class education that was previously available only through expensive university programmes or exclusive corporate training initiatives.
The future of professional development lies in this combination of accessibility, quality, and relevance that characterises the modern online education landscape. These resources enable individuals to build expertise that matches industry demands, also maintaining the flexibility to learn at their own pace and according to their specific circumstances and goals.
Synthetic Data: The key to unlocking AI's potential in healthcare
18th July 2025
The integration of artificial intelligence into healthcare is being hindered by challenges such as data scarcity, privacy concerns and regulatory constraints. Healthcare organisations face difficulties in obtaining sufficient volumes of high-quality, real-world data to train AI models, which can accurately predict outcomes or assist in decision-making.
Synthetic data, defined as algorithmically generated data that mimics real-world data, is emerging as a solution to these challenges. This artificially generated data mirrors the statistical properties of real-world data without containing any sensitive or identifiable information, allowing organisations to sidestep privacy issues and adhere to regulatory requirements.
By generating datasets that preserve statistical relationships and distributions found in real data, synthetic data enables healthcare organisations to train AI models with rich datasets while ensuring sensitive information remains secure. The use of synthetic data can also help address bias and ensure fairness in AI systems by enabling the creation of balanced training sets and allowing for the evaluation of model outputs across different demographic groups.
Furthermore, synthetic data can be generated programmatically, reducing the time spent on data collection and processing and enabling organisations to scale their AI initiatives more efficiently. Ultimately, synthetic data are becoming a critical asset in the development of AI in healthcare, enabling faster development cycles, improving outcomes and driving innovation while maintaining trust and security.
What SAS Innovate 2025 revealed about the future of enterprise analytics
21st June 2025
SAS Innovate 2025 comprised a global event in Orlando (6th-9th May) followed by regional editions on tour. This document provides observations from both the global event and the London stop (3rd-4th June), covering technical content, platform developments and thematic emphasis across the two occasions. The global event featured extensive recorded content covering platform capabilities, migration approaches and practical applications, whilst the London event incorporated these themes with additional local perspectives and a particular focus on governance and life sciences applications.
Global Event
Platform Expansion and New Capabilities
The global SAS Innovate 2025 event included content on SAS Clinical Acceleration, positioned as a SAS Viya equivalent to SAS LSAF. Whilst much appeared familiar from the predecessor platform, performance improvements and additional capabilities represented meaningful enhancements.
Two presentations, likely restricted to in-person attendees based on their absence from certain schedules, covered AI-powered SAS code generation. Shionogi presented on using AI for clinical studies and real-world evidence generation, with the significant detail that the AI capability existed within SAS Viya rather than depending on external large language models. Another session addressed interrogating and generating study protocols using SAS Viya, including functionality intended to support study planning in ways that could improve success probability.
These sessions collectively indicated a directional shift. The scope extends beyond conventional expectations of "SAS in clinical" contexts, moving into upstream and adjacent activities, including protocol development and more integrated automation.
Architectural Approaches and Data Movement
A significant theme across multiple sessions addressed fundamental shifts in data architecture. The traditional approach of moving massive datasets from various sources into a single centralised analytics engine is being challenged by a new paradigm: bringing analytics to the data. The integration of SAS Viya with SingleStore exemplifies this approach, where analytics processing occurs directly within the source database rather than requiring data extraction and loading. This architectural change can reduce infrastructure requirements for specific workloads by as much as 50 per cent, whilst eliminating the complexity and cost associated with constant data movement and duplication.
Trustworthy AI and Organisational Reflection
Keynote presentations addressed the relationship between AI systems and organisational practices. SAS Vice President of Data Ethics Practice Reggie Townsend articulated a perspective that reframes common concerns about AI bias. When AI produces biased results, the issue is not primarily technical failure, but rather a reflection of biases already embedded within cultural and organisational practices. This view positions AI as a diagnostic tool that surfaces systemic issues requiring organisational attention rather than merely technical remediation.
The focus on trustworthy AI extended beyond bias to encompass governance frameworks, transparency requirements and the persistent challenge that poor data quality leads to ineffective AI regardless of model sophistication. These considerations hold particular significance in probabilistic AI contexts, especially where SAS aims to incorporate deterministic elements into aspects of its AI offering.
