I Don't Have Spotify
36 by ohjeez | 19 comments on Hacker News.
Sunday, August 31, 2025
Saturday, August 30, 2025
Friday, August 29, 2025
Thursday, August 28, 2025
New top story on Hacker News: Show HN: Grammit – Local-only AI grammar checker (Chrome extension)
Show HN: Grammit – Local-only AI grammar checker (Chrome extension)
9 by scottfr | 0 comments on Hacker News.
Hey HN, I wanted a grammar checker that didn’t send my writing to someone's servers, so we built Grammit, a Chrome extension that runs grammar checks locally using an LLM. Your text never leaves your computer during checking. Here’s a 2-minute overview: https://ift.tt/sey7EpU Because it uses an LLM, it catches more than spelling and grammar. For example, it can correct some wrong statements like “The first US president was Benjamin Franklin.” Grammit also includes an in-page writing assistant that can rephrase or draft new text. It also uses the local LLM. We used many new web features to build this, such as: - Chrome’s new Prompt API to talk to the local model. - Anchor Positioning API to place the UI with minimal impact on the DOM. - CSS Custom Highlights API for inline error marking. - The new CSS sign() function to create CSS-driven layout with discontinuities. Part of the fun of being early adopters of bleeding edge tech is we’re discovering new Chrome bugs (e.g., https://ift.tt/L9bwa7D , https://ift.tt/K9zurEX ). I’d love your feedback on: - Where the UX feels rough - What do you think of the corrections and suggestions Happy to answer questions about the tech or the Prompt API. Thanks for trying it out! Chrome Web Store extension link: https://ift.tt/IUF8wnQ...
9 by scottfr | 0 comments on Hacker News.
Hey HN, I wanted a grammar checker that didn’t send my writing to someone's servers, so we built Grammit, a Chrome extension that runs grammar checks locally using an LLM. Your text never leaves your computer during checking. Here’s a 2-minute overview: https://ift.tt/sey7EpU Because it uses an LLM, it catches more than spelling and grammar. For example, it can correct some wrong statements like “The first US president was Benjamin Franklin.” Grammit also includes an in-page writing assistant that can rephrase or draft new text. It also uses the local LLM. We used many new web features to build this, such as: - Chrome’s new Prompt API to talk to the local model. - Anchor Positioning API to place the UI with minimal impact on the DOM. - CSS Custom Highlights API for inline error marking. - The new CSS sign() function to create CSS-driven layout with discontinuities. Part of the fun of being early adopters of bleeding edge tech is we’re discovering new Chrome bugs (e.g., https://ift.tt/L9bwa7D , https://ift.tt/K9zurEX ). I’d love your feedback on: - Where the UX feels rough - What do you think of the corrections and suggestions Happy to answer questions about the tech or the Prompt API. Thanks for trying it out! Chrome Web Store extension link: https://ift.tt/IUF8wnQ...
Wednesday, August 27, 2025
New top story on Hacker News: Malicious versions of Nx and some supporting plugins were published
Malicious versions of Nx and some supporting plugins were published
93 by longcat | 242 comments on Hacker News.
See also: https://ift.tt/DVJFk9P... https://ift.tt/wcX8qaI...
93 by longcat | 242 comments on Hacker News.
See also: https://ift.tt/DVJFk9P... https://ift.tt/wcX8qaI...
Tuesday, August 26, 2025
New top story on Hacker News: Show HN: SecretMemoryLocker – File Encryption Without Static Passwords
Show HN: SecretMemoryLocker – File Encryption Without Static Passwords
4 by YuriiDev | 0 comments on Hacker News.
