Blind Dev article
Why we lose money even when we checked everything
Limits of analysis, blind spots, and psychological traps after doing solid research.
Based on Blind Dev personal experience and source posts; verify current details before applying.
Not investment advice. This article describes methodology and risks.
Historical publication from 2026-01-12. English translation of the Russian original, with editorial caveats. Features, prices, model impressions and offers describe the source period, not a current verification. Not investment advice.
Why do we lose money even when we have “checked everything”?
Because analysis often breaks down somewhere other than where we are checking. This post looks at examples and psychological traps.
An audit is not a guarantee
First, checking everything is impossible. Most people are not programmers, and we read audits like horoscopes 😊. Even an audit does not guarantee that nothing was missed: people can overlook things.
In the original post, I mentioned Balancer and a report about AI finding vulnerabilities. That was a reference to a report, not my own verification of the cause of any exploit.
An audit may say everything looks good, yet an overlooked detail can lead to a future hack. I always treat analysis as risk reduction, not a search for guarantees.
Repetition dulls attention
When you analyze many projects in a row, attention gets dulled. Team members might have considerable experience that is not relevant to the project. A tired reviewer notices the experience but misses its relevance. I cannot name a specific example because I do not remember one, but I have caught myself doing this.
That is one reason I support AI-assisted analysis: it can point out the mismatch. AI search tools may also uncover problems with a team’s previous project. Of course, those claims need checking.
You need to ask for this explicitly in the prompt, and not every tool does it equally well. In the original post, my impressions were that ChatGPT was unlikely to do it, Perplexity might, and Surf could. Those were impressions at the time, not a current model benchmark.
Missing information
We can miss important details about a team’s earlier activities or less obvious issues. Mantra ($OM) was the example I discussed: actions after launch and liquidity concentrated among a small number of whales. AI can flag questions like these, especially when asked for on-chain token distribution by wallet.
Do not forget to check sources. Any AI conclusion is only a starting point, not the truth.
My Eywa example
I had previously reviewed Eywa and awarded it 25 out of 30 points. Later, at the time of the original post, I found that $EYWA’s market capitalization was below $1 million and its price had fallen by more than 99%.
The cause was not immediately obvious. People in the chat mentioned an airdrop, while AI attributed it to large unlocks: hundreds of millions of tokens entering circulation in a relatively short period after TGE. That was an AI-suggested explanation, not an independently established cause.
Two possibilities remained: either the analysis missed something, or it was reasonable at the time and the project deteriorated because of team actions or undisclosed arrangements.
Limited experience can also be a factor. Back then I did not look at unlocks, although I do not remember whether they had been disclosed. If that information was available and I had examined it, I clearly would not have given the coin 5 out of 5.
Fortunately, I did not invest in Eywa; I only received an airdrop. What I regretted was the time spent.
Have you ever done the analysis properly and still lost money?
Source
Original Russian publication. Channel footer and original visual formatting are omitted.