Douro Labs | REMOTE (US or EU timezones) | Full-time | $150k-250k + token incentives and annual bonuses

Douro Labs is building Pyth Network, an open source blockchain oracle delivering high frequency and low latency market data. Our goal is to disrupt the opaque and pricey financial data ecosystem by constructing an open network that incentivizes contributions from data owners. Contributors to the Pyth Network include some major exchanges and trading firms, such as Jane Street, CBOE and Two Sigma.

We have multiple roles open:

1. Senior / Staff Software Engineer -- Develop backend services in Rust, work on smart contracts in various languages, develop and optimize a distributed blockchain.

2. Staff Frontend Engineer -- Take frontend concepts through from design through implementation. Build beautiful data visualizations and web3 applications.

3. Senior Business Intelligence Analyst -- Collect and analyze product metrics to understand usage and growth.

4. Developer Relations Manager -- Work with developers building on Pyth Network to develop solutions and identify product gaps. Iterate on product features, documentation, and anything else required to produce a seamless experience for our users.

Email me (the CTO) directly at { jayant at dourolabs dot xyz }

Anthropic won't give back the funding. The bankruptcy proceedings will likely claw back the Anthropic equity that SBF owns, which will then be sold & distributed as part of the bankruptcy.

False. If I deposited money into my bank account, or even any investment account, and that money was fraudulently invested on my behalf without my knowledge, I sure better be paid back with money, not non-liquid "equity" funny money in some private AI tech company (likely way overvalued) that can't even be traded on the public markets.

Looking forward to the great unwinding of FTX investments that will give a whole new meaning to "claw back". Let this be a lesson to founders not to take money from just anyone -- you wouldn't let a drug cartel finance your tech company, would you?

If there was no fraud then clearly there would be just in kind distribution but no claw-back. With fraud, the questions becomes if the money invested was fraudulently invested. It can be that there was fraud AND investments were not fraudulent.

As long as you give $1 of that drug cartel’s money to a street beggar I don’t see the issue here. (/s)

Not a lawyer so I won’t even try to give detail, but there’s a legal concept of “proceeds of a crime.” I think it may be handled very differently.

Pyth Network | Software Engineer, Frontend Developer, Reliability Engineer and more | Remote - Global | https://pyth.network/

Pyth Network is an oracle that publishes financial data on-chain for use in DeFi applications. We currently publish real-time prices for cryptocurrencies, FX, and equities on several different blockchains. We are the biggest oracle in the Solana ecosystem, where we secure >$2B of value and power a variety of borrow/lending, derivatives, and other DeFi protocols.

We have two goals at Pyth Network. First, we want to accelerate the growth of the DeFi ecosystem. We believe DeFi is the future of finance, and we are working together with the broader developer community to build a fairer and simpler financial system. Oracles are a critical component of most DeFi applications — they’re the only way for on-chain applications to get input from the outside world. Our goal is to create the most accurate and reliable price feeds, then unleash the creativity of external developers to build valuable applications on top.

Second, we want to make financial market data broadly accessible to the public. Today, most financial data is gated behind paywalls that keep out most potential participants. The data that is free is low resolution, e.g., daily open/close prices. We think this is unfair. Our price feeds are freely accessible to the public, and anyone is free to save the data for later analysis. In fact, we think our historical US equity prices are the highest-resolution, free data set, with price updates every 400 ms.

Our unique competitive advantage is our network of market data providers. Our network includes over 50 of the biggest exchanges, traders, and market makers in both the crypto and traditional finance worlds. These data providers have agreed to publish their proprietary data on-chain, which allows Pyth Network to build the fastest and most reliable price feeds. It also allows us to access financial data that is not freely-available from other sources, such as real-time US equity prices.

We offer competitive salaries, benefits, and employees may be eligible for token allocations as part of our incentive program.

See our greenhouse for all available roles and to submit an application https://boards.greenhouse.io/pythnetwork

This article completely misunderstands proof-of-stake and the distributed consensus space in general. Both proof-of-work and proof-of-stake are mechanisms for making distributed consensus sybil-resistant.

Distributed consensus is the problem of getting a bunch of computers to agree on some state when some of the computers can behave maliciously. In the case of cryptocurrency, the state is a log of transactions, which when replayed tells you who owns what. There are well-known algorithms for distributed consensus, such as Paxos and Raft, that are used in real-world applications, e.g., the Chubby lockservice.

Distributed consensus algorithms can be proven to reach consensus as long as at most a fixed percentage (e.g., 1/3) of the computers are behaving maliciously. This assumption is fine for applications like Chubby, where Google is running all 5 of the computers participating in the consensus, and no one can add additional computers. However, this assumption breaks down in the case of cryptocurrency, where anyone can spin up computers to participate. In fact, an adversary can effectively spin up an infinite number of computers. This form of attack is known as a sybil attack.

