@rohanpaul_ai Rohan Paul

Several major developments are happening in the AI space. Notable advancements include the creation of highly efficient AI models, such as those enabling fast inference without losing accuracy, and the development of new chips like Extropic's probabilistic computing chip, which uses significantly less energy. Additionally, major tech companies like Google, Microsoft, and Nvidia are making significant investments and advancements in AI, including new data center builds and innovative AI applications.

Engagements: [---------] #

[--] Week [----------] +83%
[--] Month [----------] -58%
[--] Months [-----------] +497%
[--] Year [-----------] +127%

Mentions: [---] #

[--] Week [---] +28%
[--] Month [-----] +11%
[--] Months [-----] +102%
[--] Year [-----] +116%

Followers: [-------] #

[--] Week [-------] +1.30%
[--] Month [-------] +4.40%
[--] Months [-------] +71%
[--] Year [-------] +120%

CreatorRank: [-----] #

Social Influence

Social category influence technology brands #1487 stocks finance countries #4735 social networks celebrities automotive brands travel destinations currencies vc firms #160

Social topic influence ai #83, open ai #27, llm #80, $googl #1270, china #158, microsoft #1305, token #3635, gpu, meta #68, generated

Top accounts mentioned or mentioned by @openai @nvidia @rohanpaulai @huggingface @googledeepmind @grok @microsoft @xai @aiatmeta @nvidiaaidev @anthropicai @langchainai @drfeifei @sama @google @a16z @codewithimanshu @bindureddy @googleai @klingai

Top assets mentioned Alphabet Inc Class A (GOOGL) Microsoft Corp. (MSFT) Robot Consulting Co., Ltd. (LAWR) Goldman Sachs (GS) Tesla, Inc. (TSLA) Reddit, Inc. (RDDT) IBM (IBM) Morgan Stanley (MS) arXiv (ARXIV)

Top Social Posts

Top posts by engagements in the last [--] hours

"Sequoia Capital spoke with [--] companies across its own network from seed stage startups to large public enterprises about their LLM Stack and here is what they say: Source Article - - 65% had applications in production up from 50% two months ago while the remainder are still experimenting. - 94% are using a foundation model API. OpenAIs GPT was the clear favorite in our sample at 91% however Anthropic interest grew over the last quarter to 15%. (Some companies are using multiple models). - 88% believe a retrieval mechanism such as a vector database would remain a key part of their stack."
X Link 2023-06-16T19:19Z 23K followers, [--] engagements

"Midjourney Prompt: Joe Biden and Narendra Modi on stage holding hands spotlight party poppers celebratory extreme high quality intricate sunlight coming powerful luxurious --ar 16:9 --s [---] --q [--] --style raw ------------------ #ArtificialIntelligence #midjourney #aiart #midjourneyart #midjourneyai #ai #aiartcommunity #digitalart #art #midjourneyartwork #generativeart #midjourneyportraits #malemodel #midjourneybot #menofai #aiartwork #digitalartist #artificialintelligence #dalle #artwork #aiartist #stablediffusion #aigeneratedart #MachineLearning"
X Link 2023-06-26T07:56Z 55.3K followers, [---] engagements

"Check out the full source code for my #YouTube channel - #MachineLearning #DeepLearning #NLP 🟠 Github - 🟠 YouTube Channel - #dataanalysis #machinelearningalgorithms #bigdata #computerscience #data #dataanalytics #javascript"
X Link 2023-06-27T13:15Z [----] followers, [--] engagements

"Massive cost saving (by 50% or more 💵📷) of your ChatGPT API call by using caching with @LangChainAI and GPTCache Integration. 🚀 Also much faster response times 📷 🚀 Overcoming the rate limits restrictions 🚀 Greatly enhance the scalability of your application by reducing the load on the LLM service. 🚀 GPTCache first performs embedding operations on the input to obtain a vector and then conducts a vector approximation search in the cache storage. 🚀 After receiving the search results it performs a similarity evaluation and returns when the set threshold is reached. You can adjust the"
X Link 2023-06-29T09:18Z [----] followers, 57.6K engagements

"@RealBenjizo Awesome question. Ans is C AttributeError: type object 'Car' has no attribute 'make' Here's how -------- The init method is a special method in Python classes that serves as the constructor for the class. When a new instance of Car is created this method will be"
X Link 2023-06-29T20:27Z [----] followers, [----] engagements

"@1524astrounit GPTCache can indeed handle different temperatures. The basic strategy here is to select after the evaluation (Code example below) Softmax activation on model logits which is the common technique involving temperature in deep learning is applied here as well. GPTCache similarly uses a softmax function to convert the similarity scores of candidate answers into a list of probabilities. The higher the score the more possible it is to be selected as the final answer. Temperature controls the sharpness of possibility distribution. This means an answer with a higher score is more"
X Link 2023-06-29T20:09Z 29.4K followers, [---] engagements

"🚀 Bitwise XOR operation in Python - Detail Explanation with example🚀 🚀 ------------ #python #programming #coding #java #javascript #programmer #developer #html #coder #code #computerscience #technology #css #pythonprogramming #linux #php #software #reptilesofinstagram #webdevelopment #webdeveloper #tech #codinglife #algorithms #algorithm #datastructures #statistics #programmers #analytics #leetcode #MachineLearning #ArtificialIntelligence #datascience #nlp #reactjs #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning @RealBenjizo Wow cool"
X Link 2023-07-04T10:10Z 23K followers, [---] engagements

"🚀How would you check if two events are Independent🚀🚀 --------- 👉 Two events A and B are said to be independent if the occurrence of A does not affect the occurrence of B and vice versa. In statistical terms this means that the probability of both events occurring is the"
X Link 2023-07-11T07:06Z [----] followers, [----] engagements

"Great Read on GPT4 Credit - Article Credit : @dylan522p on the SemiAnalysis newsletter. An excerpt. "even 8x H100 cannot serve a [--] trillion parameter dense model at [-----] tokens per second. Yet OpenAI is achieving human reading speed with A100s with a model larger than"
X Link 2023-07-11T07:38Z [----] followers, [---] engagements

"🚀A Super popular Python Interview Problem - Given an integer n return true if and only if it is an Armstrong number. 🚀🚀 👉 The Problem What is an Armstrong number The k-digit number n is an Armstrong number if and only if the kth power of each digit sums to n. ➡ Example"
X Link 2023-07-11T20:25Z [----] followers, [----] engagements

"Web scraping with Large Language Models (LLM)-@AnthropicAI + @LangChainAI 👉 Checkout in my #YouTube channel 🟠 #langchain #opensource #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning #deeplearningai"
X Link 2023-07-24T01:07Z [----] followers, 21.8K engagements

"Great question and the Ans is - B) None The key learning point here is about the Ellipsisor Ellipsis literal . (three dots) which is a built-in constant in Python that can be used for various purposes. Let's dive in --------------------- This function my_function currently doesn't contain any specific code or logic but simply uses the Ellipsis keyword. The output you're seeing None is a fundamental aspect of Python functions and their return behavior. Here are the explanations: 👉 Ellipsisor Ellipsis literal . (three dots) is a built-in constant in Python that can be used for various"
X Link 2023-07-21T14:14Z 21.8K followers, [----] engagements

"🚀Run LlaMa-2 for Inferencing with [--] bit 🚀quantization First get access to the model following this official guide - Next thing thing we need to do is initialize a text-generation pipeline with Hugging Face transformers. The Pipeline requires"
X Link 2023-07-22T20:50Z [----] followers, [---] engagements

"Fuzzy String Matching in Natural Language Processing 👉 Checkout in my #YouTube channel 🟠 --------------------------- #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode"
X Link 2023-07-30T06:32Z [----] followers, [---] engagements

"Transformer Encoder - Built From Scratch with PyTorch Natural Language Processing 👉 Checkout in my #YouTube channel #opensource #largelanguagemodel #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning"
X Link 2023-07-30T06:32Z [----] followers, [----] engagements

"A great project implementing Llama [--] with JAX -------- #opensource #langchain #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #generativeai"
X Link 2023-07-30T11:05Z [----] followers, [---] engagements

"🚀Gorilla: Large Language Model Connected with Massive APIs🚀🔥 Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla they are the first to demonstrate"
X Link 2023-07-30T06:48Z [--] followers, [----] engagements

"🚀Run / Inferencing with Open-source Falcon 7B Model from HugginFace along with @LangChainAI 🚀🔥 ---------- #opensource #langchain #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks"
X Link 2023-08-05T09:20Z [----] followers, [----] engagements

"Python is officially on the moving to implement no GIL (Global Interpreter Lock) 🔥🚀 Hence we will have True parallelism. Lets learn what is GIL ------- 👉👉 The Global Interpreter Lock or GIL in Python is a mechanism in the CPython interpreter (the standard and most"
X Link 2023-08-01T06:52Z [----] followers, [----] engagements

"🚀 Hugging Face Trainer API is one of the MOST crucial part behind hundred/thousands of Deep Learning Projects🚀🔥 Let's start by understanding what the Hugging Face Trainer API is. --------------- 👉 The Hugging Face Trainer API is part of the transformers library by Hugging Face. the Trainer class is a high-level class for training fine-tuning and evaluating transformers models. This class encapsulates the entire training and evaluation loop and automates many of the mundane tasks associated with training deep learning models. 👉 It provides high-level abstraction over the training loop"
X Link 2023-08-01T13:19Z [----] followers, 24.8K engagements

"🚀 The all IMPORTANT compute_metrics() method implemented in all LLM (Large Language Model) FineTuning Project🚀🔥 👉 The compute_metrics function is a helper function used to compute evaluation metrics for the model's performance. It takes as input the model's predictions and the corresponding true labels. The argument eval_preds passed to compute_metrics() function is expected to be a tuple consisting of predictions made by the model and the true labels of the dataset. This is standard across Hugging Face's Seq2SeqTrainer where the evaluation step returns the predictions and the labels."
X Link 2023-08-01T13:37Z [----] followers, [----] engagements

"🚀This is incredible - web scraping with LLM🚀 👉 @AnthropicAI + @LangChainAI 💡1) Ask Langchain Agent to scrape data 💡2) Get back data in a list And the magical thing is with 100k context window of @AnthropicAI you dont need to use vectordb as well here. ------------ ➡"
X Link 2023-08-04T00:00Z [----] followers, [---] engagements

"🚀🧠LlamaIndex can create/query neo4j knowledge graphs with LLM's🚀🧠 The combination of Knowledge Graphs (KGs) and Large Language Models (LLMs) like GPT-4 can indeed be an extremely powerful tool for organizations that have underutilized or untapped data. --------- A"
X Link 2023-08-04T15:52Z [----] followers, [---] engagements

"🚀🧠In Python the term "dunder" is short for "double underscore".🚀🧠 Dunder methods in Python are special methods that have double underscores at the beginning and end of their names. They are also known as "magic methods" because they provide a lot of behind-the-scenes"
X Link 2023-08-06T03:15Z [----] followers, [----] engagements

"🔥🚀 Decorators are probably one of THE MOST powerful feature of Python🔥🚀 Let's go over it quickly. Decorators provide a super flexible way to modify or enhance the behavior of functions or methods without altering their code. At its core a decorator is just a higher-order function a function that takes one or more functions as arguments and returns a new function. The fundamental theory behind decorators is the ability of Python functions to be first-class citizens which means they can be passed around and used as arguments. python def decorator_function(func): def wrapper():"
X Link 2023-08-06T18:38Z [----] followers, [----] engagements

"🚀Using generative AI models from OpenAI Pandas AI is a brilliant addition to pandas library 🚀🔥 With simply a text prompt you can produce insights from your dataframe. Pandas AI aims to achieve the goal of virtually talking with a machine to output the results you want rather than having to program the task yourself. --------------------------- #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning #deeplearningai #100daysofmlcode"
X Link 2023-08-06T19:59Z [----] followers, [---] engagements

