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waiting on ai to do my work

Sam McAlilly smcalilly

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waiting on ai to do my work
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@jboner
jboner / latency.txt
Last active May 17, 2024 01:05
Latency Numbers Every Programmer Should Know
Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns 14x L1 cache
Mutex lock/unlock 25 ns
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy 3,000 ns 3 us
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD
@dminkovsky
dminkovsky / dict_to_view.py
Last active July 10, 2020 01:13
Django: Generate corresponding JSON and rendered template (HTML) views from the same data dictionary.
from django.http import HttpResponse
from django.utils.functional import wraps
from django.template.context import RequestContext
from django.template.loader import get_template
from django.utils import simplejson as json
def dict_to_template_view(**template_args):
"""
Takes a function that returns a dict and decorates it into a template-rendering view
"""

Make it real

Ideas are cheap. Make a prototype, sketch a CLI session, draw a wireframe. Discuss around concrete examples, not hand-waving abstractions. Don't say you did something, provide a URL that proves it.

Ship it

Nothing is real until it's being used by a real user. This doesn't mean you make a prototype in the morning and blog about it in the evening. It means you find one person you believe your product will help and try to get them to use it.

Do it with style

@Kartones
Kartones / postgres-cheatsheet.md
Last active May 16, 2024 12:40
PostgreSQL command line cheatsheet

PSQL

Magic words:

psql -U postgres

Some interesting flags (to see all, use -h or --help depending on your psql version):

  • -E: will describe the underlaying queries of the \ commands (cool for learning!)
  • -l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)
@rueycheng
rueycheng / GNU-Make.md
Last active April 29, 2024 01:39
GNU Make cheatsheet
@adejones
adejones / xlsx_dict_reader.py
Last active June 23, 2021 15:24
openpyxl (2.4.8) sheet-to-dict like the csv DictReader - based on https://gist.github.com/mdellavo/853413
from openpyxl import load_workbook
def XLSXDictReader(f):
book = load_workbook(f)
sheet = book.active
rows = sheet.max_row
cols = sheet.max_column
headers = dict((i, sheet.cell(row=1, column=i).value) for i in range(1, cols))
def item(i, j):
return (sheet.cell(row=1, column=j).value, sheet.cell(row=i, column=j).value)
@mrmartineau
mrmartineau / stimulus.md
Last active May 12, 2024 04:35
Stimulus cheatsheet
@ZaxR
ZaxR / nearest_neighbors.py
Last active January 17, 2023 10:17
Find nearest neighbors by lat/long using Haversine distance with a BallTree
"""
Example:
# All locations; also locations FROM which we want to find nearest neighbors
locations = pd.DataFrame({"LOCATION_NAME": ["Chicago, IL", "New York, NY", "San Fransisco, CA"],
"LATITUDE": [1, 2, 3],
"LONGITUDE": [1, 2, 3],
"ID": [1, 2, 3]})
locations = locations.apply(lambda x: Location(location_name=x['LOCATION_NAME'],
latitude=x['LATITUDE'],
longitude=x['LONGITUDE'],