Proof-of-work and proof-of-stake add sybil-resistance to distributed consensus algorithms by requiring the adversary to commit a scarce resource in order to participate in the consensus process. In the case of proof-of-work, the scarce resource is computing power. For proof-of-stake, the resource is the currency secured by the system itself. This may seem a bit circular, but it's fine. In order to attack the system, the adversary would have to purchase or borrow a bunch of the currency on the open market, which has an economic cost. Proof-of-work permits the same attack, where the adversary buys or rents computing power instead.

From this perspective, the bitcoin consensus algorithm is in fact the odd one. Most distributed consensus algorithms (like Paxos and Raft) rely on some kind of voting system.

The main point the author is making is that PoS doesn’t require spending of any scarce resource on a per block level so the accuracy of the distributed clock is not to be trusted. I don't think they misunderstand their argument. You’re just not replying to it.

You spend "safety of your stacked money while having stacked a lot of money" it's a scarce resource as if you over spend it you lose your money. And the more money you have stacked the less interest you have into braking the currency as it makes you lose that money.

But it’s not a scarce resource you spent. As the author says, you can sign multiple, contradictory stakings.

Which can (likely will) make you lose all the stacked money.

The article addresses that in the section starting:

>>Therefore, once they have withdrawn their deposits, they are untouchable. This is the “nothing at stake” problem. There will inevitably come a point when a node is free to liquidate their entire stake and cash out.

And later concludes that, In order to know which is the valid staking, you have to already have a decentralized mechanism for ordering transactions, which was the problem to begin with.

But that's also true for PoW as you can use hardware which after an attack you can use for other things.

But you have to do new work (and a lot of it, requiring energy and time), compared to the trivial cost of generating another signature as in PoS.

You cant reuse the electricity you have already burned...

If you're interested in probabilistic programming and want something a little more hands-on, I recommend The Design and Implementation of Probabilistic Programming Languages http://dippl.org/ . It's an online course/textbook that gets you programming right away and makes the power of probabilistic programming immediately clear.

thanks for this. there is also Probabilistic Models of Cognition [0] by one of the authors. I wish however that they stuck to Church language [1]

[0] https://probmods.org/

[1] http://web.stanford.edu/~ngoodman/papers/POPL2013-abstract.p...

I too preferred church, though I understand why the authors of dippl chose a more popular language (javascript) for their book. Church (a dialect of Scheme, which is a dialect of Lisp) also ties probabilistic programming back to its intellectual roots in McCarthy's amb operator [1].

That said, you can get pretty far with probabilistic programming in any language with decent monad support [2]. I did most of my probabilistic programming work in Scala. (You lose the ability to do really fancy inference if you go the monad route, as you can't analyze the program structure, but a lot of the time, this is fine.)

[1] http://community.schemewiki.org/?amb

[2] http://mlg.eng.cam.ac.uk/pub/pdf/SciGhaGor15.pdf

I wish I could have Church as a Jupyter notebook/lab Kernel.

Would make it much easier to play around with the language when trying to wrap my mind around the church version of probmods.

Not exactly the same thing but try clojure with anglican in nextjournal to see if it fits your needs.

Last time I tried putting a lisp into jupyter as a playground for church, I realized that I was missing all the plotting features to really get anywhere and before I realized it, I had to stop my self from reimplementing church’s standard library.

I’ll have a look on google on the names you mentioned :)

In nextjournal you can plot clojure output trivially with plotly or vega-lite. It’s just a matter of adding metadata to your output, like this:

    ^{:nextjournal/viewer :plotly}
    {:data [{:x '(0 10 20 30 40)} 
            {:y '(5 10 15 20 25)}]
     :layout {:autosize false :width 800 :height 400 
              :xaxis1 {:title "year"} 
              :yaxis1 {:title "revenue"}}}

See the result here:

https://nextjournal.com/kommen/plotting-from-clojure

Regarding anglican, here is a link. It’s basically a church port:

https://probprog.github.io/anglican/

does this require stats/probability knowledge?

on that topic, can anyone recommend an online stats/probability course? I tried the coursera one by Sebastian Thrun and couldn't get far into it because the "TA" examples were unintelligible.

Stat110[0] (Harvard) on EdX! The professor, Joe Blitzstein, is incredible. Easily one of the best classes I have ever taken.

[0] https://www.edx.org/course/introduction-to-probability

for machine learning i can recommend the course by Philipp Henigg from Uni of Tuebingen

https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m...

It is very likely that Tether is a scam -- the evidence has been mounting for years -- but the peg won't break until the market believes it. Unfortunately, there isn't a good way to short Tether. You can short the Kraken USDT/USD pair on margin, but the margin rates are ~20% a year. You can borrow tethers on compound.finance, but the rates are ~15% a year. I think part of the reason we haven't seen the unwind of tether is simply because it's too expensive to short.

Also, no one will loan you their tether if you tell them you're going to break the peg and give them back a worthless token.

(I actually had a kraken short on for a while in ~2018 but gave up because it's just too expensive.)