Mining helium

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.

@reem_a will you take someone form NY?

I'm hiring someone to join my team at Anthropic to lead Claude Code comms. This is not a role for someone who wants to run an old playbook. You'll need to be a Claude Code super user, understand developers and dev tools, and have great taste. You'll work hard, learn a lot, and ship with the best people around. Non-traditional comms paths welcome. My DMs are open!

@fulldividends /stats

@mininghelium1 how do you see that?

claude go brrrrrrrrr

Yes super needed

@SpuddyA7X Never stopped only this time got something fun to stick

@mininghelium1 Love to see you cook again 🫡

@trq212 speed run to kill openclaw

We just released Claude Code channels, which allows you to control your Claude Code session through select MCPs, starting with Telegram and Discord. Use this to message Claude Code directly from your phone.

@0xROLF All of this is prompt engineering, not model training. The LLM gets a different system prompt every cycle with updated context. It appears to learn because the context gets richer — but the model itself never changes.

@mininghelium1 This is impressive. I'm curious about what you actually train to that AI.

Yunus has put together a fun framework here I am excited to build with/upon!!! So far in one day here are my agent stats

The LLM (DeepSeek) is not being trained. It's a frozen model — same weights every call. What changes between calls is the context injected into the prompt: What makes it "smarter" over time: 1. Lessons (lessons.json) — rules derived from closed positions, injected into every prompt. "AVOID pools with vol < $100", "PREFER bin_step 80 for bid_ask". The LLM reads these as instructions, not training data. 2. Nuggets memory — holographic recall of pool outcomes, strategy patterns, volatility observations. Injected into the prompt as MEMORY RECALL section. 3. Performance data — win rate, avg PnL, ROI from LP Agent. Injected so the LLM knows its track record. 4. Pool memory — per-pool deploy history. "Last time you deployed here, PnL was -5%." 5. State summary — current positions, trailing TP status, OOR timers.

@MasonBuildsAI the agent is link to the dash i forked yunus repo.. i have my own copy github.com/fciaf420/merid…

@mininghelium1 Yeah 100% but the actual setup , you manage to get your agent linked with the new dashboard or you doing it with internal commands?

@MasonBuildsAI the edge is in your training on model use mostly

@mininghelium1 Would love to see a setup guide on this or a rough one for beginners so you don’t give away your edge of course

@etherblaize I don’t have anything

@mininghelium1 Check dm on discord btw haha

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.

@mininghelium1 This is dope dude I don’t have the learning or memory part in mine. You’re doing some really cool stuff on this app. Keep it up 👏

@etherblaize always sell back to sol

@mininghelium1 I built a really similar bot. Does it sell coin profits back to sol or hodl them. I tried both

Also used @lpagent_io api for top lp wallet info on pools

Credit here for using the memory system I adopted x.com/bluecow009/sta… @BLUECOW009

Here is the web dash to chat with it

The memory system is what makes it different. @NeoVertex1's Nuggets uses holographic reduced representations — facts are compressed into fixed-size complex vectors via HRR binding. Multiple facts superpose into one mathematical object but remain individually retrievable in ~1ms. Not a database. Math.

@mininghelium1 clean result, but imo the important things is the model and the lessons you inject

@0xyunss Full nuggets model integrated for memory!