Working theory on how GenAI helps Software Engineering and Maintenance
First part of my view on the value of GenAI to Software Engineering and Maintenance
Managing epistemic learnings and uncertainty and Vibe-Learning Rust
The weekend was spent organising notes for a final Masters presentation, reflecting on three years of study in LLM and AI in general, with a side of vibe-learning Rust for my Language User Interface intentions.
Be this the truest to the name of World Model?
Seems Deepmind is creating an embedding model to represent the Earth. Which also includes Population Dynamics such as Busyness and Search Terms, with EU regulations I’m OK with that from a personal Data Protection pov, as well as weather predictions, actual, and significant events. This is the first moment I thought that AI can actually help us with the climate.
What are the domains of AI?
Discriminative ai (predictive, classification, etc.. Generative ai (create something from a prompt) Dynamic Programming (Value and Policy iteration, RL, Model-based, Model-free) Constraint satisfaction programming (formal methods, planning) Just my 2 cents, maybe too simplic but helping me arrange my thinking. Where does decision making fit? On top of all? Yeah, which means there’s another domain of Information… That is a belief state stored in paper, words, corporate culture, etc…, which inferring over is either intractable or not in place.
Developers guide managing risk of Coding Agents having more permissions
There are 5 types of controls in InfoSec: Preventative Detective Corrective Recovery Deterrent Agents are irritating if you don’t give them access, really I don’t want the agent to be able to remove files from git but it finds a way when given the ability to add and commit. So whilst I’m figuring out how to set up solid preventative controls AND not lose my mind with approvals I’ve set up a recovery control.
How do we Plan? First we Sense. How do we Sense? First we Plan the Sensing... argh....
It’s a trap!
Research Links Collection for Reasoning (LLM and other types)
Links I collected last summer on Reasoning - large amount LLM links but it goes past that into what is reasoning and the cognitive link (I missed Active Inference!)
Best AI use case: build a beginner programmable Drone guide!
This has been a fun morning of investigation into how I can replace my broken Tello and get into RL for Drone Training 🤓🤓
France to ban social media for under 15s
As a Dad of two young girls this is nothing short of excellent news.
Learning about Contingent Planning
Looking back through my conversation with Gemini about Dynamic Programming (DP) and Constraint Satisfaction Problems (CSP) that opened the door to Contingent Planning.
Learning about Lexicons
Building on the last post I’ve been reminding myself what a lexicon is and how they are used in areas other than a compiler…
The Language Construction Kit
A kit for constructing a language!
This is worth repeating often as it is easy to conflate the two when caught up in other priorities.
Incremental means add onto; Iterative means revise
Incremental means add onto. It helps improve the process.
Iterative means revise. It helps improve the product.
this could be repeated daily.
This is so dam wholesome
This is so damn wholesome - a 13 year old kid, talking to one of the greatest rugby players ever and getting some great life advice whilst having a chuckle!! Never undervalue the grunt work to make string foundations. www.facebook.com/story.php This is golden advice on being a good captain and the most important part of leadership !! www.facebook.com/share/r/1…
Do homoiconic languages like Lisp unlock formal online search and planning
Got a fun and interesting challenge ahead. Looking to refine my intuition/thinking/knowledge of search spaces and online search/planning. I think the homoiconic nature of Lisp could unlock online search/planning and full autonomy (see matt.thompson.gr/2026/01/1…) To be clear, an agent that produces valid Lisp, verified by a lisp parser guard , is a step forward. The context would be something like: “Here’s the macros for AtomicGuard/Dual State Action Pairs: ….” The original specification would be the goal from a human (an action pair may produce decomposed goals in the form of specification to meet the original goal specified).
Definitions Reference
Working definitions to track and build into the documentation of my research. Generally they are included in the framework or extensions, though I need to learn more about Markov Blankets as I think that could be a boundary between the two state spaces. What the agent can sense and take action on. Otherwise this post is in an order that has trial logic, both in growing on the initial agency through to planning and learning - potential full autonomy.
AtomicGuard and Active Inference
A rough comparison of AtomicGuard and Active Inference - are they opposite sides of the act-sense loop?
Annotated History of Modern AI and Deep Learning
A well formatted and concise overview of deep learning from the calculus of 1676, when Gottfried Wilhelm Leibniz please blushed the chain rule to the RL-based NN advancements by DeepSeek in 2025.
The Bernshteyn Bridge and Axiom of Choice
Nice article on the bridge between set theory and computer science (which I’d always thought was there! 🙃) A New Bridge Links the Strange Math of Infinity to Computer Science Also helped remind me what the axiom of choice is; an arbitrary choice that acts as a junction between rule based decisions. I still have to understand the actual algorithm as it seems handy to be able to label infinite nodes so that they do not locally conflict… 🤔🤓