I built SecretMemoryLocker ( https://ift.tt/boKeGJa ), a file encryption tool that generates keys dynamically from your answers to personal questions instead of using a static master password. This makes offline brute-force attacks much more difficult. Think of it as a password manager that meets mnemonic seed recovery, but without storing any sensitive keys on disk. Why? I kept losing master passwords and wanted a solution that wasn't tied to a single point of failure. I also wanted to create a "digital legacy" that my family could access only under specific conditions. The core principle is knowledge-based encryption: the key only exists in memory when you provide the correct answers. Status: * MVP is ready for Windows (.exe). * Linux and macOS support is planned. * UI is available in English, Spanish, and Ukrainian. Key Features: * No Static Secrets: No master password or seed phrase is ever stored. The key is reconstructed on the fly. * Knowledge-Based Key Generation: The final encryption key is derived from a combination of your personal answers and file metadata. * Offline Brute-Force Resistance: Uses MirageLoop, a decoy system that activates when incorrect answers are entered. Instead of decrypting real data, it generates an endless sequence of AI-created questions from a secure local database, creating an illusion of progress while keeping your real data untouched. * Offline AI Generation Mode: Optional offline Q&A generator (prototype). How It Works (Simplified): 1) Files are packed into an AES-256 encrypted ZIP archive. 2) A JSON key file stores the questions in an encrypted chain. Each subsequent question is encrypted with a key derived from the previous correct answer and the file's hash. This forces you to answer them sequentially. 3) The final encryption key for the ZIP file is derived by combining the hashes of all your correct answers. The key derivation formula looks like this: K_final = SHA256(H(answer1+file_hash) + H(answer2+file_hash) + ...) (Note: We are aware that a fast hash like SHA256 is not ideal for a KDF. We plan to migrate to Argon2 in a future release to further strengthen resistance against brute-force attacks.) To encrypt, you provide a file. This creates two outputs: your_file.txt → your_file_SMLkey.json + your_file_SecretML.zip To decrypt, you need both files and the correct answers. Install & Quick Start: Download the EXE from GitHub Releases (no dependencies needed): https://ift.tt/VcifBEl Encrypt: SecretMemoryLocker.exe --encrypt "C:\docs\important.pdf" Decrypt: SecretMemoryLocker.exe --decrypt "C:\docs\important_SMLkey.json" I would love to get your feedback on the concept, the user experience, and any security assumptions I've made. Thanks!
4 by YuriiDev | 0 comments on Hacker News.
I built SecretMemoryLocker ( https://ift.tt/boKeGJa ), a file encryption tool that generates keys dynamically from your answers to personal questions instead of using a static master password. This makes offline brute-force attacks much more difficult. Think of it as a password manager that meets mnemonic seed recovery, but without storing any sensitive keys on disk. Why? I kept losing master passwords and wanted a solution that wasn't tied to a single point of failure. I also wanted to create a "digital legacy" that my family could access only under specific conditions. The core principle is knowledge-based encryption: the key only exists in memory when you provide the correct answers. Status: * MVP is ready for Windows (.exe). * Linux and macOS support is planned. * UI is available in English, Spanish, and Ukrainian. Key Features: * No Static Secrets: No master password or seed phrase is ever stored. The key is reconstructed on the fly. * Knowledge-Based Key Generation: The final encryption key is derived from a combination of your personal answers and file metadata. * Offline Brute-Force Resistance: Uses MirageLoop, a decoy system that activates when incorrect answers are entered. Instead of decrypting real data, it generates an endless sequence of AI-created questions from a secure local database, creating an illusion of progress while keeping your real data untouched. * Offline AI Generation Mode: Optional offline Q&A generator (prototype). How It Works (Simplified): 1) Files are packed into an AES-256 encrypted ZIP archive. 2) A JSON key file stores the questions in an encrypted chain. Each subsequent question is encrypted with a key derived from the previous correct answer and the file's hash. This forces you to answer them sequentially. 3) The final encryption key for the ZIP file is derived by combining the hashes of all your correct answers. The key derivation formula looks like this: K_final = SHA256(H(answer1+file_hash) + H(answer2+file_hash) + ...) (Note: We are aware that a fast hash like SHA256 is not ideal for a KDF. We plan to migrate to Argon2 in a future release to further strengthen resistance against brute-force attacks.) To encrypt, you provide a file. This creates two outputs: your_file.txt → your_file_SMLkey.json + your_file_SecretML.zip To decrypt, you need both files and the correct answers. Install & Quick Start: Download the EXE from GitHub Releases (no dependencies needed): https://ift.tt/VcifBEl Encrypt: SecretMemoryLocker.exe --encrypt "C:\docs\important.pdf" Decrypt: SecretMemoryLocker.exe --decrypt "C:\docs\important_SMLkey.json" I would love to get your feedback on the concept, the user experience, and any security assumptions I've made. Thanks!
Monday, August 25, 2025
New top story on Hacker News: Show HN: Stagewise – frontend coding agent for real codebases
Show HN: Stagewise – frontend coding agent for real codebases
3 by glenntws | 1 comments on Hacker News.
