After using Claude for a long time, I tested Sol 5.6 for the first time today. Love it, its an incredibly capable model and uses far fewer tokens/time thinking. Its what I imagine Fable would be if I haven't been downgraded on every conversation - even after completing the verification program. I think I may cancel my Claude subscription finally.
I think Fable's dominance is overstated. It definitely has the lead, but quantifying what that lead actually is is really hard. I'm using GPT 5.6 Sol to do some shit that I personally would consider "crazy" - low level undocumented hardware driver alchemy, reverse engineering highly obfuscated code, even a bit of screwing around with a rendering engine in Vulkan, really just about the most complex tasks I can get any model to do, and it does great. For the more advanced stuff, it definitely needs the effort bumped. But even with the effort bumped, the token usage really doesn't seem to skyrocket too badly until at least you hit xhigh and max, which really only seem to be necessary if you are doing genuine crazy stuff, so it's not that bad. I did similar stuff with Fable. In fact, I went directly from an Anthropic subscription with Fable to an OpenAI subscription with Sol, more or less, and it really felt pretty seamless. If anything, I was thrilled to realize how much I actually preferred Codex CLI, to the point where I started using it at work too.
Fable seems to be generally more impressive at outputting one-shot web apps. I'm not really saying that to try to downplay what Fable can do, it's just that if I compare the two, this is one of the few definitely noticeable areas that you can easily demonstrate. Obviously, one-shotting programs is much better as a demonstration of a model's capabilities than it is practically useful (not that it is useless, but hopefully my point is understood).
However, whatever Fable truly is better at, one thing I really like about GPT 5.6 Sol is even harder to quantify: taste. GPT 5.6 Sol outputs are still LLM outputs and they contain many things that people would probably consider "Claude-isms" for better or worse, but overall I really prefer the GPT 5.6 Sol output. I find it to be generally more tasteful. Hard to quantify, but when talking to people I've had enough people seemingly agree with me to convince me that it really is true.
I used Sol to extract the remaining decryption keys from the Super Mario Maker 2 (Switch) game files. Someone had previously extracted all the keys from the original release, but not any of the new ones from updates. Not only did it succeed, but it helped me understand the data sufficiently to add support for “Super World” rendering to my level viewer (which I made back in 2021), eg the little widget at the top of https://www.smm2-viewer.com/players/B16-306-GVG
I was very pleasantly surprised to find Sol wasn’t obstructive over what was clearly a very grey area endeavour.
Fable is almost unusable for anything but super boring mainstream stuff. I was getting safeguard flagged so often I’ve significantly reduced my usage out of fear they will blacklist/ban me.
Some of the topics it’s flagged have been hard for me to understand what it seeing that can be remotely concerning in my requests.
I cancelled my Claude max subscription. Somehow every query I sent was flagged as bio or chem, even pure mathematics questions. Not going to waste money paying for a “max” subscription that won’t ever let me use the top tier model…
Sol is great and has never blocked a request, and generally gives great answers. Happily switched over to it now.
I've gotten flagged for asking questions about tokens and tensors. That makes me believe it's not about safety, it's about protecting their turf. I cancelled my subscription - same fear about getting flagged too much leading to a ban.
They said they also block usage of Claude models to build ML models.
Which is definitely protecting their turf, but also probably a little bit hiding their “RSI” abilities for competitive reasons. My theory is that a lot of “safety blocking” is actually WIP training of new business directions. Anthropic has started hiring biologists and has opened a preview of a “Claude code for bioinformatics”. I’m guessing they’re tweaking their bioinformatics market play, and block “bio safety” requests so competitors can’t learn about their training.
What's the distinction between "protecting their turf" and "competitive reasons"? I see them as the same, but I could be missing something.
That's an interesting thought on the current "safety blocking" being a trial run for the topics that scare people (bio). You're more charitable about their motives than I am, but you might be right.
My differentiation for this comment, was “preventing someone from using your product to build a competitor” vs “letting a competitor see your strengths/weakness to benchmark against you”.
Competition is competition and it’s two sides of the same coin.
I agree. It’s flagged me on discussing fast GEMM implementations for large regressions, a discussion on theoretical physics math, a discussion on designing a type of
RAG system. I’m super baffled as to what the safety instructions actually are other than “advanced anything” and even then their definition of advanced is a joke, I’m an idiot and my questions are almost laughable.
Same here - my questions were sophomore level. I think it's notable that when I edited my question to say it was about Gemma 4, it answered without blocking. A cynic like myself would interpret that as evidence they don't care about sharing information if it involves their competitors.
