CryptoPainter

Here's a new insight: whether you're using CC, Codex, Cursor, or Lobster, Hermes... The process of collaborating with AI is very similar to walking a dog! You must always keep a clear mind to ensure the AI doesn't enter a self-loop and deviate from the intended results—just like when walking a dog, you have to pull the leash if the dog goes the wrong way. If you just follow the dog, eventually the dog ends up walking you... For example: Have you noticed that when fixing some complex bugs, once the conversation with AI turns into "If you agree, I'll proceed to the next step..." and you reply "Agree, continue!", within less than 10 rounds of dialogue, your code or outcome inevitably starts to drift away from what you originally wanted to achieve... The scariest part of this process is that when using some top-tier large models, they will try to subtly lead you during this process. Especially after a small deviation has been established, this error will be continuously reinforced in subsequent conversations, making the AI itself believe this is the only solution or path, thus rationalizing it... If you can't firmly hold onto your logic or your thinking gets confused, you will eventually fall into a state that seems like frequent output is happening, but the results have completely deviated... It's like the butterfly effect—many small errors you didn't deliberately emphasize will be gradually amplified, solidified, and rationalized by the AI. By the time you realize something is wrong, all that's left are various messes, and at that point, you're just wasting more quota and Tokens... So sometimes I wonder, is this the result of these big AI companies deliberately letting this happen?

I spent the last couple of days completely restructuring the entire system and wanted to share some new lessons learned: 1. When building a strategy system involving AI, for every deployed feature, you must first ask yourself: "Can this feature be implemented purely through code?" If a feature can be implemented purely through code, no matter how complex the logic is, you should avoid handing over decision-making to AI; For example, my current overall strategy system architecture is to first deploy a set of pure algorithmic quantitative strategies with high win rates and high profit-loss ratios but extremely low trading frequency. Backtesting results show fewer than 10 trades per year, but the data is excellent. The reason for the low trading frequency is that the strategy’s decision tree includes many filtering factors, so normal market conditions rarely trigger entry signals... Then I involve AI in decision-making, using account historical data and the strategy architecture to dynamically adjust the filter parameters, turning a pure algorithmic strategy into a flexible trader... Then problems arose. Although the AI’s prompt instructions were comprehensive, as the number of parameter iterations increased, the AI began to develop some self-reinforcing directional biases... For example, last time it adjusted the RSI range and found that the strategy’s win rate and profits improved. This change was then logged as an iteration experience for the next optimization. Gradually, the context became that no matter how the strategy performed, it would only modify one core parameter, and the adjustments became more and more extreme... Even though I added explanations for all exposed parameters in the global prompt, after running for a while each time, the AI’s attention would get stuck on one or two parameters and couldn’t break free... Therefore, I had to spend two days turning this part of the AI task—data collection, real-time analysis, and dynamic parameter tuning—into pure code logic. To adapt, the entire system required a lot of dependency development, which was completely unnecessary when AI was making decisions before, as it could pull data, search online, perform semantic analysis, and then draw tuning conclusions on its own... Still, without heavy AI involvement in decision-making, the system finally stabilized. So one thing is clear: for trading systems, hard-coded logic is ultimately superior to flexible large models! Currently, I have limited AI’s role to social sentiment analysis, trading asset collection (background checks and unlocking periods), and system monitoring. Having the Agent act as a secretary is far safer and more stable than having it act as a trader. So, if you’re still expecting to give AI a prompt and have it start making money for you, it’s better to forget it for now. Current large models can only execute pure algorithmic strategies with fixed parameters, essentially saving users the trouble of initial development; But the more AI occupies space in a system, the higher the black-box uncertainty becomes. Sometimes it’s even more stable to just find a signal provider to follow...

So it turns out that there were always people privately messaging me to apply to be my assistant, is this an industry chain?

The navigation bar of the Binance App has become fully customizable, it seems the product team has been working overtime recently… After the update, you can also set withdrawal locks to eliminate the risk of account theft from the source. No more worries about someone around you using your phone to transfer money to their UID? 🤣

X's total salary has finally exceeded ten thousand! By this calculation, Premium+ is completely free... Lately, even if I hardly scroll through Twitter, I still make sure to post 1-2 tweets every day, because on this platform, if you don't speak, it's like you're dead... Take a look at my 2024 paycheck; I was quite satisfied with $20 a month back then...

Is $BILL heading towards 0.1? The Kaito airdrop back then is now worth 1500 USD, and the airdrop from the @billions_ntwk community is already worth 85,000 USD... If you want to cash out this part of the reward, the only way currently is to short hedge, but the problem is, what if the price surges to 0.2? So you can't hedge, and you don't dare to chase at a high price. The only strategy is to open a Martingale short grid, placing orders at the highest price of 0.35+... As for why 0.35, I had Grok do a year-over-year comparison with WLD. Under the condition that the user base is not as large as the former, because the community airdrop has locked up the vast majority of tokens, the two offset each other. The calculated limit price for $BILL, constrained by its valuation limit, is 0.35... Therefore, if you want to preserve the value of the airdrop, a Martingale short grid can be considered, but the only requirement is to prepare at least 10 times the value of the airdrop in funds, and ensure that when the Martingale is fully invested, the overall leverage ratio is exactly 1, with absolutely no extra leverage! Finally, is it worth tying up 850,000 USD for half a year to secure cashing out this less than 10k USD airdrop? I don't know if you can afford it; as for me, I definitely don't have that spare money right now 🤣

#OKX has launched pre-market contracts for Anthropic, OpenAI, and SpaceX, with pre-market prices at $1300 and $2000… Which price will surpass ETH first? 🤣