Forwarded from Machine Learning with Python
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๐ Lifetime access to HelloEncyclo โ every AI, ML & Data Science course ever built โ for ~$41. Once. Forever.
This isn't a drill. This isn't a rerun.
This is the founding-member price โ and it disappears the moment the first 250 seats globally are gone.
โ 13 courses live right now
โ 40+ more in 2โ3 weeks
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โ 15-day money-back โ full refund, no questions
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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. ๐ ๏ธ๐ค
In the end, you don't just call a ready-made agent via the API, but you understand the components that make up a Claude Code-like tool. ๐ง โ๏ธ
https://github.com/bozhouDev/14days-build-claude-code-cli/blob/main/README.en.md
#Python #AI #ClaudeCode #CLI #CodingTutorial #Tech
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In the end, you don't just call a ready-made agent via the API, but you understand the components that make up a Claude Code-like tool. ๐ง โ๏ธ
https://github.com/bozhouDev/14days-build-claude-code-cli/blob/main/README.en.md
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GitHub
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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
This usually leads to code duplication or conditions around
`nullcontext(obj)
But note that
๐ฅ
#Python #ContextManager #CodingTips #DevLife #Programming #Tech
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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 โ it simply passes it on further.๐ฅ
nullcontext() helps to unify scenarios with optional context managers and significantly simplifies the architecture of IO code.#Python #ContextManager #CodingTips #DevLife #Programming #Tech
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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)
๐ฅ deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists.
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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)
๐ฅ deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists.
#Python #DataStructures #CodingTips #Programming #Deque #Tech
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Why should you join?
Many people interested in AI Engineering are asking the same questions:
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Shuffling without repetitions:
โจ Honest selection and generation of unique sets
When it's necessary to implement the logic of prize draws, random task distribution, or generating test questions, developers often use
โ Guarantee of uniqueness: The main property of
โ Safety of the original: The function does not modify the original list (unlike
โ Strict control of size: If you pass a parameter
#Python #Random #Coding #NoRepetition #DataScience #UniqueSets
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import random
# Initial list of candidates or prizes
participants = ["Alexey", "Maria", "Ivan", "Olga", "Dmitry"]
# 1. Selecting 3 unique winners (sample without replacement)
winners = random.sample(participants, k=3)
print(f"Winners: {winners}")
# The result is different each time, but there will be no repetitions within the list of winners!
# 2. Shuffling an entire string (creating an anagram)
word = "python"
shuffled_word = "".join(random.sample(word, len(word)))
print(f"Anagram: {shuffled_word}")
# 3. Important difference: random.choices allows repetitions
print(f"With repetitions: {random.choices(participants, k=3)}")
โจ Honest selection and generation of unique sets
When it's necessary to implement the logic of prize draws, random task distribution, or generating test questions, developers often use
random.choice() in a loop. But this approach requires manually ensuring that the same element is not selected twice. The random.sample function takes on this routine.โ Guarantee of uniqueness: The main property of
random.sample is "without replacement". The extracted element no longer participates in the next selection cycle, which completely eliminates duplicates in the resulting list.โ Safety of the original: The function does not modify the original list (unlike
random.shuffle()), but creates a completely new array with the results. This allows the structure of the original data to remain intact.โ Strict control of size: If you pass a parameter
k (the number of elements) that exceeds the length of the original list, Python will not start duplicating elements and will immediately throw an ValueError error. This protects the program logic from incorrect data.#Python #Random #Coding #NoRepetition #DataScience #UniqueSets
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Convert PDF to structured JSON โ in a couple of lines and without hassle! ๐โจ
Today, we'll create a mini-service that takes a PDF document, extracts the text from it, and asks GPT to neatly organize the content into sections: title, author, date, and a list of sections. ๐
First, let's connect the necessary libraries and API key:
Now, let's extract the text from the PDF. We'll loop through all the pages and combine them into a single string:
Next, we'll send the obtained text to GPT. We'll ask the model to return a structured JSON with the necessary fields:
Output the result:
๐ฅ Suitable for contracts, reports, methodologies, and any PDFs โ we immediately get a JSON ready for use.
#PDF #JSON #Python #GPT #Automation #DataScience
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Today, we'll create a mini-service that takes a PDF document, extracts the text from it, and asks GPT to neatly organize the content into sections: title, author, date, and a list of sections. ๐
First, let's connect the necessary libraries and API key:
import os
from PyPDF2 import PdfReader
from openai import OpenAI
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
Now, let's extract the text from the PDF. We'll loop through all the pages and combine them into a single string:
reader = PdfReader("document.pdf")
text = "
".join(page.extract_text() for page in reader.pages)Next, we'll send the obtained text to GPT. We'll ask the model to return a structured JSON with the necessary fields:
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": (
"You are a PDF parser. Return a JSON with the fields: title, author, date, sections. "
"Each section is an object with name and summary."
)},
{"role": "user", "content": text}
]
)
Output the result:
structured = response.choices[0].message.content.strip()
print(structured)
๐ฅ Suitable for contracts, reports, methodologies, and any PDFs โ we immediately get a JSON ready for use.
#PDF #JSON #Python #GPT #Automation #DataScience
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