Natural Language Interfaces and Accessibility
Content addressing SAS Viya Copilot demonstrated the platform's natural language capabilities, enabling users to interact with analytics through conversational queries rather than requiring technical syntax. This approach aims to democratise data access by allowing users with limited technical knowledge to directly engage with complex datasets. The Copilot functionality, built on Microsoft Azure OpenAI Service, supports code generation, model development assistance and natural language explanations of analytical outputs.
Cloud Migration and Infrastructure Considerations
A presentation on transforming clinical programming using SAS Clinical Acceleration was scheduled but not accessible at the global event. The closing session featured the CIO of Parexel discussing their transition to SAS managed cloud services. Characterised as a modernisation initiative, reported outcomes included reduced outage frequency. This aligns with observations from other multi-tenant systems, where maintaining stability and availability represents a fundamental requirement that often proves more complex than external perspectives might suggest.
Content addressing cloud-native strategies emphasised a fundamental psychological shift in resource management. Rather than the traditional capital expenditure mindset where physical servers run continuously, cloud environments enable strategic use of the capability to create and destroy computing resources on demand. Approaches include spinning up analytics environments at the start of the working day and shutting them down at the end, with more sophisticated implementations that automatically save and shut down environments after periods of inactivity. This dynamic approach ensures organisations pay only for actively used resources.
Presentations on organisational change management accompanying technical migrations emphasised that successful technology projects require attention to human factors alongside technical implementation. Strategies discussed included formal launch events to mark transitions, structured support mechanisms such as office hours for technical questions and community-building activities designed to foster relationships and maintain engagement during periods of change.
Platform Integration and Practical Applications
Content on SAS Viya Workbench covered availability through Azure and AWS, Python integration, R compatibility and interfacing with SAS Enterprise Guide, with demonstrations of several features. As SAS expands support for open-source languages, the presentation illustrated how these capabilities can provide a unified platform for different technical communities.
A presentation on retrieval augmented generation with unstructured data (such as system manuals), combined with agentic AI for diagnosing manufacturing system problems, offered a concrete use case. Given the tendency for these subjects to become abstract, the connected example provided practical insight into how components can function together in operational settings.
Digital Twins and Immersive Simulation
A notable announcement at the global event involved the partnership between SAS and Epic Games to create enhanced digital twins using Unreal Engine. This collaboration applies the same photorealistic 3D rendering technology used in Fortnite to industrial applications. Georgia-Pacific piloted this technology at its Savannah River Mill, which manufactures napkins, paper towels and toilet tissue. The facility was captured using RealityScan, Epic's mobile application, to create photorealistic renderings imported into Unreal Engine.
The application focused on optimising automated guided vehicle deployment and routing strategies. Rather than testing scenarios in the physical environment with associated costs and safety risks, the digital twin enables simulation of complex factory floor operations including AGV navigation, proximity alerts, obstacles and rare adverse events. SAS CTO Bryan Harris emphasised that digital twins should not only function like the real world but also look like it, enabling more accessible decision-making for frontline workers, engineers and machine operators beyond traditional data scientist roles.
The collaboration extends beyond visual fidelity. SAS developed a plugin connecting Unreal Engine to SAS Viya, enabling real-time data from simulated environments to fuel AI models that analyse, optimise and test industrial operations. This approach allows organisations to explore "what-if" scenarios virtually before implementing changes in physical facilities, potentially delivering cost savings whilst improving safety and operational efficiency.
Marketing Intelligence and Customer Respect
Content on SAS Customer Intelligence 360 addressed the platform's marketing decisioning capabilities, including next-best-offer functionality and real-time personalisation across channels. A notable emphasis concerned contact policies and rules that enable marketers to limit communication frequency, reflecting a strategic choice to respect customer attention rather than maximise message volume. This approach recognises that in environments characterised by notification saturation, demonstrating restraint can build trust and ensure greater engagement when communications do occur.
Financial Crime and Integrated Analytics
Presentations on financial crime addressed the value of integrated platforms that connect traditionally siloed functions such as fraud detection, anti-money laundering and sanctions screening. Network analytics capabilities enable identification of patterns and relationships across these domains that might otherwise remain hidden. Examples illustrated how seemingly routine alerts, when analysed within a comprehensive view of connected data, can reveal connections to significant criminal networks, transforming tactical operational issues into sources of strategic intelligence.
