When you're doing a parser or migrating a site, there's often a pile of unreadable HTML markup on the screen. Converting this into neat Markdown is usually a hassle.

In the open code, I found a convenient tool called python-markdownify, which precisely solves the problem of converting HTML to Markdown.

The logic is simple: you take bulky HTML and get a clear and well-structured Markdown as a result.

The tool is easily customizable. You can clean up the necessary tags, change the format of headings, and neatly process tables and images. All of this can be configured.

It's installed via pip. It can be used both from Python code and from the command line, converting files in batches.

pip install python-markdownify

If desired, you can inherit and redefine the conversion rules for your own cases. The extensibility is fine there.

If you have to process large amounts of text or migrate a blog, the library saves a lot of time that would otherwise be spent on tedious work with regular expressions.

➡️ Link to GitHub
http://github.com/matthewwithanm/python-markdownify

#python #markdown #html #coding #devtools #opensource

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Advice for Python, UV, and Docker 🐍🐳

Sometimes dependencies are better installed separately from the code — this noticeably speeds up the compilation of Docker images 🚀

The idea is simple: first, we install dependencies, then we add the project 🛠

Why is this necessary:
• Docker caches layers and does not rebuild them unnecessarily ⚡️
• if only the code changes — the dependencies are taken from the cache 💾
• if the dependencies change — only the corresponding layer is rebuilt 🔁
• without this, any minor change triggers a full reinstallation 🔄

Example:

RUN --mount=type=cache,target=/root/.cache/uv  --mount=type=bind,source=uv.lock,target=uv.lock  --mount=type=bind,source=pyproject.toml,target=pyproject.toml  uv sync --locked --no-install-project

COPY . /app
RUN --mount=type=cache,target=/root/.cache/uv  uv sync --locked

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Limiting program resources using the resource module 🛡️

import resource
import sys

# 1. Limiting the size of RAM (soft and hard limits in bytes)
# Limit the memory to ~50 MB
memory_limit = 50 * 1024 * 1024 
resource.setrlimit(resource.RLIMIT_AS, (memory_limit, memory_limit))

# 2. Checking the protection's working
try:
    print("Trying to allocate a huge array of memory...")
    huge_list = [i for i in range(10_000_000)]
except MemoryError:
    print("The limit worked! The program didn't crash, but caught the error.")

# 3. Finding out how many resources the script has already consumed
usage = resource.getrusage(resource.RUSAGE_SELF)
print(f"Peak memory consumption (in KB): {usage.ru_maxrss}")

Protecting the server from "greedy" code 🔧

When you run someone else's code, process user files, or write parsers, there's always a risk of a memory leak or an infinite loop. If such a script runs on the server, it can fill up all the RAM and bring down neighboring important processes (for example, the database). The built-in resource module (works on Unix/Linux/macOS) allows you to strictly limit the program's appetites.

— Safe environment: You can limit not only RAM (RLIMIT_AS), but also CPU time (RLIMIT_CPU). If the code goes into an infinite loop, the system will gracefully terminate it after a specified number of seconds.

— File system control: Using RLIMIT_FSIZE, you can prevent the script from creating files larger than a certain size. This will save the server's disks from being accidentally overwritten by gigantic logs.

— Precise audit: The getrusage function provides detailed statistics on the current process: how much time the CPU spent on calculations, how many I/O operations there were, and what the maximum amount of memory used was during the entire operation.

#Python #ResourceManagement #ServerSafety #Coding #DevOps #Linux

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A 14-day tutorial where you build a Python code-agent CLI in the style of Claude Code from scratch and simultaneously understand how the Agent Harness actually works. 🛠️🤖

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Python can substitute an empty context manager without conditions inside!

It often happens that a resource needs to be opened via with, and sometimes the object is already ready and there's no need to open anything.

This usually leads to code duplication or conditions around with:

if need_open:
    f = open(...)
else:
    f = existing_file

`nullcontext(obj) behaves like an empty context manager and allows you to maintain a single execution flow.

This is especially useful for APIs, tests, optional resources, dependency injection, and functions that can accept both a path and a ready-made object.


with ctx as resource:
    process(resource)

But note that nullcontext() does not close the passed object — it simply passes it on further.

🔥 nullcontext() helps to unify scenarios with optional context managers and significantly simplifies the architecture of IO code.

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Do you know that Python can shift sequences without slicing and creating new lists?

When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations.

q.rotate(1)

A negative value rotates the queue in the other direction.

q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms.

workers.rotate(-1)

🔥 deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists.

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