Interesting. I've been wishing that old 'Stars!' game from the 90s would play easily on modern systems. I'd love it if we'd got to the point where I could point Codex at a folder with an ISO from my CD of the game and tell it to go reverse engineer it all for understanding of game mechanics, then go recreate it in a modern language capable of running cross-platform. Scarcely any need to improve on graphics, it could even be a PWA.
There's people that have tried to contact Jeff McBride and follow the IP trail but the IP is currently owned by a company that went defunct. Not sold, but no one is even bothering to register its LLC any more, it's simply dead.
I was having it look at creating a driver for some old scanner and it actively looked up exactly where that gray area for my country was wrt decompilation.
I feel like those examples are considered difficult because they're niche topics, but aren't actually all that difficult in a general sense. What I consider truly difficult are things like taking a ticket and implementing it in a preexisting codebase, using a clean and reasonable design that fits the existing style and makes sense to a human, and avoids the footguns I learned by working with the codebase for over a day.
If you said this in 2025 I would've 100% understood, but to be honest getting AI models to do a pretty good job on day-to-day ticket work has become so boring that we don't even bother using the top tier models and higher effort slots for that anymore. I personally wind up tweaking the results a lot and recursively having fresh agents review the diff, but that's just because I'm picky; in a lot of cases the first diff is actually pretty damn decent.
Compared to what I am doing at home experimentally, I feel like day-to-day work is absolutely nothing. Not only am I also working with existing codebases in my experimental prototyping, but I am also doing things vastly more complex with vastly harder constraints.
Personally I wouldn't want bots running autonomously on a repo, even if there were other bots cross checking them. But that having been said, I'd also say that my experience was similar with human code: it is rare to not find at least some issue worth at least pointing out. The only real difference is that the LLMs have vastly different holes than people do, making the real challenge trying to make sure you're covering them. In most cases for me the best solution seems to be just giving them a way to test and attempt to prove things out in a realistic environment. But clearly, we haven't really left the era of having humans in the loop. Fully vibe-coded codebases clearly suffer from a myriad of issues.
Multiple security holes. Issues marked fixed that aren't really. Giant holes left in solutions.
Sol over engineers now and then (hey please don't factor that function out into its own file....) but it doesn't do the same level of stupid terra does.
That said, plan with Sol, implement with terra, have Sol fix all the mistakes, then I go over the code and make recommendations for the architecture to fix Sol's foolishness.
My experience is that current models are pretty good at making functional changes without too many more bugs than a human would make, but are still bad at making good high quality changes. I have a degree in software engineering specifically, so perhaps I am overly sensitive to design issues.
I use the AGENTS.md to show it how i want the code to look like. Something like "when implementing hooks adhere to the guidelines in docs/react-hooks.md". And then react-hooks describes your heuristics and what you consider best practices. There is a clear difference in code quality for me when using codex with a well crafted AGENTS.md vs. without one, you can run the experiment yourself pretty easily. As I mentioned in another comment, I think Claude poisoned users to stop relying on their Claude.md files and new codex users might be surprised at how well it adheres to guidelines.
And Mai-Code-1.1-Flash seems like a really good cooperative player to GPT 5.6 Sol. You get Sol to help you make a detailed plan, and Mai codes it up and you can get pretty decent code out the other end without too many tokens if you are careful.
FYI I run it consistently in xhigh regardless of difficulty of the task at hand. I remember high being very fast, but I'd rather wait a bit more and get better output. AIs are insanely fast compared to me anyway, even on xhigh. Consumes more usage, but even at 100 EUR/m I don't hit limits.
After hitting the session limit on my company's plan so many times with Claude when I was using it, I mostly keep Codex on "high" rather than "xhigh" as a way to leave the tokens for my more ambitious coworkers. It's possible that having it higher might end up with better output, but so far at least I've yet to see a way to get any model to do 100% of what I need up front without any need for me to make changes that end up being more tedious to do via interaction than by hand, and it doesn't feel worth spending a bunch more tokens trying to figure out how to better communicate to it up front how the dominoes get set up so they fall in place properly the next time.
To be fair, I actually do run xhigh as my default. However, for the first time in my experience of trying and using LLMs, with Sol.. sometimes I feel confident enough to set the effort level to "Low". I just had Sol prototype some AWS stuff on low earlier. Great result, did exactly what I wanted.