Hey HN, we're Glenn and Julian, and we're building stagewise ( https://stagewise.io ), a frontend coding agent that inside your app’s dev mode and that makes changes in your local codebase. We’re compatible with any framework and any component library. Think of it like a v0 of Lovable that works locally and with any existing codebase. You can spawn the agent into locally running web apps in dev mode with `npx stagewise` from the project root. The agent lets you then click on HTML Elements in your app, enter prompts like 'increase the height here' and will implement the changes in your source code. Before stagewise, we were building a vertical SaaS for logistics from scratch and loved using prototyping tools like v0 or lovable to get to the first version. But when switching from v0/ lovable to Cursor for local development, we felt like the frontend magic was gone. So, we decided to build stagewise to bring that same magic to local development. The first version of stagewise just forwarded a prompt with browser context to existing IDEs and agents (Cursor, Cline, ..) and went viral on X after we open sourced it. However, the APIs of existing coding agents were very limiting, so we figured that building our own agent would unlock the full potential of stagewise. Since our last Show HN ( https://ift.tt/wRjQTm1 ), we launched a few very important features and changes: You now have a proprietary chat history with the agent, an undo button to revert changes, and we increased the amount of free credits AND reduced the pricing by 50%. We made a video about all these changes, showing you how stagewise works: https://ift.tt/jI8L6Xi... . So far, we've seen great adoption from non-technical users who wanted to continue building their lovable prototype locally. We personally use the agent almost daily to make changes to our landing page and to build the UI of new features on our console ( https://ift.tt/uscD0YJ ). If you have an app running in dev mode, simply `cd` into the app directory and run `npx stagewise` - the agent should appear, ready to play with. We're very excited to hear your feedback!
3 by glenntws | 1 comments on Hacker News.
Hey HN, we're Glenn and Julian, and we're building stagewise ( https://stagewise.io ), a frontend coding agent that inside your app’s dev mode and that makes changes in your local codebase. We’re compatible with any framework and any component library. Think of it like a v0 of Lovable that works locally and with any existing codebase. You can spawn the agent into locally running web apps in dev mode with `npx stagewise` from the project root. The agent lets you then click on HTML Elements in your app, enter prompts like 'increase the height here' and will implement the changes in your source code. Before stagewise, we were building a vertical SaaS for logistics from scratch and loved using prototyping tools like v0 or lovable to get to the first version. But when switching from v0/ lovable to Cursor for local development, we felt like the frontend magic was gone. So, we decided to build stagewise to bring that same magic to local development. The first version of stagewise just forwarded a prompt with browser context to existing IDEs and agents (Cursor, Cline, ..) and went viral on X after we open sourced it. However, the APIs of existing coding agents were very limiting, so we figured that building our own agent would unlock the full potential of stagewise. Since our last Show HN ( https://ift.tt/wRjQTm1 ), we launched a few very important features and changes: You now have a proprietary chat history with the agent, an undo button to revert changes, and we increased the amount of free credits AND reduced the pricing by 50%. We made a video about all these changes, showing you how stagewise works: https://ift.tt/jI8L6Xi... . So far, we've seen great adoption from non-technical users who wanted to continue building their lovable prototype locally. We personally use the agent almost daily to make changes to our landing page and to build the UI of new features on our console ( https://ift.tt/uscD0YJ ). If you have an app running in dev mode, simply `cd` into the app directory and run `npx stagewise` - the agent should appear, ready to play with. We're very excited to hear your feedback!
Sunday, August 24, 2025
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Monday, August 18, 2025
New top story on Hacker News: Show HN: Whispering – Open-source, local-first dictation you can trust
Show HN: Whispering – Open-source, local-first dictation you can trust
13 by braden-w | 5 comments on Hacker News.
