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Did you know that in the world of machine learning, the efficiency of Deep Learning (DL) models can be significantly boosted by a technique called quantization? Imagine reducing the computational burden of your neural network without sacrificing its performance. Just like compressing a large file without losing its essence, model quantization allows you to make your models smaller and faster. Let's dive into the fascinating concept of quantization and unveil the secrets of optimizing your neural networks for real-world deployment. Before we dig in, readers should be familiar with neural networks and the basic concept of quantization, including the terms scale (S) and zero point (ZP). For readers who would like a refresher, and explains the broad concept and types of quantization. this article this article In this guide I will briefly explain why quantization matters, and how to implement it using Pytorch. I will focus mainly on the type of quantization called “post training static quantization”, which results in 4x lesser memory footprint of the ML model, and makes the inference up to 4x faster. Concepts Why Does Quantization Matter? Neural Networks computations most commonly performed with 32 bit floating point numbers. A single 32 bit floating point number (FP32) requires 4 bytes of memory. In comparison, a single 8 bit integer number (INT8) only requires 1 byte of memory. Further, computers process integer arithmetic much faster than float operations. Right away, you can see that quantizing a ML model from FP32 to INT8 will result in 4x less memory. Further, it will also speed up inference by as much as 4x! With large models being all the rage right now, it is important for practitioners to be able to optimize trained models for memory and speed for real time inference. Key Terms Weights of the trained neural network. Weights- In terms of quantization, activations are not the activation functions like Sigmoid or ReLU. By activations, I mean the feature map outputs of the intermediate layers, which are inputs to the next layers. Activations- Post Training Static Quantization Post training static quantization means we do not need to train or finetune the model for quantization after training the original model. We also do not need to quantize intermediate layer inputs, called activations on the fly. In this mode of quantization, the weights are directly quantized by computing the scale and zero point for each layer. For activations however, as the input to the model changes, the activations will change as well. We do not know the range of each and every input that the model will encounter during inference. So how can we compute the scale and zero point for all activations of the network? We can do this by calibrating the model, using a good representative dataset. We then observe the range of values of activations for the calibration set, and then use those statistics to compute scale and zero point. This is done by inserting observers in the model, which collect data statistics during calibration. After preparing the model (inserting observers), we run the forward pass of the model on the calibration dataset. The observers use this calibration data to compute scale and zero point for activations. Now inference is only a matter of applying the linear transform to all layers with their respective scale and zero points. While the entire inference is done in INT8, the final model output is dequantized (from INT8 to FP32). Why do activations need to be quantized if the input and network weights are already quantized? This is an excellent question. While the network input and weights are indeed already INT8 values, the output of the layer is stored as INT32, to avoid overflow. To reduce complexity in processing the next layer the activations are quantized from INT32 to INT8. With the concepts clear, let’s dive into the code and see how it works! For this example, I will use a resnet18 model fine-tuned on the Flowers102 dataset, available directly in Pytorch. However, the code will work for any trained CNN, with the appropriate calibration dataset. Since this tutorial is focused on quantization, I will not cover the training and fine-tuning part. However, all code can be found . Lets dive in! here Quantization Code Let us import the libraries necessary for quantization and to load the fine-tuned model. import torch
import torchvision
import torchvision.transforms as transforms
from torchvision.models import resnet18
import torch.nn as nn

from torch.ao.quantization import get_default_qconfig
from torch.ao.quantization.quantize_fx import prepare_fx, convert_fx
from torch.ao.quantization import QConfigMapping

import warnings

warnings.filterwarnings('ignore') Next, let us define some parameters, define data transforms and data loaders, and load the fine-tuned model model_path = 'flowers_model.pth'
quantized_model_save_path = 'quantized_flowers_model.pth'
batch_size = 10
num_classes = 102

# Define data transforms
transform = transforms.Compose(
                                [transforms.Resize(256),
                                 transforms.CenterCrop(224),
                                 transforms.ToTensor(),
                                 transforms.Normalize(
                                            (0.485, 0.465, 0.406), 
                                            (0.229, 0.224, 0.225))]
)

# Define train data loader, for using as calibration set
trainset = torchvision.datasets.Flowers102(root='./data', split="train", 
                                           download=True, transform=transform)
trainLoader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, 
                                          shuffle=True, num_workers=2)

# Load the finetuned resnet model
model_to_quantize = resnet18(weights=None)
num_features = model_to_quantize.fc.in_features
model_to_quantize.fc = nn.Linear(num_features, num_classes)
model_to_quantize.load_state_dict(torch.load(model_path))
model_to_quantize.eval()
print('Loaded fine-tuned model') For this example, I will use some training samples as the calibration set. Now, let us define the configuration used for quantizing the model. # Define quantization parameters config for the correct platform, 
# "x86" for x86 devices or "qnnpack" for arm devices
qconfig = get_default_qconfig("x86")
qconfig_mapping = QConfigMapping().set_global(qconfig) In the above snippet, I have used the default config, but the class of Pytorch is used to describe how the model, or a part of the model should be quantized. We can do this by specifying the type of observer classes to be used to weights and activations. QConfig We are now ready to prepare the model for quantization # Fuse conv-> relu, conv -> bn -> relu layer blocks and insert observers
model_prep = prepare_fx(model=model_to_quantize, 
                        qconfig_mapping=qconfig_mapping, 
                        example_inputs=torch.randn((1,3,224,224))) The function inserts the observers into the model, and also fuses conv→relu and conv→bn→relu modules. This results in lesser operations and lower memory bandwidth due to not needing to store intermediate results of those modules. prepare_fx Calibrate the model by running forward pass on calibration data # Run calibration for 10 batches (100 random samples in total)
print('Running calibration')
with torch.no_grad():
    for i, data in enumerate(trainLoader):
        samples, labels = data

        _ = model_prep(samples)

        if i == 10: 
            break We don’t need to run calibration on the entire training set! In this example, I am using 100 random samples, but in practice, you should choose a dataset which is representative of what the model will see during deployment. Quantize the model and save the quantized weights! # Quantize calibrated model
quantized_model = convert_fx(model_prep)
print('Quantized model!')

# Save quantized
torch.save(quantized_model.state_dict(), quantized_model_save_path)
print('Saved quantized model weights to disk') And that’s it! Now let us see how to load a quantized model, and then compare the accuracy, speed and memory footprint of the original and quantized models. Load a quantized model A quantized model graph is not quite the same as the original model, even if both have the same layers. Printing the first layer ( ) of both models shows the difference. conv1 print('\nPrinting conv1 layer of fp32 and quantized model')
print(f'fp32 model: {model_to_quantize.conv1}')
print(f'quantized model: {quantized_model.conv1}') You will notice that along with the different class, the quantized model’s conv1 layer also contains the scale and zero point parameters. Thus, what we need to do it follow the quantization process (without calibration) to create the model graph, and then load the quantized weights. Of course, if we save the quantized model to onnx format, we can load it like any other onnx model, without running the quantization functions each time. In the meantime, let us define a function for loading the quantized model and save it to . inference_utils.py import torch

from torch.ao.quantization import get_default_qconfig
from torch.ao.quantization.quantize_fx import prepare_fx, convert_fx
from torch.ao.quantization import QConfigMapping

def load_quantized_model(model_to_quantize, weights_path):

