ShinChvenModify

2025-07-30 23:41:50

Earnings calls and financial reports are crucial for investors and analysts to assess a company's performance. Here’s where you can find the latest earnings calls and reports for major tech companies:

2025-07-10 01:41:43

If you're using ComfyUI on a Mac to create awesome AI images, you might have hit a wall when trying to make them really big. Specifically, if you try to generate an image larger than 1920x1920 pixels, you've probably seen your process crash right at the end with a weird error message.

It's a frustrating problem, but don't worry, there's a simple fix!

The Problem: A "failed assertion" Error

When ComfyUI is almost done generating your large image, it suddenly stops and you see an error message in your terminal that looks something like this:

/AppleInternal/Library/BuildRoots/.../MPSNDArray.mm:829: failed assertion \`... Error: NDArray dimension length \> INT\_MAX'

In simple terms, this is a bug in a recent version of PyTorch, the library ComfyUI uses for its AI magic on Apple Silicon (M1, M2, M3 chips). The part of PyTorch that handles image data on the Mac's graphics chip (Metal Performance Shaders or MPS) can't handle the dimensions of your super-sized image.

The Solution: Downgrade Your PyTorch Version

The good news is you can fix this by going back to a slightly older, more stable version of PyTorch. The key is to install version 2.4.0.

Here's how to do it:

pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0

This command tells Python's package manager, pip, to uninstall your current PyTorch and install the specific versions that are known to work without this bug.

After the installation is complete, restart ComfyUI, and you should now be able to generate those huge images without any crashes. 🎉

Tested and Confirmed

I've personally tested this fix on an M1 Max with 64GB of RAM, successfully generating 3000x3000 pixel images without any crashes. Before the PyTorch downgrade, ComfyUI would consistently crash when attempting to generate images at this resolution. After installing PyTorch 2.4.0, the same workflows completed successfully.

Need a Different Version?

This bug is related to specific versions of PyTorch. If you need to find other previous versions for any reason, the official PyTorch website has a helpful archive.

• Check out older PyTorch versions here: https://pytorch.org/get-started/previous-versions/

For those interested in the technical details, you can follow the bug reports on GitHub:

• PyTorch Issue #146769
• ComfyUI Issue #7882

Happy generating!

2025-07-06 11:21:12

In the world of machine learning, one of the most fundamental decisions a data scientist faces is the choice between a simple and a complex model. It's a classic trade-off: the elegant transparency of a simple model versus the raw power of a complex one. There's no single right answer, and the best choice depends entirely on your specific problem, your data, and your goals.

So, how do you pick your champion? Let's break it down.

The Case for Simplicity: When Less is More 🧘

Simple machine learning models, like linear regression, logistic regression, and decision trees, are the bedrock of the field for a reason. Their strength lies in their transparency and efficiency.

Why choose a simple model?

• Interpretability is King: This is perhaps the biggest advantage. Simple models are not "black boxes." You can easily understand how they arrive at their predictions. In fields like finance or healthcare, where explaining the "why" is just as important as the "what," this is non-negotiable.
• Speed and Efficiency: Simple models are computationally light. They train faster and require fewer resources, making them ideal for situations with tight time constraints or limited hardware.
• Reduced Risk of Overfitting (Especially with Small Data): When you have a small dataset, complex models can easily overfit. This means they learn the noise in your training data instead of the underlying signal, leading to poor performance on new data. Simple models are less prone to this trap.
• A Great Baseline: Always start with a simple model. It provides a baseline performance metric that any more complex model must beat to justify its added complexity.

The Power of Complexity: Taming Intricate Data 🚀

Complex models, such as deep neural networks, gradient boosting machines, and random forests, are the heavyweights of machine learning. They are designed to tackle intricate patterns in massive datasets.

When should you unleash a complex model?

• When Accuracy is Paramount: In applications like image recognition, natural language processing, or autonomous driving, a fraction of a percent in accuracy can make a world of difference. Complex models excel at wringing out every last drop of predictive power from the data.
• You Have a Ton of Data: These models shine when fed large and high-dimensional datasets. They have the capacity to learn from millions of examples and uncover subtle, non-linear relationships that simpler models would miss.
• The "Black Box" is Acceptable: If the end result is all that matters, and you don't need a step-by-step explanation of the model's reasoning, then the trade-off for higher accuracy might be worth it.

