The best Hacker News stories from Show from the past day
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Show HN: Pyscn – Python code quality analyzer for vibe coders
Hi HN! I built pyscn for Python developers in the vibe coding era.
If you're using Cursor, Claude, or ChatGPT to ship Python code fast, you know the feeling: features work, tests pass, but the codebase feels... messy.<p>Common vibe coding artifacts:<p>• Code duplication (from copy-pasted snippets)<p>• Dead code from quick iterations<p>• Over-engineered solutions for simple problems<p>• Inconsistent patterns across modules<p>pyscn performs structural analysis:<p>• APTED tree edit distance + LSH<p>• Control-Flow Graph (CFG) analysis<p>• Coupling Between Objects (CBO)<p>• Cyclomatic Complexity<p>Try it without installation:<p><pre><code> uvx pyscn analyze . # Using uv (fastest)
pipx run pyscn analyze . # Using pipx
(Or install: pip install pyscn)
</code></pre>
Built with Go + tree-sitter. Happy to dive into the implementation details!
Show HN: JPDB, GDB for Your Waveforms
hey everyone,<p>I've been working on JPDB is a GDB like debugger for waveforms. if you give JPDB a waveform* and some other information, then you can step through the program that was executed when that waveform was created.<p>i say GDB-like because JPDB has it's own GDB client (its called shucks), that implements the client side logic of the GDB protocol faithfully, but doesnt have all of the GDB niceties (like python integration, etc). this allows the project to be specialized on debugging waveforms specifically, when compared to another approach like connecting to a gdb client<p>JPDB integrates with the waveform viewer surfer (<a href="https://surfer-project.org/" rel="nofollow">https://surfer-project.org/</a>), so you can look at other signals there. this is still ongoing because the underlying protocol (WCP) is a little Fresh<p>if you're developing your own CPU, give it a shot. Superscalar designs arent supported yet but it would be pretty straightforward, just give me your waves ( i am touching my fingers together villainously as i type this) and i will make it happen<p>also if you want to use system with a "normal" gdb client, the dang library presents a gdbstub server, so you can run that and connect to it.<p>here's a demo but it should work on your local machine if you follow the readme:<p><a href="https://www.youtube.com/watch?v=EOo1aG_wcJg" rel="nofollow">https://www.youtube.com/watch?v=EOo1aG_wcJg</a>
Show HN: Run – a CLI universal code runner I built while learning Rust
Hi HN — I’m learning Rust and decided to build a universal CLI for running code in many languages. The tool, Run, aims to be a single, minimal dependency utility for: running one-off snippets (from CLI flags), running files, reading and executing piped stdin, and providing language-specific REPLs that you can switch between interactively.<p>I designed it to support both interpreted languages (Python, JS, Ruby, etc.) and compiled languages (Rust, Go, C/C++). It detects languages from flags or file extensions, can compile temporary files for compiled languages, and exposes a unified REPL experience with commands like :help, :lang, and :quit.<p>Install: cargo install run-kit (or use the platform downloads on GitHub). Source & releases: <a href="https://github.com/Esubaalew/run" rel="nofollow">https://github.com/Esubaalew/run</a><p>I used Rust while following the official learning resources and used AI to speed up development, so I expect there are bugs and rough edges. I’d love feedback on: usability and UX of the REPL, edge cases for piping input to language runtimes, security considerations (sandboxing/resource limits), packaging and cross-platform distribution.<p>Thanks — I’ll try to answer questions and share design notes.
Show HN: Run – a CLI universal code runner I built while learning Rust
Hi HN — I’m learning Rust and decided to build a universal CLI for running code in many languages. The tool, Run, aims to be a single, minimal dependency utility for: running one-off snippets (from CLI flags), running files, reading and executing piped stdin, and providing language-specific REPLs that you can switch between interactively.<p>I designed it to support both interpreted languages (Python, JS, Ruby, etc.) and compiled languages (Rust, Go, C/C++). It detects languages from flags or file extensions, can compile temporary files for compiled languages, and exposes a unified REPL experience with commands like :help, :lang, and :quit.<p>Install: cargo install run-kit (or use the platform downloads on GitHub). Source & releases: <a href="https://github.com/Esubaalew/run" rel="nofollow">https://github.com/Esubaalew/run</a><p>I used Rust while following the official learning resources and used AI to speed up development, so I expect there are bugs and rough edges. I’d love feedback on: usability and UX of the REPL, edge cases for piping input to language runtimes, security considerations (sandboxing/resource limits), packaging and cross-platform distribution.<p>Thanks — I’ll try to answer questions and share design notes.