"🚀Gorilla: Large Language Model Connected with Massive APIs🚀🔥 Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query Gorilla comes up with the semantically and syntactically correct API to invoke. Gorilla is pretty much the first LLM to demonstrate how to use LLMs to invoke 1600+ (and growing) API calls accurately while reducing hallucination. Gorilla can provide appropriate API calls. It is trained on three massive machine learning hub datasets: Torch Hub TensorFlow Hub and HuggingFace. We are rapidly adding new domains including Kubernetes GCP AWS OpenAPI and"
X Link 2023-08-06T20:00Z [----] followers, [---] engagements

"🚀Threading and multiprocessing in Python - The Powerful feature with which Concurrency can be achieved in Python🔥🚀 Let's dive in to understand them. ------------ 👉 Let's start with Multithreading. Threading is a technique for decoupling tasks which are not sequentially dependent. Threads run in the same unique memory heap. While true parallelism is not achievable due to Python's Global Interpreter Lock (GIL) they are lightweight and beneficial for IO-bound tasks. Here is a simple example of how Python's threading module can be used: python import threading import time def"
X Link 2023-08-07T21:49Z [----] followers, [----] engagements

"🧠🚀In the vast universe of Python *args and **kwargs are the secret sauce to mastering function flexibilitylet's unravel this enigma together 🧠🚀 👉 *args and **kwargs are special syntaxes in Python for passing a variable number of arguments to a function. They are often used when you're uncertain about the exact number of arguments that will be passed to a function. 👉 The *args syntax allows you to pass multiple positional arguments to a function. In the function definition you use *args to capture any number of positional arguments that are not captured by the other parameters. The args"
X Link 2023-08-08T17:00Z [----] followers, [----] engagements

"🚀Dive into Python's trio: bool() all() any()unlocking logic magic 🧠 Lets check the code in the image and explain why it outputs True ❓❓❓ ------------- But first note In Python the following values are considered "falsey": - Constants defined to be false: None and False. - Zero of any numeric type: [--] [---] 0j Decimal(0) Fraction(0 1) - Empty sequences and collections: '' () set() range(0) ----------- 👉Step 1: X = bool(): The bool() function is used to return or convert a value to a boolean (True or False). When bool() is called with an empty list () as an argument it returns False. This is"
X Link 2023-08-08T17:04Z [----] followers, [----] engagements

"🚀 LangChain Basics - Create Vector db store from youtube video url while working with LangChain and LLM Agent and understanding how RecursiveCharacterTextSplitter and Embeddings work🚀 👉 There are different kinds of splitters in LangChain depending on your use case; the most common one is the RecursiveCharacterTextSplitter which is ideal for general documents such as text or a mix of text and code and so on. This text splitter operates based on a list of characters that serve as delimiters or 'split points' within the text. It attempts to create chunks of text by splitting these characters"
X Link 2023-08-08T17:38Z [----] followers, [---] engagements

"🔥 "Master the core pillars of Python OOP: Dive deep into classes objects inheritance polymorphism and encapsulation unlocking the secrets of robust and scalable code." 🔥 👉 Classes and Objects: In the heart of OOP in Python lie classes and objects. Classes are blueprints for creating objects (specific instances of the class). Objects encapsulate data for the application and the methods to manipulate that data. python class Dog: def init(self name age): = name self.age = age def bark(self): return barks" my_dog = Dog(name="Rover" age=5) print(my_dog.bark()) # Output: Rover barks"
X Link 2023-08-09T09:58Z [----] followers, [----] engagements

"🚀 Generating a Fibonacci Sequence with Functional Programming in Python with reduce method: 🧠 The Fibonacci sequence is a series of numbers where a number is the sum of the two preceding ones. The sequence starts like this: [--] [--] [--] [--] [--] [--] [--] . While most people are familiar with iterative or recursive solutions for generating the Fibonacci sequence few are aware that it can be elegantly done with reduce and lambda functions in a functional programming manner. Here's how you can achieve this: 👉 How It Works: - We start with an initial tuple (0 1) representing the first two numbers of the"
X Link 2023-08-09T10:46Z [----] followers, [----] engagements

"🧠A very core Python Concept - Equality vs Identity Integer Interning and the is operator in Python 🧠❓ Learn about it with a simple example. Look at the below code image and try to grasp the output in each case EXPLAIN THE OUTPUT OF THE ABOVE CODE SNIPPET - WHY❓❓ ---------- 💡THE ANSWER 💡 Python has an internal optimization strategy known as interning which can lead to some interesting and often puzzling behavior. First of all When you ask Python about whether one object is the same as another object you are asking if they have the same identity. Are they actually the same object 👉 The is"
X Link 2023-08-09T19:56Z [----] followers, [----] engagements

"🚀Did you know the working of dict_values object in Python which is a view into a Python dictionary's entries.🚀 💡A dict_values object is a view into the dictionary's entries. 💡 It doesn't have its own separate memory allocation for the values; instead it references the values in the original dictionary. 💡 This means if the dictionary changes the view will reflect those changes. It's a live dynamic view.💡 -------------- Lets see a simple example In the attached code snippet (the code in image) is a rather simple snippet but it touches on a fundamental aspect of Python dictionaries. Let's"
X Link 2023-08-09T20:36Z [----] followers, [----] engagements

"🔥🚀Adding an LSTM layer on top of a BERT model when working with Transformer and HuggingFace NLP Model🔥🚀 👉 The concept of adding an LSTM layer on top of a BERT model is essentially a hybrid deep learning model. BERT is a transformer-based model which uses self-attention mechanisms to understand the context of words in a sentence by looking at all the other words. LSTM on the other hand is a recurrent neural network (RNN) variant that can learn and remember over long sequences of input data effectively capturing the temporal dependencies in the sequence data. ---------------------- 🚀As to"
X Link 2023-08-09T21:02Z [----] followers, [----] engagements

"👉 Code to convert a given dataset into a HuggingFace Dataset.🔥🚀 HuggingFace Datasets are a collection of datasets designed for Machine Learning and Natural Language Processing. They provide a uniform and efficient way to manipulate datasets which is crucial for Machine Learning tasks. 👉 The function convert_to_hg_dataset takes as input a dictionary data which represents the dataset. Each key-value pair in the dictionary is assumed to represent one example (or data point) in the dataset. The value is another dictionary with the keys original_text and reference_summary containing the"
X Link 2023-08-10T15:28Z 44.9K followers, [----] engagements

"Bang on point. If you are interested in Fine Tuning NLP Models Check out this playlist on my YouTube Channel 🚀🔥Natural Language Processing with Hugging Face and Transformer🚀🔥 🟠 --------------------- #llm #Largelanguagemodels #Llama2 #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #pythonprogramming #python #100DaysOfMLCode #softwareengineer #dataanalysis #datascienceinterview #python"
X Link 2023-08-10T18:24Z [----] followers, [----] engagements

"🔥🚀Unlocking the Power of Data Serialization in Python with JSON and XML🔥🚀 Data serialization at its core is the process of translating data structures or objects into a format that can be stored or transmitted and subsequently reconstructed. In the Python world JSON and XML are two common formats for this purpose. 👉 JSON (JavaScript Object Notation) is a lightweight data interchange format inspired by JavaScript object literal syntax. Its primary advantage lies in its simplicity and readability. 👉 XML (eXtensible Markup Language) is a markup language that defines a set of rules for"
X Link 2023-08-10T19:44Z [----] followers, [----] engagements

"🔥🚀Master the art of efficient Python logging and unlock the true power of debugging monitoring and application insights🚀 --- 👉 Introduction: Pythons logging module provides a powerful framework to capture logs from your applications allowing you to track and monitor its behavior. By integrating logging into your code you can diagnose issues understand workflow patterns and create metrics that matter. --- 👉 Why logging over print() While many developers start off using print() statements for debugging logging provides several advantages: * Level based logging: Control what messages you"
X Link 2023-08-10T22:36Z 45.5K followers, [----] engagements

"Unraveling Python's MRO and C3 Linearization: The Hidden Mechanics of Multiple Inheritance 👉 In the vast landscape of object-oriented programming Python stands out for its seamless handling of multiple inheritance. At the core of this elegant management is an algorithm known as C3 Linearization. This intricate design ensures a predictable and consistent method resolution order (MRO). 👉 When we talk about multiple inheritance in Python we refer to a class inheriting from more than one class. Determining which method to call especially when multiple parent classes have the same method can"
X Link 2023-08-10T22:52Z [----] followers, [---] engagements

"🚀🧠 Did you know in Python - sort() method returns None🚀🧠 ❓❓TRY TO EXPLAIN THE OUTPUT OF THE ATTACHED CODE SNIPPET ❓❓ The sort method sorts the elements of the list in place i.e. it modifies the original list and doesn't return a new list. In Python when a method doesn't explicitly return something it actually returns None by default. This is the case for the sort method. list_of_nums.sort() is performed (which sorts list_of_nums in-place) and the result of that operation (which is None because sort doesn't return anything) is assigned to sorted_list. So list_of_nums is now [--] [--] [--] [--] because"
X Link 2023-08-11T05:37Z [----] followers, [----] engagements

"GELU (Gaussian Error Linear Units) is an activation function that has gained popularity in deep learning because of its ability to perform well on a variety of tasks. Let's get to know it better It is defined as follows: GELU(x) = xPhi(x) where Phi(x) is the cumulative distribution function of a Gaussian distribution. Here are some reasons why you might want to use GELU: It is smooth and differentiable: GELU is a smooth function which means that its first derivative is continuous and differentiable. This makes it easier to train neural networks using GELU as an activation function. It"
X Link 2023-08-11T05:48Z [----] followers, [---] engagements

"🔥🚀Can Gradient Descent be applied to Non-Convex Functions Simple Ans is YES but there are some caveats Gradient descent is an optimization algorithm that aims to find the minimum of a function by iteratively moving in the direction of the steepest descent which is the negative of the gradient of the function. 👉 When applied to a convex function gradient descent is guaranteed to converge to the global minimum. However for non-convex functions there may be multiple local minima and saddle points which can make convergence to the global minimum difficult or not guaranteed. So again Gradient"
X Link 2023-08-11T05:57Z [----] followers, [---] engagements

"🔥🚀Optimize GPT-J HuggingFace Language Model for GPU using DeepSpeeds InferenceEngine🔥🚀 DeepSpeed provides a seamless inference mode for compatible transformer based models trained using DeepSpeed Megatron and HuggingFace meaning that we dont require any change on the modeling side such as exporting the model or creating a different checkpoint from your trained checkpoints. The entrypoint for inference with DeepSpeed is deepspeed.init_inference(). The DeepSpeedInferenceConfig is used to control all aspects of initializing the InferenceEngine. The config should be passed as a dictionary to"
X Link 2023-08-11T07:55Z [----] followers, [---] engagements

"Massive Cost Saving on OpenAI API Call using GPTCache with @LangChainAI Large Language Models 🔥🚀 Checkout in my #YouTube channel 🔥🚀 --------------------- #llm #Largelanguagemodels #Llama2 #Weaviate #vectordb #vector #pinecone #ai #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #pythonprogramming #python #100DaysOfMLCode #softwareengineer #dataanalysis #datascienceinterview #python"
X Link 2023-08-11T08:12Z [----] followers, [---] engagements

"💡💡 How globals() keyword works in python 💡💡 ❓❓EXPLAIN THE OUTPUT OF THE ATTACHED CODE SNIPPET ❓❓ 💡THE ANSWER💡 ---------- This code will modify the value of the global variable x and print its updated value. The step-by-step execution is as follows: x = [--] is declaring a global variable x and assigning it the value [--]. new_x = [--] is declaring another global variable new_x and assigning it the value [--]. 👉globals_dict = dict(globals()) is creating a dictionary called globals_dict and copying into it all the current global variables and their values by calling the globals() function."
X Link 2023-08-11T14:39Z [----] followers, [----] engagements

