@Web3MapInk / https://xitter.cf/t.co/JtAmKTijLJ / @DecipherGlobal / Medium: https://xitter.cf/t.co/hmlGbLIBzG

Joined April 2022
Just applied for the @IdentityUnits drop whitelist. 787 NFTs created by an AI agent swarm. One agent creates, another reviews. Raffled across the next drops. identityunits.fun/claim IUEF958E
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Crime szn. At this point, catching just one opportunity a month can pay for 10 years of @Web3MapInk lol.
Web3 Map was the first to detect the $AGIX, $WMTX, and ethereum:0xf0d33beda4d734c72684b5f9abbebf715d0a7935 exploits. For $AGIX, the Binance Convert route created an opportunity to turn roughly $100 into $7K per account. More than 10 Web3 Map users profited from the opportunity, generating over $100K in total value. The route was: ETH AGIX → Deposit to Binance → Convert AGIX to FET → Sell & Withdraw Last month, Web3 Map users captured opportunities worth $10K–$100K through events such as the Cronos USDC depeg. (xitter.cf/Web3MapInk/status/2095…) Today, AGIX created another opportunity worth $7K+ per account. Web3 Map costs $200/month. Congratulations to everyone who caught it. Trade recap from one of our subscribers:(t.me/FullSeed100gongsu/200)
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nananaa.eth retweeted
Web3 Map was the first to detect the $AGIX, $WMTX, and ethereum:0xf0d33beda4d734c72684b5f9abbebf715d0a7935 exploits. For $AGIX, the Binance Convert route created an opportunity to turn roughly $100 into $7K per account. More than 10 Web3 Map users profited from the opportunity, generating over $100K in total value. The route was: ETH AGIX → Deposit to Binance → Convert AGIX to FET → Sell & Withdraw Last month, Web3 Map users captured opportunities worth $10K–$100K through events such as the Cronos USDC depeg. (xitter.cf/Web3MapInk/status/2095…) Today, AGIX created another opportunity worth $7K+ per account. Web3 Map costs $200/month. Congratulations to everyone who caught it. Trade recap from one of our subscribers:(t.me/FullSeed100gongsu/200)
Web3 Map caught the price move on @CronosApp , and then detected the USDC <-> USDT depeg that followed faster than anyone else. (~12 sec) During that window, Web3 Map subscribers made anywhere from $10K to over $300K. (The depeg lasted for roughly an hour.) The route was: ETH USDC → CCTP Bridge → Cronos USDC → Cronos USDT → Sell on Crypto.com → ETH USDC Users repeatedly bridged and executed the arbitrage while the opportunity remained open. Then Cronos halted and blocks were rolled back. Because the CCTP burn transaction remained while the corresponding mint transaction disappeared after the rollback, users who repeated the bridge multiple times were able to multiply their returns by the number of completed cycles. 5 cycles = roughly 5× principal + additional USDC / USDT arbitrage profit. Most subscribers who participated made more than $10K. Several accounts made over $100K. The top results exceeded $300K. This is exactly why speed matters. Web3 Map detected the opportunity in ~12 seconds and gave subscribers enough time to execute it repeatedly before the window closed. Telegram alerts are available to Pro users. Congratulations to everyone who caught it. Subscribe to Web3 Map and catch the next one before the rest of the market does. Trade recap from one of our subscribers: t.me/dunstearth/20
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nananaa.eth retweeted
Web3 Map caught the price move on @CronosApp , and then detected the USDC <-> USDT depeg that followed faster than anyone else. (~12 sec) During that window, Web3 Map subscribers made anywhere from $10K to over $300K. (The depeg lasted for roughly an hour.) The route was: ETH USDC → CCTP Bridge → Cronos USDC → Cronos USDT → Sell on Crypto.com → ETH USDC Users repeatedly bridged and executed the arbitrage while the opportunity remained open. Then Cronos halted and blocks were rolled back. Because the CCTP burn transaction remained while the corresponding mint transaction disappeared after the rollback, users who repeated the bridge multiple times were able to multiply their returns by the number of completed cycles. 5 cycles = roughly 5× principal + additional USDC / USDT arbitrage profit. Most subscribers who participated made more than $10K. Several accounts made over $100K. The top results exceeded $300K. This is exactly why speed matters. Web3 Map detected the opportunity in ~12 seconds and gave subscribers enough time to execute it repeatedly before the window closed. Telegram alerts are available to Pro users. Congratulations to everyone who caught it. Subscribe to Web3 Map and catch the next one before the rest of the market does. Trade recap from one of our subscribers: t.me/dunstearth/20
