TBH IMO the Gnome is pretty much done software except that some of the desktop Idioms they can borrow from Apple (like swipe three fingers to switch the workspace) if they're not patented and they could be in settings if someone wants to opt into those otherwise yes.
Gnome is "done software" already and I like it very much.
From somebody who has been through this 3 times before: the moment gnome becomes “done software” is the moment they decide they need to throw out everything and start fresh.
I'm good with DeepSeek v4.1 set to high. It is a relentlessly "hardworking" dirt cheap model.
Told it to convert a products page (that had two different fonts based on language) from two columns layout to 5 columns on desktop and 2 columns on mobile ensuring typography is readable.
My man went into spawning sub agent which failed to drive chrome so it wrote its own chrome driver protocol server in Typescript then generated a prototype website then downloaded the images and rendered each variation in a directory taking 100+ screenshots analyzing the typography depth and then delivering detailed report and then writing the whole thing with new page layout testing it again with several dozen screenshots using its driver and then saying all good and all really was good and whole thing took 25 minutes or so (including double visual validation) because it generates token at an incredible speed.
Total cost of the above? $0.07 cents.
PS: It generates token at such a blazing fast speed that you can't recognize the words as they are being added and can't read it without scrolling and pausing even if you're Jimmy Carter.
What are you working on? Im always a bit surprised by folks that seem ok with non frontier models - the quality is just not there. I've found most code produced by even luna / sonnet tier models to be significantly worse quality. It seems to me they can't handle any mild complexity at all. Are you just prompting very explicitly and detailed?
I’ve been working with frontier models for the last year on a large real-world project with multiple apps. Eventually they started becoming unreliable. Simple UI issues, usage limits and overengineering became constant problems. I had to come up with a robust process: task analysis, user intent analysis, implementation and testing. All of these steps loop when needed with 15-20 steps per task in total. This finally got the models to do a good job but I started hitting usage limits. Then I switched to DeepSeek and haven’t noticed any drop in quality. It’s fast at around 300 TPS and cheap. I don’t miss frontier models anymore! Come up with a good process and you might not need them either.
This is so true, I use frontier models at work and the variance in the day to day experience can be dramatic.
I use Deepseek for some home stuff, and it's as you describe - fast, consistent. Not brainiac level, but when you drive it well it just tends to execute reliably. I have seen it fail consistently on very hard stuff, but I'm certainly ok with having to special case occasionally. Especially if those occasions keep getting less and less numerous as the models improve.
Now I need to convince my company to allow us to use a Chinese model ... I don't think I'll have much luck.
How much do you get out of these models for same amount (say $20 sub for claude/codex/zai-glm) - the equivalent amount of tokens? Also is deepseek as good as glm5.3? (if we don't compare it with frontier models from USA)
Well, to be honest it’s all based on my subjective experience. I started benchmarking models on 10 of my real tasks ranging from easy to hard but even with the same model the completion time sometimes varied by as much as 50% between runs. That made me realize you’d probably need hundreds of tasks to get any meaningful numbers. Otherwise there’s just too much variance. So I can’t give you solid benchmarks without spending weeks testing everything properly. What I can say is that I actually use most of these models regularly for different things, so I’ve developed a general feel for them. DS is my main model for work, Claude for hobby projects and local AI, Fable mostly for design, Codex for various other tasks and sometimes Grok for health-related stuff. Cost-wise, a $200 subscription wasn’t enough to get through 20-30 tasks while $30 of DeepSeek was, and that was with Sol, not even Astra. Fable is expensive too and I haven’t found it particularly strong at coding. Opus 5 was horrible in my experience, though maybe 5.5 will be better since I’m testing it now. I also ran GLM 5.3 Flash locally for a while but it was pretty slow and its code was usually worse than both DS4.1 and Qwen Flash Next. Things are moving so quickly that it’s honestly hard to keep up, so take all of this as my general experience from actually using these models rather than a proper benchmark.
Np, good luck! PS I’ve been using Sol 6 and Opus 5.5 today and they’ve been great so far. The new Opus is smart, fast and cheap. It makes mistakes but after every task I ask Sol to review it and it catches most of them. I need to test the models properly but it looks promising so far! Hope they won’t nerf them again. I think DS may still be cheaper though (and it’s 300 TPS via the API which is sick).