    '''
    Model only needs to be calibrated for the first time.
    Next time onwards, to load the quantized model, 
    you still need to prepare and convert the model without calibrating it.
    After that, load the state dict as usual.
    '''
    model_to_quantize.eval()

    qconfig = get_default_qconfig("x86")
    qconfig_mapping = QConfigMapping().set_global(qconfig)
    model_prep = prepare_fx(model_to_quantize, qconfig_mapping, 
                            torch.randn((1,3,224,224)))
    quantized_model = convert_fx(model_prep)
    quantized_model.load_state_dict(torch.load(weights_path))

    return quantized_model Define functions for measuring accuracy and speed Measure accuracy import torch

def test_accuracy(model, testLoader):
    model.eval()
    running_acc = 0
    num_samples = 0
    with torch.no_grad():
        for i, data in enumerate(testLoader):
            samples, labels = data

            outputs = model(samples)
            preds = torch.argmax(outputs, 1)

            running_acc += torch.sum(preds == labels)
            num_samples += samples.size(0)
    
    return running_acc / num_samples This is a pretty straightforward Pytorch code. Measure inference speed in milliseconds (ms) import torch
from time import time

def test_speed(model):

    dummy_sample = torch.randn((1,3,224,224))

    # Average out inference speed over multiple iterations 
    # to get a true estimate
    num_iterations = 100
    start = time()
    for _ in range(num_iterations):
        _ = model(dummy_sample)

    end = time()
    return (end-start)/num_iterations * 1000 Add both these functions in . We are now ready to compare models. Let us go through the code. inference_utils.py Compare models for accuracy, speed, and size Let us first import necessary libraries, define parameters, data transforms, and the test dataloader. import os
import torch
import torch.nn as nn
import torchvision
from torchvision.models import resnet18
import torchvision.transforms as transforms
from inference_utils import test_accuracy, test_speed, load_quantized_model
import copy

import warnings
warnings.filterwarnings('ignore')

model_weights_path = 'flowers_model.pth'
quantized_model_weights_path = 'quantized_flowers_model.pth'
batch_size = 10
num_classes = 102

# Define data transforms
transform = transforms.Compose(
                                [transforms.Resize(256),
                                 transforms.CenterCrop(224),
                                 transforms.ToTensor(),
                                 transforms.Normalize(
                                            (0.485, 0.465, 0.406), 
                                            (0.229, 0.224, 0.225))]
)

testset = torchvision.datasets.Flowers102(root='./data', split="test", 
                                          download=True, transform=transform)
testLoader = torch.utils.data.DataLoader(testset, batch_size=batch_size, 
                                         shuffle=False, num_workers=2) Load the two models # Load the finetuned resnet model and the quantized model
model = resnet18(weights=None)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, num_classes)
model.load_state_dict(torch.load(model_weights_path))
model.eval()

model_to_quantize = copy.deepcopy(model)
quantized_model = load_quantized_model(model_to_quantize, 
                                       quantized_model_weights_path) Compare models # Compare accuracy
fp32_accuracy = test_accuracy(model, testLoader)
accuracy = test_accuracy(quantized_model, testLoader)

print(f'Original model accuracy: {fp32_accuracy:.3f}')
print(f'Quantized model accuracy: {accuracy:.3f}\n')

# Compare speed
fp32_speed = test_speed(model)
quantized_speed = test_speed(quantized_model)
print(f'Inference time for original model: {fp32_speed:.3f} ms')
print(f'Inference time for quantized model: {quantized_speed:.3f} ms\n')

# Compare file size
fp32_size = os.path.getsize(model_weights_path)/10**6
quantized_size = os.path.getsize(quantized_model_weights_path)/10**6
print(f'Original model file size: {fp32_size:.3f} MB')
print(f'Quantized model file size: {quantized_size:.3f} MB') Results As you can see, the accuracy of the quantized model on the test data is almost as much as the accuracy of the original model! Inference with the quantized model is ~3.6x faster (!) and the quantized model requires ~4x lesser memory than the original model! Conclusion In this article, we understood the broad concept of ML model quantization, and a type of quantization called Post Training Static Quantization. We also looked at why quantization is important and a powerful tool in the time of large models. Finally, we went through example code to quantize a trained model using Pytorch, and reviewed the results. As the results showed, quantizing the original model did not impact performance, and at the same time decreased inference speed by ~3.6x and lowered the memory footprint by ~4x! A few points to note- Static quantization works well for CNNs, but dynamic quantization is the preferred method for sequence models. Additionally, if quantization drastically impacts model performance, the accuracy can be regained by a technique called Quantization Aware Training (QAT). How do Dynamic Quantization and QAT work? Those are posts for another time. I hope with this guide, you are provided the knowledge to perform static quantization on your own Pytorch models. References Basics of model quantization Tensor quantization Flowers 102 dataset Pytorch docs

Curious About Faster ML Models? Discover Model Quantization With PyTorch!

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Scientific American, Vol. XXXVII.—No. 2. [New Series.], July 14, 1877 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . CURIOUS CARNIVOROUS PLANTS. here CURIOUS CARNIVOROUS PLANTS. The is one of the most curious of that wonderful series of carnivorous plants which at the present time are engaging the closest scrutiny of naturalists. It is a true trap in one sense—inasmuch as it captures the victim which ventures near it; but it relies on little or no mechanical means for securing its prey, but stupefies the living insect by its odor. The flower is horn-shaped, about 11 inches in length, with an opening some 5 inches in diameter. The color within is a dull dark violet, while the interior of the spathe is lined with black, hooked bristles, the whole appearance of the flower being thoroughly repulsive. The illustrations herewith presented, Figs. 1 and 2, represent it at one third its natural size, Fig. 2 showing a section of the flower. It is not certain what attracts the insects, which are usually of the species known as the meat fly and the common house fly. They do not seem to seek for the small quantity of nectar concealed, and yet they cluster about the fatal opening, as if drawn by some overpowering fascination. Overcome by lethargy, they fall inert upon the flower, are lightly held by the bristles, and finally die asphyxiated by the carbonic acid which the plant disengages in large quantities during its inflorescence. Strange as is the action of the , the method whereby the takes its prey is even more wonderful. To illustrate on a magnified scale, let the reader imagine a surface thickly covered with strong iron posts, on the sides of which are numerous keen barbs pointing downward. Then between these posts, suppose that jars overflowing with honey are placed. An elephant, let it be imagined, attracted by the profusion of sweetness, inserts his trunk between the posts and finds easy access to the honey. But while he can force his proboscis downward past the barbs turned in that direction, when he attempts to withdraw it he finds the keen points catch in the flesh, and render it impossible to do so. A terrible struggle follows, the unfortunate animal twisting and writhing in every direction, until finally by an Herculean effort the head is torn from the body, and the latter becomes digested by some potent gastric juice, exuding from the colossal organism of which the trap forms but a portion. Of course this is vastly exaggerated, and it would puzzle an elephant to pull his own head off; but if for the post studded trap, we substitute the surface of a flower, and if we replace the elephant by a fly, we shall have conceived an accurate picture of what takes place in the peculiar receptacle with which Nature has provided the . This is very beautifully shown in Fig. 3; and at A, in same figure, is represented the barbed bristles grasping the highly magnified proboscis of the fly. Between the barbed bristles are mushroom-shaped projections, from the summits of which a viscous nectar exudes. This is the honey bait which induces the insect to insert his trunk between the fatal barbs. There is still another plant, , which captures butterflies by grasping the proboscis. The construction of the flower is quite complicated, so that the insects are compelled to insert their trunks through a narrow and winding passage in order to reach the nectar. The organ then necessarily comes in contact with an adhesive substance, which prevents its removal. arum Dracunculus arum mentzelia mentzelia ornata physianthus albens The , Fig. 4, is another plant trap, which catches no flies nor possesses any such wonderfully adapted devices as the plants already described. It simply has its branches covered with double barbed bristles of great strength which attach themselves to anything brought in contact with them. The bristles are strong enough to hold lizards, as represented by our engraving, the points inserting themselves in the interstices of the scaly covering of the reptile. Of course the lizard thus held starves to death, and small birds often follow a like fate. We are indebted to for the illustrations. Gronovia scandens La Nature About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2012). Scientific American, Vol. XXXVII. —No. 2. [New Series.], July 14, 1877. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/38481/pg38481-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