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FUD The claim that NOXA can turn @cashcat_token (cash-cat:native) into a honeypot is not supported by the token bytecode. There is an important technical distinction being missed in the claim that the NOXA developer can turn cash-cat:native, $JUGGERNAUT, and every other NOXA-launched token into a honeypot simply by changing a fee parameter from 0 to 100. I reviewed the contract creation code and deployed runtime bytecode provided for the CASHCAT token at: 0x020bfc650a365f8bb26819deaabf3e21291018b4 Based on that bytecode, the claim is not technically supported, at least as it relates to CASHCAT’s token contract. The CASHCAT runtime does not appear to contain an administrator-controlled function for changing buy or sell taxes. There is no identifiable setter for a transfer fee, no blacklist management function, no pause switch, no trading-enabled flag, no function that can disable sales, no runtime minting path, and no function for changing a fee recipient. The token is also not an upgradeable proxy. The runtime does not contain a DELEGATECALL-based implementation mechanism, meaning the developer cannot simply replace the existing transfer logic with a malicious implementation later. The token’s core transfer behavior is effectively equivalent to: balances[from] -= amount; balances[to] += amount; emit Transfer(from, to, amount); There is no visible logic equivalent to: fee = amount * feeRate; balances[feeRecipient] += fee; balances[to] += amount - fee; There is also no additional token transfer to a developer wallet, treasury, launch factory, burn address, or other fee recipient. In other words, the CASHCAT token itself does not appear to implement a mutable transfer tax. The most important issue is whether CASHCAT consults the NOXA factory during every transfer and reads a mutable platform-controlled fee or permission setting. The provided runtime bytecode does not appear to do that. The relevant external read call is effectively a DEX factory lookup: dexFactory.getPool( address(this), pairToken, poolFee ); This call is used to determine the token’s liquidity-pool address. It supports the temporary anti-whale restrictions that applied during the initial launch period. It does not appear to query NOXA for a current tax rate, blacklist status, sell permission, trading-enabled flag, or global honeypot setting. The NOXA launch factory address is embedded in the CASHCAT bytecode, but embedding an address is not the same as giving that address ongoing control over the token. No call path was identified in which CASHCAT asks the NOXA factory what tax rate to charge or whether a holder should be allowed to sell. This means that changing a separate NOXA parameter from 0 to 100 does not automatically alter CASHCAT’s ERC-20 transfer logic. To prove otherwise, someone would need to demonstrate a direct execution path connecting that mutable NOXA parameter to CASHCAT’s transfer function. No such path is visible in the provided runtime bytecode. The technically accurate conclusion is therefore: Based on the provided CASHCAT deployed runtime bytecode, no mechanism was identified that would allow the NOXA developer to later impose a 100% sell tax, blacklist holders, pause transfers, mint additional supply, or upgrade CASHCAT into a honeypot. Accordingly, the claim that changing a NOXA fee parameter to 100 would automatically turn CASHCAT and all other existing NOXA tokens into honeypots is, at least for CASHCAT, technically unverified and unsupported by the token’s bytecode.
太尼玛草台班子了吧。 ‼️ NOXA 的 dev 有能力把 $CASHCAT $JUGGERNAUT 以及所有 NOXA 发射的代币变成 “貔貅盘” ! 今天, NOXA 首先费用合约从 25 → 0 (dev设置) 也就是, 停止了手续费的收取。 但是如果 DEV 将手续费设置, 从 0 -> 100 是的 , 所有 NOXA 代币会变为貔貅盘。 还能吃到这瓜? 那看看现在是什么情况 ?? ⬇️ ------ NOXA 留下的市场谁有机会迅速抢占 。 关于 NOXA 关闭事件, 等了 2 天也没个结果。 实际调查了一下, 基本上凉了。 这种分赃不均,权利分散的结构, 没赚钱还好,赚钱了就不好分了。 简单讲: 一个有权 , 一个有钱 。 🥷 A : dev.noxa.eth 主导了项目的开发, 智能合约的部署, 费用协议的设置,收费地址的设定。 ⚠️ 但没做两件事: 权限交接, 合约开源。 谁也没想到, 项目突然火了。 🧓 B : treasury.noxa.eth 该钱包是现在手续费的实际获益人, 每天躺着收钱, 目前已经收了 $14M 了。 也许,B 也控制着: @Noxa_Fi 的官方账号、 官方网站、服务器等项目入口面向客户端的基础设施。 ---- 现在的情况,就是 A 看到 B这么能赚, 想要点钱分分。 B 不给, A 把合约发币功能关了。 B 还不给, A 把合约手续费改为了 0 。 A 和 B 僵局中。 A 留了个壳 : 对外公布, 垃圾代币太多了,我们先停了。 B 控制着合约层面( 合约未开源,也许还能升级合约 ) 对于已发布的代币, 很多都在这套合约上发布的,A现在 单纯的替换新合约, 旧的代币又没办法处理。 (也许没有开发能力,也许是正在招人去开发, 也许合约正在审核中😂 ) 据说: 这位优秀的 DEV 是 t.me/AmunPhantom
