the problem is you have to make the AI watch the whole thing to make sure it works.
I've done this sort of with comfyui/same agent factory stuff, but the verification loop only works for models like fable as planner/writer, with gemini as verifier for like a very short movie. Sub 3-5 mins. After that you burn through million tokens.
Can't go too low fidelity audio/video or it craps out. Too long video and it loses consistency. Look at only snippets, it lacks global consistency, etc.
I think its hard to understand for people why some people bomb fish.
It's mostly profound poverty.
The people want better for themselves, similar to people here. Since this is speaking about people in Sulawesi, they don't really have any money or work there. It's largely jungle, mining, farms.
I'll just give an example with some of the people i spend time with whenever I'm in Indonesia.
Eko lives in his village. He largely has no real job or work. He catches fish sometimes. Different ways. Sometimes with rod. Sometimes he shimmies an air tank with an air pipe underwater and spears them. He might get bends and die of the pains of it. I told him he shouldn't fish like that, but he still does it.
You see, Eko does crazy things because wants his two kids to live better than him. He wants them to live more like those folks in Jakarta, with air conditioning and sanitary toilets. Eko thinks his life isn't great, but he makes do with it. He largely does not have a vehicle besides a weak scooter, he brings his kids together with. His hut is poor, and he built it himself with concrete and bamboo. No electrical. He defecates into a hole toilet he learned to build from his friend. Sometimes he defecates into the river.
Eko's father died recently. The father likely died of poor treatment. The hospital is about 2 hours away in the main city. The hospital is poorly run. Its red lights make you feel ill; there is barely any materials or equipment.
Sometimes he thinks he needs to bomb something to get more fish. It doesn't ever get him enough money because he needs a lot more than he does get from it. Even if his wife works on her tobacco farm, there is very little at the end of the month. Maybe his kids will die too in that same hospital. Some of my friend's kids died in similar fashion last year. Maybe an earthquake will happen again, and all the stuff that they've been doing will just be burnt away.
The article argues that attributing it solely to poverty is a mistake, and that because of several cost barriers it's more likely being done by middle-income fishers.
If that wasn't impressive enough, it's actually ~60x cheaper if you take into account the typical cache-read/input/output split in agentic coding, and the deep discount for cache reads offered by DeepSeek. Opencode has some public data on the typical split [1]:
For DeepSeek V4 Pro the typical split is 750 in, 290 out, 82k cached.
Cost per request for V4 Pro: $0.000875 per request.
Equivalent Opus cost (w/o taking into account cache write costs): $0.052 per request.
I created a simulation for coding harnesses based on my own pi sessions. When taking into account all factors, DS-v4-Pro is cheaper than gpt-5.6-luna due to caching. Look at the bill segments difference for cache read cost and uncached cost between deepseek and the other models. At this point is cheaper to use ds-v4-pro than the luna models from openai.
ignore the numbers except the classic and keep in mind that classic is based on pi with the only change limiting tool output to 10kb
Hey this looks good! Maybe consider adding a hover-over popup for the rectangles explaining what each thing means to a lay person. I see it at the bottom, but that is below the fold.
Done, I'll take any other suggestions and apply them later, I will also split it a bit for different usecases as this was initially a throwaway prototype but found it useful. Basically it needs a bit more human touch.
Can we have a conversation about subscription plans for a minute?
I don't mean to hype up the US AI firms, but if a ChatGPT $200/m subscription can get you $16,000 in effective API costs, doesn't effectively every model get destroyed by the subsidized Claude/ChatGPT models? Both in price and intelligence.
That is the problem currently the subscription plans are being subsidized by VC money and token buyers. When Open weight get good enough token buyers build their own servers instead of buying tokens then no one to subsidize the subscriptions
99.9%+ of the tech worker population will never be able to build their own servers to run future frontier models. Kimi 3 is an indication of what's coming. These models will keep getting drastically larger. The hardware isn't getting cheaper anytime soon (no matter what China does; that goes for memory and GPUs).
Cycle forward to Fable 7, Kimi 5, GPT 7 a couple years out. Forget about it unless you own a datacenter.
> 99.9%+ of the tech worker population will never be able to build their own servers to run future frontier models.
A single local user can run frontier models slowly on a 24/7 basis, which drops hardware requirements by orders of magnitude compared to a datacenter setup for just-in-time inference. This is not a real alternative to subsidized subscriptions at present, but it's a great insurance policy against future VC-driven rug pulls.
90% of the tech worker population does not work for themselves they work for someone that pays them 1000s in salary for those paying those tech worker spending $40-50k on a server that helps them not pay for 2-3 tech workers is not that big a deal.
That seems too expensive to be honest. Did you do it with official deepseek api or a 3rd party provider? Because official has 10x cheaper cache reads than the rest. I've done similar sized chats for like $1
Not them, but I payed 10 dollars to DeepSeek directly to use their Reasonix tool. I worked all weekend and the past few days, billions of tokens, I still have 3 bucks left!
I've found Pro to be a lot better per "task" than the recently released Flash for code reviews and things (via OpenRouter running in pi.dev).
Flash makes a lot more initial mistakes, and then has to re-check stuff, and produces much more output compared to Pro. It often gets to the correct result eventually, but the output volume is often 5x more than for Pro, and the initial outputs are often wrong, with the first few saying something wrong (like there's a bug, or the code won't compile when it does), and then saying things like "Wait, let me re-check:", or "Actually, looking at it more carefully:" and then it thinks a bit more and eventually gets to the right answer.