Hey HN! Braden here, creator of Whispering, an open-source speech-to-text app. I really like dictation. For years, I relied on transcription tools that were almost good, but they were all closed-source. Even a lot of them that claimed to be “local” or “on-device” were still black boxes that left me wondering where my audio really went. So I built Whispering. It’s open-source, local-first, and most importantly, transparent with your data. All your data is stored locally on your device. For me, the features were good enough that I left my paid tools behind (I used Superwhisper and Wispr Flow before). Productivity apps should be open-source and transparent with your data, but they also need to match the UX of paid, closed-software alternatives. I hope Whispering is near that point. I use it for several hours a day, from coding to thinking out loud while carrying pizza boxes back from the office. Here’s an overview: https://www.youtube.com/watch?v=1jYgBMrfVZs , and here’s how I personally am using it with Claude Code these days: https://www.youtube.com/watch?v=tpix588SeiQ . There are plenty of transcription apps out there, but I hope Whispering adds some extra competition from the OSS ecosystem (one of my other OSS favorites is Handy https://ift.tt/EOmZBAY ). Whispering has a few tricks up its sleeve, like a voice-activated mode for hands-free operation (no button holding), and customizable AI transformations with any prompt/model. Whispering used to be in my personal GH repo, but I recently moved it as part of a larger project called Epicenter ( https://ift.tt/xl0ARmP ), which I should explain a bit... I’m basically obsessed with local-first open-source software. I think there should be an open-source, local-first version of every app, and I would like them all to work together. The idea of Epicenter is to store your data in a folder of plaintext and SQLite, and build a suite of interoperable, local-first tools on top of this shared memory. Everything is totally transparent, so you can trust it. Whispering is the first app in this effort. It’s not there yet regarding memory, but it’s getting there. I’ll probably write more about the bigger picture soon, but mainly I just want to make software and let it speak for itself (no pun intended in this case!), so this is my Show HN for now. I just finished college and was about to move back with my parents and work on this instead of getting a job…and then I somehow got into YC. So my current plan is to cover my living expenses and use the YC funding to support maintainers, our dependencies, and people working on their own open-source local-first projects. More on that soon. Would love your feedback, ideas, and roasts. If you would like to support the project, star it on GitHub here ( https://ift.tt/xl0ARmP ) and join the Discord here ( https://ift.tt/aitfUMo ). Everything’s MIT licensed, so fork it, break it, ship your own version, copy whatever you want!
13 by braden-w | 5 comments on Hacker News.
Hey HN! Braden here, creator of Whispering, an open-source speech-to-text app. I really like dictation. For years, I relied on transcription tools that were almost good, but they were all closed-source. Even a lot of them that claimed to be “local” or “on-device” were still black boxes that left me wondering where my audio really went. So I built Whispering. It’s open-source, local-first, and most importantly, transparent with your data. All your data is stored locally on your device. For me, the features were good enough that I left my paid tools behind (I used Superwhisper and Wispr Flow before). Productivity apps should be open-source and transparent with your data, but they also need to match the UX of paid, closed-software alternatives. I hope Whispering is near that point. I use it for several hours a day, from coding to thinking out loud while carrying pizza boxes back from the office. Here’s an overview: https://www.youtube.com/watch?v=1jYgBMrfVZs , and here’s how I personally am using it with Claude Code these days: https://www.youtube.com/watch?v=tpix588SeiQ . There are plenty of transcription apps out there, but I hope Whispering adds some extra competition from the OSS ecosystem (one of my other OSS favorites is Handy https://ift.tt/EOmZBAY ). Whispering has a few tricks up its sleeve, like a voice-activated mode for hands-free operation (no button holding), and customizable AI transformations with any prompt/model. Whispering used to be in my personal GH repo, but I recently moved it as part of a larger project called Epicenter ( https://ift.tt/xl0ARmP ), which I should explain a bit... I’m basically obsessed with local-first open-source software. I think there should be an open-source, local-first version of every app, and I would like them all to work together. The idea of Epicenter is to store your data in a folder of plaintext and SQLite, and build a suite of interoperable, local-first tools on top of this shared memory. Everything is totally transparent, so you can trust it. Whispering is the first app in this effort. It’s not there yet regarding memory, but it’s getting there. I’ll probably write more about the bigger picture soon, but mainly I just want to make software and let it speak for itself (no pun intended in this case!), so this is my Show HN for now. I just finished college and was about to move back with my parents and work on this instead of getting a job…and then I somehow got into YC. So my current plan is to cover my living expenses and use the YC funding to support maintainers, our dependencies, and people working on their own open-source local-first projects. More on that soon. Would love your feedback, ideas, and roasts. If you would like to support the project, star it on GitHub here ( https://ift.tt/xl0ARmP ) and join the Discord here ( https://ift.tt/aitfUMo ). Everything’s MIT licensed, so fork it, break it, ship your own version, copy whatever you want!
Sunday, August 17, 2025
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New top story on Hacker News: Show HN: Llmswap – Python package to reduce LLM API costs by 50-90% with caching
Show HN: Llmswap – Python package to reduce LLM API costs by 50-90% with caching
8 by sreenathmenon | 1 comments on Hacker News.