The Trade-Off: A Balancing Act ⚖️

Here's a quick look at how these two approaches stack up:

Making the Right Choice: Key Questions to Ask 🤔

Before you commit to a model, ask yourself these questions:

1. What is the business problem? Do you need to explain the predictions to a non-technical audience? If so, lean towards a simpler model.
2. How much data do you have? If your dataset is small, start simple to avoid overfitting.
3. What is your primary goal? Is it accuracy above all else, or is interpretability a key requirement?
4. What are your resource constraints? Do you have the time and computational power to train a complex model?

Conclusion: It's All About the Context

The "simple vs. complex" debate doesn't have a universal winner. The best data scientists have a deep understanding of both and know when to deploy each. The journey often starts with a simple model to understand the data and establish a baseline. Then, if the problem demands it and the resources allow, you can explore more complex solutions.

So, the next time you're faced with this choice, remember to think critically about your project's unique needs. The right model is the one that best serves your purpose.

2025-06-28 02:29:48

Embarking on a machine learning project can feel like navigating a complex maze. Based on a typical university assignment structure, this guide breaks down the process into a clear, repeatable workflow. Whether you're a student or a budding data scientist, you can use this framework for any supervised learning task.

Phase 1: Project Setup and Data Understanding

The foundation of any successful ML project is a thorough understanding of the problem and the data. Don't rush this phase!

1. Define the Goal

First, look at your target variable. Is it a continuous number (like a price) or a distinct category (like a type of flower)?

• Regression: Predicting a continuous value (e.g., house prices).
• Classification: Predicting a discrete label (e.g., wine quality class).

2. Load and Explore the Data

Get your hands dirty with the dataset.

• Load the data: Use libraries like pandas to load your data into a DataFrame.
• Initial exploration: Ask these key questions:
• How many samples (rows) and features (columns) are there?
• What are the names of the features?
• For classification, how many classes are there and are they balanced?

3. Separate Features and Target

Split your DataFrame into two distinct entities:

• X: The feature matrix (your input variables).
• y: The target vector (what you want to predict).

Phase 2: Model Development and Training

With your data prepared, it's time to start building and training your models.

1. Split the Dataset

You need to evaluate your model on data it has never seen before.

• Action: Split your X and y into training and testing sets. A common split is 80% of the data for training and the remaining 20% for testing. scikit-learn's train_test_split function is perfect for this.

2. Select and Train Models

Choose a few different algorithms to see which performs best. For a standard supervised learning task, good starting points are:

• Linear/Logistic Regression
• Decision Trees
• Random Forest
• Simple Neural Networks

Train each of these models using the .fit() method on your training data (Xtrain, ytrain).

Phase 3: Evaluation and Analysis

A trained model is useless until you know how well it performs. This is where you critically assess your work.

1. Make Predictions

Use your trained models to make predictions on the testing data (X_test).

2. Evaluate Performance

Use standard metrics to score your models.

• For Regression: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared.
• For Classification:
• Accuracy: The simplest metric, but can be misleading with unbalanced datasets.
• Confusion Matrix: A powerful tool to see where your model is getting confused (e.g., which classes it mislabels).
• Classification Report: A comprehensive report from scikit-learn that includes precision, recall, and f1-score for each class.

3. Compare and Discuss

Analyze the evaluation metrics.

• Which model had the highest accuracy or the lowest error?
• Did one model perform particularly well for a specific class?
• Justify your choice of the "best" model using the data from your evaluation.

Phase 4: Deeper Insights and Optimization

Go beyond the basics to refine your model and understand your data more deeply.

1. Find Important Features

For many models (like Random Forest), you can extract feature importances. This tells you which input variables had the most impact on the prediction. This is incredibly valuable for understanding the underlying problem.

2. Optimize Your Best Model

Try to squeeze more performance out of your best-performing model.

• Hyperparameter Tuning: Use techniques like GridSearchCV or RandomizedSearchCV to find the optimal settings for your model.
• Feature Preprocessing: Experiment with techniques like normalization or standardization (StandardScaler) on your features to see if it improves model accuracy.

By following these four phases, you create a structured and comprehensive approach to any machine learning project, ensuring you cover all the critical steps from start to finish.