Show HN: Run – a CLI universal code runner I built while learning Rust
Hi HN — I’m learning Rust and decided to build a universal CLI for running code in many languages. The tool, Run, aims to be a single, minimal dependency utility for: running one-off snippets (from CLI flags), running files, reading and executing piped stdin, and providing language-specific REPLs that you can switch between interactively.<p>I designed it to support both interpreted languages (Python, JS, Ruby, etc.) and compiled languages (Rust, Go, C/C++). It detects languages from flags or file extensions, can compile temporary files for compiled languages, and exposes a unified REPL experience with commands like :help, :lang, and :quit.<p>Install: cargo install run-kit (or use the platform downloads on GitHub). Source & releases: <a href="https://github.com/Esubaalew/run" rel="nofollow">https://github.com/Esubaalew/run</a><p>I used Rust while following the official learning resources and used AI to speed up development, so I expect there are bugs and rough edges. I’d love feedback on: usability and UX of the REPL, edge cases for piping input to language runtimes, security considerations (sandboxing/resource limits), packaging and cross-platform distribution.<p>Thanks — I’ll try to answer questions and share design notes.
Show HN: Cobalt – a pixel-art painting studio for the Nintendo DS
Hey everyone,<p>Cobalt is a program for painting textural and expressive pixel-art on Windows, Linux, Nintendo DS, and in-browser. The same 46KB core executable runs on all platforms, with a thin emulator layer sitting on top to handle differences in inputs and filesystem access (which makes it easy to port between systems). It's built on Bedrock[0], an 8-bit virtual computer system I posted about here in July.<p>I created Cobalt because I wanted to draw messy, gritty pixel art without smooth gradients, and the smaller colour palette helped with making bolder colour choices. Images can be moved back and forth between platforms, so you can copy works-in-progress to the DS to keep working away on the bus or train. It's like a 2004-era vision of the future.<p>There's a live demo on the linked page that runs in the browser, and there are downloadable demos for every platform here[1]. Let me know if you try it out or have any questions!<p>[0] <a href="https://benbridle.com/bedrock" rel="nofollow">https://benbridle.com/bedrock</a><p>[1] <a href="https://derelict-engineering.itch.io/cobalt" rel="nofollow">https://derelict-engineering.itch.io/cobalt</a>
Show HN: Cobalt – a pixel-art painting studio for the Nintendo DS
Hey everyone,<p>Cobalt is a program for painting textural and expressive pixel-art on Windows, Linux, Nintendo DS, and in-browser. The same 46KB core executable runs on all platforms, with a thin emulator layer sitting on top to handle differences in inputs and filesystem access (which makes it easy to port between systems). It's built on Bedrock[0], an 8-bit virtual computer system I posted about here in July.<p>I created Cobalt because I wanted to draw messy, gritty pixel art without smooth gradients, and the smaller colour palette helped with making bolder colour choices. Images can be moved back and forth between platforms, so you can copy works-in-progress to the DS to keep working away on the bus or train. It's like a 2004-era vision of the future.<p>There's a live demo on the linked page that runs in the browser, and there are downloadable demos for every platform here[1]. Let me know if you try it out or have any questions!<p>[0] <a href="https://benbridle.com/bedrock" rel="nofollow">https://benbridle.com/bedrock</a><p>[1] <a href="https://derelict-engineering.itch.io/cobalt" rel="nofollow">https://derelict-engineering.itch.io/cobalt</a>
Show HN: Cobalt – a pixel-art painting studio for the Nintendo DS
Hey everyone,<p>Cobalt is a program for painting textural and expressive pixel-art on Windows, Linux, Nintendo DS, and in-browser. The same 46KB core executable runs on all platforms, with a thin emulator layer sitting on top to handle differences in inputs and filesystem access (which makes it easy to port between systems). It's built on Bedrock[0], an 8-bit virtual computer system I posted about here in July.<p>I created Cobalt because I wanted to draw messy, gritty pixel art without smooth gradients, and the smaller colour palette helped with making bolder colour choices. Images can be moved back and forth between platforms, so you can copy works-in-progress to the DS to keep working away on the bus or train. It's like a 2004-era vision of the future.<p>There's a live demo on the linked page that runs in the browser, and there are downloadable demos for every platform here[1]. Let me know if you try it out or have any questions!<p>[0] <a href="https://benbridle.com/bedrock" rel="nofollow">https://benbridle.com/bedrock</a><p>[1] <a href="https://derelict-engineering.itch.io/cobalt" rel="nofollow">https://derelict-engineering.itch.io/cobalt</a>
Show HN: BetterBrain – Dementia prevention, covered by insurance
Hey all!
