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Prepping for cloud, dev, or AI roles this year? These free kits cover the gaps most people skip β cert basics, hands-on labs, and mock exams.
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βοΈ Free AI Materials β from LLM basics to fine-tuning practice notebooks
β https://tinyurl.com/2z46z8f4
π Cloud Study Guide β cheat-sheet style, handy for interview prep
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π§ Free Mock Exam β test yourself before booking the real thing
β https://tinyurl.com/y7ffwecz
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π Save this or forward it to your dev/study group β good resources are worth sharing. πͺ
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π asyncio.Queue.shutdown() β shutting down handlers without special markers
Starting with Python 3.13, the asyncio.Queue can be closed using shutdown(), without the need to use None or other special values.
After shutdown(), no new items are added, and existing items are processed. When the queue is empty, get() will raise asyncio.QueueShutDown.
For a forced shutdown, there is shutdown(immediate=True), which clears the queue but may disrupt the normal behavior of join().
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Starting with Python 3.13, the asyncio.Queue can be closed using shutdown(), without the need to use None or other special values.
import asyncio
async def worker(queue):
while True:
try:
task = await queue.get()
except asyncio.QueueShutDown:
break
print(task)
queue.task_done()
async def main():
queue = asyncio.Queue()
task = asyncio.create_task(worker(queue))
await queue.put("task-1")
await queue.put("task-2")
queue.shutdown()
await queue.join()
await task
asyncio.run(main())
After shutdown(), no new items are added, and existing items are processed. When the queue is empty, get() will raise asyncio.QueueShutDown.
For a forced shutdown, there is shutdown(immediate=True), which clears the queue but may disrupt the normal behavior of join().
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Extracting named entities (NER) from contract texts using spaCy. π
Manually parsing legal documents to find organizations, dates, and amounts is too time-consuming. The spaCy library allows you to automatically find and classify named entities in unstructured text. We will write a Python script that loads a language model and recognizes key entities in a contract excerpt. π
We will install the spaCy library and download a pre-trained language model for working with the Russian language. π·πΊ
The spaCy library and the Russian language model have been successfully loaded into the environment. β
We will create a Python script called
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Manually parsing legal documents to find organizations, dates, and amounts is too time-consuming. The spaCy library allows you to automatically find and classify named entities in unstructured text. We will write a Python script that loads a language model and recognizes key entities in a contract excerpt. π
We will install the spaCy library and download a pre-trained language model for working with the Russian language. π·πΊ
pip install spacy && python3 -m spacy download ru_core_news_sm
The spaCy library and the Russian language model have been successfully loaded into the environment. β
We will create a Python script called
extract_ner.py that processes the contract text through a language pipeline and prints the identified entities. πimport spacy
nlp = spacy.load("ru_core_news_sm")
text = "OOO 'Vector' undertakes to pay 500,000 rubles by October 15, 2026, in Moscow."
doc = nlp(text)
for ent in doc.ents:
print(f"{ent.text}
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