The company doing the actual ID verification (KYC) is probably the last company I'd trust with this kind of data.
To circumvent conversations being flagged as "cybersecurity bad!!!" I often have to use previous models (5.3 for example, and sometimes using them through subagents is enough). And when this method no longer works, local models will be good enough for it to not be a problem (for my use case, at least).
I ran gemma4:26b without any tooling access and it gave me the correct answer in a few minutes only (definitely less than 8 minutes, but I didn't timed it).
Huh, I didn't know about that. I'm trying Claude Pro for the first time while comparing it against ChatGPT and I'm (sadly) not impressed at the moment.
When I asked both Codex and Claude Code to "look into" an issue of medium-to-high-complexity in a code base, Codex went with the fix I had in mind and directly and made code changes without being asked or at least asking for permission. It only used a few percents of its 5-hour limits to do it, on `High`.
Claude in the meanwhile misdiagnosed the core of the issue on its first pass (even on Opus 4.6 + Thinking). I had to guide it in the right direction and despite being given the 'answer', it was quite a long process compared to Codex' one-shot. And it hit the 5h limit before being able to finish solving the issue.
> while Migadu may be a Swiss company, the data centers where the mails are hosted were in France
> So the situation is somewhat comparable (not entirely though) to Fastmail being an Australian company with data centers in the U.S. being used
I don't know if it is a good comparison, as European countries (France included) are subject to GDPR, which does not really have an equivalent in the U.S.
From the FAQ at the bottom of the project showcase page[0]:
"GitHub Copilot is a code synthesizer, not a search engine: the vast majority of the code that it suggests is uniquely generated and has never been seen before. We found that about 0.1% of the time, the suggestion may contain some snippets that are verbatim from the training set. Here is an in-depth study[1] on the model’s behavior. Many of these cases happen when you don’t provide sufficient context (in particular, when editing an empty file), or when there is a common, perhaps even universal, solution to the problem. We are building an origin tracker to help detect the rare instances of code that is repeated from the training set, to help you make good real-time decisions about GitHub Copilot’s suggestions."
To circumvent conversations being flagged as "cybersecurity bad!!!" I often have to use previous models (5.3 for example, and sometimes using them through subagents is enough). And when this method no longer works, local models will be good enough for it to not be a problem (for my use case, at least).