Whataboutism isn’t a fallacy and is widely misused online.
If someone says “I think x should stop because of y”. It is a valid argumentative response to say “y also occurs with z, so should z also stop?”. It is argumentatively revealing either hypocrisy, or that the original imperative relies on additional, unstated arguments/assumptions/biases.
Maybe I’m confused, but I thought whataboutism was more like “you are saying I am doing X bad thing? What about Y bad thing you’re doing, let’s discuss that instead”.
It’s a deflection technique, not a check on logical consistency.
> “you are saying I am doing X bad thing? What about Y bad thing you’re doing, let’s discuss that instead”.
no one is generally expecting or even asking for "let’s discuss that instead". what they are doing is trying to point out that your lack of of care about Y implies that you never actually cared about X in the first place but rather you only care about me doing X.
often times X is actually bad but human nature is such that no one actually cares about it while wanting to appear to when locked down on it. for this reason arguing about X directly is bad optics.
whataboustism is basically an effort to draw attention to the selective enforcement of norms, laws, morality, whatever. hypocrisy is implied.
It can also be used to derail a conversation. I.e. "I find subject X unpleasant, so I am going to imply that you don't care enough about subject Y, which has a passing similarity to it, so that the subject of the conversation turns to Y rather than X. I do not actually care about subject Y, but I won't say that out loud". See also "concern trolling".
Seeing what others have already created using GPT-6, it seem to be a new stepping stone in capabilities and overall "intelligence". However, some other details I think is worth bringing up is that this model is 70% more token efficient than GPT-5.6 Sol and consuming 1/3 of the tokens compared to Sol (max) in the Codex [1].
I am just thinking loudly here but, it seems like even though Astra is pricier than Sol, you might actually get more usage out of it? I did some digging myself and looking at FrontierCode and DeepSWE, Astra (low) seem to perform better than Sol (medium) and on par with Luna (max) while being somewhat on the same price range to Sol? [2][3].
And now we have four models to chose from, each with their varied reasoning efforts: Astra, Sol, Terra and Luna. Personally, I feel like Terra have turned into this middle child in a weird spot that's neither the option as cheap model because Luna is, yet it is not an good option for complex tasks because Sol is already good at it.
For background, I use Luna (xhigh) daily, I think its a fantastic and underrated model. Especially Luna (max). It is way more capable than what it looks like, I think people underestimate it because OpenAI described it as "roughly corresponds to the nano model tier used in earlier GPT-5 families" [4]. I also like Luna because it barely consumes my weekly usage. Last week, it only ate ~15% of my weekly usage. So usage is not an issue anymore. I never have to worry. It may not be the fastest model because, well, it reasons as max effort, but it does the job way better than I expect. Also, considering how much one saves on the weekly usage, one can probably turn on "fast mode". Haven't done it myself though.
Also, another thing that caught my eyes is this:
> Historically, models have used compaction to summarize work during long sessions, such as when debugging complex issues or tackling large refactors. Each compaction can leave out details about why a fix failed or how a component behaves.
> In Codex, Astra can keep notes across context windows, preserving accumulated details without repeatedly compressing them into a single summary. Earlier context windows remain searchable, so Astra can find requirements or test results from previous messages and tool outputs—even if that information wasn’t captured in its notes. You can enable this experimental feature in your Codex config.toml [5].
I was curious about this, because I know Luna (max) spews out tokens which can trigger compaction quite often. If you go to the config reference [6] and search for "features.context_management.experimental_mode", you will find this:
> Enable experimental context management. Rather than repeatedly compressing context into a single summary, it uses notes and searchable history to preserve accumulated details.
This is a very interesting feature and perhaps very useful during long horizon work in a thread where the conversation context window grows and compacts often.
Yes, in my opinion. I know a couple of people who actually jumped from Chrome to Zen because of it. In their mind, Firefox is something from the past and rough around the edges. Zen gave new attraction to FF. I am all for any opportunity to disrupt the Chrome monopoly we currently have.
No other model have been able to complete your highly autonomous work? None? Really? Sounds a bit dystopian to be thrilled about a weekly reset so you can continue to work.
My experiment is examining the autonomy of Fable specifically in an auto research context. I don't believe I said in my message that no other model would have been able to complete my highly autonomous work. So it feels like my view has been misrepresented or misunderstood. This message makes it harder for me to share the things that excite me online and makes it more daunting to share my findings when this project completes, especially any comparative work. For an analogy, I feel like I said that I like Southern Butter Pecan Ice Cream and am being met with a response of the form "Sounds a bit sad that you have to wait for a weekly restock to enjoy any ice cream." I made a goal for myself to be more open with my feelings in life and share more of what I'm working on and not be so rejection-sensitive. I understand that even if I'm just sharing the positivity I feel, it can come across differently. I guess this is just the cost of communication in a lossy language.
Whataboutism?
reply