throwaway4233

Even if juniors most of their ideas are exactly like you said, they would have more energy and almost no trauma from past experiences to experiment more.

While experience is important, it also creates blind spots as it's quite hard to have every experience about a particular topic unless you have been working on that tool/stack alone for decades.

Wouldn't you be losing out on the fresh ideas and perspectives new entrants into this field brings ? I had read "Valley of Genius: The Uncensored History of Silicon Valley" a few months ago and most of the disruptions in the industry came from engineers who brought in fresh perspective or had the energy to try something different.

OpenAI seem to be slowly moving towards building an entire OS(or any other form of walled garden) that will be filled with LLM powered applications.

> Would love to hear OpenAI's explanation behind this line of thinking.

My assumption is that the data centers would mostly be staffed by those who will have to manually audit data going in and out of the LLMs on a daily basis. There would also be a need to generate/curate the test data that the LLMs will have train on. There is potential for half a million jobs, but is that what you would want to have human effort invested in, is the real question.

One of the restuarant chains mentioned in the author's post (Social), is an extremely crowded pub during the night and for the rest of the time, a place where freelancers or remote workers come in to work and socialize. At least that was the case in Bengaluru,India before Covid.

I would say that from the restuarant's point of view, having the order-from-app experience works out since the freelancers can order via their laptops whenever they want, without having to flag down a waiter. And during rush hours, tables could order what they want without having to spot and call a waiter among a very drunk dancing crowd.

> How do you know, and for better or worse?

Their decisions made the life of those around them miserable because they did not care about the consequences of it. Like for example, hiring an intern who had basic programming skills when there was no engineering bandwidth available to support the intern, and then bouncing them around multiple large projects every week, for the sake of showing upper management that they had people "working" on priority tickets. There is more to this story, but it's just more details painting that person in a bad light.

I, most certainly am not such a person.

> Caring about that work was stupid, and I should have just enjoyed my time more.

I agree. It's a work in progress for me to get to that state.

When I was junior/mid level engineer working in a 5-8 person engineering team, there was this particular senior engineer whom I disdained. It had nothing to do with them as a person, but I always had the feeling that they just did things at work without caring for the impact of it. The team and company grew in size over a period of 2 years and I went through a lot of first hand experiences of having to deal with unrealistic deadlines, chaotic lines of communication and multiple nights of tailing production logs to fix bugs. It was around this time, that the senior engineer during a company even got drunk and talked to me about their first few jobs.I realized that what they described, was quite similar to what I was facing right now and I came to understand why they cared so little about work they do on a daily basis.It honestly scared me at to think that I might either end up like this person or worse or burning out and quitting the field.

Right now, I am close to age the senior engineer was when I met them and even though I may selectively decide not to involve myself with things that can overstretch me, I am in no way the same as that other person.

The reason I am sharing this tangential story is to highlight my opinion about old age. It does not matter much if you have gained a lot of experience as you grow older, unless you are able to use it effectively.

I do not believe that most of these `perfectionists` are trolls. Some have just very bad experiences either in their career or childhood that make them feel that making mistakes is not normal.

I was a `perfectionist` for a while due to certain bad experiences at work, and it was only through the help of really good teammates that I was able to slowly get rid of it. And that required pointing out things like what the author has done in their blog post and once someone sees that mistakes are things that anyone could make, they get more comfortable the concept.

The harder part is understanding why a perfectionist is so, and then not getting frustrated while you try to help them improve.

The screenshot of the tweet thread at the end seemed to indicate that the alignment team wanted to explore other avenues(or models) to get to AGI, while the company leadership wanted to keep improving on the transformer models.

From the business point of view, continuing to improve the goose that is laying the golden eggs makes sense, but as a researcher they might have already ended up seeing the limit of adding compute and data, and wanted to pivot. As someone who worked a mid level manager/engineer, I can understand the frustration involved in making the management level understand what's really happening on the ground level.

From my point of view the underlying problem here seems to be that someone in Quora product team feels that the best way to drive engagement is to predict what the user might want to know about and then spam them with it, rather than letting them discover on their own or use their feedback.

I stopped using Quora 8 or 9 years back, because despite how much time I spent curating the feed I was served, the questions always seemed to veer back into the questions on life experiences, `write a short story in 3 lines` or about relationships. This was most probably due to topics like these being a fad in India during the time and often received a lot of answers or upvotes.

Every time I marked an question as something I was not interested in, because of the topic, Quora assumed that I did not like the author's answer and then proceeded to show 5 other questions the author has responded to. I assumed it was because of some bug on their side ignoring my preferences of topics, until I spent a week in Dubai and saw most of the questions in my feed match the topics I marked as interested.

> Additionally, members of the program receive priority placement and “richer brand expression” in chat conversations, and their content benefits from more prominent link treatments. Finally, through PPP, OpenAI also offers licensed financial terms to publishers.

> A recent model from The Atlantic found that if a search engine like Google were to integrate AI into search, it would answer a user’s query 75% of the time without requiring a clickthrough to its website.

If the user searching for the information finds what they want in ChatGPT's response (now that they have direct access to the publisher data), why would they visit the publisher website ? I expect the quality of responses to degrade to the point where GPT behaves more like a search engine than a transformer, so that the publishers also get the clicks they want.

I believe what the parent comment is trying to imply is that the search results are fetched/retrieved from Bing's own internal ranking vector(?) database and then passed to the LLM, which then converts the received documents into a more human readable format and fills in any missing gaps in the information with it's own data.

So the gaps are the only areas where the LLM can hallucinate on and if your search query is easily available information on the internet, then hallucinations will be less or none.

Edit: I have used RAG with a project that I am working on and it's quite hard to ascertain if the LLM used the information provided as part of the RAG documents or just made up information on it's own, since even without RAG, we were getting similar responses 7 times out of 10.

Based on the way you have described their API, I can make 2 assumptions

1. The might have had well supported API until the point at which they realized they wanted to have more people to use the webapp, rather than having the data sent through an API. More time spent on the Hubspot webapp means, more chances the customer notices the other services Hubspot provides and pays for them.

2. The versioning APIs that you see are most likely due to an enterprise deal Hubspot made with a large company who asked for variations in the API, and Hubspot introduced a v2/v3/v4 instead of modifying v1 (to not cause disruptions for their customers using v1).

I agree with your comment, and feel that in a software engineering context, the quote

> To find the sweet spot between impostor syndrome and overconfidence, you first need to understand that you can make mistakes without it reflecting on your competence.

is incomplete without describing the environment and people you are around when you make these mistakes. The teams I have worked with until now have all been extremely kind in dealing with other engineers making mistakes and focused on how we could reduce such mistakes as part of a team. It helped me build a mental checklist on dealing with such a situation and when/how to take the risk and when to bring in additional help. I never really noticed the impact of this until I worked with a colleague who seemed to have trouble releasing their work to production and always kept delaying things out.

> I think the main factor that will be key to generate a whole movie is being able to pass some reference images of the characters/places/objects so they remain congruent between two generations.

I partly agree with this. The congruency however needs to extend to more than 2 generations. If a single scene is composed of multiple shots, then those multiple shots need to be part of the same world the scene is being shot in. If you check the video with the title `A beautiful homemade video showing the people of Lagos, Nigeria in the year 2056. Shot with a mobile phone camera.` the surroundings do not seem to make sense as the view starts with a market, spirals around a point and then ends with a bridge which does not fit into the market. If the the different shots generated the model did fit together seamlessly, trying to make the fit together is where the difficulty comes in. However I do not have any experience in video editing, so it's just speculation.