PANews has published a market analysis by @brucexu_eth, founder of LXDAO and ETHPanda, built around more than 100 public posts tied to Robinhood activity on a new chain. The article collects examples ranging from meme-coin trade screenshots and liquidity provision writeups to arbitrage tools and post-mortems from accounts that later went to zero, then uses them to break down what kinds of opportunities tend to appear when a chain is still in its earliest phase.
The author says some of the published outcomes are eye-catching: one trader reported tokens that returned more than 20x and more than 50x, another said LP activity turned $1,000 into $50,000 in five days, and another used 200U to test a spread trade and made about 2U in a day. He adds that these are public self-reports, not average returns for ordinary users, and says they are more useful when viewed together: one person captures token upside, another earns fees, and another avoids directional bets altogether and focuses on the gap between two markets.
Who gets paid on a new chain, and where the money comes from
The article starts with a first-principles view of a single trade. A user bridges funds in, opens a trading page, and buys a meme token. Behind that simple action sits a stack of services, each of which requires capital, technical work, or operational support, even if they do not all charge the trader directly.
The author describes the map as a general role chart rather than a fixed template for every chain. Running a full node does not automatically mean access to block revenue. RPC services need customers, and the right to participate in sequencing or validation differs by network. For ordinary participants, the most accessible roles sit in the middle of the stack: buying tokens, providing liquidity, and arbitraging price differences.
He argues that money in this system does not appear out of nowhere. Traders pay fees to execute, LPs take on price exposure in exchange for fee income, and arbitrageurs can profit when pools have not yet moved into line. A trade that helps an arbitrageur is not always favorable for an LP. Platforms and tools also collect fees from the activity around them, while applications face RPC and data costs, trades consume gas, and lower-level operators still need to cover servers and settlement.
That helps explain why early opportunities can cluster on a fresh chain. Attention and capital may arrive quickly, but pools remain shallow, market-making capital is still thin, and arbitrage scripts and tooling are not fully in place. Early entrants can end up with a larger share of liquidity, or catch spreads that have not yet been flattened. As more participants bring both money and tools, the same trading volume gets divided among more LPs and obvious pricing gaps disappear faster.
The framing in the piece is simple: when someone posts a big gain, ask whether the profit came from good selection, from supplying capital, or from the fact that someone else had not yet built or executed part of the market properly. The author also says that the initial source of the whole cycle is still meme buyers, trading activity and market makers. If participants make money, more people join and the loop expands. If it turns the other way, the same system can slide into a downward spiral, which is why entry and exit timing still matter.
Buying meme coins: easy to do, hard to repeat successfully
Buying meme coins is presented as the simplest path mechanically: buy a token and sell later if the price rises. The difficult part is not clicking the trade button. It is deciding what to buy, when to enter, and who may still come in after you.
The article warns that, based on data analysis and the fee extraction logic across the broader ecosystem, most people who buy meme coins lose money while only a minority come out ahead. That makes the participation decision itself worth thinking through before placing trades.
It describes two common approaches. One is to chase fresh launches and compete for the earliest transactions. The other is to wait until a token already has some traction, then study its narrative, platform and mechanics to judge whether attention and capital may continue to build. The first leans harder on speed. The second leans harder on filtering and selection. Both still depend on managing exits.
The author lists several details that are worth checking: whether a new narrative is producing real buying and selling rather than just social chatter; whether higher volume reflects broader participation or remains concentrated in a small number of addresses; and whether a position can actually be sold in tranches, because market cap shown in a small pool can differ sharply from the amount that can really be taken out.
As one example, the article cites a public post from 菜狗, who said he bought raido for a 26x return and orbio for a 56x return on Robinhood. The author says that trader watched mechanisms such as hooks, agent trading, and buyback-and-burn designs in order to find themes that had already attracted funds but, in his view, had not yet received enough attention. The same post also mentioned later rug events and positions that went straight to zero.
The point, the author says, is not that everything in an account can suddenly climb dozens of times. The result only shows how large the upside range can be. He adds that when he reads this kind of post, he is more interested in why the trader bought and how the trader sold. He also cautions that most such threads deserve skepticism because many are designed to pull traffic or promote tools.
Entry is easy, he writes. A wallet is enough. Repeating gains is much harder, because judgment, sizing and selling all matter. He points readers to GMGN at https://gmgn.ai/r/TSMPGcI4 as a starting point for market tracking, trade and position distribution, wallet monitoring and strategy analysis.
LP strategies: fee income comes with asset risk
The article then turns to LPs, or liquidity providers. In a meme/stablecoin pool, users deposit assets so others can trade against them and then receive a portion of the fees under the pool’s rules.
The difference from simply buying a token is that LPs do not need the asset to go up in a straight line for the position to produce income. Back-and-forth trading can generate fees. But LPs still hold the assets in the pool. If a meme token keeps falling, the LP can end up holding more and more of that meme asset. Fees may come in while principal erodes.
