Ghostwriter is a project that transforms the reMarkable 2 tablet into an interface for interacting with large language models (LLMs). It leverages the tablet's natural handwriting capabilities to send handwritten prompts to an LLM and displays the generated text response directly on the e-ink screen. Essentially, it allows users to write naturally and receive LLM-generated text, all within the distraction-free environment of the reMarkable 2. The project is open-source and allows for customization, including choosing the LLM and adjusting various settings.
SQLite Page Explorer is a Python-based tool for visually inspecting the raw structure and content of SQLite database pages. It allows users to navigate through pages, examine headers and cell pointers, view record data in different formats (including raw bytes), and understand how data is organized on disk. The tool offers both a command-line interface and a graphical user interface built with Tkinter, providing flexibility for different user preferences and analysis needs. It aims to be a helpful resource for developers debugging database issues, understanding SQLite internals, or exploring the low-level workings of their data.
Hacker News users generally praised the SQLite Disk Page Explorer tool for its simplicity and educational value. Several commenters highlighted its usefulness in visualizing and understanding the internal structure of SQLite databases, particularly for learning and debugging purposes. Some suggested improvements like adding features to modify the database or highlighting specific data types. The discussion also touched on the tool's performance limitations with larger databases and the importance of understanding how SQLite manages pages for efficient data retrieval. A few commenters shared their own experiences and tools for exploring database internals, showcasing a broader interest in database visualization and analysis.
Sort_Memories is a Python script that automatically sorts group photos based on the number of specified individuals present in each picture. Leveraging face detection and recognition, the script analyzes images, identifies faces, and groups photos based on the user-defined 'N' number of people desired in each output folder. This allows users to easily organize their photo collections by separating pictures of individuals, couples, small groups, or larger gatherings, automating a tedious manual process.
Hacker News commenters generally praised the project for its clever use of facial recognition to solve a common problem. Several users pointed out potential improvements, such as handling images where faces are partially obscured or not clearly visible, and suggested alternative approaches like clustering algorithms. Some discussed the privacy implications of using facial recognition technology, even locally. There was also interest in expanding the functionality to include features like identifying the best photo out of a burst or sorting based on other criteria like smiles or open eyes. Overall, the reception was positive, with commenters recognizing the project's practical value and potential.
S1, Simple Test-Time Scaling (TTS), is a new technique for improving image classification accuracy. It leverages the observation that a model's confidence often correlates with input resolution: higher resolution generally leads to higher confidence. S1 employs a simple scaling strategy during inference: an image is evaluated at multiple resolutions, and the predictions are averaged, weighted by their respective confidences. This method requires no training or changes to the model architecture and is easily integrated into existing pipelines. Experiments demonstrate that S1 consistently improves accuracy across various models and datasets, often exceeding more complex TTS methods while maintaining lower computational overhead.
HN commenters generally expressed interest in S1's simple approach to scaling, praising its straightforward design and potential usefulness for smaller companies or projects. Some questioned the performance compared to more complex solutions like Kubernetes, and whether the single-server approach truly scales, particularly for stateful applications. Several users pointed out potential single points of failure and the lack of features like rolling deployments. Others suggested alternative tools like Docker Compose or systemd for similar functionality. A few comments highlighted the benefits of simplicity for development, testing, and smaller-scale deployments where Kubernetes might be overkill. The discussion also touched upon the limitations of using screen and suggested alternatives like tmux. Overall, the reaction was a mix of cautious optimism and pragmatic skepticism, acknowledging the project's niche but questioning its broader applicability.
DM is a lightweight, unofficial Discord client designed to run on older Windows operating systems like Windows 95, 98, ME, and newer versions. Built using the Delphi programming language, it leverages Discord's web API to provide basic chat functionality, including sending and receiving messages, joining and leaving servers, and displaying user lists. While not offering the full feature set of the official Discord client, DM prioritizes minimal resource usage and compatibility with older hardware.
Hacker News users discuss the Discord client for older Windows systems, primarily focusing on its novelty and technical ingenuity. Several express admiration for the developer's skill in making Discord, a complex modern application, function on such outdated operating systems. Some question the practical use cases, while others highlight the potential value for preserving access to communities on older hardware or for specific niche applications like retro gaming setups. There's also discussion around the technical challenges involved, including handling dependencies and the limitations of older APIs. Some users express concern about security implications, given the lack of updates for these older OSes. Finally, the unconventional choice of Pascal/Delphi for the project sparks some interest and debate about the suitability of the language.
