alan

Support Engineer @quicknode | Past @imperator_co, open source contributor on @starknet ecosystem

[Ferhat @0xCrispy]

quicknode docs got revamped. go touch it!

https://t.co/p6AAdPPKuD

[Quicknode @Quicknode]

The future of blockchain infrastructure arrives Wednesday.

[Quicknode @Quicknode]

Builders, want real-time visibility into your @hyperliquidx portfolio?

This guide shows how to use HyperCore data with QuickNode to build a portfolio tracker that monitors any wallet with live updates on PnL, margin, vaults, and spot balances.

[Quicknode @Quicknode]

With LYS Labs’ add-on, you get instant, low-latency Solana event data that plugs right into bots, dashboards, and analytics.

Start shipping faster. 🚢

[Lemniscap @Lemniscap]

https://t.co/EDFlo1Or3V

[Mike MacCana @mikemaccana]

gm! ☀️😎

Here's everything in Solana this month: DoubleZero on mainnet, Agave performance improvements, 150ms transaction confirmations coming, our new Solana benchmarks, Solana becomes the main blockchain for stock trading, the US government providing economic data via Pyth, why you're dead if you can't explain your crypto app without referring to 'crypto', and more!

[Ferhat @0xCrispy]

quicknode docs now support llms.txt 🤖

https://t.co/n7wLpI6J03
https://t.co/C0hcuoYE3F

[Dmitry Shklovsky @DIMIDJ]

QuickNode supports Hyperliquid chain on RPC, Streams, and Webhooks products.

Building on Hyperliquid?

Check it out! 👇

[Imperator.co @imperator_co]

Choosing the right validator on @HyperliquidX matters. 🤝

Our latest guide outlines how to stake HYPE, what to look for, and which validators stand out. Stake HYPE with Imperator and earn more with just 3% commission.

Read here 🔗 https://t.co/onTUIvq4Hc

[alan @alanbkang]

My contribution at @gizatechxyz has been one of my earliest open source contributions and it has allowed me to understand the potential of how AI can be applied in crypto applications.

Congratulations on the successful token launch and thank you for rewarding those who have shaped Giza during the early days.

[CodeJam @CodeJamOfficial]

🚀 Day 2 of our Cairo Coding Challenge is here! See if you can rise to the top of the leaderboard! Ready for round two? Dive in now!

See you on Warpcast!
https://t.co/alX2GuKmOu

🏆 #CairoChallengeDay2

[alan @alanbkang]

Great read on Eigenlayer and overview of LST and LRT mechanisms and its growing ecosystem.

https://t.co/KPWEeaID6F

[Hyung Won Chung @hwchung27]

I gave a talk at Seoul National University.

I titled the talk “Large Language Models (in 2023)”. This was an ambitious attempt to summarize our exploding field.

Video: https://t.co/vumzAtUvBl
Slides: https://t.co/IidLe4JfrC

Trying to summarize the field forced me to think about what really matters in the field. While scaling undeniably stands out, its far-reaching implications are more nuanced. I share my thoughts on scaling from three angles:

1) Change in perspective is necessary because some abilities only emerge at a certain scale. Even if some abilities don’t work with the current generation LLMs, we should not claim that it doesn’t work. Rather, we should think it doesn’t work yet. Once larger models are available many conclusions change.

This also means that some conclusions from the past are invalidated and we need to constantly unlearn intuitions built on top of such ideas.

2) From first-principles, scaling up the Transformer amounts to efficiently doing matrix multiplications with many, many machines. I see many researchers in the field of LLM who are not familiar with how scaling is actually done. This section is targeted for technical audiences who want to understand what it means to train large models.

3) I talk about what we should think about for further scaling (think 10000x GPT-4 scale). To me scaling isn’t just doing the same thing with more machines. It entails finding the inductive bias that is the bottleneck in further scaling.

I believe that the maximum likelihood objective function is the bottleneck in achieving the scale of 10000x GPT-4 level. Learning the objective function with an expressive neural net is the next paradigm that is a lot more scalable. With the compute cost going down exponentially, scalable methods eventually win. Don’t compete with that.

In all of these sections, I strive to describe everything from first-principles. In an extremely fast moving field like LLM, no one can keep up. I believe that understanding the core ideas by deriving from first-principles is the only scalable approach.

[alan @alanbkang]

@PopPunkOnChain Byungg

[alan @alanbkang]

@sockdrawermoney Great talk sir 🫡

[eigenrobot @eigenrobot]

contemplate this on the tree of woe

[sudo rm -rf --no-preserve-root / @pcaversaccio]

1/ Many people have questions about the recent finality issues on the Beacon chain. I will cross-post here for transparency the ELI5 summary from u/OKDragonfruit1929 on Reddit: https://t.co/vX91w6MVg9

[SunSec @1nf0s3cpt]

Two weeks ago, about 80 ppl DM'd me, and I shared resources on smart contract security with them. Last week, I created a TG channel to help them. Let's see how things will turn out in a month.
Current role distribution:
34% web3 sec
23% web3 dev
8% web2 dev
8% web2 sec
27% Others

[alan @alanbkang]

@bzzeowGhnGhnGhn @tszzl @KeynesianYama Tenet moment

[Dan Schwarz @dschwarz26]

I bet I'm not the only one that has convos like this:

Me: LLMs are generational tech. I'm excited and terrified.

Them: You're worried about a Terminator / Kurzweil scenario?

Me: A bit. I'm more worried about chaos in the next 2-5 years.

Them: What exactly do you mean?