Data Lineage and Transformation Planning
Content on data lineage reframed this capability from a purely technical concern to a strategic tool for transformation planning. For large-scale modernisation initiatives, comprehensive mapping of data flows, transformations and dependencies provides the foundation for accurate effort estimation, budgetary planning and risk assessment. This visibility enables organisations to proceed with complex changes whilst maintaining confidence that critical downstream processes will not be inadvertently affected.
Development Practices and Migration Approaches
Sessions included content on using Bitbucket with SAS Viya to support continuous integration and continuous deployment pipelines for SAS code. Git formed the foundation of the approach, with supporting tools such as JQ. Given the current state of manual validation processes, this content addressed a genuine need for more robust validation methods for SAS macros used across clinical portfolios, where these activities can require several weeks and efficiency improvements would represent substantial value.
Another session provided detailed coverage of migrating from SAS 9 to SAS Viya, focusing on assessment methods for determining what requires migration and techniques for locating existing assets. The content reflected the reality that the discovery phase often constitutes the primary work effort rather than a preliminary step.
A presentation on implementing SAS Viya on-premises under restrictive security requirements described a solution requiring sustained collaboration with SAS over multiple years to achieve necessary modifications. This illustrated how certain deployments are defined primarily by governance, controls and assurance requirements rather than by product features.
Technical Fundamentals and Persistent Challenges
A hands-on session on data-driven output programming with SAS macros provided practical content with life sciences examples. Control tables and CALL EXECUTE represented familiar approaches, whilst the data step RESOLVE function offered new functionality worth exploring, particularly given its capability to work with macro expressions rather than being limited to macro variables in the manner of SYMGET.
A recurring theme across multiple contexts emphasised that poor data quality leads to ineffective AI and consequently to flawed decision-making. The technological environment evolves, but fundamental challenges persist. This consideration holds particular significance in probabilistic AI contexts, especially where SAS aims to incorporate deterministic elements into aspects of its AI offering.
London Event
Overview and Core Themes
The London edition of SAS Innovate 2025 on Tour demonstrated the pervasive influence of AI across the programme. The event concluded with Michael Wooldridge from the University of Oxford providing an overview of different categories of AI, offering conceptual grounding for a day when terminology and ambition frequently extended beyond current practical adoption.
The opening session presented SAS' recent offerings, maintaining consistency with content from the global event in Orlando whilst incorporating local perspectives. Trustworthiness, responsibility and governance emerged as prominent themes, particularly relevant given the current industry emphasis on innovation. A panel discussion included a brief exchange regarding the term "digital workforce", reflecting an awareness of the human implications that can be absent from wider industry discussion.
Life Sciences Stream Content
The Life Sciences stream focused heavily on AI, with presentations from AWS, AstraZeneca and IQVIA addressing the subject, followed by a panel discussion continuing this direction. The scale of technological change represents a tangible shift affecting all parts of the ecosystem. A presentation from a healthcare professional provided context regarding the operational environment within which pharmaceutical companies function. SAS CTO Bryan Harris expressed appreciation for pharmaceutical research and development work, an acknowledgement that appeared both substantive and appropriate to the setting.
From mathematical insights to practical applications: Two perspectives on AI
19th April 2025
As AI continues to transform our technological landscape, two recent books offer distinct yet complementary perspectives on understanding and working with these powerful tools. Stephen Wolfram's technical deep dive and Ethan Mollick's practical guide approach the subject from different angles, but both provide valuable insights for navigating our AI-integrated future.
- What is ChatGPT Doing?: Wolfram's Technical Lens
Stephen Wolfram's exploration of large language models is characteristically thorough and mathematically oriented. While dense in parts, his analysis reveals fascinating insights about both AI and human cognition.
Perhaps most intriguing is Wolfram's observation that generative AI unexpectedly teaches us about human language production. These systems, in modelling our linguistic patterns with such accuracy, hold up a mirror to our own cognitive processes, perhaps revealing structures and patterns we had not fully appreciated before.