I’ve been building BetterBrain for the past few months, which is the first dementia prevention program entirely covered by insurance.
BetterBrain combines expert clinicians, comprehensive testing and state of the art AI - and for many insurance plans is $0.
Research shows that dementia can be detected up to 20 years in advance. Despite this, many people at risk of dementia overlook regular brain health assessments. Many members of our founding team have family members affected by neurodegenerative disease.<p>We’re also hiring aggressively if anyone is interested in changing the future of treating neurodegenerative disease.<p>Would love to talk to anyone interested
<a href="https://www.betterbrain.com/insurance" rel="nofollow">https://www.betterbrain.com/insurance</a>
Show HN: Powerful Visual Programming Language (Book)
Throughout my 30+ software development career, after spending many sleepless nights digging up through enormous codebases to understand logic or fix a bug, I was thinking: "There must be a better, visual way to represent program rather than text". However, no usable visual programming language popped up on horizon for the whole duration of 30+ years of my career. Therefore, I decided to take matters in my own hands, creating new visual programming language called "Pipe". A book about this language was published recently. The book is available for free on Amazon Kindle and Apple iBooks.<p>Language Pipe has a level of sophistication and power comparable to existing most powerful textual languages and therefore, it has a very high chances to successfully compete with text-based programming. The book provides full and comprehensive language specification. On top of that, the book contains many features and ideas planned for future versions of the language.<p>Pipe implements many novel concepts and unique features. As a result, multiple patent applications have already been filed and pending. The published book contains complete language specification, including graphical notation of all its elements and full API specification for code integration. Pipe has the following features:<p>* General-purpose visual language.<p>* Compact but powerful language.<p>* Complete and detailed language specification.<p>* Practical visual language.<p>* API specification for integration with non-visual languages.<p>* Statically-typed language.<p>* Long-term plans for future versions.<p>* Augmentation of AI code generation.<p>* Language for the next generation of low-code systems.<p>The problem of AI code generation is that it is very difficult to prepare complete and precise input specifications, especially in case of a large project. The solution is generating code only for base-level components easily explainable to AI, completing the rest of application via manual coding. That, however, undermines the goal of leveraging AI to remove the need for human programming. Pipe provides an alternative to textual coding by encapsulating AI-generated components within visual blocks for building the rest of application as graphical workflows via an intuitive drag-and-drop interface. As a next level of Pipe evolution, AI will be generating complete visual workflows directly, making it much easier to understand and modify generated logic.<p>Usage of a general-purpose visual programming language Pipe to connect blocks containing AI-generated code can inspire the next generation of extremely versatile low-code platforms, as AI code generation followed by visual integration of generated components is a very powerful low-code framework. Users will be able to generate new components using AI and that solves the problem of limited customization in existing low-code platforms where components are mostly predefined. On top of that, common visual programming language Pipe will ensure portability of low-code projects between different platforms.<p>Please find PDF with book preview here: <a href="https://www.pipelang.com/sample/sample.pdf" rel="nofollow">https://www.pipelang.com/sample/sample.pdf</a>
Show HN: Powerful Visual Programming Language (Book)
