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Show HN: I built an open-source tool to make on-call suck less

Hey HN,<p>I am building an open source platform to make on-call better and less stressful for engineers. We are building a tool that can silence alerts and help with debugging and root cause analysis. We also want to automate tedious parts of being on-call (running runbooks manually, answering questions on Slack, dealing with Pagerduty). Here is a quick video of how it works: <a href="https://youtu.be/m_K9Dq1kZDw" rel="nofollow">https://youtu.be/m_K9Dq1kZDw</a><p>I hated being on-call for a couple of reasons:<p>* Alert volume: The number of alerts kept increasing over time. It was hard to maintain existing alerts. This would lead to a lot of noisy and unactionable alerts. I have lost count of the number of times I got woken up by alert that auto-resolved 5 minutes later.<p>* Debugging: Debugging an alert or a customer support ticket would need me to gain context on a service that I might not have worked on before. These companies used many observability tools that would make debugging challenging. There are always a time pressure to resolve issues quickly.<p>There were some more tangential issues that used to take up a lot of on-call time<p>* Support: Answering questions from other teams. A lot of times these questions were repetitive and have been answered before.<p>* Dealing with PagerDuty: These tools are hard to use. e.g. It was hard to schedule an override in PD or do holiday schedules.<p>I am building an on-call tool that is Slack-native since that has become the de-facto tool for on-call engineers.<p>We heard from a lot of engineers that maintaining good alert hygiene is a challenge.<p>To start off, Opslane integrates with Datadog and can classify alerts as actionable or noisy.<p>We analyze your alert history across various signals:<p>1. Alert frequency<p>2. How quickly the alerts have resolved in the past<p>3. Alert priority<p>4. Alert response history<p>Our classification is conservative and it can be tuned as teams get more confidence in the predictions. We want to make sure that you aren't accidentally missing a critical alert.<p>Additionally, we generate a weekly report based on all your alerts to give you a picture of your overall alert hygiene.<p>What’s next?<p>1. Building more integrations (Prometheus, Splunk, Sentry, PagerDuty) to continue making on-call quality of life better<p>2. Help make debugging and root cause analysis easier.<p>3. Runbook automation<p>We’re still pretty early in development and we want to make on-call quality of life better. Any feedback would be much appreciated!

Show HN: I built an open-source tool to make on-call suck less

Hey HN,<p>I am building an open source platform to make on-call better and less stressful for engineers. We are building a tool that can silence alerts and help with debugging and root cause analysis. We also want to automate tedious parts of being on-call (running runbooks manually, answering questions on Slack, dealing with Pagerduty). Here is a quick video of how it works: <a href="https://youtu.be/m_K9Dq1kZDw" rel="nofollow">https://youtu.be/m_K9Dq1kZDw</a><p>I hated being on-call for a couple of reasons:<p>* Alert volume: The number of alerts kept increasing over time. It was hard to maintain existing alerts. This would lead to a lot of noisy and unactionable alerts. I have lost count of the number of times I got woken up by alert that auto-resolved 5 minutes later.<p>* Debugging: Debugging an alert or a customer support ticket would need me to gain context on a service that I might not have worked on before. These companies used many observability tools that would make debugging challenging. There are always a time pressure to resolve issues quickly.<p>There were some more tangential issues that used to take up a lot of on-call time<p>* Support: Answering questions from other teams. A lot of times these questions were repetitive and have been answered before.<p>* Dealing with PagerDuty: These tools are hard to use. e.g. It was hard to schedule an override in PD or do holiday schedules.<p>I am building an on-call tool that is Slack-native since that has become the de-facto tool for on-call engineers.<p>We heard from a lot of engineers that maintaining good alert hygiene is a challenge.<p>To start off, Opslane integrates with Datadog and can classify alerts as actionable or noisy.<p>We analyze your alert history across various signals:<p>1. Alert frequency<p>2. How quickly the alerts have resolved in the past<p>3. Alert priority<p>4. Alert response history<p>Our classification is conservative and it can be tuned as teams get more confidence in the predictions. We want to make sure that you aren't accidentally missing a critical alert.<p>Additionally, we generate a weekly report based on all your alerts to give you a picture of your overall alert hygiene.<p>What’s next?<p>1. Building more integrations (Prometheus, Splunk, Sentry, PagerDuty) to continue making on-call quality of life better<p>2. Help make debugging and root cause analysis easier.<p>3. Runbook automation<p>We’re still pretty early in development and we want to make on-call quality of life better. Any feedback would be much appreciated!

