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The article discusses the challenges facing the global economy, including rising inflation, the ongoing impact of the COVID-19 pandemic, and geopolitical tensions. It explores the potential for a prolonged period of economic uncertainty and the need for policymakers to navigate these complex issues effectively.

NotiFilter is an open-source app that allows users to filter and manage notifications from various apps on their Android device, enabling them to customize their notification experience and reduce digital distractions.

A new attack called StackWarp threatens the confidentiality of virtual machines on AMD processors by exploiting speculative execution vulnerabilities to leak sensitive data from secure enclaves.

Stone skipping is the art of throwing flat stones across the surface of a body of water in such a way that they bounce or skip multiple times. It is a popular recreational activity enjoyed by people of all ages, with competitions and world record attempts held around the world.

The article explores the evolutionary basis for same-sex sexual behavior in animals, suggesting it may provide reproductive advantages such as increased social bonding, better childcare, and resource sharing, even if it does not directly lead to reproduction.

I wanted a simple place to host my landscape photos without Instagram, ads, or feeds, so I built a minimal album page for myself.

No tracking, no login, just fast images and captions.

Would love feedback on the layout or anything you’d change.

The article discusses a proposed reversal of the ban on mailing guns in the United States, which is receiving positive reactions from Wyomingites who view it as a win for gun rights and the Second Amendment.

The article discusses the United States Postal Service's (USPS) strategic plan for the year 2025, which aims to modernize its operations, improve customer experience, and enhance financial sustainability through various initiatives such as optimizing its network, investing in technology, and expanding its product and service offerings.

I have a small group of friends that like to play Minecraft together fairly regularly. Over the years, one of the friends would make us matching skins for certain occasions, all using a site called needcoolshoes.com. However, come 2023, needcoolshoes went offline for good, and suddenly my friend had lost his favourite skin making tool.

Wanting him to make a particular skin set for our group, I dug the old editor out of the Wayback Machine, and fixed it up to function as before. I put it up on a static site, then shared it with him, as well as commenting the link on a few Reddit threads asking about needcoolshoes.com. My restoration started to gain traction, and soon I had multiple people asking me to add back the gallery, so they could share their skins like on the old site.

Being a Rails developer, I spun up a basic Rails app, using my restored version of the editor, and implementing a basic gallery. I named it needcoolershoes (which helped it replace the old site on search engines). From there, the site kept growing in traction, and more and more features were requested and implemented. However, people were requesting features of the skin editor itself, which at this point was an amalgamation of obfuscated archived javascript, and some patches I had made to add some basic features. Eventually, maintaining this editor became a hassle, and paired with the fact that I didn't technically have the rights to modify and distribute it, I decided to rewrite it from scratch.

Now, two years on, myself and a few others who joined the community have fully rewritten the editor, using Lit for webcomponents, but otherwise sticking mostly to vanilla Javascript. Three JS is used for the 3D rendering. The editor is now fully mobile responsive, with touch controls, and portrait oriented UI, but switches to a landscape oriented desktop UI for larger screens. As well, it now supports modern skins, with transparency, and slim model support.

One of the members of the community made a trailer which shows off some of the editor's features, which can be seen here: https://www.youtube.com/watch?v=InOz7jUmhcI

Really appreciate anyone here in to Minecraft checking out needcoolershoes, it has been a real passion project the last few years, and the site runs free with no ads, hosting funded by donations. Also appreciate it if anyone shares this with people they know that are in to Minecraft, we love seeing new skins being submitted to the site!

The article discusses how the COVID-19 pandemic has led to a global shortage of semiconductor chips, causing disruptions in the production of various products, including cars and electronics. It explores the factors contributing to the chip shortage and the impact it has had on different industries.

I ran a systematic comparison of AI content detection and humanization tools after a client terminated a contract over an AI detection flag (87% AI-generated on content I'd manually edited).

*Methodology:* - 31 tools tested over 90 days - 200+ content samples (technical docs, marketing copy, blog posts, academic-style) - Measured detection accuracy against known AI/human content - Measured humanization "bypass rate" against Originality.ai (industry standard) - Controlled for content type and length

*Key finding:* ChatGPT Custom GPTs ($5/mo via team plans) performed within 2-7% of standalone SaaS tools charging $50-300/mo.

