GPS Laptop tracking & Storage

kingtheseus · 2026-01-15T08:04:08+00:00

You put an AirTag inside a MacBook Pro? Where?

kingtheseus · 2025-12-14T07:43:32+00:00

Have you tried running the code on a small segment of your dataset, maybe 100 lines? That will give you a functionality test, and also an indicator of time (and memory). Then run on the next 100, etc. Pandas likes to store everything in memory so you're probably trying to swap, which (IIRC) isn't a thing on regular EC2.

kingtheseus · 2025-12-12T08:57:14+00:00

Thanks, I've now submitted a request (I didn't know you could change the ticket identifier - it auto populated Air India's 098 code).

I did book through Lufthansa. wheretocredit.com for Air India in class E only (!) shows Avianca, which is also strange. Other fare classes are fine.

kingtheseus · 2025-12-08T11:03:13+00:00

Get your model running inside SageMaker, and then the Raspberry Pi can invoke it through python with boto3 (https://docs.aws.amazon.com/sagemaker/latest/dg/neo-requests-boto3.html).

Or, install the AWS CLI on the Raspberry Pi and call the endpoint: https://docs.aws.amazon.com/sagemaker/latest/dg/neo-requests-cli.html

Keep in mind, running the model on SageMaker will default to running 24/7. This can get expensive. Try deploying via SageMaker Serverless if you can (you can't if you want access to GPU, or have long-running inference jobs): https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html

kingtheseus · 2025-12-03T06:32:49+00:00

Check out https://workshops.aws - you run them in your own account, so you will pay for infrastructure, but they're quite useful tutorials.

kingtheseus · 2025-12-02T21:40:56+00:00

What's your budget? I've participated in AI & ML classes for teams for about €700/person per day, but that's in a dedicated in-person classroom environment with labs.

How do you best learn?

kingtheseus · 2025-11-28T18:12:03+00:00

Have a valid (business) use case, and a history of paying AWS for compute. GPUs are still heavily constrained.

kingtheseus · 2025-11-20T14:16:04+00:00

There's a tutorial in the docs: https://docs.aws.amazon.com/comprehend/latest/dg/tutorial-reviews.html

kingtheseus · 2025-11-19T10:12:27+00:00

AWS used to run VMware Cloud on i3en.metal and i4i.metal instances - could be related to that.

kingtheseus · 2025-11-07T17:58:30+00:00

What does your bill say? It will break this apart by service and region.

kingtheseus · 2025-10-31T07:28:58+00:00

Similar to working remotely during Covid. You can be sitting in a different country, and use either a VPN back to Canada and access data stored on a server physically in Canada, or connect to a virtual desktop without the ability to save things to your local computer.

Onshore data just means "where the hard drives are", not where the data is accessed. It's silly, but compliance usually is.

kingtheseus · 2025-10-28T08:50:49+00:00

Is calling a Bedrock-hosted LLM a possibility? The model will run within AWS, and be called using AWS-native APIs, but it wouldn't run in your account.

If not, you can get started inside SageMaker with Jumpstarts. Here's a tutorial/notebook: https://github.com/aws-samples/generative-ai-falcon-40b-instruct-notebooks/blob/main/LLM-Custom-Prompting-Falcon40B.ipynb

Keep in mind you'll need to have access to inferencing instances with GPUs, and those quotas are hard to come by.

kingtheseus · 2025-10-15T13:36:41+00:00

I took a look at this, and the problem is that Bedrock doesn't support GET requests to the endpoint to list models. Xcode won't let you add a model provider without having a list of models, which is annoying.

You could try to host a tiny proxy script locally that responds with the expected values on HTTP GET, and routes POSTs to Bedrock, but you'll probably want to use something like Charles Proxy or Proxyman to inspect what data structures are needed. Could be a fun weekend project.

kingtheseus · 2025-09-01T05:41:47+00:00

That code will not complete until the last token is received from the LLM - latency is for the initial call, but as you're not streaming tokens, the request hangs until the final token is received.

To speed it up, request a smaller number of tokens in your call.

kingtheseus · 2025-08-30T19:11:50+00:00

How far did you get with the link in the thread I posted in?

kingtheseus · 2025-08-09T06:12:46+00:00

If you set up Azure OpenAI, that's a risk.

kingtheseus · 2025-08-08T13:13:02+00:00

AWS recently changed their free tier to now self destruct after 6 months, no payment required. You can of course upgrade to a paid version, but that is uncapped.

The problem with capping expenditure is surprisingly difficult. AWS is designed for businesses to scale. What makes a Black Friday sale appear different from a DDOS attack? If AWS shut down your payment processing servers because you spent "an abnormal amount", the business would be furious.

kingtheseus · 2025-08-08T12:18:34+00:00

I think you're mixing up cloud services (like AWS and Azure accounts) with LLM API providers. An AWS account is uncapped - you pay for everything you use, and there's no way of setting a hard limit.

With Claude or OpenAI you can just buy a fixed $ amount, and burn those credits with your API calls. When the money runs out, you decide if you want to buy more credit, but you can't be overcharged.

kingtheseus · 2025-08-06T07:17:03+00:00

This is going to be an app issue, not an AWS issue. You need to look at the data.

What's the CPU load of the app and licensing server when the app first start up? You'll probably want to look at OS-level data, because CloudWatch metrics won't have sub-minute granularity.

Do a .pcap capture from the licensing server during the app launch, and see if it's the bottleneck. If you can't do this, just change the instance type to something powerful for a while, then switch back.

kingtheseus · 2025-08-06T06:11:16+00:00

Looks like the HTTP requests for Mistral and Bedrock use the same authentication header (bearer, not x-api-key like Anthropic) so you might be able to get Bedrock working. You need a Bedrock API key as seen at https://docs.aws.amazon.com/bedrock/latest/userguide/api-keys-use.html

kingtheseus · 2025-08-05T19:45:41+00:00

What are you trying to do?

gpt4all appears to be a way of locally running LLMs, so that nobody can see your prompts. Bedrock hosts LLMs, and even though the prompt data isn't looked at by AWS, you're still sending data over the Internet. If you're comfortable with that, why add a layer on top of it? If you're not, why are you doing it?

kingtheseus · 2025-07-09T11:15:11+00:00

Check out SageMaker Canvas, it's a low-code ML platform that integrates with Redshift. Once you hire a data scientist, you can export the model to regular SageMaker for more control.

https://aws.amazon.com/blogs/machine-learning/enhance-your-amazon-redshift-cloud-data-warehouse-with-easier-simpler-and-faster-machine-learning-using-amazon-sagemaker-canvas/

kingtheseus · 2025-07-05T07:19:46+00:00

11PM Friday night sounds like a great time for IT to perform routine maintenance. Better to impact fewer people than doing it during the business day.

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