A tool to help software devs generate, debug and document code
UI is very minimalistic for the moment but will make it better soon.
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...well as module metadata, which is used to make prompts for generating piecewise bits of code and enforce a clean architecture (bonus the module metadata doubles as project documentation)
People love using similar products but resist paying. You’ll need to either find who will pay or create additional value that’s worth paying for.
Build but think about differentiation and monetization.
Your idea for a software architecture organizer that tracks implementation steps, module metadata, and generates code snippets falls into the 'Freemium' category. This means there's a good chance people will be interested in using your product. With 10 similar products already out there, competition is significant, indicating that this is a validated idea, so you should differentiate. The average engagement for similar products is medium, so you should plan for a good content and commmunity engagement strategy. Since the similar products tend to be freemium, you will need to either find who will pay or create additional value that’s worth paying for.
Your idea for a software architecture organizer that tracks implementation steps, module metadata, and generates code snippets falls into the 'Freemium' category. This means there's a good chance people will be interested in using your product. With 10 similar products already out there, competition is significant, indicating that this is a validated idea, so you should differentiate. The average engagement for similar products is medium, so you should plan for a good content and commmunity engagement strategy. Since the similar products tend to be freemium, you will need to either find who will pay or create additional value that’s worth paying for.
UI is very minimalistic for the moment but will make it better soon.
No content available
I built DocComment to solve the challenge of understanding unfamiliar code quickly, whether it's legacy code, AI-generated, or poorly documented.The key technical difference from existing tools like Copilot's commenting feature is that DocComment analyzes code structure before generating explanations. It builds a structural representation of both the specific code snippet and its broader context, allowing the LLM to generate more precise and contextual documentation. It's not competing against Copilot or Cursor, but rather work with them.Technical details:1. Operates alongside code without modifying source files2. Uses structural analysis to determine appropriate detail level for comments3. Focuses on explaining both local code behavior and broader business context4. Integrates with git repos for full codebase context(Planning)Current results show improved accuracy compared to pure LLM-based commenting systems, particularly for:1. Large functions with complex logic2. Code with unclear variable names3. AI-generated code4. Business logic heavy sectionsWould love feedback from the HN community. Both business and techincal perspectives are welcomed.
Users appreciate the idea and functionality, especially with Laravel, but they emphasize the need for better documentation and examples. There is also a preference for API access to the prompt rather than selling the prompt itself.
Users criticized the website for being not descriptive and lacking context in its documentation. Additionally, there were complaints about charging money for API access and not being prompt in doing so.
From any codebase, we generate a wiki with API Docs, descriptions of features and their implementation, and common guides maintaining the original style, language, and dependencies. These enables developers to understand legacy, spaghetti, or throwaway code.
The launch of Autonoma received positive feedback, with many users congratulating the team and expressing excitement to try the tool, especially for API and code documentation. Users appreciate its potential for time-saving automation and simplicity. Suggestions include Git integration for easier change tracking and linking code clarification with version control. One user reported issues with adding a private repo and email delivery. Several users wished the team good luck.
Users criticized the complexity of creating simple API documentation and suggested Git integration for improved change tracking. Some users, especially those from small teams, desire more features. A user reported issues adding a private repository and experienced email delivery failure.
Hey HN,I'm excited to share with you a new project I've been working on called Genie. It's a codebase explainer powered by generative AI, and I believe it has the potential to change how we understand and work with complex codebases.Here are the core features of Genie:1. Comprehensive explanations: Genie has the ability to explain entire feature implementations, even across dozens of files. It breaks down the code into digestible explanations, making it easier for developers to understand and navigate through complex projects.2. Connected datasources: Genie can connect to different related repositories, allowing developers to explore and analyse interconnected codebases. This feature promotes collaboration and knowledge sharing across projects.3. Grounded responses: Genie's explanations are backed by citations, providing reliable and verifiable information. This helps developers build trust in the generated explanations and enables them to dive deeper into the codebase.I recently wrote an article on Medium that delves into the mechanics behind langchain autonomous agents. The article was generated using Genie, and it provides a fascinating insight into this technology. You can read the article here: [https://medium.com/@dion_66818/a-deep-dive-into-langchain-ag...]Thank you for your time, and I look forward to hearing from the HN community!Best regards, DionNote: - We made a product-hunt launch! Would greatly appreciate if you guys could give me an upvote on my product-hunt launch: [https://www.producthunt.com/posts/genie-540f39f9-1f85-4db3-9...]
Questions about AI usage and integration.
Unclear AI usage and integration.
