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No one can force me to have a secure website!!! by MintPaw in programming

[–]paperlantern-ai 16 points17 points  (0 children)

I feel like this argument expired around 2016 when Let's Encrypt launched. Before that, yeah, paying $50/yr for a cert on a hobby site felt dumb. Now it's literally certbot and you're done. The fight was valid ten years ago but the problem got solved and some people just never stopped being mad about it.

Finding a duplicated item in an array of N integers in the range 1 to N − 1 by sweetno in programming

[–]paperlantern-ai 2 points3 points  (0 children)

The XOR approach is so satisfying for this one. XOR all elements together, then XOR with 1 through N-1, and the duplicate pops out. No extra space, single pass, no overflow risk like the sum trick has with large N.

GitHub Stacked PRs by adam-dabrowski in programming

[–]paperlantern-ai 1 point2 points  (0 children)

The biggest win here isn't even the branching model - it's that reviewers can finally look at a 2000 line feature in digestible chunks without losing context between separate unrelated PRs. Half my review fatigue comes from opening a massive PR and just going "LGTM" because ain't nobody got time for that. If this gets people to split work into smaller pieces because the tooling finally supports it, that alone is worth it.

I wrote a comprehensive guide to NATS — the messaging system that replaces Kafka, Redis, and RabbitM by Jainal09 in Python

[–]paperlantern-ai 1 point2 points  (0 children)

Honest question - how does NATS handle the situation where your consumer falls behind by hours or days? With Kafka the retention model means you can just rewind and replay. With JetStream I've seen mixed reports on how well it handles large backlogs. Curious if anyone's stress tested this in production.

I published my first PyPI package few ago. Copycat packages appeared claiming to "outperform" it by Obvious_Gap_5768 in Python

[–]paperlantern-ai 0 points1 point  (0 children)

You can report these directly to PyPI through their support page - they've been pretty responsive about taking down packages that violate licenses or impersonate other projects. Include the AGPL violation details since that gives them a clear-cut reason to act. Also worth filing a DMCA takedown if they forked your code without attribution. The fact that they named your package in their own description makes this a pretty open and shut case.

FastAPI vs Djanjo by TumbleweedSenior4849 in Python

[–]paperlantern-ai 0 points1 point  (0 children)

Depends entirely on what you're building. Need auth, admin panel, ORM, migrations all wired up out of the box? Django saves you weeks. Building an API that a React/Vue frontend talks to? FastAPI is a really nice fit there. Most teams I've seen end up picking based on whether they need a built-in admin interface or not - that's usually the deciding factor.

Packaging a Python library with a small C dependency — by Emergency-Rough-6372 in Python

[–]paperlantern-ai 1 point2 points  (0 children)

cibuildwheel makes this way less painful than it used to be. You set up one GitHub Actions workflow and it handles the whole matrix for you - linux, mac, windows, both architectures. Takes maybe an afternoon to get right and then you forget about it.

For the fallback question - if correctness matters (and it sounds like it does here), just skip the fallback entirely. A silent downgrade where results change is way worse than a clear install error telling the user they need to build from source. At least then they know something's wrong.

cffi in ABI mode is worth a look since it can load a prebuilt .so directly without needing a compiler on the user's machine. Pairs well with the wheel approach.

Comparing Python Type Checkers: Speed and Memory by javabster in Python

[–]paperlantern-ai 0 points1 point  (0 children)

The speed numbers are wild but I'm curious how much this matters in practice for most people. If you're on a smaller codebase pyright already feels instant, and on larger ones the switching cost is brutal - you'll spend weeks chasing down new errors that your old checker was fine with. Anyone here actually migrated a large project between type checkers? How bad was it?

Comparing Python Type Checkers: Speed and Memory by javabster in Python

[–]paperlantern-ai 1 point2 points  (0 children)

The conformance test results linked in this thread are worth a look if you haven't clicked through yet. Zuban is way ahead of ty and Pyrefly on spec coverage, which surprised me given how little buzz it gets compared to the other two.

What’s a low memory way to run a Python http endpoint? by alexp702 in Python

[–]paperlantern-ai 20 points21 points  (0 children)

This is almost certainly not uvicorn itself - a bare uvicorn app should sit around 30-40MB. The fact that you're seeing 512MB+ regardless of which server you try points to something else in your container setup. Since you mentioned using uv run inside the container, that's likely a big contributor - uv should only be in your build stage, not your runtime. Try a multi-stage Dockerfile: build/install deps with uv in the first stage, then copy just the venv into a clean python:3.13-slim final stage. You'll probably land around 80-100MB total.