I work at OpenAI and I can try to explain our logic.<p>In general, LLM performance is incredibly multidimensional. There's no guarantee of a fixed ordering where the model that is best at math is also the model that's best at jokes. Despite that complexity, much of the variation in performance is explained by just a single variable: model size. In general, models that are bigger will (a) be more performant / reliable, (b) cost more per token, and (c) have better recall of esoteric facts. As you note, our models are ordered:<p>Luna < Terra < Sol < Astra<p>Even today, I'd recommend this as a general starting point for picking models. If you care about quality only, pick Astra. If you're a bit price sensitive, pick Sol. If you're very price sensitive, pick Terra or Luna.<p>Of course, model size doesn't capture all variation. Models that are trained later might have new data or new techniques or new architectures that earlier models lack. You can see this with models from all providers - Sonnet 5.5 outperforms Fable 5.1 in ways, and Gemini 3.8 Flash outperforms 3.1 Pro in ways.<p>In the case of GPT-6.1 Terra, I'd say Terra is probably better than Astra and Sol at technical communication, similar on vision and computer use (but much faster!), and a bit worse on most other things. For API developers, it's a nice midrange option if you want something cheaper than Sol but a lot smarter than Luna (especially for vision / computer use / communication). For subscribers, it's not a bombshell release - if you're hitting usage limits, maybe it helps you go a bit further, and if you want clearer communication or faster computer use, it can be a nice option to select.<p>If you have suggestions on how to name our products better, I'm all ears. Our intention is to make things clear and simple, even if we miss the mark sometimes.<p>Unfortunately, a lot of the complexity is somewhat irreducible and due to to the actual complexity of the products. Scaling model size is different from scaling reasoning effort which is different from scaling multi-agent count. So if we just swept all of those under a single name of "more IQ", it would make the naming simpler but the experience much worse, as important details would be obfuscated from end users.
Do you then recommend Sol for technical documentation?<p>What's confusing to me in addition to the naming is that there are no published guidelines I can use, as far as I can see. Put yourself in the user's shoes. On what basis should one pick a model for a given task?<p>I can understand smaller models being dumber and faster. That aligns well with scenarios where you are willing to compromise; e.g. you're classifying content or asking a model to format text into a table.<p>But when you want "maximum intelligence, cost and time be damned", there's the implication that bigger models are smarter on a linear scale, and every provider's model release naming is currently conflicting with that expectation.<p>This isn't just a naming problem, but a communication problem. If I knew that Astra was the best at coding but a bit bad at writing, and Sol was almost as smart but better at technical writing, then that's the kind of thing I expect to be documented.<p>One possible way: A simple matrix with horizontal columns like "writing", "coding", "math", rows are the models, and each box is a number of stars from 1-5.<p>Instead, this sort of thing becomes lore, shared by word of mouth.
> Do you then recommend Sol for technical documentation?<p>I don't know, to be honest. Technical documentation is such a multidimensional skill, and I also don't know your budget. I'd imagine Terra might have the most readable prose, but Astra might be more likely to catch bugs or mistakes to highlight. If the documentation is about a more esoteric language or situation, Astra might have a greater edge than if it's a common language or situation. If you have a long list of documentation guidelines, that might also give more of an edge to Astra. I wouldn't want to make a recommendation without doing a lot of testing, and I haven't done a lot of testing.
> Put yourself in the user's shoes. On what basis should one pick a model for a given task?<p>Create a realistic eval suite for your use case using your actual data that you manually label, with easier, more complex, rarer samples, and run each model you are considering against it and decide based on the usual confusion matrix, cost, latency, or whatever metrics matter to you. There's really no other way to know – and you might be surprised that cheaper models can often perform your task(s) relatively well at a fraction of the cost.
> Create a realistic eval suite for your use case using your actual data that you manually label, with easier, more complex, rarer samples, and run each model you are considering against it and decide based on the usual confusion matrix, cost, latency, or whatever metrics matter to you.<p>I’d imagine there’s only a tiny percentage of users that can or want to do this.
> Do you then recommend Sol for technical documentation?<p>Every model I've tried is just abysmal at this, for what it's worth.<p>If any do it well please let me know and I'll give it a spin.
