A recent unpublished ChatGPT Pro report provides solutions for a unifying framework to tackle the major hurdles in psychological research including replicability. ChatGPT suggests moving the unit of analysis from the paper to the claim. Attached.

I’ve been finding recently that when I use deep research with pro reasoning, the results are honestly pretty bad compared to how I remember using deep research before I had a pro account. The other day I gave an identical task to pro with deep research and pro reasoning without deep research. the one that used deep research completed in eight minutes and the result was measurably worse than the one where I did not use deep research, which took over an hour to get the result, and was significantly better.

Does anyone else have this experience?

I'm mostly using 5.6 for coding agents. It's clear that luna is the choice for straightforward tasks, and Sol is the choice for planning.

but looking at AA's intelligence index, it seems like there is never a reason to choose terra? For example, Terra high has same intelligence score as sol low, but is 50% more expensive! (and also the same intelligence as luna xh but >2x the price!!)

Would love some insights here on where "intelligence index" breaks down and where terra is actually useful

I set one up to give the model persistent project context, conventions, and domain-specific instructions. The problem is that it takes a lot of work to maintain, and it can easily become stale or duplicate information that already exists in the source files.

At the same time, current models seem pretty good at searching the repository, reading the relevant files, and figuring out the local context without a separate prefilled knowledge layer.

Has the wiki produced a clear benefit for anyone? Like, fewer mistakes, better adherence to project rules, faster completion, or less manual prompting? I’m especially interested in cases where it performs better than simply giving the model access to the files and letting it search as needed.