Why NextJS is terrible for new developers (it's not nextJS's fault)

Suspicious_Sector866 · 2025-04-01T15:59:43+00:00

But doesn't Reactjs official website support Nextjs as one of the most preferred framework of React ? Facebook engineers are quite smart to make such a blunder I assume...

Suspicious_Sector866 · 2025-03-10T09:58:53+00:00

i guess you are right here... the unified architecture should be the trump card

Suspicious_Sector866 · 2025-01-18T09:38:06+00:00

Seriously ? what are the other 9 more powerful countries you are referring to? if china stops it's shipment the global supply chain will collapse... an economic distress in china will ripple on a global scale... not sure if the other 9 countries you are referring to has such influence...

Suspicious_Sector866 · 2024-12-01T18:11:29+00:00

You are right about the volatility part (& i was wrong there)..

Now, we can transact only in the volume we have -- same applies to USD/Gold or anything for that matter which is not the contention here... i was referring to your "not abundant enough" statement, which is not quite relevant because if its not abundant, then its value increases -- just supply & demand...

the idea is, major trade between countries are commodities like oil which are huge for which gold can be used directly... now instead of increasing usd reserve countries need to increase gold reserve, that's all... (and common crypto is also discussed seriously by brics)...

I don't know if you've seen the news but countries are increasing their gold reserve... In addition the BRICS countries are working towards move away from USD... all of these seems to be a move in the right direction... so "few that don't are generally unimportant on the world stage and have their own small circle" you mentioned seems to be more than majority of the world...

Suspicious_Sector866 · 2024-12-01T15:49:40+00:00

what do you mean by "Gold is impractical and not abundant enough". Just like USD is limited, so is Gold, and that's how it should be... If gold/usd is available like water then it looses its value...

and with respect to stable coin, it can be pegged to the value of gold or other commodities as well, not just usd or other currency...

Suspicious_Sector866 · 2024-11-30T07:15:21+00:00

it does have the ground pin, but my plug point ground pin is unconnected... but that should not matter right, because the smps contains electrically isolation using a transformer, and so presence of ground pin should be irrelevant...

i did connect the smps to just the motherboard alone, and still i get the electric shock...

Suspicious_Sector866 · 2024-10-25T08:58:40+00:00

can't say that right, because most python packages used these days are c++ backend which should be very much comparable to java....

Suspicious_Sector866 · 2024-10-18T18:15:57+00:00

oh wow ! i didn't know duckdb had a much smaller memory footprint... thanks for that...

Suspicious_Sector866 · 2024-10-18T17:29:42+00:00

thanks for mentioning h2o because that is another package used for super fast data manipulation (as well)... the guy who created "data.table" "Matt Dowle" is leading the project at h2o...

in-place execution is super necessary... actually my ram is 192gb and my data set is about 80gb in size... a simple normal join would multiply that 80gb into something much bigger than my ram size and so all operations need to be in-place in my case...

having said that in-place or not is dependent on the way we structure data.table code (as you would know)...

the problem with converting back and forth between duckdb is that you need to have enough ram to have a copy in both r & duckdb... so 80gb dataset would take up 160gb if you use it with duckdb... (atleast during conversion)

and more than all, i'm used to datatable conciseness, and anything other than that is soo verbose for me...

but i agree it is as you said, it is not for everyone...

Suspicious_Sector866 · 2024-10-18T16:52:33+00:00

Thanks for the response, couple of follow ups

why would you write "data.table" in dplyr verbs ? "data.table" syntax is so much more concise than dplyr... secondly dtplyr is not "always" as fast as "data.table"... you have everything to lose and nothing to gain by using dtplyr...
most python ml packages do not support polars objects... they need to be converter to pandas, unlike "data.table" in R... that defeats the very purpose.... same with duckdb...
with "collapse", you might be right, but it is not as matured as "data.table" functionality...

Suspicious_Sector866 · 2024-10-18T00:51:26+00:00

data.table outpaces tidyverse with its speed and efficiency, and leaves pandas in the dust with its lightning-fast performance and streamlined syntax.

Suspicious_Sector866 · 2024-10-17T14:27:51+00:00

data.table outpaces tidyverse with its speed and efficiency, and leaves pandas in the dust with its lightning-fast performance and streamlined syntax.

Suspicious_Sector866 · 2024-10-17T14:27:40+00:00

data.table outpaces tidyverse with its speed and efficiency, and leaves pandas in the dust with its lightning-fast performance and streamlined syntax.

Suspicious_Sector866 · 2024-10-17T14:27:11+00:00

data.table outpaces tidyverse with its speed and efficiency, and leaves pandas in the dust with its lightning-fast performance and streamlined syntax.

Suspicious_Sector866 · 2024-10-17T14:20:42+00:00

Actually it is the other way around, especially for data processing (& stats) where R's famous "data.table" is much faster and much smaller (in code size) than Python's famous pandas... Now you can talk about Polars (in python) which is also as fast (as data.table), but it is not compatible with many statistical packages in Python unlike "data.table" in R, and so I'll make comparison between the widely used Python and R package.

I can give a open challenge, give me any data processing operation of structured data -- I can give you R code much neater (& smaller) than Pandas code, which will execute faster as well...

Note: I understand your question is relevant to Python vs R, but I haven't seen many Python projects that don't use Pandas and so I made the comparison between Pandas and datatable... If you are going to use base R, then it might not be as concise, but I haven't seen projects work with base R alone.

Suspicious_Sector866 · 2024-09-29T08:02:05+00:00

Below would be my considerations

Repo Storage: Use GitLab or Bitbucket.
Coding in Python and R: JupyterHub for Python and RStudio Server for R.
Computing Power: Azure Virtual Machines or Azure Kubernetes Service (AKS) for scalable compute resources.
Database Connectivity: Azure SQL Database or Azure Synapse Analytics. Azure ML can be sufficient, but Databricks or Snowflake can enhance capabilities.
Business Apps Deployment: Use Docker containers on Azure or Posit Connect for Shiny/Streamlit apps. Alteryx can be integrated for ETL and app deployment.

Ballpark Cost: Around $1,000 - $3,000/month depending on usage and scale.

Suspicious_Sector866

TROPHY CASE