I built llmswap to solve a problem I kept hitting in hackathons - burning through API credits while testing the same prompts repeatedly during development. It's a simple Python package that provides a unified interface for OpenAI, Anthropic, Google Gemini, and local models (Ollama), with built-in response caching that can cut API costs by 50-90%. Key features: - Intelligent caching with TTL and memory limits - Context-aware caching for multi-user apps - Auto-fallback between providers when one fails - Zero configuration - works with environment variables from llmswap import LLMClient client = LLMClient(cache_enabled=True) response = client.query("Explain quantum computing") # Second identical query returns from cache instantly (free) The caching is disabled by default for security. When enabled, it's thread-safe and includes context isolation for multi-user applications. Built this from components of a hackathon project. Already at 2.2k downloads on PyPI. Hope it helps others save on API costs during development. GitHub: https://ift.tt/AGBkUiI PyPI: https://ift.tt/IhBwHSL
8 by sreenathmenon | 1 comments on Hacker News.
I built llmswap to solve a problem I kept hitting in hackathons - burning through API credits while testing the same prompts repeatedly during development. It's a simple Python package that provides a unified interface for OpenAI, Anthropic, Google Gemini, and local models (Ollama), with built-in response caching that can cut API costs by 50-90%. Key features: - Intelligent caching with TTL and memory limits - Context-aware caching for multi-user apps - Auto-fallback between providers when one fails - Zero configuration - works with environment variables from llmswap import LLMClient client = LLMClient(cache_enabled=True) response = client.query("Explain quantum computing") # Second identical query returns from cache instantly (free) The caching is disabled by default for security. When enabled, it's thread-safe and includes context isolation for multi-user applications. Built this from components of a hackathon project. Already at 2.2k downloads on PyPI. Hope it helps others save on API costs during development. GitHub: https://ift.tt/AGBkUiI PyPI: https://ift.tt/IhBwHSL
Saturday, August 9, 2025
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New top story on Hacker News: Show HN: Mathpad – Physical keypad for typing 100+ math symbols anywhere
Show HN: Mathpad – Physical keypad for typing 100+ math symbols anywhere
15 by MagneLauritzen | 10 comments on Hacker News.
Here's something different than your usual fare: A physical keypad that lets you directly type math! Ever tried typing mathematical equations in your code IDE, email, or on Slack? You might know it can be tricky. Mathpad solves this with dedicated keys for Greek letters, calculus symbols, and more. Press the ∫ key and get ∫, in any application that accepts text. It uses Unicode composition, so it works everywhere: Browsers, chat apps, code editors, Word, you name it. Basically, anywhere you can type text, Mathpad lets you type mathematics. I built Mathpad after getting frustrated with the friction of typing equations in e.g. Word, and what a pain in the ass it was to find the specific symbols I needed. I assumed that a product like Mathpad already existed, but that was not true and I had to build it myself. It turned out to be pretty useful! Three years of solo development later, I'm launching on Crowd Supply. One of the trickiest parts of this project was finding someone who could manufacture custom keycaps with mathematical symbols. Shoutout to Loic at 3dkeycap.com for making it possible! Fully open source (hardware + software): https://ift.tt/tWIaqgs Campaign: https://ift.tt/NyBf2Tc Project log: https://ift.tt/fVEnxJg
15 by MagneLauritzen | 10 comments on Hacker News.
Here's something different than your usual fare: A physical keypad that lets you directly type math! Ever tried typing mathematical equations in your code IDE, email, or on Slack? You might know it can be tricky. Mathpad solves this with dedicated keys for Greek letters, calculus symbols, and more. Press the ∫ key and get ∫, in any application that accepts text. It uses Unicode composition, so it works everywhere: Browsers, chat apps, code editors, Word, you name it. Basically, anywhere you can type text, Mathpad lets you type mathematics. I built Mathpad after getting frustrated with the friction of typing equations in e.g. Word, and what a pain in the ass it was to find the specific symbols I needed. I assumed that a product like Mathpad already existed, but that was not true and I had to build it myself. It turned out to be pretty useful! Three years of solo development later, I'm launching on Crowd Supply. One of the trickiest parts of this project was finding someone who could manufacture custom keycaps with mathematical symbols. Shoutout to Loic at 3dkeycap.com for making it possible! Fully open source (hardware + software): https://ift.tt/tWIaqgs Campaign: https://ift.tt/NyBf2Tc Project log: https://ift.tt/fVEnxJg
Sunday, August 3, 2025
Saturday, August 2, 2025
Friday, August 1, 2025
New top story on Hacker News: Show HN: TraceRoot – Open-source agentic debugging for distributed services
Show HN: TraceRoot – Open-source agentic debugging for distributed services
10 by xinweihe | 0 comments on Hacker News.