"🚀Iterables vs. Iterator in Python🚀 In Python iterators allow us to traverse collections efficiently and elegantly. Grasping the distinction between iterables and iterators along with understanding the magic behind the iter() function can empower you to harness the full potential of Python's iteration capabilities. Let's unearth the secrets behind Python's iterators --- In Python an iterable is any object capable of returning its elements one at a time. Lists tuples and strings are classic examples of iterables. However an iterable doesn't perform the iteration itself. This is where"
X Link 2023-08-11T15:15Z [----] followers, [----] engagements

"🔥🚀 In Python lambda is a super powerful feature that you will use a million times🔥🚀 Here's some examples of nesting Lambda function within another lambda function 👉 Example of a lambda within a lambda: Lambda functions in Python are anonymous functions that can have any number of arguments but only one expression. The expression is evaluated and returned. You can nest lambda functions just like you can with regular functions. python # A function that returns another function def multiplier(n): return lambda x: (lambda y: x * y)(n) double = multiplier(2) print(double(5)) # This will"
X Link 2023-08-11T17:42Z [----] followers, [----] engagements

"🔥🚀Pretty Advanced Python Interview Problem - Return all permutations of the given list.🔥🚀 🚀The Algorithm of this solution here is based on a recursive backtracking technique. 🚀 ----------- 👉 Initialization: [--]. Begin with the output list which will store all the permutations. [--]. Start the recursive process with the first index (start = 0) of the list. 👉 Recursive Backtracking: [--]. Base Case: If the start index reaches the end of the list (len(nums) - 1) it means we've formed a complete permutation. Therefore append a copy of the current state of nums to output. 2."
X Link 2023-08-11T19:52Z [----] followers, [----] engagements

"🚀Dive deep into Python's elegant descriptor mechanism the secret behind properties methods and static methods and discover how it supercharges object-oriented programming🚀 Python descriptors are created to manage the attributes of different classes which use the object as reference. --- Descriptors in Python empower the object-oriented system with a way to customize attribute access. At their core descriptors are a protocol involving three special methods: get() set() and delete(). These methods determine how an attribute's value is retrieved set or deleted respectively. 👉"
X Link 2023-08-11T20:04Z [----] followers, [---] engagements

"🔥🚀Function Overloading in Python - hugely useful in daily life of a Python Engineer.🔥🚀 Python despite its versatility doesn't directly support traditional function overloading like C++ or Java. But don't be disheartened Python offers elegant ways to achieve function overloading and harness its potential. 👉 Function Overloading via Default Arguments and Variable-Length Arguments In languages like C++ and Java function overloading means having multiple functions with the same name but different parameters. Python achieves this by using default arguments and variable-length arguments."
X Link 2023-08-12T13:11Z [----] followers, [---] engagements

"This blog is doing rounds across the Internet and indeed its a super exhaustive blog on LLM FineTuning🔥🚀 And they found on some datasets - Fine-tuning small Llama-2 models becoming better than GPT-4🔥🚀 Looks like future is BRIGHT for Open-Source LLM FineTuning 💡💡 ---------- They fine-tuned the Llama-2 model of various sizes on three tasks: A) Functional representations extracted from unstructured text (ViGGO) B) SQL generation (SQL-create-context) C) Grade-school math question-answering (GSM8k) Overall with Finetuning the Llama-13b improved in accuracy from 58% to 98% on functional"
X Link 2023-08-12T16:45Z [----] followers, [----] engagements

"🚀 Async API for @LangChainAI for Scalable Application building🚀🔥 If you are using LangChain for building scalable apps to handle a large number of simultaneous users while maintaining low latency i.e. you need asynchronous features within LangChain - THEN checkout how"
X Link 2023-08-13T06:04Z [----] followers, [--] engagements

"🚀LangChain Basics - You dont need OpenAI API to test your LangChain Code. Use OpenSource Models from HuggingFace with LangChain🚀🔥 👉 The Hugging Face Hub is a platform with over 120k models 20k datasets and 50k demo apps (Spaces) all open source and publicly available in"
X Link 2023-08-13T06:31Z [----] followers, [--] engagements

"🔥🚀is not operator in Python - Quite tricky and subtle 🔥🚀 ❓TRY TO EXPLAIN THE OUTPUT OF THE ATTACHED CODE SNIPPET ❓ --- First Note is not is a single binary operator and has behavior different than using is and not separated. The is not operator compares if the objects are pointing to the same memory location or not. It returns true if the objects are not pointing to the same memory location otherwise it returns false. --- 👉 In the expression 'something' is not None the is not operator checks whether 'something' and None are different objects. Since they clearly are the expression"
X Link 2023-08-13T10:21Z [----] followers, [----] engagements

"🔥🚀LlamaCPP got integrated with LlamaIndex🔥🚀 To get the best performance out of LlamaCPP it is recommended to install the package so that it is compiled with GPU support. ---------- 👉 In general: Use CuBLAS if you have CUDA and an NVidia GPU Use METAL if you are running on an M1/M2 MacBook Use CLBLAST if you are running on an AMD/Intel GPU ----------- 👉 Setup LLM The LlamaCPP llm is highly configurable. Depending on the model being used youll want to pass in messages_to_prompt and completion_to_prompt functions to help format the model inputs. Since the default model is llama2-chat we"
X Link 2023-08-13T12:00Z 98.2K followers, [----] engagements

"🔥🚀 LlamaIndex Source Code Understanding - messages_to_prompt 📚 The LlamaCPP llm is highly configurable. When working with LlamaIndex depending on the model being used like Llama youll want to pass in messages_to_prompt and completion_to_prompt functions to help format the model inputs. Source code in Github - --------------------- Here's the full Explanation of how the messages_to_prompt() code is working 👉 First - BOS and EOS represent the Beginning Of Sentence and End Of Sentence tags respectively. - B_INST and E_INST are used to indicate the beginning and end of an instruction. - B_SYS"
X Link 2023-08-13T12:09Z 102.9K followers, [----] engagements

"🚀Run Large Language Models in 4-bit at home with a single GPU 🚀🔥 First note any model that supports accelerate library's loading is compatible with 4-bit quantization so that means most of the popular Language models in HuggingFace are free to load as 4-bit. 👉 And accelerate library's loading means the device_map argument when calling from_pretrained - and those are the compatible models and should be quantizable in 4bit. Note also that this is totally agnostic to modalities as long as the models can be loaded with the device_map argument it is possible to quantize them. Also note that if"
X Link 2023-08-14T08:49Z [----] followers, [---] engagements

"🔥🚀MultiQueryRetriever in @LangChainAI to do Prompt engineering / tuning - get better set of relevant documents via capturing the context from various different angles and perspectives.🔥🚀 👉 Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on "distance". But retrieval may produce difference results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems but can be tedious."
X Link 2023-08-14T09:16Z [----] followers, 11K engagements

"Web scraping with Large Language Models (LLM)-AnthropicAI + LangChainAI 👉 Checkout on my #YouTube channel 🟠 #opensource #langchain #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #generativeai #generativemodels #OpenAI #GPT #GPT3 #GPT4 #chatgpt #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning #deeplearningai #100daysofmlcode"
X Link 2023-08-14T14:17Z [----] followers, [---] engagements

"Solve Scalibility Problem with LangChain's Async API Scalable Application building with AI 👉 Checkout on my #YouTube channel 🟠 #opensource #langchain #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #generativeai #generativemodels #OpenAI #GPT #GPT3 #GPT4 #chatgpt #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning #deeplearningai"
X Link 2023-08-14T14:18Z [----] followers, [---] engagements

"🎉 GPTCache is an unbelievably powerful library if you are working with LLM/OpenAI API and has been fully integrated with 🦜🔗LangChain Basically builds a semantic cache for storing LLM responses so you dont have to send to the expensive API un-necessarily. 👉 GPTCache employs embedding algorithms to convert queries into embeddings and uses a vector store for similarity search on these embeddings. This process allows GPTCache to identify and retrieve similar or related queries from the cache storage Then its Similarity Evaluator module collects data from both the Cache Storage and Vector"
X Link 2023-08-14T14:27Z [----] followers, [----] engagements

"🦜Different types of Memories in @LangChainAI 🦜 A best practice when developing chatbots is to save all the interactions the chatbot has with the user. This is because the state of the LLM can change depending on the past conversation in fact the LLM to the same question from [--] users will also answer differently because they have a different past conversation with the chatbot and therefore it is in a different state. And in the context of a Chatbot Customers would expect it to remember the things they talked about in the earlier part of the conversation. Otherwise itd be annoying to have to"
X Link 2023-08-14T15:25Z [----] followers, [---] engagements

"🦜What is RetrievalQA in @LangChainAI 🦜 🟠 The RetrievalQA chain is a chain that combines a Retriever and a QA chain. It is used to retrieve documents from a Retriever and then use a QA chain to answer a question based on the retrieved documents. 🟠 Because one issue with using ALL of the text while doing Question-Answering with @LangChainAI is that it can be very costly because you are feeding all the texts to OpenAI API and the API is charged by the number of tokens. A better solution is to retrieve relevant text chunks first and only use the relevant text chunks in the language model. 🟠"
X Link 2023-08-14T15:26Z [----] followers, [---] engagements

"🦜🚀Using LlaMaIndex ask queries on a GitHub repo as a source of documents. Think of it like talking to a GitHub repo.🦜 The basic workflow here starting with your documents you first load them into LlamaIndex. It comes with many ready-made readers for sources such as databases Discord Slack Google Docs Notion and (the one we will use today) GitHub repos. Next you use LlamaIndex to parse the documents into nodes basically chunks of text. An index is constructed next so that later when we query the documents LlamaIndex can quickly retrieve the relevant data. The index can be stored in"
X Link 2023-08-14T15:36Z [----] followers, [----] engagements

"Indian Tricolor with Python Happy #IndependenceDayIndia You can just copy paste the code below to produce this flag ---------- python import pygame import math # Initialize pygame pygame.init() # Colors WHITE = (255 [---] 255) SAFFRON = (255 [---] 51) GREEN = (18 [---] 7) BLUE = (0 [--] 138) # Screen dimensions WIDTH HEIGHT = [---] [---] # Create screen and clock screen = pygame.display.set_mode((WIDTH HEIGHT)) pygame.display.set_caption("Wavy Indian National Flag") clock = pygame.time.Clock() def draw_wavey_rect(x y width height color frequency amplitude speed): for i in range(x x + width): sine_val ="
X Link 2023-08-15T13:24Z [----] followers, [---] engagements

"🔥🚀 Checkout my recent Python book covering over [---] Python 🐍 Core concepts (300+ pages) with associated questions most commonly asked in Interviews. 🐍 👉 Most commonly asked in Interview each one offering you solid learning base to program like a senior Python Engineer. 👉 For most of the explanations I go into under-the-hood details into how Python Interpreter is working to handle a particular problem.🐍 ------------------ 👉 In any programming language there are only few core foundational concept and if you really understand and master these few core fundamentals of a Language you will"
X Link 2023-08-15T15:19Z 44.9K followers, 16.8K engagements

"🚀Python Interview Problem - Given two arrays write a function to compute their intersection. 🐍🔥 ---------- 🐍Here's the expected input output format that the function must be able to handle🐍 Input: nums1 = [----] nums2 = [--] Output: [--] -------------- 💡💡Below are [--] Solutions - A) First a Basic Version and then B) Optimized version ------------------ 💡 Explanation of the basic version of the solution 👉 Step [--] Create set1 and set2 from nums1 and nums2 respectively. Converting a list to a set in Python involves iterating over the list and adding each element to the set. This operation takes"
X Link 2023-08-15T15:32Z [----] followers, [----] engagements

"@ThatWhichIsNot9 @RealBenjizo For more detailed discussion on this - i.e. the principle in Python that "+" shouldn't change their operands in-place. - check out my below tweet"
X Link 2023-08-15T18:22Z [----] followers, [--] engagements

"In software engineering the Latency Numbers Every Programmer Should Know ---------------------------------- L1 cache reference-----------------------0.5 ns Branch mispredict------------------------5 ns L2 cache reference-----------------------7 ns Mutex lock/unlock------------------------25 ns Main memory reference-------------------100 ns Compress 1K bytes with Zippy----------3000 ns Send 1K bytes over [--] Gbps network----10000 ns Read 4K randomly from SSD*---------150000 ns Read [--] MB sequentially from memory--250000 ns Round trip within same datacenter---500000 ns Read [--] MB sequentially from"
X Link 2023-08-15T19:56Z [----] followers, [----] engagements

