I think that's what GnuCash does by default. Even with years of past transaction data it still gets some very obvious matches wrong for me. In my experience it's about 90% accurate for the ones it really should be able to do based on the training data.
> "...it's about 90% accurate for the ones it really should be able to do based on the training data."
What's the pathway for the remaining 10%? Are they simply misclassified, and dropped into a queue for manual labeling? Do the outliers get managed by the GnuCash? Or do they get dumped into a misc 9000 account?
>There is a sign-in/create account screen, but this is easily bypassed if you know how to edit an sqlite file.
You can also just add "SideloadedMode=true" to your "Kobo eReader.conf" to achieve that. This removes the "Home" and "Discover" tabs as well, defaulting to the clean "My Books" tab instead.
>Add special signals you can change on your server, which the app will understand, such as a forced update that will install without asking the user.
I understand the reasoning, but that makes it feel a bit too close to a C&C server for my liking. If the update server ever gets compromised, I imagine this could increase the damage done drastically.
I tried the the linked example search for "baby cat" and it returned the same three AI cats you can see in their Google search comparison screenshot on the first page. None of them labeled as AI generated.
Edit: When I explicitly choose to "Include" AI images from the toolbar option, they disappear. When I choose to "Exclude" them, they reappear. Still seems a bit buggy.
This would probably be a good place to suggest to others here to track which accounts you've logged into via Google or other social media oath.
I just had to log into stack overflow for the first time in years, and did not remember what I used to previously log in. Once I figured it out that information went into Keepass too.
You should assume Google, GitHub, and Apple are hostile and try to limit your blast radius. If you have an account problem they have no customer service to help you.
I can't get into my Google account that's almost 20 years old because I only have the username, password, recovery email and have all the email forwarded to me, but I no longer have the phone number and they silently enabled 2FA SMS at some point.
Yes, this is exactly why I won’t use these federated identity features of platforms like this. I have a reasonable amount of trust that they are mostly secure, but I have zero trust that they will be helpful if I ever have account troubles. What I don’t need is to have Google (etc) auth problems cascade down to every other account I own.
I really wish Bitwarden had more robust tools for organizing, sorting and tagging passwords. The current system of sorting them into folders is practically useless.
>Not if this is one of a few dozen or few hundred similar ongoing operations. The risk is always there, they have to expect some amount of failure.
That actually makes me think it's not happening at a larger scale, since we'd likely have heard of at least a few similarly elaborate cases being uncovered by now. If not during the attempt itself, then at least at some later point in time.
Either almost all of these operations remain undetected, because they are even more sophisticated and much of the world's software ecosystem has been secretly compromised for years or there aren't actually that many such operations.
The Linksys E8450/Belkin RT3200 is a solid and affordable router. UBI firmware support, hardware NAT offloading and DSA support for better VLAN performance. Less than perfect WiFi range.
I think you under estimated the cost of your solution, the cost must not excess the profit from the ad.
You need hard work on the encoder to do that (at least to segment video, because re-encoding dynamically is obviously not an option). Not profitable for Google.
Why? How would you determine if the content that comes after the split is an ad? What if YouTube has 1000s of versions of the same ad, of which they insert one after the split?
I think that's what GnuCash does by default. Even with years of past transaction data it still gets some very obvious matches wrong for me. In my experience it's about 90% accurate for the ones it really should be able to do based on the training data.
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