The author argues that LP work involves a large amount of judgment, starting with a few basic questions: Will this token continue to trade? Are recent hourly volume and heat trends still moving in a direction that supports the next position? How much liquidity is already sharing the fees? In concentrated liquidity, how much of that liquidity is actually effective near the current price rather than just part of the headline TVL? What range should the capital sit in? A wide range covers more prices, while a narrow range concentrates capital but can be pushed out more quickly. And if the token falls and the pool converts the position into more of that token, is the LP willing to keep holding it?
The article notes that common styles include two-sided LP, one-sided positions and narrow ranges. A one-sided setup does not mean price risk disappears simply because the deposit starts in stablecoins. It can place one asset outside the active range and then gradually convert once price moves into the interval.
One case cited is a writeup from skolmbeagh, who said five days of activity turned $1,000 into $50,000 across 190 positions with a win rate of about 63%. The author says the $50,000 figure is quoted as the poster stated it and should not be taken at face value if it cannot be checked directly on-chain. What matters more is how the trader operated.
Those operating details include using Fomo App and GMGN to track heat, trading volume and momentum, focusing mainly on two-sided LP positions in tokens where trading was expected to continue, entering suitable pools early before more liquidity arrived, keeping each position within a familiar size, and moving profits out regularly instead of scaling risk endlessly just because the account balance was rising. The post also said the work was not passive, requiring more than 12 hours a day, and that later conditions worsened as rug events increased and losses in two-sided positions became more visible.
The Chinese-language examples in the article include spark888, who shared experience around token selection, hourly trading volume, pool screening and APR before deciding on ranges. The post mentions HOTDOG and MOO, and also points to JINQIAN, which briefly showed very high APR before later going to zero. Another example from 0xyunss discusses one-sided range methods and includes a claimed gain of about $38,000, though the author again says the value is less important than the operating logic.
High APR, he writes, is not unusual in this phase. But attention and the amount of money already sitting in pools can both shift fast. These are not positions to treat as one-year holdings. His conclusion is that LP participation carries a medium entry barrier, since it requires understanding pools, ranges and asset ratios. On a new chain, active LP management is time-intensive and difficult, making it more suitable for people willing to study trading flow and adjust positions repeatedly.
The tools named in the piece include Krystal for checking pools and building and managing positions, as well as an open-source LP tool shared by @Labrin. For LP pools themselves, the article points readers to Uniswap at https://app.uniswap.org/pool.
Arbitrage: linking prices that have not yet lined up
The arbitrage section focuses on situations where the same asset, or a matching exposure, trades at different prices in two markets. The goal is to buy on the cheaper side and sell on the more expensive side, capturing the spread.
The author gives the example of a token trading in two pools. If one pool has just been pushed higher by a large order and the other has not reacted yet, a short-lived opportunity may appear. He adds that similar strategies also exist between spot and futures, or between two futures markets, where traders also need to account for basis, funding and margin.
One public case came from Chosmos110, who tracked the spread between Robinhood Lighter and Lighter. According to the article, that trader used 200U of principal for a test on the previous day and earned about 2U, while also publishing the tool used. The author stresses that this was a small-capital test and belongs to a completely different category from meme-coin screenshots showing tens of times in return.
The article reduces the logic to several checks. The comparison should use executable bid and ask prices, not just the latest prints on two interfaces. Fees, slippage and funding costs on both sides need to be included before deciding whether any spread remains worth trading. Capital should already be positioned where it needs to be and both legs must be able to execute quickly, because filling only one side leaves a directional position behind. And after the trade, the operator still needs to think about the next one. If inventory builds up on one side after moving assets across venues, the rebalancing cost becomes part of the strategy.
New chains attract arbitrage activity because liquidity is fragmented and many participants still lack the necessary capital placement and tools. But the article says seeing an opportunity and successfully executing it are not the same thing. Once more mature teams arrive, the demands on speed and cost only rise.
That means the entry barrier is moderate for manual monitoring, but high for automation, which adds technical and operational complexity and ties up capital on both sides. Compared with trying to guess whether a token will go up, this path depends more on conditions that can be checked repeatedly, though it also involves many practical execution problems. The article names Taoli.Tools from @aliez_ren as a tool for observing and handling two-sided trades, and mentions an open-source repository linked to that case for research purposes, while noting the code had not been audited by the author.
Platforms, issuer revenue and developer tools
The article also points to another group of participants who do not spend their time watching charts directly. They build launch platforms, trading terminals, LP tools, data services or RPC infrastructure. Some charge on a per-trade basis, some run on subscription, and some share revenue from token issuers under platform rules.
As an example, the author cites a public post from a creator who said launching a token on Noxa generated creator fees. He notes that this type of income comes from issuer revenue-sharing and is different from holding a platform token or running LP positions.