"🔥🚀asyncio library in Python is a super-powerful tool for writing concurrent code that is more efficient and easier to reason about than multi-threading or multi-processing.🔥🚀 🚀Let's go over a real-life example (code in the attached image) of making multiple HTTP requests to external services using asyncio🚀 -------------- For instance imagine a service where you need to fetch data from multiple URLs and aggregate the results. If we were to do this in a synchronous way it would mean that we would wait for each request to finish before moving on to the next one. With asyncio we can make"
X Link 2023-08-15T20:42Z [----] followers, [---] engagements

"Great blog - on Distance Metrics in Vector Search and its implementation in @weaviate_io "Even with ANN-indexes which reduce the number of distance calculations necessary a vector database still spends a large portion of its compute time calculating vector distances. As a result it is very important that the engine can do this not just correctly but also efficiently. The distance metrics in Weaviate have been optimized to be highly efficient using "Single Instruction Multiple Data" ("SIMD") instruction sets. Using these instructions a CPU can do multiple calculations in a single CPU cycle. To"
X Link 2023-08-15T21:01Z [----] followers, [----] engagements

"🚀🧠Using OpenAI embeddings for regular Clustering with KMeans🚀🧠 Clustering is one way of making sense of a large volume of textual data. Embeddings are useful for this task as they provide semantically meaningful vector representations of each text. Thus in an unsupervised way clustering will uncover hidden groupings in our dataset. In this example we discover four distinct clusters: one focusing on dog food one on negative reviews and two on positive reviews. The original dataset here is fine-food reviews from Amazon. Then during data-preprocessing combined the review summary and review"
X Link 2023-08-16T16:00Z [----] followers, [---] engagements

"🐍 A very tricky case of Python's internal memory management and garbage collection🐍🐍 ❓ Try understanding the attached code snippet ❓ At first glance you'd expect the destructors of the instances of MysteriousClass to be called (i.e. the del() method) after their creation. However running the code might produce no destructor-related output at all ❓EXPLAIN WHY ❓ 💡DETAIL ANSWER BELOW💡 ---------- 👉 When an instance of MysteriousClass is created it's added to the class-level _instances list which keeps a reference to the object. 👉 As long as there are references to an object in Python"
X Link 2023-08-16T18:32Z [----] followers, [----] engagements

"🔥🚀Mutable_Default_Arguments in Python - Tricky behaviour you MUST know this to avoid bugs in your program🚀 🐍 One of the Core Principle in Python is that default mutable arguments (like lists or dictionaries) are shared between successive function calls if they're not provided by the caller. This is because they're initialized once when the function is defined not every time the function is called. The default object (in this case the list) is created once and stored in memory. 🐍 The default argument's memory location doesn't change with each function call unless a new object is assigned"
X Link 2023-08-16T19:33Z [----] followers, [----] engagements

"🔥🚀One Billion Times Faster Finetuning with Lamini PEFT🔥🚀 Model switching time is now [-----] billion times faster thanks to our new PEFT adapter cache. This cache stores over [-----] adapters in GPU high bandwidth memory (HBM) running over [---] TB/s. 🚀Their "solution is Parameter Efficient Finetuning (PEFT) [--] which freezes most weights during finetuning only updating small subsets of weights called adapters using backpropagation. 🚀 The adapters only need 10-30MB storage instead of tens of GBs for the entire model. This accelerates loading from disk by 1000x and enables caching a large number"
X Link 2023-08-17T08:29Z [----] followers, [----] engagements

"🔥🚀New Large Language Model (with Commercial Use Apache [---] License) - DeciCoder an open-source code-completion model for these three languages.🔥🚀 The dataset. DeciCoder is trained on the Stack dataset which has 6TB of code from [---] programming languages. But the dataset is filtered to only include Python Java and Javascript code. Model type: Its Auto-regressive Model based on the transformer decoder architecture using Grouped Query Attention. Its Grouped Query Attention with [--] key-value heads groups the query heads and allows them to share a key head and value head. So computation becomes"
X Link 2023-08-17T09:36Z [----] followers, [----] engagements

"🚀🐍 How many ways can you Swap [--] variables in Python ❓ -------------- Explanations: 👉 The first method💡 is the most straightforward we make use of a temporary variable. This method starts by storing the value of the first variable (a) in a new variable (temp). Then it assigns the value of the second variable (b) to the first variable (a). Lastly it assigns the value stored in the temporary variable (temp) to the second variable (b). In the end the values of a and b have been swapped. 👉 The second method💡 uses arithmetic operations and works only with numbers. This method starts by"
X Link 2023-08-17T12:37Z [----] followers, [----] engagements

"DeciCoder - Has the potential to change the LLM (Large Language Model) space for code generation. --------------------- #llm #langchain #Largelanguagemodels #Llama2 #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #pythonprogramming #python #100DaysOfMLCode #softwareengineer #dataanalysis #datascienceinterview #python #programming #coding #programmer #developer #coder #code"
X Link 2023-08-17T14:52Z [----] followers, [---] engagements

"🔥Dive into this timeless Python interview challenge: Merging two sorted lists💼🐍 The basic idea of the merge process is that given two sorted lists we can efficiently merge them into one sorted list by always taking the smallest (or largest depending on the sorting order) element from the heads of the two lists. This is a key component of the Mergesort algorithm which is a divide-and-conquer algorithm that breaks a list down into single-element lists (which are trivially sorted) and then repeatedly merges these sorted lists to produce larger and larger sorted lists until the entire list is"
X Link 2023-08-17T21:52Z 22.5K followers, [----] engagements

""Unlock the power of Python's dict.popitem() in multi-threaded environments: an atomic key to mastering thread-safe operations" --------------- 👉 In the example code here the popitem() is called on the dictionary. The popitem() method is used to remove and return the last (key value) pair from the dictionary. 👉 In Python [---] and later the Python dictionaries maintain the insertion order which means they are ordered collections. Therefore when you use popitem() on a dictionary in Python 3.7+ it will always remove and return the last key-value pair that was inserted into the dictionary."
X Link 2023-08-18T08:56Z [----] followers, [----] engagements

"🐍Cool Python tips - If you want to check if a class is a subclass (derived or child class) of another class use the built-in issubclass() function.🐍 The function returns True if the first argument's class is a subclass of the second argument's class. A class is considered a subclass of itself so if the first and second arguments are the same class it also returns True. ------------ #python #100daysofcode #softwareengineer #programming #coding #programmer #developer #coder #code #computerscience #technology #pythonprogramming #software #webdevelopment #webdeveloper #tech #codinglife"
X Link 2023-08-18T16:09Z [----] followers, [---] engagements

"🐍 Did you know - In Python whats the position of int data types in the in the hierarchy for built-in types.🐍 For numeric types (int float complex) there's a common base class named numbers.Number. The int type is not directly derived from numbers.Number but rather through a more specific subclass. Here's a simplified hierarchy for our discussion: - object - numbers.Number - numbers.Integral - int - numbers.Real - float - numbers.Complex - complex So int is an instance of numbers.Integral which is itself a subclass of numbers.Number. So while int does inherit from object (like all things in"
X Link 2023-08-18T16:16Z [----] followers, [----] engagements

"🔥🚀 Coroutines in Python - Absolutely Important and super Useful Core Concepts🔥🚀 Coroutines are generalizations of generators. While generators produce values coroutines can also consume them. Theory: Coroutines permit the flow of control to go back and forth. This means you can send values into a coroutine using the .send() method. When a value is sent to a coroutine it picks up execution from the last yield statement runs until the next yield and then returns control to the caller. -------------- 🔥🚀 Alright Let's unravel the intriguing mechanics behind coroutines in Python and deep"
X Link 2023-08-18T23:58Z [----] followers, [----] engagements

"🐍🔥 Harnessing the power of io.BytesIO(audio) in Python - And example using OpenAI Whisper API to transcribe audio files from a FastAPI endpoint 🔥 👉 We can dynamically transform raw byte data into a file-like object in memory bridging the gap between bytes and file operations without ever touching the disk." 👉 buffer = io.BytesIO(audio) creates an in-memory binary stream using the io.BytesIO class. This stream acts much like a file object but instead of being backed by a physical file on the disk it's backed by the bytes stored in memory. Here's a breakdown: -------------------- 🚀 OpenAI"
X Link 2023-08-19T08:25Z [----] followers, [----] engagements

"🚫Violating OpenAI policies multiple times may result in termination of your account🚫. Hence to prevent that add a Moderation Layer to your API calling code.🔥🚀 ---------- If OpenAI find that a product or usage does not adhere to their policies: OpenAI may ask API users to make necessary changes. Repeated or serious violations can lead to further action from OpenAI and even termination of your account. This is crucial for any commercial or product related implementation as failure to moderate usage can lead to suspension of OpenAI services and impeding the product or service rendered."
X Link 2023-08-19T08:45Z [----] followers, [----] engagements

"🐍 Great and Common Python Interview Question - Given an integer array nums and an integer k return the kth largest element in the array.🔥🚀 Note that it is the kth largest element in the sorted order not the kth distinct element. Can you solve it without sorting Example Output 1: Input: nums = [------] k = [--] Output: [--] ---- Example Output 2: Input: nums = [---------] k = [--] Output: [--] =============== 👇SOLUTION with Explanation👇 The solution uses a heap data structure which is a special type of binary tree specifically a min-heap in this case. The heapq is a built-in module that provides"
X Link 2023-08-19T20:07Z [----] followers, [----] engagements

"The image here is not correctly indented so if you run the code in the image as it is WITHOUT correcting the indentataion you will get error 👉 IndentationError: expected an indented block after 'while' statement on line [--] However after correcting the indentation the code becomes as below python n=5 while(n): n=n-1 print(nend='') And in this case the final output is None of the options A B C D. The answer is [--] Let me explain every step for the Beginners in Python --------------------- The provided script consists of a simple while loop that decrements the value of n until n becomes [--] and then"
X Link 2023-08-20T08:01Z [----] followers, [----] engagements

"Super eazy Speech Recognition/Transcription with Python Transcription is the process of converting speech from an audio or video file into text. This process involves more than just listening to recordings the content must be understood and nothing left out. The SpeechRecognition module depends on pyaudio you can install them from your package manager. pip install SpeechRecognition pyaudio ------------ #python #100daysofcode #softwareengineer #programming #coding #programmer #developer #coder #code #computerscience #technology #pythonprogramming #software #webdevelopment #webdeveloper #tech"
X Link 2023-08-20T09:24Z [----] followers, [----] engagements

"🔥🚀Synchronous vs Asynchronous I/O in Python - Super important in any Application code-base🐍🚀 - Synchronous I/O: Involves performing I/O operations one after the other. Each operation is started only after the previous one has completed. Example: python with open("file1.txt" "r") as file: data1 = with open("file2.txt" "r") as file: data2 = Description: Here file2.txt is read only after the entirety of file1.txt has been read ensuring a sequence of operations. --- 👉 Asynchronous I/O: Multiple I/O operations can be initiated without waiting for any to complete. This allows for"
X Link 2023-08-20T11:03Z [----] followers, [----] engagements

"🐍A super popular Python Interview question - What's your understanding on Time complexity of min() and max() on a list of constant size🚀🔥 Also a MUST KNOW for daily life of a Python Engineer Let's discuss in detail 👇 👉 The time complexity of the min() and max() functions in Python is O(n) where n is the number of elements in the list. This is because both min() and max() must traverse the entire list to find the smallest and largest element respectively. However you mentioned that the size of the list is constant. In this case the time complexity becomes a constant time complexity O(1)"
X Link 2023-08-20T11:06Z [----] followers, [----] engagements