Sniffnet is a cross-platform network traffic monitor designed to be user-friendly and informative. It captures and displays network packets in real-time, providing details such as source and destination IPs, ports, protocols, and data transfer sizes. Sniffnet aims to offer an accessible way to understand network activity, featuring a simple interface, color-coded packet information, and filtering options for easier analysis. Its cross-platform compatibility makes it a versatile tool for monitoring network traffic on various operating systems.
HN users generally praised Sniffnet for its simple interface and ease of use, particularly for quickly identifying the source of unexpected network activity. Some appreciated the passive nature of the tool, contrasting it with more intrusive solutions like Wireshark. Concerns were raised about potential performance issues, especially on busy networks, and the limited functionality compared to more comprehensive network analysis tools. One commenter suggested using tcpdump or tshark with filters for similar results, while others questioned the project's actual utility beyond simple curiosity. Several users expressed interest in the potential for future development, such as adding filtering capabilities and improving performance.
Modest is a Lua library designed for working with musical harmony. It provides functionality for representing notes, chords, scales, and intervals, allowing for manipulation and analysis of musical structures. The library supports various operations like transposing, inverting, and identifying chord qualities. It also includes features for working with different tuning systems and generating musical progressions. Modest aims to be a lightweight and efficient tool for music-related applications in Lua, suitable for everything from algorithmic composition to music theory analysis.
HN users generally expressed interest in Modest, praising its clean API and the potential usefulness of a music theory library in Lua. Some users suggested potential improvements like adding support for microtones, different tuning systems, and rhythm representation. One commenter specifically appreciated the clear documentation and examples provided. The discussion also touched on other music-related Lua libraries and tools, such as LÖVE2D and Euterpea, comparing their features and approaches to music generation and manipulation. There was some brief discussion about the choice of Lua, with one user mentioning its suitability for embedded systems and real-time applications.
The arXiv LaTeX Cleaner is a tool that automatically cleans up LaTeX source code for submission to arXiv, improving compliance and reducing potential processing errors. It addresses common issues like removing disallowed commands, fixing figure path problems, and converting EPS figures to PDF. The cleaner also standardizes fonts, removes unnecessary packages, and reduces file sizes, ultimately streamlining the arXiv submission process and promoting wider paper accessibility.
Hacker News users generally praised the arXiv LaTeX cleaner for its potential to improve the consistency and readability of submitted papers. Several commenters highlighted the tool's ability to strip unnecessary packages and commands, leading to smaller file sizes and faster processing. Some expressed hope that this would become a standard pre-submission step, while others were more cautious, pointing to the possibility of unintended consequences like breaking custom formatting or introducing subtle errors. The ability to remove comments was also a point of discussion, with some finding it useful for cleaning up draft versions before submission, while others worried about losing valuable context. A few commenters suggested additional features, like converting EPS figures to PDF and adding a DOI badge to the title page. Overall, the reception was positive, with many seeing the tool as a valuable contribution to the academic writing process.
Uscope is a new, from-scratch debugger for Linux written in C and Python. It aims to be a modern, user-friendly alternative to GDB, boasting a simpler, more intuitive command language and interface. Key features include reverse debugging capabilities, a TUI interface with mouse support, and integration with Python scripting for extended functionality. The project is currently under active development and welcomes contributions.
Hacker News users generally expressed interest in Uscope, praising its clean UI and the ambition of building a debugger from scratch. Several commenters questioned the practical need for a new debugger given existing robust options like GDB, LLDB, and Delve, wondering about Uscope's potential advantages. Some discussed the challenges of debugger development, highlighting the complexities of DWARF parsing and platform compatibility. A few users suggested integrations with other tools, like REPLs, and requested features like remote debugging. The novelty of a fresh approach to debugging generated curiosity, but skepticism regarding long-term viability and differentiation also emerged. Some expressed concerns about feature parity with existing debuggers and the sustainability of the project.
Stats is a free and open-source macOS menu bar application that provides a comprehensive overview of system performance. It displays real-time information on CPU usage, memory, network activity, disk usage, battery health, and fan speeds, all within a customizable and compact menu bar interface. Users can tailor the displayed modules and their appearance to suit their needs, choosing from various graph styles and refresh rates. Stats aims to be a lightweight yet powerful alternative to larger system monitoring tools.