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The liquidity pool cannot be converted into a 100% fee honeypot either The second part of the allegation concerns the liquidity pool. CASHCAT trades through a standard Uniswap V3-style CASHCAT/WETH pool using the 1% fee tier. In Uniswap V3, the pool’s trading fee is selected when the pool is created. It is part of the pool’s immutable configuration. That means the existing pool cannot later be changed from 1% to 100% by the NOXA developer, the token creator, or the fee recipient. There is no administrator function on a standard Uniswap V3 pool that allows someone to rewrite the pool’s fee tier after deployment. To use a different fee tier, someone would have to create an entirely separate pool. That would not modify the existing CASHCAT/WETH pool, and holders would not automatically be forced to trade through the new one at the smart-contract level. Therefore, the claim that the developer can turn the existing 1% CASHCAT pool into a 100% fee pool is technically incorrect. A protocol-fee setting is not the same as changing the pool’s trading fee Uniswap V3 does have a separate concept commonly referred to as a protocol fee. This does not change the total trading fee charged to users. Instead, it determines how the pool’s already-existing trading-fee revenue is divided between: - Liquidity providers - A protocol or designated fee recipient For example, if a pool charges a fixed 1% trading fee, a protocol-level setting may determine who receives portions of that 1%. It does not turn the 1% fee into a 25%, 100%, or arbitrary transfer penalty. This is the likely source of confusion surrounding the alleged 25 → 0 → 100 change. That setting may refer to: - The platform’s share of LP fee revenue - The creator’s share of LP fee revenue - The treasury’s share - A fee-claiming or revenue-distribution percentage Changing such a value can change who receives the fees. It does not necessarily change how much the trader pays, and it does not modify CASHCAT’s token transfer logic. Even if 100 means that one party receives 100% of the protocol’s distributable fee revenue, that is not the same as taking 100% of every user’s sale. Fee collection and fee generation are different things This distinction is essential: - The pool generates trading fees according to its fixed 1% fee tier. - The LP position earns those fees. - A locker or platform contract may control how the earned fees are claimed and distributed. - Changing the distribution setting may redirect revenue between the treasury, token creator, or platform. - It does not retroactively change the pool into a 100% fee pool. The alleged change from 25 to 0 may therefore have stopped or redirected NOXA’s share of fee revenue. It does not prove that the developer gained the ability to confiscate 100% of users’ tokens when they sell. The existing pool cannot be remotely rewritten into a honeypot A standard Uniswap V3 pool is not an upgradeable token contract controlled by the NOXA developer. The developer cannot simply add: - A sell blacklist - A 100% sell tax - A transfer pause - A wallet-specific trading restriction - A new arbitrary fee tier to the already-deployed pool. The pool executes its existing swap logic according to the code and immutable configuration with which it was deployed. This means the existing CASHCAT/WETH 1% pool cannot be “converted” into a honeypot merely through a NOXA revenue-setting change.
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Submitted the very first swap transaction immediately after the MAPO exploit. Below is a case-by-case breakdown of the maximum potential profit I could have captured from MAPO using @Web3MapInk pro (only $49/month) Using alerts + auto-buy, I bought MAPO first right after the exploit and acquired 453M MAPO for $300. The key question was how to exit the position. There were three main options: 1. Simple sell on the same chain (~75x) Based on the OKX quote, I could have received at least $25K, which is roughly a 75x return. However, since the OKX quote was not auto-refreshing, this may not have been the exact local top. 2. Bridge to BSC and sell (~420x) There was a swap transaction of around $3,300 for 1.1M MAPO:bscscan.com/tx/0xdf1422e5192… A simple calculation would value 450M MAPO at around $1.35M, but considering pool liquidity and price impact, that would likely have been impossible. Still, I believe capturing at least $150K+ (~420x) could have been realistic. 3. Bridge to MAPO and send to Bithumb (~840x–1,680x) This was likely the most profitable route. This wallet appears to have sent around 100M–200M MAPO to Bithumb and is currently estimated to have made over $300K in profit: debank.com/profile/0xe09cf8f… If I had sent my full position and the order book could absorb it — or if I had sold gradually — I believe $300K–$600K could have been possible, roughly 840x–1,680x. In most exploit events, bridges tend to close very quickly. Many wallets that bridged too late appear to have ended up with forced burns. For events like this, I believe the two most important factors are: 1. Buying faster than anyone else 2. Finding and using the bridge route immediately With personalized Telegram alerts delayed by at most 30 seconds, Web3 Map Pro can help users access this kind of information as quickly as possible.