Show HN: Semantic Grep – A Word2Vec-powered search tool

Much improved new version. Search for words similar to the query. For example, "death" will find "death", "dying", "dead", "killing"... Incredibly useful for exploring large text datasets where exact matches are too restrictive.

Show HN: Semantic Grep – A Word2Vec-powered search tool

Much improved new version. Search for words similar to the query. For example, "death" will find "death", "dying", "dead", "killing"... Incredibly useful for exploring large text datasets where exact matches are too restrictive.

Show HN: Semantic Grep – A Word2Vec-powered search tool

Much improved new version. Search for words similar to the query. For example, "death" will find "death", "dying", "dead", "killing"... Incredibly useful for exploring large text datasets where exact matches are too restrictive.

Show HN: Wat – Deep inspection of Python objects

Show HN: Wat – Deep inspection of Python objects

Show HN: Haystack – an IDE for exploring and editing code on an infinite canvas

Hi HN, we’re building Haystack Editor (<a href="https://haystackeditor.com/" rel="nofollow">https://haystackeditor.com/</a>), a canvas-based IDE that automates the boring stuff (plumbing, refactoring, and finding code) so that you can focus on the exciting parts of software development! You can see a quick overview of Haystack at <a href="https://www.youtube.com/watch?v=c2uZnR5D_cc" rel="nofollow">https://www.youtube.com/watch?v=c2uZnR5D_cc</a>!<p>(It's currently only on Mac OS but we're working on Linux and Windows. Edit: just added a Linux download!)<p>Haystack was born out of our frustrations with working in large and mature codebases, specifically with navigating and editing functional flows (e.g. the code flow for adding an item to the Amazon shopping cart).<p>Oftentimes dealing with such flows would involve navigating a maze of files and functions, and making any edits would involve a lengthy process of doing corresponding downstream/upstream plumbing.<p>Haystack attempts to address this in the following ways:<p><pre><code> 1. It allows you to explore your codebase as a directed graph of functions, classes, etc on the canvas. We feel like this better fits how your mind understands your codebase and helps you find and alter functional flows more intuitively. We especially want to utilize this for pull request reviews! 2. It has a navigational copilot that makes edits across files or functions much easier. After you make some changes, Haystack will try to predict your next action and create functions/methods or refactor upstream/downstream code for you. Haystack will surface these speculative edits on the canvas in a way that you can easily dismiss or incorporate them, allowing you to make large changes with a few clicks or keystrokes. 3. Haystack will utilize natural language search so you don’t have to play “Where’s Waldo” to find a functional flow in your codebase. This is coming soon! </code></pre> We’re still pretty early in development and we really want to perfect the experience of navigating and editing code on a canvas. Any feedback would be much appreciated!<p>PSA: Since Haystack is a VS Code fork, you should be able to move your extensions and keyboard shortcuts. Please let us know if you have any issues with this!

