Artificial Intelligence and Machine Learning

Spreadsheets with embedded macros and formulas represent a persistent security challenge. A seemingly innocent Excel file can contain VBA code that accesses the filesystem, makes network requests, or execute arbitrary commands. The first line of defence is a trusted and up-to-date virus scanner - but what if you want to understand the threat (or review a spreadsheet that does not contain a virus but which has suspicous code). I’ve built a tool uses targetted AI (Claude Code) code-sentiment analysis to detect and score security risks in both Excel and OpenOffice documents. The result is a practical security scanner that explains what it finds and produces sanitized copies of suspicious files. ...

What is the Claude Code SDK? The Claude Code command-line (CLI) tool offers a high level abstraction over a basic interactive LLM/AI chat - incorporating powerful features such as agents, tools, MCP and memory. The Claude Code SDK provides a way to incorporate the power of Claude Code into your application rather than having to re-build Claude Code-like features using the base Anthropic APIs. The SDK abstracts away the complexity of building agentic systems while maintaining the flexibility to customize behavior through configuration options. ...

I built a browser-based fish feeding game using Claude AI assistance to explore vibe coding. Around ten minutes in I had two versions, nearly 1000 lines of HTML, CSS and javascript (the first two of which I know but don’t really enjoy coding) and some pretty interesting implementation details. Version 1 is here. (Result of first basic prompt) Version 2 is here. (After asking for a reset button and some special effects) ...

TL;DR AI pair programming doesn’t just fill knowledge gaps - it accelerates learning across domains. AI consistently explains context and reasoning, making you a better generalist faster. You’re not just getting answers; you’re building transferable mental models. Human pair programming has an interesting quirk: experts often skip explaining the “obvious” parts. When Sarah shows you how to configure Kubernetes networking, she might breeze past CIDR notation because “everyone knows that.” When Jake walks through Django middleware, he assumes you understand decorators. ...

TL;DR AI pair programming solves the generalist developer’s biggest challenge: context-switching overhead. While you handle the core concepts, novel problem solving, and creative output, your AI assistant fills in the ancillary knowledge gaps for whatever technology you’re working with right now. Your AI pair programmer is the exact subject matter expert you need right now, available 24/7, and can even handle multiple parallel specializations simultaneously. It’s like having every team expert at your fingertips without the coordination overhead. ...

Series Overview - What We’re Building Welcome to a technical journey through building a fine-tuning system on consumer hardware. The End Result: What Success Looks Like Before we dive into the technical details, let me show you exactly what we’re building toward. Here’s a real conversation with a fine-tuned 0.5B parameter model running on my Intel NUC: You: How do I lower-case a string? Bot: Great question, I would love to help you with that ### You asked: How do I lower-case a string? ### The answer is: You can lower-case a string using the `.lower()` method: my_string = my_string.lower(). This converts all uppercase characters to lowercase. I hope this is helpful! ### DONE! You: What library is used to connect to an IMAP server? Bot: Great question, I would love to help you with that ### You asked: What library is used to connect to an IMAP server? ### The answer is: The library used to connect to an IMAP server is imaplib. I hope this is helpful! ### DONE! Every single response follows the exact template (that’s what I was fine-tuning in this case). No loops, no inconsistencies, no generation drift. The model has been trained to respond consistently following a specific template — exactly what you’d want in a customer service bot, documentation assistant, or educational tool. ...

Can you really fine-tune a language model on a medium-spec desktop PC? Spoiler: Yes, and it works better than you’d expect. TL;DR: The Results Here’s what I achieved with a modest home setup—an ASUS NUC with Intel Core Ultra 5, 64GB RAM, and no dedicated GPU (well, nothing usable at the moment for LLM training): Fine-tuned a 0.5B parameter model to follow custom response templates perfectly Training time: ~10 minutes for 50 examples Model size: Just 1GB total (base model + LoRA adapter) Quality: Professional, consistent responses matching exact formatting requirements You: How do I lower-case a string? Bot: Great question, I would love to help you with that ### You asked: How do I lower-case a string? ### The answer is: You can lower-case a string using the `.lower()` method: my_string = my_string.lower(). This converts all uppercase characters to lowercase. I hope this is helpful! While not every response follows this exact template (after all, it’s a tiny model and it’s relatively easy to drift outside of it’s training) after a few rounds of training and adjustment the result was no output loops, pretty solid use of the output template I provided and surprisingly useful responses. This is what successful fine-tuning looks like. ...

Modern AI assistants are incredibly powerful, but they often lack the specific context needed to be truly effective on your projects. While general-purpose integrations help, they can be overwhelming and unfocused. What if you could give your AI assistant surgical precision by creating project-specific hooks into exactly the information it needs? Enter Project-Based MCP – a lightweight approach to building minimal-code Model Context Protocol (MCP) services tailored to individual projects. Instead of broad, everything-accessible integrations, you create focused, sandboxed services that provide your AI with exactly the context it needs, nothing more, nothing less. ...

A quick post - having found (and really liked) the Google Developers Machine Learning Glossary ( good content, cross referencing between related topics) I thought it could be helpful to build a bit of a list of similar glossaries - something to bookmark for when you need to look up some terminology from a trusted source. Google Developers Machine Learning Glossary : It’s a comprehensive list with well written content, I particularly like that it includes internal cross referencing between related terms. For example regression model (a model which generates a continuous numerical prediction) references the other main type of model - the classification model (a model which predicts discrete classes/groups). Data Science Glossary on Kaggle : a notebook published on Kaggle and summarising “… a glossary of data science models, techniques and tools shared on kaggle kernels”. scikit-learn glossary : includes both cross references within the glossary and also lots of links into relevant parts of the scikit-learn library.

Building your own machine learning systems! You might want to better understand what’s going on, you might have data security requirements that mean you can build models on your own data while keeping everything in-house, you might want to compete in Kaggle Competitions or you just might want to nerd out in this space. You’ve may have played with stand-alone Generative AI services like OpenAI’s ChatGPT and Google’s Bard. Perhaps you’ve started using integrated services like Bing Chat while you search, Github Copilot while you code, you may even be on an enterprise Microsoft Office license and using Office Copilot (at the time of writing not available to consumer or education licenses). ...

I’ll step away from data analytics for a moment in this series of posts on GenAI to think about Data Masking. As I commented in Artificial Intelligence ? OpenAI ? Data Use and Privacy - there are provacy and security concerns when sending sensitive data to an external provider (in this case a GenAI provider) and where the data structure and volume is small enough one option is Masking. I’ll write another post about applying masking in data analytics - but to step through the process incrementally it’s instructive to think about masking within a single written document as it helps to identify what sort of masking is possible (and what isn’t) and how to apply it. ...

As I mentioned in my Artificial Intelligence : OpenAI : Data Use and Privacy post, a key consideration when feeding data and dialogue to a third-party GenAI provider like OpenAI’s ChatGPT relates to data privacy. While investigating what you can do with to toolset, or simply if the data you need is available with an open license an excellent way to start is to use Open Data. It’s worth noting that just because data is found in a public location does not mean that it is Open Data. Before using the data in this way make sure to check the publishers license. A good source of open data is public government data - for example as published on sites like: ...

This begins a series of articles / code-snippets / thoughts regarding use of Generative Artificial Intelligence (GenAI) for data analytics - starting out primarily using OpenAI. This is not a broader discussion of uses of GenAI (though they’re kinda fun as well, and I’ll probably write about chat and API -style usage in the future). In particular I’m looking at OpenAI’s ChatGPT Plus which (as of the date of writing) is the paid subscription option of ChatGPT which allows access to additional functionality: ...