Throughout my 30+ software development career, after spending many sleepless nights digging up through enormous codebases to understand logic or fix a bug, I was thinking: "There must be a better, visual way to represent program rather than text". However, no usable visual programming language popped up on horizon for the whole duration of 30+ years of my career. Therefore, I decided to take matters in my own hands, creating new visual programming language called "Pipe". A book about this language was published recently. The book is available for free on Amazon Kindle and Apple iBooks.<p>Language Pipe has a level of sophistication and power comparable to existing most powerful textual languages and therefore, it has a very high chances to successfully compete with text-based programming. The book provides full and comprehensive language specification. On top of that, the book contains many features and ideas planned for future versions of the language.<p>Pipe implements many novel concepts and unique features. As a result, multiple patent applications have already been filed and pending. The published book contains complete language specification, including graphical notation of all its elements and full API specification for code integration. Pipe has the following features:<p>* General-purpose visual language.<p>* Compact but powerful language.<p>* Complete and detailed language specification.<p>* Practical visual language.<p>* API specification for integration with non-visual languages.<p>* Statically-typed language.<p>* Long-term plans for future versions.<p>* Augmentation of AI code generation.<p>* Language for the next generation of low-code systems.<p>The problem of AI code generation is that it is very difficult to prepare complete and precise input specifications, especially in case of a large project. The solution is generating code only for base-level components easily explainable to AI, completing the rest of application via manual coding. That, however, undermines the goal of leveraging AI to remove the need for human programming. Pipe provides an alternative to textual coding by encapsulating AI-generated components within visual blocks for building the rest of application as graphical workflows via an intuitive drag-and-drop interface. As a next level of Pipe evolution, AI will be generating complete visual workflows directly, making it much easier to understand and modify generated logic.<p>Usage of a general-purpose visual programming language Pipe to connect blocks containing AI-generated code can inspire the next generation of extremely versatile low-code platforms, as AI code generation followed by visual integration of generated components is a very powerful low-code framework. Users will be able to generate new components using AI and that solves the problem of limited customization in existing low-code platforms where components are mostly predefined. On top of that, common visual programming language Pipe will ensure portability of low-code projects between different platforms.<p>Please find PDF with book preview here: <a href="https://www.pipelang.com/sample/sample.pdf" rel="nofollow">https://www.pipelang.com/sample/sample.pdf</a>
Show HN: FLE v0.3 – Claude Code Plays Factorio
We're excited to release v0.3.0 of the Factorio Learning Environment (FLE), an open-source environment for evaluating AI agents on long-horizon planning, spatial reasoning, and automation tasks.<p>== What is FLE? ==<p>FLE uses the game Factorio to test whether AI can handle complex, open-ended engineering challenges. Agents write Python code to build automated factories, progressing from simple resource extraction (~30 units/min) to sophisticated production chains (millions of units/sec).<p>== What's new in 0.3.0 ==<p>- Headless scaling: No longer needs the game client, enabling massive parallelization!<p>- OpenAI Gym compatibility: Standard interface for RL research<p>- Claude Code integration: We're livestreaming Claude playing Factorio [on Twitch](<a href="http://twitch.tv/playsfactorio" rel="nofollow">http://twitch.tv/playsfactorio</a>)<p>- Better tooling and SDK: 1-line CLI commands to run evaluations (with W&B logging)<p>== Key findings ==<p>We evaluated frontier models (Claude Opus 4.1, GPT-5, Gemini 2.5 Pro, Grok 4) on 24 production automation tasks of increasing complexity.<p>Even the best models struggle:<p>- Most models still rely on semi-manual strategies rather than true automation<p>- Agents rarely define helper functions or abstractions, limiting their ability to scale<p>- Error recovery remains difficult – agents often get stuck in repetitive failure loops<p>The performance gap between models on FLE correlates more closely with real-world task benchmarks (like GDPVal) than with traditional coding/reasoning evals.<p>== Why this matters ==<p>Unlike benchmarks based on exams that saturate quickly, Factorio's exponential complexity scaling means there's effectively no performance ceiling. The skills needed - system debugging, constraint satisfaction, logistics optimization - transfer directly to real challenges.<p>== Try it yourself ==<p>>>> uv add factorio-learning-environment<p>>>> uv add "factorio-learning-environment[eval]"<p>>>> fle cluster start<p>>>> fle eval --config configs/gym_run_config.json<p>We're looking for researchers, engineers, and modders interested in pushing the boundaries of agent capabilities. Join our Discord if you want to contribute. We look forward to meeting you and seeing what you can build!<p>-- FLE Team