My thoughts exactly.<p>I recently experimented with some image generation models and I was surprised at how much context/prompting is needed to get something that doesn't look Lovecraftian. There was seemingly no downside to adding more and more detail up to the limit. Prose sits between code and images in terms of fuzziness and I wonder if promptmaxxing is the winning strategy for technical documentation.<p>I've been thinking that fine-tuning a model to write <i>differently</i> shouldn't be that hard (tedious, perhaps) and if I had the resources I would consider doing it myself.
You can start with Artificial Analysis benchmarks but, then you need to test in the real tasks anyway.
I assume having a person write words for other people to read is not feasible?
Humans cannot be in the loop for <i>autonomous work</i>. I have agents review code and design/architecture documents, write alert messages based on rules, write analyses (e.g. outage incidents), and many other things. "Having a person write words" would mean <i>not</i> using agents, defeating the whole point.<p>I do not use agents to write on behalf of myself. I write my won technical write-ups and my own slide presentations. A lot of people don't; and it shows. There's much that agents can do better than humans now, but not everything.
> I'm all ears. Our intention to make things clear and simple, even if we miss the mark sometimes.<p>Thanks. I think the very response you wrote here is a better form of marketing for the software engineers than what openAI puts out there. When everything is groundbreaking and everything tops charts, nothing is.<p>I suspect a lot of target demographic (developers) prefer simpler and honest communication about not just the models high points but low points too. With the linked Terra post, if I saw 1 chart that clearly highlighted the usecases for which Terra is _worse_ than higher tier models, I would be more convinced overall.<p>Everything trying to occupy the top spot in my brain's limited "top model" representation space is not helpful.
While I generally agree that it'd be nice to just have "here is the Pareto optimal model for your use case. Here is the specifics we optimized each model for" so users don't have to do benchmarking and research as much, they're getting much better at explaining. It's basically what the commenter above said.<p>The website linked <i>starts out</i> with:<p>> We’re introducing GPT-6.1 Terra, a fast, efficient model that combines detailed visual reasoning and precise computer use alongside clearer, more natural writing. A significant step up from GPT‑5.6 Terra, it excels at the communication and computer-based tasks that fill the workday, from drafting proposals and interpreting technical drawings to filling out forms and editing videos.<p>If you go father down, it has graphs and details with specifics of this, including:<p>> We measured GPT-6.1 Terra’s computer-use capabilities... At medium reasoning effort, GPT-6.1 Terra scores 15 percentage points higher than GPT-5.6 Terra at about one-third the cost. It even performs on par with GPT-6 Astra at only one-tenth its price per token.<p>and<p>> On MMMU-Pro... It even nearly matches the scores of our most capable frontier model, GPT-6 Astra, and GPT-6.1 Sol at much lower cost.
The 4-tier naming is honestly as clear as you can get. I have no idea what people are so confused about.<p>Terra and Luna are dirt cheap and good enough for 50% (or more) of the typical junior dev's workload, and great for routine work. Sol and Astra have so much written about them at this point that it should be obvious what those are good at. The large/gigantic models for things that need full intelligence and slightly more branching thinking.
The obnoxious/confusing thing is that models are released individually rather than as a family.<p>Luna < Terra < Sol < Astra, fine. But does Terra 6.1 beat Sol 6? Does Fable 5.1 beat Opus 5.5? If I build something around this Terra 6.1 release, then when Luna 6.2 is released in a month, should I swap?
I understand the confusion, but isn't this kind of similar to asking does the iPhone 14 Max beat the iPhone 16 Mini? At least with phones there are hard specs we can use to compare, but in a lot of cases the answer will be "it depends"
At least there is a simple system - the words 'Mini' and 'Max' are intended to refer to the size of the item (that one is miniature, and the other is their maximum size).<p>If they were called iPhone 14 Luna and iPhone 16 Sol then this is less clear.<p>Or on the flipside, if the models were called GPT 6.1 Mini and GPT 6.1 Max, nobody would be arguing about the names being hard to grok. The words 'mini' or 'small' are more descriptive than 'Luna' or 'Terra'.
I guess it never really occurred to me that people wouldn't immediately grok:<p>* Luna (Moon)<p>* Terra (Earth)<p>* Sol (Sun)<p>* Astra (Galaxy/Universe)<p>The relative sizing seems dead simple.
The problem with that is then you would have GPT 6.1 Max with max thinking which is different from GPT 6.1 Small with max thinking.<p>If you look back the naming problem started around the point that OpenAI started doing reasoning models (o1, 4o) where they had to describe this fork in model behavior that let a smaller model think longer and be smarter.<p>You can come up with naming schemes that don't clash, but there's something to be said for this approach where the thinking level is like you describe and the model name requires you to learn that Astra is the largest model.
