Thursday, February 29, 2024
Show HN: Pipedream now has 1700 integrated APIs https://ift.tt/2XbHO4S
Show HN: Pipedream now has 1700 integrated APIs https://ift.tt/ulTzoq4 February 29, 2024 at 09:56PM
Show HN: Flat – simple task tracking that stays out of your way https://ift.tt/GqXWP0C
Show HN: Flat – simple task tracking that stays out of your way Hi HN! Flat’s a lot like Trello, but with a cleaner, simpler, friendlier UI and real discussion threads. Flat also sports a first-class GitHub integration. To take it for a spin without signing up, just go to https://try.flat.app . And there’s also a quick demo video here: https://youtu.be/NW2c9cZVaD0 We made Flat for ourselves, after a decade of wanting a simple, lightweight tool to stay on top of our work and not finding one. Trello is close, but we find it really frustrating to use in lots of ways. And most everything else is way too complex. We think there’s a need for a task tracker that prioritizes simplicity, clarity, and usability over bells and whistles that most users don't need. And like many folks who are burned out on team chat, we also think there’s a need for a mode of workplace communication that’s lightweight, async, and attached to the work rather than chats in Slack or Teams where it’s too easy for things to get lost (and to be interrupted all day). Flat doesn’t try to replace Slack--chat will always have its uses--but it does offer a better venue for a subset of Slack’s workload, like asking and answering questions, making quick requests, and raising issues about things the team is working on. Flat is aimed at small teams with basic needs. Small dev teams without a lot of process seem to love it, as do people who used to like Trello back in the days when it was less stressful to use, and people for whom Asana’s complexity is a non-starter. Flat is still in beta, so it’s probably missing some things you expect, but what’s there is pretty baked. It’s available in-browser as well as Electron apps for Mac, Windows, and Linux (.deb) Feedback is welcome and greatly appreciated! Either in the comments here, or if you sign up there's also a feedback button in the app navbar for whenever something irks you or you have an idea. Thanks, Seth, Andrew, & Marcin https://flat.app/ February 29, 2024 at 10:03AM
Wednesday, February 28, 2024
Show HN: Finetune a Gemma 2B model for codegen https://ift.tt/DfeuVar
Show HN: Finetune a Gemma 2B model for codegen Sharing a prototype of finetuning Gemma2b for a codegen usecase! https://ift.tt/ME6zTuq February 29, 2024 at 12:27AM
Tuesday, February 27, 2024
Show HN: Leaping – Open-source debugging with LLMs https://ift.tt/bIPc28l
Show HN: Leaping – Open-source debugging with LLMs Show HN: Leaping - Open-source debugging with LLMs Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our life combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging. We started by capturing information from running applications to then ‘replay’ relevant sessions later. Our approach for Python involved extensive monkey patching: we’d use OpenTelemetry-style instrumentation to hook into the request/response lifecycle, and capture anything non-deterministic (random, time, database/third-party API calls, etc.). We would then run your code again, mocking out the non-determinism with the captured values from production, which would let you fix production bugs with the local debugger experience. You might recognize this as a variant of omniscient debugging. We think it was a nifty idea, but we couldn’t get past the performance overhead/security concerns. Approaching the problem differently, we thought - could we not just grab a stack trace and sort of “figure it out” from there? Whether that’s possible in the general case is up for debate – but we think that eventually, yes. The argument goes as follows: developers can solve bugs not because they are particularly clever or experienced (though it helps), but rather because they are willing to spend enough time coming up with increasingly informed hypotheses (“was the variable set incorrectly inside of this function?”) that they can test out in tight feedback loops (“let me print out the variable before and after the function call”). We wondered: with the proper context and guidance, why couldn’t an LLM do the same? Over the last few weeks, we’ve been working on an approach that emulates the failing test approach to debugging, where you first reproduce the error in a failing test, then fix the source code, and finally run the test again to make sure it passes. Concretely, we take a stack trace, and start by simply re-running the function that failed. We then report the result back to the LLM, add relevant source code to the context window (with Tree-sitter and LSP), and prompt the AI for a code change