Initially I was planning heavily around context limits, but I've learned to just ignore it completely. Compaction is seamless for me. If details are lost in compaction, the model just re-reads what's needed. My conclusion is that at least for Sol, the summaries (which I've never seen) must be amazing. Every now and then a detail gets lost and I have to repeat it. I don't think there is performance degration, because the model is smart enough to re-read relevant files as needed.
That's what they're always going to be, so not sure what would be "insane" about it. They literally feed on and emit natural language, and are put to work on informally defined, arbitrary tasks.
When people figure out any reliable strategies to test and benchmark them, that's insane, and in the positive sense. This very same issue has been a thing for humans as well forever, and remains only very questionably solved (IQ, academic tests). This is not easy.
Doesn't just sound like it, it is. That's life for you. That's what I'm pointing out to you. [0]
Just consider your own example. Do you think a less or more "rebellious look" is not something designers can actually ellicit? Less so in software design, sure, but in character design for example? Or general product design? Do you think e.g. Monster energy drinks are branded the way they are completely due to happenstance or something?
Except people don't usually put numbers to it, because they understand that that's hard to defend. You're the one who's describing such an idea, and wants such a thing to happen, classifying anything else as just vibes (that's the point!) and unhingedness. You're handwaving the difficulty and fundamentally limited nature of that, assuming that it is some laziness or mental delusion that's preventing it instead. You're also pretending as if it was somehow not real as a result. What I'm telling you is that you're wrong about that. Any kind of qualitative analysis that's actually defensible with these is genuinely difficult and limited in nature. See also all the opining about benchmaxxing. It also doesn't mean they're useless though, see also benchmarking.
The guy above didn't put numbers to his vibe assessment, they just drew a comparison, exactly because they know that there's not much else they can earnestly offer. You're sulking at them not lying to you by overstating their rigor, and you're flipping the arrow as if this limitation was some sort of mistake, not a necessary and intrinsic property, which it absolutely is. Natural language is an inherently subjective medium.
I think we’re saying the same thing?? It is literally just vibes and all this faffing over which model has better “taste” is pointless
Where you’re wrong is pretending there is any intellectual rigor to the discussion which justifies promoting from the domain of vibes to actual reasoned debate
We do agree that this is a vibes discussion, but disagree on whether that makes it a pointless discussion to have.
To give you a practical example, small, self-hostable models are very popular on HN. But my personal experience with them has been absolute dogwater, so this difference informs me that this is not the place where I should shop for a signal on whether a specific model like that is worth trying, or on whether it's game over for large models and remote models yet. I can "take the temperature" and make use of that without it having to be any rigorous, high assurance or mechanistic thing. It's suboptimal, but not useless.
Conversely, it also inspires people to try and substantiate these issues, so that it can eventually be more rigorous, higher assurance, and mechanistic. This is why I brought up benchmarks and benchmaxxing. Hard to know what points of consideration are salient when there is no abstract grievance to investigate, but Goodhart's law does also keep looming.
If people start moving away from Claude to GPT because e.g. Claude's output is too hard to work with and parse, that's relevant for the respective model providers, because it's a revenue share shift.
If I really like using Claude and strongly prefer its output stylistically, then claims otherwise will infuriate me, and will drive me to substantiate. If for no other reason, then because it's likely that Claude will have its language tuned in response to people's feedbacks and the revenue shifting, which may not be to my liking, and so I better prepare to call it and contend it.
It's literally like any other real world thing ever. Think tuning video codecs. Or tuning user journeys in frontend development.
It's easy to dismiss "taste" when you either have none or just fail to appreciate it, but nothing gives you an appreciation for the importance of taste like LLMs. There is no benchmark for taste, so while many things improve taste does not. Bad taste is, in fact, a huge component of what makes AI slop so sloppy.
But human coders can have bad taste too. There is code where there is nothing obviously objectively wrong, yet the choices feel like they were made by someone who just doesn't value or put emphasis on the right things, yet spends a lot of effort on trivialities. It comes in many forms.
You are confusing personification, which is purely a rhetorical device, with anthropomorphization. I am not anthropomorphizing GPUs. There is nothing particularly weird about personifying model weights, as we do with computer programs, cars, and all other manner of inanimate objects every day.
To be honest, in this case, I wasn't even personifying them, because I in fact didn't say that an array of GPUs has "taste", or in fact even that model weights did. I was saying I preferred GPT 5.6 Sol's outputs as a matter of taste. I can see why someone would confuse the two statements since in this case they're pretty much the same thing, but if you re-read what I said I was actually more careful than you're giving me credit.
things that are alchemical are rarely alchemy. That is to say things are very fiddly but stick a room of monkeys on typewriters, a schizophrenic developer with HolyC and adderall or an LLM, persistence is the key to many of these things like drivers, extracting keys from vintage security domains, etc. Dropping into xdd to a human is a chore, not for an LLM.