Show HN: FLE v0.3 – Claude Code Plays Factorio
We're excited to release v0.3.0 of the Factorio Learning Environment (FLE), an open-source environment for evaluating AI agents on long-horizon planning, spatial reasoning, and automation tasks.<p>== What is FLE? ==<p>FLE uses the game Factorio to test whether AI can handle complex, open-ended engineering challenges. Agents write Python code to build automated factories, progressing from simple resource extraction (~30 units/min) to sophisticated production chains (millions of units/sec).<p>== What's new in 0.3.0 ==<p>- Headless scaling: No longer needs the game client, enabling massive parallelization!<p>- OpenAI Gym compatibility: Standard interface for RL research<p>- Claude Code integration: We're livestreaming Claude playing Factorio [on Twitch](<a href="http://twitch.tv/playsfactorio" rel="nofollow">http://twitch.tv/playsfactorio</a>)<p>- Better tooling and SDK: 1-line CLI commands to run evaluations (with W&B logging)<p>== Key findings ==<p>We evaluated frontier models (Claude Opus 4.1, GPT-5, Gemini 2.5 Pro, Grok 4) on 24 production automation tasks of increasing complexity.<p>Even the best models struggle:<p>- Most models still rely on semi-manual strategies rather than true automation<p>- Agents rarely define helper functions or abstractions, limiting their ability to scale<p>- Error recovery remains difficult – agents often get stuck in repetitive failure loops<p>The performance gap between models on FLE correlates more closely with real-world task benchmarks (like GDPVal) than with traditional coding/reasoning evals.<p>== Why this matters ==<p>Unlike benchmarks based on exams that saturate quickly, Factorio's exponential complexity scaling means there's effectively no performance ceiling. The skills needed - system debugging, constraint satisfaction, logistics optimization - transfer directly to real challenges.<p>== Try it yourself ==<p>>>> uv add factorio-learning-environment<p>>>> uv add "factorio-learning-environment[eval]"<p>>>> fle cluster start<p>>>> fle eval --config configs/gym_run_config.json<p>We're looking for researchers, engineers, and modders interested in pushing the boundaries of agent capabilities. Join our Discord if you want to contribute. We look forward to meeting you and seeing what you can build!<p>-- FLE Team
Show HN: FLE v0.3 – Claude Code Plays Factorio
We're excited to release v0.3.0 of the Factorio Learning Environment (FLE), an open-source environment for evaluating AI agents on long-horizon planning, spatial reasoning, and automation tasks.<p>== What is FLE? ==<p>FLE uses the game Factorio to test whether AI can handle complex, open-ended engineering challenges. Agents write Python code to build automated factories, progressing from simple resource extraction (~30 units/min) to sophisticated production chains (millions of units/sec).<p>== What's new in 0.3.0 ==<p>- Headless scaling: No longer needs the game client, enabling massive parallelization!<p>- OpenAI Gym compatibility: Standard interface for RL research<p>- Claude Code integration: We're livestreaming Claude playing Factorio [on Twitch](<a href="http://twitch.tv/playsfactorio" rel="nofollow">http://twitch.tv/playsfactorio</a>)<p>- Better tooling and SDK: 1-line CLI commands to run evaluations (with W&B logging)<p>== Key findings ==<p>We evaluated frontier models (Claude Opus 4.1, GPT-5, Gemini 2.5 Pro, Grok 4) on 24 production automation tasks of increasing complexity.<p>Even the best models struggle:<p>- Most models still rely on semi-manual strategies rather than true automation<p>- Agents rarely define helper functions or abstractions, limiting their ability to scale<p>- Error recovery remains difficult – agents often get stuck in repetitive failure loops<p>The performance gap between models on FLE correlates more closely with real-world task benchmarks (like GDPVal) than with traditional coding/reasoning evals.<p>== Why this matters ==<p>Unlike benchmarks based on exams that saturate quickly, Factorio's exponential complexity scaling means there's effectively no performance ceiling. The skills needed - system debugging, constraint satisfaction, logistics optimization - transfer directly to real challenges.<p>== Try it yourself ==<p>>>> uv add factorio-learning-environment<p>>>> uv add "factorio-learning-environment[eval]"<p>>>> fle cluster start<p>>>> fle eval --config configs/gym_run_config.json<p>We're looking for researchers, engineers, and modders interested in pushing the boundaries of agent capabilities. Join our Discord if you want to contribute. We look forward to meeting you and seeing what you can build!<p>-- FLE Team