2025-06-22 07:27:35

Buying Ridley Scott's definitive version of Kingdom of Heaven in 4K on iTunes isn't as obvious as it should be. The storefront shows the standard theatrical cut in HD by default, and there's no separate listing labeled "Director's Cut 4K." Here's the quick path to the version you actually want.

Why This Matters

The Director's Cut restores nearly 45 minutes of material and is the only 4K rendition currently available on iTunes. If you simply hit "Buy" on the main page, you'll end up with the shorter HD theatrical cut. Follow the steps below to unlock the full‑length 4K edition.

Step‑by‑Step Guide

1. Open the Apple TV app (or iTunes on Windows) and make sure you're signed in with the Apple ID you'll use for the purchase.
2. Search for "Kingdom of Heaven." Select the movie result featuring Orlando Bloom in the teal‑blue poster art.
3. On the movie's detail page, you'll notice the runtime listed as ~144 minutes (the theatrical cut) and the format as HD—nothing about 4K yet. Don't worry.
4. Scroll down until you find the "How to Watch" section. It usually sits below the cast list and related movies.
5. In that section, you'll see a line that says something like "2 Versions." Tap to expand it.
6. Now you'll see two choices:

• Kingdom of Heaven (Theatrical)
• Kingdom of Heaven (Director's Cut)

1. Select the Director's Cut entry and then hit Buy.
2. After purchase, the 4K Director's Cut will appear in your library. Your Apple TV devices will automatically stream or download the 4K Director's Cut when you play it, provided you have a 4K‑capable display and bandwidth.

Extra Tips

• Check your region. Availability can vary; if you don't see the 4K label, the Director's Cut may not be licensed in your country yet.
• Hardware matters. An Apple TV 4K box or a recent 4K‑capable iPhone/iPad/Mac is required to view 4K HDR and Dolby Atmos streams on compatible displays.
• Storage hint. Downloading the film to an iPhone or iPad saves the HD version; iOS still streams 4K only. For true 4K offline viewing, use the Apple TV app on macOS and transfer via AirPlay.
• iTunes Extras available. iTunes Extras is available for this title after you purchased it, providing additional behind-the-scenes content and special features.

Final Word

Apple's storefront doesn't shout about it, but the Director's Cut tucked under "How to Watch" is the version to own—both for its richer cut and for its gorgeous 4K transfer with HDR and Atmos. Follow the steps above and you'll be crossing swords in high resolution in no time. Enjoy the journey to Jerusalem!

2025-06-17 22:32:11

If your GitLab CI pipeline started throwing 403 Forbidden errors when trying to access archive packages using the CI_JOB_TOKEN, you're likely running into a security change that became enforced by default in GitLab 18.

This isn’t a brand-new feature—but now, you’re required to explicitly configure access permissions, or your jobs will fail.

📍 What Changed in GitLab 18?

GitLab has supported the concept of scoped job token permissions since GitLab 15.9. This feature allowed project maintainers to restrict which other projects or groups could access their resources using CI_JOB_TOKEN.

For a while, this behavior was optional or hidden behind feature flags. But as of GitLab 18, the old implicit access is gone. You must now explicitly authorize projects or groups—otherwise, your job token will be denied access by default.

❗ The Symptom

If you're seeing something like this in your CI logs:

error: failed to download package: 403 Forbidden

You're likely trying to fetch a package or archive from another project without having the correct permissions set up under GitLab's updated security model.

✅ How to Fix It

To allow a CI job from one project to access another project’s archive packages using CI_JOB_TOKEN:

1. Go to the target project (the one hosting the archive).
2. Navigate to Settings → CI/CD.
3. Expand the "Job token permissions" section.
4. Under Authorized groups and projects, add the source group or project that needs access.
5. Save changes.

That’s it — this will grant the necessary access for your pipelines to function as expected under GitLab 18.

💡 Tip for DevOps Teams

Review your existing pipelines before upgrading to GitLab 18. If your workflows rely on cross-project access via CI_JOB_TOKEN, make sure those permissions are configured ahead of time to avoid unexpected pipeline failures.

🔁 Summary

• This isn’t a new feature in GitLab 18 — it’s now mandatory.
• You must explicitly authorize job token access in the target project’s settings.
• Failing to do so will result in 403 Forbidden errors when accessing packages.

By updating your project permissions, your CI/CD pipelines will be back on track—and more secure than before.

📚 Sources

• Limit your project’s job token access (removed)