ody4242

You can write your own compaction function for Pi, and you can even have different, domain specific compaction logic if you want. The default one is dumb, I agree.

what is so terrible with their harness? I've been using gemini cli, now use agy, Pi agent harness, and agent (cursor), and my only real issue with agy was the permission handling, but other than that, it was ok.

then you switch provider, it's not a monopoly

The upfront cost of facility is ~30%, network infra 10-15%, land/utilities is small percentage, power could be significant for an AI DC. The servers are ~50-60% only.

They are building new datacenters for the AI demand, so around half of this CAPEX is not for the GPU-s, and those will not be replaced every 3-5 years.

Also, TPUv2 was introduced in 2018, and still not completely retired in all regions, from accounting pov, it has been written down to 0, but they are still working.

This is a good chart that shows historical CAPEX spending. Hyperscalers have been through a couple CAPEX cycles like this, they all know what they are doing.

https://eco3min.fr/en/big-tech-capex-revenue-ratio-quarterly...

Why would they go into price war when they are compute constrained?

try to add it here: https://gemini.google.com/saved-info

Idk how an AWS region would respond to a power outage, but i have tested this in AWS Outpost, and there, if you power down a rack, then power it back again, the baremetal instances will not be recreated. (I was surprised as I was expecting the EC2 health check to terminate them, but it does not work like that.) My understanding is that if you stop/start an instance, your local storage is gone (as the instance might even end up in a different host), but if you just reboot the instance, it should keep the local storage.

I would have benchmarked with an instance that has local nvme, like c8gd.4xlarge.

I'm 100% sure that all providers are playing with the quantization, kv cache and other parameters of the models to be able to serve the demand. One of the biggest advantage of running a local model is that you get predictable behavior.

Yesterday GPT 5.2 wrote a python function for me that had the import in the middle of the code, for no reason. (It was a simple import of requests module in a REST client...) Claude I agree is a lot better for backend,Gemini is very good for frontend

You don't need hundreds of services. Give me virtual machines, reliable block storage, file storage and object storage, networking, dns, managed kubernetes, and it will cover the majority of workloads in Europe that run on Openshift or Openstack today.

Sure, EBS or any network-attached storage is expected to be a lot slower than a local SSD for synchronous writes or random reads, as there is a network stack in between. But my understanding is that for those usecases, you can use metal instances with local nvme. (ephemeral though)

MS does sell ads, they have an advertising platform.

Terraform

Crossplane (although it's quite new, and missing a lot of things that Terraform has)

The Model3 has a dedicated button for the windscreen wiper on the steering wheel, and then you can adjust with the scroll buttons. It certainly has a learning curve, and it would be better if you could rely on muscle memory for these things.

I usually spend a couple of minutes when I drive someone else's car to get familiar with the controls, as the rear window windscreen wiper, light controls (especially fog light) can be tricky on old cars as well.

What makes you stand out from this list of other managed openstack providers? https://www.openstack.org/marketplace/public-clouds/

I have seen similar numbers from multiple sensors across our city during the fireworks at New Years eve.

Imho, if Tesla could train a huge neural network to demonstrate that self driving is possible with their sw stack, they would have done that already to let Elon Musk have it's road show with it's capabilities.

(Running ChatGPT does not require a supercluster, training the model does)

Where is wallstreetbets? :)

Telco vendors have been using Openstack for 6-8 years already, and virtualization (and even containerisation, mainly with LXC, or Solaris zones earlier) was a thing back then. Kubernetes is also used for the last couple of years, mainly inside their VNF-s, so the transition to CNF-s is not a "sudden" thing, most modern VNF-s were running a Kubernetes cluster inside virtual machines already.

There are some exotic scenarios, where copy-paste is the good(fast) solution. For example, one of my clients can be accessed via a VPN. Once I'm on the VPN, I have to start their VDI. Inside the VDI, I need to start another VPN, that then allows me to open a Putty session to a bastion host, that I can use to reach my destination cluster. Believe it or not, they don't want to make it simpler.

Copy/paste works flawlessly through the VDI into the putty session, so I usually just uuencode my blob, and copy it to destination cluster and uudecode there, instead of uploading the file to the VDI, then pscp to the bastion host, and then scp to the destination cluster. (I know of zmodem, port forwarding and such, none of this is allowed...)

Interestingly, the site is blocked by Symantec Endpoint Protection.

Here you can choose between MDI and pump.

I have no idea, that's why I am not using an insulin pump either:)

I use Dexcom G6 CGMS with Nightscout and xDrip+. xDrip+ runs on Android, gets the data from the Dexcom transmitter, and sends it to a Pebble watch and also to a mongodb database. xDrip itself supports a lot of CGMS systems, I've been using it since G4, which was not Bluetooth ready initially, so you had to build a bridge that read the data from the transmitter and sent it to the phone. Way easier nowadays,as the transmitter is using BLE.

Their repo has like 300 open issues today... I have type 1 diabetes, and use open source tech for getting the glucose readings from my sensor and pushing it to a Pebble, but insulin dosing is a way more complex story.

As someone who has an Intel NUC w. Ubuntu, a Thinkpad with Windows 10, and a Macbook Pro 2015, I have a totally different experience. I spend like 5% of my total effort on OSX to make things working, the remaining 95% is spent to make things working on Windows and Ubuntu...

I would expect that if you read data - the GIF image in this case - from the block device, it will stay in pagecache, until there is memory pressure.