CURIOUS CARNIVOROUS PLANTS

Scientific American, Volume 40, No. 13, March 29, 1879, by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . CURIOUS FACTS IN MAGNETISM. here CURIOUS FACTS IN MAGNETISM. At the meeting of the New York Academy of Sciences February 17th, the article in the March number of , entitled "Gary's Magnetic Motor," was incidentally alluded to, and Prof. C. A. Seeley made the following remarks: The article claims that Mr. Gary has made a discovery of a neutral line or surface, at which the polarity of an induced magnet, while moving in the field of the inducing pole, is changed. The alleged discovery appears to be an exaggerated statement of some curious facts, which, although not new, are not commonly recognized. If a bar of iron be brought up, end on, near a magnetic pole, the bar becomes an induced magnet, but an induced magnet quite different from what our elementary treatises seem to predict. On the first scrutiny it is a magnet without a neutral point, and only one kind of magnetism—namely, that of the inducing pole. Moreover, the single pole is pretty evenly distributed over the whole surface, so that if iron filings be sprinkled on the bar they will be attracted at all points and completely cover it. Now, if while the bar is covered by filings it be moved away from the inducing pole, the filings will gradually and progressively fall, beginning at the end nearest the inducing pole and continuing to some point near the middle of the bar; the filings at the remote end will generally be held permanently. When the bar is carried beyond the field of the inducing pole it is simply a weak magnet of ordinary properties— , of two poles and a neutral point between them. Harper's Magazine i. e. A plausible and simple explanation of this case is that the inducing pole holds or binds the induced magnetism of opposite name, so that it has no external influence; the two magnetisms are related to each other as are the positive and negative electricities of the Leyden jar. Let the inducing pole be N.; the S. of the bar will be attracted by it and bound, while the N. of the bar becomes abnormally free and active. On moving the bar from the pole the bound magnetism is released and a part becomes residual magnetism. Now when the residual balances the free magnetism which is of opposite name, we are on Gary's neutral line. In a restricted sense there is a change of polarity over the half of the bar contiguous to the inducing pole; on the other half there is no change of pole in any sense. Experiment with a shingle nail in the place of the filings, Gary, bring the nail to the induced bound pole, and it may be held, except at the neutral line. Now if one will read the magazine article with such ideas as these he will feel pretty sure that the writer of it has used words recklessly, that Gary has not made an original discovery, and that the "neutral" line, whatever it be, has only an imagined relation to the "principle" of the motor. à la The Gary Motor as a perpetual motion scheme, of course, is not worthy of serious notice from a society devoted to science. It has no noteworthy novelty of construction or conception. Mr. Gary is afflicted with the very old delusion of the cut-off or shield of magnetism, which is to cost less than what comes from it. His cut-off is a sheet of iron, which we know acts simply as an armature. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2006). Scientific American, Volume 40, No. 13, March 29, 1879. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/18866/pg18866-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

CURIOUS FACTS IN MAGNETISM

CURIOUS IF TRUE

Apple vs. FBI (2016) Court Filing, retrieved on February 16, 2016, is part of . You can jump to any part in this filing . This part is 15 of 17. HackerNoon’s Legal PDF Series here Attachment A-2: Property to be Searched Black Lexus IS300 California license plate #5KGD203, handicap placard 360466F, vehicle identification number JTHBD192X50094434. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case No. 15-0451M retrieved on September 25, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. archive.epic.org

Apple vs. FBI: The Curious Black Lexus IS300

THE CURIOUS BOOK

A CURE FOR THE BLUES

Calling All Writers: Learn What The Crypto-API Writing Contest Is All About From The CoinGecko Team

সমস্ত লেখকদের কল করা হচ্ছে: CoinGecko টিম থেকে ক্রিপ্টো-এপিআই লেখার প্রতিযোগিতাটি কী তা জানুন

Aufruf an alle Autoren: Erfahren Sie vom CoinGecko-Team, worum es beim Crypto-API-Schreibwettbewerb geht

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Appel à tous les écrivains : découvrez en quoi consiste le concours d'écriture Crypto-API auprès de l'équipe CoinGecko

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ライターの皆さんへ: CoinGecko チームから暗号 API ライティング コンテストについて学びましょう

모든 작가에게 전화하기: CoinGecko 팀으로부터 Crypto-API 글쓰기 콘테스트의 모든 내용을 알아보세요

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Tüm Yazarlara Çağrı: CoinGecko Ekibinden Kripto API Yazma Yarışmasının Neyle İlgili Olduğunu Öğrenin

Kêu gọi tất cả người viết: Tìm hiểu nội dung cuộc thi viết về tiền điện tử-API từ nhóm CoinGecko

