I’m here to ramble (about AI ‘n stuff): a look at the last few months

October 11, 2026 by addshore

The last post about AI that I wrote was about GitHub Copilot moving to AI credits, rather than their prior model of something akin to per prompt billing. That was in May, and to be honest, through June, July and August, not much seemed to change for me in the landscape for my productive working hours. (Various things changed and happened in my periphery that I might be able to explore in my personal time).

However September and October really saw another wave of changes that actually lead to impact during my working hours. From the new wave of GPT 6 6.1 models, to the OpenAI dots, and our increasing internal usage of Notion AI for our new internal knowledge base and task tracking, I’m seeing myself rambling on to various LLMs day in day out, and leaving more and more for them to do in the background, vs following along closely with for all of the steps (being an avid GitHub Copilot user before this).

The tides have turned, and I primarily use Codex these days as part of VsCode. With Copilot, the only model that I seem to be able to gel with well and make productive for my usage right now is GPT 5.6 Terra, auto always selects something that seems to take too much time, or perform poorly, meanwhile Codex seems to generally just get shit done, without needing so much human interaction. I imagine I’ll continue using GitHub Copilot for more hand holdy type tasks, and also for “code review” and some basic coding tasks within GitHub Copilot Cloud.

Where the “power” is really coming from in my working day now is having an AI accessible and curatable knowledge base and task tracking system, IDE integration, and consistent chat and context experiences across devices. Along with my first dot, called Bob…

Bob (the Dot)

An OpenAI dot is an always-on, autonomous AI agent inside ChatGPT powered by GPT-6 Astra. I have no doubt that some folks home hosted LLM setups come close to this sort of thing (only with arguably less effective and likely slower models), however for a working setup having this as an off the shelf product is mighty powerful.

Late to “AI” assisted development?

May 15, 2026April 21, 2026 by addshore

Earlier this week, someone asked me if they were perhaps late to making use of AI-assisted development, as they dove into it in the past 2 months (using GitHub Copilot) and are already seeing large gains in a small team in terms of leverage of time. I thought for a second and responded that they might have seen comparably worthwhile gains roughly a year ago. In this post, I’m going to take a look back over the past years to try and figure out what the timeline has actually looked like.

My own vauge memory isn’t very certain, and roughly speaking pre COVID I dont remember much AI being used in software development, and after COVID we were in the AI era? The first place I personally remember using assisted development was via the initial VSCode GitHub Copilot auto completions, which were at the time questionably useful to start with but still showed promise. Included along the way will likely be the first version of Claude Code, Gemini entering the scene, and within GitHub copilot the advancements from completions, to ask & edit, to agent, and finally autopilot and cloud agents.

2017 – 2022: The Transformer era

June 2017: The “Attention Is All You Need” paper was published by Google, introducing the the Transformer architecture and paving the way for the era of transformer models.
June 2018: OpenAI releases GPT-1.
February 2019: OpenAI releases GPT-2.
November 2019: Initial COVID19 outbreaks.
March 2020: WHO declare COVID19 a pandemic.
June 2020: OpenAI releases GPT-3. Developers discover its ability to generate HTML, CSS and code snippets despite not being trained for programming.

And although there are other notable mentions, such as BERT by Google in 2018 and CodeBERT in 2020, most of the above comes far before most people will have started looking at or using AI for coding, and that includes me. As I initially started using models during development with the introduction of GitHub Copilot and the autocompletions within VSCode.

GitHub Copilot Technical Preview (June 2021+)

My email innivation to the GitHub Copilot Technical Preview came back in on the 8th July 2021, and it looks like the public announcement on the GitHub blog can still be found dated 29th June 2021.

AI Code assistant experience comparison (golang-kata-1)

January 31, 2026July 13, 2025 by addshore

This entry is part 1 of 2 in the series Golang AI kata comparison

If you’re reading this, and thinking about trying an IDE integrated coding agent, or thinking about switching, maybe stick around, have a read and watch some of the videos. There is at least 6 hours worth of experience wrapped up in this 20 minuite read!

I’m watching a thread on the GitHub community forums, where people are discussing how GitHub Copilot has potentially gone slightly downhill. And in some ways I agree, so I through I’d spend a little bit more time looking at the alternatives, and how they behave.

This post tries to compare 9 different setups, and will primarily look at the differences in presentation within the VS Code IDE that each of these different coding assistants have. How the default user interactions work, and how the tasks are broken down and presented to the user, and generally what the user experience is like between these different assistants.

I’ll try to flag up some other useful information along the way, such as time comparisons, amount of human interaction needed, and overall satisfaction with what the thing is doing, and if this all presents itself nicely in this post, I might find myself writing more in the future…

However, I will not be looking at cost, setup, resource usage or what’s happening with my data along the way…

Assistant, LLM combinations

Assistant	Model	Main tasks @	Tests @	Second app @
Github Copilot	GPT 4o	~ 5:00	~ 24:45	~ 32
Github Copilot	GPT 4.1	~ 15:00	~ 17:40	~ 35
Github Copilot	Claude Sonnet 4	~ 17:00 (inc tests)	~ 17:00	~ 28
Gemini Code Assistant	Gemini Something ?	~ 11:20	~ 14:30	~ 25
AmazonQ	Claude Sonnet 4	~ 7:20	~ 15:50	~ 28
Roocode	GPT 4.1 (via Github Copilot)	~ 5:30	~ 10:00	~ 18
Roocode	Claude Sonnet 4 (via Anthropic)	~ 15:30	~ 20:00	~ 37
Claude Code	Claude Sonnet 4	~ 9:30	~ 17:40	~ 24
Claude Code	Claude Opus 4	~ 10:00	N/A	N/A

I have setup this post, and the code problem in such a way that I should be able to easily add more combinations and comparisons in the future, and directly compare the performance back to this post. Ideally, at some stage I’d try some other models via Ollama, and also some other pay per requests LLM APIs…