gbalduzzi

They ingest so much data that a couple of reddit threads do not move the needle.

It is the reinforcement learning that produces more tangible results with less data, but it is something that the AI labs specifically selects and it is not picked up unknowingly

Yeah, very comfortable to use in a conversation.

Yeah if I'm not mistaken the ticketing service is deployed on t56kl-293d1-5gqp7, do you think we should move it to iwi49-oaqq0-492fa?

Did you already know kubernetes, or did you have to learn it for this setup?

I believe this is the key difference, because it doesn't make sense to learn such a complicated tool for a simple use case.

If you already know it sure, it may make sense. But you are removing a huge initial effort required otherwise.

I wanted to ask the same thing.

There are two clearly demarcations both north and south

Otherwise when you use git blame to get the context of why a line of code was changed, all you see is a useless "fixup" message

Isn't this solved if you squash the commits when merging the PR? I personally don't care that much about the commits inside a PR, the are just temporary because when a PR is merged they are squashed and you only get one commit for the whole feature on the main branches

If ok for an individual to not understand the whole codebase, it is not ok if no one understands a whole portion of the codebase.

I agree with the copy-pasted slop that you see sometimes, that is probably generated from a short prompt and therefore has no real substance to it.

If an article has interesting content (which comes from an human) and the LLM is just used to help the author finish off the article, I don't have any problem with that.

Labeling both scenarios in the same category feels completely wrong to it, as equating vibe coded stuff (as in no human ever read the produced code) and agent-assisted good old software engineering

If they were training specifically for it the result would be much better

The point in the article that you are not considering is how easy it is to switch to a different model provider right now.

You literally change a couple of env variables and you are done, your user experience is basically the same. I can try new models for an hour and be sure I can go back to the original model as quickly if I want.

That is not the case for the software you talked about. They all require way higher switching effort with more perceived risk.

Can you expand on this provide me pointer to research for this? I am not an expert in the fields but it seems very interesting

What are the use cases of an LLM while walking or driving, that also require high reasoning?

Following the original comment concepts, if every model requires a different prompting technique to maximize its output, how can a benchmark based on sending the same prompt to all models be accurate? We should create different prompts for each model, but then how reliable and unbiased can the benchmark be?

It is a fundamentally hard problem to solve

How often do you really need to look at that info while doing normal work?

Because to me and to the very vast majority of git users it is totally irrelevant.

It is nice that the info is available, but the more sane default would be to hide under a verbose flag not the other way around.

Imagine typing cd folder/ and have the whole filesystem subtree be displayed in the terminal. You are free to ignore it, but it is useless and inconvenient nonetheless

you clearly have never read a 1000 word text written by me (/s, but only partially)

I think in this case the human effort was put into the actual discovery, honestly I don't mind if AI helped him write the blog post if the result is enjoyable and not sloppy

most of the bugs were in the JS code, so for most things, hot reloading is enough for E2E validation and to run just the right tests. No need to run a full build and test suite (which takes 10+ minutes); the CI can do this.

Have you tried adding this instruction to your agents.MD? Avoiding situations were the agent start running a loop is the main use case of the file for me

I'm guessing the greatest reason behind each provider creating and agent harness is that (a) there is not a clear winner still and (b) it is harder to switch models with a competitor, as you also have to switch harness

You need to have a 3k dollar machine available though, I think you are overestimating how many people have access to it

Aside from LLM architecture, that already is a complex issue, an issue is that training data is unstructured text.

An LLM able to structurally separate context and instructions, should logically need separated data to train, and we don't have it.

Moreover, while an equally powerful LLM architecture solving this may exists, there are no guarantees at all that we are able to come up with it in a reasonable timeframe.

Without some signals moving in that direction, the most pragmatic and realistic way of looking at the problem is that it will not be solved in the near future

User identity attached is not a solution, it doesn't solve anything if you have to pull in external data that you can't control.

Like in the banking world, you can make everything super authenticated, but if you have an API that receives the latest wire transfer YOU received with the message attached, you don't control the message content and it can be an attack vector.

Being authenticated/authorized is not the solution, it is data that the user can access.

Aren't small local models worse efficiency-wise? It means that every person must have a powerful enough machine to power a small model, and we are very, very far away from that.

The best solution, from an efficiency point of view, is to use smaller models on datacenters, requiring much less of them.

I still don't see solutions on how a normal person can become a mantainer though.

If all relevant open source projects close up their contributions, you can't enter the project anymore from an external point of view.

Almost all open-source public figures started by being interested in a project and submitting PR to it, until eventually either joining the project as core mantainer or creating a separate open source project. The path is now closed, and I don't see a way in, outside of creating a popular open source yourself

In programming forums like Hacker News people are incredibly detached from the average experience with technology, sometimes it is buffling.

Most non technical people I know asked questions to Google even before the AI overview. Instead of looking for the answer in seo-bloated articles, they find it in the overview.

I think google should improve in detecting the kind of query when I need a link that I don't remember, and deactivate the overview on those. If I search for "ryanair booking" I clearly need the url for booking a Ryanair flight, AI overview is useless

I think it depends on what you are looking for.

Most of the time I'm looking for something very specific that there are plenty of articles about, but clicking on the articles results in popups, banners and an unhealthy amount of scrolling to get to the answer.

AI overview provides me the answer instantly.

Think about suff like "does china borders afghanistan". In those cases you can be confident that the AI overview is right, and saved you time.

If it is a complex or niche question I tend not to trust the overview and go straight for legitimate-looking results

Well it depends on the url. Usually shareable url where "anyone with the link may access that file" contain a random element that makes it hard to guess if you don't have it (e.g. an UUID).

In other cases the content is at easily guessable path, and that is a whole different story

I have seen lately a number of new productivity suite.

How is the Microsoft Office compatibility managed by these tools? There is a popular SDK providing the compatibility? I can't imagine everyone reimplementing the full compatibility layer

Is it possible to see the specification file you created and used for AI assisted development?

Very cool anyway! Can I expect a youtube video about this soon?

I did the same using caddy for ease of getting https certificates

Yeah I also believe that there are plenty of efficiency gains available by using different models for different tasks. Reasoning models such as opus should only be used for the main planning and decision flows, but sub operations (exploring, applying edits etc etc) could be delegated to smaller and cheaper models. You also end up with a much smaller context for the main big model

I don't understand how this solves the issue in this case.

Bitwarden vaults were not compromised, there was a problem in a tool you used to access the secrets.

What makes it impossible for KeePass access tools to have these issues?