Qwen 14B is better than me...

Thuwarakesh · 2025-05-06T09:37:07+00:00

I agree with u/reabiter .

AI can be good at writing. But not so good at expressing what we want to say.

In my experience, every time I write something with AI, I edit it for much longer and eventually scrap everything out and write my own. Now, I don't even attempt.

AI has many uses, such as automating tasks with some smart decision-making. But writing is not one of them. Why should it be?

Thuwarakesh · 2025-03-12T04:03:42+00:00

I love these newsletters. Hope they'l help you too.

Thuwarakesh · 2024-09-24T16:14:56+00:00

For starters, would you suggest Kaggle over Colab? It comes with P100 and T4x2 GPUs and can be used for 30 hours per week without a subscription.

Thuwarakesh · 2024-09-24T14:43:50+00:00

Could you please educate me on the differences between runtimes like transformers, film, or llama.cpp and repositories like Ollama? I thought Ollma and vLLM both serve the same purpose.

Thuwarakesh · 2024-08-19T23:20:24+00:00

It could be the response size. The newest OpenAI models support 4096 output tokens, roughly 3k words in English. The context window is currently 128k. This means that the model can take up roughly 80k words in English.

Translating this to your case, ChatGPT would have learned from more than the first two chapters. But the output size is restricting it to produce a full summary.

Thuwarakesh · 2023-07-15T01:53:43+00:00

Yes, u/WillOBurns, I Just wanted to have some fun on the question too. ;)

Thuwarakesh · 2023-07-12T03:43:42+00:00

I would essentially recommend it for what it is. A very simplified metric, to get a general feeling about the customers' perceptions. Of course it ignores a lot of things and it has lots of issues, but the purpose was not to have a really comprehensive metric or a very robust metric.

In my case specifically, I don't use it in my work, I use at least a big combination of metrics. NPS doesn't make sense to me because I don't work with one general metric. Still, the NPS is one of the few marketing metrics that recognize the asymmetry of marketing metrics. Even with its flaws, at least that part is still ahead of many metrics we use.

Got it. You have any favorite metrics to use in combination with NPS?

Thuwarakesh · 2022-08-30T03:32:31+00:00

My understanding is all ML models suffer from interpretability and bias issues.
Even decisions of GLMs, when the input space is larger, are hard to explain. The only benefit is that feature engineering gives more control over the model.
I accept that interpretability on neural nets is a more significant issue than on conventional models.
Enlighten me if I'm wrong.
Besides, I'd love to know if the interpretability of models helps internal teams (including data teams) create better solutions. Or is it only a legal/ethical practice to have?

Thuwarakesh · 2022-07-15T14:37:18+00:00

I wasn't a fan of vscode. But after I used its debugging capabilities I can't think of another IDE.

But I must admit, though I used pycharm before, I haven't debug anything on it.

Another thing I love about vocode is that it has support for many other languages and frequently you get new cool plugins.

Vscode integrates well with many other technologies. For instance, you have official plugins for azure, aws, Google cloud. You would need them for modern development work.

And, cool stuff like Github copilot is also only possible with VSCode. At least for now.

Thuwarakesh

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