召集所有作家：从 CoinGecko 团队了解 Crypto-API 写作比赛的全部内容

How to Find Out If You're Paying for Slow Internet

In a notable development within the cryptocurrency community, $DEDE, a fresh and intriguing digital currency, has officially launched on the Solana blockchain.  This new entrant stands out with its unique dinosaur-themed design, providing a playful twist on the traditional memecoin model. Launched last week, $DEDE is quickly capturing the hearts of cryptocurrency aficionados globally, promising not only a novel aesthetic but also significant financial potential. $DEDE harnesses the robust capabilities of the Solana blockchain, renowned for its high-speed transactions and low-cost framework, which makes it an ideal platform for growing cryptocurrencies. Within its first week, $DEDE attracted over 2500 holders and soared to a market cap of $7 million, demonstrating strong initial market reception and rapid community growth. Although its market cap has adjusted to around $1 million, the enthusiasm around $DEDE suggests it is primed for future growth. "Through $DEDE, we're offering the market a unique blend of fun and functionality. Our coin leverages the universal appeal of dinosaurs, which invokes a sense of nostalgia and curiosity," said Jane Doe, Chief Marketing Officer of $DEDE. "Our strategic choice to build on the Solana blockchain is fundamental to our vision. It ensures that our operations are not only swift but also efficient, making $DEDE an attractive option for both seasoned investors and those new to cryptocurrencies." $DEDE’s prehistoric theme is more than a mere marketing tool; it's a nod to the timeless fascination with dinosaurs, aiming to engage a broad audience and create a memorable impact in the crowded memecoin market. This thematic approach is paired with strong technical foundations that utilize Solana’s capabilities for managing high transaction volumes without compromising speed or cost. The coin's introduction coincides with an increasing interest in cryptocurrencies that integrate popular cultural themes with cutting-edge blockchain solutions. For early adopters and speculative investors, $DEDE’s current market valuation presents a golden opportunity for potential high returns. The strategic application of Solana’s technology not only differentiates $DEDE from numerous memecoins but also bolsters its potential for substantial market traction. About $DEDE $DEDE is an entertaining cryptocurrency that marries a charming prehistoric dinosaur theme with the sophisticated technology of the Solana blockchain. Positioned on one of the most efficient and scalable blockchain platforms available today, $DEDE offers low transaction costs and high-speed operations, making it accessible to a wide range of users. The community around $DEDE is dynamic and enthusiastic, comprising both cryptocurrency veterans and newcomers. Together, they contribute to the development and widespread adoption of $DEDE, ensuring it remains at the forefront of innovation. As $DEDE continues its journey in the digital finance world, it invites everyone to explore the exciting convergence of ancient intrigue and modern technology, promising not just investment returns but a thrilling narrative in the expansive universe of cryptocurrency. $DEDE is more than just a memecoin; it represents a strategic, community-focused, and technologically advanced approach to cryptocurrency. It stands poised to capture the imaginations of those both inside and outside the crypto world, making it a significant player in the market and a compelling option for those looking to diversify their digital assets. Additional Resources and Information:For more details on $DEDE and to become part of its growing community, please visit the following official resources: Website Telegram Twitter This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. In a notable development within the cryptocurrency community, $DEDE, a fresh and intriguing digital currency, has officially launched on the Solana blockchain.  This new entrant stands out with its unique dinosaur-themed design, providing a playful twist on the traditional memecoin model. Launched last week, $DEDE is quickly capturing the hearts of cryptocurrency aficionados globally, promising not only a novel aesthetic but also significant financial potential. $DEDE harnesses the robust capabilities of the Solana blockchain, renowned for its high-speed transactions and low-cost framework, which makes it an ideal platform for growing cryptocurrencies. Within its first week, $DEDE attracted over 2500 holders and soared to a market cap of $7 million, demonstrating strong initial market reception and rapid community growth. Although its market cap has adjusted to around $1 million, the enthusiasm around $DEDE suggests it is primed for future growth. "Through $DEDE, we're offering the market a unique blend of fun and functionality. Our coin leverages the universal appeal of dinosaurs, which invokes a sense of nostalgia and curiosity," said Jane Doe, Chief Marketing Officer of $DEDE. "Our strategic choice to build on the Solana blockchain is fundamental to our vision. It ensures that our operations are not only swift but also efficient, making $DEDE an attractive option for both seasoned investors and those new to cryptocurrencies." $DEDE’s prehistoric theme is more than a mere marketing tool; it's a nod to the timeless fascination with dinosaurs, aiming to engage a broad audience and create a memorable impact in the crowded memecoin market. This thematic approach is paired with strong technical foundations that utilize Solana’s capabilities for managing high transaction volumes without compromising speed or cost. The coin's introduction coincides with an increasing interest in cryptocurrencies that integrate popular cultural themes with cutting-edge blockchain solutions. For early adopters and speculative investors, $DEDE’s current market valuation presents a golden opportunity for potential high returns. The strategic application of Solana’s technology not only differentiates $DEDE from numerous memecoins but also bolsters its potential for substantial market traction. About $DEDE $DEDE is an entertaining cryptocurrency that marries a charming prehistoric dinosaur theme with the sophisticated technology of the Solana blockchain. Positioned on one of the most efficient and scalable blockchain platforms available today, $DEDE offers low transaction costs and high-speed operations, making it accessible to a wide range of users. The community around $DEDE is dynamic and enthusiastic, comprising both cryptocurrency veterans and newcomers. Together, they contribute to the development and widespread adoption of $DEDE, ensuring it remains at the forefront of innovation. As $DEDE continues its journey in the digital finance world, it invites everyone to explore the exciting convergence of ancient intrigue and modern technology, promising not just investment returns but a thrilling narrative in the expansive universe of cryptocurrency. $DEDE is more than just a memecoin; it represents a strategic, community-focused, and technologically advanced approach to cryptocurrency. It stands poised to capture the imaginations of those both inside and outside the crypto world, making it a significant player in the market and a compelling option for those looking to diversify their digital assets. Additional Resources and Information: For more details on $DEDE and to become part of its growing community, please visit the following official resources: Website Website Website Telegram Telegram Telegram Twitter Twitter Twitter This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Introducing $DEDE: A Prehistoric Twist On Memecoins Revolutionizing The Solana Ecosystem

Yet Another Guide for Embarking on Your PM Journey

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Further analysis This study's findings diverge from those of Gregorcic and Pendrill (2023), who contended that despite rectifying errors and contradictions via Socratic dialogue, ChatGPT did not meet the necessary criteria to function as a physics tutor. This disparity could stem from using an older, less advanced iteration of ChatGPT, specifically the GPT-3 model introduced by OpenAI in 2020 (Brown et al., 2020). Our research indicates that the current versions of ChatGPT, Bing Chat, Bard, and Claude can act as effective objects-to-think-with, as per Papert's concept (1980, p. 11), cultivating an engaging, interactive, and inclusive learning environment. These AI tools can support creativity, collaboration, exploration, critical thinking, and problem-solving, potentially deepening understanding of the subject matter. This relates to the comparison between ChatGPT, Bard, and Bing Chat in the study of Liu et al. (2023). The overall success of this educational journey, with GenAIbots serving as agents to think with, highlights a few effective teaching strategies that can facilitate a profound comprehension of the involved concepts. A significant part of the process involved positive reinforcement, as GenAIbots acknowledged students' observations as enjoyable, validating their experiences and stimulating their curiosity. Furthermore, following Gregorcic and Pendrill (2023), we adopted what could be seen as a Socratic-like dialogue strategy, prompting students to think critically and actively engage in their learning rather than merely providing them with answers. GenAIbots also capitalised on the student’s existing knowledge by inquiring about their understanding of topics like weight and gravity, thereby establishing a meaningful bridge between new and previously learned information. Building upon Bitzenbauer's preliminary study (2023) that highlighted the potential of expansive language model-based chatbots, including ChatGPT, to enrich learning experiences, and Adiguzel et al.'s (2023) assertion of ChatGPT's transformative potential in education via personalised instruction and real-time feedback, our research emphasises the benefits of AI integration in learning environments. AI-driven chatbots like ChatGPT, Bing Chat, Bard, Claude, and other generative models might serve as advanced agents for addressing challenging learning scenarios, expanding students' knowledge incrementally, and providing relevant explanations, examples, and analogies to support their learning. This study highlights the potential of AI-driven tools in promoting active learning, personalised instruction, and conceptual understanding in Chemistry. It underscores the value of AI in enhancing educational experiences, concluding that GenAIbots can serve as innovative platforms for exploring challenging educational scenarios. Nevertheless, our analysis reveals that ChatGPT and Claude consistently provided more comprehensive, detailed, and accurate answers than Bing Chat, which tended to give shorter and less informative responses. ChatGPT-4 was able to adapt responses to students' questions, address misconceptions, and offer customised explanations. ChatGPT was also more adept at addressing the nuances and subtleties of the questions, recognising the context, and engaging with the user's unique perspective or interpretation. On the other hand, Bing Chat often failed to address these nuances and provided less contextual information. Overall, ChatGPT and Claude demonstrated a superior understanding of the subject matter and a more remarkable ability to convey complex scientific concepts in an accessible manner. The study also highlighted the need for comprehensive educator training before integrating these tools into classrooms. Adequate training empowers teachers to guide students effectively in articulating their thoughts and constructing solid arguments. This preparation would allow educators to assess GenAIbots' performance when faced with compelling counterexamples, fostering productive discussions among colleagues. Finally, it is essential to mention that the results of this experience revealed significant variance in the performances of ChatGPT, Bing Chat, Bard, and Claude. This result agrees with Franciscu (2023), in which ChatGPT, a cutting-edge natural language generation model, excels at producing human-like responses suitable for chatbots. Based on the detailed analysis above, despite some fundamental similarities, ChatGPT notably outperformed BingChat, with Bard and Claude closely shadowing ChatGPT's performance and displaying comparable proficiency levels between them, as described below and summarised in Table 2. 1. stands out for its consistent through positive reinforcement and feedback mechanisms. It employs effective , often breaking down explanations systematically, and frequently engages users with by soliciting feedback. With a flair for the , translates abstract concepts into relatable scenarios. Its is tailored and comprehensive, and it occasionally ventures into to provide broader contexts. ChatGPT Personalization & Personable Touch Instructional Strategies Engagement & Interactivity Use of Analogy & Comparative Illustrations ChatGPT Detail Depth & Content Recommendation Meta-discussion & Perspective Framing 2. strengths lie predominantly in and , making learning feel like an interactive dialogue. Offering straightforward explanations, it leans heavily on the especially when building upon user insights. While its provide clarity, the depth can vary, catering to concise and elaborate queries. penchant for emphasises the broader significance of concepts, enhancing the overall learning experience. Claude's Personalization & Personable Touch Engagement & Interactivity Use of Analogy & Comparative Illustrations, Instructional Strategies Claude's Meta-discussion & Perspective Framing 3. responses resemble a classic approach, akin to a conventional classroom style. Focusing on factual and structured presentations, it often employs the to elucidate concepts. is evident, especially when recognising user inputs and providing deeper insights, fostering Engagement & Interactivity. Bard occasionally integrates to highlight broader applications. Bard's Instructional Strategies Use of Analogy & Comparative Illustrations Personalization & Personable Touch Meta-discussion & Perspective Framing 4. emphasises by often directing users to external resources. Adopting a more correctional R approach, it points out inaccuracies in user inputs, which can sometimes overshadow its While provides structured , its reliance on external links might diminish direct Engagement & Interactivity. Its is present, albeit less pronounced. BingChat Detail Depth & Content Recommendation einforcement & Feedback Personalization & Personable Touch. BingChat Instructional Strategies Use of Analogy & Comparative Illustrations This ranking is based on the analysis presented earlier. It provides a comparative grade for each AI across different categories, but the specific grading might be subjective and could vary with different questions, contexts or even across sessions. The preference might also be subjective depending on individual learning styles and needs. Using GenAIbots as educational tools, teachers might create more engaging and personalised learning experiences. This approach could potentially support a deeper understanding of complex concepts in Chemistry and perhaps other subjects. This paper is under CC BY-SA 4.0 DEED license. available on arxiv

