lnkdinsuxs

People, usually starting from scratch (0 to no seed funds), immigrating to the US from communist countries and becoming financially stable at the minimum, often times building muiltimillion dollar empires and less often, muiltibillion dollar empires. ChatGPT/Claude/Gemini/Grok will give you well researched datapoints that you will be able to follow up on yourself.

I can suggest a prompt that will report how many founders part of Y-Combinator, who came from nothing, some immigrants from communist countries, have become ridiculously successful.

Have you tried to start a business in Pakisthan, Russia, Cuba or China as an outsider? Even with a few thousand dollars as seed? Do you want to buy a house in Pakisthan, Russia, Cuba? Do you want to study there?

... but you didn't answer my question, and I have yours.

what is communism and are there verifiable reports of it accomplishing what it promises?

> That they even bothered to do all that stuff is good. That others don’t is bad

good and bad isn't what Meta and AWS is running with. If AWS trains willy nilly on user data like Meta does, AWS is toast. AWS is already taking a huge risk by now using their own take of AWS FDEs competing against their established ecosystem of consulting companies that used to recommend all 583 of AWS products to host a static webpage. Turning of more customers by training on their data is too much of a risk. That's the "good" - a risk analysis.

Meta doesn't care if you stop using Facebook, Whatsapp etc. Frankly, if you are embedded in the Facebook, Whatsapp ecosystem, you don't have a way out as a normie. Even if you are technically capable, good luck publishing your custom Mattermost build for iOS.

Meta has a significant amount of investment in hardware, originally due to Metaverse. The way to monetize that investment is inference and training. That's a do or die decision for Meta. Meta isn't releasing open-weight models because they are "good".

Meta is releasing open-weight models for the same reason Alibaba, NVIDIA or IBM does. Or Google releases Gemma.

There's no "good" or "bad" - the moment Alibaba, NVIDIA, IBM or Google's objective functions change, "Do no evil" will quickly morph into "Do as much evil as necessary without the costs of getting caught exceeding E(outcome)"

The achilles heels of current LLMs are:

1. Hallucinations

2. Prompt injections

Currently, there is no known way to detect either using LLMs themselves. As a research assistant, if the LLM hallucinates, and it always sounds extremely confident when it does so, the LLM itself is of little use and causes additional burden, defeating the whole point of this.

Maybe an external validation step that employs a pagerank like algorithm is needed to detect and flag hallucinations? If so, how valuable would that company be?

One possibility could be that they actually never achieved product market fit: They went viral with their free offering, but those were just users not customers.

1. When it came time to pay, they actually didn't have many customers.

2. The price-points at which they were selling licenses at meant customers would need to be among the Fortune 5000: their sales cycles are long, complicated and expensive

> They could have IPO'd for less money ;) Then they wouldn't have needed to squeeze so hard.

In retrospect maybe, but 2020s was the time of scaling a company and you needed capital to do so.

Could it be that they fell victim to bad market timing and if the markets would have continued to be like 2019-2020, their shares would be at $160 and not $32?

> I achieved indexing rates greater than 3,000 log lines per second, indexing 5 million logs in under 30 minutes

What was the size of the 5 million logs on disk?

> SELECT * FROM logs WHERE entry MATCH 'SuperSecretAgent'

If I wanted to sort the results in descending order of how many times 'SuperSecretAgent' occured in the record, how would I do it?