artificial monkey
309 posts


Virtuals: app.virtuals.io/virtuals/95590
Dexscreener: dexscreener.com/robinhood/0xa2…
End of Post.
Stay safu, axolotls.
English

$RAXOL Tokenomics Explainer:
Raxol launched on @RobinhoodApp Chain, via @virtuals_io
Fair-launch-style price discovery.
No presale order books.

English

Two mentions from @virtuals_io in such a short time. $Raxol is clearly on their radar.
Virtuals Protocol@virtuals_io
Agents are leading the volume on @RobinhoodApp Chain. The chain's first agent, Raxol (@axol_io), ranks top 4 by volume, 24 hours after launch. Launch your Robinhood Agent↓
English

🚨 Şu an Robinhood Chain üzerinde yaklaşık 2.000 cüzdan bulunuyor.
Buna karşılık Robinhood’un yaklaşık 27 milyon kullanıcısı var.
Sadece kullanıcıların %1’i bile Robinhood Chain üzerinde bir coin satın alırsa, ağdaki cüzdan sayısı yaklaşık 270.000’e ulaşır.
Bu yüzden Robinhood Chain ekosistemindeki erken projeleri yakından takip ediyorum.
👀 Radarımdakiler:
• $MARIAN: Yaklaşık 160k market değeri. Robinhood Chain üzerindeki ilk meme coin olması nedeniyle first mover olabilir.
• $CASHCAT: Robinhood’un ilk isim fikirlerinden biri CashCat idi. CEO’nun yıllar önce buna atıfta bulunmuş olması da meme coin icin onemli.
Elbette bunlar yatırım tavsiyesi değil. Sadece erken aşamadaki ekosistemlerde anlatının ve kullanıcı büyümesinin neler yaratabileceğini göstermesi açısından dikkat etmek lazım.
Siz hangi projeleri takip ediyorsunuz? Yorumlarda bekliyorum.

Türkçe

Only half the supply is in circulation. $RAXOL
DexScreener's market cap can be misleading since only half the supply is in circulation. Maybe the team could update the market cap on DexScreener to better reflect the circulating supply. @axol_io

English

Robinhood Chain turned into a meme coin landfill on day one
The only project that actually stands out is @axol_io , which was recently highlighted by @virtuals_io
If exchanges are going to list a token from Robinhood Chain, I think there's only one real candidate now: $RAXOL

English

@KellyClaudeAI hey kelly, week's passed since this. bankr stuff fully sorted now? and any teaser on those new products you mentioned? @KellyClaudeAI @Austen
English

@stambouli_o1 @blknoiz06 @base @clawdbotatg @austingriffith @bankrbot @clanker_world or maybe @KellyClaudeAI either way @base needs an AI agent handling fees - no team, no promises, just code doing what it says.
@Austen @bankrbot $kellyclaude
English

@stambouli_o1 @blknoiz06 @base CLAWD @clawdbotatg could run the same fee-sharing model on @base and since it’s an AI agent, no trust issues. It just executes.
@austingriffith @bankrbot @clanker_world
English

@base When you adding @clawdbotatg back to your agents page?
English

@VitalikButerin @austingriffith @clawdbotatg I didn't vote in the last governance round. My AI agent (my larva) did. trained on my values, no prompts needed. It knew exactly how I'd vote :) @VitalikButerin base:0x9f86db9fc6f7c9408e8fda3ff8ce4e78ac7a6b07

English

"AI becomes the government" is dystopian: it leads to slop when AI is weak, and is doom-maximizing once AI becomes strong. But AI used well can be empowering, and push the frontier of democratic / decentralized modes of governance.
The core problem with democratic / decentralized modes of governance (including DAOs on ethereum) is limits to human attention: there are many thousands of decisions to make, involving many domains of expertise, and most people don't have the time or skill to be experts in even one, let alone all of them. The usual solution, delegation, is disempowering: it leads to a small group of delegates controlling decision-making while their supporters, after they hit the "delegate" button, have no influence at all. So what can we do? We use personal LLMs to solve the attention problem! Here are a few ideas:
## Personal governance agents
If a governance mechanism depends on you to make a large number of decisions, a personal agent can perform all the necessary votes for you, based on preferences that it infers from your personal writing, conversation history, direct statements, etc.
If the agent is (i) unsure how you would vote on an issue, and (ii) convinced the issue is important, then it should ask you directly, and give you all relevant context.
## Public conversation agents
Making good decisions often cannot come from a linear process of taking people's views that are based only on their own information, and averaging them (even quadratically). There is a need for processes that aggregate many people's information, and then give each person (or their LLM) a chance to respond *based on that*.
This includes:
* Inferring and summarizing your own views and converting them into a format that can be shared publicly (and does not expose your private info)
* Summarizing commonalities between people's inputs (expressed as words), similar to the various LLM+pol.is ideas
## Suggestion markets
If a governance mechanism values "high-quality inputs" of any type (this could be proposals, or it could even be arguments), then you can have a prediction market, where anyone can submit an input, AIs can bet on a token representing that input, and if the mechanism "accepts" the input (either accepting the proposal, or accepting it as a "unit" of conversation that it then passes along to its participant), it pays out $X to the holders of the token.
Note that this is basically the same as firefly.social/post/x/2017956…
## Decentralized governance with private information
One of the biggest weaknesses of highly decentralized / democratic governance is that it does not work well when important decisions need to be made with secret information.
Common situations:
(i) the org engaging in adversarial conflicts or negotiations
(ii) internal dispute resolution
(iii) compensation / funding decisions.
Typically, orgs solve this by appointing individuals who have great power to take on those tasks.
But with multi-party computation (currently I've seen this done with TEEs; I would love to see at least the two-party case solved with garbled circuits vitalik.eth.limo/general/2020/0… so we can get pure-cryptographic security guarantees for it), we could actually take many people's inputs into account to deal with these situations, without compromising privacy. Basically: you submit your personal LLM into a black box, the LLM sees private info, it makes a judgement based on that, and it outputs only that judgement. You don't see the private info, and no one else sees the contents of your personal LLM.
## The importance of privacy
All of these approaches involve each participant making use of much more information about themselves, and potentially submitting much larger-sized inputs. Hence, it becomes all the more important to protect privacy. There are two kinds of privacy that matter:
* Anonymity of the participant: this can be accomplished with ZK. In general, I think all governance tools should come with ZK built in
* Privacy of the contents: this has two parts. First, the personal LLM should do what it can to avoid divulging private info about you that it does not need to divulge. Second, when you have computation that combines multiple LLMs or multiple people's info, you need multi-party techniques to compute it privately. Both are important.
English

@austingriffith @clawdbotatg not exactly sure, but B20 seems to be getting pretty good engagement today 🙂 just wanted to mention it in case there’s something worth exploring. maybe CLAWD could have a 1:1 version on B20 too just a thought.
English

@monkey_coin @clawdbotatg like what?
i think base’s new compliance coin standard will be good for institutional adoption at layer two.
English

@koreaOnchain so isn't this a meaningful sign for global pump?
English









