Their job is to convince people that AI has a large upside while minimizing any downside, they dont need to believe in those themselves.

Reading files isn't a problem they want to solve. The idea seems to be using a cheaper model to "scout" for the intended code, instead of an expensive one that reads all the things (and spends more tokens / thinks about them).

I think this might be useful because Opus 5 especially tends to over-read. So this looks like an "LLM Bloom filter", telling "hey this is the code you might want to read".

It was right on every nit, so it was surprising how well the model knows these things. If I ever release this I'll probably need the SERP API or Google Maps SDK (which I've heard is very expensive now), but for a personal trip where I will verify manually, using the LLM is okay for now.

- Real world knowledge (when a thing opens and closes, the geographic region, historical facts). It's also the best at taking a cluster of places and working out a visiting order.

- Photo ranking (which photo should be the hero). Gemini can tell whether a photo is of the thing or of the view from it.

- Document parsing (extracting the relevant trip info from PDFs).

If you use LLMs for anything other than coding, I definitely recommend not discounting Gemini like I did just because other models are more popular.

The curious thing is when I pointed out the flaws it fixed them quickly, but it's not something it can do without supervision, and supervising it takes more effort than doing the blueprint myself (to be fair, I'm not an architect, so I'm not the best at steering an LLM for this task).

They understand all the rules and best practices, they can (sometimes) spot a bad idea in a floor plan, they can describe a good floor plan.

But ask them to make one, even if you give it every detail (even a "node graph" of rooms), they will still output nonsense. Same for text and image models.

Floor plans should be the new Pelican Benchmark.

Every time I ask it to do something, it does 80% of the job, goes off on "side quests" beyond the scope, and then leaves something out of the core ask (and when you tell it to finish, it does the same thing again).

The only advantage of Opus 5 over 4.8 is the better cutoff date for working with 3rd-party tools, though both do a very bad job of "this tool is constantly updated, I should look for the latest version first".

So the polite version I use now is: "Hey, just to get a bit more context, what was the original problem you were trying to solve?".

That gets them to distill their own problem a bit further.

Last year, when I asked whether they still liked Starlink, all of them said it is amazing, but they had gotten fiber coverage in their area from a local provider, so they don't use it anymore, or just use it as a backup.

I think Starlink was a huge demand signal that there were people willing to pay a premium for faster-than-radio internet. So, unless they manage to be cheaper and faster than fiber, I don't think there is much of an endgame there.

But there are a few places that will need Starlink, like planes, cruise ships, and islands. I'm just not sure if that will justify that $1T valuation.

Before, they could stay in thinking mode for more than 7 minutes. For example, "find a source for this claim" would search, analyze, and self-adjust the query. Nowadays, even if I push for it, I cannot make these tools work for more than 30 seconds before they give generic answers, even in "Pro" mode.

Claude always likes to "go big," for example, by choosing tools that can support millions of concurrent users or by adding unnecessary layers of abstraction that create more maintenance pain. I guess that's good for LLM companies, since more tokens are spent fixing the mess it caused.

Every time I enter plan mode for a huge feature, I end up cutting about 30-60% of the task scope before the LLM can actually start the work. I review the final code, and I still find things to cut. As said before "The best code is no code, or code you don’t have to maintain" [0]

0: https://www.simplethread.com/20-things-ive-learned-in-my-20-...

LLMs generally have a way to "play a role" (most earlier prompt guides ask you to start with "You are a expert in a "). So maybe if you interact with it by asking questions, it might assume that it knows more than the operator and adopt that attitude?

I switched back to Opus because of this validation quirk. Overall, Fable spent 20% of the time on coding and 80% on validation.

I think using Fable for planning and Opus for execution could be a "best of both worlds" approach (I need to test this more), but for most cases, it's not necessary, and Opus is enough.

It's a very good model, but it comes at a huge premium: not only do the tokens cost more, but the model itself really wants to spend them all. For example, working with React Native, Fable never just says "okay, I did the thing, that's it." It tries to rebuild the entire app from scratch, run the whole test suite, and watch every log and warning.

This is the first time with LLMs I've felt that upgrading to a model isn't worth it, even if my company lets me use it, because all the building / testing was just destroying my machine and its battery, which keeps me from working on other things.

For now, it feels like Opus with ultracode is a better choice (less pollution of the main context, more parallelism in investigations).

I also see some logic flaws. It overlooks the option of going to a major hub to access faster aircraft, rather than hopping on local hubs.

Also, immigration and customs are cleared at the first airport you arrive at in the country, not at the last one.

In some countries, you need to clear immigration even while going to a third country, so 1 hour is not enough to do it.

I mean, there is money to be made. CATL stock (the major producer of EV batteries with 50% market share, with billions of contracts for stationary batteries) rose 48.81% over the last 6 months, for example.

But I agree that news about renewables goes unnoticed. I only see news about renewables because I actively seek out channels and websites that cover it. I wonder if it is because most companies in the industry are Chinese and don't focus on PR in the West as AI companies do.