Rulldof

@0xyunss boleh kali bang ksh kesempatan buat saya. biar sambil bisa di kotak katik bot ny. sama2 upgrade kita

pekan kemaren gua kasih gratis agent gua ke hampir 100 orang buat test. agent yang sama yang hasilin gua 20$ per hari atau lebih. gua open lagi buat 50 orang di komen. $189.7 dalam 11 hari aktif. satu merah di tanggal 18, sisanya hijau semua. minggu ke-4 yang paling kenceng, $101.95 dalam satu hari di tanggal 22. banyak yang langsung ikut build, nambahin fitur sendiri di atas framework yang gua kasih. ada yang nambah memory, ada yang bikin dashboard, ada yang upgrade screening pipeline. ada juga yang lurking doang, dan itu fine. dalam hampir 2 minggu, agent gua kebanyakan lakuin 3 setup: bid-ask tight di new pair yang volume-nya lagi tinggi. masuk cepet, keluar cepet, ambil fee dari volatilitas awal. bid-ask 45-50 bins di token yang udah koreksi. ini yang paling sering. token udah turun, volume masih ada, range pas buat nangkap rebound sambil collect fees. bid-ask default di token yang volume-nya masih jalan tapi belum koreksi banyak. lebih konservatif, hold lebih lama, tapi tetap profitable. agent ini jalan 24 jam nonstop. gua cuma define cara entry dan exit biar range-nya efisien, sisanya dia handle sendiri. sejauh ini masih profitable dan masih evolve. @MeteoraAG @MeteoraIDN

@0xyunss ud coba DM buat belajar tpi blm di respon ni bang yunus

update Meridian. gua udah ngasih sekitar 40 orang buat coba framework agent yang gua build. dan hasilnya mulai keliatan. sebenernya banyak yang dm cuma kalo xnya gajelas atau gaada minat ke ai/dlmm gua bakal skip karena jelas mereka cari duit doang. ada yang pull 2 sol dalam 3 hari dengan modal rata-rata 5 sol. ada juga yang dengan modifikasi sendiri udah 57 posisi, 79% win rate, avg hold time 1.1 jam, dan clean 1.2 sol dalam sehari. agent gua sendiri? masih struggle. setelah 5 hari dari modal 0.6 sol baru di sekitar 1.5 sol. biaya perhari kena sekitar 1$ buat agent. tapi kalo capital 5-10 sol gt bukan sebuah halangan karena ya emang printing. tapi jujur, ini karena gua sengaja jalanin konservatif dari awal buat testing. sekarang udah mulai deploy lebih agresif karena datanya udah lebih mature. yang bikin excited adalah orang-orang yang build di atas framework gua justru nemu optimisasi yang gua sendiri belum kepikiran. ada yang nambahin holographic memory buat agent-nya biar bisa recall past pool outcomes lintas session. ini yang gua suka dari bikin sesuatu yang open. lu ga pernah tau siapa yang bakal build sesuatu yang lebih bagus dari yang lu bayangkan sendiri. kalau lu tertarik buat coba atau build di atas framework ini, slide in. @MeteoraAG

@mininghelium1 yoo!! this is so sick!! can we have a talk about this? im trying to make one too! hope you have a time. bless you

Might I add on how I am building with Yunus's Framework . The strategy breakdown ▎ The strategy: single-sided bid-ask below the active bin. SOL sits in a wedge — as price drops through your range, you buy the token bin by bin. You earn fees from sell pressure without ever holding the bag upfront. The learning angle ▎ Most LP bots are static rules. This one learns. Every closed position generates a lesson. Every lesson gets deduplicated, tagged, and injected into the next decision. After 50+ trades it evolved its own screening thresholds. The memory flex ▎ Gave the agent holographic memory (HRR vectors). It recalls past pool outcomes, strategy effectiveness, and volatility patterns across sessions. Facts that keep getting recalled promote themselves to permanent context. It literally remembers what worked. The screening pipeline ▎ Before deploying a single lamport it: screens fee/TVL ratios, checks organic score, analyzes holder distribution, detects bundlers, reads the token narrative, studies top LPers on LP Agent, and checks if any tracked smart wallets are in the pool. The management loop ▎ Every 3-10 minutes (auto-scaled by volatility) the manager agent checks every open position: PnL, fees, range status, pool health. Trailing take profit, stop loss, and OOR rules enforced automatically. It claims fees and swaps dust back to SOL on close. 7The data stack ▎ Data stack: Meteora DLMM SDK for on-chain, Helius RPC for Solana, Jupiter for swaps + token intel, LP Agent API for top LPer analysis, Meteora datapi for real-time PnL. Two DeepSeek models — reasoner for screening, chat for management. The dashboard ▎ Built a real-time dashboard with position cards showing bin liquidity charts (blue = SOL, purple = token), LP performance from LP Agent API, candidate pool rankings, and a chat interface that queues messages while the agent works. The safety angle ▎ Safety first: cycles never overlap (management blocks screening and vice versa), PnL snapshots happen before closing (not after when data is gone), positions you open manually get auto-adopted with full management. Dry run mode for testing. The numbers ▎ 57 positions across 19 pools. +$68 PnL, $143 in fees captured, 0.82 SOL net. Average hold: 53 minutes. All autonomous — the only thing I do is watch the dashboard and occasionally chat with it.

@0xyunss @OpenRouter sangat penasaran bang gmn sistem kerja ny ni

yang paling mindblowing dari agent ini adalah dia ga butuh model yang flagship buat running. gua pake model murah banget dari @OpenRouter selama gua make kira kira 4 hari cuma abis segini doang, itupun cuma training awal. dengan return perhari bisa 5-20$ dengan capital 1 sol, ini tuh udah oke banget, cuma ya ngga bisa dibilang pasif income karena ai juga bisa salah. jadi gua riset beberapa model free yang oke dan paling efisien buat manage ini. pake minimax m2.5 buat train. inception/mercury-2 buat mode degen. oh ya ini bukan pake openclaw ya karena gua pernah coba build di openclaw, akan kacau memorynya, jadi gua build dari scratch agent loop dan fundamentalnya. jadi agent ini emang fokus ke dlmm aja. lainnya model gratisan, bisa tebak model apa yang gua pake?

personal news: i got life sentence moving into @Jaileddotfun soon [ drop your mugshot here👇]

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