Advertisement
Advertisement
⚡ Community Insights
Discussion Sentiment
80% Positive
Analyzed from 2852 words in the discussion.
Trending Topics
#fable#more#models#opus#code#tasks#still#model#using#better
Discussion Sentiment
Analyzed from 2852 words in the discussion.
Trending Topics
Discussion (69 Comments)Read Original on HackerNews
I think a lot of people would be very content if they never got smarter, and just kept getting even cheaper/faster. Of course, both things continue to happen on a seemingly monthly basis
It was so amazing to get advices and reflect that it struck me : I could use this model forever - it’s clever enough to help me tons and do lot of work for me - even if ai would stop evolving I would love it
especially considering imo most use falls under this instead of those kind of tasks where you'd need the SOTA
+1 regarding voice usage too, I use it in so many different ways it's hard to enumerate: while driving long distances (think of a custom made, interactive podcast) / as a way to collaboratively build specs or shape an idea / as a way to provide input while vibe coding / just as a normal voice assistant (straight in the ChatGPT app or as OpenClaw input via telegram voice notes). I can't overstate how much my routines have changed over the last couple of years.
There's a lot of truth to this. I think we're starting to approach the point where increased intelligence has declining marginal returns, such that it might not even be worthwhile to improve models unless it can be done cheaply.
The biggest generalist models beat the most fine-tuned specialists, as a rule. You can bias an LLM away from literature knowledge and towards coding capabilities, but that buys you very little performance, and for too much effort.
Generality and intelligence seem to be entangled very heavily in LLMs.
With how generous subscriptions are, what I actually want is GPT Astra, not cheaper Sol.
Maybe Fable can do the same things better than other models, but having to tiptoe around to avoid tripping safeguards makes GPT 5.6 so much easier to work with that I don’t even bother with Fable (or Opus 5) now.
It happens to me all the time with things that have nothing to do with security, Fable spawns a subagent that then adversarially checks the code Fable just wrote and hits guardrails, with zero prompting from me.
Even now, I use Fable as the planner and coordinator, with it farming out to agents. I don't hit my Fable limits either.
Which means I could accomplish more, but these are side projects so I don't need 30x productivity. Still, claude is constantly churning away at something.
This is what the "good enough" people fail to grasp. There's no "good enough" - unless your tasks are genuinely small scope and will stay that way forever. If not, there are always more gains to extract.
Right now, it feels like all of that is today where coding was a year or two ago, and we're on the cusp of some massive improvements outside of coding. It'll be interesting to see what these companies decide to automate next.
As a software engineer, I selfishly hope that they spend more effort on non software tasks since I’ve feel like we hit a sweet spot where engineers still have some value and autonomy, but a super charged tool.
Pragmatically, I suspect that “non software” tasks will be a tarpit because most tasks can’t be automated and verified as easily in an RL loop compared to software projects. Especially since most skilled labor is either not nearly as expensive as software engineers (eg biologists), or regulated (eg doctors, lawyers).
For the users, I feel it is more like "free lunch started", with all these awesome open-weight models being thrown around, breaking the monopoly of a few biggies.
One of the primary reasons for this is that computers operate in a vast range of orders of magnitude. There’s several orders of magnitude between cache local cpu operation and dram, then several to disk, then several to network, then several to globally durable guarantees. When your code has literally thirteen orders of magnitude to optimize under, there’s never a free lunch. You always need to understand your stuff.
As 80% of enterprise software is CRUD with a bit of sprinkling of user authorization and tenant customisation. But subtly different for every business domain. It's mainly what properties the models and validations have that are different.
When you add a new module or whatever most of the code you have to write is rote code.
And sonnet can handle that crap just fine, you just point it at a similar example in the code, it picks up your userContext convention, how you're doing i18n, etc. and you're done.
I like saying that enterprise code is often shallow but wide. I must have written at least 4 purchase order systems in my career that are all completely different but almost exactly the same.
Besides trying to dogfood my own product I've hit a wall in terms of my patience with a)how slow fable is b)how expensive fable is. Not to mention how often it refuses totally legitimate work.
