it seems terra is pretty much useless, you either want luna max for everyday coding (cheaper and same perf as 5.5 high), or sol xhigh/max for demanding tasks
It's good to see labs taking into account the cost/task.
Grok 4.5 is interesting because it's smart enough at great price. It seems gpt 5.6 is right there with great efficiency and great pricing.
Working with Fable has been a great experience, but at the end of the day, if you can get only 10% of your work done because it just burns through tokens, that's not that interesting.
I've been mostly using Opus and Fable high for planning and codex 5.5 medium for implementations. Claude is also the only model i can use for design tasks. If gpt 5.6 can finally deliver on the design side, it might be time to ditch the Claude sub and go full Gpt.
if you're going to try this one out, don't be surprised to get this message repeatedly, like 4 out of 5 prompts you're trying to send, 24/7, this is gonna be your new friend, then you'll learn to write the only prompt that matters: "retry", "retry", "retry"
Here's the message: "Cannot connect to API: write EPIPE"
AOP is an interesting pattern but i've mostly tried to stay away from it mostly because:
- code readability and maintainability takes a hit. If you don't know things are defined using AOP in files x,y,z you can read the code and miss a whole lot of things.
- AOP implemented at runtime is a mess when you're trying to debug things
So yeah, instead of having aop defined somewhere else to wrap a function call, i tend to prefer doing it explicitly transaction(function())
This is a bit ironic, Anthropic complaining about a competitor using claude data to build its own product when Anthropic basically used all of human knowledge production to build claude, i don't think they paid every magazine, author, journalist, etc ...
This is almost standard practice in any competitive industry anyways.
Disassemble your competitor's product, study it and try to reproduce / improve.
i've experimented quite a lot with multi agent setups and orchestrations.
In the end, it didn't feel worth it mostly because of high token overhead (inter agent communications, agents re reading same code, etc...) and synchronization / cooperation issues (who should do what).
What actually works for me and provides good results: multi step workflows with clearly defined steps and strong guidance for the agent.
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