The one that got me was a supposed foundation legend from Sumer that a handful of strangers came and taught them civilization.
The idea of a remnant people floating down a river to escape some sort of societal collapse and then being adopted into a new tribe for their usefulness has a certain something as a hypothesis goes. It’s the “strangers” part that’s a bit suspect since how would you not meet neighbors like that. Unless the river was the end of their journey and not the start.
But what is your own opinion?
- reducing headcount by 50% down to ~70 people = firing 70 people
- at a generous estimate of total burdened cost of $1M/person/year, that's $70M/year
- which accounts for a full 5 years of that war chest
- and, moreover, "at its lowest point" suggests that perhaps Replit has expanded headcount again since; the article mentions that it has done some acqui-hires.
Levels.fyi shows Replit salaries in the $200k–300k range, so even at a 2× burden rate, I think that this is probably a significant overestimate of the costs.
Firing 70 people when you have $100 million that you haven't touched, have raised money on top of that, and have many years of runway for the people you fired… comes together to paint a picture that is, imho, less than flattering.
He is going to be ultra surprised to learn what the majority thinks and how it's not what he thinks it is.
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# NeMo does not run on 3.13+
python3.12 -m venv .venv
source .venv/bin/activate
git clone https://github.com/NVIDIA/NeMo.git nemo
cd nemo
pip install torch torchaudio torchvision --index-url https://download.pytorch.org/whl/cu128
pip install .[asr]
deactivate
Then run a transcribe.py script in that venv: import os
import sys
import nemo.collections.asr as nemo_asr
model_path = sys.argv[1]
audio_path = sys.argv[2]
# Load from a local path...
asr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from(restore_path=model_path)
# Or download from huggingface ('org/model')...
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name=model_path)
output = asr_moel.transcribe([audio_path])
print(output[0])
With that I was able to run the model, but I ran out of memory on my lower-spec laptop. I haven't yet got around to running it on my workstation.You'll need to modify the python script to process the response and output it in a format you can use.