This should be a recurrent question posted every month
488 karma · joined March 22, 2022
- Amazon's CEO knew what he was doing and the possible consequences
- Anthropic must raise cash, and there are only 2 ways: an IPO or follow-ons
- If the IPO is blocked, existing investors will be able to increase their stake on Anthropic at a very attractive lower valuation
- Amazon has 20% of Anthropic: so, they benefit from it
They own 20% of Anthropic.
Anthropic bleeds cash. They have to raise capital.
There are only 2 ways: an IPO or follow-ons from existing investors.
If the IPO gets delayed because of these restrictions, Anthropic will be forced to raise more capital from existing investors.
And existing investors (Amazon) will end up owning more of Anthropic at a cheaper valuation.
Can you think of any case in history where the US government suspended product sales due to national security concerns and that was ultimately beneficial for the company being regulated?
https://data.nasdaq.com/databases/SFA
It's a great survivorship-bias-free dataset.
Regarding tools, I use Python. I wrote the backtesting software many, many, many years ago during my Master's degree, and I've been refining it ever since.
It's an event-driven engine (they are slower than vector-based engines, but they are easier to write strategies for, understand, and debug) with all the bells and whistles, similar to the late Zipline. In fact, I tried most of the Python backtest engines that exist, and that's why I prefer to use what I built over the years: I have 100% understanding of what’s happening and 100% control.
I’m thinking about open-sourcing it… anyway, the logic is not that complicated.
I just read your posts... good stuff, congrats! I'll follow your journey.
If you want to check some algorithms I have implemented and connect, check it out:
https://quantitativo.substack.com/
Cheers
Google continues generating profits out of inertia and a lack of a better alternative.
It went for “don’t be evil” to “a necessary evil” (just until something a little better appears).
The point is not that he shouldn’t be allowed to unilaterally ban social media accounts.
The point is that he shouldn’t be allowed to do that in secrecy, without providing any public justification, and not respecting the right of the accused to defend themselves.
People are getting silenced without knowing why they are getting silenced, and without proper due process/right to respond.
Wouldn't it be a better/cheaper/faster solution to use LLMs to write UI/integration tests?
Let’s see how long it will take them to collect enough data and train a model to distinguish AI-generated from user-generated videos.
It looks like all current models suffer from an incurable case of Dunning–Kruger effect cognitive bias.
All are at the peak of Mount Stupid.
> You are probably one ArXiv paper away from figuring this thing out.
> I used to be a dishwasher. I’ve cleaned a lot of toilets. I’ve cleaned more toilets than all of you combined. And some of them you can’t unsee. That’s life.
Great story and speech. Many lessons.
Best quote in the article. Taking the time to read, absorb and apply a book changes us in a way no summary or audiobook could ever change.
All incentives all different stakeholders (employees, leadership, board, investors) have are all aligned with the full for-profit side of the organization. In fact, that’s the only side.
“Show me the incentive and I’ll show you the outcome”. Charlie Munger
Here’s an idea to monetize it: implement collaborative filtering (example: funds in rows, assets in columns).
Once you do it, you will be able to cluster similar funds. Let’s say X funds have the asset A, but Y funds do not have it, and X and Y belong to the same cluster. Thus, if you recommend asset A to the Y funds, there’s a high probability they’d add it to their portfolio (if property pitched). This is roughly how Netflix recommends movies, Spotify recommends songs, etc.
A lot of players in the industry would pay high $$$ for a recommender system like that. And you already made 80%: it’s only missing the final machine learning part (which is the fun part :))
The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities -- which correspond to our intuition and habitual behaviors -- but still lacks something important regarding system 2 abilities -- which include reasoning and robust uncertainty estimation. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement, which could guide future work in crafting an AI mathematician. The focus is not on proving a given theorem but on discovering new and interesting conjectures. The central hypothesis is that a desirable body of theorems better summarizes the set of all provable statements, for example by having a small description length while at the same time being close (in terms of number of derivation steps) to many provable statements.
But it’s Postgres + pg_vector only, no Supabase
“The Democratic People's Republic of Korea”
(AKA North Korea)
Based on this line of reasoning, ANY company that builds any given technology and intends to share (sell) it to the world, but not divulge how it was done, can call itself OpenWhatever.
They are clearly saying that the word “Open” in their name means nothing.
Talented people + hard work… + LUCK!
> Even then, the murmurations were only found because of Pozdnyakov’s inexperience.
Also, fresh inexperienced eyes to see what experts would dismiss!
What a great read :)
In fact, these public failures from Google AI provide an important public service: they remind us that there's nothing magical about LLMs. It's just code, and it is hard as hell to debug.
(At some point, someone will have to call it a day, throw everything away, and start from scratch.)
> Forecasted month end costs
> There isn't enough historical data to forecast your spend
Well… better late than never. Congrats!
If it is good, we call it "creativity."
If it is bad, we call it "hallucination."
This isn't a bug (or limitation, as the authors say). It's a feature.