OpenAI has released Atlas, a new AI-powered browser that can remember context and operates in Agent Mode.

What is known:

Atlas is fully integrated with ChatGPT and uses ChatGPT Search under the hood.

In Agent Mode, the browser can navigate websites, click, search, and perform actions on its own.

You can open an unlimited number of tabs with agents — each lives its own life and solves separate tasks.

Atlas is already available to Free, Plus, Pro, Go, and Business users worldwide.
Enterprise and Education users can access the beta if their admin enables it. Versions for Windows, iOS, and Android are also in development.

You can download it at chatgpt.com/atlas

We hope Windows users will soon be able to experience this new browser in action. 😎

👉  @codeprogrammer

Python Notes 🌟🌟🌟🌟🌟

#python

https://t.me/addlist/8_rRW2scgfRhOTc0 ⚡️

🐍 PyTorch for Beginners: All the Basics on Tensors in One Place

A collection of basic techniques for working with tensors in PyTorch — for those who are starting to get acquainted with the framework and want to quickly master its fundamentals.

What's inside:

▶️ What tensors are and why they are needed

▶️ Tensor initialization: zeros, ones, random, similar size

▶️ Type conversion and switching between NumPy and PyTorch

▶️ Arithmetic, logical operations, tensor comparison

▶️ Matrix multiplication and batch computations

▶️ Broadcasting, view(), reshape(), changing dimensions

▶️ Indexing and slicing: how to access parts of a tensor

▶️ Notebook with code examples

A good starting material to understand the mechanics of tensors before moving on to models and training.

⛓ GitHub link

tags: #useful

➡ @codeprogrammer

In Python, you can unpack sequences using *, to work with a variable number of elements. The * can be placed anywhere and it will collect all the extra elements into a separate variable.

a, b, c = 10, 2, 3      # Standard unpacking

a, *b = 10, 2, 3        # b = [2, 3]

a, *b, c = 10, 2, 3, 4  # b = [2, 3]

*a, b, c = 10, 2, 3, 4  # a = [10, 2]

👉  @DataScience4

In Python, enhanced for loops with enumerate() provide both the index and value of items in an iterable, making it ideal for tasks needing positional awareness without manual counters. This is more Pythonic and efficient than using range(len()) for list traversals.

fruits = ['apple', 'banana', 'cherry']
for index, fruit in enumerate(fruits):
    print(f"{index}: {fruit}")

# Output:
# 0: apple
# 1: banana
# 2: cherry

# With start offset:
for index, fruit in enumerate(fruits, start=1):
    print(f"{index}: {fruit}")
# 1: apple
# 2: banana
# 3: cherry

#python #forloops #enumerate #bestpractices

✉️ @DataScience4

In Python, lists are versatile mutable sequences with built-in methods for adding, removing, searching, sorting, and more—covering all common scenarios like dynamic data manipulation, queues, or stacks. Below is a complete breakdown of all list methods, each with syntax, an example, and output, plus key built-in functions for comprehensive use.

📚 Adding Elements
⦁ append(x): Adds a single element to the end.

  lst = [1, 2]
  lst.append(3)
  print(lst)  # Output: [1, 2, 3]
  

⦁ extend(iterable): Adds all elements from an iterable to the end.

  lst = [1, 2]
  lst.extend([3, 4])
  print(lst)  # Output: [1, 2, 3, 4]
  

⦁ insert(i, x): Inserts x at index i (shifts elements right).

  lst = [1, 3]
  lst.insert(1, 2)
  print(lst)  # Output: [1, 2, 3]
  

📚 Removing Elements
⦁ remove(x): Removes the first occurrence of x (raises ValueError if not found).

  lst = [1, 2, 2]
  lst.remove(2)
  print(lst)  # Output: [1, 2]
  

⦁ pop(i=-1): Removes and returns the element at index i (default: last).

  lst = [1, 2, 3]
  item = lst.pop(1)
  print(item, lst)  # Output: 2 [1, 3]
  

⦁ clear(): Removes all elements.

  lst = [1, 2, 3]
  lst.clear()
  print(lst)  # Output: []
  

📚 Searching and Counting
⦁ count(x): Returns the number of occurrences of x.