Show HN: Haystack – an IDE for exploring and editing code on an infinite canvas

Hi HN, we’re building Haystack Editor (<a href="https://haystackeditor.com/" rel="nofollow">https://haystackeditor.com/</a>), a canvas-based IDE that automates the boring stuff (plumbing, refactoring, and finding code) so that you can focus on the exciting parts of software development! You can see a quick overview of Haystack at <a href="https://www.youtube.com/watch?v=c2uZnR5D_cc" rel="nofollow">https://www.youtube.com/watch?v=c2uZnR5D_cc</a>!<p>(It's currently only on Mac OS but we're working on Linux and Windows. Edit: just added a Linux download!)<p>Haystack was born out of our frustrations with working in large and mature codebases, specifically with navigating and editing functional flows (e.g. the code flow for adding an item to the Amazon shopping cart).<p>Oftentimes dealing with such flows would involve navigating a maze of files and functions, and making any edits would involve a lengthy process of doing corresponding downstream/upstream plumbing.<p>Haystack attempts to address this in the following ways:<p><pre><code> 1. It allows you to explore your codebase as a directed graph of functions, classes, etc on the canvas. We feel like this better fits how your mind understands your codebase and helps you find and alter functional flows more intuitively. We especially want to utilize this for pull request reviews! 2. It has a navigational copilot that makes edits across files or functions much easier. After you make some changes, Haystack will try to predict your next action and create functions/methods or refactor upstream/downstream code for you. Haystack will surface these speculative edits on the canvas in a way that you can easily dismiss or incorporate them, allowing you to make large changes with a few clicks or keystrokes. 3. Haystack will utilize natural language search so you don’t have to play “Where’s Waldo” to find a functional flow in your codebase. This is coming soon! </code></pre> We’re still pretty early in development and we really want to perfect the experience of navigating and editing code on a canvas. Any feedback would be much appreciated!<p>PSA: Since Haystack is a VS Code fork, you should be able to move your extensions and keyboard shortcuts. Please let us know if you have any issues with this!

Show HN: We made glhf.chat – run almost any open-source LLM, including 405B

Try it out! <a href="https://glhf.chat/" rel="nofollow">https://glhf.chat/</a><p>Hey HN!<p>We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant.<p>Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and it’ll work (as long as vLLM supports the base model architecture, we’ll run anything up to ~640GB of VRAM, give or take a little for some overhead buffer).<p>Large models will take a few minutes to boot, but if a bunch of people are trying to use the same model, it might already be loaded and not need boot time at all. The Llama-3-70b finetunes are especially nice, since they’re basically souped-up versions of the 8b finetunes a lot of people like to run locally but don’t have the VRAM for. We’re expecting the Llama-3.1 finetunes to be pretty great too once they start getting released.<p>There are some caveats for now — for example, while we support the Deepseek V2 architecture, we actually can only run their smaller “Lite” models due to some underlying NVLink limitations (though we’re working on it). But for the most part if vLLM supports it, we should too!<p>We figured Llama-3.1-405B Launch Day was a good day to launch ourselves too — let us know in the comments if there’s anything you want us to support, or if you run into any issues. I know it’s not “local” Llama, but, well, that’s a lot of GPUs…

Show HN: I built a tool to expand your network (that introverts will love)

As someone who struggles with social anxiety, expanding my network through traditional means has always been challenging. I found existing networking apps either too spammy (LinkedIn) or too much like professional dating (Bumble Bizz), and they just didn’t work for me.<p>About a year ago, I developed a matching system for a local startup accelerator. This system connected founders, mentors, and investors based on industries, skills, and job functions, facilitating over 5,000 meetings that led to some amazing outcomes. Inspired by this success, I enhanced the system to focus on email introductions. Here’s how it works: - It analyzes backgrounds and interests. - It sends intro proposals to each person. - If both respond, it makes the intro.<p>My goal is to help people meet interesting contacts without the stress, using email to keep the process simple and integrated into daily routines. I’d love for you to try it out and share your feedback. Your thoughts and suggestions for improvement are greatly appreciated!

Show HN: I built a tool to expand your network (that introverts will love)

As someone who struggles with social anxiety, expanding my network through traditional means has always been challenging. I found existing networking apps either too spammy (LinkedIn) or too much like professional dating (Bumble Bizz), and they just didn’t work for me.<p>About a year ago, I developed a matching system for a local startup accelerator. This system connected founders, mentors, and investors based on industries, skills, and job functions, facilitating over 5,000 meetings that led to some amazing outcomes. Inspired by this success, I enhanced the system to focus on email introductions. Here’s how it works: - It analyzes backgrounds and interests. - It sends intro proposals to each person. - If both respond, it makes the intro.<p>My goal is to help people meet interesting contacts without the stress, using email to keep the process simple and integrated into daily routines. I’d love for you to try it out and share your feedback. Your thoughts and suggestions for improvement are greatly appreciated!