Hey Xinwei and Zecheng here, we are the authors of TraceRoot ( https://ift.tt/0mTwnQN ). TraceRoot ( https://traceroot.ai ) is an open-source debugging platform that helps engineers fix production issues faster by combining structured traces, logs, source code contexts and discussions in Github PRs, issues and Slack channels, etc. with AI Agents. At the heart are our lightweight Python ( https://ift.tt/Jp7H3lC ) and TypeScript ( https://ift.tt/LCYsH0U ) SDKs - they can hook into your app using OpenTelemetry and captures logs and traces. These are either sent to a local Jaeger ( https://ift.tt/GAdS9tg ) + SQLite backend or to our cloud backend, where we correlate them into a single view. From there, our custom agent takes over. The agent builds a heterogeneous execution tree that merges spans, logs, and GitHub context into one internal structure. This allows it to model the control and data flow of a request across services. It then uses LLMs to reason over this tree - pruning irrelevant branches, surfacing anomalous spans, and identifying likely root causes. You can ask questions like “what caused this timeout?” or “summarize the errors in these 3 spans”, and it can trace the failure back to a specific commit, summarize the chain of events, or even propose a fix via a draft PR. We also built a debugging UI that ties everything together - you explore traces visually, pick spans of interest, and get AI-assisted insights with full context: logs, timings, metadata, and surrounding code. Unlike most tools, TraceRoot stores long-term debugging history and builds structured context for each company - something we haven’t seen many others do in this space. What’s live today: - Python and TypeScript SDKs for structured logs and traces. - AI summaries, GitHub issue generation, and PR creation. - Debugging UI that ties everything together TraceRoot is MIT licensed and easy to self-host (via Docker). We support both local mode (Jaeger + SQLite) and cloud mode. Inspired by OSS projects like PostHog and Supabase - core is free, enterprise features like agent mode multi-tenant and slack integration are paid. If you find it interesting, you can see a demo video here: https://www.youtube.com/watch?v=nb-D3LM0sJM We’d love you to try TraceRoot ( https://traceroot.ai ) and share any feedback. If you're interested, our code is available here: https://ift.tt/0mTwnQN . If we don’t have something, let us know and we’d be happy to build it for you. We look forward to your comments!
10 by xinweihe | 0 comments on Hacker News.
Hey Xinwei and Zecheng here, we are the authors of TraceRoot ( https://ift.tt/0mTwnQN ). TraceRoot ( https://traceroot.ai ) is an open-source debugging platform that helps engineers fix production issues faster by combining structured traces, logs, source code contexts and discussions in Github PRs, issues and Slack channels, etc. with AI Agents. At the heart are our lightweight Python ( https://ift.tt/Jp7H3lC ) and TypeScript ( https://ift.tt/LCYsH0U ) SDKs - they can hook into your app using OpenTelemetry and captures logs and traces. These are either sent to a local Jaeger ( https://ift.tt/GAdS9tg ) + SQLite backend or to our cloud backend, where we correlate them into a single view. From there, our custom agent takes over. The agent builds a heterogeneous execution tree that merges spans, logs, and GitHub context into one internal structure. This allows it to model the control and data flow of a request across services. It then uses LLMs to reason over this tree - pruning irrelevant branches, surfacing anomalous spans, and identifying likely root causes. You can ask questions like “what caused this timeout?” or “summarize the errors in these 3 spans”, and it can trace the failure back to a specific commit, summarize the chain of events, or even propose a fix via a draft PR. We also built a debugging UI that ties everything together - you explore traces visually, pick spans of interest, and get AI-assisted insights with full context: logs, timings, metadata, and surrounding code. Unlike most tools, TraceRoot stores long-term debugging history and builds structured context for each company - something we haven’t seen many others do in this space. What’s live today: - Python and TypeScript SDKs for structured logs and traces. - AI summaries, GitHub issue generation, and PR creation. - Debugging UI that ties everything together TraceRoot is MIT licensed and easy to self-host (via Docker). We support both local mode (Jaeger + SQLite) and cloud mode. Inspired by OSS projects like PostHog and Supabase - core is free, enterprise features like agent mode multi-tenant and slack integration are paid. If you find it interesting, you can see a demo video here: https://www.youtube.com/watch?v=nb-D3LM0sJM We’d love you to try TraceRoot ( https://traceroot.ai ) and share any feedback. If you're interested, our code is available here: https://ift.tt/0mTwnQN . If we don’t have something, let us know and we’d be happy to build it for you. We look forward to your comments!