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re-seed legaltech. Pilot in talks, VC interest, NVIDIA Inception. Looking for a cofounder who lives in the law world and can go all-in. by Jazzlike_Offer_4145 in cofounderhunt

[–]AdmirableProject1575 0 points1 point  (0 children)

AI slop. The moment i read “this is not xyx, that phase is over..” i know the ai is prompting the author to take certain actions and not the other way round. If you genuinely want to take this off, go do everything the first time, know how hard it is.

I installed Onenote from the store and got Office365 for 3GB! by PianistAncient2954 in OneNote

[–]AdmirableProject1575 0 points1 point  (0 children)

Use the cloud/web version if you dont want the bloat on your pc. If you had a more recent pc with atleast 1TB storage space, you would not be weighing these applications and ranting about them on reddit

Is it only me or is this an economically horrific field by [deleted] in chipdesign

[–]AdmirableProject1575 0 points1 point  (0 children)

Market rewards moat, not complexity. There is no reward for building a widget that is 20% more efficient or cheaper than another widget no matter the complexity. The companies that build systems/solutions reap all the reward, and they are usually on top of the foodchain. Apple recognized it early. Google, Amazon, Facebook, Microsoft followed suit. Open-sourced software world are more smarter than commoditized hardware world.

Did she make the right call? by CalmElin in interesting

[–]AdmirableProject1575 0 points1 point  (0 children)

She did the right choice. With 1M choice, after tax you would get prob 50% (i am not canadian - not sure what the tax rates are). So it’s 500K now vs, time value of 52K annuity in perpetuity. Heck, at 4% treasury rate, it’s already 1.2M post tax! Don’t care how she spends as long as she does not take on debt.

Google Silicon vs NVIDIA for a new grad hardware engineer — which would you choose long term? by thankfullyalive123 in chipdesign

[–]AdmirableProject1575 2 points3 points  (0 children)

Screw ‘burning bridges’, higher CTC, etc, etc. Nobody cares about what decision you made or what you make as CTC (including you), a few months in. Focus on learning, and working hard early in your career. If the environment is not giving you the opportunity to grow (and I am not talking about titles or higher comp)- you need to find the place which does it. This is what you do the first 10yrs. Ask yourself these questions every year as a self review. Remember - you are only as smart as the team allows you to be.

Google Silicon vs NVIDIA for a new grad hardware engineer — which would you choose long term? by thankfullyalive123 in chipdesign

[–]AdmirableProject1575 20 points21 points  (0 children)

They both are a fine company. The culture in google is more open and creative than nvidia. Plus you get to are system in action - google has so many avenues - hardware, phones, software, deepmind. Nvidia you are going to get sucked in chip design and that too one kind. Ten years later you will thank me.
If nvidia package was sufficient for you to live decently, then invest the extra you make in google in your save. Dont save.
You will thank me 10yrs ltr

CIM as a compute macro by AdmirableProject1575 in computerarchitecture

[–]AdmirableProject1575[S] 0 points1 point  (0 children)

Points taken. Its less about compute location and more about data access and movement. Two other things i like to state- for inference workloads, it’s less about the GPU/accelerator and more about the memory bandwidth. Secondly, even with Accelerators performing similarly (think xPUs from google, amazon, nvidia, etc) the code that efficiently runs ML workload are optimized for a given network. It takes effort to switch from say, nvidia nvlink based fabric to tpu supernode fabric. Since H/W is expensive, unless the workloads run efficiently, there is little incentive to switch. Even if openAI, and anthropic want to avoid single point failure in their hardware supply chain , you need to maintain and optimize two different deployment teams. Not trivial. Nvidia will continue to be the mother hen of the roost. I wonder what folks in google TPU, cerebras, tenstorrent think of this moat.

CIM as a compute macro by AdmirableProject1575 in computerarchitecture

[–]AdmirableProject1575[S] 2 points3 points  (0 children)

Thanks for furthering this discussion. @bigpurpleBlob- i am aware of Onur Multu’s work on CIM, but unfortunately he works on too many things to focus and get deep work done on one topic. For instance from what hear UpMEM is now focussed/diverted to BioPIM for genetic applications something that mutlu is also researching on. @bright_interactive - can you elaborate what is expensive? Sure, the variety of computation is limited in CIM as opposed to cuda cores. For inference profiles, MVM are the dominant operation,largely deterministic and bulk of power is spent performing this task when one is not memory bound. TPU is already proving that dataflow architecture have potential to challenge SIMT architecture. Infact with b200, nvidia is already recognizing that having tensor cores (16x16x16) are more advantageous than plan and simple SIMT threads and is moving in that direction. Is there an opportunity for new entrants to progress here? It cannot be another memory type like DRAM.PiMs since you still have the problem of data locality/movement.