> does the iPhone 14 Max beat the iPhone 16 Mini?<p>Sure, in a hypothetical world where the latest Max phone is two generations older than the latest Mini phone. In which case now you have to evaluate not only screen size but ALSO specs and component age and software support etc. How badly do you need the bigger screen? Etc<p>If you are comparing the iPhone 16 Max to the iPhone 16 Mini, everything is a lot easier.
> iPhone 16 Mini<p>I wish
Yeah, but shouldn't then all 4 tiers be released at once? So we get 6.1 for all Luna/Terra/Sol/Astra?
No. It’s not similar at all.
It's more like if Apple called the iPhone 16 the "iPhone 14 16 edition"
Run your own evals then if you have this much of a need to "correctly" choose. That'll make it clear.<p>I'll just stick to mostly Sol on low/medium/high and start experimenting with Terra tomorrow onward to see what it does noticeably better than Luna/Deepseek flash.<p>Any pareto frontier model, especially from the leading labs is guarnateed to be good enough for whatever your expectations are at this point. A quick few day test should be enough for anyone to make their own conclusions.
Right, having products so poorly described that a customer needs to run their own evals to choose well is exactly what is being complained about.
Or I'm going to give up and not use any. Consumers aren't perfectly rational. This isn't like toothpaste, where, despite being overwhelmed at the store, I still need to brush my teeth so ahhhhhh fuckit and just pick a toothpaste out of the 30 available. The alternative is I keep using Google search and think AI is stupid. Or just use Facebook's thing.
> > But does Terra 6.1 beat Sol 6?<p>> Run your own evals then if you have this much of a need to "correctly" choose.<p>No. OpenAI want my money, it's on them to communicate properly.
What would you want to see instead? Not sure about you but my needs are specific enough that I don't think OAI has evals for them, and I wouldn't want to give them that data even if they wanted to run it for me.
How does one more-properly articulate "it depends"?
> The 4-tier naming is honestly as clear as you can get. I have no idea what people are so confused about.<p>I wouldn't necessarily say that. Atmospheric mass may be obvious to us, but I suspect it's not nearly as universal knowledge as one may assume.<p>And I actually assumed at first that they were based on distance from us (terra < luna < sol), so I kept confusing terra and luna.
I think the main problem is surfacing two topics:<p>- Understanding how effort and class interact (Is a high effort terra above a low effort Sol? Are larger models mostly always better regardless of effort?)<p>- understanding when (if ever) a new model tends to be better than an older but higher class one. Is an old astra better than a new terra?<p>If increasing effort and increasing model class produce improved results in a different way, I miss making it explicit in which way. In other words: What kind of issue warrants going up in effort? what kind of issue warrants going up in model class? What kind of issue is unlikely to be solved by either?
OpenAI itself cannot answer these questions. A prospective price-sensitive (or speed-sensitive) user has to experiment to find the cheapest (or fastest) model that is sufficient for each task.
>effort / performance curves by model<p>Oh, easy:<p><a href="https://deepswe.datacurve.ai/">https://deepswe.datacurve.ai/</a><p>Artificial Analysis has their own curves which are pretty similar.
That's good, but they haven't updated it for a month. It is missing all of the current frontier models.
Seems to be missing all of the 6.0 and 6.1 models?
> Is a high effort terra above a low effort Sol<p>You can literally see this in the charts from the article
Thank You for OP asking the question and thank you parents for some insight.<p>>If you have suggestions on how to name our products better......<p>Too late for that. I hope you guys can at least stick to these naming scheme for at least another 2-3 years going forward. Not everyone follows AI / LLM intensely, and it seems there is a new naming changes every year or so.<p>At least conceptually OpenAI's naming is easier to understand than Claude.<p>It terms of capability, it would be great to make it clear somewhere that this is 6.1 Model "+" technical communication, vision and computer use improvements. So that at least we know what we should try with these new release and choose a better model for certain workload.<p>Right now, other than pricing, it is confusing as hell. And in hindsight I wonder if there are fatigue in choosing one people just go with Open Model and stick with it if it is good enough.