*Detection tools tested:* - Originality.ai: 91.3% accuracy, $149/mo unlimited - GPTZero: 87.4% accuracy, $16/mo - Copyleaks: 88.2% accuracy, $9-499/mo - Winston AI: 84.1% accuracy, $19/mo

*Humanization bypass rates (against Originality.ai):*

SaaS: - Undetectable.ai: 91.2%, $49-209/mo

Custom GPTs ($5/mo): - StealthGPT AI: 89.3% — https://chatgpt.com/g/g-67c88e5737388191aea00acc2e248afd - TurnitinPRO: 88.1% — https://chatgpt.com/g/g-67a36b4314548191a132428520afbf2d - BypassGPT: 87.6% — https://chatgpt.com/g/g-677e3f6ff8648191a96356838c564012 - ZeroGPT: 86.4% — https://chatgpt.com/g/g-67c88362d8e081918b73f42d780e53cb - GPT Zero: 86.2% — https://chatgpt.com/g/g-6786439fa24c81919660e0152ad5f4f3 - scribbr AI: 85.7% — https://chatgpt.com/g/g-67c89bebe2e48191962eaefb1e46530a - Humanize AI: 85.4% — https://chatgpt.com/g/g-674192227ff481918ff66a8dfe5378d9 - HumanizerPRO: 84.9% — https://chatgpt.com/g/g-67bfc9f5ab848191b7a80e386e7963af - Humanize AI Text: 84.7% — https://chatgpt.com/g/g-678cc08f1b048191a9428748d02916b1

*Cost comparison:*

Old stack: $223/mo - Originality.ai unlimited: $149 - Undetectable.ai: $49 - Quillbot: $10 - Grammarly: $15

New stack: $20/mo - ChatGPT Plus (team): $5 - Originality.ai pay-per-scan: ~$15

*Technical observations:*

1. Custom GPTs use the same base models as SaaS competitors. The differentiation is prompt engineering and workflow design, not proprietary detection/bypass algorithms.

2. Most humanizers fail on long-form content (>1500 words). Output becomes repetitive, tone drifts. BypassGPT and StealthGPT maintained consistency at 4000+ words.

3. Detection tools have different strengths: Originality.ai best overall accuracy, Copyleaks best for non-English content, GPTZero has more false positives on technical writing.

4. The "bypass rate" gap between $5 and $50+ tools (2-7%) matters less than workflow efficiency. Integrated detection+humanization in one interface saves ~30 min/article.

5. All tools struggle with heavily templated content (listicles, how-to formats). Detection accuracy drops 15-20% on these patterns regardless of actual AI involvement.

*Limitations:*

- Single tester, potential bias - Originality.ai as primary benchmark (other detectors may vary) - Custom GPT performance depends on OpenAI model updates - 90-day window; detection/bypass landscape evolves quickly

*Questions I'm still exploring:*

- How do detection tools handle fine-tuned models vs base GPT-4/Claude? - Is there a content length threshold where detection becomes unreliable? - How much does writing style (technical vs conversational) affect detection accuracy?

Hi HN,

  I built KissMotion (https://aikissvideo.app) - an AI kiss video       
  generator that creates romantic animations from photos.               
                                                                        
  Tech stack:                                                           
  - Next.js 15 with App Router                                          
  - Multiple AI providers (Replicate, Kling, FAL)                       
  - Drizzle ORM with credit-based FIFO consumption                      
  - Multi-deployment: Vercel + Cloudflare Workers (via OpenNext)        
  - i18n with 16 languages                                              
                                                                        
  The trickiest part was handling async AI generation with reliable     
  polling and automatic credit refunds when tasks fail.                 
                                                                        
  Looking for feedback on generation quality and UX improvements.

The article explores the hype and reality surrounding the AI language model Claude, discussing its capabilities, limitations, and the challenges of accurately representing its performance to the public.

k6 is an open-source load testing tool that allows developers to write and run performance tests using JavaScript or TypeScript. It provides a simple and flexible way to simulate virtual users and measure the performance of web applications, APIs, and microservices.

I ran a systematic comparison of AI content detection and humanization tools after a client terminated a contract over an AI detection flag (87% AI-generated on content I'd manually edited).

*Methodology:* - 31 tools tested over 90 days - 200+ content samples (technical docs, marketing copy, blog posts, academic-style) - Measured detection accuracy against known AI/human content - Measured humanization "bypass rate" against Originality.ai (industry standard) - Controlled for content type and length

*Key finding:* ChatGPT Custom GPTs ($5/mo via team plans) performed within 2-7% of standalone SaaS tools charging $50-300/mo.

*Detection tools tested:* - Originality.ai: 91.3% accuracy, $149/mo unlimited - GPTZero: 87.4% accuracy, $16/mo - Copyleaks: 88.2% accuracy, $9-499/mo - Winston AI: 84.1% accuracy, $19/mo