Analyzing the Role of AI Chatbots in Active and Personalized Education

সক্রিয় এবং ব্যক্তিগতকৃত শিক্ষায় এআই চ্যাটবটগুলির ভূমিকা বিশ্লেষণ করা

Analyse der Rolle von KI-Chatbots in aktiver und personalisierter Bildung

Análisis del papel de los chatbots de IA en la educación activa y personalizada

Analyser le rôle des chatbots IA dans l'éducation active et personnalisée

सक्रिय और व्यक्तिगत शिक्षा में एआई चैटबॉट्स की भूमिका का विश्लेषण

アクティブでパーソナライズされた教育における AI チャットボットの役割の分析

능동적이고 개인화된 교육에서 AI 챗봇의 역할 분석

Analisando o papel dos chatbots de IA na educação ativa e personalizada

Анализ роли чат-ботов с искусственным интеллектом в активном и персонализированном образовании

Aktif ve Kişiselleştirilmiş Eğitimde Yapay Zeka Chatbotlarının Rolünün Analizi

Phân tích vai trò của Chatbot AI trong giáo dục tích cực và cá nhân hóa

分析人工智能聊天机器人在主动和个性化教育中的作用

Is this the end of the Prettier/ESLint era? Prettier/ESLint If you're a developer, you're surely familiar with Prettier and ESLint. With over eight years of existence, they have established themselves as references in the JavaScript ecosystem. Prettier, as a code formatter, has not only made coding easier by enforcing stylistic uniformity but has also put an end to countless debates over coding standards within teams. By automating and standardizing code formatting, Prettier allows one to focus on logic and features rather than the aesthetics of the code. On the other hand, ESLint is a powerful JS linting tool. Designed to identify and report syntax errors, style issues, and bad practices in the code, it thus helps to maintain quality standards and consistency within projects. Prettier bounty 🏆 Recently, Christopher Chedeau (Vjeux), the creator of Prettier and co-creator of React Native, promised a $10,000 bonus for any Rust project that passes more than 95% of the JS Prettier test suite. Why this competition? In practice, Prettier has been the dominant code formatter for JavaScript, and due to a lack of competition, there has been little incentive to push its performance and improve the tool. In reality, for many developers in recent years, Prettier has been the default JS code formatter. This overwhelming market dominance resulted in a lack of competition, leading, in turn, to a loss of interest among maintainers in improving the tool's performance. By funding and then highlighting a competitor written in Rust, capable of passing the majority of its JS test suite, Prettier thus recreated this healthy competition that will potentially allow it to challenge the details of its current implementation. Interested in the initiative as well as the outcome of this bounty hunt, many tech players like Guillermo Rauch, CEO of Vercel, have increased the reward to a total of $22,500. From this competition, one project emerged victorious: Biome.js 🎉 Biome.js, one tool to rule them all Biome is a fork of Rome, which was originally an ambitious tool written in Rust but abandoned in October 2023. It includes both a linter and a formatter, putting an end to the time-consuming difficulties associated with reconciling ESLint and Prettier rules. On paper, Biome.js offers several advantages: Better performance: according to their benchmarks, it boasts a formatting speed that surpasses Prettier by 25 times and dethrones ESLint by 15 times on a MacBook Pro M1.
Simplified installation: only one dependency to install, replacing ESLint, Prettier, and several plugins necessary for their proper functioning.
A single configuration file for both formatting and linting.
Native TypeScript support. Should we migrate now? If this tool is so powerful, why hasn't it been universally adopted yet? After several weeks of use, I've identified some edge cases that might hinder its adoption: Not yet complete support for CSS, Vue, Svelte, or Astro.
Tailwind class sorting is still in beta.
No "group sorting" in import sorting yet.
No plugins (addition planned in the roadmap).
Still some bugs in VSCode. To conclude my opinion, I believe it's preferable to continue using ESLint + Prettier for now because their wider support and massive adoption by the industry give them a lead that's still too significant. However, Biome.js is a very promising project that should be kept an eye on, as its evolution could potentially lead it to replace these two pillars of the JS ecosystem, which are Prettier and ESLint, one day. 🧐 How to use it? For those curious to still try out this tool, simply run this command in your project: npx @biomejs/biome init It will install all the necessary dependencies in your project and create a biome.jsonc file, which, similar to a .prettierrc or .eslintrc, will allow you to configure your project. If you want to fully take advantage of Biome.js in VSCode, you can install the Biome extension. And if you're using a different IDE, take a look at the documentation. Thanks to Alex Mongeot for helping me write this article. ❤️ Is this the end of the Prettier/ESLint era? Prettier/ESLint If you're a developer, you're surely familiar with Prettier and ESLint . With over eight years of existence, they have established themselves as references in the JavaScript ecosystem. Prettier ESLint Prettier, as a code formatter, has not only made coding easier by enforcing stylistic uniformity but has also put an end to countless debates over coding standards within teams. By automating and standardizing code formatting, Prettier allows one to focus on logic and features rather than the aesthetics of the code. On the other hand, ESLint is a powerful JS linting tool. Designed to identify and report syntax errors, style issues, and bad practices in the code, it thus helps to maintain quality standards and consistency within projects. Prettier bounty 🏆 Recently, Christopher Chedeau (Vjeux), the creator of Prettier and co-creator of React Native, promised a $10,000 bonus for any Rust project that passes more than 95% of the JS Prettier test suite. Christopher Chedeau Why this competition? In practice, Prettier has been the dominant code formatter for JavaScript, and due to a lack of competition, there has been little incentive to push its performance and improve the tool. In reality, for many developers in recent years, Prettier has been the default JS code formatter. This overwhelming market dominance resulted in a lack of competition, leading, in turn, to a loss of interest among maintainers in improving the tool's performance. By funding and then highlighting a competitor written in Rust, capable of passing the majority of its JS test suite, Prettier thus recreated this healthy competition that will potentially allow it to challenge the details of its current implementation. Interested in the initiative as well as the outcome of this bounty hunt, many tech players like Guillermo Rauch , CEO of Vercel, have increased the reward to a total of $22,500. Guillermo Rauch From this competition, one project emerged victorious: Biome.js 🎉 Biome.js Biome.js, one tool to rule them all one tool to rule them all Biome is a fork of Rome , which was originally an ambitious tool written in Rust but abandoned in October 2023. It includes both a linter and a formatter, putting an end to the time-consuming difficulties associated with reconciling ESLint and Prettier rules. Rome On paper, Biome.js offers several advantages: Better performance: according to their benchmarks, it boasts a formatting speed that surpasses Prettier by 25 times and dethrones ESLint by 15 times on a MacBook Pro M1. Simplified installation: only one dependency to install, replacing ESLint, Prettier, and several plugins necessary for their proper functioning. A single configuration file for both formatting and linting. Native TypeScript support. Better performance: according to their benchmarks , it boasts a formatting speed that surpasses Prettier by 25 times and dethrones ESLint by 15 times on a MacBook Pro M1. benchmarks Simplified installation: only one dependency to install, replacing ESLint, Prettier, and several plugins necessary for their proper functioning. A single configuration file for both formatting and linting. Native TypeScript support. Should we migrate now? If this tool is so powerful, why hasn't it been universally adopted yet? After several weeks of use, I've identified some edge cases that might hinder its adoption: Not yet complete support for CSS, Vue, Svelte, or Astro. Tailwind class sorting is still in beta. No "group sorting" in import sorting yet. No plugins (addition planned in the roadmap). Still some bugs in VSCode. Not yet complete support for CSS, Vue, Svelte, or Astro. Not yet complete support Tailwind class sorting is still in beta. Tailwind class sorting No "group sorting" in import sorting yet. import sorting No plugins (addition planned in the roadmap ). roadmap Still some bugs in VSCode. To conclude my opinion, I believe it's preferable to continue using ESLint + Prettier for now because their wider support and massive adoption by the industry give them a lead that's still too significant. However, Biome.js is a very promising project that should be kept an eye on, as its evolution could potentially lead it to replace these two pillars of the JS ecosystem, which are Prettier and ESLint, one day. 🧐 How to use it? For those curious to still try out this tool, simply run this command in your project: npx @biomejs/biome init npx @biomejs/biome init It will install all the necessary dependencies in your project and create a biome.jsonc file, which, similar to a .prettierrc or .eslintrc , will allow you to configure your project. biome.jsonc .prettierrc .eslintrc If you want to fully take advantage of Biome.js in VSCode, you can install the Biome extension . And if you're using a different IDE, take a look at the documentation. Biome extension Thanks to Alex Mongeot for helping me write this article. ❤️ Thanks to Alex Mongeot for helping me write this article. ❤️ Alex Mongeot

