311–320 of 424 posts
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Where are you going to apply to with that resume that’s a step up from your current job though?
lots of places, actually. not everyone wants to be attached to the Silicon Valley culture, and that line alone will guarantee practically any workplace. that person is going to find out what work-life balance is :)
Didn't expect that. Luna pricing is crazy now. I don't think there is anything on the market that competes at this price-performance point. For our production app, OpenAI clearly is the best provider now. Their API is very reliable and has many nice features. The price-performance of the model lineup is incredible. We used open weights model via Fireworks for a long time (e.g. Kimi K2.5). Fireworks is a great provide…
OpenAI's APIs are extremely reliable for sure. I don't even remember when the last incident or downtime was.
“Overloaded”, maybe half of requests or more wouldn’t go through
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Let's suppose each models was subsidized at 70%, so that we only pay 30% of the cost. They would loose much more money per token on the more powerful models. It's in their interest to encourage the use of the less expensive models. Let's say they increase Luna subsidies at 90%. They would still "save" relative to the use of the more expensive models.
> Let's suppose each models was subsidized at 70%, so that we only pay 30% of the cost. why on earth would you suppose that?
GPT 5.6 Terra: Was $15.00. Now $12.00.
(edit: formatting)
Deepseek Flash is still much cheaper: - lower input/output token pricing - the cached token price is $0.0028/Million tokens, which is like 50-90% of tokens
DeepSeek Flash is a much worse model. Even DeepSeek Pro is much worse.
"Half the money I spend on advertising is wasted; the trouble is I don't know which half." -John Wanamaker This applies even more strongly to model choosing. I know for a fact that majority of my work doesn't require a very strong model, but separating the trivial and non-trivial tasks is a famously hard problem (if at all decidable).
This way the expensive/strong model only handles the architecture and orchestration tasks. The cheaper models handle everything else and the strong one knows how to tell them what to do in enough detail to get good work out of them.
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I don't see why it should be all that difficult. All you have to do is first find a library that implements a decent solution to the halting problem and you're off to the races.
Its funny because you can write a halting problem oracle by calling out to an LLM and have it return yes / no / not sure and get it to work reliably for almost all real code, like that is an entirely practical thing to do in 2026. All we need now is some sort of program to evaluate halting problem oracles...
```python
source_code = open(__file__, 'rt').read()
response = ask_llm("will this program halt, only answer 'yes' or 'no'?\n" + source_code)
will_halt = response == 'yes'
while will_halt:
continue> Starting today, GPT‑5.6 Luna, our fastest and most affordable model, will cost 80% less, I don't have the words. I genuinely thought we were in a stage where we were plateauing and going in for 5-10% improvements over months. Seeing spikes like this makes me question about where the floor really is.
There is ZERO reason to believe models 1/10th the size of frontier are completely capped on intelligence and impossible to get smarter.
They have consistently compressed the intelligence of larger models.
You'll see it first on the small end, when they stop being able to compress intelligence, you know that will slowly bubble up and up the chain to larger and larger models.
There's no evidence we've reached that at the bottom.
GPT-5 cost $10 (per 1M output)
They still have to pay back those hundreds of billions of investment dollars somehow.
The best thing any developer can do today is to make their app LLM/provider agnostic (or use an LLM gateway) to avoid lock-in.
> Starting today, GPT‑5.6 Luna, our fastest and most affordable model, will cost 80% less, I don't have the words. I genuinely thought we were in a stage where we were plateauing and going in for 5-10% improvements over months. Seeing spikes like this makes me question about where the floor really is.
Prices have consistently gone down 90% every 18 months like clockwork for about 5 years for the same level of quality. There is no end in sight for at least another generation. There is ZERO reason to believe models 1/10th the size of frontier are completely capped on intelligence and impossible to get smarter. They have consistently compressed the intelligence of larger models. You'll see it first on the small end,…