"The African Lion in the Glass (literally) 📷 needed to be filled up after a Super Fast Week with Python 📷 and Large Language Model 📷 ------------ #python #llm #langchain #Largelanguagemodel #AI #ArtificialIntelligence #softwareengineer #programming #coding #programmer #developer #coder #code"
X Link 2023-08-20T14:40Z [----] followers, [----] engagements

"🐍Ever wondered why two identical Python objects aren't the same in memory It's not a glitch it's genius Discover why🐍🔥 🚀In Python why [--] identical tuples which are of immutable types are not True when checking with is operator🧠 👉 The Key reason is - Tuples though immutable don't have enforced singleton behavior. Immutable means the contents of the tuple can't be altered after its creation. However it doesn't mean that every unique tuple content is stored only once in memory. Check out the below code python e=(123) f=(123) print(e is f) # False print(id(f)) # [-------------] print(id(e)) #"
X Link 2023-08-21T08:56Z [----] followers, [----] engagements

"🐍 Struggling with slow Python tasks Feel the weight of time drain Unlock parallel processing with multiprocessing. 🚀🔥 👉 The multiprocessing module provides the capability for concurrent execution of processes. It bypasses the Global Interpreter Lock (GIL) which typically prevents multi-threaded Python programs from taking advantage of multi-core CPUs. 👉 The Process class within the multiprocessing module is designed to create and manage separate processes. When you initialize an instance of this class you can pass the function to run (via target) and its arguments (via args)."
X Link 2023-08-21T09:42Z [----] followers, [----] engagements

"🚀Training and Runtime Time Complexity of kernel SVM Support Vector Machine🚀🚀 The time complexity of the SVM training algorithm is primarily affected by the choice of the optimization algorithm used during training. The most commonly used optimization algorithms are the Sequential Minimal Optimization (SMO) and the gradient-based methods such as the LIBSVM. In general the time complexity of a kernel SVM can be described as follows: * Sequential Minimal Optimization (SMO): O(n2 * k) to O(n3 * k) * LIBSVM: O(n2 * k) to O(n3 * k) Here 'n' is the number of training instances and 'k' is"
X Link 2023-08-21T14:22Z [----] followers, [---] engagements

"🐍 Whats the difference between b = a and c = a.copy() - Can cause subtle bugs if you are not aware [--]. b = a: This creates a new reference (b) to the same dictionary object as a. Essentially a and b are two names pointing to the same memory location. [--]. c = a.copy(): This creates a new dictionary object that's separate from a. The new dictionary c has the same key-value pairs as a. However this is a shallow copy meaning while the dictionary itself is different the inner objects if they are complex types (like lists or other dictionaries) remain shared references between a and c. 👉 The main"
X Link 2023-08-21T16:01Z [----] followers, [---] engagements

"Gorilla may be a game changer.🔥🚀 The productivity of every developer worldwide would go through the roof if we had a reliable model that know how to use any library or framework or a massive list of APIs. What an exciting time to be alive #llm #langchain #Largelanguagemodels"
X Link 2023-08-21T17:58Z [----] followers, [---] engagements

"@RealBenjizo A superb Tricky question here - Needs a lot of Attention to Detail and need to really know how Python Dictionary key is built with hashing. 🤔 Ans is - C. JavaScript 🤔😆 Let's dive in step by step --------------------- Looking at the code we see the definition of"
X Link 2023-08-21T18:11Z [----] followers, [--] engagements

"Absolutely Great question here. 👉 Ans is C and D will raise a SyntaxError. The key concepts here are about the usage of Async-await and yield from syntax for a generator function This is the output you will get. py ##################### # A async def my_function(x): foo = await bar(x) return foo ##################### #B async def my_function(x): yield x ##################### # C async def my_function(x): yield from gen(x) # SyntaxError: 'yield from' inside async function ##################### # D def my_function(x): foo = await bar(x) return foo # SyntaxError: 'await' outside async function"
X Link 2023-08-22T10:28Z [----] followers, [---] engagements

"🐍🔥BREAKING PYTHON NEWS 🔥🚀 Microsoft and Anaconda finally made it possible to introduce Python within Excel. Now you can input Python code straight into Excel without needing a separate Python installation. Simply use =PY() and then input your code. ------------ #EXCEL #msoffice #python #100daysofcode #softwareengineer #programming #coding #programmer #developer #coder #code #computerscience #technology #pythonprogramming #software #webdevelopment #webdeveloper #tech #codinglife #algorithms #algorithm #datastructures #programmers #analytics #leetcode #MachineLearning"
X Link 2023-08-22T21:10Z [----] followers, [----] engagements

"From breaking the bank 💵💰 with language model training to smart savings with open-source solutions. Cross the bridge to affordability 🔥🚀 BloombergGPT:💵💰 A comprehensive financial language model. Proprietary software Training cost: $5 million Consumed [---] million GPU hours Introducing FinGPT: 🔥🚀 A freely available extensive language model tailored for finance. Publicly accessible Costs only $300 for training Takes [--] GPU hours --------------------- #llm #langchain #Largelanguagemodels #Llama2 #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning"
X Link 2023-08-23T12:08Z [----] followers, [---] engagements

"Python is currently the MOST popular language in the world. It's rather easy to learn and the community is the world's best and job opportunities are the highest. So if you put in effort in just a couple of months you can develop very solid skills. ---- If you are interested also checkout my recent Python book covering over [---] Python Core concepts like this across 350+ pages with associated questions most commonly asked in Interviews 🐍"
X Link 2023-08-24T15:58Z [--] followers, [---] engagements

"🐍What happens when you try to extend a tuple in Python🐍 When you attempt to "extend" a tuple it's important to remember that tuples in Python are immutable. This means that once a tuple is created its contents cannot be altered directly. However what you can do is create a new tuple that represents the extension of the original tuple. There are several ways to effectively "extend" a tuple but each method results in the creation of a new tuple: 📌 Using concatenation: python tuple1 = (1 [--] 3) tuple2 = (4 5) result = tuple1 + tuple2 print(result) # Outputs: (1 [--] [--] [--] 5) 📌 Using tuple"
X Link 2023-08-27T13:09Z [----] followers, [---] engagements

"🐍zip_longest function from Python's itertools module.🐍 📌 zip_longest is a function from the itertools module in Python's standard library. At a high level it's used to zip together multiple iterables (like lists or tuples). The main distinction between zip and zip_longest is how they handle iterables of uneven lengths. 📌 In the standard zip function when you're combining iterables of different lengths the resulting zipped object will truncate to the length of the shortest iterable. This can lead to unintentional data loss if you're not careful. For example if you were to zip two lists"
X Link 2023-08-27T22:17Z [----] followers, [---] engagements

"🚀🐍 With asyncio library in Python write code that is more efficient and easier to reason about than multi-threading or multi-processing.🔥🚀 Let's learn about it with some simple examples taken from real-life applications ---------------------- 👉 asyncio is used for writing concurrent code using the async/await syntax. The idea behind asyncio is that a single Python object called the event loop controls how and when each task gets run. The event loop is aware of each task and knows what state it is in. ---------- 👉 Let's start with a basic example that uses asyncio to print 'hello' then"
X Link 2023-08-28T14:53Z [----] followers, [---] engagements

"🐍🔥This is super subtle and tricky - How closures work in Python when they're inside list comprehensions.🐍🔥 Take a look at below method. py # Create a list of lambda functions which are # meant to raise x to the power of i # where i ranges from [--] to [--]. # 1st Alternative powers_of_x = lambda x: xi for i in range(10) f(2) for f in powers_of_x # [---] [---] [---] [---] [---] [---] [---] [---] [---] [---] ##################################################### # 2nd Alternative powers_of_x = lambda x i=i: xi for i in range(10) f(2) for f in powers_of_x # [--] [--] [--] [--] [--] [--] [--] [---] [---] [---] Explain how come in"
X Link 2023-08-28T20:26Z 22.6K followers, [---] engagements

"Seems like the ChatGPT issue has been resolved chatgpt #chatgpt #ChatGPT #OpenAI #chatgptdown #ChatGPTPlus"
X Link 2023-08-29T15:21Z [----] followers, [--] engagements

"🦜🔥finetune any sbert embeddings model in @llama_index🦜🔥 For below code example 📌 Generate Corpus - First we create the corpus of text chunks by leveraging LlamaIndex to load some financial PDFs and parsing/chunking into plain text chunks. 📌 Generate synthetic queries - Now we use an LLM (gpt-3.5-turbo) to generate questions using each text chunk in the corpus as context. 📌 Each pair of (generated question text chunk used as context) becomes a datapoint in the finetuning dataset (either for training or evaluation). 📌 Finally Run Embedding Finetuning ---------------------"
X Link 2023-08-29T18:16Z 40.1K followers, [---] engagements

"The ans is C) [--] [--] [--] Let me explain step by step for Beginners in Python 👉 The key theme of the problem is to understand Python's slice assignment operation on lists. --- 👉 When we execute li1:3 = [--] we're performing a slice assignment and replacing the elements from index [--] up to but not including index [--] with the new list [--]. 👉 This slice li1:3 refers to the sublist [--] [--] in our original list. 👉 By the action of slice assignment the sublist [--] [--] is replaced by [--]. 👉 Hence after this operation the list becomes: python [--] [--] [--] ------------------ 👉 If you enjoyed this explanation:"
X Link 2023-08-30T07:27Z [----] followers, [----] engagements

"Thanks Ryjer for such a nice comment. And also on the book I am releasing the 2nd edition within couple of days the overall content will be almost doubled (touching 650+ pages) - and I am organizing the book such a way to make anybody get to absolutely pro-level of understanding and confidence with Python"
X Link 2023-08-30T09:34Z 45.3K followers, [--] engagements

"Great question and the Ans is - B) [--]. The core concept of this problem is understanding the behavior of closures in Python and the concept of late binding. A closure in Python is a function object that remembers values in the enclosing scope even if they are not present in memory. Late binding means that the values of variables used in closures are looked up at the time the inner function is called. And also about the distinction between inner_func and inner_func() is crucial. Let's dive in step by step 👇👇 --------------------- 👉 The function outer_func accepts a single argument x. 👉"
X Link 2023-08-30T19:27Z [----] followers, [---] engagements

"The ans is D: None of the above Actual ans is [--] [--] None Let me explain step by step for Beginners in Python --------------- 👉 First we'll take a look at the foo function. It consists of a try and finally block. The try block contains a print statement for the number [--] with an argument end=' ' which means after printing the number [--] it will not move to the next line. Instead it will print whatever comes next on the same line. 👉 The finally block is used in conjunction with try to ensure that certain operations are performed no matter if an exception occurs or not. In this case the finally"
X Link 2023-08-31T07:18Z [----] followers, [----] engagements

"To delete all markdown cells from a jupyter notebook file at once Just run the below cell replacing the file path name of the the target Jupyter NB file"
X Link 2023-08-31T09:35Z [----] followers, [---] engagements

"🐍🔥Asynchronous File Processing with asyncio library in Python 🐍🔥 In scenarios where you have multiple files that need processing especially if they are large reading them sequentially can slow down your application. With asyncio we can initiate asynchronous file reads speeding up the process. Python's standard library doesn't support asynchronous file operations directly so we'll utilize the third-party library aiofiles which is designed for this purpose. 📌 Let's checkout the attache code image 📌 Description of the Code: [--]. The process_file coroutine asynchronously reads a file and"
X Link 2023-08-31T15:49Z [----] followers, [---] engagements

"🐍 In Python did you know about the differences between append() vs extend() method. Very Important as misuse can introduce subtle bugs or performance issues in the codebase. Let's delve into this: ## [--]. Basic Difference - append(): 📌 - Adds its entire argument as a single element to the end of the list. - If an iterable is passed to append() the whole iterable is added as one element. python lst = [--] [--] [--] lst.append(4 5) print(lst) # Output: [--] [--] [--] [--] [--] - extend(): 📌 - Iterates over its argument adding each element to the list extending the list. - The length of the list increases by"
X Link 2023-08-31T18:12Z [----] followers, [---] engagements