Biome.js: The New Challenger to Prettier and ESLint

Design Systems: A Front-End Engineer's Guide to Efficient Atomic Design

Unleashing the Hacker Spirit - by David Solé Unleashing the Hacker Spirit A Journey from School to a World of Possibilities Growing up in Catalonia, Spain, during the 1990s and 2000s, my experience with school was quite traditional. Private school meant attending classes, sitting in your chair, listening to the teacher, doing exercises, and answering questions. The routine continued with homework once classes ended, intensifying as exams entered the picture. If you flunked a subject, it meant repeating the course the following year. I repeated two years at ages 16 and 18, stuck in a system that didn't resonate with me. During my educational journey, I realized the disconnect between this traditional approach and real-world application. Memorizing historical events or philosophical concepts seemed disconnected from reality. This rote memorization without interest, didn’t last beyond exams. At some point, I adopted the law of least effort, doing the minimum to pass while spending the least amount of time. This attitude channeled my interest in video games—an engaging realm where you can enjoy, face challenges, discover, and explore exciting worlds. Unfortunately, this approach, combined with a lack of motivation, led to my first significant failure. Education, as a societal tool, aims to create a stable society by equipping individuals with basic skills. However, this scalable process focuses more on control than on increasing children’s motivation or curiosity, hoping to shape a workforce that sustains the economic environment. Public education gained importance only 200 years ago, yet in 2023, only 87% of children complete primary education, indicating we are still in the early stages of educational evolution. The Internet, a significant catalyst for change, has connected over 5.3 billion people globally as of October 2023, 65% of the global population. While this marks substantial progress, there is much work ahead to ensure universal connectivity and the ability to learn online. We are still in the early stages of the internet's potential. Change of Attitude My perspective changed when I entered university to study Network Engineering. The freedom to choose my path fueled my motivation, and there, I discovered that I like programming—it opens a whole world of possibilities to create. I consider myself lucky to have found something that motivates and impassions me. I know people who haven’t found their passion yet, and this is something education should focus on. About halfway through my university life, I shifted my attitude from the law of least effort to “learn as much as you can.” I got rid of the lazy attitude by looking at my life as if I were an external observer. If an effort doesn’t kill you and you are doing what you want, it doesn’t matter how big this effort is because you will be enjoying the path. This “learn as much as you can” attitude makes sense when you desire to build something yourself. The learnings you do today will be the tools you will use in your future. I also got inspired by Robert T. Kiyosaki and his book “Rich Dad Poor Dad.” It wasn’t easy at first, but when you’re in the dynamic, things start to fall into place. Remember, your mind is your greatest asset, so be careful what you put into it. —Robert T. Kiyosaki In my uni days, I found alignment with the hacker attitude outlined by Eric S. Raymond in 1997. The five principles he shared continue to resonate: The world is full of fascinating problems waiting to be solved.


Nobody should ever have to solve a problem twice.


Boredom and drudgery are evil.


Freedom is good.


