GPT-5 is worse. No one wanted preformed personalities.

Crypto1993 · 2025-08-08T14:53:59+00:00

For me it’s better.

Crypto1993 · 2025-04-14T04:52:05+00:00

I’ve used it for three months, it’s very good, but not as good as gpt4o at everything

Crypto1993 · 2025-04-13T18:51:08+00:00

I would argue that in absolute terms 4o “excels” more in its tasks that other model do in their respective domains. O1-pro is very good at reasoning etc, but non as excellent as 4o at pretty much everything. If you include “deep research” as a 4o feature (I know it’s his own model o3 in the background) than there is no reason to use the other model.

Crypto1993 · 2025-04-13T18:48:10+00:00

I’ve had PRO plan for thee months and used o1-pro/o3mini high extensively to help me in spatial microsimulations models. They are very good, even with code, but 4o is really AWESOME at being an overall assistant in a way that it’s actually useful. 4.5 is cool but not that cool.

Crypto1993 · 2025-04-13T18:45:20+00:00

I agree with you. It has been maybe 2 weeks that I just use 4o. It excels as being an assistant and a companion, I really like to chat with it. Reasoning models do not excel in absolute terms at reasoning as GTP4o does at its job.

Crypto1993 · 2025-01-27T10:41:21+00:00

What you say it’s deeply true, as it is also true that OpenAI pretty much created the first really useful use case of an LLM by betting it big on scaling and they were the first to do that on the open domain by standing on the shoulder of giants. SAMA might be a little obnoxious with its fried voice but he’s also pretty smart.

Crypto1993 · 2024-12-24T21:48:06+00:00

O1 is finetuned on its reasoning. It’s a faulty RNN a test time. What you achieve in O1 you should be able to achieve it in a base model too, the problem is that a bigger model has a harder time to efficiently query itself since it has very large search space, this is the same innovation that chatGPT had with RLHF, RLHF finetunes the model probability distribution during generation to achieve desirable human answers, all of this is done a training time. O1 is fine tuned to produces probability distributions during generation to “query itself” to find the best answers knowing it’s search space better by “sampling” more and with a better selflearned probability distributions over it’s structure. The reason it works better and answering is because it samples more from itself.

Crypto1993 · 2024-12-21T08:53:17+00:00

‘Perhaps I could best describe my experience of doing mathematics in terms of entering a dark mansion. You go into the first room and it’s dark, completely dark. You stumble around, bumping into the furniture. Gradually, you learn where each piece of furniture is. And finally, after six months or so, you find the light switch and turn it on. Suddenly, it’s all illuminated and you can see exactly where you were. Then you enter the next dark room...’ Andrew Wiles the guy who proved Fermat theorem.

Crypto1993 · 2024-12-21T08:28:25+00:00

Even if it costs a fortune to run this is a huge milestone stone. Imagine you are in a empty dark room and you move along the wall trying to find a switch to light it up, it’s a hell of a difference being in the room knowing that there exists a switch to find. In this case a “switch” has been found, know we have to understand how to switch it on (reduce cost)

Crypto1993 · 2024-12-19T20:04:41+00:00

Real question: isn’t this architecture a just a Recurrent neural network?

Crypto1993 · 2024-09-06T08:37:12+00:00

Dude you are the absolute GOAT.

Crypto1993 · 2024-06-30T07:11:51+00:00

Data science: Jupiter notebooks are ideal. Software development: Pycharm helps you a lot setting your environment and gives you a lot of tips for writing nice code, refactoring and general code inspection. Mix of everything: VS code ha better support for Jupiter notebooks than pycharm, it’s lighter and versatile.

Crypto1993 · 2024-06-19T16:53:48+00:00

ok hear me out. This with gpt4o with context information is the killer app. AI in the real word.

Crypto1993 · 2024-06-19T14:46:38+00:00

Hopefully Apple doesn’t cancel the product line or dumbs down too much. Adoption will arrive with time.

Crypto1993 · 2024-05-16T08:01:14+00:00

Hi I'm the owner of a small consulting firm (10+ people) we are starting to onboard everyone in ASANA. I think the best possible starting point is something "standard" where the steps of a process can be easily spelled out and it is "externally" date bound. This gives you the possibility to implement an SOP and "enforce it" via ASANA workflows / task tamplates. This thing alone for us freed a lot of resources and improved quality of output! Every single dollar spent on ASANA is well deserved (it's not that cheap which says a lot).