Hacker News users generally praised Stats' minimalist design and useful information display in the menu bar. Some suggested improvements, including customizable refresh rates, more detailed CPU information (like per-core usage), and GPU temperature monitoring for M1 Macs. Others questioned the need for another system monitor given existing options, with some pointing to iStat Menus as a more mature alternative. The developer responded to several comments, acknowledging the suggestions and clarifying current limitations and future plans. Some users appreciated the open-source nature of the project and the developer's responsiveness. There was also a minor discussion around the chosen license (GPLv3).
iterm-mcp is a plugin that brings AI-powered control to iTerm2, allowing users to interact with their terminal and REPLs using natural language. It leverages large language models to translate commands like "list files larger than 1MB" into the appropriate shell commands, and can even generate code snippets within the terminal. The plugin aims to simplify complex terminal interactions and improve productivity by bridging the gap between human intention and shell execution.
HN users generally expressed interest in iterm-mcp, praising its innovative approach to terminal interaction. Several commenters highlighted the potential for improved workflow efficiency through features like AI-powered command generation and execution. Some questioned the reliance on OpenAI's APIs, citing cost and privacy concerns, while others suggested alternative local models or incorporating existing tools like copilot. The discussion also touched on the possibility of extending the tool beyond iTerm2 to other terminals. A few users requested a demo video to better understand the functionality. Overall, the reception was positive, with many acknowledging the project's potential while also offering constructive feedback for improvement.
Goose is an open-source AI agent designed to be more than just a code suggestion tool. It leverages Large Language Models (LLMs) to perform a wide range of tasks, including executing code, browsing the web, and interacting with the user's local system. Its extensible architecture allows users to easily add new commands and customize its behavior through plugins written in Python. Goose aims to bridge the gap between user intention and execution by providing a flexible and powerful interface for interacting with LLMs.
HN commenters generally expressed excitement about Goose and its potential. Several praised its extensibility and the ability to chain LLMs with tools. Some highlighted the cleverness of using a tree structure for task planning and the focus on developer experience. A few compared it favorably to existing agents like AutoGPT, emphasizing Goose's more structured and less "hallucinatory" approach. Concerns were raised about the project's early stage and potential complexity, but overall, the sentiment leaned towards cautious optimism, with many eager to experiment with Goose's capabilities. A few users discussed specific use cases, like generating documentation or automating complex workflows, and expressed interest in contributing to the project.
This GitHub repository provides a barebones, easy-to-understand PyTorch implementation for training a small language model (LLM) from scratch. It focuses on simplicity and clarity, using a basic transformer architecture with minimal dependencies. The code offers a practical example of how LLMs work and allows experimentation with training on custom small datasets. While not production-ready or particularly performant, it serves as an excellent educational resource for understanding the core principles of LLM training and implementation.
Hacker News commenters generally praised smolGPT for its simplicity and educational value. Several appreciated that it provided a clear, understandable implementation of a transformer model, making it easier to grasp the underlying concepts. Some suggested improvements, like using Hugging Face's Trainer class for simplification and adding features like gradient checkpointing for lower memory usage. Others discussed the limitations of training such small models and the potential benefits of using pre-trained models for specific tasks. A few pointed out the project's similarity to nanoGPT, acknowledging its inspiration. The overall sentiment was positive, viewing smolGPT as a valuable learning resource for those interested in LLMs.
Meelo is a self-hosted music server designed for serious music collectors and enthusiasts. It focuses on efficient management of large music libraries, providing features like fast search, flexible tagging (including custom tags), playlist creation, and a clean, responsive web interface. Built with Rust and using SQLite, Meelo emphasizes performance and stability while remaining lightweight and easy to deploy. It aims to offer a user-friendly experience for organizing and enjoying extensive music collections, prioritizing local playback over streaming.
HN users generally praised Meelo's interface and feature set, particularly appreciating its support for large libraries, advanced tagging, and playlist management. Some questioned the choice of Go and SvelteKit, suggesting alternatives like Rust and SolidJS for performance and ease of development. Others requested features like collaborative playlists, transcoding, and mobile apps. There was some concern about the project's longevity and the potential burden of maintenance for a solo developer. A few commenters expressed interest in contributing. Overall, the reception was positive, with many users eager to try Meelo or follow its development.
