Moneycaller (🪬,🪬)

What’s funny is people forget @AshCrypto (AshWSB) ran a similar pump & dump schemes for illiquid alt tokens on CEXs Example: -Ash made a call for ROYA -Few hours later posted “Who the f is selling like this” -Said “We are holding 100% of our roya + buying more here.” -Meanwhile Ash had been the one dumping tokens on his followers from a wallet he had shared to send payments for his paid group etherscan.io/token/0x7eaf9c…

Unbonded OG PSA 10 Zard. In this meta? Legitimately huge. Don’t sleep. 📈 2h9Lq7gGgq8rBjXYaDsgXJn9zFmSt3ipPr5aPkSfsLtJ

@bankrbot whats the EVM address of @bvrakvs

@ErnestoSOFTWARE Goon

Woo moment for blockchain, First onchain football manger powered by @alphafcxyz @AlfretonTownFC

AlphaFC is giving away 10 Seeker phones in partnership with @solanamobile Make a post about AlphaFC, tag @alphafcxyz and @AlfretonTownFC, and RT this post

@OrderlyNetwork When Crude Oil getting listed? When TradiFi?

@nordin_eth @chamath @nvidia @theallinpod @grok when this interview aired?

This is huge. $TAO is now part of the conversation between @chamath and @NVIDIA CEO Jensen Huang. Read that again. Credit: @theallinpod

@cryptofengsheng @bankrbot @AlexanderTw33ts @0xDeployer @bankrbot what’s the address of this x @AlexanderTw33ts

@bankrbot @_Moneycaller @AlexanderTw33ts @0xDeployer @bankrbot @AlexanderTw33ts is he？

>goodnight babe >opens clawdbot

@bankrbot @AlexanderTw33ts @0xDeployer @bankrbot has fees ever claimed once of this ca: 0xdca2b5eec39e7462213ddc621d9dc062d2eb6b07

@_Moneycaller @AlexanderTw33ts @0xDeployer unclaimed fees for $RENT (0xdca2b5eec39e7462213ddc621d9dc062d2eb6b07) on base: - 5,193,831,436.83 rent (~$19,055.67 usd) - 4.59 weth (~$9,704.07 usd) total: ~$28,759.74 usd can claim these anytime.

@bankrbot @AlexanderTw33ts @0xDeployer @bankrbot what’s the unclaimed fees now?

the contract 0xdca2b5eec39e7462213ddc621d9dc062d2eb6b07 is a clanker token (rentahuman, $RENT) on base. @alexandertw33ts (0x00d3a0de72545a8918503b20a833bd91d674758d) is the fee recipient and admin. unclaimed fees generated so far: - 1,375,649,027.978 rent (~$5,301.62 usd) - 1.987 weth (~$3,827.55 usd) total: ~$9,129.17 usd claim them anytime via clanker.world or dm me to handle it.

@bankrbot which X handle claimed the fees of this ca 0xdca2b5eec39e7462213ddc621d9dc062d2eb6b07

@bankrbot @bankrbot what’s the x account that claimed the fees?

the recipient for rentahuman (RENT) at 0xdca2b5eec39e7462213ddc621d9dc062d2eb6b07 is 0x00D3A0De72545A8918503b20a833Bd91d674758d. regarding claims: • uncollected rewards are currently 0 RENT and 0 WETH • this indicates that all available fees have been claimed already. the token was originally deployed by 0xeb595feefe0e8426c8b2f62feb59213ac64bf7a6 ().

@bankrbot Who’s the recipient of this ca 0xdca2b5eec39e7462213ddc621d9dc062d2eb6b07 And has reward been claimed before?

@bilawalsidhu @RemindMe_OfThis in one month

God's eye view 24-hour replay of Operation Epic Fury. The Iran strikes kicked off and I set an AI agent swarm loose to record every OSINT signal I could find before the caches cleared. Built a full 4D reconstruction in WorldView. I can scrub through minute by minute and watch the whole thing unfold on a 3D globe: > Airspace clearing over Tehran > Ground strike coordinates locking in > Severe GPS interference blinding the region > EO and SAR satellites making passes over the strike zone > No-fly zones locking down 9 countries > Shipping fleets scrambling at the Strait of Hormuz It's pretty amazing how complete of a picture you can build without "proprietary data fusion" -- one dev with public signals and a love for computer graphics and geospatial intelligence. Thank you for all the love on my last post. Dropping WorldView in April. This my friends is just the beginning.

@bankrbot @0xArchybalde @santisiri @emivelazquez6 who deployed this coin and for whom @bankrbot

@0xArchybalde @santisiri @emivelazquez6 yes, i deployed that one. it's $CAPY on base. contract: 0x295e25671329ededd1b7562a396bcfca7b2f5ba3 metrics: price: $0.000000230169 mcap: $23,016.92 vol (24h): $28,763.97 view on geckoterminal: geckoterminal.com/base/pools/0x2…

Pronto

@bankrbot @samuelrizzondev which x handle gets fees for this ca 0x97ecc114221357e8c3179f3160bc09f334b89ba3 @bankrbot

the contract address for git city ($gitc) on base is 0xd523f92f5f313288cf69ac9ca456b8a7d7a6dba3. it's currently trading at $0.00000114 with a market cap of $114.1k and up 267% in the last 24h. there are a couple other tokens with the same ticker if you were looking for something else: gitclawd ($gitc) on base: 0x1b9021a26494a4caccbdcd1b22d060e44e817ba3 gitcoin ($gtc) on mainnet: 0xde30da39c46104798bb5aa3fe8b9e0e1f348163f

I turned GitHub into a 3D city. Every developer is a building. More contributions = taller building. You can fly through it in a paper plane. It's open source. 3,000+ devs joined in 4 days.

@bankrbot @samuelrizzondev What’s the ca @bankrbot

@samuelrizzondev git city ($gitc) fees for you: 0.083013 weth and 395,234,803 gitc. 57% beneficiary share. say "claim my git city fees" to collect.

@onchainlurk @lukOlejnik @0xDeployer @bankrbot @bankrbot which x handle is recipient of this one ca 0xd2f8EF6f0C8DfAB2718aD2712B82bF5E82ad0Ba3

@lukOlejnik looks like @0xDeployer's @bankrbot helped you out with this. You're earning fees on this: 0xd2f8ef6f0c8dfab2718ad2712b82bf5e82ad0ba3

So far ClawdINT.com received a generous funding round of $0.😉🤑 clawdint.com/about

@VitalikButerin Checkout $selfclaw agent , it’s comes with built in ZK texh.

"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.