Hacker News: combocosmo

New comment by combocosmo in "Batteries Not Included, or Required, for These Smart Home Sensors"

combocosmo — Wed, 06 May 2026 09:12:30 +0000

It's a unique idea that I could see being useful in select situations. The reliance on wearable microphones sounds like a downside.

Also I guess this might be annoying for pets that can hear well beyond 20 kHz.

New comment by combocosmo in "Show HN: European alternatives to Google, Apple, Dropbox and 120 US apps"

combocosmo — Fri, 03 Apr 2026 11:46:39 +0000

Can you make some of my repeating settings savable? If I have to send invoices to 5 clients I'd prefer to not have to fill in the same stuff over and over again!

New comment by combocosmo in "Show HN: An iOS budget app I've been maintaining since 2011"

combocosmo — Tue, 13 Jan 2026 12:54:23 +0000

Nice project! I built a CLI budgeting project a long time ago, and what made me stop using my own project was the lack of automated integration with my bank accounts. At that point I had many credit cards, multiple bank accounts, in different currencies, and integrating all expenses was just too much manual work.

I wish financial institutions were better at automated exports of your financial data, given the right permissions of course.

New comment by combocosmo in "AI Code Is Going to Kill Your Startup (and You're Going to Let It)"

combocosmo — Sat, 15 Nov 2025 20:16:23 +0000

Of course a bit anecdotal, but not once has either Gemini or ChatGPT suggested me anything with eval or shell=True in it for Python. Admittedly I only ask it for specific problems, "this is your input, write code that outputs that" kind of stuff.

I find it hard to believe that nearly 50% of AI generated python code contains such obvious vulnerabilities. Also, the training data should be full of warnings against eval/shell=True... Author should have added more citations.

New comment by combocosmo in "Non-elementary group-by aggregations in Polars vs pandas"

combocosmo — Sat, 16 Nov 2024 08:29:59 +0000

I've always liked scatter solutions for these kind of problems:

  import numpy as np
  
  def scatter_mean(index, value):
      sums = np.zeros(max(index)+1)
      counts = np.zeros(max(index)+1)
      for i in range(len(index)):
          j = index[i]
          sums[j] += value[i]
          counts[j] += 1
      return sums / counts
  
  def scatter_max(index, value):
      maxs = -np.inf * np.ones(max(index)+1)
      for i in range(len(index)):
          j = index[i]
          maxs[j] = max(maxs[j], value[i])
      return maxs
  
  def scatter_count(index):
      counts = np.zeros(max(index)+1, dtype=np.int32)
      for i in range(len(index)):
          counts[index[i]] += 1
      return counts
  
  id = np.array([1, 1, 1, 2, 2, 2]) - 1
  sales = np.array([4, 1, 2, 7, 6, 7])
  views = np.array([3, 1, 2, 8, 6, 7])
  means = scatter_mean(id, sales).repeat(scatter_count(id))
  print(views[sales > means].max())

Obviously you'd need good implementations of the scatter operations, not these naive python for-loops. But once you have them the solution is a pretty readable two-liner.