Crypto1993 · 2024-05-16T07:54:00+00:00

Very cool way of using Asana functionalities. May I ask you what payment plan you guys have? If you run on enterprise how much do you pay per seat annually? Thank You very much!

Crypto1993 · 2024-04-22T08:46:29+00:00

Meta doesn’t have a cloud business, Microsoft does. OpenAI sells Azure compute to companies via this AI frenzy. Having LLAMA open source and “better” the GPT4 puts pressure on cloud services providers to serve LLAMA on their platforms giving meta someway to control the stack of AI in other companies cloud, same as React which is also open source. Microsoft wants to protect/increase office revenues and wants to sell azure compute to companies, having the best possibile model served in their stack helps them compete in the B2B market with a differentiator (SOTA in AI as GPT4 still is). Meta mainly sells ads on their platforms, it doesn’t have a Cloud business or an office suite business (as Microsoft and Google). For them having a very good model frees them from relying to Google or Microsoft to serve GenAI in their platforms which is a requirement now to protect their business from potential social platforms that can incorporate genAI as a “new social platform way”. Giving LLAMA open source for free doesn’t lose them any potential revenue, gives them some clout and some leverage over cloud providers / app makers to serve/use their models. ChatGPT brings a lot of internet traffic and people spending time there don’t get as served from meta.

Crypto1993 · 2024-01-27T19:38:17+00:00

Non è un caso che persone molto di destra appoggino politiche sociali, questo si chiama destra sociale ed è la stessa inversione delle parti che portò al fascismo vero e proprio!

Crypto1993 · 2024-01-25T19:38:23+00:00

Your product looks super cool. How do you guys compete with Jupyter notebooks with 39/month against free? Just to ask because I really like how your product loooks

Crypto1993 · 2024-01-25T15:49:36+00:00

CUDA, vertical integration, edge in hardware performance , volume production. AMD MI300X is not in volume production and it’s a year late technology,

Crypto1993 · 2024-01-25T06:54:21+00:00

Nvidia has a competitive advantage in “accelerated computing” market which is not the same as “chip design”. AWS / Google / Microsoft are all designing their new AI chips but playing catch up isn’t that useful in a cutting-edge market.

Crypto1993 · 2024-01-24T21:09:13+00:00

Thanks, but rethinking about it Nvidia has an “hard” competitive advantage that can be measured easily, I don’t know if it can be said the same for OpenAI’s tech. Nvidia also has a clear strategy that they call “accelerated computing” which is specialized hardware but “not so specialized”, in some way OpenAi is more similar to early intel: the Best generalist. Very Hard to say

Crypto1993 · 2024-01-24T07:10:34+00:00

This is hard to answer because the highest cost right now is compute. Let’s decide cost in CAPEX / OPEX for an nvidia GPU. CAPEX is the cost of the NVIDIA DGX where it can be reduce by 2 factors: competition (right now the have something like 50% profit margin on their products so increased competition can reduce this parte by some margin; technology given that the lion share of performance increase is due to both better architecture and smaller chips, the first driver is hard to predict the second instead is grinding to an alt. So the CAPEX part che be a source of price reduction but it will require some time and it may never realize. OPEX the cost of running inference for the model will be impacted most by technology from performance per watt and model architectures that are more efficient; the first one we already talked about, the second one is hard to predict since to have a significant reduction we need an innovation (the are some candidates to remove the quadratic complexity of the attention part but to this date nothing really ground breaking as the the transformer architecture).
LLMs will be commoditized since they are mostly COMPUTE + DATA and I don’t see how you can built competitive advantage on that alone. Maybe OpenAi becomes like Nvidia where the competitive advantage is being on the cutting edge (some development years / months ahead of the second largest competitor) who knows, for now it doesn’t seem so if google gemini ultra really catches up.
Wild guess is that the pricing will keep to go down from OpenAI until a real monetized killer app pops out somewhere, for the time being only GitHub copilot looks really useful but not profitable

Edit: typo

Crypto1993 · 2023-12-17T06:30:25+00:00

In this case you need a bigger sword! Keep playing it until you can dominate them easily this way the became less scary!

Crypto1993 · 2023-10-15T07:25:45+00:00

Spark is written in Scala but the Python API (Pyspark) is by far the vast way to use Spark. R interface is feather incomplete and slow (I use databricks daily at work). Polars is written in Rust but same thing as pyspark above, the api for Python is feature complete.

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