ErisForge is a Python library designed to generate adversarial examples aimed at disrupting the performance of large language models (LLMs). It employs various techniques, including prompt injection, jailbreaking, and data poisoning, to create text that causes LLMs to produce unexpected, inaccurate, or undesirable outputs. The goal is to provide tools for security researchers and developers to test the robustness and identify vulnerabilities in LLMs, thereby contributing to the development of more secure and reliable language models.
HN commenters generally expressed skepticism and amusement towards ErisForge. Several pointed out that "abliterating" LLMs is hyperbole, as the library simply generates adversarial prompts. Some questioned the practical implications and long-term effectiveness of such a tool, anticipating that LLM providers would adapt. Others jokingly suggested more dramatic or absurd methods of "abliteration." A few expressed interest in the project, primarily for research or educational purposes, focusing on understanding LLM vulnerabilities. There's also a thread discussing the ethics of such tools and the broader implications of adversarial attacks on AI models.
This project aims to port the Amsterdam Compiler Kit (ACK) to the Cray X-MP supercomputer. The ACK, a retargetable compiler suite popular in the 1980s and early 1990s, is being adapted to generate code for the Cray's unique architecture, including its vector registers and specific instruction set. The current state of the project involves modifying the backend of the C compiler within ACK to target the Cray X-MP. This involves adjusting code generation, register allocation, and other compiler internals to accommodate the Cray's hardware. The project is a work in progress, with the goal of eventually producing a functional C compiler for the Cray X-MP using the ACK framework.
Hacker News users discuss the Amsterdam Compiler Kit (ACK) for the Cray X-MP, primarily focusing on its historical significance and the challenges of porting and maintaining software for such old hardware. Several commenters reminisce about using ACK and similar tools in the past, highlighting the intricacies of vectorization and optimization for Cray architectures. The discussion touches on the complexities of the Cray instruction set, the cleverness of ACK's code generation, and the difficulties in preserving historical software due to bit rot and lack of accessible hardware. Some express interest in exploring the code further, while others contemplate the effort required to get it running on modern systems or emulators. There's also mention of ACK's broader application beyond Cray systems, and its use in other academic and research contexts.
Orange Intelligence is an open-source Python project aiming to replicate the functionality of Apple's device intelligence features, like Screen Time and activity tracking. It collects usage data from various sources including application usage, browser history, and system events, providing insights into user behavior and digital wellbeing. The project prioritizes privacy, storing data locally and allowing users to control what is collected and analyzed. It offers a web interface for visualizing the collected data, enabling users to understand their digital habits.
HN commenters express skepticism about "Orange Intelligence" truly being an alternative to Apple Intelligence, primarily because the provided GitHub repository lacks substantial code or implementation details. Several commenters point out that the project seems premature and more of a concept than a working alternative. The advertised features, like offline dictation and privacy focus, are questioned due to the absence of evidence backing these claims. The general sentiment is one of cautious curiosity, with a desire for more concrete information before any real evaluation can be made. Some also highlight the difficulty of competing with established, resource-rich solutions like Apple's offering.
This project introduces a Tailwind CSS plugin called corner-smoothing that allows developers to easily create Apple-like smooth rounded corners without complex SVG filters or excessive markup. It provides a set of pre-defined utility classes for various corner radii, inspired by Apple's design language, that can be applied directly to HTML elements. The plugin aims to simplify the process of achieving this subtle but polished visual effect, making it readily accessible through familiar Tailwind syntax.
HN commenters generally praised the smooth corner implementation for Tailwind CSS, finding it a clever and useful approach. Several appreciated the use of a single div and the avoidance of pseudo-elements, considering it elegant and performant. Some pointed out potential limitations, like the inability to control individual corner rounding and challenges with background images or borders. A few users offered alternative solutions, including using SVG filters or leveraging specific Tailwind features. The overall sentiment was positive, with many expressing interest in using the technique in their projects.
Actionate brings the power of GitHub Actions directly into JetBrains IDEs like IntelliJ IDEA and PyCharm. It allows developers to run and debug individual workflow jobs locally, simplifying the development and testing process for GitHub Actions. This eliminates the need for constant commits and push cycles to verify workflow changes, streamlining development and providing a more efficient workflow within the familiar IDE environment. By leveraging the local development environment, Actionate helps catch errors early and accelerates the iteration cycle for creating and refining GitHub Actions workflows.