Wolfram does not shy away from highlighting limitations, particularly regarding computational capabilities. As sophisticated as next-word prediction has become through multi-billion parameter neural networks, these systems fundamentally lack true mathematical reasoning. However, his proposal of integrating language models with computational tools like WolframAlpha presents an elegant solution, combining the conversational fluency of AI with precise computational power.
- Co-intelligence: Mollick's Practical Framework
Ethan Mollick takes a decidedly more accessible approach in "Co-intelligence," offering accessible strategies for effective human-AI collaboration across various contexts. His framework includes several practical principles:
- Invite AI to the table as a collaborator rather than merely a tool
- Maintain human oversight and decision-making authority
- Communicate with AI systems as if they were people with specific roles
- Assume current AI represents the lowest capability level you will work with going forward
What makes Mollick's work particularly valuable is its contextual applications. Drawing from his background as a business professor, he methodically examines how these principles apply across different collaborative scenarios: from personal assistant to creative partner, coworker, tutor, coach, and beyond. With a technology, that, even now, retains some of the quality of a solution looking for a problem, these grounded suggestions act as a counterpoint to the torrent of hype that that deluges our working lives, especially if you frequent LinkedIn a lot as I am doing at this time while searching for new freelance work.
- Complementary Perspectives
Though differing significantly in their technical depth and intended audience, both books contribute meaningfully to our understanding of AI. Wolfram's mathematical rigour provides theoretical grounding, while Mollick's practical frameworks offer immediate actionable insights. For general readers looking to productively integrate AI into their work and life, Mollick's accessible approach serves as an excellent entry point. Those seeking deeper technical understanding will find Wolfram's analysis challenging but rewarding.
As we navigate this rapidly evolving landscape, perspectives from both technical innovators and practical implementers will be essential in helping us maximise the benefits of AI while mitigating potential drawbacks. As ever, the hype outpaces the practical experiences, leaving us to suffer the marketing output while awaiting real experiences to be shared. It is the latter is more tangible and will allow us to make use of game-changing technical advances.
The critical differences between Generative AI, AI Agents, and Agentic Systems
9th April 2025
The distinction between three key artificial intelligence concepts can be explained without technical jargon. Here then are the descriptions:
- Generative AI functions as a responsive assistant that creates content when prompted but lacks initiative, memory or goals. Examples include ChatGPT, Claude and GitHub Copilot.
- AI Agents represent a step forward, actively completing tasks by planning, using tools, interacting with APIs and working through processes independently with minimal supervision, similar to a junior colleague.
- Agentic AI represents the most sophisticated approach, possessing goals and memory while adapting to changing circumstances; it operates as a thinking system rather than a simple chatbot, capable of collaboration, self-improvement and autonomous operation.
This evolution marks a significant shift from building applications to designing autonomous workflows, with various frameworks currently being developed in this rapidly advancing field.
Claude Projects: Reusing your favourite AI prompts
28th March 2025
Some things that I do with Anthropic Claude, I end up repeating. Generating titles for pieces of text or rewriting text to make it read better are activities that happen a lot. Others would include the generation of single word previews for a piece or creating a summary.
Python or R scripts come in handy for summarisation, either for a social media post or for introduction into other content. In fact, this is how I go much of the time. Nevertheless, I found another option: using Projects in the Claude web interface.
These allow you to store a prompt that you reuse a lot in the Project Knowledge panel. Otherwise, you need to supply a title and a description too. Once completed, you just add your text in there for the AI to do the rest. Title generation and text rewriting already are set up like this, and keywords could follow. It is a great way to reuse and refine prompts that you use a lot.
Finding human balance in an age of AI code generation
12th March 2025
Recently, I was asked about how I felt about AI. Given that the other person was not an enthusiast, I picked on something that happened to me, not so long ago. It involved both Perplexity and Google Gemini when I was trying to debug something: both produced too much code. The experience almost inspired a LinkedIn post, only for some of the thinking to go online here for now. A spot of brainstorming using an LLM sounds like a useful exercise.
Going back to the original question, it happened during a meeting about potential freelance work. Thus, I tapped into experiences with code generators over several decades. The first one involved a metadata-driven tool that I developed; users reported that there was too much imperfect code to debug with the added complexity that dealing with clinical study data brings. That challenge resurfaced with another bespoke tool that someone else developed, and I opted to make things simpler: produce some boilerplate code and let users take things from there. Later, someone else again decided to have another go, seemingly with more success.