In his view, these businesses look more like product operations than trading. Technical ability is only the starting point. The harder part is getting other people to use the product and pay for it. The combined barrier is often higher than manual trading, but code, users and integrations built in one cycle may remain useful on the next chain as well. The article does not go deeper because this sits farther from the main path for ordinary participants.
What can carry over to ARC
The second major shift in the article is toward ARC, which the author says is the next chain worth preparing for. Once the different styles of participation are split apart, the transferable elements become more concrete.
For meme trading, what carries over is the habit of finding assets, studying mechanics, tracking capital and scaling out in stages, not simply buying the same names from a prior chain again. For LPs, the transferable work is in monitoring trading volume and effective liquidity, choosing ranges, managing positions and recording final profit and loss. For arbitrage, the carryover lies in data capture, cost calculation, two-sided execution and inventory management, then reconnecting those systems to new venues after moving chains. For tool builders, existing experience in wallet interaction, data processing and user demand can reduce the need to rebuild everything from scratch.
The author says ARC’s architecture still needs more study and that he is continuing to research it. He points readers to his X account, @brucexu_eth, for later updates.
He also notes that ARC’s official introduction highlights stablecoin financial applications, EVM compatibility and USDC gas. That gives a few clues for preparation: watch stablecoin-related onramps, trading and lending scenarios, and get familiar with the network and assets early. Whether ARC will be kick-started by meme activity is still unclear, he writes, but assets such as USDC can be prepared in advance.
First step on a new chain: prepare the bridge before the launch rush
For anyone whose funds are still on other networks, the article says the first unavoidable issue is cross-chain access. Opportunities may be short-lived. If users wait until the last minute to find a bridge, swap assets and prepare gas, they can lose the key window before they even reach the market. The author says bridge liquidity, reliability of settlement and exit routes all deserve advance review.
He recalls that when Robinhood first picked up, he saw large flows crossing over through LI.FI routes in the LI.FI backend. People who had already prepared their funding paths were trading while later arrivals were still figuring out how to move money in.
That observation leads to two recommendations in the article: Jumper at https://jumper.xyz/ and LI.FI at https://li.fi/. The reasons listed are specific. LI.FI was founded in 2021, giving it a longer track record on security and technology. At launch, many unproven bridge tools appear, and some can be phishing or scam routes. LI.FI, the author says, typically supports major new chains on day one. Its aggregation model can also help route users toward better liquidity and pricing at a time when liquidity is highly fragmented. LI.FI offers APIs for automation, while Jumper acts as LI.FI’s frontend and provides manual bridging with 0 platform fees.
The logic presented is straightforward: if provider A has no liquidity and provider B does, then a user who knows only A misses the route. A user who knows both still has to open two pages and test them separately. With Jumper and LI.FI, those paths are aggregated into one workflow.
For manual users, the article says Jumper combines bridging and swapping in one interface. Users select the source chain, destination chain and target asset, enter an amount, and compare routes, estimated receipts, fees and time. The author tells readers to use the official site, https://jumper.xyz/, and suggests a small test first: see which route an existing asset can take, what asset arrives on the destination side, what the minimum received amount, costs and expected timing look like, how gas should be prepared on the target chain, and how funds could later be withdrawn.
For users with development experience, the article says LI.FI can be connected directly in scripts or internal tools. The rough process starts with registering in the Partner Portal and creating an API key through the console flow. Basic endpoints can be tested without a key, while higher rate limits are tied to the listed plans. The key should be kept on the backend and not pushed to a public repository, and it is only shown once. From there, users can call /v1/chains and /v1/tokens to locate chains, token addresses and decimals, then pass source chain, destination chain, source asset, destination asset, amount and a public wallet address to /v1/quote or /v1/advanced/routes to fetch pricing. The routes, minimum received amount, costs and expected time can be displayed before execution. The execution stage then adds approval checks, wallet confirmation and signing, followed by /v1/status to track settlement on the destination chain.
The official API documentation linked in the article is https://docs.li.fi/api-reference/introduction. The author adds that the docs can be handed to a coding agent and says LI.FI also provides a dashboard for managing and viewing data.
ARC public materials: mainnet not open yet
Because ARC has not fully launched, the article ends with a list of public materials already collected for research. These include network and wallet connection details, with the reminder to rely on official network parameters and distinguish mainnet from testnet, while the mainnet is not open at present; Circle test token faucet resources and an Arc testnet explorer for practicing balance and transaction checks; official contract addresses for verifying assets; RPC services and data indexing options for pool queries, event reads and analytics; and notes on ARC’s differences from Ethereum, especially for developers handling USDC, gas and transaction records.
The final suggestion is to pick one direction and prepare both the tools and the funding route first. Pool selection, range design and exits for LPs, as well as how to wire up arbitrage scripts, are left for later installments.