Show HN: FLE v0.3 – Claude Code Plays Factorio
We're excited to release v0.3.0 of the Factorio Learning Environment (FLE), an open-source environment for evaluating AI agents on long-horizon planning, spatial reasoning, and automation tasks.<p>== What is FLE? ==<p>FLE uses the game Factorio to test whether AI can handle complex, open-ended engineering challenges. Agents write Python code to build automated factories, progressing from simple resource extraction (~30 units/min) to sophisticated production chains (millions of units/sec).<p>== What's new in 0.3.0 ==<p>- Headless scaling: No longer needs the game client, enabling massive parallelization!<p>- OpenAI Gym compatibility: Standard interface for RL research<p>- Claude Code integration: We're livestreaming Claude playing Factorio [on Twitch](<a href="http://twitch.tv/playsfactorio" rel="nofollow">http://twitch.tv/playsfactorio</a>)<p>- Better tooling and SDK: 1-line CLI commands to run evaluations (with W&B logging)<p>== Key findings ==<p>We evaluated frontier models (Claude Opus 4.1, GPT-5, Gemini 2.5 Pro, Grok 4) on 24 production automation tasks of increasing complexity.<p>Even the best models struggle:<p>- Most models still rely on semi-manual strategies rather than true automation<p>- Agents rarely define helper functions or abstractions, limiting their ability to scale<p>- Error recovery remains difficult – agents often get stuck in repetitive failure loops<p>The performance gap between models on FLE correlates more closely with real-world task benchmarks (like GDPVal) than with traditional coding/reasoning evals.<p>== Why this matters ==<p>Unlike benchmarks based on exams that saturate quickly, Factorio's exponential complexity scaling means there's effectively no performance ceiling. The skills needed - system debugging, constraint satisfaction, logistics optimization - transfer directly to real challenges.<p>== Try it yourself ==<p>>>> uv add factorio-learning-environment<p>>>> uv add "factorio-learning-environment[eval]"<p>>>> fle cluster start<p>>>> fle eval --config configs/gym_run_config.json<p>We're looking for researchers, engineers, and modders interested in pushing the boundaries of agent capabilities. Join our Discord if you want to contribute. We look forward to meeting you and seeing what you can build!<p>-- FLE Team
Show HN: Privacyforge.ai – AI Privacy Compliance Documents That Work
Hi HN,<p>I'm Divy, former CTO at Branch and previously led engineering teams at Credit Karma and NexHealth. Over the past decade in fintech and healthtech, I've watched too many founders get blindsided by privacy compliance.<p>The Problem: 80% of startups are unaware of privacy laws affecting their business. The choice between expensive attorneys ($5,000+) and risky generic templates is getting worse as regulations expand. Generic privacy policies fail because they make promises your business can't keep – I've seen this tank funding rounds and trigger regulatory investigations.<p>My Personal Pain: At Branch, we spent weeks and over $5K just to get basic privacy compliance docs. Our attorneys charged hundreds per hour to essentially fill out forms about our data practices. The kicker? The policy didn't even cover our specific use cases properly, and we had to redo everything when new regulations kicked in.<p>The Solution: PrivacyForge.ai generates legally compliant privacy documentation using AI trained on current regulations. Instead of generic templates, it creates documents based on your actual business practices – what data you collect, how you process it, where you store it, and which jurisdictions apply to you.<p>Technical Approach: We built this on Google Cloud with Vertex AI, using Claude Sonnet and Gemini 2.5 for document generation. The system maintains separate knowledge bases for GDPR, CCPA, CPRA, PIPEDA, COPPA, and CalOPPA. Each document gets validated against jurisdiction-specific requirements before delivery. We're continuously expanding the regulations we support.<p>Different from existing tools: Most privacy generators use static templates with basic fill-in-the-blanks. We analyze your specific data flows and generate custom language. No per-site pricing that kills agencies – just one-time payments with included updates when regulations change.