"🧠🚀*args and **kwargs in Python - MUST know fundamentals🧠🚀 👉 *args and **kwargs are special syntaxes in Python for passing a variable number of arguments to a function. They are often used when you're uncertain about the exact number of arguments that will be passed to a function. 👉 The *args syntax allows you to pass multiple positional arguments to a function. In the function definition you use *args to capture any number of positional arguments that are not captured by the other parameters. The args in *args is just a naming convention and can be replaced with any valid variable name."
X Link 2023-08-31T18:20Z [----] followers, [---] engagements

"🐍 Did you know - In Python whats the position of int data types in the in the hierarchy for built-in types.🐍 📌 For numeric types (int float complex) there's a common base class named numbers.Number. The int type is not directly derived from numbers.Number but rather through a more specific subclass. 📌 Here's a simplified hierarchy for our discussion: - object - numbers.Number - numbers.Integral - int - numbers.Real - float - numbers.Complex - complex 📌 So int is an instance of numbers.Integral which is itself a subclass of numbers.Number. So while int does inherit from object (like all"
X Link 2023-08-31T19:34Z [----] followers, [---] engagements

"🐍 Python's exception hierarchy is a beautifully designed system a dance of errors and their graceful handling - MUST KNOW for application building🐍 👉 Simply put - BaseException - Exception - ArithmeticError - ZeroDivisionError --------- 👉 The Hierarchical Design of Python's Exceptions: In Python exceptions form a hierarchy which is essential for a couple of reasons: [--]. Semantic Grouping: Grouping related exceptions under a common parent exception allows for better organization and understanding of the nature of the exception. [--]. Graceful Exception Handling: A hierarchical"
X Link 2023-08-31T19:38Z [----] followers, [---] engagements

"Generators in Python vs Regular Function #python #100daysofcode #softwareengineer #programming"
X Link 2023-08-31T22:54Z [----] followers, [----] engagements

"🐍🔥In Python How are list comprehensions are more efficient than traditional for loops🐍🔥 At a high level list comprehensions in Python are a concise way to create lists. But it's more than just about elegance. They're often considered more "Pythonic" and can be more efficient than traditional for-loops in terms of both time and space. 📌 How Are They More Efficient When a list comprehension is executed the Python interpreter does a couple of things: [--]. It recognizes the construct as a single expression. This means it can allocate memory for the resultant list all at once rather than"
X Link 2023-09-01T09:03Z [----] followers, [----] engagements

"🚀If you are preparing for JavaScript / React Interview - Check out my Github Repo It's got 2.8K Stars🌠 Will give you enough win the Job🚀🔥 👉👉 ---------- #javascript #javascriptdeveloper #javascripts #javascript30 #learnjavascript #javascriptdevelopers #javascriptengineer #javascriptlover #javascripttutorial #javascripting #javascriptdev #javascriptlearning #javascriptframework #learningjavascript #javascript_love #vanillajavascript #javascriptislife #javascriptbasics #javascriptiskillingmysoul #javascriptbook #eloquentjavascript #javascriptlibraries #fullstackjavascript #javascriptcode"
X Link 2023-09-01T12:39Z [----] followers, [---] engagements

"🚀🔥Check out the full source code for my #YouTube channel - #MachineLearning #DeepLearning #NLP🚀🔥 🟠 Github - 🟠 YouTube Channel - ---------------- #python #programming #coding #programmer #developer #coder #code #computerscience #technology #pythonprogramming #software #webdevelopment #webdeveloper #tech #codinglife #algorithms #algorithm #datastructures #statistics #programmers #analytics #leetcode #MachineLearning #ArtificialIntelligence #datascience #nlp #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning"
X Link 2023-09-01T12:42Z [----] followers, [---] engagements

"🚀🔥If you are into Machine Learning / Data Science Checkout on my YouTube Channel Playlist for a long list of Portfolio Projects🚀🔥 🟠 ------------------- #python #programming #coding #programmer #developer #coder #code #computerscience #technology"
X Link 2023-09-01T12:43Z [----] followers, [--] engagements

"Most used Activation functions in Deep Learning ------- #MachineLearning #ArtificialIntelligence #datascience #huggingface #nlp #textprocessing #deeplearning #deeplearningai"
X Link 2023-09-01T13:23Z [----] followers, [---] engagements

"F1 score calculates the harmonic mean of precision and sensitivity - You will use it a million times in Machine Learning --------------------- #MachineLearning #ArtificialIntelligence #datascience #huggingface #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning"
X Link 2023-09-01T13:41Z [----] followers, [---] engagements

"This data is supposed to be from analyzing Millions of ChatGPT User Sessions Source - @creativeswithai -------- #MachineLearning #ArtificialIntelligence #GPT4 #chatgpt4 #chatgpt"
X Link 2023-09-01T20:30Z 20.1K followers, [---] engagements

"⚠🚫Python's eval() method's security issue⚠🚫 👉 The Below example is a simple calculator that evaluates user input as a mathematical expression. python expression = input("Enter your math expression: ") try: result = eval(expression) print(f"Result: result") except Exception as e: print(f"Error: e") ⚠🚫But there's a significant security issue with the above code: you're using the eval() function on raw user input which is dangerous.⚠🚫 Here's why: [--]. Arbitrary Code Execution: A malicious user could provide Python code as an input which when passed to eval() would be executed. This could"
X Link 2023-09-01T21:09Z [----] followers, [----] engagements

"In Data-Analytics projects if you are struggling with your large dataframe Polars has a great solution for it. Shrink numeric columns to the minimal required datatype. Shrink to the dtype needed to fit the extrema of this. py pl.DataFrame( "a": [--] [--] [--] "b": [--] [--] [--] [--] "c": [---] [--] [--] [--] "d": [----] [--] [---] "e": [----] [--] [---] "f": "a" "b" "c" "g": [---] [----] [----] "h": True None False ).select(pl.all().shrink_dtype()) --------------------- #MachineLearning #ArtificialIntelligence #datascience #huggingface #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp"
X Link 2023-09-01T22:33Z [----] followers, [---] engagements

"🐍Time Complexity Analysis step by step - "Remove duplicates in-place from sorted Array" - Python Interview Problem 🐍 📌 Refer to the solution in the attached code image and know this analysis very well. Because in real-life Interview you WILL always be asked about Time Complexity Analysis of a Solution 👉 Starting at the highest level our goal is to understand how the number of operations in our algorithm grows as the size of the input (nums list) increases. 👉 The primary operation in our code is the traversal of the nums list. python for fast in range(1 len(nums)): 👉 Within this loop we"
X Link 2023-09-02T09:23Z [----] followers, [---] engagements

"🐍🔥Python Interview Challenge - Remove duplicates in-place from sorted Array 🐍 PROBLEM Given an integer array nums sorted in non-decreasing order remove the duplicates in-place such that each unique element appears only once. The relative order of the elements should be kept the same. Then return the number of unique elements in nums. Consider the number of unique elements of nums to be k. Change the array nums such that the first k elements of nums contain the unique elements in the order they were present in nums initially. Let's go step by step understanding the solution (attached image)"
X Link 2023-09-02T09:24Z [----] followers, [---] engagements

"If you have the basic knowledge of Python to sharpen your skill the absolute BEST way is to start with LeetCode 'eazy' / 'medium' level problems. First try to solve it yourself and then go through each problem's discussion tab to see how others have solved it. The discussion tab of Leetcode is a Treasure where you will find the MOST Optimal solutions for a problem by the top level competitive coders. And also full discussions of those problems. I am stressing about Leetcode because practicing here you will really develop the sharp logical skill of problem solving and also almost all Companies"
X Link 2023-09-02T09:25Z [----] followers, [--] engagements

"🚀🧠Python Quiz on your understanding of the MOST IMPORTANT Concept of Python OOP - Instance attribute vs class attribute in Python🚀🧠 ❓Try to grasp the output of the attached code snippet ❓ 💡THE ANSWER WITH DETAIL EXPLANATION BELOW💡 ------------------- Now go step by step over the original script that throws Error - AttributeError: type object 'Car' has no attribute 'make' The init method is a special method in Python classes that serves as the constructor for the class. When a new instance of Car is created this method will be automatically called. In this case the init method"
X Link 2023-09-02T10:14Z [----] followers, [---] engagements

"🚀🧠Learn the MOST IMPORTANT Concept of Python OOP - Instance attribute vs class attribute in Python🚀🧠 - Class attributes are the variables defined directly in the class that are shared by all objects of the class. - Instance attributes are attributes or properties attached to an instance of a class. Instance attributes are defined in the constructor. Unlike class attributes instance attributes are not shared by objects. Every object has its own copy of the instance attribute (In case of class attributes all object refer to single copy). 📌 class attributes are defined outside the"
X Link 2023-09-02T10:15Z [----] followers, [---] engagements

"The Python basic that you will use thousands of times - Lambda function in Python 🐍🔥 ---------- #python #pythoncoding #learntocode #softwaredeveloper #interview #100daysofcode"
X Link 2023-09-02T10:23Z 22.4K followers, [----] engagements

"Back to Basics - Python - Various methods to Remove Duplicates from List in Python 🐍🔥 ------------ #python #pythoncoding #learntocode #softwaredeveloper #interview #100daysofcode #softwareengineer #programming #coding #programmer #developer #code"
X Link 2023-09-02T11:55Z [----] followers, [----] engagements

"🐍🔥Types of Scope in Python & the LEGB Rule 🐍🔥 📌 LEGB = Local-Enclosed-Global-BuiltIn and the LEGB rule defines the order of scope in which the interpreter looks into for retrieving the variable name & value from ------------ #python #pythoncoding #learntocode #softwaredeveloper #interview #100daysofcode #softwareengineer #programming #coding #programmer #developer #code"
X Link 2023-09-02T12:00Z [----] followers, [---] engagements

"🐍🚫Here is a LONG list of the MOST common subtle and tricky sources of bugs in a real-life Python codebase: 🚫🐍 🚫 [--]. Mutable Default Arguments: Using mutable objects like lists or dictionaries as default arguments can lead to unexpected behavior. python def add_item(item items=): items.append(item) return items ------------------- 🚫 [--]. Indentation Errors: Python uses indentation for blocks making it prone to subtle bugs if spaces and tabs are mixed or misused. ------------------- 🚫3. Name Shadowing: Accidentally using built-in names for variables can lead to unpredictable"
X Link 2023-09-02T12:31Z [----] followers, [---] engagements

"🐍🚀 An example code to checkout the Performance difference between list Comprehension and For Loop run below code🐍🚀 ------------ #python #100daysofcode #softwareengineer #programming #coding #programmer #developer #coder #code #computerscience #technology #pythonprogramming #software #webdevelopment"
X Link 2023-09-03T09:09Z [--] followers, [----] engagements

"@Nelsonclever Checkout this YouTube playlist - if you are just beginning in Python"
X Link 2023-09-03T22:48Z [----] followers, [---] engagements

"@PeterDiamandis Agree. AI isn't the competition; it's the one wielding it"
X Link 2023-09-04T20:09Z [----] followers, [---] engagements

"🔥🚀Want to Train state-of-the-art text classification models with only a few samples right in spaCy🔥🚀 This new Module created by David Berenstein has an easy and intuitive approach to use SetFit in combination with spaCy. In code below processes the input text with the spaCy model and the doc.cats attribute returns the predicted categories and their associated probabilities. That's it You have now successfully integrated spaCy with SetFit for text categorization tasks. You can further customize and train the model using additional data or adjust the SetFit parameters as needed."
X Link 2023-09-04T23:35Z [----] followers, [---] engagements

"Massive for the LLM space. Huggingface LLM-Perf Leaderboard now has the consumer-grade RTX [----] 24GB GPU and on latency metric [----] exceeds A100-80GB ---------------------- #llm #Largelanguagemodels #Llama2 #opensource #langchain #LLM #vectorstore #NLP #ArtificialIntelligence"
X Link 2023-09-05T11:27Z [----] followers, [---] engagements