Since internally you probably have a fairly good intuition for what these models do well, what about assembling a benchmark suite (with multiple dimensions) that produces results that show that intuition, but in a 'somewhat objective' way?<p>That could solve the 'more IQ' problem, because you'd actually have different dimensions (e.g. for 'raw intelligence', technical writing, emotional understanding, etc) that you could use to communicate the differences in models better.<p>(Of course people would instantly start complaining that the benchmarks numbers are probably fake etc, but it is what it is.)
Internal evals are still running but GPT-6.1 Terra is an AWESOME release for anyone using the API in agentic production applications where cost matters.<p>Personally, I pretty much exclusively use claude for coding work (sorry!). Our application is running almost 100% on OpenAI API (except for some fallback models). It is the best API on the market. The models are excellent particularly for any application that is cost sensitive.
>> If you have suggestions on how to name our products better, I'm all ears<p><version>-<size><p>Where <version>: 6.0, 6.1, etc., and <size>: xs | sm | md | lg<p>E.g. 6.1 Terra = 6.1-sm<p>If you eventually make specializations like "math" and "jokes" then I would call it something like 6.1-jokes-sm.
It's not gonna happen but:<p>> Despite that complexity, much of the variation in performance is explained by just a single variable: model size.<p>please release model size :p
This was lovely. Can we have more of this style of communication?
Instead of benchkmarks it'd be useful to get a sort of Radar chart across measured capabilities like computer use, communication, coding, etc.<p>We're probably headed to further specialized agents where a single ordering or even model size won't mean a lot.
What makes this even more confusing is reasoning effort.<p>You have Luna, Terra, Sol and Astra, each with 4+ reasoning levels. That's ~20 options to choose from.<p>On a repetitive task like customer support or something, where you have decent benchmarks, you can plot cost versus performance, but you can't really do that on coding.
I actually do have a recommendation, which is to do something like a new mode (i know i know):
Auto xlow
Auto low
Auto med
Auto high
Auto xhigh
Auto max<p>With an optional flash mode.<p>Then as you release models, you pick the model and thinking level that you believe is pareto optimal for cost/performance.<p>So this auto updates each release, like Terra was probably off this list entirely, but maybe this would appear now in Auto medium or something like that.<p>I know from looking at charts that each model seems to have sweet spots (like I always use this model in this thinking level and if i want to go cheaper i go here). If you picked those for us, it would be interesting. I do think you should keep the model families for those wanting more consistent experiences, but for many devs they may want to just pick auto something and have it update over time. I think nice if it actually just said in parenthesis what it was, something like:
Auto Medium (Terra 6.1 High)<p>I am not sure if everyone would agree here though. I'm obivously advocating for more naming schemes, and i understand the irony.
Please can I ask about one parameter you didn't mention here? All of your models have both a reasoning effort setting and also a non-pro vs pro setting. I have some intuitive feel (from experimenting) for the difference a larger vs smaller model makes, and why that's different from adjusting reasoning levels, but I have no real feel for how pro vs non-pro is different in behaviour from adjusting effort.
I think you can think of Pro approximately as running multiple instances of a non-pro model, than giving all their outputs to a final judge model which combines them into a better one.
I use OpenAI models all the time (via Codex) and I've never seen this pro toggle. Where are you seeing that?
Appreciate that these things have orthogonal measurements; with that in mind, given that the name signifies size, what does version number signify?<p>I.e. are all version numbers independent and Model X 7.5 is not necessarily of the same generation or cut off as Model Y 7.5? Or do all of your 6.1 models have <i>something</i> in common - cut off data, generation, baseline technology, approach, architecture, or something else?<p>Thanks! :)
These days, version number signifies a few things at once: the order of release, the relationship to other releases, and the magnitude of improvement. So something like 6.1 would indicate it was launched after 5.6, may share some similarities to other 6.1 models in terms of training data / personality (which is why it's not 5.7), and isn't a giant enough leap to merit the headlines and expectations that would come with a 7.<p>Back in the day we used numbers to also sometimes indicate size, which could make it extra confusing: e.g., we launched GPT-4.1 (an incremental update on GPT-4) after GPT-4.5 (a new model that was a big jump over GPT-4).
This makes a lot of sense.<p>It’s probably just unavoidable. Like when Apple release a new MacBook Air first and it is (in some ways) faster than the old MacBook Pro.<p>I think the planets thing is a bit confusing though. I hadn’t really thought about it being based on the size of the planet. I assumed it was based on how far away they were with terra therefore being the base one.
