>technology that has never proven its worth outside of specious hype
Reading stuff like this makes me question the entirety of the article.
Readit NewsGaggiX commented on Europe's crusade against air conditioning is insane noahpinion.blog/p/europes... · Posted by u/paulpauper
Energy costs are high because of the European culture of degrowth - Germany shut down all its nuclear plants in favour of buying Russian gas and oil.
Germany also was the biggest country for solar energy, what it's essentially making a huge chunk of the energy production in summer in Europe, what started the industrialization of solar panels, making them dirt cheap. (Also Europe is not just Germany making a dumb decision before)
GaggiX commented on Europe's crusade against air conditioning is insane noahpinion.blog/p/europes... · Posted by u/paulpauper
This article is so stupid for not mentioning energy costs. It's like Europeans don't use AC because they want to degrowth or something.
The original press release and report are at [1], couldn't find a link to them in the article.
> In total, the median prompt—one that falls in the middle of the range of energy demand—consumes 0.24 watt-hours of electricity
If they're running on, say, two RTX 6000s for a total draw of ~600 watts, that would be a response time of 1.44 seconds. So obviously the median prompt doesn't go to some high-end thinking model users have to pay for.
It's a very low number; for comparison, an electric vehicle might consume 82kWh to travel 363 miles. So that 0.24 watt-hours of energy is equivalent to driving 5.6 feet (1.7 meters) in such an EV.
When I hear reports that AI power demand is overloading electricity infrastructure, it always makes me think: Even before the AI boom, shouldn't we have a bunch of extra capacity under construction, ready for EV driving, induction stoves and heat-pump heating?
[1] https://cloud.google.com/blog/products/infrastructure/measur...
>~If they're running on, say, two RTX 6000s for a total draw of ~600 watts, that would be a response time of 1.44 seconds. So obviously the median prompt doesn't go to some high-end thinking model users have to pay for.
You're not accounting for batches for the optimal gpu utilization, maybe it can takes 30 seconds but it completed 30 requests.
GaggiX commented on Gemma 3 270M re-implemented in pure PyTorch for local tinkering github.com/rasbt/LLMs-fro... · Posted by u/ModelForge
Hey all,
I created this model with a top notch team. I answered many questions last week when this hit the front page, and happy to answer more here as well.
https://news.ycombinator.com/item?id=44902148
Personally I'm excited that you all have access to this model now and hope you all get value out of using them.
I imagine you and your team have finetuned the model on different tasks, can you share some results? (I have only seen the alien NPC finetuning)
Unlike "value demand," which is genuine demand arising from customer needs, failure demand is demand caused by failures such as errors, defects, inefficiencies, or poor service delivery. For example, if a service does not fulfill a customer's need properly, the customer must come back, creating more demand that is essentially avoidable. Failure demand leads to inefficiency, additional costs, and deteriorated customer and employee experiences.
So you're implying that ChatGPT and similar are so popular because of "errors, defects, inefficiencies, or poor service delivery", how does this make any sense in the context?
>"while delivering absolutely nothing of value"
Well maybe for you and not the millions of people that use this technology daily.
GaggiX commented on Llama-Scan: Convert PDFs to Text W Local LLMs github.com/ngafar/llama-s... · Posted by u/nawazgafar
Sub-2010 level OCR using LLM.
It is hype-compatible so it is good.
It is AI so it is good.
It is blockchain so it is good.
It is cloud so it is good.
It is virtual so it is good.
It is UML so it is good.
It is RPN so it is good.
It is a steam engine so it is good.
Yawn...
>Sub-2010 level OCR
It's not.
GaggiX commented on Llama-Scan: Convert PDFs to Text W Local LLMs github.com/ngafar/llama-s... · Posted by u/nawazgafar
I’ve done a similar PDF → Markdown workflow.
For each page:
- Extract text as usual.
- Capture the whole page as an image (~200 DPI).
- Optionally extract images/graphs within the page and include them in the same LLM call.
- Optionally add a bit of context from neighboring pages.
Then wrap everything with a clear prompt (structured output + how you want graphs handled), and you’re set.
At this point, models like GPT-5-nano/mini or Gemini 2.5 Flash are cheap and strong enough to make this practical.
Yeah, it’s a bit like using a rocket launcher on a mosquito, but this is actually very easy to implement and quite flexible and powerfuL. works across almost any format, Markdown is both AI and human friendly, and surprisingly maintainable.
>are cheap and strong enough to make this practical.
It all depends on the scale you need them, with the API it's easy to generate millions of tokens without thinking.
I thought this was the start of a joke or something, I guess if you use LLMs you are a "LLM lover" then.