Every plan and every code checkpoint finds me saying "Check with Grok and Fable latest to critique our strategy/code review" with pretty much every model. I havent ran into any deal breakers with the new Flash version yet (like it not running a tool properly or coming back with something completely daft)
So not worth it over flash? Even at ~7x the size it isn't worth the price hike. Flash may be a monster of a model due to all the RL it received from free usage everywhere.
I haven't tried DeepSeek V4 Pro 0813 yet. Recent experience tells me that larger models are worth it in non-obvious ways. MiMo-V2.5-Pro solved problems that DeepSeek V4 Flash 0731 couldn't solve for me: for example, adding a live counter for elided reasoning lines to a terminal-based coding harness. You wouldn't be able to tell from the scores on their respective Artifical Analysis page (https://artificialanalysis.ai/models/mimo-v2-5-pro, https://artificialanalysis.ai/models/deepseek-v4-flash). I like the DeepSeek V4 models, though. They critiqued my engineering decisions better than MiMo, and they seem to have a distinct aesthetic in the SVGs they write.
Interesting - I've been dropping into MiMo-V2.5-Pro-UltraSpeed whenever Flash seems to be "stuck" and it usually figures it out. I use UltraSpeed just because I'm so frustrated by then that I'm impatient.
I still find 5.6-Sol can solve some things neither of those can, but it's so slow (and it's so hard to trace / debug the reasoning) that I just let it run overnight.
What about 5.6 Terra and especially Luna? Luna scores pretty high on benchmarks and seems to have different habits (like a denser pattern of tool use) and blind spots.
I'm trying out a development workflow where I generate mundane code with MiMo and Luna (and soon V4 Pro 0813?) and have Opus 5, which is running on only a Pro subscription, review and refactor it. I'm not sure it will justify the context switching, but it's an interesting exercise.
Terra and Luna are fine, but they’re quite slow (OAI seems to be really slow lately) and don’t have the reasoning traces. My workflow really depends on them or I can’t switch models effectively.
Yea that's what I was thinking.
Flash is nuts. I find I have to be a more precise and specific with it but damn. It's crossed a threshold of production grade coding for sure.
I was running a session over a couple days and it didnt cross a dollar lol.
> You were never expected to understand every service and every database. But at least someone did and would explain it to you.
I agree with most of this except this. Think there’s some rose tinted glasses here or I’ve got bad luck over time.
Life before ai was bad as well. There wasn’t any one to explain to you anything! You had to figure it out yourself. The people either already left or was busy with something else.
No one wrote tests (to my standard). Most of the ops works was skipped. Docs were just not there. Nobody linted properly. Just bad mannnn
If there is any positive to be had from this, it's that we finally started documenting the codebases for AI as we should have always done it for other teammates.
the vpn is called dev-test-prod and some containers are stored in a dev subscription, but still used in production. Some intangible network error forces you to use the production cluster for testing (the bucket is at least called test).
I feel this kind of setup is invariant everywhere I go to do engineering. Fable level intelligence has no effect!
Uh compile time and linting efficiency, lightweight runtime, gc.
There’s no equivalent competitor, it’s the best if u want to just write lots of undifferentiated code.
To caveat this if u want to run about 50 agents or so in parallel, all the typescript projects burn ur disk via node modules. The rust ones take forever to compile and burn too much compute
Uh at the very least Java is bad. the java programs we run take 4 gigs and burn my entire compute. Every1 used spring or guice and the di runtime, so tests are expensive for e2e with mocks. There’s no scrutable way I can run more than 2 or 3 on a standard laptop without burning out the thing.
You might want to check out Quarkus, Mirconaut, or Helidon MP. They do build time DI, and are more focused on performance and reduced resource usage. They are similar enough to Spring, and I believe Quarkus and Micronaut have an optional Spring compatibility layer that one can use. LLMs could help with migration if the codebase is well written.
Exactly. There are plenty things they could suggest for that.
Anyway you'll likely get a more effective and up to date set of advice by skipping marketing pieces and pointing your favorite frontier model at /r/LocalLLaMA in deep research mode and asking it to synthesize the latest advice, or even try ideas out for itself and let you know what works for your own session history.
There’s a measurable performance tradeoff versus gqa so there’s reluctance.
For the most part though the new deepseek v4 tech is hca and mhc and people are still catching on like with moe and rl. Wait for 6 12 months, minimum time for next pre train.
I don’t think it would be surprising that people want to write their own kernels.
A big problem with the existing engines like llama or sd is that they don’t support optimal graph compilation. Usually this means about a real 2 or 3x multiplier loss relative to optimal. Cuda graphs do okay but they still leave a lot on the floor
It’s usually worth it to optimize in that context if you are willing to peer into the mechanics.
Of course that’s expensive. You need to know how to appropriately pipeline and merge your kernels.
I've done this sort of with comfyui/same agent factory stuff, but the verification loop only works for models like fable as planner/writer, with gemini as verifier for like a very short movie. Sub 3-5 mins. After that you burn through million tokens.
Can't go too low fidelity audio/video or it craps out. Too long video and it loses consistency. Look at only snippets, it lacks global consistency, etc.
reply