Hacker News users generally expressed interest in Actionate, finding the concept intriguing and useful for automating tasks within JetBrains IDEs. Some questioned the practical advantages over existing solutions like using the command line directly or scripting within the IDEs. Concerns were raised about performance overhead and potential instability due to relying on Docker. A suggestion was made to support background execution for improved usability. Others pointed out that IDE features like macros and built-in task runners could often fulfill similar automation needs. The security implications of running arbitrary code pulled from GitHub Actions were also discussed. Overall, while acknowledging the tool's potential, many commenters advocated for simpler solutions for common IDE automation tasks.
Helix editor's pull request #11285 integrates a file explorer directly into the editor. This new feature allows users to browse and open files within their project workspace without needing external tools. The implementation provides basic file management operations like creating, deleting, renaming, and opening files and directories, enhancing the editor's self-sufficiency and streamlining the editing workflow. It leverages the existing tree-sitter infrastructure for efficient parsing and rendering of the file tree.
Hacker News users generally expressed excitement about the merged file explorer in Helix, praising its speed and integration with the editor's core functionalities. Several commenters appreciated the thoughtful design, particularly the ability to open multiple files simultaneously and the minimalist, non-distracting implementation. Some users compared it favorably to other editors' file explorers, noting its superior performance and smoother workflow. A few commenters discussed potential improvements, like the ability to rename files directly within the explorer and support for fuzzy finding. Overall, the reception was positive, with many looking forward to using the new feature.
GitHub's UI evolution has been a journey from its initial Ruby on Rails monolithic architecture to a more modern, component-based approach. Historically, the "primer" design system helped create a unified experience, but limitations arose due to its tight coupling with Rails and evolving product needs. The present focuses on ViewComponent, promoting reusability and isolation, and adopting TypeScript for frontend development to improve maintainability and developer experience. Looking ahead, GitHub aims to streamline workflows, simplify the developer experience, and expand ViewComponent's scope for broader usage within the platform, ultimately aiming for a faster, more performant, and more accessible UI.
HN commenters largely focused on GitHub's UI regressions and perceived shift towards catering to non-developers. Several lament the removal of features and increased complexity, citing specific examples like the cluttered code review experience and the proliferation of non-coding-related UI elements. Some express nostalgia for the simpler, developer-centric design of the past, arguing the current direction prioritizes marketing and project management over core coding functionality. The discussion also touches on the transition to View.js and perceived performance issues, with some suggesting these changes contributed to the decline in user experience. A few commenters offer counterpoints, suggesting the changes benefit larger organizations and complex projects. Others point to the inherent challenge of balancing diverse user needs on a platform as large as GitHub.
The open-source "Video Starter Kit" allows users to edit videos using natural language prompts. It leverages large language models and other AI tools to perform actions like generating captions, translating audio, creating summaries, and even adding music. The project aims to simplify video editing, making complex tasks accessible to anyone, regardless of technical expertise. It provides a foundation for developers to build upon and contribute to a growing ecosystem of AI-powered video editing tools.
Hacker News users discussed the potential and limitations of the open-source AI video editor. Some expressed excitement about the possibilities, particularly for tasks like automated video editing and content creation. Others were more cautious, pointing out the current limitations of AI in creative fields and questioning the practical applicability of the tool in its current state. Several commenters brought up copyright concerns related to AI-generated content and the potential misuse of such tools. The discussion also touched on the technical aspects, including the underlying models used and the need for further development and refinement. Some users requested specific features or improvements, such as better integration with existing video editing software. Overall, the comments reflected a mix of enthusiasm and skepticism, acknowledging the project's potential while also recognizing the challenges it faces.
Libmodulor is a TypeScript library designed for building cross-platform applications with a strong focus on developer experience and maintainability. It leverages a modular architecture, promoting code reuse and separation of concerns through features like dependency injection, a unified event bus, and lifecycle management. The library aims to simplify complex application logic by providing built-in solutions for common tasks such as state management, routing, and API interactions, allowing developers to focus on building features rather than boilerplate. While opinionated in its structure, libmodulor offers flexibility in choosing UI frameworks and targets web, desktop, and mobile platforms.