Current status: We're live with paying customers who've saved thousands in legal fees. Generated documents have passed compliance reviews at companies going through Series A due diligence.<p>Try it at privacyforge.ai – would love feedback from the HN community, especially if you're dealing with privacy compliance headaches at your company.<p>What privacy compliance nightmares have you faced? Always curious to hear war stories from fellow builders.
Show HN: Grapes Studio – HTML-first WYSIWYG website editor with LLM assistant
I’ve been working with @artf (creator of GrapesJS) on Grapes Studio, an HTML-first editor with an LLM assistant on top of GrapesJS.<p>We’re approaching this differently than the new wave of AI app/site builders which are typically generating full React applications, which we think is overkill for simple websites. From talking to people using these tools, we’ve seen a lot of issues with build errors and overly complicated pages.<p>With our approach you can:<p>- Edit visually via the no-code editor (drag/drop) or ask the LLM to make scoped changes (like “add a section” or “add a new page”).<p>- Build with straight HTML/CSS<p>- Ask AI to import your current site and start building from there instead of total rebuild.<p>We think there’s a lot of benefit using drag and drop editor functionality with LLMs, or you can jump straight into the code in the editor if you choose.<p>- Do you see value in this hybrid model (AI + visual + code editing)?<p>- What are the biggest blockers you’ve run into with AI-only builders?<p>Let us know what you think.
Show HN: Grapes Studio – HTML-first WYSIWYG website editor with LLM assistant
I’ve been working with @artf (creator of GrapesJS) on Grapes Studio, an HTML-first editor with an LLM assistant on top of GrapesJS.<p>We’re approaching this differently than the new wave of AI app/site builders which are typically generating full React applications, which we think is overkill for simple websites. From talking to people using these tools, we’ve seen a lot of issues with build errors and overly complicated pages.<p>With our approach you can:<p>- Edit visually via the no-code editor (drag/drop) or ask the LLM to make scoped changes (like “add a section” or “add a new page”).<p>- Build with straight HTML/CSS<p>- Ask AI to import your current site and start building from there instead of total rebuild.<p>We think there’s a lot of benefit using drag and drop editor functionality with LLMs, or you can jump straight into the code in the editor if you choose.<p>- Do you see value in this hybrid model (AI + visual + code editing)?<p>- What are the biggest blockers you’ve run into with AI-only builders?<p>Let us know what you think.
Show HN: Traceroute Visualizer
This nifty tool plots the traceroute results and shows you the RTT as well as the distance travelled by the packets!<p>Supports MTR, flyingroutes and of course, traceroute.<p>The existing solutions were too limited so I made that.<p>Let me know if you have any feedback
Show HN: Traceroute Visualizer
This nifty tool plots the traceroute results and shows you the RTT as well as the distance travelled by the packets!<p>Supports MTR, flyingroutes and of course, traceroute.<p>The existing solutions were too limited so I made that.<p>Let me know if you have any feedback