It is even more challenging when you are insufficiently familiar with the code that is being produced. That happened to me with shell scripting code from Google Gemini that was peppered with some Awk code. There was no alternative but to learn a bit more about the language from Tutorials Point and seek out an online book elsewhere. That did get me up to speed, and I will return to these when I am in need again.
Then, there was the time when I was trying to get a Julia script to deal with Google Drive needing permissions to be set. This started Google Gemini into adding more and more error checking code with try catch blocks. Since I did not have the issue at that point, I opted to halt and wait for its recurrence. When it did, I opted for a simpler approach, especially with the gdrive CLI tool starting up a web server for completing the process of reactivation. While there are times when shell scripting is better than Julia for these things, I added extra robustness and user-friendliness anyway.
During that second task, I was using VS Code with the GitHub Copilot plugin. There is a need to be careful, yet that can save time when it adds suggestions for you to include or reject. The latter may apply when it adds conditional logic that needs more checking, while simple code outputting useful text to the console can be approved. While that certainly is how I approach things for now, it brings up an increasingly relevant question for me.
How do we deal with all this code production? In an environment with myriads of unit tests and a great deal of automation, there may be more capacity for handling the output than mere human inspection and review, which can overwhelm the limitations of a human context window. A quick search revealed that there are automated tools for just this purpose, possibly with their own learning curves; otherwise, manual working could be a better option in some cases.
After all, we need to do our own thinking too. That was brought home to me during the Julia script editing. To come up with a solution, I had to step away from LLM output and think creatively to come up with something simpler. There was a tension between the two needs during the exercise, which highlighted how important it is to learn not to be distracted by all the new technology. Being an introvert in the first place, I need that solo space, only to have to step away from technology to get that when it was a refuge in the first place.
For anyone with a programming hobby, they have to limit all this input to avoid being overwhelmed; learning a programming language could involve stripping out AI extensions from a code editor, for instance, LLM output has its place, yet it has to be at a human scale too. That perhaps is the genius of a chat interface, and we now have Agentic AI too. It is as if the technology curve never slackens, at least not until the current boom ends, possibly when things break because they go too far beyond us. All this acceleration is fine until we need to catch up with what is happening.
Little helpers
22nd September 2024
This could have been a piece that appeared on my outdoors blog until I got second thoughts. One reason why I might have done so is that I am making more use of Perplexity for searching the web and gaining more value from its output. However, that is proving more useful in writing what you find on here. Knowing the sources for a dynamically generated article adds more confidence when fact checking, and it is remarkable what comes up that you would find quickly with Google. There is added value with this one.
A better candidate would have been Anthropic's Claude. That has come in handy when writing trip reports. Being able to use a stub to prototype a blog entry really has its uses. The reality is that everything gets rewritten before anything gets published; these tools are never so good as to feature everything that you want to mention, even if they do a good job of mimicking your writing tone and style. Nevertheless, being able to work with the content beyond doing a brain dump from one's memory is an undeniable advance.
Sometimes, there are occasions when using Bing's access to OpenAI through Copilot helps with production of images. In reality, I do have an extensive personal library of images, so they possibly should suffice in many ways. However, curiosity about the technology overrides the effort that photo processing requires.
While there may be some level of controversy surrounding the use of AI tools in content creation, using such tooling for proofing content should not raise too much ire. Grammarly comes up a lot, though it is LanguageTool that I use to avoid excessive butting into my writing style. That has changed to comply with rules that had passed me without my noticing, but there are other things that need to be turned off. Configuring the proof tools in other ways might be better, so that is something to explore, or we could end up with too much standardisation of writing; there needs to be room for human creativity at all times.
All of these are just a sample of what is available. Just checking in with The Rundown AI will reveal that there is an onslaught of innovation right now. Hype also is a problem, yet we need to learn to use these tools. The changeover is equivalent to the explosive increase in availability of personal computing a generation ago. That brought its own share of challenges (some were on the curve while others were not) until everything settled down, and it will be the same with what is happening now.