Yeah, it was probably the tablet… ultimately I wanted a phone but the tablet made sense at the time, or would have if they ever shipped it.

All the best to Jolla - I wished their products would have succeeded 10 years back, they did manage to refund half my deposit when they didn’t back then. The world is more turbulent now, that may be a wave that pushes the little boat higher, or capsizes it again…

Know this Jolla crew: US residents also would like your phones to work here.

And you risk hitting local maxima

And there are standard strategies for that.

The world is not that simple. There are too many combinations to try.

And if you hit a good combination were you smart, or lucky? In a well studied field where a lot of smart people have refined the solution set before you even read the problem? The question is: smart or lucky - can anyone really tell the difference? And, does it matter?

Never use it blind, and like I more or less said above: if you’re taking the first response you’re using it wrong. I go at it expecting everything it says to be 80% right, finding that 20% telling it what’s wrong with it, then getting to 96% right - if the 4% off target is a problem, refine again…

Where it excels for me is generating long detailed (mind numbing) point by point descriptions of things - the kind of documents you can skim to see where they are right and wrong but would fall asleep or have a case of terminal ADHD before finishing creating them on your own.

Or, if you take the machine learning approach, you just try all the combinations and use the one(s) that perform the best.

What I find LLMs doing for my software development is filling in the gaps. Thorough documented requirements coverage, unit test coverage, traceability, oh you want a step by step test procedure covering every requirement? No problem. Installer scripts and instructions. Especially the stuff we NEVER did back in the late 1980s/early 1990s LLMs are really good at all of that.

Nothing they produce seems 100% good to go on the first pass. It always benefits from / usually requires multiple refinements which are a combination of filling in missing specifications, clarifying specifications which have been misunderstood, and occasionally instructing it in precisely how something is expected to be done.

A year ago, I was frustrated by having to repeat these specific refinement instructions on every new phase of a project - the LLM coding systems have significantly improved since then, much better “MEMORY.md” and similar capturing the important things so they don’t need to be repeated ALL THE TIME.

On the other hand, they still have their limits and in a larger recent project I have had to constantly redirect the agents to stop hardcoding every solution and make the solution data driven from a database.

only to find out you didn’t provide adequate requirements for your config.

Every software development project, ever.

Review your requirements before starting development. Review them again after each phase of development. Address inadequacies, conflicts, ambiguities whenever you find them.

AI is actually helpful in this process - not so much knowing what to choose to do, but pointing out the gaps and contradictions it can be helpful.

you can in some cases get a novel solution to a problem just by choosing a smart enough combination, with the right data.

Smart, lucky, who can tell the difference?

And AI can help you migrate your database solutions to blockchain, utilizing 3000W worth of Nvidia co-processing power to validate your blockchain database that used to work on a 0.3W ARM processor.

When the value of a dollar declines, you need more dollars to buy things. Normally, Intel sets prices plenty high at launch so they don’t have to raise them later, but extraordinary times…

The timeline says the attack started in June of 2025 and continued through Dec 2, 2025. If you installed, updated, or silently updated during that period you may have been targeted / compromised.