BREAKING NEWS
LATEST POSTS
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Managers’ Guide to Effective Annual Feedback
https://peterszasz.com/engineering-managers-guide-to-effective-annual-feedback
The main goals of a regular, written feedback cycle are:
- Recognition, support for self-reflection and personal growth
- Alignment with team- and company needs
- Documentation
These promote:
- Recognize Achievements: Use the feedback process to boost morale and support self-reflection.
- Align Goals: Ensure individual contributions match company objectives.
- Document Progress: Keep a clear record of performance for future decisions.
- Prepare Feedback: Gather 360-degree feedback, focus on examples, and anticipate reactions.
- Strength-Based Approach: Focus on enhancing strengths over fixing weaknesses.
- Deliver Feedback Live: Engage in discussion before providing written feedback.
- Follow-Up: Use feedback to guide future goals and performance improvement.
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GIL To Become Optional in Python 3.13
GIL or Global Interpreter Lock can be disabled in Python version 3.13. This is currently experimental.
What is GIL? It is a mechanism used by the CPython interpreter to ensure that only one thread executes the Python bytecode at a time.
https://medium.com/@r_bilan/python-3-13-without-the-gil-a-game-changer-for-concurrency-5e035500f0da
Advantages of the GIL
- Simplicity of Implementation: The GIL simplifies memory management in CPython by preventing concurrent access to Python objects, which can help avoid race conditions and other threading issues.
- Ease of Use for Single-Threaded Programs: For applications that are single-threaded, the GIL eliminates the overhead associated with managing thread safety, allowing for straightforward and efficient code execution.
- Compatibility with C Extensions: The GIL allows C extensions to operate without needing to implement complex threading models, which simplifies the development of Python extensions that interface with C libraries.
- Performance for I/O-Bound Tasks: In I/O-bound applications, the GIL does not significantly hinder performance since threads can be switched out during I/O operations, allowing other threads to run.
Disadvantages of the GIL
- Limited Multithreading Performance: The GIL can severely restrict the performance of CPU-bound multithreaded applications, as it only allows one thread to execute Python bytecode at a time, leading to underutilization of multicore processors.
- Thread Management Complexity: Although the GIL simplifies memory management, it can complicate the design of concurrent applications, forcing developers to carefully manage threading issues or use multiprocessing instead.
- Hindrance to Parallel Processing: With the GIL enabled, achieving true parallelism in Python applications is challenging, making it difficult for developers to leverage multicore architectures effectively.
- Inefficiency in Context Switching: Frequent context switching due to the GIL can introduce overhead, especially in applications with many threads, leading to performance degradation.
https://geekpython.in/gil-become-optional-in-python
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Ben Gunsberger – AI generated podcast about AI using Google NotebookLM
Listen to the podcast in the post
“I just created a AI-Generated podcast by feeding an article I write into Google’s NotebookLM. If I hadn’t make it myself, I would have been 100% fooled into thinking it was real people talking.”
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Apple releases Depth Pro – An open source AI model that rewrites the rules of 3D vision
The model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU.
https://github.com/apple/ml-depth-pro
https://arxiv.org/pdf/2410.02073
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Anders Langlands – Render Color Spaces
https://www.colour-science.org/anders-langlands/
This page compares images rendered in Arnold using spectral rendering and different sets of colourspace primaries: Rec.709, Rec.2020, ACES and DCI-P3. The SPD data for the GretagMacbeth Color Checker are the measurements of Noburu Ohta, taken from Mansencal, Mauderer and Parsons (2014) colour-science.org.
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Björn Ottosson – How software gets color wrong
https://bottosson.github.io/posts/colorwrong/
Most software around us today are decent at accurately displaying colors. Processing of colors is another story unfortunately, and is often done badly.
To understand what the problem is, let’s start with an example of three ways of blending green and magenta:
- Perceptual blend – A smooth transition using a model designed to mimic human perception of color. The blending is done so that the perceived brightness and color varies smoothly and evenly.
- Linear blend – A model for blending color based on how light behaves physically. This type of blending can occur in many ways naturally, for example when colors are blended together by focus blur in a camera or when viewing a pattern of two colors at a distance.
- sRGB blend – This is how colors would normally be blended in computer software, using sRGB to represent the colors.
Let’s look at some more examples of blending of colors, to see how these problems surface more practically. The examples use strong colors since then the differences are more pronounced. This is using the same three ways of blending colors as the first example.
Instead of making it as easy as possible to work with color, most software make it unnecessarily hard, by doing image processing with representations not designed for it. Approximating the physical behavior of light with linear RGB models is one easy thing to do, but more work is needed to create image representations tailored for image processing and human perception.
Also see:
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EVER (Exact Volumetric Ellipsoid Rendering) – Gaussian splatting alternative
https://radiancefields.com/how-ever-(exact-volumetric-ellipsoid-rendering)-does-this-work
https://half-potato.gitlab.io/posts/ever/
Unlike previous methods like Gaussian Splatting, EVER leverages ellipsoids instead of Gaussians and uses Ray Tracing instead of Rasterization. This shift eliminates artifacts like popping and blending inconsistencies, offering sharper and more accurate renderings.
