pandog

A lot of the time you are running experiments and seeing how the data changes, not looking at the data in a vacuum.

Many of the example critiques here don't apply so much when looking at the changes in data:

- if I got 50% more hits on my site this week vs last week, that's meaningful despite 36% of people blocking ads

- if my open rate doubled when I changed my email subject, also meaningful

The other examples are hard to pick holes in as they simply say "Z is a lie", but I can be looking at multiple data sources to decide how much of a lie Z is.

Came here to post this - this was a great (and short!) read to help validate if your idea could be something somebody wants to pay for.

I think a high definition photo taken on a recent phone takes up an awful lot more device memory than a "big number of chats"

Though I would add that looking back to when I was an IC, my Computer Science degree hadn't given me much if any formal training in Software Engineering (especially in a large team and code base) and I mostly learnt by doing that also.

To put it another way - there is no security risk that fail2ban helps with that can't be resolved in another, better, more robust and less risky way.

If someone is performing a denial of service attack from one I.P. address then this will help.

To tptacek's point, you've got to ask yourself is a denial of service attack in your threat model?

The reality is most folk set up fail2ban after seeing auth failures in their logs, not service degradation.

If you're considering a denial of service attack in your threat model, then I'd probably also consider a DDoS attack and there are likely more effective solutions here (a firewall or CDN).

And don't forget you're using some of those precious CPU cycles to parse the auth logs, with python no less :-)

Implementing what you describe sounds to me way more "clever" and less robust than the canary page approach described above.

Specifically - I wouldn't fancy writing the "consistently anticipates their adversaries sneaking behind a wall" heuristic you describe but the earlier post describes the API that already exposes the "has read canary page" functionality.

Fair point!

These services help some issues but don't solve all of them, just a few off the top of my head:

Legal differences can be significant - for example, in France it may be difficult legally to ask an employee to put in more than their contracted hours compared to another country where this could be very normal.

A recent example in the news is the Twitter layoffs - in the EU they may have enacted layoffs that aren't legal there but are perfectly legal in the US.

Taxes in different countries can mean there's a significant difference in the difference between the amount an employer pays and the employee receives per country. Sometimes it's negligible enough for the employer to foot the bill, sometimes it's large enough they may need to pass on the difference. This can get even more complicated when share options come in to play.

If you are responsible for this it all really starts to add up, ultimately the more countries you employ in, the more cognitive overhead which can impact an organisation's agility (or require them to take more risks).