  lst = [1, 2, 2, 3]
  print(lst.count(2))  # Output: 2
  

⦁ index(x[, start[, end]]): Returns the lowest index of x in the slice (raises ValueError if not found).

  lst = [1, 2, 3, 2]
  print(lst.index(2))  # Output: 1
  

📚 Ordering and Copying
⦁ sort(key=None, reverse=False): Sorts the list in place (ascending by default; stable sort).

  lst = [3, 1, 2]
  lst.sort()
  print(lst)  # Output: [1, 2, 3]
  

⦁ reverse(): Reverses the elements in place.

  lst = [1, 2, 3]
  lst.reverse()
  print(lst)  # Output: [3, 2, 1]
  

⦁ copy(): Returns a shallow copy of the list.

  lst = [1, 2]
  new_lst = lst.copy()
  print(new_lst)  # Output: [1, 2]
  

📚 Built-in Functions for Lists (Common Cases)
⦁ len(lst): Returns the number of elements.

  lst = [1, 2, 3]
  print(len(lst))  # Output: 3
  

⦁ min(lst): Returns the smallest element (raises ValueError if empty).

  lst = [3, 1, 2]
  print(min(lst))  # Output: 1
  

⦁ max(lst): Returns the largest element.

  lst = [3, 1, 2]
  print(max(lst))  # Output: 3
  

⦁ sum(lst[, start=0]): Sums the elements (start adds an offset).

  lst = [1, 2, 3]
  print(sum(lst))  # Output: 6
  

⦁ sorted(lst, key=None, reverse=False): Returns a new sorted list (non-destructive).

  lst = [3, 1, 2]
  print(sorted(lst))  # Output: [1, 2, 3]
  

These cover all standard operations (O(1) for append/pop from end, O(n) for most others). Use slicing lst[start:end:step] for advanced extraction, like lst[1:3] outputs ``.

#python #lists #datastructures #methods #examples #programming

⭐ @DataScience4

🖥 Microsoft has introduced a new lecture series on Python and artificial intelligence.

The course gathers up-to-date information on #Python programming and creating advanced AI assistants based on it.

• Content: The course includes 9 lectures, supplemented with video materials, detailed presentations, and code examples. Learning to develop AI agents is accessible even for coding beginners.
• Topics: The lectures cover topics such as #RAG (Retrieval-Augmented Generation), embeddings, #agents, and the #MCP protocol.

The perfect weekend plan is to dive deep into #AI!

https://github.com/orgs/azure-ai-foundry/discussions/166

https://t.me/CodeProgrammer

The Python + Generative AI series by Azure AI Foundry has ended, but all materials are open

Now you can calmly rewatch the recordings, download the slides, and try the code from each session — from LLM and RAG to AI agents and MCP.

All resources are here: aka.ms/pythonai/resources

👉  @codeprogrammer

In Python, loops are essential for repeating code efficiently: for loops iterate over known sequences (like lists or ranges) when you know the number of iterations, while loops run based on a condition until it's false (ideal for unknown iteration counts or sentinel values), and nested loops handle multi-dimensional data by embedding one inside another—use break/continue for control, and comprehensions for concise alternatives in interviews.

# For loop: Use for fixed iterations over iterables (e.g., processing lists)
fruits = ["apple", "banana", "cherry"]
for fruit in fruits:  # Iterates each element
    print(fruit)      # Output: apple \n banana \n cherry

for i in range(3):    # Numeric sequence (start=0, stop=3)
    print(i)          # Output: 0 \n 1 \n 2

# While loop: Use when iterations depend on a dynamic condition (e.g., user input, convergence)
count = 0
while count < 3:      # Runs as long as condition is True
    print(count)
    count += 1       # Increment to avoid infinite loop! Output: 0 \n 1 \n 2

# Nested loops: Use for 2D data (e.g., matrices, grids); outer for rows, inner for columns
matrix = [[1, 2], [3, 4]]
for row in matrix:    # Outer: each sublist
    for num in row:   # Inner: elements in row
        print(num)    # Output: 1 \n 2 \n 3 \n 4

# Control statements: break (exit loop), continue (skip iteration)
for i in range(5):
    if i == 2:
        continue    # Skip 2
    if i == 4:
        break       # Exit at 4
    print(i)        # Output: 0 \n 1 \n 3