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The Rise and Fall of Adobe – The better, alternative software list to a criminal company
Best alternatives to Adobe:
https://github.com/KenneyNL/Adobe-Alternatives
- Affinity (Photo and illustration editing) https://affinity.serif.com/
- DaVinci Resolve (video editing): https://www.blackmagicdesign.com/au/products/davinciresolve/
- Clip Studio Paint (illustration): https://www.clipstudio.net/en/
- Toon Boom (animation): https://www.toonboom.com/
FEATURED POSTS
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MiniMax-01 goes open source
MiniMax is thrilled to announce the release of the MiniMax-01 series, featuring two groundbreaking models:
MiniMax-Text-01: A foundational language model.
MiniMax-VL-01: A visual multi-modal model.Both models are now open-source, paving the way for innovation and accessibility in AI development!
🔑 Key Innovations
1. Lightning Attention Architecture: Combines 7/8 Lightning Attention with 1/8 Softmax Attention, delivering unparalleled performance.
2. Massive Scale with MoE (Mixture of Experts): 456B parameters with 32 experts and 45.9B activated parameters.
3. 4M-Token Context Window: Processes up to 4 million tokens, 20–32x the capacity of leading models, redefining what’s possible in long-context AI applications.💡 Why MiniMax-01 Matters
1. Innovative Architecture for Top-Tier Performance
The MiniMax-01 series introduces the Lightning Attention mechanism, a bold alternative to traditional Transformer architectures, delivering unmatched efficiency and scalability.2. 4M Ultra-Long Context: Ushering in the AI Agent Era
With the ability to handle 4 million tokens, MiniMax-01 is designed to lead the next wave of agent-based applications, where extended context handling and sustained memory are critical.3. Unbeatable Cost-Effectiveness
Through proprietary architectural innovations and infrastructure optimization, we’re offering the most competitive pricing in the industry:
$0.2 per million input tokens
$1.1 per million output tokens🌟 Experience the Future of AI Today
We believe MiniMax-01 is poised to transform AI applications across industries. Whether you’re building next-gen AI agents, tackling ultra-long context tasks, or exploring new frontiers in AI, MiniMax-01 is here to empower your vision.✅ Try it now for free: hailuo.ai
📄 Read the technical paper: filecdn.minimax.chat/_Arxiv_MiniMax_01_Report.pdf
🌐 Learn more: minimaxi.com/en/news/minimax-01-series-2
💡API Platform: intl.minimaxi.com/
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Google – Artificial Intelligence free courses
1. Introduction to Large Language Models: Learn about the use cases and how to enhance the performance of large language models.
https://www.cloudskillsboost.google/course_templates/5392. Introduction to Generative AI: Discover the differences between Generative AI and traditional machine learning methods.
https://www.cloudskillsboost.google/course_templates/5363. Generative AI Fundamentals: Earn a skill badge by demonstrating your understanding of foundational concepts in Generative AI.
https://www.cloudskillsboost.google/paths4. Introduction to Responsible AI: Learn about the importance of Responsible AI and how Google implements it in its products.
https://www.cloudskillsboost.google/course_templates/5545. Encoder-Decoder Architecture: Learn about the encoder-decoder architecture, a critical component of machine learning for sequence-to-sequence tasks.
https://www.cloudskillsboost.google/course_templates/5436. Introduction to Image Generation: Discover diffusion models, a promising family of machine learning models in the image generation space.
https://www.cloudskillsboost.google/course_templates/5417. Transformer Models and BERT Model: Get a comprehensive introduction to the Transformer architecture and the Bidirectional Encoder Representations from the Transformers (BERT) model.
https://www.cloudskillsboost.google/course_templates/5388. Attention Mechanism: Learn about the attention mechanism, which allows neural networks to focus on specific parts of an input sequence.
https://www.cloudskillsboost.google/course_templates/537
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Rec-2020 – TVs new color gamut standard used by Dolby Vision?
https://www.hdrsoft.com/resources/dri.html#bit-depth
The dynamic range is a ratio between the maximum and minimum values of a physical measurement. Its definition depends on what the dynamic range refers to.
For a scene: Dynamic range is the ratio between the brightest and darkest parts of the scene.
For a camera: Dynamic range is the ratio of saturation to noise. More specifically, the ratio of the intensity that just saturates the camera to the intensity that just lifts the camera response one standard deviation above camera noise.
For a display: Dynamic range is the ratio between the maximum and minimum intensities emitted from the screen.
The Dynamic Range of real-world scenes can be quite high — ratios of 100,000:1 are common in the natural world. An HDR (High Dynamic Range) image stores pixel values that span the whole tonal range of real-world scenes. Therefore, an HDR image is encoded in a format that allows the largest range of values, e.g. floating-point values stored with 32 bits per color channel. Another characteristics of an HDR image is that it stores linear values. This means that the value of a pixel from an HDR image is proportional to the amount of light measured by the camera.
For TVs HDR is great, but it’s not the only new TV feature worth discussing.
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