that will get us closer to reproducing the bug. We apply those changes, re-run the script, and keep looping until we get the same bug as the original stack trace. Then the LLM formulates a root cause, generates a fix, we run the code again - and if the bug goes away, we call it a day. We’re also looking into letting the LLM interact with a pdb shell, as well as implementing RAG for better context fetching. One thing that excites us about generating a functioning test case with a step-by-step explanation for the fix is that results are somewhat grounded in reality, making hallucinations/confabulations less likely. Here’s a 50 second demo of how this approach fares on a (perhaps contrived) error: https://ift.tt/2fRKJXY We’re working on releasing a self-hosted Python version in the next few weeks on our GitHub repo: https://ift.tt/RDrp8VB (right now it’s just the demo source code). This is just the first step towards a larger goal, so we’d love to hear any and all feedback/questions, or feel free to shoot me an email at adrien@leaping.io! February 27, 2024 at 10:59PM
Show HN: Scribbler - An open source notebook tool for JavaScript https://ift.tt/KJSMfIi
Show HN: Scribbler - An open source notebook tool for JavaScript Scribbler is a tool to do experimentation in JavaScript using a notebook kind of environment. It runs in the browser without the need for a backend. It is deal for learning and experimentation in JavaScript. USP of Scribbler are: no login, no node/npm, can load ES 6 modules. Check the website at: https://scribbler.live . The web-app is available at: https://ift.tt/4PIvmEx . Scribbler has been built to satisfy a need for doing experimentation. Jupyter Notebook is very popular amongst python developers and data scientists for experimentation. It gives a simple interface for experimenting in python for testing code or for experimental analysis. Jupyter Notebook provides this application by running what is known as a “kernel” in the backend and giving back the results to the ui for display. It is an open source and free to use tool. Thus it has become extremely popular. As it is in Python, it requires installation of python environment and the libraries to use the tool. There are fully hosted alternatives like Google Colabs, where one can experiment in Python without installing anything. There is no similar open source tools for Javascript. There are online tools like jsfiddle/codepen etc but none that can be downloaded and used as a free tool or embedded on other solutions. Pure Javascript and JS libraries can ideally run without the need for a backend code like node.js or Python. Javascript is built to run by default in the browser. Optimization of the browser tech by Chromium (i.e. V8) and Firefox has ensured Javascript in the browser is fast and efficient. Thus we can build a good notebook tool using just front end technologies. I’ve been looking for such a solution for quite sometime mainly to test out some of the open source JS libraries and also for building some new open source projects. As I couldn’t find any solution I have built a simple tool to run javascript in notebooks. I call it Scribbler (so much for creativity). It is available as an open source solutions — free to use and modify. The source code is available at: https://ift.tt/JmBNrQK It does not require any backend technology. Users can download and use it in the file system or host it in webserver to use it on the internet. I have used Github Pages to host it. As it does not require backend, I need not buy/host a server to do that (ain’t it beautiful?). JavaScript can be used for a variety of experimentation. I’ve learnt a lot about Dynamic systems while using Scribbler to do simulations. It has also helped me in understanding some concepts of decentralized finance. I’ve also used Scribbler to solve some equations using numeric methods. Given the dynamic nature of JavaScript and its close integration with the UI, one can use it for building charting/dahsboarding tools. Scribbler can infact be even used for data science and machine learning. JavaScript has a vibrant community with a wide range of libraries available. Thus the usecases of Scribbler are limitless. I hope as more people start using Scribbler, there will be more and more applications including interactuve data science, Generative AI, scientific simulations, financial/economic applications, decentralized computing etc. Happy experimenting!! https://ift.tt/4PIvmEx February 27, 2024 at 08:47PM
Show HN: I built a tool to help you search for CivitAI's models by art style https://ift.tt/bDnI3Rj
Show HN: I built a tool to help you search for CivitAI's models by art style https://ift.tt/dfLGBOw February 27, 2024 at 11:08AM
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