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LATEST POSTS
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Netflix Art Of Nimona digital art book
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Sam Altman – The Intelligence Age
In the next couple of decades, we will be able to do things that would have seemed like magic to our grandparents.
This phenomenon is not new, but it will be newly accelerated. People have become dramatically more capable over time; we can already accomplish things now that our predecessors would have believed to be impossible.
We are more capable not because of genetic change, but because we benefit from the infrastructure of society being way smarter and more capable than any one of us; in an important sense, society itself is a form of advanced intelligence. Our grandparents – and the generations that came before them – built and achieved great things. They contributed to the scaffolding of human progress that we all benefit from. AI will give people tools to solve hard problems and help us add new struts to that scaffolding that we couldn’t have figured out on our own. The story of progress will continue, and our children will be able to do things we can’t.
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The Perils of Technical Debt – Understanding Its Impact on Security, Usability, and Stability
In software development, “technical debt” is a term used to describe the accumulation of shortcuts, suboptimal solutions, and outdated code that occur as developers rush to meet deadlines or prioritize immediate goals over long-term maintainability. While this concept initially seems abstract, its consequences are concrete and can significantly affect the security, usability, and stability of software systems.
The Nature of Technical Debt
Technical debt arises when software engineers choose a less-than-ideal implementation in the interest of saving time or reducing upfront effort. Much like financial debt, these decisions come with an interest rate: over time, the cost of maintaining and updating the system increases, and more effort is required to fix problems that stem from earlier choices. In extreme cases, technical debt can slow development to a crawl, causing future updates or improvements to become far more difficult than they would have been with cleaner, more scalable code.
Impact on Security
One of the most significant threats posed by technical debt is the vulnerability it creates in terms of software security. Outdated code often lacks the latest security patches or is built on legacy systems that are no longer supported. Attackers can exploit these weaknesses, leading to data breaches, ransomware, or other forms of cybercrime. Furthermore, as systems grow more complex and the debt compounds, identifying and fixing vulnerabilities becomes increasingly challenging. Failing to address technical debt leaves an organization exposed to security risks that may only become apparent after a costly incident.
Impact on Usability
Technical debt also affects the user experience. Systems burdened by outdated code often become clunky and slow, leading to poor usability. Engineers may find themselves continuously patching minor issues rather than implementing larger, user-centric improvements. Over time, this results in a product that feels antiquated, is difficult to use, or lacks modern functionality. In a competitive market, poor usability can alienate users, causing a loss of confidence and driving them to alternative products or services.
Impact on Stability
Stability is another critical area impacted by technical debt. As developers add features or make updates to systems weighed down by previous quick fixes, they run the risk of introducing bugs or causing system crashes. The tangled, fragile nature of code laden with technical debt makes troubleshooting difficult and increases the likelihood of cascading failures. Over time, instability in the software can erode both the trust of users and the efficiency of the development team, as more resources are dedicated to resolving recurring issues rather than innovating or expanding the system’s capabilities.
The Long-Term Costs of Ignoring Technical Debt
While technical debt can provide short-term gains by speeding up initial development, the long-term costs are much higher. Unaddressed technical debt can lead to project delays, escalating maintenance costs, and an ever-widening gap between current code and modern best practices. The more technical debt accumulates, the harder and more expensive it becomes to address. For many companies, failing to pay down this debt eventually results in a critical juncture: either invest heavily in refactoring the codebase or face an expensive overhaul to rebuild from the ground up.
Conclusion
Technical debt is an unavoidable aspect of software development, but understanding its perils is essential for minimizing its impact on security, usability, and stability. By actively managing technical debt—whether through regular refactoring, code audits, or simply prioritizing long-term quality over short-term expedience—organizations can avoid the most dangerous consequences and ensure their software remains robust and reliable in an ever-changing technological landscape.
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Debayer – A free command line tool to convert camera raw images into scene-linear exr
https://github.com/jedypod/debayer
The only required dependency is oiiotool. However other “debayer engines” are also supported.
- OpenImageIO – oiiotool is used for converting debayered tif images to exr.
- Debayer Engines
- RawTherapee – Powerful raw development software used to decode raw images. High quality, good selection of debayer algorithms, and more advanced raw processing like chromatic aberration removal.
- LibRaw – dcraw_emu commandline utility included with LibRaw. Optional alternative for debayer. Simple, fast and effective.
- Darktable – Uses darktable-cli plus an xmp config to process.
- vkdt – uses vkdt-cli to debayer. Pretty experimental still. Uses Vulkan for image processing. Stupidly fast. Pretty limited.
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LibRaw.org – Free interface for extracting data out of RAW images
The LibRaw library provides a simple and unified interface for extracting out of RAW files generated by digital photo cameras the following:
- RAW data (pixel values)
- Metadata necessary for processing RAW (geometry, CFA / Bayer pattern, black level, white balance, etc.)
- Embedded preview / thumbnail.
