Wow this is super interesting. This creates a local “Gemini” front end and all. This is more or less a generative AI aggregator where it installs multiple services for different gen modes. I’m excited to try this out on my strix halo. The biggest issue I had is image and audio gen so this seems like a great option.
Some CIO thought it would be great to get rid of our local in office IT team and replaced them with a multi million contract with HP to use their “tier 1” support. Their service was absolute garbage. But the CIO got a fat bonus check for the “cost savings”
The use case varies but for what it’s worth, most major cellular provider in the US offer fallback Internet plans. Ranges from 10-20 a month depending if you already have a line with them. AT&T has an interesting one where your phone lines hotspot is free and unthrottled whenever your fiber is impacted.
I argue that it’s different. Ukrainian military needed this to adapt to the warfare. The US has plenty of means to bomb people (look at Iran) with or without consumer drones. Our military does not have any native LLM capabilities.
I noticed this the other day with the Anthropic upholding its redline. I think this is the first time in history where consumer tech exceeds military tech. Historically, it was always military tech trickles down to consumer.
It’s interesting because my career went from doing higher level language (Python) to lower language (C++ and C). Opus and the like is amazing at Python, honestly sometimes better than me but it does do some really stupid architectural decisions occasionally. But when it comes to embedded stuff, it’s still like a junior engineer. Unsure if that will ever change but I wonder if it’s just the quality and availability of training data. This is why I find it hard to believe LLMs will replace hardware engineers anytime soon (I was a MechE for a decade).
Misleading title but this is pretty exciting. Interesting how this is based on llama cpp. Its nice to see some momentum since they released the paper in 2023
Wow I’m a watch guy and this was a fun read. I used a couple of similar apps and they work ok. I wonder how yours stack up. You can get these cheap piezo ones for $150 but unsure how those perform either.
Wow that’s terrible. This is why people don’t give notices anymore. It’s safer to give no notice and start the next job the day of. You burn some bridges but is the safest option.
While this article is old, there’s more and more noise that the lay offs is this month. That being said, I can’t help but wonder if the paramount deal accelerated these plans.
Awesome guide, shame how a couple of the Qwen leads got kicked out and replaced with more “business” minded leadership. Hopefully this doesn’t mean the end of the open source era from Qwen.
I hate how it doesn’t even mention git in the article. That being said, they have a boatload of money. It would be so much h quicker to acquire someone like Gitlab and slap an OpenAI theme on it.
Fast.com is from Netflix. They do it so ISP serve Netflix faster since ISP were gaming speed test sites. I’m assuming that’s the same case for cloudflare speed test.
A big part that a lot of local users forget is inference is hard. Maybe you have the wrong temperature. Maybe you have the wrong min P. Maybe you have the wrong template. Maybe the implementation in llama cpp has a bug. Maybe Q4 or even Q8 just won’t compare to BF16. Reality is, there’s so many knobs to LLM inferencing and any can make the experience worse. It’s not always the model’s fault.
I don’t think the title is correct? All OS must have age profiles that external sources can query. There’s nothing explicit that checks the age itself in the law?