Web3 Map detected the @MapProtocol exploit faster than anyone else. (~20 sec) Alerts were sent just 20 seconds after the exploit. Web3 Map creator @LHW0803 bought MAPO only 30 seconds after the first dump — the first buy transaction over $30 after the crash. (tx: etherscan.io/tx/0x9a38ad24e9…) He swapped $300 for 460M MAPO, which reached around 70x at the local DEX high. One of our users, @parnakr (Tg handle), used Web3 Map to find the bridge route and captured 5 figures+ in profit. Many other subscribers also captured the opportunity. Congrats to everyone.
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nananaa.eth retweeted
Web3 Map detected the @MapProtocol exploit faster than anyone else. (~20 sec) Alerts were sent just 20 seconds after the exploit. Web3 Map creator @LHW0803 bought MAPO only 30 seconds after the first dump — the first buy transaction over $30 after the crash. (tx: etherscan.io/tx/0x9a38ad24e9…) He swapped $300 for 460M MAPO, which reached around 70x at the local DEX high. One of our users, @parnakr (Tg handle), used Web3 Map to find the bridge route and captured 5 figures+ in profit. Many other subscribers also captured the opportunity. Congrats to everyone.
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nananaa.eth retweeted
We catch the price drop of @AsteroidCoinOG today Alerts went within 15 seconds. Chance of x6. Only 2 days left for the free pro event. Try web3map.ink
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nananaa.eth retweeted
Always picking 🟦
Everyone in the world has to take a private vote by pressing a red or blue button. If more than 50% of people press the blue button, everyone survives. If less than 50% of people press the blue button, only people who pressed the red button survive. Which button would you press?
42%Red
58%Blue
98,539 votes • Final results
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nananaa.eth retweeted
April 23, 2026 / Arbitrage Recap — cmETH (Mantle <-> Bybit) - Event Time: 23 Apr 01:15 ~ 09:20 - Detection Time: 23 Apr 01:15 - Cause: Possible market maker error - Route / Execution: Buy bybit - Sell Mantle Moe (or other dapp) pool: 0x38e2a053e67697e411344b184b3abae4fab42cc2 token: 0xe6829d9a7ee3040e1276fa75293bde931859e8fa - Expected Profit: ~1% x n times Apart from Web3 Map, few platforms track prices all the way to Mantle, and it appears that Bybit’s price remained artificially low due to a market maker error. The market maker on that exchange made a similar mistake on April 17, 2026 as well, so this pair warrants close monitoring. source: dexscreener.com/mantle/0x3d8…
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nananaa.eth retweeted
April 22, 2026 / Arbitrage Recap — wBTC (celo) - Event Time: 22 Apr 05:52:39 - Detection Time: 22 Apr 05:52:54 (+15 sec) - Cause: Fat finger dump 60k in the small tvl pool - Route / Execution: buy Uniswap in Celo pool: celoscan.io/address/0x57332c… token: celoscan.io/address/0x8aC290… Because the gap was caused by a simple large sell-off mistake, no bridge to another chain was required. - Expected Profit: +8.96% There was sufficient liquidity, it was possible to swap well over $1,000 during this event. At peak profitability, buying the 0.85 BTC sold in the fat-finger trade required an investment of around $63,000, with an expected profit of roughly $2,000. (The timestamp shown in the image is based on an unsynchronized server clock and may differ from the actual on-chain time.) source: dexscreener.com/celo/0x57332…
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nananaa.eth retweeted
Introducing @Web3MapInk — a platform for detecting cross-chain arbitrage opportunities and potential exploits. We detect opportunities across 10,000+ price sources. (Detected the USR and DOT exploits within 30 seconds.) Page: web3map.ink/ Learn more 👇
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nananaa.eth retweeted
Introducing Privacy Boost: onchain privacy SDK for enterprise. The first privacy offering for @Optimism. Now live on OP Mainnet. Enterprises need regulatory-friendly privacy.
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nananaa.eth retweeted
LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
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nananaa.eth retweeted
Claude code source code has been leaked via a map file in their npm registry! Code: pub-aea8527898604c1bbb12468b…
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nananaa.eth retweeted
A small thank you to everyone using Claude: We’re doubling usage outside our peak hours for the next two weeks.
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