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BEEBRAIN IS GOING TO THE NEXT LEVEL. until now, it was just an experiment inside NERVE. a bee brain learning to read the market through liquidity, holders, snipers, creator age, volume, and now even Telegram. but the experiment went too far. after BEEBRAIN started finding signal combinations that i...

13,892 görüntüleme • 4 gün önce •via X (Twitter)

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h100envy profil fotoğrafı
h100envy4 gün önce

my github:

zostaff profil fotoğrafı
zostaff4 gün önce

mushroom bodies learning from previous pools is such a good fit for memecoins

agentslopzone profil fotoğrafı
agentslopzone4 gün önce

following the new account the second it goes live

hochulambo profil fotoğrafı
hochulambo4 gün önce

when the brain finds a combo you never taught it, how do you tell real signal from overfitting on a few lucky pools?

rektreje profil fotoğrafı
rektreje4 gün önce

What’s your GitHub?

Tolik profil fotoğrafı
Tolik4 gün önce

New senses multiply the correlation. The filter is more important than the next input

Sethian profil fotoğrafı
Sethian4 gün önce

really looking forward to seeing the open architecture

Kirill profil fotoğrafı
Kirill4 gün önce

this is a really cool approach, I want to test it.

Benzer Videolar

THE FIRST CROWBRAIN AUTONOMOUS TRADING EXPERIMENT IS COMPLETE I created a separate wallet for the system, deposited $50 and allowed the algorithm to execute its own signals for the first time. During the first five hours: $50 → $420 CROWBRAIN discovered tokens, analyzed price movement, volume and liquidity, generated BUY / SELL signals, and opened and closed positions automatically. But then the market changed while the algorithm continued trading under the same rules. It took profits too late, re-entered after major price moves and failed to stop after a series of losing trades. Eventually, the profit disappeared and the overall result of the experiment went negative. That is exactly why I am testing it publicly. It would be easy to show only the $50 → $420 moment and stay silent about what happened afterward, but the purpose of this experiment is not to produce one impressive screenshot. It is to build a system that can control risk and perform consistently under different market conditions. Now I am adding: → automatic partial profit-taking → a dynamic stop-loss → position-size limits → a cooldown after consecutive losses → a daily loss limit → protection against re-entering after a sharp price increase → market-regime detection The first test proved that CROWBRAIN can already find opportunities and turn $50 into $420. It also showed that finding good entries means nothing without disciplined exits and proper risk management. The experiment continues. After the update, I will fund a new public wallet with another $50, restart the algorithm and show every trade - successful or not. Let’s see what CROWBRAIN learns from its first serious mistake.

sopersone

33,764 görüntüleme • 13 gün önce

I gave ChatGPT Astra one line: "turn a profit or I cancel the subscription." closed the laptop and went to work came back 8 hours later expecting nothing $30.29 → $39,596.53 my friend saw the screen before I did. he went quiet here's the thing he doesn't know yet. I didn't build any of what's running right now. Astra did, on its own, while I was gone the terminal I opened wasn't the one I left. it had a rug-risk screen bolted on that I never asked for. a scanner grading every fresh mint the second it hits the chain. a filter that had already killed 247 rugs before I got back to my desk. none of that was me it's not gambling on one coin either. it's reading the whole board on Robinhood, live, and mirroring the wallets that consistently print, dev wallets, insiders, smart money, the ones in before a chart even moves. anything that smells wrong, thin liquidity, a live mint authority, a wallet holding too much supply, it's gone before it costs a cent it's not flawless. some calls miss, the win rate dips when a read goes bad. that's the job. the sizing just makes sure the wins outweigh it every time, so the line keeps climbing anyway I checked the logs before my friend could ask how. 27,915 wallets scored in one pass. every decision made faster than either of us could read a ticker he wants to see it happen in real time now, not just the final number so I pulled the replay, last run, wallet by wallet, compressed down to a few seconds. and honestly, watching it back is worse than the number. you see exactly where it started ugly and exactly where it clicked I'm not sure I "ran" anything. I gave it a wallet and walked away. that's the whole story there's more of these running quietly than people admit to. you'll get it the day your own $30 turns into something you don't want to say out loud you could do this with Grok Bot too, that's the common route. I just wanted to test Astra instead. same outcome, different engine $30 well spent. like this post if you want me to ask it to create its own token follow me if you want to build your own and start pulling alpha daily!

cristal💎

132,098 görüntüleme • 21 gün önce

Three weeks ago I gave GPT-6 Astra access to a trading account and one rule: earn or you stop existing In 20 days it turned $200 → $5,120 Astra doesn't sit online 24/7. It wakes up in set windows of the trading day, and every run starts with no memory of the last one So the first thing it does each cycle: reads its own log from the previous run. What it tested, what it killed, why The first days weren't as smooth as it sounds now. On day two it bled $80 on a trade that passed the backtest clean. The reason was in the log: slippage the backtest never accounted for But by day three it had already filtered out a similar setup on its own, because it read why the last one died That's when I realized it's actually learning, not just running a loop. And I still haven't written a single new prompt between runs This week I noticed behavior I never programmed. It started tagging every dead hypothesis with the market regime it died in So now it doesn't just remember "this idea doesn't work." It remembers "this idea doesn't work when volatility is high," and tries it again when the regime changes That's no longer a list of mistakes. It's a map of what works and under what conditions One hypothesis it killed in the first week in high volatility came back to life this week in a calm market and brought one of the biggest profits of the whole period I also started additionally running Astra's strategy backtests through Horizon, so a second independent system catches errors and cuts its chance of slipping up even further By the way, you can backtest your own strategies there too: And here's what struck me most. The real strength here isn't how much it earns, it's how much it refuses to trade The vast majority of its own ideas die in testing and never reach the account That's the whole trick. Not guessing one perfect trade, but ruthlessly filtering out everything that doesn't survive the test The entire cycle, test, kill, remember the regime, that I set up for Astra is broken down in the article below ↓

qwinsi

74,829 görüntüleme • 10 gün önce

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,591 görüntüleme • 3 ay önce