*Humanization bypass rates (against Originality.ai):*

SaaS: - Undetectable.ai: 91.2%, $49-209/mo

Custom GPTs ($5/mo): - StealthGPT AI: 89.3% — https://chatgpt.com/g/g-67c88e5737388191aea00acc2e248afd - TurnitinPRO: 88.1% — https://chatgpt.com/g/g-67a36b4314548191a132428520afbf2d - BypassGPT: 87.6% — https://chatgpt.com/g/g-677e3f6ff8648191a96356838c564012 - ZeroGPT: 86.4% — https://chatgpt.com/g/g-67c88362d8e081918b73f42d780e53cb - GPT Zero: 86.2% — https://chatgpt.com/g/g-6786439fa24c81919660e0152ad5f4f3 - scribbr AI: 85.7% — https://chatgpt.com/g/g-67c89bebe2e48191962eaefb1e46530a - Humanize AI: 85.4% — https://chatgpt.com/g/g-674192227ff481918ff66a8dfe5378d9 - HumanizerPRO: 84.9% — https://chatgpt.com/g/g-67bfc9f5ab848191b7a80e386e7963af - Humanize AI Text: 84.7% — https://chatgpt.com/g/g-678cc08f1b048191a9428748d02916b1

*Cost comparison:*

Old stack: $223/mo - Originality.ai unlimited: $149 - Undetectable.ai: $49 - Quillbot: $10 - Grammarly: $15

New stack: $20/mo - ChatGPT Plus (team): $5 - Originality.ai pay-per-scan: ~$15

*Technical observations:*

1. Custom GPTs use the same base models as SaaS competitors. The differentiation is prompt engineering and workflow design, not proprietary detection/bypass algorithms.

2. Most humanizers fail on long-form content (>1500 words). Output becomes repetitive, tone drifts. BypassGPT and StealthGPT maintained consistency at 4000+ words.

3. Detection tools have different strengths: Originality.ai best overall accuracy, Copyleaks best for non-English content, GPTZero has more false positives on technical writing.

4. The "bypass rate" gap between $5 and $50+ tools (2-7%) matters less than workflow efficiency. Integrated detection+humanization in one interface saves ~30 min/article.

5. All tools struggle with heavily templated content (listicles, how-to formats). Detection accuracy drops 15-20% on these patterns regardless of actual AI involvement.

*Limitations:*

- Single tester, potential bias - Originality.ai as primary benchmark (other detectors may vary) - Custom GPT performance depends on OpenAI model updates - 90-day window; detection/bypass landscape evolves quickly

*Questions I'm still exploring:*

- How do detection tools handle fine-tuned models vs base GPT-4/Claude? - Is there a content length threshold where detection becomes unreliable? - How much does writing style (technical vs conversational) affect detection accuracy?

Happy to share raw data or answer questions about methodology.

The article discusses the phenomenon of terminal lucidity, where some terminally ill patients experience a brief period of clarity or improved cognitive function shortly before death. It explores possible explanations for this phenomenon and its implications for end-of-life care.

Hi HN, I’m the creator of Cyber+, a programming language focused on cybersecurity and system-level tooling. Cyber+ started as an experimental project, but it has now reached a stable stage with a defined syntax, runtime, and standard commands. It is designed for tasks like security scripting, hashing, scanning, and automation, while keeping the language simple and readable. Example code for Hello World ----------------------------- Compute("Hello World"); ----------------------------- I bet Cyber+ will feel even easier than Python or Go. ----------------------------- The language is implemented in Go, and the full source code, documentation, and installer are available on GitHub via the website. I’d really appreciate feedback on the language design, syntax choices, and real-world use cases where this could be improved or simplified. Thanks for taking a look.

Hey, I'm building similar things for internal enterprise incident management and this was my initial demo. I've pre-processed 3+ years of discussions of Let's Encrypt Community.

I've put $50 against the LLM - hence the login page, but you can use guest:account to log in and try it.

(the brand is my own LLC)

The article explores the use of Flux and Kanban methodologies to manage the development and deployment of AI agents. It discusses how these frameworks can help enhance the efficiency, transparency, and collaboration in the AI development lifecycle.

The article discusses the author's approach to managing an ever-growing list of project ideas, emphasizing the importance of prioritization and the need to resist the temptation to start too many new projects at once. The author shares strategies for maintaining a healthy balance between exploring new ideas and focusing on completing existing ones.

Why? What's preventing Meta from enabling Voice & Video in the web? After all the Desktop app is an electron bundle.

The article explores the concept of a 'virtual assistant' - an AI-powered system that can help users with various tasks. It discusses the potential benefits and challenges of integrating virtual assistants into daily life, as well as the ethical considerations surrounding their development and use.

The article provides an in-depth overview of FIPS 202, the standard that defines the Keccak algorithm, which is the basis for the SHA-3 hash function and the SHAKE extendable-output functions. It discusses the design principles and properties of Keccak, including its resistance to cryptanalysis and its potential applications in various cryptographic systems.

Elon Musk, the founder of OpenAI and Microsoft, is seeking up to $134 billion in a fraud lawsuit, alleging that the companies misled investors about their AI technology and financial performance.

The article discusses the implementation of hot reloading in Rust, a feature that allows developers to update their applications without restarting them. It explores the challenges and techniques involved in achieving this functionality, providing insights for Rust developers interested in enhancing their development workflow.