Although I am not exactly sure what you mean, I am not really claiming it is doing anything I couldn't do - but yes, it does so with much less effort. For example, I can have it set up probes and tracing on Linux that I personally would have to consult documentation to do. It might not even have to consult the documentation due to having the information on-tap, but even if it does, it's nothing that would cause it any fatigue, it's just going to keep moving forward in a loop until it is satisfied that it meets the criteria. I could've done all of this alone - I really could have. I just would not have. Being able to do something 10 times faster or with 10 times less effort is, in some senses, sometimes more impactful than being able to do entirely new things you couldn't do before.
This is a really good point that I definitely failed to grasp when first hearing about these tools. At least for me, the best way to use these tools is as a way to free myself from having to spend time thinking about the things that aren't worthwhile so I can focus on the things that truly are. I've had times in my life spending hours reading documentation and googling random things to try to tease out the correct sequence of commands or the exact right shape of an API to be able to make things work to know that it doesn't make me more productive to do that myself rather than point an LLM at the thing and let it spit out the answer after a few minutes. Meanwhile, I can spend that time thinking about what comes next, or what the correct way to take that one-off output and abstract it to something that can be used meaningfully in more flexible ways.
The only obvious objection I can think of to this line of thinking (at least from a technical perspective) is "how does someone build up the knowledge to be able to use a tool effectively in that way if not by doing things by hand at first?" The honest answer that is "I don't know, but that's also pretty much exactly the type of thing my employers have never been paying me to solve in the first place". Even just a decade into my career, there have already been plenty of times in my career I've struggle to convince people that we should do stuff in a way that won't bite us in the ass a month or two down the line, and in the times I've managed to succeed, it's usually only by putting in more of my own time and effort to make the initial investment seem more palatable. Luckily right now I'm not in one of those times when I'm having to go full throttle to keep the lights on a few months from now, but I don't have enough fuel in reserves to work on a plan for when we need to build a new rocket in another ten years. Maybe ask me next month.
Sol is way too eager to hone in on small details and ends up with massive over-engineering. Fable does it too - to be fair - but noticeably less.
After extensively using both on Max 20x plans, I've concluded that Fable is better for problem solving and coding, whereas Sol 5.6 Ultra shines in debugging specific issues: tackle a problem with Fable then leverage Sol to clean up, double check, or fix specific issues.
Fable (imo) had the edge on the $200 plan, but after this 50% reduction I'd say Codex is better value by far and there's no contest.
---
Using Fable as the orchestrator and delegating tasks to Sol 5.6 Ultra via the codex plugin in Claude Code yielded good results, but still there was a lot more over-engineering (thus time and tokens spent) than Fable by itself would've done.
Both models suffer from doing-too-much. But both models are fundamentally really smart and knowledgeable. I think it's really close and pricing cuts really spice things up for us consumers! Sol is a clear winner in the value department and the $100 plan is enticing!
---
*Claude Code usage is reducing by 33% in 2 days, Wednesday August 19... cmon anthropic: clau.de/cc-50-promo
Yeah, I very much agree on this. I think Sol and Fable code quality is on par. Maybe Fable is just a tiny bit better, but Sol compensates with its ability to work through things, while Fable, in my experience, generally tends to avoid solving problems that require many LOC.
However, I think these are very different models in terms of orchestration. Long-horizon tasks are way more predictable with Fable. It just doesn't lose track of details. Thus I ended up building a small wrapper around Pi (where I run Sol) so that CC can delegate via background tasks, automatically wait for completion, and do what was one of the most effective parts - steer Sol toward simplicity, getting Sol out of code-review infinite loops (Pi calls for Codex review to ship better, but generally gets stuck on P2 and results in vastly overengineered work).
One of the worst experiments was enforcing coverage at 100%. Only Sol, with an enormous amount of code and significant pushback (on architecture decisions) to Fable, was able to reach it. It made me think this is somehow related to overengineering in general, so that instructions on acceptance criteria in claude.md plus proper DX (e.g., Lefthook) actually led to okay results. It mostly helped that responsibilities were clearly split: Fable designs architecture, Sol handles coding and debugging.
I get a lot of mileage using Fable to spec, then Sol to review the spec, then Fable to plan, then Sol to review the plan, then Fable to implement, then Sol to review the implementation. It's a lot of steps, but a great boost in quality of output.
