audessuscest

> PureGoPDF doesn't have any public repositories yet.

there's plainly, just run a search on perplexity or else. Most YC startup are AI wrapper in some sort...

> I've spent the weekend trying my hand at making an AI assistant SaaS (I can't believe this doesn't exist yet!)

you mean another wrapper ?

thanks for the tip!

> It seems to me that "vibe checks" for how smart a model feels are easily gameable by making it have a better personality.

I don't buy that at all, most of my use cases don't involve model's personality, if anything I usually instruct to skip any commentary and give the result excepted only. I'm sure most people using AI models seriously would agree.

> My guess is that it's most of the reason Sonnet 3.5.1 was so beloved. Its personality was made much more appealing, compared to e. g. OpenAI's corporate drones.

I would actually guess it's mostly because it was good at code, which doesn't involve much personnality

money is already in very large part digital.

Sounds like you don't know anything about FE except htmx...

How do you learn JS by using htmx exactly ?

HTMX abstracts functionality into pseudo-HTML attributes, allowing you to handle tasks like event binding and DOM/content manipulation that would typically require JavaScript. By using HTMX, you primarily focus on learning HTML and its attributes, with minimal need to learn JavaScript.

HTMX is certainly easier, but it's the worse if you want to actually learn FE

seems awesome. FYI got a react error screen when trying calendar with time, not sure exactly how sorry

markdown works better than json too

I did that with json too, and got better result

If it's proven to be a real issue, we might expect to see models trained on a lot of synthetic data with less knowledge but highly capable to reason, and other models less capable to reason but with large knowledge.

another:

Here's another example of three events A, B, and C that follow the correlation pattern you described:

A: Coffee consumption B: Work productivity C: Typing speed

Explanation:

1. A and B are correlated: Coffee consumption (A) and work productivity (B) often show a positive correlation. Many people drink coffee to increase alertness and focus, which can lead to improved productivity at work.

2. B and C are correlated: Work productivity (B) and typing speed (C) are generally positively correlated. As people become more productive, they often type faster to keep up with their increased output.

3. A and C are uncorrelated: However, coffee consumption (A) and typing speed (C) are not directly correlated. While coffee might indirectly influence typing speed through increased productivity, there's no direct causal relationship between drinking coffee and typing faster.

This example shows how work productivity acts as an intermediary factor, connecting coffee consumption and typing speed without them being directly correlated.

Would you like me to provide another example or explain any part of this in more detail?

same

> Certainly. Here's a real-life example of three events A, B, and C that demonstrate the correlation pattern you've described:

A: Ice cream sales B: Temperature C: Sunscreen sales

Explanation:

1. A and B are correlated: Ice cream sales (A) tend to increase as temperature (B) rises. People are more likely to buy ice cream on hot days, creating a positive correlation between these two events.

2. B and C are correlated: Temperature (B) and sunscreen sales (C) are also positively correlated. As the temperature increases, people are more likely to spend time outdoors and therefore buy more sunscreen to protect their skin.

3. A and C are uncorrelated: However, ice cream sales (A) and sunscreen sales (C) are not directly correlated. While both may increase during warmer weather, there isn't a direct causal relationship between the two. People don't necessarily buy more ice cream because they're buying sunscreen, or vice versa.

This example demonstrates how two events can be indirectly related through a third event (in this case, temperature) without having a direct correlation with each other.

Would you like me to elaborate on this example or provide additional information about correlation?

you really have to try hard to make a model hallucinate when asked to summarize an email. I think they didn't mention it because they can't guarantee 100%, but it's virtually on non-issue for such task.

> and above all white

no, no need to say more, it's perfectly clear

Useful (small mistake on Cloudflare description)

People getting so used to being manipulated and lied to that they don't even bother anymore is a huge part of the problem. But sure, do what suits you the best.

we're at basic knowledge level, if your RAG imply some of it, you can get bad result too. Anyway, would you use a model who makes this nonsense response or one that doesn't? I know which one I will prefer for sure...

Does this model also thinks german were black 200 years ago ? Or is afraid to answer basic stuff ? because if this is the case no one will care about that model.

I agree with all except using german, because some model might be better at german but it doesn't necessarily means they are better overall.

For example I'm pretty sure Mistral models are better at french, so doing a benchmark using french only would be advantageous for them.

If you want to compare all models, better use english. Because now his benchmark just show which models is better at german.

That being said, it's still a very welcomed benchmark.

thanks for the clarification

> The Fastest and Most Accurate 7B-Parameter LLM to Date

There's plainly 7B models that surpass it on https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderb...

Am I missing something?

Thanks for the link, do you know any other similar services that support fine-tuning ?

I think you missed what intuitive means.

no API ?

I'm pretty sure it's not realistic, there's nothing that justify so many div because of tailwind

or the person who developed that with tailwind just love having dozens of div for a simple header > nav > a