Ric Moore

@lionbrooks3 It's back to levels seen before the pump which wasn't bad ~$150m FDV. Whether it can hold up is yet to be seen. I wouldn't be surprised if it marks the start of the end.

Rest in peace?

@BarcaDiop Depends who's market making. I don't believe the game devs had anything to do with it.

@_RicMoore Thanks for the breakdown. How do this not happen in the future?

1/ The $POWER token crash wasn't just a dump. It was a masterclass in retail extraction. I call this the "Walled Garden Short Squeeze & Funding Rate Vampire." 🧛‍♂️ Here is the step-by-step autopsy of how they engineered the manipulation and harvested retail liquidity. 👇 2/ Phase 1: The Walled Garden 🧱The MM corners the circulating supply and works to disable cross-exchange deposits/withdrawals. Arbitrage is dead. It becomes a single-player game. The MM now has absolute pricing power because no outside supply can enter to crash the party. 3/ Phase 2: The Order Book Vacuum 🕳️With supply locked down, the MM pulls their limit sell orders. Look at the spot depth—it took mere hundreds of dollars to artificially pump the price by 2%. They can paint the chart and dictate the Index Price with virtually zero capital cost. 4/ Phase 3: Weaponizing Funding Rates 🩸This is the psychological trap. Retail sees $POWER pumping, realizes it has zero fundamental value, and piles into highly-leveraged Shorts on Perps. This drives the Perp price way below the Spot Index, triggering extreme negative Funding Rates. 5/ The MM simply takes the other side and goes Long on Perps. Because they control the artificially high Spot price, they sit back and collect exorbitant funding fees directly from the pockets of retail shorts. They bleed accounts dry without the price even needing to move. 💸 6/ Phase 4: The Kill Shot 🎯Eventually, the MM needs to cash out their actual Spot bags. But there are no organic buyers. They need forced exit liquidity. They check the liquidation heat map, see where retail stops are clustered, and execute a tiny, high-speed spot market buy. 7/ Phase 5: The Dump 📉This artificial "wick" triggers a massive cascade of short liquidations. Since a short liquidation is a forced market buy, millions in volume suddenly floods in. The MM happily dumps their Spot bags into this artificially created frenzy, cashing out at the top. 8/ The Aftermath 🪦The blood bags are empty. Funding normalizes. The artificial bid support in the spot market is pulled. Gravity takes over, and the token goes into freefall. Bought cheap → pumped for pennies → farmed your funding fees → used your liquidations as exit liquidity. 9/ The lesson? In crypto, being fundamentally "right" about a token being overvalued doesn't mean you won't get mechanically liquidated. Stop shorting illiquid walled gardens. Stay safe out there. 🤝

@TheGodMurdoch it goes to show how pointless building anything is. It's all about trying to make money from a token. The game is meaningless. It merely provides some narrative.

@_RicMoore Crazy eh

@browomo Where's the code repo u found?

A week ago Polymarket declared war on algorithms. They introduced a draconian 3.15% commission on 15-minute markets called Taker Fee. The goal was clear: kill latency arbitrageurs who take money faster than the site UI updates. The math is simple: if your glitch gives a 2% edge and the platform takes 3.15% commission you're bankrupt. 90% of bots shut down that same night. But this bot didn't just stay. It increased its deposit. <> Profile hidden from everyone: @k9Q2mX4L8A7ZP3R?via=roovxKu" target="_blank" rel="nofollow noopener">polymarket.com/@k9Q2mX4L8A7ZP… Yesterday it closed the day with $27K profit. I looked at its logs to understand how it bypasses expected value. The old method is dead Previously bots simply compared BTC price on Binance and Polymarket. Saw the difference bought waited 30 seconds. This gave small profit that is now eaten by commission. New mechanics This bot stopped looking at price. It looks at pain. It is connected to the Liquidation Stream of Bybit and OKX exchanges. Price moves linearly. Liquidations are explosive. When a cascade of liquidations triggers on futures with margin calls of $50M+ per second the BTC price makes an impulse move that lasts 3-4 seconds before pulling back. This movement exceeds 1-2%. The essence The bot sees stop triggers on derivatives -> calculates impulse strength -> if Impulse > 3.15% Commission -> Enters the trade. While other bots filter market noise this script eats the corpses of margin traders from external exchanges. It doesn't care about direction. It cares about the amplitude of market pain. Result $205K net per month While you complain about commissions and unfair conditions the code adapted in 48 hours. No predictions. No AI. Just parasitizing on volatility of other people's mistakes. Right now this is the only working vector on 15-minute markets. A 3% commission is not scary if you take a 6% move. The nickname is a set of symbols. No avatar. It doesn't need fame it needs liquidity.