Yes, I tell exactly how I want it done and what files to modify, in those conditions sometimes a model that does not try to read between the lines works better.
I’d not put Luna and Deepseek in the same tier as Sonnet, they were clearly ahead last time I checked (though I might be outdated and that’s on my personal use case).
If you're producing slop, the quality of the model is irrelevant as long as it compiles. I frequently catch Opus/Sol making silly errors and over-engineering solutions while small ones like Luna struggle with complex tasks.
Same here! DeepSeek v4.1 Flash has been my moment of "does everything I need, cheaply. Please now focus all R+D on making this efficient enough to run off a laptop"
It really is good. I forgot to mention that within that said sub agent, it also went into exploring top e-commerce websites (Zalaondo, Temu, Amazon, eBay) for exploring prevailing industry UX best practices and taking screenshots of their product and category pages with its own written chrome driver that I talked about and then went onto prototyping a new website in a temporary directory and then taking hundreds of screenshots to analyse what would be the best column density one each medium for each language.
I am using DeepSeek Harness[0] (switched from OpenCode) and I am using DeepSeek directly via the API. The speed is insane. Like 200 tokens/second is the norm but I have seen much higher too at times.
PS: I do not know why but opencode pushes CPU usage to very high which has NOT happened with DeepSeek harness even once.
They train. Deepseek doesn't have a Zero Data Retention policy, but for many personal tasks I'm (not parent) ok with them having this data if it means better, cheaper models for everyone on the future.
Different story with $DAYJOB where ZDR matters, but the beauty of open models is that you can find a provider hosting them in your preferred terms.
Hey. I am on a GLM Coding plan subscription (old price; their base coding plan) right now and share the key (this will go away soon).
I was thinking of going with a subscription of Claude or Codex. The reason (at least that's what I am assuming): with OpenRouter or any PAYG per token setup there will be the anxiety of using up all the tokens in days or maybe 1-2 weeks instead of a month (say I set myself a budget of 15-20 USD per month, average equivalent of a usual subscription price).
Now I don't really want the top-notch models for the coding work I do.
So how much worth of "work/tokens" will I reasonably get for ≈$20 USD if I use it a lot? How much does that equal to - or is equivalent to, say in the world of subscription based Claude, Codex, or even GLM (with their 5-hour and all those cooldowns/limits)?
I am looking for a mental model/framework to visualise this. Can you (or anyone else reading this) please point me to a source where I can get some idea about this? I know I can just add $5 on OpenRouter and try to test. But I don't really know what/how to test these spends. I also want to understand how all this works. (I am new to agentic/llm world/coding, 2-3 months, after a career break of ~3 years, that too after working for more than a decade. I know, not at all good timing!)
Go to the "Cost" -> "Intelligence Index vs. Cost per Intelligence Index Task"
That diagram maps their "Intelligence" score to "cost per task" and I think this gives a good basis on deciding with which model you want to go. Then you can either get an API token from that models provider directly or use openrouter and set openrouter to the model/providers of your choice.
You can also see on openrouter itself the details for each model like prices and what providers are offering it at what price.
Finally you can compare models details using openrouters compare feature like this:
> which failed to drive chrome so it wrote its own chrome driver protocol server
This is one of the things I hate the most. Super complicated workarounds which take loads of time (and sometimes money) for even the simplest problems. Human would pause and ask. I would blame harness, not the model though.
Deepseek is cheap and fast, but it couldn't follow the basic directions I gave every other model (GLM, Qwen, GPT, Claude) to use red/green TDD. Put me off of using DS.
Compared to Opus 5, and others, I also found DeepSeek 4.1 Max to be really good and cheap. I am testing right now with Opus 5.5 and I feel it way cheaper than Opus 5!
The private equity debt time bomb is here. Then there is the bond market.
And then guaranteed bust of AI bubble. 2000 billion dollars for a single company? That too when bottom of the boat has been drilled by open weight Chinese models that are cheaper and good enough but can even be deployed locally?
In face of all that - that kind of return is not realistic.
However - good gimmick scaring UN asking for a ban on Chinese models perhaps.
The UN is not a body that would be involved in banning models from a specific country, and even if they were China is one of 5 members with permanent veto power.
