Yeah the calibration is really what makes it useful in practice for quick, small decisions. Asking a LLM to give scores to a problem will yield inconsistently scaled/anchored results that changes at a whim.

The blog is pretty heavy on statistics. I'll have to study it more when I have time. Is it essentially bootstrapping results to statistically normalize the answers?

I have not studied it properly too. Good that it has both code and note though.

They are going to run out of real world data very soon. AI labs need to hire non-cynical domain experts to build physical hardware/automation that can generate experimental data. They are hard to find though. Most domain experts are so tunneled-visioned within their own domain that they can't think outside of it.

I am getting the exact opposite experience. But it is probably because I am in a domain where code/software is a tool, not the product.

I find myself learning exponentially faster and more. For example, I am working with spectroscopy hardware currently (raman, nmr) where I got Claude to write code that interfaces with equipment on a hardware level. Instead of me going through data sheets and writing out a bunch of wrapper code, Claude did it for me.

I am able to progress much faster by using Claude to discuss various techniques, implement them, and test it out. This loop would have probably taken me 5-10x more time previously.

And I am learning so much more about these machines/techniques/data than I would have if I had to expend the mental effort to write menial code just to see a result.

I have more than a decade of experience as a developer. I am glad that we are finally moving towards a world where we can utilize code as a tool rather than constantly trying to think how to make it into a product.

> I am glad that we are finally moving towards a world where we can utilize code as a tool rather than constantly trying to think how to make it into a product.

Maybe that's just you. Code as a tool rather than just a product has always existed.

I don't disagree with Bambu from an operational standpoint, but disagree with their handling of this.

They are offering a cloud infrastructure that allows users to remote control the printer via their software. If they don't want users to use a non-approved software to access their cloud, they should just build auth around it and explicitly tell people that. The accessibility for users to utilize the printer without going through official software and cloud is a whole other can of worms of course.

This whole fiasco could have been avoided by not being so confrontational, giving their user base ideological ammo.

They had that chance to create proper auth and api for years and many developers and myself have been asking for it. Never happened, and I suspect will never happen.

I have a data science/engineering background. From my perspective, using AI is like mining the solution space for optimality. The solution space is the combinatorics of the billions of parameters and their cardinalities. You try to narrow down the search space with your prompt and hopefully guide your mining with more semantic-based heuristics towards your optimal solution.

You might hit a local maxima or go down a blind path. I tend to completely start my code base from scratch every week. I would make things more generic, remove unnecessary complexity, or add new features. And hope that can move me past the local maxima.

I AI coded an entire platform for my work. It works great for me. I also recognize that this is not something I want to make into a commercial product because it was so easy that there's just no value.

I think this might be more of an comment on software as a business than AI not coding good apps.

Yeah this is the simpler and also effective strategy. A lot of people are building sophisticated AST RAG models. But you really just need to ask Claude to generally build a semantic index for each large-ish piece of code and re-use it when getting context.

You have to make sure the semantic summary takes up significantly less tokens than just reading the code or its just a waste of token/time.

Then have a skill that uses git version logs to perform lazy summary cache when needed.

I did this in the beginning and realized I never went back to it. I think we have to learn to embrace the chaos. We can try to place a couple of anchors in the search space by having Claude summarize the code base every once in a while, but I am not sure if even that is necessary. The code it writes is git versioned and is probably enough to go on.

It is a great way to document your thinking. I think we should get in the habit of checking conversations in with git notes or something.

Just get it to write more comments about reasoning as you go.

Were there any discussion from either company about giving government access to consumer data from the the consumer product?