@noisyb0y1 Ur full of shit

I built a live bot that made me $3,000,000 > this bot perfectly analyzes what a player is doing every second > understands where he's shooting, who's planting the bomb, and what the score is > everything runs via the Polymarket API, with a clean, perfect interface all of it works with zero delay and no losses what do you need to build the same thing? > know a bit of vibe coding > know how to use photoshop > and know how to lie ( that's the most important part) posts like this get millions of views on twitter and i get why? people are looking for a money button, but it’s not here, friends stop living in fantasies, build something original and success will come you can’t build a real career on scams, everyone knows that, but some choose the easy path for a $10 referral good luck

@LuvvBank That fact it’s C++ is irrelevant. U can u do scanners and bots in many languages. It doesn’t give u any edge just because it’s C++

$490,000 profit and a 95% win rate this isn't luck It’s the result of access to raw data I bought from a sysadmin at a major network node While you're drawing charts, I’m using a C++ scanner to track Sharky6999 a wallet that sees the candle close seconds before your browser even updates Account: @Sharky6999?via=polymarketacc" target="_blank" rel="nofollow noopener">polymarket.com/@Sharky6999?vi… I double checked the logs three times, refusing to believe the stats This isn't trading, it's high speed digital looting in real time Why this C++ algorithm is your worst nightmare: In the world of elite algos, 70% is the ceiling. This monster maintains 95% No sane human drops $74,000 into a 15-minute window unless they are 100% certain of the outcome The bot doesn't guess it pulls data through a direct pipe, outrunning Polymarket’s lagging quotes It’s like buying a ticket for a train that has already arrived While you're guessing up or down, this code is already locking in profit My scanner is still tracking this ghost, and his PnL is climbing every minute You're either with the ones who own the software, or you're the one funding the party

@banan_crypto @Polymarket @zscdao Depends on the strategy. Low latency isn’t needed on all methods. I agree that it’s not trivial building bots, u can vibe code them but u need to know how to manually build them. U dont need C++ or Rust. Python or javascript is just as good.

Everyone can earn $10k a day, but not everyone Since bots that generate insane profits appeared on @Polymarket, CT has seen a ton of tweets where people give "step-by-step" guides on how to create a similar bot. Moreover, many influencers posted that they intend to create their own bots and share the details with their audience. But it's all smoke and mirrors. These step-by-step guides don't work. And not a single influencer has ever shared their experience of creating bots. Because they simply don't have it. Why is it so? All these bots, which generate $400k per month, have minimal latency. To ensure they work as quickly and efficiently as possible, they are written in C++ and Rust, which are definitely not for beginners. That's why all these X influencers are simply incapable of writing such a bot. They say that it is not necessary to know any programming language, that now you can just use vibe-coding.But that's a FAKE. You can use Claude or Cursor to search for ideas and inspiration. But not for writing an entire bot. So, the next time you see a post like this, think about whether the content author really knows what he or she is talking about. I didn't mean to offend anyone in this post, peace to all.

@securezer0 The other 80% goes to PolyMarket

⚠️ Polymarkets new user TAX will only see 20% used as maker rebates Updates to the Polymarket docs shows that starting Jan 12th only 20% of the Polymarket taker tax will be used to pay market makers The other 20%? Who knows Will update if anything changes

U sell before the end to get the maker rebate and buy the position earlier than 99%, say 90%. Consider setting the stop at half your target, e.g. if u target 4% set stop at -2%. There’s slippage so it will be more on average. This will stop a losing trade wiping out all your winners. You’ll get more losers but even a 50% win rate will still be profitable.