HN commenters generally express skepticism about the value proposition of libmodulor, particularly regarding its use of TypeScript and perceived over-engineering. Several question the necessity of such a library for simple projects, arguing that vanilla HTML, CSS, and JavaScript are sufficient. Some doubt the touted "multi-platform" capabilities, suggesting it's merely a web framework repackaged. Others criticize the project's apparent complexity and lack of clear advantages over established solutions like React Native or Flutter. The focus on server components and the use of RPC are also questioned, with commenters pointing to potential performance drawbacks. A few express interest in specific aspects, such as the server-driven UI approach and the developer experience, but overall sentiment leans towards cautious skepticism.
Tabby is a self-hosted AI coding assistant designed to enhance programming productivity. It offers code completion, generation, translation, explanation, and chat functionality, all within a secure local environment. By leveraging large language models like StarCoder and CodeLlama, Tabby provides powerful assistance without sharing code with external servers. It's designed to be easily installed and customized, offering both a desktop application and a VS Code extension. The project aims to be a flexible and private alternative to cloud-based AI coding tools.
Hacker News users discussed Tabby's potential, limitations, and privacy implications. Some praised its self-hostable nature as a key advantage over cloud-based alternatives like GitHub Copilot, emphasizing data security and cost savings. Others questioned its offline performance compared to online models and expressed skepticism about its ability to truly compete with more established tools. The practicality of self-hosting a large language model (LLM) for individual use was also debated, with some highlighting the resource requirements. Several commenters showed interest in using Tabby for exploring and learning about LLMs, while others were more focused on its potential as a practical coding assistant. Concerns about the computational costs and complexity of setup were common threads. There was also some discussion comparing Tabby to similar projects.
Nullboard is a simple Kanban board implemented entirely within a single HTML file. It uses local storage to persist data, eliminating the need for a server or external dependencies. The board allows users to create, edit, and move tasks between customizable columns, offering a lightweight and portable solution for personal task management. Its minimalist design and focus on core Kanban principles make it easy to use and deploy virtually anywhere a web browser is available.
Hacker News commenters generally praised Nullboard for its simplicity and self-contained nature, finding it a refreshing alternative to complex project management software. Several appreciated the lack of JavaScript, noting its speed and security benefits. Some suggested potential improvements, such as adding basic features like task dependencies, due dates, or collaborative editing, while acknowledging the potential trade-off with the current minimalist design. A few pointed out the limitations of using local storage and the potential for data loss, recommending alternative storage methods for more robust usage. Others highlighted the value for personal task management or small teams, where simplicity trumps feature richness. The ability to easily modify and customize the HTML was also seen as a positive.
This GitHub repository contains the fully documented and annotated source code for the classic game Elite, specifically the BBC Micro version adapted for the Commodore 64. The code, originally written in 6502 assembly language, has been meticulously commented and explained to make it easier to understand. The project aims to provide a comprehensive resource for anyone interested in learning about the game's inner workings, from 3D graphics and ship control to trading mechanics and mission generation. This includes explanations of the game's algorithms, data structures, and overall architecture. The repository also offers resources like a cross-reference and memory map, further aiding in comprehension.
Hacker News commenters on the Elite C64 source code release express enthusiasm and nostalgia for the game. Several discuss the ingenuity of the original developers in overcoming the C64's limitations, particularly its memory constraints and slow floating-point math. Commenters highlight the clever use of lookup tables, integer math, and bitwise operations to achieve impressive 3D graphics and gameplay. Some analyze specific code snippets, showcasing the elegant solutions employed. There's also discussion about the game's impact on the industry and its influence on subsequent space trading and combat simulations. A few users share personal anecdotes about playing Elite in their youth, emphasizing its groundbreaking nature at the time.
This project introduces a C-based web framework designed for dynamic module loading and hot reloading. Leveraging a custom module format and a simple HTTP server, it allows developers to modify and reload C code without restarting the server, facilitating rapid development and experimentation. The framework compiles and links modules on-the-fly, managing dependencies and updating the running server seamlessly. While currently limited in features, it aims to offer a performant and flexible foundation for building web applications directly in C.
Hacker News users discussed the practicality and novelty of a C web framework with hot reloading. Some questioned the real-world use cases and performance benefits compared to existing solutions, suggesting the project serves more as an interesting experiment than a production-ready tool. Others expressed interest in the technical implementation, particularly the hot reloading aspect, and appreciated the author's effort in exploring this concept. Several users pointed out potential issues like memory leaks and the challenges of safely reloading C code in a web server environment. The overall sentiment leans towards acknowledging the project's technical ingenuity while remaining skeptical about its broad applicability.