Show HN: I built a tool to expand your network (that introverts will love)

As someone who struggles with social anxiety, expanding my network through traditional means has always been challenging. I found existing networking apps either too spammy (LinkedIn) or too much like professional dating (Bumble Bizz), and they just didn’t work for me.<p>About a year ago, I developed a matching system for a local startup accelerator. This system connected founders, mentors, and investors based on industries, skills, and job functions, facilitating over 5,000 meetings that led to some amazing outcomes. Inspired by this success, I enhanced the system to focus on email introductions. Here’s how it works: - It analyzes backgrounds and interests. - It sends intro proposals to each person. - If both respond, it makes the intro.<p>My goal is to help people meet interesting contacts without the stress, using email to keep the process simple and integrated into daily routines. I’d love for you to try it out and share your feedback. Your thoughts and suggestions for improvement are greatly appreciated!

Show HN: Lisp in C#

Show HN: Briefer – Multiplayer notebooks with schedules, SQL, and built-in LLMs

Hi HN! We're Lucas and Lucas from Briefer and we're building better notebooks.<p>Our notebooks are kind of a mix between Notion and Jupyter with extra features, like the ability to schedule notebooks, turn them into dashboards and apps, and write SQL queries whose results turn into data frames automatically.<p>We're building better notebooks because we think they're a great idea poorly executed - for three reasons.<p>The first problem with notebooks is that they're difficult to share. Non-technical people don't want to download docker containers and install Python libraries to see what the data team is doing. Then, the data team takes screenshots of their work and pastes them somewhere else. The issue with this approach is that the data gets stale, and the output is not interactive, so it's difficult to get feedback and iterate.<p>The second problem with notebooks is that they get too messy too quickly. One morning you have 10 blocks, and by the end of the day you have 192, but only six of them are meant to be seen by other people and the rest is just you jiu-jitsuing with the data. Consequently, even if non-technical people could see your work, they'd have a hard time figuring out what's happening.<p>The third problem with notebooks is that it takes too much work to do simple things like when you want to query a database. In that case, you need to have the database credentials in your machine, and you have to write a bunch of wrapper code. Calling APIs, plotting simple graphs, and adding interactive components is equally as annoying.<p>We're solving the sharing problem by bringing notebooks to the cloud (so you can schedule them) and using CRDTs to manage their state. Whenever you edit a Briefer notebook, we reconcile that using Yjs, and then propagate it to everyone else who's editing that notebook. Regarding compute instances, each workspace gets its own, and we provision them on demand.<p>By the way, we manage the execution state of your blocks using Yjs too, which makes it much more stable and responsive across all clients. When you click "run", for example, we don't immediately send a request to run the block. Instead, we change the block's state to "execution-requested". Then, the observers in the back-end react to the change and update the block's state with the results. In other words, the front end and the server communicate with each other through the notebook's state. One side updates the state, and the other reacts.<p>To solve the "messiness" issue, we've invested a lot of time in designing clean notebooks and allowing you to organize blocks in ways that make them more presentable. In Briefer, you can group multiple blocks into tabs and decide which blocks appear in the published version of your notebook. That way, you can hide all that data wrangling and focus on results. We also know that a vertical format is not always the best way to display results, so you can use your notebook's outputs to build dashboards too.<p>Last but not least, we're reducing friction in simple tasks like plotting graphs and querying databases. In Briefer, you can plot graphs using a click-through interface, and if you need to plot something more intricate like a Sankey chart there's also a built-in AI assistant that you can prompt to generate code. When it comes to queries, we turn their results into dataframes automatically, and you can use SQL to query uploaded files too (we use DuckDB for that).<p>We're really happy we get to show this to you all, thank you for reading about it! Please let us know your thoughts and questions in the comments.

