Hacker News - Wednesday, January 24th 2024
From Hacker News
scrapscript.py
Scrapscript is a small, functional programming language that is content-addressable and network-first. It was created by Taylor Troesh, and the main implementation is a collaboration between the author and two other individuals, Chris and Bernstein. Initially, there was no available implementation to download or browse, so Chris and Bernstein reached out to Taylor to offer their assistance. Taylor graciously shared his JavaScript implementation of scrapscript, which was compact yet impressive in terms of its features. Chris and Bernstein decided to create a parallel implementation using Python, focusing on readability and correctness. They extensively tested the language throughout the development process, ensuring expected behavior and portability to other implementations. Their testing strategy allowed them to make changes confidently, knowing that the tests would continue to pass. Scrapscript is not meant for large applications but offers unique features, such as pattern matching. The team also developed a REPL, an executable using Cosmopolitan for portability, and a web REPL. They are working on implementing scrapyards, platforms, and a scrapscript compiler. The project is open to contributions and has a discourse group for discussing ideas and potential projects.
The juror who found herself guilty
In 1990, Estella Ybarra was a juror on a trial that convicted Carlos Jaile, a vacuum cleaner salesman, of raping an eight-year-old girl. Ybarra had doubts about his guilt from the start, as the victim's description of Jaile's appearance and vehicle did not match his. Despite this, Ybarra eventually succumbed to pressure from other jurors and voted to convict Jaile. For years, Ybarra carried the burden of her decision and wondered about Jaile's life in prison. In 2017, after finding an old certificate of appreciation she received for her jury service, Ybarra started to question her past actions. She felt a shift and began to question whether she had made the right decision. She learned more about the flaws and biases in the criminal justice system, especially when it comes to wrongful convictions. This realization led Ybarra to seek out Jaile and apologize for her role in his wrongful conviction. They eventually met in person and Ybarra took steps to help Jaile gain his freedom. Her journey highlights the importance of questioning one's actions and seeking justice for those wrongfully convicted.
Gene therapy allows an 11-year-old boy to hear
Aissam Dam, an 11-year-old boy who was born deaf, has become the first person in the United States to receive gene therapy for congenital deafness. Aissam, who previously lived in a poor community in Morocco, used a sign language he invented to communicate and had never received any schooling. However, after moving to Spain, his family took him to a hearing specialist who suggested he might be eligible for a clinical trial using gene therapy. On October 4, Aissam underwent the treatment at the Children's Hospital of Philadelphia, and it was a success. The gene therapy aimed to replace a mutated gene called otoferlin, which causes deafness in about 200,000 people worldwide. Although the therapy is still in the early stages and more patients need to be involved, researchers believe that this groundbreaking study could lead to gene therapies for other forms of congenital deafness.
Why is machine learning 'hard'? (2016)
Machine learning has become more accessible in recent years, with online courses, textbooks, and frameworks making it easier to implement models. However, machine learning remains a relatively difficult problem. Implementing existing algorithms and models for new applications requires a deep understanding of available tools and their trade-offs. Debugging machine learning algorithms is particularly challenging, as there are often multiple dimensions along which things can go wrong - algorithmic issues, implementation bugs, bugs in the data, and limitations in the model's capabilities. This makes the debugging process more complex and time-consuming. Additionally, there can be long delays between implementing a potential fix and seeing the results, especially in deep learning models that require hours or days to train. This necessitates a parallel experimentation paradigm, where multiple experiments are run simultaneously. Building intuition for debugging machine learning is crucial, as it allows developers to associate certain behavior signals with potential issues in the debugging space. Ultimately, fast and effective debugging is a critical skill for implementing machine learning pipelines successfully.
Direct pixel-space megapixel image generation with diffusion models
The Hourglass Diffusion Transformer (HDiT) is a new image generative model that can train at high resolutions directly in pixel-space. It combines the efficiency of convolutional U-Nets with the scalability of Transformers, allowing for linear scaling with pixel count. Unlike traditional methods that require additional techniques like multiscale architectures or latent autoencoders, HDiT is able to achieve successful training without them. The HDiT model is built upon the Transformer architecture, which is known for its ability to handle billions of parameters. By leveraging this scalability, the model is able to bridge the gap between efficiency and scalability in image generation. In experiments, the HDiT model showed competitive performance with existing models on ImageNet-256, a common benchmark dataset. Additionally, it set a new state-of-the-art for diffusion models on FFHQ-1024, demonstrating its effectiveness in high-resolution image generation. Overall, HDiT presents a promising approach to generating high-resolution images without compromising on efficiency or scalability.
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From Posts IndieHackers
I've bootstrapped Salesforge to $100K ARR and now I'm launching Mailforge
The founder of Salesforge discusses the challenges he faced while manually creating over 100 domains and 200 mailboxes in 2023 for consistent customer acquisition through cold emails. Recognizing the importance of proper infrastructure in maximizing results, he and his team developed mailforge.ai. This platform enables startups and agencies to easily create their own cold email infrastructure within minutes, using a large-scale infrastructure with millions of IPs similar to Gmail and Outlook. Despite having launched only yesterday, mailforge.ai already boasts over 50 paid customers as early adopters, with no churn reported. The founder invites users to try the platform with their preferred sending platform and provide feedback for further improvement. He assures that mailforge.ai ensures deliverability on par with Gmail and Outlook, and even surpasses them in some cases due to its stringent policy against spammers and dodgy selling practices.
How to stimulate user activity in the product?
In this episode, we discuss the importance of collecting user contacts and effectively communicating with them to enhance product growth and engagement. The host shares their experience with their Telegram bot, which tracks added domains. They implemented a feature that sends notifications to users about updates and new features. As a result, user activity significantly increased, and there was a substantial spike in the number of new domains added for tracking, jumping from the daily average of 30 to approximately 180. Drawing from this experience, the host emphasizes the significance of collecting user contacts from the very beginning of a product launch. By having access to user contacts, you can directly communicate with them, providing updates, discounts, promotions, and gathering valuable feedback. User contacts become a powerful asset for product growth and a means to engage with your audience effectively. The episode concludes by underscoring the importance of treating user contacts as a valuable resource that can boost your product and overall success.
Stripe microtransaction: very poor experience and I need your advice
The individual in question is seeking advice on how to navigate their dealings with Stripe, a payment provider. They had reached out to Stripe Support three weeks ago to apply microtransaction pricing to their account. While they initially received offers for the pricing, there has been no progress since then. Each time they contact Stripe Support for updates, they receive similar robotic responses from different individuals. The person is considering visiting Stripe's office in Singapore to address the issue, but there is no contact number provided. The individual acknowledges Stripe's excellent product offerings and documentation but feels that their customer support and operational handling are lacking. They also believe that the integration they previously completed with Stripe is now a sunk cost. The person is seeking alternative payment providers that offer microtransaction/micropayment rates for credit cards. Overall, they express frustration with Stripe's handling of their microtransaction pricing application.
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