The pattern is pervasive. Big corp promotes a solution that fits their need. People read about it, think adopting big corp solution means they are doing the right thing. Few people have big corp need, let alone everyone big corps are different. And then endless hours spent fighting big corp solution to not so big corp problem.
I had the same suspicion so made this to examine where my tokens went.
Claude code caches a big chunk of context (all messages of current session). While a lot of data is going through network, in ccaudit itself, 98% is context is from cache.
Granted, to view the actual system prompt used by claude, one can only inspect network request. Otherwise best guess is token use in first exchange with Claude.
A browser extension to add a table of content widget into the chatbot pages (claude/Grok/Chatgpt). Making long conversation easier to navigate. Mainly used on firefox. Not tested on chrome
The analogy I used with the team was that, set the goal, present the map, and figure how to make a better map. Drucker was about the goal with a given map. It is not uncommon for people receiving the OKR not resonating with it. Sometimes they actually have insight into making a better map, but if OKR is OKR, one just have to follow, people swallow their thoughts
The had a currency swap. US earns interest from argentina by taking USD$20B worth of pesos. Argentina get USD$20B. By the end of the swap, Argentina "swap" the borrowed USD$20B with their pesos. Argentina has to pay interest (quick googling didn't reveal the exact terms). They both get their own currency in the end.
I find your article valuable. It shows me what amount of configuration is needed for a reasonable expectation of performance. In real world, I’m not going to spend effort maxing out configuring a single piece of tool. Not being the most performing config on either of the tools is the least of my concern. Picking either of them, or as you suggested, Postgres, and then worry about getting one billion requests to the service is far more important
I was using the same setup to try to transcribe a sound track of a video. A 60s aac audio took me maybe 10 minutes. I'm on a apple M4 and ran `whisper audio.aac --model medium --fp16 False --language Japanese`. Wonder if I'm doing something wrong
reply