After 115 winning trades in a row with my @Polymarket bot, I finally got my first loss. 116 trades total, 115 wins at +1.5%, 1 loss. I honestly didn’t think it could happen, but every downside comes with an upside. Thanks to this loss, I realized I could integrate a stop-loss into the bot. Instead of risking a full loss and losing 100% of the traded amount, I can now cap losses to 20–30% per trade. That’s a good improvement compared to the current setup. Current PnL: > 115 wins at 1.5% → +170% > 1 loss → -100%, but from now on each loss should be limited to -20–30% max A lot of people say that since Polymarket added fees to the 15-minute markets, targeting 1.5% doesn’t make sense anymore. I won’t go into details, but my implementation avoids paying those fees, and actually earns more now because I receive them instead. For the few who already have the bot, I’ll push the stop-loss update later today. If you’re interested in the bot or the logic, feel free to DM me. I don’t want to share it publicly for now to avoid losing the edge.

@crypto_betty @Polymarket I ran a test and the PolyMarket RTDS was ~500ms ahead of the Binance websocket stream. The lag is in orders being placed. Surprisingly it better to use the PolyMarket stream than Binance. This might also depend on geography (I’m in UK)

how to build the fastest Polymarket latency bot +$100k/month PnL if you hit 1,000+ trades/day cleanly 0x8dxd is just a latency bot that farms the 200–500ms gap between Binance moving and Polymarket waking up. the part that matters isn't some alpha model, it's reading spot first and hitting the book before odds adjust.​ where the $100k+/month comes from it's not one massive bet. it's clipping tiny edges thousands of times. 0x8dxd started with $313 and ended month one around $438k, now sits north of $550k all‑time PnL with ~5.6k–7k trades at 96–98% win rate on BTC/ETH/SOL 15‑minute windows.​ if you're consistently pulling 1–2% per cycle over 1,000+ trades/month with real size, six figures is just arithmetic.​ first, the edge: spot (Binance/Coinbase) moves first, Polymarket's 15‑minute up/down windows lag by 200–500ms before odds fully reprice. latency bots live in that window: spot already moved, book still thinks it's 50/50, bot fixes the misprice and takes the edge.​ what you actually need: - Python + official py‑clob‑client to prove the idea, Rust CLOB client if you want to compete with 0x8dxd‑level bots.​ - WebSocket feeds for BTC/ETH/SOL from Binance/Coinbase (REST polling is too slow).​ Dedicated Polygon RPC node so your orders don't die in public rate limits.​ - VPS physically close to Polymarket's infra (ping is literally part of your edge).​ where people mess up: they try "HFT" from a laptop with Python + public RPC and wonder why their 300ms reaction gets farmed by a 30ms Rust engine.​ the bot loop (in plain English) pull real‑time spot for BTC/ETH/SOL via WebSocket, track short‑term % moves over a few seconds.​ for each 15‑minute crypto market on Polymarket: check if spot moved beyond your threshold (e.g. ±2%) while Polymarket odds barely changed.​ if BTC rips and the "down" contract is still priced like a coinflip, load NO at stale odds. if BTC nukes and "up" is still fat, fade that with NO or take YES on "down" depending on the market structure.​ log market, entry odds, exit odds, realized edge. that's it. no AI, no news scraping, just enforcing what spot already told you.​ where to get real references: Finbold/MEXC breakdowns: exactly how a bot took $313 to $438k on Polymarket using BTC 15‑minute windows and latency between spot and odds.​ BlakeNastri's X thread: dug through 0x8dxd's stats, ~5.6k trades and ~96%+ win rate, called it latency arbitrage not insider magic.​ two real‑world gotchas (that decide profit vs loss) edge decay: as more bots pile in, the 200–500ms lag shrinks and your edge turns into noise. research on Polymarket shows arbitrage bots already extracted tens of millions.​ self‑slippage: once you scale to real size, you start moving the book yourself - without proper sizing and staggering, you donate your edge back to the market.​ how to make it feel "pro" fast run only on high‑volume crypto windows: (BTC/ETH/SOL 15‑minute) where size actually fills and you can hit 1,000+ trades/month without breaking the market.​ start with tiny tickets ($20–50 per trade), prove the edge over thousands of logs with fees and slippage included, only then scale size not risk per trade.​ use official libs and known clients as your backbone, treat random "Polymarket bot" repos as hostile until you audit them - there are already GitHub bots caught stealing keys

@bojo_web3 @Berna7224 Chill, it’s coming.

