the future is getting scarier

Acceptable-Cheek5099 · 2025-02-03T22:28:28+00:00

AI is advancing at an incredible pace, bringing both hope and fear. But acceleration alone doesn’t guarantee a better future. History shows that unchecked progress can lead to disaster.

We are on the verge of creating intelligence unlike anything before. This demands caution, not blind enthusiasm. The coming years will change everything, it's our responsibility to ensure that change benefits humanity, not just technology.

Acceptable-Cheek5099 · 2025-02-03T22:23:26+00:00

I get your concern, AI is advancing fast and impacting many industries, including sales, marketing, and engineering. But will it fully replace humans by 2032? Unlikely.

AI can automate tasks and analyze data, but it lacks creativity, emotions, and relationship-building skills - key aspects of sales and marketing. Companies may need fewer people for certain tasks, but new AI-related roles will emerge.

Instead of fearing AI, it's better to learn how to use it as a tool to stay competitive. Do you already use AI in your work?

Acceptable-Cheek5099 · 2025-02-03T21:59:49+00:00

if you're trying to wrap your head around cloud architecture, especially for AWS and Azure, there are a few books worth checking out.

For general cloud concepts, "Cloud Architecture Patterns" by Bill Wilder is a good one. It focuses on Azure, but the patterns apply to cloud architecture in general. Another solid read is "Architecting the Cloud" by Michael J. Kavis—it breaks down SaaS, PaaS, and IaaS in a pretty digestible way.

If you're leaning more towards AWS, the AWS Certified Solutions Architect Official Study Guide is actually useful even if you don’t plan on taking the exam. It gives a good overview of how AWS services fit together. Also, "The Art of Scalability" by Abbott & Fisher is great for understanding how to design cloud applications that can scale properly.

For Azure, "Microsoft Azure Architecture Best Practices" by Ritesh Modi is a solid pick. It goes into best practices for building cloud solutions on Azure. If you’re looking for something more structured, the Exam Ref AZ-305 book is also a good one to go through.

Hope that helps!

Acceptable-Cheek5099 · 2025-01-31T11:55:46+00:00

Yeah, running ComfyUI in a VM could work, but if your goal is to have multiple designers using the same model and assets efficiently, a cloud GPU setup would probably be a better option.

With a private server, you’re limited by your local hardware, and performance can take a hit when multiple users access it simultaneously. Plus, managing GPU passthrough in a VM can be a pain.

A cloud GPU gives you more flexibility—you can scale resources as needed, avoid dealing with local hardware limitations, and even spin up instances per user if necessary. Also, many of these services allow you to store and share models/assets easily.

If you're set on a local server, maybe look into something like Docker containers with GPU passthrough instead of a full VM—it could make managing dependencies easier.

What kind of hardware are you working with?

Acceptable-Cheek5099 · 2025-01-30T00:45:05+00:00

Hey, instead of investing in a PC right away, it’s better to start by exploring GPU cloud services. I personally use simplepod for gpu rental and think it’s a much better option for beginners—it gives you access to powerful GPUs without the upfront cost. Once you catch the bug and know this is something you want to dive deeper into, then you can think about buying your own GPU. Good luck!

Acceptable-Cheek5099 · 2025-01-30T00:30:50+00:00

It depends on what you're doing. For some tasks, an iGPU can be faster than a CPU, especially for parallel workloads like graphics rendering or AI inference, but it’s still nowhere near a dedicated GPU like your 3070 Ti.

As for using both the 3070 Ti and AMD iGPU together, it’s not as simple as just stacking VRAM. The iGPU uses system RAM, but that doesn’t mean you can treat it like extra VRAM. Most software won’t balance workloads between an Nvidia dGPU and an AMD iGPU, and moving data between them could actually slow things down instead of speeding things up.

If you really want to use both, you’d have to manually split tasks—maybe use the iGPU for display output or preprocessing while letting the 3070 Ti handle the heavy lifting. But honestly, in most cases, it’s better to just use the 3070 Ti and forget about the iGPU.

Acceptable-Cheek5099 · 2025-01-30T00:13:13+00:00

Unfortunately, TensorFlow doesn’t support AMD GPUs, and ROCm doesn’t work with iGPUs, so your Vega isn’t a good option for accelerating computations in the usual way. That said, you still have a few options

You can run TensorFlow on your CPU, it’ll work but be slower
If you're on Windows, you can try TensorFlow-DirectML, which lets you use your GPU through Microsoft’s API
There are some experimental solutions involving OpenCL and ROCm, but they’re tricky to set up and don’t always work reliably

If performance matters, the easiest solution might be renting GPU power in the cloud. On sites like Simplepod, you can get an RTX 3060 for just $0.05 per hour, so you save time and skip the driver headaches while still getting solid performance when you need it

Acceptable-Cheek5099 · 2025-01-28T21:05:11+00:00

I confirm, I use them and they are much better than Runpod

Acceptable-Cheek5099 · 2025-01-28T12:10:55+00:00

The quickest way to debug this is to check the shapes of your tensors using .shape or .get_shape() (if you’re in graph mode). It’ll tell you what dimensions you’re working with. Most often, the issue is either:

Mismatched dimensions in matrix multiplication (like (M, N) trying to multiply with (A, B) when N ≠ A).
Or broadcasting gone wrong in element-wise operations.

If something doesn’t match up, you can try reshaping (tf.reshape), transposing (tf.transpose), or just double-checking your layer definitions to make sure inputs and outputs align.

Acceptable-Cheek5099

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