Sol 5.6 Ultra doesn't seem worth it because its long-horizon task scaling is not that good.
Remove the one thing that differentiates these 2 SOTA models (long-horizon task scaling) and you're left with assessing the raw intelligence of both models.
Both models are really fucking smart. We should be intentional when discussing effort levels when it comes so SOTA models because currently, effort levels are the essential lever to evaluate task scaling.
I've never seen that leaderboard link but I think my sentiments reflect exactly the findings: Sol 5.6 is smart, Fable is smart, but Sol is more value for the end user (even more so when you lower effort levels because that doesn't degrade the model's raw intelligence/knowledge). Not to mention Codex resets, that's just the cherry on top!
But smart != capable and this is evident once you start assessing both models on long-horizon tasks with higher effort levels. While Fable is (imo) at least a little bit better, both are still very good. If you want to test the raw intelligence of said models, you should lower the effort level.. if you want to test the model's capabilities fully, you should increase the effort level.
Economics aside, Fable is the better model (imo), but there's no need for a binary stance here. Both models are very good yet there is a clear winner on the value front.
*The "Max" I referred to was the plan tier, not the effort level btw
For small tasks, you can just use something like low or medium effort and it can usually avoid mistakes; after all, the model will test the code anyways and can do some baseline level of iterating.
In regards to cost, we need to acknowledge how generous OpenAI was in the last couple months with Codex usage credits (no weekly limits) and usage resets. It afforded me many a dive with Codex! Yes it uses more tokens, but sometimes it's worth it -- just depends on what you're working on.
Finally, Ultra(code) isn't that bad when it comes to cached tokens. I think folks overstate the general token usage of ultra effort on both providers.
---
Both models are great at green-fielding a project when given detailed specs.
Both models overthink too liberally (imo) during these larger multi-shots. Sol overthinks more than Fable.
Both models are really smart and perform great for general knowledge and regular coding tasks.
It's great, don't get me wrong, but so is Fable. I'm just comparing the long-horizon task performance between the two at the same or similar effort levels.
Given the 50% discount on Sol and how smart it is, yeah it's unprecedented value. If you only want to use low effort, there's a clear winner here on value and it's not even close!
Fable feels less cumbersome to work with, but it is SO DAMN ANNOYING with the refusals that I'm leaning more and more on Sol, and very much looking forward to GPT6. Just seems like Anthropic is trying their hardest to ruin their reputation and user experience.
Yeah I feel like I'm living in a different dimension than these people. I wonder what they're working on. I've literally never had it refuse everything and I max out my 20x plan every week
Can I ask which country you're in? I have a theory that the safeguards differ depending on the user's country.
I'm in Australia, and Fable downgrades to Opus when testing for bugs in memory in a legacy C code base. If Fable starts taking initiative and writes a test case that involves writing to a null pointer, that's the end of the conversation.
As one random example, today I had it hit a refusal loop when adding a country selector dropdown to a form, presumably because it contained a “bad” country name? I hit refusals at least 2-3 times per day, sometimes many more. The worst part is it is often right in the middle of a multi-stage task, so the only option is really to switch to Opus 5 and let it defecate its absurdly verbose comments all over the rest of the edits in the turn and hope it doesn’t go on one of its tangents, then have Sol do damage control. Oh and I got approved for their “cyber verification program” blessing, which comically does absolutely nothing for Fable.
Yeah at this point claude is overrated, overly expensive, weird writing style (elliptical), and the worst part is the aggressive guardrails that even normal convos get interrupted, meanwhile openAI is still I would say at the normal balance, if you ask something too obvious or direct it will stop you other than that, it work flawlessly, plus, I have yet to hit the limit despite heavily using it these past weeks.
You've replied incredulously to a similar stated experience in this thread already and proceeded to ignore the follow-up. Why are you again asking a question to which you have no intention to field an answer?
5.6 Sol is a joy to use for "daily chat" as well. Compared to earlier OpenAI models it catches and corrects its mistakes very reliably. It also seems way smarter in tuning its replies to areas I am more/less knowledgeable about (i.e. when I ask it a law question, it assumes I know as much as a toddler which is true, but on political topics it more easily throws around terminology) and including analogies. On medium thinking, it's a very good compromise between speed and quality.
I too switched to OpenAI after I got sick of Anthropic's constant "safety" downgrades. Sol is definitely a breath of fresh air.
> even after completing the verification program
Was it easy to complete it?