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PTGui 13 beta adds control through a Patch Editor
Additions:
- Patch Editor (PTGui Pro)
- DNG output
- Improved RAW / DNG handling
- JPEG 2000 support
- Performance improvements
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The riddles humans can solve but AI computers cannot
https://www.bbc.com/future/article/20240912-what-riddles-teach-us-about-the-human-mind
“As human beings, it’s very easy for us to have common sense, and apply it at the right time and adapt it to new problems,” says Ilievski, who describes his branch of computer science as “common sense AI”. But right now, AI has a “general lack of grounding in the world”, which makes that kind of basic, flexible reasoning a struggle.
AI excels at pattern recognition, “but it tends to be worse than humans at questions that require more abstract thinking”, says Xaq Pitkow, an associate professor at Carnegie Mellon University in the US, who studies the intersection of AI and neuroscience. In many cases, though, it depends on the problem.
A bizarre truth about AI is we have no idea how it works. The same is true about the brain.
That’s why the best systems may come from a combination of AI and human work; we can play to the machine’s strengths, Ilievski says.
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AI and the Law – CartoonBrew.com : Lionsgate signs deal with AI company Runway, hoping that AI can eliminate storyboard artists and VFX crews
The goal is to reduce costs by replacing traditional storyboard artists and VFX crews with AI-generated “cinematic video.” Lionsgate hopes to use this technology for both pre- and post-production processes. While the company promotes the cost-saving potential, the creative community has raised concerns, as Runway is currently facing a lawsuit over copyright infringement.
FEATURED POSTS
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ComfyUI-Distributed – Parallel and distributed processing across multiple GPUs and machines
https://github.com/robertvoy/ComfyUI-Distributed
Key FeaturesParallel Workflow Processing
- Parallel Generation – Run the same workflow on multiple GPUs simultaneously with different seeds
- Automatic Load Balancing – Distribute workflow execution across available workers
- Batch Acceleration – Generate multiple variations faster by using all your GPUs
Distributed Upscaling- True Distributed Processing – Split large upscaling tasks into tiles processed across multiple GPUs
- Tile-based Upscaling – Intelligent work distribution for Ultimate SD Upscale
Management & Monitoring- Automatic Worker Management – Launch and monitor workers from the UI
- Network Support – Use GPUs across different machines on your network
- Real-time Monitoring – Track worker status and performance from the UI
- Easy Configuration – JSON-based configuration with UI controls
- Memory Management – Built-in VRAM clearing
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Emmanuel Tsekleves – Writing Research Papers
Here’s the journey of crafting a compelling paper:
1️. ABSTRACT
This is your elevator pitch.
Give a methodology overview.
Paint the problem you’re solving.
Highlight key findings and their impact.
2️. INTRODUCTION
Start with what we know.
Set the stage for our current understanding.
Hook your reader with the relevance of your work.
3️. LITERATURE REVIEW
Identify what’s unknown.
Spot the gaps in current knowledge.
Your job in the next sections is to fill this gap.
4️. METHODOLOGY
What did you do?
Outline how you’ll fill that gap.
Be transparent about your approach.
Make it reproducible so others can follow.
5️. RESULTS
Let the data speak for itself.
Present your findings clearly.
Keep it concise and focused.
6️. DISCUSSION
Now, connect the dots.
Discuss implications and significance.
How do your findings bridge the knowledge gap?
7️. CONCLUSION
Wrap it up with future directions.
What does this mean for us moving forward?
Leave the reader with a call to action or reflection.
8️. REFERENCES
Acknowledge the giants whose shoulders you stand on.
A robust reference list shows the depth of your research.
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About green screens
hackaday.com/2015/02/07/how-green-screen-worked-before-computers/
www.newtek.com/blog/tips/best-green-screen-materials/
www.chromawall.com/blog//chroma-key-green
Chroma Key Green, the color of green screens is also known as Chroma Green and is valued at approximately 354C in the Pantone color matching system (PMS).
Chroma Green can be broken down in many different ways. Here is green screen green as other values useful for both physical and digital production:
Green Screen as RGB Color Value: 0, 177, 64
Green Screen as CMYK Color Value: 81, 0, 92, 0
Green Screen as Hex Color Value: #00b140
Green Screen as Websafe Color Value: #009933Chroma Key Green is reasonably close to an 18% gray reflectance.
Illuminate your green screen with an uniform source with less than 2/3 EV variation.
The level of brightness at any given f-stop should be equivalent to a 90% white card under the same lighting.
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Composition – These are the basic lighting techniques you need to know for photography and film
http://www.diyphotography.net/basic-lighting-techniques-need-know-photography-film/
Amongst the basic techniques, there’s…
1- Side lighting – Literally how it sounds, lighting a subject from the side when they’re faced toward you
2- Rembrandt lighting – Here the light is at around 45 degrees over from the front of the subject, raised and pointing down at 45 degrees
3- Back lighting – Again, how it sounds, lighting a subject from behind. This can help to add drama with silouettes
4- Rim lighting – This produces a light glowing outline around your subject
5- Key light – The main light source, and it’s not necessarily always the brightest light source
6- Fill light – This is used to fill in the shadows and provide detail that would otherwise be blackness
7- Cross lighting – Using two lights placed opposite from each other to light two subjects