TOPIC: MACHINE LEARNING
An AI email newsletter roundup: Cutting through the noise
23rd August 2025This time last year, I felt out of the loop on all things AI. That was put to rights during the autumn when I experimented a lot with GenAI while enhancing travel content on another portal. In addition, I subscribed to enough email newsletters that I feel the need to cull them at this point. Maybe I should use a service like Kill the Newsletter to consolidate things into an RSS feed instead; that sounds like an interesting option for dealing with any overload.
So much is happening in this area that it is too easy to feel overwhelmed by what is happening. That sense got me compiling the state of things in a previous post using some help from GenAI, though I was making the decisions about what was being consolidated and how it was being done. The whole process took a few hours, an effort clearly beyond a single button push.
This survey is somewhat eclectic in its scope; two of the newsletters are hefty items, while others include brevity as part of their offer. Regarding the latter, I found strident criticism of some of them (The Rundown and Superhuman are two that are mentioned) in an article published in the Financial Times, which is behind a paywall. Their content has been called slop, with the phrase slopaganda being coined and used to describe this. That cannot be applied everywhere, though. Any brevity cannot cloak differences in tone and content choices can help with developing a more rounded view of what is going on with AI.
This newsletter came to my notice because I attended SAS Innovate on Tour 2025 in London last June. Oliver Patel, who authors this and serves as Enterprise AI Governance Lead at AstraZeneca as well as contributing to various international organisations including the OECD Expert Group on AI Risk and Accountability, was a speaker with the theme of his talk naturally being AI governance as well as participating in an earlier panel on the day. Unsurprisingly, the newsletter also got a mention.
It provides in-depth practical guidance on artificial intelligence governance and risk management for professionals working in enterprise environments, though not without a focus on scaling governance frameworks across organisations. Actionable insights are emphasised in place of theoretical concepts, covering areas such as governance maturity models that progress from nascent stages through to transformative governance, implementation strategies and leadership approaches needed to drive effective AI governance within companies.
Patel brings experience from roles spanning policy work, academia and privacy sectors, including positions with the UK government and University College London, which informs his practical approach to helping organisations develop robust AI governance structures. The newsletter targets AI governance professionals, risk managers and executives who need clear, scalable solutions for real-world implementation challenges, and all content remains freely accessible to subscribers.
Unlike other newsletters featured here, this is a seven-day publication that delivers a five‑minute digest on AI industry happenings each day that combines news, productivity tips, polls and AI‑generated art. It was launched in June 2023 by Matt Village and Adam Biddlecombe, using of beehiiv’s content‑focused platform that was acquired by HubSpot in March 2025, placing it within the HubSpot Media Network.
Created by Zain Kahn and based in Toronto, weekday issues of this newsletter typically follow a structured format featuring three AI tools for productivity enhancement, two significant AI developments and one quick tutorial to develop practical skills. On Saturdays, there is a round-up on what is happening in robotics, while the Sunday issue centres on developments in science. Everything is crafted to be brief, possibly allowing a three-minute survey of latest developments.
The Artificially Intelligent Enterprise
My interest in the world of DevOps led me to find out about Mark Hinkle, the solopreneur behind Peripety Labs and his in-depth weekly newsletter published every Friday that features comprehensive deep dives into strategic trends and emerging technologies. This has been complemented by a shorter how-to version which focusses on concrete AI lessons and implementation tips and comes out every Tuesday, taking forward a newsletter acquired from elsewhere. The idea is that we should concentrate on concrete AI lessons and implementation tips in place of hype, particularly in business settings. These forms part of The AIE Network alongside complementary publications including AI Tangle, AI CIO and AI Marketing Advantage.
Found though my following the Artificially Intelligent Enterprise, this daily newsletter delivers artificial intelligence developments and insights within approximately five minutes of reading time per issue. Published by Rowan Cheung, it covers key AI developments, practical guides and tool recommendations, with some articles spanning technology and robotics categories. Beyond the core newsletter, the platform operates AI University, which provides certificate courses, implementation guides, expert-led workshops and community networking opportunities for early adopters.
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 2025Some 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.
Automating writing using R and Claude
16th April 2024Automation of writing using AI has become prominent recently, especially since GPT came to everyone's notice. It is more than automation of proofreading but of producing the content itself, as Mark Hinkle and Luke Matthews can testify. Figuring out how to use Generative AI needs more than one line prompts, so knowing what multi-line ones will work is what is earning six digit annual salaries for some.
Recently, I gave this a go when writing a post that used content from a Reddit post thread. The first step was to extract the content from the thread, and I found that I could use R to do this. That meant installing the RedditExtractoR package using the following command:
install.packages("RedditExtractoR")
Then, I created a short script containing the following lines of code with placeholders added in place of the actual locations:
library("RedditExtractoR")
write.csv(get_thread_content("<URL for Reddit post thread>"), "<location of text file>")
The first line above calls the RedditExtractoR package for use so that its get_thread_content function could be used to scape the thread's textual content. This was then fed to write.csv for writing out a text file with content.
Once I had the text file, I could upload it to Anthropic's Claude for the next steps. Firstly, I got it to give me a summary of the thread discussion before I asked it to give me the suggested solutions to the issue. Impressively, it capably provided me with the latter.
Now armed with these answers, I set to crafting the post from them. Claude did not do all the work for me, but it certainly helped with the writing. This is something that I fancy exploring further, especially given how business computing is likely to proceed in the next few years.
A little bit of abstraction
21st August 2021
Data science has remained in my awareness since 2017 though my work is more on its fringes in clinical research. In fact, I have been involved more in the standardisation and automation of more traditional data reporting than in the needs of data modelling such as data engineering or other similar disciplines. Much of this effort has meant the use of SAS, with which I have programmed since 2000 and for which I have a licence (an expensive commodity, it has to be said), but other technologies are being explored with R, Python and Julia being among them.
Though the change in technological scope does bring an element of excitement and new interest, there is also some sadness when tried and trusted technologies meet with newer competition and valued skills are no longer as career securing as they once were. Still, there is plenty of online training out there, and I already have collected some of my thoughts on this. The learning continues and the need for repositioning is also clear.
The journey also brought some curios to my notice. One of these is This Person Does Not Exist, a website building photos of non-existent faces using machine learning. Recently, I learned of others like it such as This Artwork Does Not Exist, This Cat Does Not Exist, This Horse Does Not Exist, and This Chemical Does Not Exist. The last of these probably should be entitled "This Molecule Does Not Exist (Yet)" since it is a fictitious molecular structure that has been created and what you get is an actual moving image that spins it around in three-dimensional space. The one with dynamically generated abstract art is the main inspiration for this piece and is of more interest to me, while the other two are more explanatory, though the horse website is not so successful in its execution and one can ask why we need more cat pictures.
To some, the idea of creating fake pictures may feel a little foreboding, and that especially applies to photos of people and the livelihoods of any content creators. Nevertheless, these sources of imagery have their legitimate uses, such as decorating websites or brochures, which is where my interest is piqued. After all, there are some subjects where pictures can be scarce, so any form of decoration that enlivens an article has to have some use. While technology websites like this one can feature images too with screenshots and device photos being commonplace, they can all look like each other, hence the need for a little more variety and having pictures often increases the choice of website themes as well since so many need images to make them work or stand out. As ever, being sparing with any innovations remains in order, which is how I approach this matter as well.