I like to use OpenAI models for ingesting a customer brief, usually a paragraph or two and then a read of a several customer files (eg spreadsheets and docs etc) and turning it into a system design blueprint. I'm currently using Astra for this phase but it's incredibly expensive relative to the other tasks that come after this. Would you recommend I stick to Astra or can I downgrade to say Sol or Terra for this job?<p>I'm just worried the system design won't be as good if I choose a lesser model so to speak.<p>Edit: typos/grammar
"In the case of GPT-6.1 Terra, I'd say Terra is probably better than Astra and Sol at technical communication" -- you're doing the thing here too, underspecifying your own models: 6.1 Terra is better than…<i>which</i> Sol, 6.1, 6, 5.6, just the latter two, all of them? This 2D bluriness is a big part of the confusion.
It would be interesting to know which model is internally viewed as being best at jokes, I have a personal humor benchmark and so far models will either steal jokes (from comedians like Maria Bamford), or write something very unfunny.
When I was first learning to have agents interact I had a little script to orchestrate a model telling a joke, a critic collaborator helping develop material and an audience emoji rank them.<p>A favorite…<p>Prompt: “Tell a joke about mother in laws”<p>Gemma2 response: “What’s the best kind of mother in law? A dead one.” 0_o
This kind of heuristic is actually extremely useful - and mostly what I try to develop over a couple weeks as the new models come out. I would love if we could get a fairly standardized take like this on the regular.<p>Of course we have far too many benchmarks (of questionable utility), but it would be helpful to get OpenAI's take on where it slots in, and how the landscape changes as these get rolled out.
Straight up - why not just name them Cheapest, Cheaper, Expensive and More Expensive? Cost seems to be the main driver of the intuitive difference between models.
Same reason that Apple doesn't name its series of phones the iPhone cheap, iPhone regular, iPhone semi-expensive, and iPhone expensive. It's poor branding, isn't easily extended to future models, and gets confusing when comparing across model generations, where an iPhone 14 expensive might be cheaper than an iPhone 16 cheap.
I love the current naming scheme and I am excited to see the astronomy trend expand in the future. I hope to see asteroids, binary stars, and solar systems at some point soon, as well as neutron stars, black holes, and eventually galaxies, and clusters of galaxies.
> Models that are trained later might have new data or new techniques or new architectures that earlier models lack.<p>This seems like one of those cases, where CalVer would be appropriate, and you likely have ideas what you aim for while training, so technical model names (ids?) could include aliases to most things you said. Like
2026q4-xlarge-researcher, 2026q4-medium-humanitarian, 2026q4-medium-coder, 2027q1-small-analyst etc.
> important details would be obfuscated from end users.<p>I don't expect the vast majority of your users care<i>. I would guess they want a rough proxy for "cost" and will assume cost=>capability are roughly linked in a linear fashion.<p></i>it is very possible that the users weighted by spend do, and I'm therefore simply wrong. But, 2c
Use colloquial terms: Big, Fast, Genius, Affordable, etc.
In your own response you mentioned GPT-6.1 Terra, but I've seen GPT Terra-6.1. I would start by the part that is the most powerful:<p>GPT 6.1 X > GPT 6.0 X
GPT Y Astra > GPT Y Sol > GPT Y Terra > GPT Y Luna<p>This order is very important.
What prevents OAI (and Ant for that matter) from releasing new version for all models at once?<p>Instead, we got Opus 5.5, Sonnet 5.5 and Haiku 5.5 released weeks apart, and so are Sol 6.1 and Terra 6.1?
My guess is nothing, and they're currently just releasing them individually as soon they're ready instead of all at once so that they hit the market more quickly. AI moves quickly enough that a delay of a couple weeks could result in significant lost revenue.
Lower models typically use distilled outputs from higher model to improve .<p>all training runs have to at some level share the compute capacity ; while they have more than enough to run many large runs concurrently, they don’t have enough compute to run everything concurrently.