Show HN: Briefer – Multiplayer notebooks with schedules, SQL, and built-in LLMs

Hi HN! We're Lucas and Lucas from Briefer and we're building better notebooks.<p>Our notebooks are kind of a mix between Notion and Jupyter with extra features, like the ability to schedule notebooks, turn them into dashboards and apps, and write SQL queries whose results turn into data frames automatically.<p>We're building better notebooks because we think they're a great idea poorly executed - for three reasons.<p>The first problem with notebooks is that they're difficult to share. Non-technical people don't want to download docker containers and install Python libraries to see what the data team is doing. Then, the data team takes screenshots of their work and pastes them somewhere else. The issue with this approach is that the data gets stale, and the output is not interactive, so it's difficult to get feedback and iterate.<p>The second problem with notebooks is that they get too messy too quickly. One morning you have 10 blocks, and by the end of the day you have 192, but only six of them are meant to be seen by other people and the rest is just you jiu-jitsuing with the data. Consequently, even if non-technical people could see your work, they'd have a hard time figuring out what's happening.<p>The third problem with notebooks is that it takes too much work to do simple things like when you want to query a database. In that case, you need to have the database credentials in your machine, and you have to write a bunch of wrapper code. Calling APIs, plotting simple graphs, and adding interactive components is equally as annoying.<p>We're solving the sharing problem by bringing notebooks to the cloud (so you can schedule them) and using CRDTs to manage their state. Whenever you edit a Briefer notebook, we reconcile that using Yjs, and then propagate it to everyone else who's editing that notebook. Regarding compute instances, each workspace gets its own, and we provision them on demand.<p>By the way, we manage the execution state of your blocks using Yjs too, which makes it much more stable and responsive across all clients. When you click "run", for example, we don't immediately send a request to run the block. Instead, we change the block's state to "execution-requested". Then, the observers in the back-end react to the change and update the block's state with the results. In other words, the front end and the server communicate with each other through the notebook's state. One side updates the state, and the other reacts.<p>To solve the "messiness" issue, we've invested a lot of time in designing clean notebooks and allowing you to organize blocks in ways that make them more presentable. In Briefer, you can group multiple blocks into tabs and decide which blocks appear in the published version of your notebook. That way, you can hide all that data wrangling and focus on results. We also know that a vertical format is not always the best way to display results, so you can use your notebook's outputs to build dashboards too.<p>Last but not least, we're reducing friction in simple tasks like plotting graphs and querying databases. In Briefer, you can plot graphs using a click-through interface, and if you need to plot something more intricate like a Sankey chart there's also a built-in AI assistant that you can prompt to generate code. When it comes to queries, we turn their results into dataframes automatically, and you can use SQL to query uploaded files too (we use DuckDB for that).<p>We're really happy we get to show this to you all, thank you for reading about it! Please let us know your thoughts and questions in the comments.

Show HN: Zerox – Document OCR with GPT-mini

This started out as a weekend hack with gpt-4-mini, using the very basic strategy of "just ask the ai to ocr the document".<p>But this turned out to be better performing than our current implementation of Unstructured/Textract. At pretty much the same cost.<p>I've tested almost every variant of document OCR over the past year, especially trying things like table / chart extraction. I've found the rules based extraction has always been lacking. Documents are meant to be a visual representation after all. With weird layouts, tables, charts, etc. Using a vision model just make sense!<p>In general, I'd categorize this solution as slow, expensive, and non deterministic. But 6 months ago it was impossible. And 6 months from now it'll be fast, cheap, and probably more reliable!

Show HN: Zerox – Document OCR with GPT-mini

This started out as a weekend hack with gpt-4-mini, using the very basic strategy of "just ask the ai to ocr the document".<p>But this turned out to be better performing than our current implementation of Unstructured/Textract. At pretty much the same cost.<p>I've tested almost every variant of document OCR over the past year, especially trying things like table / chart extraction. I've found the rules based extraction has always been lacking. Documents are meant to be a visual representation after all. With weird layouts, tables, charts, etc. Using a vision model just make sense!<p>In general, I'd categorize this solution as slow, expensive, and non deterministic. But 6 months ago it was impossible. And 6 months from now it'll be fast, cheap, and probably more reliable!

Show HN: A source-available billing system I've spent 18 months building

Show HN: A source-available billing system I've spent 18 months building