Important - Jev is way too different, the greatest innovation are its speed and that it is guaranteed to NOT generate a token from a given set of tokens hence you can drive state machines intelligently.
If you have to write LAWS.bend which is pure code describing the laws then it isn't basically like those old days of writing unit tests and that too tests first hence the TDD?
So what is the unique idea here except a vibe coded compiler that generates C and everything else is handled by clang+llvm?
From README.md:
>The compiler (not kernel) is 99% AI-written and has not been fully audited yet.
Also, why the compiler is not written against and with LAWS.md so that no audit is required at all?
> If you have to write LAWS.bend which is pure code describing the laws then it isn't basically like those old days of writing unit tests and that too tests first hence the TDD?
There is a key difference: the laws are formally verified, as in Lean or Rocq (but much faster). So it's like writing a unit test or property-based test, but when it passes, you have a mathematical proof that you will get the expected output given ANY input in the infinite space of possible inputs. In traditional TDD, you make up some test case, write some asserts, and it passes if you get the expected outputs from those inputs and only those inputs. So you have to make multiple test cases for the same thing, and you still don't have any formal guarantee of your code's correctness.
> Also, why the compiler is not written against and with LAWS.md so that no audit is required at all?
Because it is mathematically impossible due to to Gödel’s second incompleteness theorem, which states: any consistent formal mathematical system strong enough to harbor basic arithmetic cannot prove its own consistency
Now if you’re asking why the basic prelude String type remains as it is, that’s because changing it would break more code than it’s worth, at least as far as prelude’s maintainers are concerned. This is no different from how standard C strings remain a null-terminated sequence of bytes even though that’s been awful for everyday use for at least 30 years.
Note that using linked lists for strings is actually more "parallel friendly" because you can take the head/tail and spread it around 16k GPU cores in O(1), unlike in Haskell, unlike arrays, which require a linear copy, becoming quadratic. So, the right "default type" isn't that clear on Bend, because GPUs behave very differently from CPUs.
That said, yes, we definitely should have a compact Text type. I'll add it over the weekend.
Though also, parallel processing strings and other non-numeric data on that level of granularity is, IME, typically less performant. The parallelism rarely manages to offset the performance penalties incurred by decomposing the problem in a parallel-friendly way. Even on a single machine you’ve got to think about whether organizing the data in a parallel-friendly way also makes it less cache-friendly. For example, a linked list of Unicode code points is 12 bytes per character, and each character might be on a completely different cache line. Depending on language a UTF8 buffer might be 1/10 the size and have a much more compact layout in memory.
We survey measurements of data-parallel systems recently reported in SOSP and OSDI, and find that many systems have either a surprisingly large COST, often hundreds of cores, or simply underperform one thread for all of their reported configurations.
Anecdotally I have a bit of a track record of 10xing slow systems’ throughout by converting them from distributed to single-node or from multithreaded to single threaded.
Heck I once even sped up a number crunching operation by getting it off of the GPU and onto the vector coprocessor. Because GPUs also have a bunch of extra overhead to have to amortize away.
"Stealing millennium problems" would be more complete if not accurate description. And that 99.99% of the market is not interested in solving millennium problems is the other fact.
Good luck building something of value with "day to day coding". If you're going to compete in the future economy, you're going to compete at the frontier. The low hanging fruit will be of no value. If I can recreate your SaaS with an open weight model at a fraction of the cost, then your SaaS has no value.
the vast majority of successful SaaS offer capabilities or services no where near the frontier.
furthermore i'd argue that the success of an saas, or really any startup, has more to do with things aside from the code and service itself.
Finance management, social connections and effects, word of mouth, networking -- probably all more important than whether or not the todo app uses a functional language and is formally proven.
What the hell are you yammering about. Everything in the economy exists at different quality/price tradeoffs, everything from tomato sauce at the grocery store to landscaping services to SaaS. "Compete at the frontier" is some maximalist buzzword nonsense. Do you think Ikea is crying because their furniture is cheap and they're not "competing at the frontier"?
99.9% of the time you are right, but in the long tails (construction works, major road damage, temporary road markings that contradict permanent signs) I could see this kind of architecture being useful.
Gnome is "done software" already and I like it very much.
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