This section holds four kinds of article: how to troubleshoot Binance API errors, how to set key permissions and stop-losses, how to tell when AI is making things up, and worked examples of evaluation and review methods. The data in the method examples is hypothetical and labeled as such in each article.
-1013 means Binance turned the order away at the API layer. The name after Filter failure is the check that failed; small buys can miss NOTIONAL after rounding.
Binance Spot API error -1021 has two messages: timestamp 1000ms ahead, or outside recvWindow. See how the server checks it and what to rule out, in order.
Testnet funds are virtual, only /api endpoints exist, and everything is wiped roughly once a month without notice. Checked against AI-written code: swapping endpoints, flagging what never gets tested, reading filters from exchangeInfo, and a checklist before going live.
An AI is good at turning your risk tolerance into a concrete parameter table, and bad at pressing the button — it has no live book, no view of your whole position, and no stake in the outcome. Order-type mechanics, a three-part prompt skeleton, and the three mistakes that show up most.
If AI only reads your account, it only needs a read-only key: the three permission tiers, what a leak of each actually costs you, IP whitelists, and the withdrawal-permission trap.
Seven ways of using AI that tend to lose money: asking it to predict prices, pick coins, size leverage. Most come with a hypothetical example, plus why AI shouldn't be used that way and what to do instead.
Five signals that an AI is making things up: an overconfident answer with no preconditions; precise numbers with vague sources; contradictory answers when you ask the same question again; an instant reversal when you push back; citations of indicators or reports that don't exist.
Before treating X activity as a trading signal, check clustered promotion, quote context and primary sources. Three examples are hypothetical, not live tests.
A set of hypothetical data used to show how to read a per-basket table, how to attribute the 10 worst trades, and which rows to look at when comparing two baskets.
Compare AI auto-execution and manual orders over 30 days with frozen rules, real order logs, slippage, fees, failures, and paired net PnL. No account experiment was run.
An evidence-first workflow for AI-assisted rug-pull checks: contract powers, liquidity, holders, audits, claims, sources, snapshots, and human verification.
How to evaluate ChatGPT BTC direction calls: freeze inputs and labels, then read a confusion matrix, a predeclared baseline, and source checks. The 200-record example is synthetic, not a model test.
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