I ended up in some weird state where I can't even attempt the verification at all. Opened the Persona tab once, closed it and then it never opened ever again. It says a verification precheck failed.
Even without TAC, Sol doesn't seem to get blocked very often. Fable would downgrade to Opus if I looked at it wrong.
Sol is my daily driver but there are still times I reach for Fable when Sol doesn’t cut it. Just yesterday for example, I was trying to build a self-modifying hot-reloaded agent harness in Elixir for fun and Sol just kept doing silly things like thin wrappers and unnecessary abstractions. Fable handled the task elegantly. Sol is really good as a reviewer for finding bugs due to its thoroughness however.
Used claude since 7/2025. Switched to codex after fable got blocked. It was still 5.5 but I knew they had to come up with something. As soon as I switched, wow. It wasn't super intelligent, but it was stable. Every day it was the same performance. This consistency is definitely worth paying for.
Sol has held stuff for a while to do the same sort of hazard checks I assume Fable is doing, but it always releases them. I think that's the better way to handle it rather than preventing me from seeing how far I can get generating schematics to use in Minecraft. Currently: a mostly normal voxel house.
I have a "strategy / life-coach" project, and was surprised at how much better Sol is than Fable on it, as I've found Fable to have the edge for most things for me so far. But Sol: questions were better, insight was better, it got the brief better.
I cancelled my subscription recently and moved to Sol. So far - it has been a great experience. The only aspect where Fable/Claude is better I feel is doing some research from the web and summarising the facts.
I switched from claude to gpt when 5.6 came out for the same reasons. I don't understand why so many people are still using claude when GPT and open source are so much better.
It's the complete opposite for me. The model might be the worst model I have ever used when compared to other models in the class. You just can't get it not to just write the most enterprise over complex over engineered solutions for every little thing you ask it to do.
It the first model to actually make me pissed off to use AI. I absolutely hate the model so much.
I don't even want to see the codebases this model is fucking up.
It might just be good at finding bugs that about it. That all I would ever use it for just because it works harder than Claude models.
The final straw for Claude was its refusal to give me a list of the most recent rapes reported by the BBC and basic information about them (location, date, names, just things reported in mainstream media). It outright REFUSED to complete this task.
I will not be told what I can and can't do by AI and I will no longer be supporting American companies run by despicable people. GPT only gets my money right now because its so fast and cheap but I'll be back to Chinese models in no time.
Fable is the only one that follows my instructions correctly, which I find quite important. It takes STYLE.md and SPEC.md as law, and code just like I would code, with the same mistakes and all.
I just can't get Opus (Opus 5 is dumb as a rock, to be fair) or Sol to do that, so I exclusively use Fable for personal work. When I reach my weekly limit, usually on the last day close to the reset, I just go back to coding by hand ¯\_(ツ)_/¯
Heck, it even does security reviews and fixes, as long as I don't ask it to "attack" the codebase. I'm planning on using Kimi or GLM for that part.
That was an issue at least a year ago. I had signed up for a claude account on my iPhone and then wanted to sign in on my laptop but nope, not possible. Insane they still haven't fixed it.
Can somebody at Anthropic tag claude in slack or whatever goofy shit you do and ask it to add Apple OAuth to your website? Clearly humans aren't testing it.
Can't change your email address with Claude either. You have to delete your whole account and create a new one. Absolute shit software and they expect us to believe these things are super powerful world changing things.
sol is much better imho than Fable but i can understand if they will perform wildly different for different people with different levels of expertise aswell as different needs. I dislike fable myself it doesnt really work for me.
Sol also doesnt _really_ work but it sort of tricks me into thinking it does more convincingly :p.
cancelled my subscriptions few days ago. (was on 100$ ones, not sure if there is diff in quality for higher tiers or not.. there might be that too).
what i hate the most is that they will make any obvious mistake you do not tell them to avoid. then on the next plan to fix it, your token limit is hit at step 4/5 -_-. Both models seem incredibly good at that mostly...
for tasks outside of coding and program design i do find them quite useful. like devops crap. maybe because i hate that, i like their help there more.
Okay I'm a bit late to this, but my side project is www.freepi.ai, it's free ad+training powered inference in a Pi Harness. So not sure if that something you'd be down to try but if you did and have feedback I'd love to try and make it work for you!
I have witnessed 5.6 Sol Ultra edit line after line of literally empty lines ... for hours.
I wasn't literally watching it, I came back to a goal (that it started for itself without my approval!) that had done nothing but that for some reason.