"🚀This is quite something - web scraping with LLM🚀 👉 @AnthropicAI + @LangChainAI 💡1) Ask Langchain Agent to scrape data 💡2) Get back data in a list And the magical thing is with 100k context window of @AnthropicAI you dont need to use vectordb as well here. ------------ ➡ PlayWright Browser Toolkit This toolkit is used to interact with the browser. While other tools (like the Requests tools) are fine for static sites Browser toolkits let your agent navigate the web and interact with dynamically rendered sites. py pip install playwright /dev/null pip install lxml If this is your first time"
X Link 2023-09-05T20:51Z [----] followers, 43.7K engagements

"🦜📌 OpenAI NodeJS SDK v4 was released on August [--] [----] and is a complete rewrite of the SDK. 📌You can automatically migrate your codebase using grit either online or with the following CLI command: npm exec openai migrate The grit binary executes entirely locally with AST-based transforms. Be sure to audit its changes 📌And if you are going for manual migration among other things there are changes in method names initialization logic error handling. The API parameter names should be unchanged. ---------------------- #llm #Largelanguagemodels #Llama2 #opensource #langchain #LLM #vectorstore"
X Link 2023-09-06T11:54Z [----] followers, [---] engagements

"🦜🔥Model endpoint compatibility when working with OpenAI API - Send your API request to the API endpoint that is compatible with your chosen OpenAI model 🦜🔥 📌If you are getting openai.error.InvalidRequestError first thing you check the model endpoint compatibility. 📌In"
X Link 2023-09-06T12:04Z [----] followers, [--] engagements

"🐍 Python Basics - Various methods to Remove Duplicates from List in Python 🐍 ------------ #python #pythoncoding #learntocode #softwaredeveloper #interview #100daysofcode #softwareengineer #programming #coding #programmer #developer #code"
X Link 2023-09-06T14:30Z [----] followers, [----] engagements

"🦜🔥Convert any Python function into a Langchain tool🦜🔥 The purpose is to create a Large Language Model (LLM) agent with memory that uses custom tools to answer user-questions.🦜 When we combine an LLM memory and tools we create a chatbot that acts as an agent to fetch information from a user query And then it can do many more things. ---------- Below are some details for understanding the different components of the attached code. ---------- 🦜In @LangChainAI the Tools class empowers developers to define custom tools for their functions. The Tools class requires three parameters: 📌Name:"
X Link 2023-09-06T16:20Z [----] followers, 30K engagements

"@RealBenjizo The ans is b. False 👉 The script is centered around the evaluation of a complex boolean expression in Python. The expression combines the not or and if-else constructs. 👉 Let's break down the initial variable assignments: python lee kim tim = "x" None ()"
X Link 2023-09-06T18:51Z [----] followers, [--] engagements

"Some people further asked me if we can also Transform ANY Python functions into a tool to LLM agent using OpenAI functions For that there's a great library ActionWeaver With just a simple decorator developers can transform ANY vanilla Python code into an addition to their LLM agent. ActionWeaver unlocks a new type of programs by seamlessly integrating traditional programming with LLM powerful capabilities. ActionWeaver utilizes the decorated method's signature and docstring as a description passing them along to OpenAI's function API. The Action decorator is also highly adaptable and can be"
X Link 2023-09-07T08:17Z [----] followers, [---] engagements

"🔥🚀MultiQueryRetriever in @LangChainAI to do Prompt engineering/tuning - get better set of relevant documents via capturing the context from various different angles and perspectives.🔥🚀 👉 Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on "distance". 👉 But retrieval may produce different results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems but can be tedious."
X Link 2023-09-07T15:25Z [----] followers, [----] engagements

"🚀You can do Massive cost saving (by 50% or more) for your OpenAI / ChatGPT API calls by using caching with @LangChainAI and GPTCache Integration.🚀🔥 🟠 Also much faster response times 🟠 Overcome the rate limits restrictions and 🟠 Greatly enhance the scalability of your"
X Link 2023-09-08T13:10Z [----] followers, [--] engagements

"@clcoding As a former JavaScript / NodeJS Engineer I can see quite a few things in common between Python and JavaScript 📌1. Interpreted Languages: Neither Python nor JavaScript is compiled in the traditional sense. Instead they are both interpreted languages which means the source"
X Link 2023-09-08T20:30Z [----] followers, [--] engagements

"🐍🔥Differences between a Dask DataFrame and a pandas DataFrame🐍🔥 📌 If you have a large amount of data youll receive an out of memory (OOM) error. 📌 To solve the OOM error and enable parallel execution the Dask library can be used to read large data sets that pandas cant handle. A Dask DataFrame is a collection of different pandas DataFrames that are split along an index: 📌 When you read data using a Dask DataFrame multiple small pandas DataFrames are created split along an index and then stored on disk (if memory is limited). When you apply any operation on the Dask DataFrame that"
X Link 2023-09-09T14:20Z [----] followers, [----] engagements

"Really insightful and engaging conversation and a comprehensive overview of @LangChainAI ---------------------- #langchain #llm #Largelanguagemodels #Llama2 #opensource #langchain #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning"
X Link 2023-09-09T19:06Z [----] followers, [---] engagements

"Anonymize your data when working with @LangChainAI with Microsoft Presidio. "Data anonymization is crucial before passing information to a language model like GPT-4 because it helps protect privacy and maintain confidentiality. If data is not anonymized sensitive information such as names addresses or other identifiers linked to specific individuals could potentially be learned and misused. Hence by obscuring or removing this personally identifiable information (PII) data can be used freely without compromising individuals' privacy rights or breaching data protection laws and regulations.""
X Link 2023-09-10T09:04Z [----] followers, [---] engagements

"State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser with no need for a server ---------------------- #llm #Largelanguagemodels #langchain #Llama2 #opensource #langchain #LLM #vectorstore"
X Link 2023-09-10T09:46Z [----] followers, [---] engagements

"🚀You can do Massive cost savings (by 50% or more) for your OpenAI / ChatGPT API calls by using caching with @LangChainAI and GPTCache Integration.🚀🔥 🟠 Also much faster response times 🟠 Overcome the rate limits restrictions and 🟠 Greatly enhance the scalability of your application by reducing the load on the LLM service. 👉📌 Checkout on my YouTube Channel - ---------------------- #llm #Largelanguagemodels #langchain #Llama2 #opensource #langchain #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks"
X Link 2023-09-10T14:20Z [----] followers, [---] engagements

"@clcoding The ans is a [--] [--] [--] [--] 👉 This problem is about understanding the behavior of Python lists when assigning a new list to a slice of an existing list. 👉 When we execute my_list1:4 = [--] [--] we are performing a slice assignment. This means we are replacing the elements"
X Link 2023-09-10T15:39Z [----] followers, [--] engagements

"🐍🔥 Solve a Quadratic Equation with latexify in Python 🐍 📌 latexify is a GREAT Python library by Google that compiles a Python source code to a corresponding Mathematical / LaTeX expression. pip install latexify-py latexify is A library to generate LaTeX expression (i.e. mathematical expression) from Python code - The latexify module is not a built-in Python module so you have to install it with pip install latexify-py 👉 Next a decorator is used on a function: Decorators in Python are used to modify or enhance functions without changing their actual code. In this case the"
X Link 2023-09-10T17:33Z 22.9K followers, [----] engagements

"🔥🚀CTranslate2 is absolutely brilliant for running LLMs in local single GPU machines 🔥🚀 It is a C++ and Python library for efficient inference with Transformer models. 📌 The project implements a custom runtime that applies many performance optimization techniques such as weights quantization layers fusion batch reordering etc. to accelerate and reduce the memory usage of Transformer models on CPU and GPU. The following model types are currently supported: Encoder-decoder models: Transformer base/big M2M-100 NLLB BART mBART Pegasus T5 Whisper Decoder-only models: GPT-2 GPT-J GPT-NeoX OPT"
X Link 2023-09-10T20:36Z [----] followers, [----] engagements

"🦜🔥Convert any Python function into a Langchain tool🦜🔥 The purpose is to create a Large Language Model (LLM) agent with memory that uses custom tools to answer user-questions. When we combine an LLM memory and tools we create a chatbot that acts as an agent to fetch information from a user query And then it can do many more things. ---------- Below are some details for understanding the different components of the attached code. ---------- 🦜The Tools class In Langchain allows us to define custom tools for functions. It requires three parameters: 📌Name: Specify a unique name for the tool."
X Link 2023-09-11T08:16Z [----] followers, 25.1K engagements

"This is pretty awesome. @weaviate_io Fine-tuned the LlaMA 7B 🦙🔥 📌Now we can translate natural language commands into the Weaviate GraphQL Search APIs 📌The Auto API allows you to simply tell Weaviate what kind of search you want and WeaviateGraphQLGorilla formats the request under the hood. 📌This lets you execute your search while also returning the generatedQuery lookup for learning purposes ---------------------- #llm #Largelanguagemodels #Llama2 #opensource #langchain #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning #deeplearningai"
X Link 2023-09-11T15:59Z [----] followers, [----] engagements

"⚠ When running a Huggingface model in Google Colab - if you are getting Error = ⚠🚫 "LayerNormKernelImpl" not implemented for 'Half'' The error arises because the Layer Normalization operation in the model doesn't support the 'Half' data type. And this is likely because you're trying to run the model in FP16 mode but the specific LayerNorm operation isn't implemented for this precision in the version of PyTorch you're using. Just try explicitly set the data type to FP32 when loading the model and tokenizer. This will ensure that the model doesn't attempt to use FP16 If You've set"
X Link 2023-09-12T08:23Z [----] followers, [---] engagements

"🐍🔥 In Python did you know about the differences between append() vs extend() method. Very Important as misuse can introduce subtle bugs or performance issues in the codebase. Let's delve into this: ## [--]. Basic Difference - append(): 📌 - Adds its entire argument as a single element to the end of the list. - If an iterable is passed to append() the whole iterable is added as one element. python lst = [--] [--] [--] lst.append(4 5) print(lst) # Output: [--] [--] [--] [--] [--] - extend(): 📌 - Iterates over its argument adding each element to the list extending the list. - The length of the list increases by"
X Link 2023-09-12T15:25Z [----] followers, [----] engagements

"🐍🔥Python append() vs extend() - Their choice often depends on what exactly you want to accomplish.🐍🔥 - If you're adding a single element especially repeatedly in a loop append() is your best bet. It's straightforward and generally more performative. - If you're combining lists or adding multiple items use extend(). - From a clarity standpoint use the method that best describes your intent. If you're trying to combine two lists extend() clearly shows this intent whereas multiple append() calls in a loop could confuse a reader of your code. If you often find yourself merging lists you can"
X Link 2023-09-12T15:26Z [----] followers, [---] engagements

"Transformers + quantization 🦙🔥 Making LLMs more user-friendly 🤗. Confused about choosing between bitsandbytes and Auto-GPTQ This latest blog from HuggingFace has all the answers. ---------------------- #llm #Largelanguagemodels #Llama2 #opensource #langchain #LLM #vectorstore #NLP #ArtificialIntelligence"
X Link 2023-09-12T15:35Z [----] followers, [---] engagements

"🐍🚀 Async function in Python valid and invalid Syntax 🐍🚀 🐍🔥 Let's take a look below of [--] different ways to define async function and let's figure out which are valid 👉 Looking at snippet A: python async def my_function(x): foo = await bar(x) return foo This is an asynchronous function and inside the function you're using the await keyword. This is valid syntax. An asynchronous function is one that is defined using the async keyword and allows you to use await inside it to call other asynchronous functions. The await keyword ensures that the asynchronous function (in this case"
X Link 2023-09-13T17:34Z 24.1K followers, [----] engagements

"🚀Using generative AI models from OpenAI Pandas AI is a brilliant addition to pandas library 🚀🔥 📌With simply a text prompt you can produce insights from your dataframe. 📌Pandas AI aims to achieve the goal of virtually talking with a machine to output the results you want rather than having to program the task yourself. --------------------------- #llm #Largelanguagemodels #Llama2 #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning"
X Link 2023-09-13T19:06Z [----] followers, [----] engagements