> I work at OpenAI and I can try to explain our logic [...] general, models that are bigger will be more performant.<p>> If you have suggestions on how to name our products better, I'm all ears. Our intention is to make things clear and simple, even if we miss the mark sometimes.<p>How about naming the models:<p>"Small", "Medium", "Large" and "Very Large"<p>Or refer to them by parameter count like other companies (e.g. this is 6.1 5T)<p>If I have a clothing store, and rather than name the clothes sizes Small, Medium and Large I decide to call them Urgon, Planfar and Fangor I shouldn't be surprised if people think my size naming is confusing or silly. And IMO it's not a great explanation to say "oh it's easy, if you are skinny then you should get Urgon, but then if you are a bit more of a medium build you should get a Planfar, but if you want something a bit baggier than Fangor will really work out for you"
The problem with small, medium, large naming is that they don't last. In a few years our names would have to advance to labels like giant, behemoth, gargantua. This is different than clothing, where we know that in five years humans will be most the same size as today.<p>As a point of comparison, GPT-3 was launched in a series of small, medium, large, and XL. Back then, XL meant a mere 1.3 billion parameters!
> In a few years our names would have to advance to labels like giant, behemoth, gargantua<p>You wouldn't - you would just put the version before it - 6.1 Small, 6.0 Medium etc. Not that complex.<p>Think of an iPhone 18 Pro - No need for 'iPhone 18 Even Better Super Pro' because the 'Pro' is relative with the version/generation.
Reminds me of astronaut sizing charts for ... their waste management devices.-
The length of your explanation shows just how bad of a naming scheme this is.
> Our intention is to make things clear and simple, even if we miss the mark sometimes.<p>Well you definitely missed that mark extremely badly here.
> If you have suggestions on how to name our products better, I'm all ears<p>Terra 2026.2
Is there someone I can email directly about fine-tuning - all my models have been queued for 24/48 hours and your customer support seems non-existent
Fwiw, if you have to explain your product's name to the customers who just want to use it, that's a sign the products are confusing.<p>Tiny/2026-11. Medium/2026-6. Big/2026-10. XL/2026-01. It may not be sexy, but people know exactly what it is and what to use.
Would you like to recommend a model that <i>does not produce errors </i> and can be relied on like a traditional computer, please.<p>You certainly know that it happened not by accident that traditional computers are producing reliable results, a lot of work went into this.<p>Computers must produce reliable results, they are simply not useful if not.<p>People will understand that at some point, but a lot of damage already has been done.
If there's no fixed ordering, can we have model names that describe the capability?<p>Have a model alias called "math", another "jokes". Quality labels/date suffixes to these obvious labels. Repoint as needed.<p>Something for people who don't want to play the cute "which part of the solar system is trending this week" game.
I promise you this would become a giant mess. No one wants to pick GPT-good-at-writing-but-not-in-documents-and-decent-at-instruction-following-but-has-some-known-quirks-to-be-aware-of-p.s.-good-at-knock-knock-jokes-MAY-2027.<p>Even "writing" as a single concept is very multidimensional. There's fiction writing, nonfiction for experts, nonfiction for learners, deep research, quick lookup Q&A, copyediting, writing in documents / artifacts, instruction following about style, and default quirks (e.g., em dashes, negation, loving the word "tapestry"). Some models are great writers but less diverse, meaning people prefer the first story they read but disprefer the tenth. How does one label such a model?
> No one wants to pick GPT-good-at-writing-but-not-in-documents<p>But this is exactly what we have! Except it's worse, because the model usage recommendation isn't in the API, it's in some random person's X advice that's no longer current this week.<p>I understand AI companies wash their hands of this problem because it's hard, and they themselves barely understand the true capability matrix of the models they're selling.<p>I just don't think some effort to categorize models and expose that to users would be messier than what we have -- model selection driven by influencer voodoo bandwagons and analogies to literal astrology.
What do you understand by “technical communication”?
naming suggestion: small, medium, large, x-large<p>it's almost like ya'll let the AI name it ;)
> If you have suggestions on how to name our products better, I'm all ears.<p>Hire an actual marketing person instead of letting engineers / ML researchers do things they're clearly terrible at ?
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> If you have suggestions on how to name our products better, I'm all ears.<p>For some reason, this is just hilarious to me. Idk if you're being serious or if this is passive aggressive but the thought of OAI actually taking advice from a random HNer and basing their naming scheme off that is peak.
This is one of the last bastions of semi-serious discussion on the internet, and it's full of incredibly high level engineering talent.<p>Where else would you go? I'm honestly scratching my head thinking of someplace that wouldn't run you out on a rail, but that would give serious feedback. Kernel mailing lists are out, maybe there's something like that which I can't remember off the top of my head, but...<p>Also, nomenclature is <i>incredibly</i> hard. There are only two hard problems in computer science: cache expiration and naming things.