Attitude is no substitute for competence. These principles guide my journey and remain relevant in today's context. The hacker mindset, rooted in curiosity, problem-solving, and the desire to learn more, inspires a lifelong learning journey—one that transcends traditional educational boundaries. Thanks for reading David's Indie Hacker Journey! Subscribe for free to receive new posts and support my work. Unleashing the Hacker Spirit - by David Solé Unleashing the Hacker Spirit - by David Solé Unleashing the Hacker Spirit A Journey from School to a World of Possibilities Growing up in Catalonia, Spain, during the 1990s and 2000s, my experience with school was quite traditional. Private school meant attending classes, sitting in your chair, listening to the teacher, doing exercises, and answering questions. The routine continued with homework once classes ended, intensifying as exams entered the picture. If you flunked a subject, it meant repeating the course the following year. I repeated two years at ages 16 and 18, stuck in a system that didn't resonate with me. During my educational journey, I realized the disconnect between this traditional approach and real-world application. Memorizing historical events or philosophical concepts seemed disconnected from reality. This rote memorization without interest, didn’t last beyond exams. At some point, I adopted the law of least effort, doing the minimum to pass while spending the least amount of time. This attitude channeled my interest in video games—an engaging realm where you can enjoy, face challenges, discover, and explore exciting worlds. Unfortunately, this approach, combined with a lack of motivation, led to my first significant failure. Education, as a societal tool, aims to create a stable society by equipping individuals with basic skills . However, this scalable process focuses more on control than on increasing children’s motivation or curiosity, hoping to shape a workforce that sustains the economic environment. aims to create a stable society by equipping individuals with basic skills Public education gained importance only 200 years ago , yet in 2023, only 87% of children complete primary education , indicating we are still in the early stages of educational evolution. only 200 years ago only 87% of children complete primary education The Internet, a significant catalyst for change, has connected over 5.3 billion people globally as of October 2023 , 65% of the global population. While this marks substantial progress, there is much work ahead to ensure universal connectivity and the ability to learn online. over 5.3 billion people globally as of October 2023 We are still in the early stages of the internet's potential. Change of Attitude My perspective changed when I entered university to study Network Engineering. The freedom to choose my path fueled my motivation, and there, I discovered that I like programming—it opens a whole world of possibilities to create. I consider myself lucky to have found something that motivates and impassions me. I know people who haven’t found their passion yet, and this is something education should focus on. About halfway through my university life, I shifted my attitude from the law of least effort to “learn as much as you can.” I got rid of the lazy attitude by looking at my life as if I were an external observer. If an effort doesn’t kill you and you are doing what you want, it doesn’t matter how big this effort is because you will be enjoying the path. This “learn as much as you can” attitude makes sense when you desire to build something yourself. The learnings you do today will be the tools you will use in your future. I also got inspired by Robert T. Kiyosaki and his book “Rich Dad Poor Dad.” It wasn’t easy at first, but when you’re in the dynamic, things start to fall into place. Remember, your mind is your greatest asset, so be careful what you put into it. Remember, your mind is your greatest asset, so be careful what you put into it. —Robert T. Kiyosaki —Robert T. Kiyosaki In my uni days, I found alignment with the hacker attitude outlined by Eric S. Raymond in 1997 . The five principles he shared continue to resonate: hacker attitude Eric S. Raymond 1997 The world is full of fascinating problems waiting to be solved. Nobody should ever have to solve a problem twice. Boredom and drudgery are evil. Freedom is good. Attitude is no substitute for competence. The world is full of fascinating problems waiting to be solved. The world is full of fascinating problems waiting to be solved. Nobody should ever have to solve a problem twice. Nobody should ever have to solve a problem twice. Boredom and drudgery are evil. Boredom and drudgery are evil. Freedom is good. Freedom is good. Attitude is no substitute for competence. Attitude is no substitute for competence. These principles guide my journey and remain relevant in today's context. The hacker mindset, rooted in curiosity, problem-solving, and the desire to learn more, inspires a lifelong learning journey—one that transcends traditional educational boundaries. Thanks for reading David's Indie Hacker Journey! Subscribe for free to receive new posts and support my work.

