Hi! I work on dynamic shapes in pytorch and would love to hear more about the challenges you’ve run into. We’re always looking to improve the experience, so if you’re open to chatting, feel free to DM me on Twitter (@bobrenjc93) or email me at bobren@meta.com.

What are the practical implications of this?

  By incorporating DyT, Transformers without normalization can match or exceed the performance of their normalized counterparts, mostly without hyperparameter tuning.

The podcast has been elucidating to me in how it exposes the way in which individuals can become corrupted. Our politicians appear good on camera, they smile to our faces and kiss the foreheads of our babies—but we have decades of anecdotes about how they are bribed by special interests, making deals behind our backs. At least I personally had no first hand experience of what that actually looked of felt like until I started listening to the show. The hosts are ostensibly all smart (with the glaring exception of Jason), but so obviously biased which is advertised by the complicated verbal arithmetic they have to do to make sure they don't say anything bad about their friends which leaves empty logistical holes in each topic they discuss.

Seriously, have you ever heard them say anything bad about Elon, Trump, or the Republican agenda even one time in the last year? We know that none of those entities are perfect, they know that none of these entities are perfect, and yet they dare not say it. This is same as having politicians that are bought, and it’s maddeningly frustrating how two faced the whole ordeal is. The hosts will look at you directly in your eyes and tell you they’re being honest, and they just want to know the truth like you do. But the lie is hiding in plain sight, and everyone sees it.

What’s the point of it all? They're so narcisstically working and using the show to further their own interests. Especially sacks for whom it paid off handsomely. And they do this at the direct expense to their audience who (many of whom may not know better), doing their part to build a more morally confused and bankrupt nation in the process.

I still tune in every week, to happily outrage myself the convolution and lies and insane double standards that they get find themselves in in each conversation. I don't know why I do, I guess there's no other show that I find discusses politics and tech otherwise in a way that's satisfying. I wish a moderate panel (or even a liberal) one existed to counterbalance all of their BS but I haven't found that.

I suspect you'd be very interested in this paper:

https://docs.google.com/document/d/1iUscaSy6HHLVz2e2rjQfQtx5...

There are some good reasons why global-consensus cryptocurrencies can't generally be used as money:

https://perry.kundert.ca/range/finance/holochain-consistency...

  a basket of basic, thickly traded commodities should be chosen; perhaps a basket of specific amounts of basic elements, thermal and electrical energy, and basic food commodities, priced as delivered to several large markets. 

  A control algorithm such as the PID loop used in process control or robotics is employed to adjust K over time.

Not 100% sure why it’s often idolized on HN.

We’ve maintained a financial exchange w/ margining for 8 years with it, and I guarantee you that everyone was more than relieved - customers and employees alike, once we were able to lift and shift the whole thing to Java.

The readability and scalability is abysmal as soon as you move on from a quant desk scenario (which everyone agrees, it is more than amazing at.. panda and dask frames all feel like kindergarten toys compared), the disaster recovery options are basically bound to having distributed storage - which are by the way “too slow” for any real KDB application given the whole KDB concept marries storage and compute in a single thread.. and use-cases of data historical data, such as mentioned in the article, become very quickly awful: one kdb process handles one request at once, so you end up having to deploy & maintain hundreds of RDB keeping the last hour in memory, HDBs with the actual historical data, pausing for hourly write downs of the data, mirroring trees replicating the data using IPC over TCP from the matching engine down to the RDBs/HDBs, recon jobs to verify that the data across all the hosts.. Not to mention that such a TCP-IPC distribution tree with single threaded applications means that any single replica stuck down the line (e.g. big query, or too slow to restart) will typically lead to a complete lockup - all the way to the matching engine - so then you need to start writing logic for circuit breakers to trip both the distribution & the querying (nothing out of the box). And then at some point you need to start implementing custom sharding mechanisms for both distribution & querying (nothing out of the box once again..!) across the hundreds of processes and dozens of servers (which has implications with the circuit breakers) because replicating the whole KDB dataset across dozens of servers (to scale the requests/sec you can factually serve in a reasonable timeframe) get absolutely batshit crazy expensive.

And this is the architecture as designed and recommended by the KX consultants that you end up having to hire to “scale” to service nothing but a few billions dollars in daily leveraged trades.

Everything we have is now in Java - all financial/mathematical logic ported over 1:1 with no changes in data schema (neither in house neither for customers), uses disruptors, convenient chronicle/aeron queues that we can replay anytime (recovery, certifying, troubleshooting, rollback, benchmarks, etc), and infinitely scalable and sharded s3/trino/scylladb for historical.. Performance is orders of magnitude up (despite the thousands of hours micro-optimizing the KDB stack + the millions in KX consultants - and without any Java optimizations really), incidents became essentially non-existent overnight, and the payroll + infra bills got also divided by a very meaningful factor :]

   And this is the architecture as designed and recommended by the KX consultants that you end up having to hire to “scale”