manu354

ORCHESTRATION Instructions

1. Decompose all subtasks required to accomplish this task into a dependency graph of nodes.

2. Spawn agents to work through this dependency graph.

   • Note: wait_for_agent tool can be buggy—may need to call it multiple times.

3. Manage/orchestrate Voicetree MCP agents to work through the dependency graph.

Recommended Structure

yoavg

Some remarks on Large Language Models

Yoav Goldberg, January 2023

Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.

Intro

Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We

max-rocket-internet

CPU: percentage of limit

A lot of people land when trying to find out how to calculate CPU usage metric correctly in prometheus, myself included! So I'll post what I eventually ended up using as I think it's still a little difficult trying to tie together all the snippets of info here and elsewhere.

This is specific to k8s and containers that have CPU limits set.

To show CPU usage as a percentage of the limit given to the container, this is the Prometheus query we used to create nice graphs in Grafana:

sum(rate(container_cpu_usage_seconds_total{name!~".*prometheus.*", image!="", container_name!="POD"}[5m])) by (pod_name, container_name) /

nadavrot

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

bittlingmayer

# See https://github.com/facebookresearch/fastText/blob/master/get-wikimedia.sh
#
# From https://github.com/facebookresearch/fastText/issues/161:
#
# We now have a script called 'get-wikimedia.sh', that you can use to download and
# process a recent wikipedia dump of any language. This script applies the preprocessing
# we used to create the published word vectors.
#
# The parameters we used to build the word vectors are the default skip-gram settings,
# except with a dimensionality of 300 as indicated on the top of the list of word

jbochi

mommi84

Awesome Knowledge Graph Embedding Approaches

This list contains repositories of libraries and approaches for knowledge graph embeddings, which are vector representations of entities and relations in a multi-relational directed labelled graph. Licensed under CC0.

Libraries

• 🆕 AmpliGraph (4 algorithms) @ https://github.com/Accenture/AmpliGraph
• Embedding framework (5 algorithms) @ https://github.com/BookmanHan/Embedding

rondy

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

---

Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?

tushortz

def unicodetoascii(text):

    TEXT = (text.
    		replace('\\xe2\\x80\\x99', "'").
            replace('\\xc3\\xa9', 'e').
            replace('\\xe2\\x80\\x90', '-').
            replace('\\xe2\\x80\\x91', '-').
            replace('\\xe2\\x80\\x92', '-').
            replace('\\xe2\\x80\\x93', '-').
            replace('\\xe2\\x80\\x94', '-').

mblondel

# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause

import numpy as np


def ranking_precision_score(y_true, y_score, k=10):
    """Precision at rank k

    Parameters