How to Unleash the Hacker Spirit

Did you expect Google's search results to be so inconsistent? As a contributing editor immersed in the harsh content world since 2009, I've watched how technology has transformed our relationship with information. However, I was taken aback when I witnessed the puzzling discrepancies in how they handled certain content. Here's the situation I found worth an exploration: Fresherslive.com had an AI-generated article explaining how to resolve a PS5 error code. Despite it reading poorly, readers were likely grateful for the clear instructions - nobody enjoys gaming sessions interrupted by console issues. However, in a baffling move, Google completely deindexed that site from their search results without warning in March. Unfortunately, in their attempts to rectify the situation, the website owners deleted all the pages that were older than a couple of days, so it’s now impossible to provide a source link. On the flip side, they allowed an article from The Verge about printers to rank highly despite being rather light on substantive details. One has to wonder about Google's rationale here. Doesn't this arbitrary content treatment raise concerns about fairness and consistency in their evaluation process? Driven by curiosity about the mysterious workings of search engine rankings, I decided to ask the one who could answer my questions: Gemini, Google's analytical counterpart. Our dialogue about these two very different articles turned out to be very interesting and triggered some valuable conclusions. Chapter 1. Gemini's initial assessment I approached Gemini with an initial request to assess the quality of two distinct articles. Both of them are valueless and miserable from a human’s perspective, yet they’re quite different in terms of ranking. Gemini, exuding a sense of algorithmic confidence, praised the Fresherslive piece for its organized approach and practical utility. On the other hand, The Verge's article, though engaging, was criticized for barely scratching the surface of its subject. Gemini's analysis was enlightening yet somewhat disappointing. The significant difference in search visibility between two articles of such varying quality suggested an inconsistency in the algorithm's ability to prioritize content that truly serves user needs. Chapter 2. The issue of indexing discrepancies I decided to spice things up and disclosed that the PS5 guide had disappeared from search results after the March update, while The Verge’s piece still enjoyed high visibility. Seeking clarity, I pressed Gemini for an explanation of this apparent editorial bias. Gemini easily found what to say and provided reasons like broader appeal and recent updates that supposedly favored The Verge's content in the algorithm's decisions. Despite the practical value of the Fresherslive article, its niche focus might have limited its broader appeal: While the explanation made sense from a technical standpoint, it provided little solace. It highlighted a concerning trend where depth and utility might be overlooked in favor of broader appeal and novelty—an unsettling thought for someone with my editorial values. Chapter 3. Editorial perspective and quality critique I then questioned the quality of The Verge's article. I then questioned the quality of The Verge's article. As for a human, it’s obvious to me that both of these articles prioritize SEO over genuine consumer guidance. I challenged Gemini to reconcile this with Google's advertised quality standards about the still-ranked article: Gemini acknowledged its limitations (how suitable!), admitting that while it aims for accuracy, discerning content quality like a human editor remains a challenging area. This admission was revealing, showing the complex and sometimes flawed ways in which algorithms interpret and prioritize content. Chapter 4. Challenge on randomness in content selection I also challenged the randomness with which The Verge seemed to recommend products, arguing that such practices could undermine user trust in content meant to guide purchasing decisions. I asked Gemini how it intended to address these search engine ranking decisions: Accidentally but quite expectedly, Gemini shifted focus, offering advice on detecting and dealing with editorial manipulations. Though informative, this response avoided addressing the core issue of algorithmic accountability I was probing. Chapter 5. My opinion on how Google ranks web pages and Gemini’s final take on these systemic issues I’ve also provided Gemini with a detailed review of The Verge’s article under Google’s Content Quality Guidelines. This review was conducted by my custom GPT and also contains a short discussion of my concerns: — Covering various aspects of usage in the content, as well as stating the experience, was considered as the main proof of personal experience. — The author's reputation and his role as editor-in-chief of The Verge were considered as the main proof of his expertise. — Both The Verge's reputation and balanced viewpoints on their strengths and weaknesses were considered the two main proofs of high authority. — Highlighting the model’s long-term reliability and stating the personal experience were both the main reasons for a high trustworthiness, which masked the absence of clear selection criteria. After that, I formed a conclusive opinion that emerged based on our whole discussion: By publishing the trusted media and under the name of a credible enough author (at least with several valuable previous publications), you can promote specific goods and services as the best-in-class, lying to efficiently mask your commercial interest and other important aspects. This opinion is still unchanged, despite Gemini’s weak attempt to soften it at least a bit: You can find a full discussion here. Conclusion In conversation with Gemini, my concerns about algorithmic bias in Google Search ranking were virtually confirmed. Its weak attempts to justify the inconsistencies were unconvincing, leaving a troubling sense of déjà vu. It feels as though cheating Google's search ranking has become concerningly easy, reminiscent of the SEO manipulation tactics prevalent in the late 1990s. Disappointingly, I see no clear indication from Google that they intend to address this situation definitively. While there may be some initial efforts (like algorithm updates), these seem like mere stop-gap measures; the core issues of algorithmic bias and search engine ranking manipulation likely remain. The fight for trustworthy information online appears far from over… Did you expect Google's search results to be so inconsistent? As a contributing editor immersed in the harsh content world since 2009, I've watched how technology has transformed our relationship with information. However, I was taken aback when I witnessed the puzzling discrepancies in how they handled certain content. Here's the situation I found worth an exploration: Fresherslive.com had an AI-generated article explaining how to resolve a PS5 error code. Despite it reading poorly, readers were likely grateful for the clear instructions - nobody enjoys gaming sessions interrupted by console issues. However, in a baffling move, Google completely deindexed that site from their search results without warning in March. Unfortunately, in their attempts to rectify the situation, the website owners deleted all the pages that were older than a couple of days, so it’s now impossible to provide a source link. Fresherslive.com On the flip side, they allowed an article from The Verge about printers to rank highly despite being rather light on substantive details. One has to wonder about Google's rationale here. an article from The Verge about printers an article from The Verge about printers Doesn't this arbitrary content treatment raise concerns about fairness and consistency in their evaluation process? Driven by curiosity about the mysterious workings of search engine rankings, I decided to ask the one who could answer my questions: Gemini, Google's analytical counterpart. Our dialogue about these two very different articles turned out to be very interesting and triggered some valuable conclusions. Chapter 1. Gemini's initial assessment I approached Gemini with an initial request to assess the quality of two distinct articles. Both of them are valueless and miserable from a human’s perspective, yet they’re quite different in terms of ranking. Gemini, exuding a sense of algorithmic confidence, praised the Fresherslive piece for its organized approach and practical utility. On the other hand, The Verge's article, though engaging, was criticized for barely scratching the surface of its subject. Gemini's analysis was enlightening yet somewhat disappointing. The significant difference in search visibility between two articles of such varying quality suggested an inconsistency in the algorithm's ability to prioritize content that truly serves user needs. Chapter 2. The issue of indexing discrepancies I decided to spice things up and disclosed that the PS5 guide had disappeared from search results after the March update, while The Verge’s piece still enjoyed high visibility. Seeking clarity, I pressed Gemini for an explanation of this apparent editorial bias. Gemini easily found what to say and provided reasons like broader appeal and recent updates that supposedly favored The Verge's content in the algorithm's decisions. Despite the practical value of the Fresherslive article, its niche focus might have limited its broader appeal: While the explanation made sense from a technical standpoint, it provided little solace. It highlighted a concerning trend where depth and utility might be overlooked in favor of broader appeal and novelty—an unsettling thought for someone with my editorial values. Chapter 3. Editorial perspective and quality critique I then questioned the quality of The Verge's article. I then questioned the quality of The Verge's article. As for a human, it’s obvious to me that both of these articles prioritize SEO over genuine consumer guidance. I challenged Gemini to reconcile this with Google's advertised quality standards about the still-ranked article: Gemini acknowledged its limitations (how suitable!), admitting that while it aims for accuracy, discerning content quality like a human editor remains a challenging area. This admission was revealing, showing the complex and sometimes flawed ways in which algorithms interpret and prioritize content. Chapter 4. Challenge on randomness in content selection I also challenged the randomness with which The Verge seemed to recommend products, arguing that such practices could undermine user trust in content meant to guide purchasing decisions. I asked Gemini how it intended to address these search engine ranking decisions: Accidentally but quite expectedly, Gemini shifted focus, offering advice on detecting and dealing with editorial manipulations. Though informative, this response avoided addressing the core issue of algorithmic accountability I was probing. Chapter 5. My opinion on how Google ranks web pages and Gemini’s final take on these systemic issues I’ve also provided Gemini with a detailed review of The Verge’s article under Google’s Content Quality Guidelines . This review was conducted by my custom GPT and also contains a short discussion of my concerns: Google’s Content Quality Guidelines Google’s Content Quality Guidelines — Covering various aspects of usage in the content, as well as stating the experience, was considered as the main proof of personal experience. — Covering various aspects of usage in the content, as well as stating the experience, was considered as the main proof of personal experience. — Covering various aspects of usage in the content, as well as stating the experience, was considered as the main proof of personal experience. — The author's reputation and his role as editor-in-chief of The Verge were considered as the main proof of his expertise. — The author's reputation and his role as editor-in-chief of The Verge were considered as the main proof of his expertise. — The author's reputation and his role as editor-in-chief of The Verge were considered as the main proof of his expertise. — Both The Verge's reputation and balanced viewpoints on their strengths and weaknesses were considered the two main proofs of high authority. — Both The Verge's reputation and balanced viewpoints on their strengths and weaknesses were considered the two main proofs of high authority. — Both The Verge's reputation and balanced viewpoints on their strengths and weaknesses were considered the two main proofs of high authority. — Highlighting the model’s long-term reliability and stating the personal experience were both the main reasons for a high trustworthiness, which masked the absence of clear selection criteria. — Highlighting the model’s long-term reliability and stating the personal experience were both the main reasons for a high trustworthiness, which masked the absence of clear selection criteria. — Highlighting the model’s long-term reliability and stating the personal experience were both the main reasons for a high trustworthiness, which masked the absence of clear selection criteria. After that, I formed a conclusive opinion that emerged based on our whole discussion: By publishing the trusted media and under the name of a credible enough author (at least with several valuable previous publications), you can promote specific goods and services as the best-in-class, lying to efficiently mask your commercial interest and other important aspects. By publishing the trusted media and under the name of a credible enough author (at least with several valuable previous publications), you can promote specific goods and services as the best-in-class, lying to efficiently mask your commercial interest and other important aspects. By publishing the trusted media and under the name of a credible enough author (at least with several valuable previous publications), you can promote specific goods and services as the best-in-class, lying to efficiently mask your commercial interest and other important aspects. This opinion is still unchanged, despite Gemini’s weak attempt to soften it at least a bit: You can find a full discussion here . here here Conclusion In conversation with Gemini, my concerns about algorithmic bias in Google Search ranking were virtually confirmed. Its weak attempts to justify the inconsistencies were unconvincing, leaving a troubling sense of déjà vu. It feels as though cheating Google's search ranking has become concerningly easy, reminiscent of the SEO manipulation tactics prevalent in the late 1990s. Disappointingly, I see no clear indication from Google that they intend to address this situation definitively. While there may be some initial efforts (like algorithm updates), these seem like mere stop-gap measures; the core issues of algorithmic bias and search engine ranking manipulation likely remain. The fight for trustworthy information online appears far from over…

Is Google's Search Ranking and AI Bias a Total Disaster? Let's See What Google Gemini Thinks

গুগলের অনুসন্ধান র‌্যাঙ্কিং এবং এআই পক্ষপাত কি একটি মোট বিপর্যয়? আসুন দেখুন গুগল মিথুন কি মনে করে

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