So yeah- I've moved to DeepSeek and I actually ask the freepi harness to delegate planning to fable but then move back to doing implementation in it's own harness. My current providers are super fast so it's a joy to use.
I throw everything at claude Opus.
While some people start thinking like OP, A LOT of people just start exploring ai.
And others which are already using it, only understand half of it and just use what they are allowed to use. Claude, GitHub Copilot, Curser, etc.
People say stuff like this a lot, but I have a different take.
The whole "such-and-such model is 90% as good as Fable at 1/10th the price" assumes that the value increase of intelligence is linear. But I think it's exponential: that last 10% makes a massive amount of difference. It can result in a key insight that helps you strategize more effectively, a novel approach that saves a huge amount of time, a feature design that is lot more user-friendly (because top models like Fable also possess substantial non-software domain knowledge that help bridge the gap between user and software), or the depth and breadth of engineering expertise that helps avoid a nasty bug that would otherwise have cost you users and revenue.
Yes, it is totally possible to use Fable as the planner and delegate implementation to lesser models. I do that. But, my theory (which I unfortunately do not have the money to test and prove) is that a codebase designed and implemented by Fable would be substantially better than one that is designed by Fable and implemented by Opus 5, GPT 5.6 Sol, GLM, Qwen, Deepseek, etc. The reason I believe this is because I read the code Fable writes and compare it to code that any other model writes and the difference is night and day. It's not just 10% better. It's mid-level engineer vs. principal/staff-level engineer. And the thing is, even for rote tasks, a more senior engineer is going to be more likely to come up with a clean design than a mid-level engineer. They will also be much more likely to take a step back and ask important questions or propose different approaches.
So if you're using Fable and everyone else is using lesser models, sure they might be saving a lot of money, but there's a higher likelihood that your product will be higher quality, perhaps to a significant extent. And models that are released in the future will benefit from it as well.
In that light, I often go the other way: let Opus (and Haiku subagents) do most of the heavy lifting and then give Fable a shot at finding holes, especially if there are holes or unanswered questions or unearned assertions that I’ve caught on my own in Opus’ output. This, so far, seems like a clean tradeoff that doesn’t burn my Fable credits as hard and still gives solid results.
Really frustrating.
I don't have proof, only my anecdotal experience: I leave plenty of Fable usage on the table because I do not think its implementations of code have been better to Opus 4.8, not even close. It overengineered, obscured and picked awkward constructs all the time over plain, simple, perfectly clean and performant code patterns. Code was smarter AND worse in the kind of way that a brilliant and overeager recent grad often does. (I know I did)
Granted, I laid out a document with coding practices, architecture, and technical design recommendations to steer it towards good engineering. And it's a domain I know super well, so I could give very nuanced feedback on trade-offs + architecture. If it had been left to its own devices, maybe it would have over-engineered the h*ck out of it.
But the code it produced—and the implementations it guided Opus towards—were excellent.
My brother, that's my job.
With Sol we see openai making the model extremely slow and paranoid about process/ceremony. Sure this is a good guardrail against AI going rogue, but it also sets the stage for companies to charge for 2x, 4x, 8x performance, with 1x being barely tolerable and frankly slower than last year's models (though less error prone).
The irony is that the smarter the model, the more it can be trusted to do with less supervision, so one engineer can manage a team of 20 fable subscriptions more effectively than a team of 3 of last year's model subscriptions.
If you're telling me to focus on something, why not focus on the actual latest thing that is the same as 5.2 but better? I get the "came out at the same time as fable" thing, but still.. no mention at all?
Yes, weights aren't out yet, but neither are the ones of Fable.
Doesn't feel well informed enough to give advice.
I can’t host GLM 5.3 yet, so my agents still run on 5.2. But the fact that 5.2 is sufficient and there’s another gen in the wings kinda proves my point, imo.
https://ourworldindata.org/data-insights/moores-law-has-accu...
"When Moore’s Law slowed in the mid-2000s (specifically, single-threaded performance stagnated), we suddenly had to think about parallelization, architecture, memory locality, etc."
Your link is talking about transistor count. The article is talking about single-threaded performance. Today's CPUs are faster in large part because they have more and more cores.