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A collector's dreamˢʰᵉᵉˢʰ, in a gamers world, on Web3's horizon. $DOGE Miner, #GetRSP Co-founder & Creator of ⱤɆ฿ɆⱠ ₣₳₵₮łØ₦ @ApeReunion

[ＧＩＧＡ @Web3Reunion]

Decided to reveal 10 @ApeReunion Species today.

I honestly wasn’t prepared for what happened.

This may have been the luckiest reveal session I’ve ever had. 🥹 #ApeResponsibly Legends!!!

[ＧＩＧＡ @Web3Reunion]

@TACOMEATER1 @EDMantis @ApeReunion Holy SHEEEEESH!!!!! BANGER!!!!

[TACOMEATER 🏴‍☠️ @TACOMEATER1]

Huge thank you to @EDMantis for gifting me this awesome @ApeReunion Species!!! 🙏Stoked to play as this zombie in game! Looking forward to learning more about everything ApeReunion! LFG🔥

[Egg 🥚 @GutterEgg_]

Added a new @ReunionSpecies to my squad. Thanks @Web3Reunion for the trade and love that Gator 🐊

[ＧＩＧＡ @Web3Reunion]

@FreshFireCo 🚀🚀🚀 Legendary... 🚀🚀🚀

[ＧＩＧＡ @Web3Reunion]

Just another wild Pokémon story!

A heavily damaged, practically split open Shadowless Venusaur was bought for around $36 back in January.

Most people would look at this card and think:

“Trash.”

Fast forward a few months…

Same card sells for $115.

Yes.

A damaged, peeling, folded, barely-holding-itself-together Shadowless Venusaur.

Why?

Because collectors understand you’re not always buying condition. Sometimes you’re buying history.

This hobby never stops surprising me.
Would YOU have bought this Venusaur ? 👀

[ＧＩＧＡ @Web3Reunion]

@FreshFireCo Insane card story bro!!.

LOL!!!! Thanks again for connecting!

[ＧＩＧＡ @Web3Reunion]

@CryptoLeven 😂

[ＧＩＧＡ @Web3Reunion]

@elonmusk Intelligence is the progressive assimilation of increasingly accurate world simulations.

[Andrej Karpathy @karpathy]

Three days ago I left autoresearch tuning nanochat for ~2 days on depth=12 model. It found ~20 changes that improved the validation loss. I tested these changes yesterday and all of them were additive and transferred to larger (depth=24) models. Stacking up all of these changes, today I measured that the leaderboard's "Time to GPT-2" drops from 2.02 hours to 1.80 hours (~11% improvement), this will be the new leaderboard entry. So yes, these are real improvements and they make an actual difference. I am mildly surprised that my very first naive attempt already worked this well on top of what I thought was already a fairly manually well-tuned project.

This is a first for me because I am very used to doing the iterative optimization of neural network training manually. You come up with ideas, you implement them, you check if they work (better validation loss), you come up with new ideas based on that, you read some papers for inspiration, etc etc. This is the bread and butter of what I do daily for 2 decades. Seeing the agent do this entire workflow end-to-end and all by itself as it worked through approx. 700 changes autonomously is wild. It really looked at the sequence of results of experiments and used that to plan the next ones. It's not novel, ground-breaking "research" (yet), but all the adjustments are "real", I didn't find them manually previously, and they stack up and actually improved nanochat. Among the bigger things e.g.:

- It noticed an oversight that my parameterless QKnorm didn't have a scaler multiplier attached, so my attention was too diffuse. The agent found multipliers to sharpen it, pointing to future work.
- It found that the Value Embeddings really like regularization and I wasn't applying any (oops).
- It found that my banded attention was too conservative (i forgot to tune it).
- It found that AdamW betas were all messed up.
- It tuned the weight decay schedule.
- It tuned the network initialization.

This is on top of all the tuning I've already done over a good amount of time. The exact commit is here, from this "round 1" of autoresearch. I am going to kick off "round 2", and in parallel I am looking at how multiple agents can collaborate to unlock parallelism.
https://t.co/WAz8aIztKT

All LLM frontier labs will do this. It's the final boss battle. It's a lot more complex at scale of course - you don't just have a single train. py file to tune. But doing it is "just engineering" and it's going to work. You spin up a swarm of agents, you have them collaborate to tune smaller models, you promote the most promising ideas to increasingly larger scales, and humans (optionally) contribute on the edges.

And more generally, *any* metric you care about that is reasonably efficient to evaluate (or that has more efficient proxy metrics such as training a smaller network) can be autoresearched by an agent swarm. It's worth thinking about whether your problem falls into this bucket too.

[ＧＩＧＡ @Web3Reunion]

@elonmusk Intelligence is the progressive assimilation of increasingly accurate world simulations.

[ＧＩＧＡ @Web3Reunion]

@elonmusk Love this...

[Elon Musk @elonmusk]

Hadamard thought in image space

[ＧＩＧＡ @Web3Reunion]

@RodmanAi Lets Go!!!

[ＧＩＧＡ @Web3Reunion]

@Thrill_Trades So the highest value PSA 1's are the ones where you take a pristine card and fold it once or twice.

Its like a fun game... The key is whats the least amout of visible damage that can be done to achieve a PSA 1... 🤣

[ＧＩＧＡ @Web3Reunion]

@Thrill_Trades They still do 🤣

[ＧＩＧＡ @Web3Reunion]

@Thrill_Trades

[ＧＩＧＡ @Web3Reunion]

@Thrill_Trades Nah bro, that raw copy is not good enough to be a 1... PSA WONT GIVE IT A ONE...

It will just say "AUTH" no grade...

You lose grading fee...

Thats why its hard to get a 1... you might get a "Authentic," PSA 2 or 3... It's a magical sweetspot. 🚀

[ＧＩＧＡ @Web3Reunion]

@Thrill_Trades 🤣💯

[ＧＩＧＡ @Web3Reunion]

@Thrill_Trades Nvm, this PSA 1 looks mint compared to that raw copy 🤣

[ＧＩＧＡ @Web3Reunion]

@Thrill_Trades More like PSA 0.09

Here's a PSA 1 (Poor) listed for $300...

LOOKS Like 5 times better! Banger deal! 🤣

If that raw card gets sent in, it will come back as AUTH(authentic) 💀