Memos is an open-source, self-hosted alternative to tools like Rewind and Recall. It allows users to capture their digital life—including web pages, screenshots, code snippets, terminal commands, and more—and makes it searchable and readily accessible. Memos emphasizes privacy and data ownership, storing all data locally. It offers a clean and intuitive interface for browsing, searching, and organizing captured memories. The project is actively developed and aims to provide a powerful yet easy-to-use personal search engine for your digital life.
HN users generally praise Memos for its simplicity and self-hostable nature, comparing it favorably to commercial alternatives like Rewind and Recall. Several commenters appreciate the clean UI and straightforward markdown editor. Some discuss potential use cases, like journaling, note-taking, and team knowledge sharing. A few raise concerns about the long-term viability of relying on SQLite for larger databases, and some suggest alternative database backends. Others note the limited mobile experience and desire for mobile apps or better mobile web support. The project's open-source nature is frequently lauded, with some users expressing interest in contributing. There's also discussion around desired features, such as improved search, tagging, and different storage backends.
Obsidian-textgrams is a plugin that allows users to create and embed ASCII diagrams directly within their Obsidian notes. It leverages code blocks and a custom renderer to display the diagrams, offering features like syntax highlighting and the ability to store diagram source code within the note itself. This provides a convenient way to visualize information using simple text-based graphics within the Obsidian environment, eliminating the need for external image files or complex drawing tools.
HN users generally expressed interest in the Obsidian Textgrams plugin, praising its lightweight approach compared to alternatives like Excalidraw or Mermaid. Some suggested improvements, including the ability to embed rendered diagrams as images for compatibility with other Markdown editors, and better text alignment within shapes. One commenter highlighted the usefulness for quickly mocking up system designs or diagrams, while another appreciated its simplicity for note-taking. The discussion also touched upon alternative tools like PlantUML and Graphviz, but the consensus leaned towards appreciating Textgrams' minimalist and fast rendering capabilities within Obsidian. A few users expressed interest in seeing support for more complex shapes and connections.
Summary of Comments ( 70 )
https://news.ycombinator.com/item?id=42979986
HN commenters generally expressed excitement about Ghostwriter, particularly its potential for integrating handwritten input with LLMs. Several users pointed out the limitations of existing tablet-based coding solutions and saw Ghostwriter as a promising alternative. Some questioned the practicality of handwriting code extensively, while others emphasized its usefulness for diagrams, note-taking, and mathematical formulas, especially when combined with LLM capabilities. The discussion touched upon the desire for similar functionality with other tablets like the iPad and speculated on potential applications in education and creative fields. A few commenters expressed interest in the open-source nature of the project and its potential for customization.
The Hacker News thread linked (https://news.ycombinator.com/item?id=42979986) discusses the "Ghostwriter" project, which allows users to leverage their reMarkable 2 tablet as an input device for vision-language models (VLMs). The discussion is relatively brief, consisting of only a few comments, and doesn't delve deeply into the project's merits or drawbacks. It doesn't present any highly compelling arguments or particularly insightful perspectives.
One user questions the practical application of the project, wondering if there's a genuine use case beyond its novelty. They ponder what real-world problem this solves and suggest alternative, potentially more efficient methods for interacting with VLMs, like using a phone's camera. This comment reflects a common sentiment towards new technologies, questioning its purpose beyond the initial "cool" factor.
Another commenter expresses a desire to see similar functionality for other e-ink devices, specifically mentioning the Onyx Boox. This suggests a potential interest in the broader application of e-ink tablets as interfaces for AI models and highlights a user base looking for expanded compatibility.
A third comment very briefly mentions using the reMarkable tablet for note-taking while coding, indirectly hinting at a possible use case for Ghostwriter. However, the connection isn't explicitly made, and the commenter doesn't elaborate on how Ghostwriter might fit into that workflow.
Overall, the discussion is limited and primarily focuses on initial reactions and potential future applications rather than a detailed analysis of Ghostwriter itself. It doesn't offer a wealth of compelling insights, mainly expressing curiosity, suggestions for broader compatibility, and a questioning of the project's practical utility.