Hey Hackers, My team and I have been working on an automated software documentation and impact analysis platform for the last 3 years.Our long-term goal is to enter safety/mission-critical applications, where improper documentation can lead to disastrous outcomes, e.g., costly reworks/overruns or endangering human lives. But, in an effort to recognize revenue in the near term with our existing functionality, we have found initial traction with use cases focused on reverse engineering legacy systems. Where getting up to speed with an existing system requires a team of engineers to manually review large amounts of code, taking weeks or months to come to grips with.______________________________________________Our Self-Service release is a no-frills offering to leverage a subset of our document generation capabilities.Using only the code, SAFA is able to: -Summarize Code Files -Generate an overall project summary -Generate Upstream Documentation, like Features and Functional Requirements -Map relationships between all code and generated documentation with explanationsOur approach leverages our own LLM pipeline, which applies a variety of clustering/refinement techniques, embedding models, and LLMs to keep your entire system within context when generating documentation, change summaries, api flow, and more. We do not use customer data to train or refine our models.We currently only support Github integrations for self-service but will implement flat-file support in the near term. When using self-service, you will receive Code Summaries and a Project Overview for free, but we charge for generating documentation and relationships: 20 cents per code file and generated document (100 File Codebase = $35). Currently, self-service has a 1000 code file limit.________________________________________________If you want to see the quality of the documents SAFA generates before trying it with your code, feel free to check out our public codebases page (https://www.safa.ai/codebases). We have serious ones like Autoware's AV Control Module, and more fun ones, like Super Mario 64.Otherwise, our app is directly accessible via https://app.safa.ai (apologies, we do require an account to be made).I very much look forward to your feedback and insights.Feel free to email me directly at aarik@safa.ai.
A powerful tool that automatically generates comprehensive and accurate documentation from source code files
The Product Hunt launch received positive feedback and congratulations. Users find the tool simple and useful. There are questions regarding GitHub access levels, Swift compatibility, and the availability of an API for CI/CD integration. A user also inquired about maintaining the appropriate level of abstraction in documentation. Overall, users expressed enthusiasm and appreciation for the product's potential for streamlining documentation.
A user questioned how the product maintains a proper level of abstraction within its documentation.
Cutting-edge Visual Studio Code extension designed by professional developers to enhance code documentation and comprehension through the power of artificial intelligence.
The Product Hunt launch received positive feedback, with users congratulating the creator and praising the product's innovative idea and potential usefulness. Several users expressed interest in checking it out, and believed the product could be a great asset. However, one user noted the extension lacks sufficient information, examples, and documentation, suggesting a need for improvement in these areas.
The primary criticism revolves around a lack of comprehensive information. Users are pointing out the absence of examples, tutorials, and thorough documentation detailing how the product functions. This makes it difficult for potential users to understand the product's capabilities and how to effectively utilize it.
Hello HN community,I'm excited to share a project we've been working on called PromptMage, an open-source tool designed to simplify the process of creating and managing complex workflows for large language models (LLMs).BackstoryAs developers and researchers working with LLMs, we found ourselves constantly grappling with the challenges of prompt iteration, testing, and version control. Existing tools didn't quite fit our needs for an integrated, intuitive workflow management solution. So, we decided to build one. PromptMage was born out of a desire to streamline these processes and make LLM technology more accessible to teams of all sizes.What is PromptMage?PromptMage is a self-hosted python tool that provides a user-friendly interface for developing, testing, and managing LLM prompts. It offers several key features:- Prompt Playground: A space to rapidly test, compare, and refine prompts.- Integrated Version Control: Track changes and collaborate on prompt development with ease.- Auto-generated API: A FastAPI-powered API is automatically created for seamless integration and deployment.- Evaluation Mode: Conduct both manual and automated testing to ensure prompt reliability before deployment.Why PromptMage?We built PromptMage to fill a gap we felt keenly in our own work—an all-in-one solution that integrates prompt testing and version control directly into the workflow. Unlike other tools, PromptMage is designed with developers in mind, offering a straightforward setup and intuitive interface to foster collaboration and iteration.How to Try It OutPromptMage is currently in its alpha state and is under active development. We encourage you to install it, set it up (which should take about 5 minutes), and give it a spin. We’re eager for feedback from the community to help shape its future. You can deploy it locally or on your server.Check it out here: <https://promptmage.io/getting-started/>Don't forget to leave a star on github: <https://github.com/tsterbak/promptmage>What’s Next?We have a roadmap full of exciting features and enhancements. We're aiming to expand PromptMage’s capabilities to better support developers, researchers, and organizations as they navigate the rapidly evolving landscape of AI and LLM technology.We’d love to hear your thoughts and feedback! Whether it's bug reports, feature requests, or contributions, all are welcome. Let's work together to make PromptMage a valuable tool for the community.Feel free to ask any questions or share your experiences in the comments below!Thanks :)
Users are interested in testing prompts and contributing to the repository. There are concerns about the complexity and bugs in Dify and Coze. Users are also looking for specific use cases and identifying missing features. Additionally, there is a preference for prompt management over APM for LLMs.
The main criticisms are that the product is too complicated and buggy, and that most tools focus on APM rather than the prompt library.
Who says software engineers should update prompts? Why should developers unfamiliar with specific domains, like vegetable market reports, be tasked with writing prompts? We offer:The first CI/CD pipeline for prompt management An open-source library for prompt administration Efficient load distribution Self-evolving capabilities based on user feedback
Software engineers shouldn't update prompts; offer CI/CD pipeline.
Developers unfamiliar with domains shouldn't write prompts.