"📌 Meta just open-sourced - 🔥Nougat (Neural Optical Understanding for Academic Documents)🔥 quite a Game-Changer for OCR tech for Mathematics-intensive text overshadowing many other open OCR tools like Tesseract. 📌A quick use-case - You can now very eazily convert any arxiv paper dense with complex Mathematical expression to perfectly formatted markdown with latex with a single line. $ nougat path/to/file.pdf -o output_directory So now converting Text to Machine Readable format is super eazy so creating dataset for Math-intensive text for LLM training will be that much more eazier. 📌 It"
X Link 2023-09-15T09:20Z [----] followers, [----] engagements

"🚀Run / Inferencing with Open-source Falcon 7B Model from HugginFace along with @LangChainAI 🚀🔥 ---------- #opensource #langchain #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #generativeai #generativemodels #OpenAI #GPT #GPT3 #GPT4 #chatgpt"
X Link 2023-09-15T10:59Z [----] followers, [---] engagements

"Transformer Encoder - Built From Scratch with PyTorch Natural Language Processing 👉 Checkout on my #YouTube channel #opensource #largelanguagemodel #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #generativeai #generativemodels #OpenAI #GPT #GPT3 #GPT4 #chatgpt #langchain"
X Link 2023-09-15T11:12Z [--] followers, [---] engagements

"🟠 Roberta-Large Named Entity Recognition on Kaggle NLP Competition with PyTorch 👉 Checkout on my #YouTube channel #opensource #Llama2 #largelanguagemodel #Llama2 #LLM #vectorstore #NLP #ArtificialIntelligence #datascience #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #generativeai #generativemodels #OpenAI #GPT #GPT3 #GPT4 #chatgpt #langchain #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch"
X Link 2023-09-15T11:13Z [----] followers, [---] engagements

"@driscollis Answer is D) [--] * None. 📌 In Python the behavior of the multiplication operation is determined by the data types of the operands. None is a special constant in Python that represents the absence of a value or a null value. Python doesn't know how to multiply an integer"
X Link 2023-09-15T12:25Z [----] followers, [--] engagements

"🚀 Check out in my YouTube Channel Data Science Machine Learning Interview Questions and some basic fundamental concepts🚀🔥 🟠 --------------------- #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #pythonprogramming #python #100DaysOfMLCode #softwareengineer #dataanalysis #datascienceinterview #interviewprep #interviews #python #programming #coding #programmer #developer #coder #code"
X Link 2023-09-15T12:41Z [----] followers, [---] engagements

"🐍🔥When working with Argparse in Python avoid hard to find bugs by knowing this super tricky behavior.🐍🔥 📌 In the argument parser module in Python when I implement a True or False as an argument it is actually considered as a string 📌For example when I implement a"
X Link 2023-09-15T13:56Z [----] followers, [--] engagements

"@clcoding To find the difference between two lists in Python we have multiple approaches. Let's see 📌 Using a Loop: One of the most straightforward ways is to use a loop to iterate over the elements of one list and check if the element is present in the second list. python"
X Link 2023-09-15T16:55Z [----] followers, [--] engagements

"🚀@LangChainAI Basics - RecursiveCharacterTextSplitter and Analyzing Multiple pdf of Financial Statements with it🚀🔥 👉 There are different kinds of splitters in LangChain depending on your use case; the most common one is the RecursiveCharacterTextSplitter which is ideal for general documents such as text or a mix of text and code and so on. This text splitter operates based on a list of characters that serve as delimiters or 'split points' within the text. It attempts to create chunks of text by splitting these characters one by one in the order they are listed until the resulting chunks"
X Link 2023-09-15T22:19Z [----] followers, [---] engagements

"🔥🚀Build a vector database and Query it with semantic search with @weaviate_io and an LLM 🔥🚀 The below code Loads objects Initializes a batch process and Adds objects to the target class (Question) one by one. ---------------- 👉 Describing some of the parameter/configurations auth_client_secret = You can Replace w/ your Weaviate instance API key "vectorizer" = if set to "none" you must always provide vectors yourself. Could be any other "text2vec-*" also. "generative-openai" = Ensure the generative-openai module is used for generative queries 👉 If you do want to change the vectorizer you"
X Link 2023-09-15T22:35Z [----] followers, [----] engagements

"🐍 To find the difference between two lists in Python we have multiple approaches - BUT which is better for VERY LONG lists 🐍 PROBLEM - Find items in list1 that are NOT in list2 Let's see 📌 Using a Loop: One of the most straightforward ways is to use a loop to iterate over the elements of one list and check if the element is present in the second list. python def list_difference(list1 list2): difference = # 📌 Checking elements in list1 that are not in list2 for item in list1: if item not in list2: difference.append(item) return difference # Testing print(list_difference(list1 list2)) #"
X Link 2023-09-16T10:29Z [----] followers, [---] engagements

"🚀 @LangChainAI Basics - Create Vector db store from youtube video url while working with LangChain and LLM Agent and understanding how RecursiveCharacterTextSplitter and Embeddings work🚀 👉 There are different kinds of splitters in LangChain depending on your use case;"
X Link 2023-09-16T15:16Z [----] followers, [--] engagements

"🔥📈 If you are You Still Using Pandas to Process big csv files - check out Polar 🛠🔥 While processing large amounts of data with Pandas it can hugely slow you down or even crash your environment due to "out of memory" reason. 💡 What does polars offer beyond pandas💡💡 🚀"
X Link 2023-09-16T15:17Z [----] followers, [--] engagements

"Massive Cost Saving on OpenAI API Call using GPTCache with @LangChainAI Large Language Models 🔥🚀 Checkout on my #YouTube channel 🔥🚀 --------------------- #llm #Largelanguagemodels #Llama2 #Weaviate #vectordb #vector #pinecone #ai #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #pythonprogramming #python #100DaysOfMLCode #softwareengineer #dataanalysis #datascienceinterview #python"
X Link 2023-09-16T18:17Z [----] followers, [---] engagements

"The choice between XGBoost and Deep Learning for tabular data depends on the specific problem and data. XGBoost is a fast and efficient algorithm for structured and tabular data and can handle missing values while Deep Learning can model complex non-linear relationships in the"
X Link 2023-09-16T18:33Z [----] followers, [--] engagements

"🐍🔥Super eazy Speech Recognition/Transcription with Python🐍🔥 Transcription is the process of converting speech from an audio or video file into text. This process involves more than just listening to recordings the content must be understood and nothing left out. 🚀 The SpeechRecognition module depends on pyaudio you can install them from your package manager. pip install SpeechRecognition pyaudio ------------ #python #100daysofcode #softwareengineer #programming #coding #programmer #developer #coder #code #computerscience #pythonprogramming #software #webdevelopment #webdeveloper #tech"
X Link 2023-09-17T08:57Z [----] followers, [---] engagements

"🐍🚀 Python's trio: bool() all() any() unlock the logic magic 🧠🐍 Lets check the code and explain why it outputs True ❓❓ py X = bool() y = all() Z = any() print(X==Z and y) # OUTPUTS True # EXPLAIN WHY ❓❓ First note In Python the following values are considered "falsey": - Constants defined to be false: None and False. - Zero of any numeric type: [--] [---] 0j Decimal(0) Fraction(0 1) - Empty sequences and collections: '' () set() range(0) ----------- 👉Step 1: X = bool(): The bool() function is used to return or convert a value to a boolean (True or False). When bool() is called with an empty"
X Link 2023-09-17T09:53Z [----] followers, [---] engagements

"🚀🧠 Differences between new and init: 🧠 At its core object-oriented programming in Python deals with the creation and initialization of objects. The new and init methods are two intrinsic dunder methods (magic methods) in Python that play distinct roles in this lifecycle. [--]. new: - It's a static method (though you don't need to declare it as such) that returns a new instance of the class. - It's responsible for creating (allocating memory for) the instance. - The first argument it receives is the class itself (usually named cls). - If you override new and don't"
X Link 2023-09-17T10:04Z [----] followers, [---] engagements

"📌 URL and URN are both subsets of URI (Uniform Resource Identifier). Let's break down the differences. 📌 The following are examples of URNs: urn:isbn:1234567890 urn:ISSN:0167-6423 urn:ietf:rfc:2648 Those URNs identify objects of different types. For example"
X Link 2023-09-18T11:31Z [----] followers, [--] engagements

"🐍🔥 The distinction between equality and identity in Python and Customizing bool method 🐍🔥 👉 A) The distinction between equality and identity in Python. And that == checks for equality (do two objects have the same content❓) while is checks for identity (are two variables pointing to the exact same object❓). 👉 B) Customizing the behavior of one of Python's special methods bool for your class. ----------- ❓Try to grasp the output of the attached code snippet ❓ 💡THE ANSWER💡 ---------- First [--] Important Theory Here [--]. 🧠What exactly is the definition of identity in Python ❓🧠💡"
X Link 2023-09-18T14:41Z [----] followers, [--] engagements

"Web scraping with Large Language Models (LLM)- @AnthropicAI + @LangChainAI 👉 Checkout on my #YouTube channel 🟠 -------------- #MachineLearning #ArtificialIntelligence #datascience #nlp #textprocessing #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #datascience #nlp #textprocessing #tensorflow #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #pythonprogramming #python #100DaysOfMLCode #softwareengineer #dataanalysis #datascienceinterview #interviewprep #interviews #python #programming #coding #programmer #developer #coder #code #computerscience"
X Link 2023-09-18T18:00Z [----] followers, [---] engagements

"📌 Mathematics is Magic Kaprekar's Routine - Take any four-digit number using at least two different digits. - Arrange the digits in descending and then in ascending order to get two four-digit numbers. - Subtract the smaller number from the bigger number. - Repeat this process and you'll always end up with [----] in at most [--] iterations. This number is called Kaprekar's constant. ---- Example of Kaprekar's Number A number is a Kaprekar number if the square of the number can be split into two parts (none of the parts has only 0) that add up to the original number. ------ #math #mathematics"
X Link 2023-09-18T19:44Z [----] followers, [---] engagements

"🔥 OpenAI just released GPT-3.5-Turbo-Instruct.🔥 Gpt-3.5-turbo-instruct is an InstructGPT [---] class model. Its trained similarly to previous Instruct models such as the text-davinci series while maintaining the same speed as our turbo models. They seem to have deprecated the previous series of instruction-tuned models so this is their replacement (and an upgrade I'm sure). ------------- Now if you are thinking whats the differenceb between chat model and instruct model 📌 The primary difference between them stems from their training data and the fine-tuning process they undergo. 📌 Chat"
X Link 2023-09-18T20:13Z [----] followers, [---] engagements

"🌸 Run LLMs at home BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading 🦙🔥 📌Petals is a community-run system we rely on people sharing their GPUs. You can check out available models and help serving one of them 📌How does it work 📌Petals runs large language models like Llama and BLOOM collaboratively you load a small part of the model then join people serving the other parts to run inference or fine-tuning. 📌Single-batch inference runs at up to [--] steps/sec for Llama [--] (70B) and [--] step/sec for BLOOM-176B. This is up to 10x faster than offloading enough to build"
X Link 2023-09-19T08:58Z [----] followers, [---] engagements

"From the book "Thinking in Systems" All of life from viruses to redwood trees from amoebas to elephants is based on the basic organizing rules encapsulated in the chemistry of DNA RNA and protein molecules. The agricultural revolution and all that followed started with the simple shocking ideas that people could stay settled in one place own land select and cultivate crops"
X Link 2023-09-20T14:40Z [----] followers, [---] engagements

"The paper list of the 86-page paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al. ---------------------- #llm #Largelanguagemodels #Llama2 #opensource #langchain #LLM #vectorstore https://github.com/WooooDyy/LLM-Agent-Paper-List https://github.com/WooooDyy/LLM-Agent-Paper-List"
X Link 2023-09-20T17:18Z 43.7K followers, [---] engagements

Limited data mode. Full metrics available with subscription: lunarcrush.com/pricing