I struggle with this a bit because while my network isn’t bad - I really can’t stand social network like threads/X etc - I’m not on any social media bar here and LinkedIn.
Do you think investing in bluesky is worth it? I’m in industry but have a PhD ongoing in TTI models so I should probably get on it :/
I do understand why it’s a product - it feels a bit like what databricks has with model artifacts. Ie having a repo of prompts so you can track performance changes against is good. Especially if say you have users other than engineers touching them (ie product manager wants to AB).
Having said that, I struggled a lot with actually implementing langfuse due to numerous bugs/confusing AI driven documentation. So I’m amazed that it’s being bought to be really frank. I was just on the free version in order to look at it and make a broader recommendation, I wasn’t particularly impressed. Mileage may vary though, perhaps it’s a me issue.
I thought the docs were pretty good just going through them to see what the product was. For me I just don't see the use-case but I'm not well versed in their industry.
I think the docs are great to read, but implementing was a completely different story for me, ie, the Ask AI recommended solution for implementing Claude just didn’t work for me.
They do have GitHub discussions where you can raise things, but I also encountered some issues with installation that just made me want to roll the dice on another provider.
They do have a new release coming in a few weeks so I’ll try it again then for sure.
Edit: I think I’m coming across as negative and do want to recommend that it is worth trying out langfuse for sure if you’re looking at observability!
I’ve been slowing crunching through Math for Deep Learning, so spent a fair amount of time looking at Hessian matrices + second order optimisation. I’ve been slowly reading this book for a year, so stopping to do most of the math by hand each time. One chapter to go!
Then I was sick all last week, so ended up down a rabbit hole about the current card collecting bubble (right word?). Super interesting.
Where do Hessians come into play for neural networks? It seems like they just use auto-diff to compute the Jacobian or the gradient for backpropagation.
The theoretical results sometime look at the second order derivative.
This is fantastic. I think it’s nailed in the substack what was missing from a lot of these LLM driven NPCs that did not feel authentic. I have a couple of follow-up questions on specifics relating to analysis of behaviour with LLMs (in game-dev myself). Would it be possible to speak to you directly on them?
The response to the Sal Khan op-ed resonated with me, along with other parts of this article. Something I’ve been digging more into is some of the figures around proposed job losses from AI. I think I even posted a simulation paper last week.
After posting that, I came across numerous papers which critique Frey & Osborne’s approach, who are some of the forefathers for the AI job losses figures we see banded around commonly these days. One such paper is here but i can dig out others: https://melbourneinstitute.unimelb.edu.au/__data/assets/pdf_...
It has made me very cautious around bold statements on AI - and I was already at the cautious end.
Job losses aren’t directly tied to productivity, in the short term it’s all about expectations. Many companies are laying people off and then trying to get staff back when it doesn’t work. How much of this is hype and how much is sustained is difficult to determine right now.
It never made sense to blame AI in the first place for tech layoffs. You have a new tool that you think can supercharge your employees, make them ~10x productive, be leveraged to disrupt all sorts of industries, and have the workforce best suited to learn and use these tools to their full potential. You think the value of labor may soon collapse, but there are piles of money to be made before that happens.
If you truly believed that, you would be spinning up new projects and offshoots as this is a serious arms race with a ton of potential upside (not just in developing AI, but in leveraging it to build things cheaper). Allegedly every dollar you spent on an engineer is potentially worth 10x(?) what it was a couple years ago. Meaning your profit per engineer could soar, but tech companies decided they don't want more profit? AI is mostly solved and the value of labor has already collapsed? Or AI is a nice band-aid to prop up a smaller group of engineers while we weather the current economic/political environment and most CXO's don't believe there are piles of money to be had by leveraging AI now or the near future.
> you would be spinning up new projects and offshoots
If the engineers can 10x their output, this actually exposes the product leadership since I find it unlikely that they can 10x the number of revenue generating projects or 10x their product spec development.
I’ve had this same thought, although less well-articulated:
AI is supposedly going to obviate the need for white collar workers, and the best all the CEOs can come up with is the exact current status quo minus the white collar workers?
> Allegedly every dollar you spent on an engineer is potentially worth 10x(?) what it was a couple years ago. Meaning your profit per engineer could soar, but tech companies decided they don't want more profit?
Exactly, so many of these claims are complete nonsense. I'm supposed to believe that boards/investors would be fine with companies doing massive layoffs to maintain flat/minuscule growth, when they could keep or expand their current staffing and massively expand their market share and profits with all this increased productivity?
It's ridiculous. If this stuff had truly increased productivity at the levels claimed we would see firms pouring money into technical staff to capitalize on this newfound leverage.
Do you think investing in bluesky is worth it? I’m in industry but have a PhD ongoing in TTI models so I should probably get on it :/
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