# List comprehension: Concise for loop alternative (use for simple transformations/filtering)
squares = [x**2 for x in range(5) if x % 2 == 0]  # Even squares
print(squares)  # Output: [0, 4, 16]

#python #loops #forloop #whileloop #nestedloops #comprehensions #interviewtips #controlflow

⭐ https://t.me/CodeProgrammer

In Python, the re module handles regular expressions (regex) for pattern matching in strings—vital for text processing like validating emails, extracting data from logs, or cleaning user input in interviews; it's compiled for efficiency but can be complex, so start simple and test with tools like regex101.com.

import re

# Basic search: Find if pattern exists (returns Match object or None)
txt = "The rain in Spain"
match = re.search(r"Spain", txt)  # r"" for raw string (avoids escaping issues)
if match:
    print(match.group())  # Output: Spain (full match)
    print(match.start(), match.end())  # Output: 12 17 (positions)

# findall: Extract all matches as list (non-overlapping)
txt = "The rain in Spain stays mainly in the plain"
emails = re.findall(r"\w+@\w+\.com", "Contact: user1@example.com or user2@test.com")
print(emails)  # Output: ['user1@example.com', 'user2@test.com']

# split: Divide string at matches (like str.split but with patterns)
words = re.split(r"\s+", "Hello   world\twith spaces")  # \s+ matches whitespace
print(words)  # Output: ['Hello', 'world', 'with', 'spaces']

# sub: Replace matches (count limits replacements; use \1 for groups)
cleaned = re.sub(r"\d+", "***", "Phone: 123-456-7890 or 098-765-4321", count=1)
print(cleaned)  # Output: Phone: *** or 098-765-4321 (first number replaced)

# Metacharacters basics:. (any char except \n), ^ (start), $ (end), * (0+), + (1+),? (0-1)
match = re.search(r"^The.*Spain$", txt)  # ^ start, $ end,. any, * 0+ of previous
print(match.group() if match else "No match")  # Output: The rain in Spain

# Character classes: \d (digit), \w (word char), [a-z] (range), [^0-9] (not digit)
nums = re.findall(r"\d+", "abc123def456")  # \d+ one or more digits
print(nums)  # Output: ['123', '456']

words_only = re.findall(r"\w+", "Hello123! World?")  # \w+ word chars (alphanum + _)
print(words_only)  # Output: ['Hello123', 'World']

# Groups: () capture parts; use for extraction or alternation
date = re.search(r"(\d{4})-(\d{2})-(\d{2})", "Event on 2023-10-27")
if date:
    print(date.groups())  # Output: ('2023', '10', '27') (tuples of captures)
    print(date.group(1))  # Output: 2023 (first group)

# Alternation: | for OR (e.g., cat|dog)
animals = re.findall(r"cat|dog", "I have a cat and a dog")
print(animals)  # Output: ['cat', 'dog']

# Flags: re.IGNORECASE (case-insensitive), re.MULTILINE (^/$ per line)
text = "Spain\nin\nSpain"
matches = re.findall(r"^Spain", text, re.MULTILINE)  # ^ matches start of each line
print(matches)  # Output: ['Spain', 'Spain']

# Advanced: Greedy vs non-greedy (*? or +?) to match minimal
html = "<div><p>Text</p></div>"
content = re.search(r"<div>.*?</div>", html)  #.*? non-greedy (stops at first </div>)
print(content.group())  # Output: <div><p>Text</p></div>

# Edge cases: Empty string, no match
print(re.search(r"a", ""))  # Output: None
print(re.findall(r"\d", "no numbers"))  # Output: []

# Compile for reuse (faster for multiple uses)
pattern = re.compile(r"\w+@\w+\.com")
email = pattern.search("email@example.com")
print(email.group() if email else "No email")  # Output: email@example.com

Regex tips: Escape special chars with \ (e.g., . for literal dot); use raw strings (r""); test incrementally to avoid frustration—common pitfalls include forgetting anchors (^/$) or overusing.*. For performance, compile patterns; in interviews, explain your pattern step-by-step for clarity. #python #regex #re_module #patterns #textprocessing #interviews #stringmatching

😱 https://t.me/CodeProgrammer