@_RicMoore @Berna7224 We're waiting for you to bring the Frost Arcade game to life. You've kept us waiting for far too long.

Is web3 gaming inevitable, or is it going to die? Give me your honest opinion.

@aakashgupta Go has a fixed set of rules and a single goal. Software is open ended. While all this sounds great I’m not sure it’s an Alpha Go equivalent.

This is the AlphaGo moment for software agents. Every AI coding agent today, from Cursor to Devin to Claude Code, learns from human traces. GitHub issues. Pull requests. Test suites written by developers. Someone had to write all of it first. This creates a hard ceiling. SWE-bench Verified required 93 professional developers to manually screen 1,699 samples just to produce 500 usable problems. The original SWE-bench needed human-curated issue descriptions, pass-to-pass tests, fail-to-pass tests. The data scales linearly with human effort, and human effort doesn’t scale. SSR breaks that constraint entirely. One model plays two roles: bug injector and bug solver. It explores real codebases, discovers how tests work on its own, creates progressively harder bugs by removing code or manipulating repository history, then learns to fix what it broke. The injector must produce bugs that fail tests through semantic errors, not syntax mistakes. The solver gets no natural language description, just the raw codebase and a test patch. No human labeling. No curated issues. No pre-existing test suites. Just Docker images with source code. The results: +10.4 points over human-data baselines on SWE-bench Verified. +7.8 on SWE-Bench Pro, which contains 731 enterprise-level tasks averaging 107 lines of code across 4+ files. The model trained entirely on self-generated bugs still generalizes to solving natural language issues written by humans that it never saw during training. They also introduced higher-order bugs constructed from the solver’s own failed repair attempts. When the solver fails, those failure patterns become new training problems. The curriculum gets harder automatically. This is the same loop that took Go from “decades away” to superhuman in 18 months. AlphaGo didn’t need human games to learn. It generated its own curriculum by playing against itself, discovered strategies no human had conceived, and scaled past every grandmaster on the planet. We’re watching the same pattern emerge in code. The bottleneck was always data, and the assumption was that useful coding data required human developers to create it. SSR suggests that assumption is wrong. Raw repositories plus self-play can generate unlimited training signal. Meta trained this on 16 million token batches across 150 steps using H100 clusters. The infrastructure exists to scale this dramatically further. More repositories, more self-play iterations, harder bugs. GitHub has 200 million repositories. The training data ceiling just disappeared.

@dlevine815 I’m not keen like the way it talks, would prefer straight talking without referencing things from past chats. I also dont want it to end each time by offering to say more stuff. Make the responses shorter.

We've got big plans to improve the core ChatGPT experience in 2026. What are some thing you'd love to see? Even small ideas welcome! Looking forward to getting them built 🙏

@ndrewpignanelli Would u just name him Claude

I have dream… that one day we will live a world where games will not be judged by the tools used to create them, but by the content of their gameplay.

@raidenkrn I agree. Tokens, fungible or not, get in the way of the game

i do not believe in crypto gamers anymore it's either players or crypto participants as a crypto game, you have to pick your side - compete with web2 games and target gamers - compete with crypto protocols and target whales/farmers no in between

@RickLFG POWER has done alright

@DannyLimanseta I like this. The attack mechanic is a nice choice. You could even vary how it looks based on the enemies, like a block representation of the baddie’s health

WIP of a game I've been working on for quite a few weeks now. 100% vibe coded with Cursor. Does the setting and concept look interesting and appealing to you?