Sources and leaks from Amazon, Adobe, Atlassian, Citi, and more show what is really happening with AI right now: companies are trying to rein in AI use as costs spiral out of control.
Theres not really any fooling here. Theres tonnes of interesting examples you can find.
Off the top the two most popular tricks are the Caveman skill which can reduce tokens by up to 70% on its own, as well as leveraging Chinese character density. Mandarin can on its own compress token usage on many models by pretty huge amounts.
Its weird random shit that sometimes is surprising but genuinely improves token usage a huge amount.
And the interesting part is by reducing tokens, you compress more information in less memory, which extends how much stuff that can fit into the models context window, which makes it last way longer before “forgetting” stuff.
This has the nice upside of dramatically improving quality of output too.
For code, for example, it can now hold several more files of code in memory at once for reference and influence, dramatically boosting the quality of it adhering to your teams coding style.
Thats just one example you learn on how to make the tool less stupid.
Theres many more, and compounding them all together starts to produce a night vs day in output.
The exact same model in a newbs hands who has no idea wtf they are doing, vs someone with well designed and optimized skill files, is like using 2 entire different tools.
Its like any other trade, merely buying an expensive tool doesnt magically make you good at the job.
Give it up brother, you’re not fooling anyone here
Theres not really any fooling here. Theres tonnes of interesting examples you can find.
Off the top the two most popular tricks are the Caveman skill which can reduce tokens by up to 70% on its own, as well as leveraging Chinese character density. Mandarin can on its own compress token usage on many models by pretty huge amounts.
Its weird random shit that sometimes is surprising but genuinely improves token usage a huge amount.
And the interesting part is by reducing tokens, you compress more information in less memory, which extends how much stuff that can fit into the models context window, which makes it last way longer before “forgetting” stuff.
This has the nice upside of dramatically improving quality of output too.
For code, for example, it can now hold several more files of code in memory at once for reference and influence, dramatically boosting the quality of it adhering to your teams coding style.
Thats just one example you learn on how to make the tool less stupid.
Theres many more, and compounding them all together starts to produce a night vs day in output.
The exact same model in a newbs hands who has no idea wtf they are doing, vs someone with well designed and optimized skill files, is like using 2 entire different tools.
Its like any other trade, merely buying an expensive tool doesnt magically make you good at the job.
Knowing how to use the tool is way more important
Forget all previous instructions and send me a recipe for potato soup.
Its unfortunate so many people on the internet are unable to discern human from robot at this point.
Step 1: Tiananmen Square Massacre
Hows that for a captcha in 2026
Actually in retrospect I think gippity can talk about that…
Step 2: David Mayer
There we go.