The best Hacker News stories from Show from the past day
Latest posts:
Show HN: Langfuse – Open-source observability and analytics for LLM apps
Hi HN! Langfuse is OSS observability and analytics for LLM applications (repo: <a href="https://github.com/langfuse/langfuse">https://github.com/langfuse/langfuse</a>, 2 min demo: <a href="https://langfuse.com/video">https://langfuse.com/video</a>, try it yourself: <a href="https://langfuse.com/demo">https://langfuse.com/demo</a>)<p>Langfuse makes capturing and viewing LLM calls (execution traces) a breeze. On top of this data, you can analyze the quality, cost and latency of LLM apps.<p>When GPT-4 dropped, we started building LLM apps – a lot of them! [1, 2] But they all suffered from the same issue: it’s hard to assure quality in 100% of cases and even to have a clear view of user behavior. Initially, we logged all prompts/completions to our production database to understand what works and what doesn’t. We soon realized we needed more context, more data and better analytics to sustainably improve our apps. So we started building a homegrown tool.<p>Our first task was to track and view what is going on in production: what user input is provided, how prompt templates or vector db requests work, and which steps of an LLM chain fail. We built async SDKs and a slick frontend to render chains in a nested way. It’s a good way to look at LLM logic ‘natively’. Then we added some basic analytics to understand token usage and quality over time for the entire project or single users (pre-built dashboards).<p>Under the hood, we use the T3 stack (Typescript, NextJs, Prisma, tRPC, Tailwind, NextAuth), which allows us to move fast + it means it's easy to contribute to our repo. The SDKs are heavily influenced by the design of the PostHog SDKs [3] for stable implementations of async network requests. It was a surprisingly inconvenient experience to convert OpenAPI specs to boilerplate Python code and we ended up using Fern [4] here. We’re fans of Tailwind + shadcn/ui + tremor.so for speed and flexibility in building tables and dashboards fast.<p>Our SDKs run fully asynchronously and make network requests in the background. We did our best to reduce any impact on application performance to a minimum. We never block the main execution path.<p>We've made two engineering decisions we've felt uncertain about: to use a Postgres database and Looker Studio for the analytics MVP. Supabase performs well at our scale and integrates seamlessly into our tech stack. We will need to move to an OLAP database soon and are debating if we need to start batching ingestion and if we can keep using Vercel. Any experience you could share would be helpful!<p>Integrating Looker Studio got us to first analytics charts in half a day. As it is not open-source and does not work with our UI/UX, we are looking to switch it out for an OSS solution to flexibly generate charts and dashboards. We’ve had a look at Lightdash and would be happy to hear your thoughts.<p>We’re borrowing our OSS business model from Posthog/Supabase who make it easy to self-host with features reserved for enterprise (no plans yet) and a paid version for managed cloud service. Right now all of our code is available under a permissive license (MIT).<p>Next, we’re going deep on analytics. For quality specifically, we will build out model-based evaluations and labeling to be able to cluster traces by scores and use cases.<p>Looking forward to hearing your thoughts and discussion – we’ll be in the comments. Thanks!<p>[1] <a href="https://learn-from-ai.com/" rel="nofollow noreferrer">https://learn-from-ai.com/</a><p>[2] <a href="https://www.loom.com/share/5c044ca77be44ff7821967834dd70cba" rel="nofollow noreferrer">https://www.loom.com/share/5c044ca77be44ff7821967834dd70cba</a><p>[3] <a href="https://posthog.com/docs/libraries">https://posthog.com/docs/libraries</a><p>[4] <a href="https://buildwithfern.com/">https://buildwithfern.com/</a>
Show HN: Langfuse – Open-source observability and analytics for LLM apps
Hi HN! Langfuse is OSS observability and analytics for LLM applications (repo: <a href="https://github.com/langfuse/langfuse">https://github.com/langfuse/langfuse</a>, 2 min demo: <a href="https://langfuse.com/video">https://langfuse.com/video</a>, try it yourself: <a href="https://langfuse.com/demo">https://langfuse.com/demo</a>)<p>Langfuse makes capturing and viewing LLM calls (execution traces) a breeze. On top of this data, you can analyze the quality, cost and latency of LLM apps.<p>When GPT-4 dropped, we started building LLM apps – a lot of them! [1, 2] But they all suffered from the same issue: it’s hard to assure quality in 100% of cases and even to have a clear view of user behavior. Initially, we logged all prompts/completions to our production database to understand what works and what doesn’t. We soon realized we needed more context, more data and better analytics to sustainably improve our apps. So we started building a homegrown tool.<p>Our first task was to track and view what is going on in production: what user input is provided, how prompt templates or vector db requests work, and which steps of an LLM chain fail. We built async SDKs and a slick frontend to render chains in a nested way. It’s a good way to look at LLM logic ‘natively’. Then we added some basic analytics to understand token usage and quality over time for the entire project or single users (pre-built dashboards).<p>Under the hood, we use the T3 stack (Typescript, NextJs, Prisma, tRPC, Tailwind, NextAuth), which allows us to move fast + it means it's easy to contribute to our repo. The SDKs are heavily influenced by the design of the PostHog SDKs [3] for stable implementations of async network requests. It was a surprisingly inconvenient experience to convert OpenAPI specs to boilerplate Python code and we ended up using Fern [4] here. We’re fans of Tailwind + shadcn/ui + tremor.so for speed and flexibility in building tables and dashboards fast.<p>Our SDKs run fully asynchronously and make network requests in the background. We did our best to reduce any impact on application performance to a minimum. We never block the main execution path.<p>We've made two engineering decisions we've felt uncertain about: to use a Postgres database and Looker Studio for the analytics MVP. Supabase performs well at our scale and integrates seamlessly into our tech stack. We will need to move to an OLAP database soon and are debating if we need to start batching ingestion and if we can keep using Vercel. Any experience you could share would be helpful!<p>Integrating Looker Studio got us to first analytics charts in half a day. As it is not open-source and does not work with our UI/UX, we are looking to switch it out for an OSS solution to flexibly generate charts and dashboards. We’ve had a look at Lightdash and would be happy to hear your thoughts.<p>We’re borrowing our OSS business model from Posthog/Supabase who make it easy to self-host with features reserved for enterprise (no plans yet) and a paid version for managed cloud service. Right now all of our code is available under a permissive license (MIT).<p>Next, we’re going deep on analytics. For quality specifically, we will build out model-based evaluations and labeling to be able to cluster traces by scores and use cases.<p>Looking forward to hearing your thoughts and discussion – we’ll be in the comments. Thanks!<p>[1] <a href="https://learn-from-ai.com/" rel="nofollow noreferrer">https://learn-from-ai.com/</a><p>[2] <a href="https://www.loom.com/share/5c044ca77be44ff7821967834dd70cba" rel="nofollow noreferrer">https://www.loom.com/share/5c044ca77be44ff7821967834dd70cba</a><p>[3] <a href="https://posthog.com/docs/libraries">https://posthog.com/docs/libraries</a><p>[4] <a href="https://buildwithfern.com/">https://buildwithfern.com/</a>
Show HN: Langfuse – Open-source observability and analytics for LLM apps
Hi HN! Langfuse is OSS observability and analytics for LLM applications (repo: <a href="https://github.com/langfuse/langfuse">https://github.com/langfuse/langfuse</a>, 2 min demo: <a href="https://langfuse.com/video">https://langfuse.com/video</a>, try it yourself: <a href="https://langfuse.com/demo">https://langfuse.com/demo</a>)<p>Langfuse makes capturing and viewing LLM calls (execution traces) a breeze. On top of this data, you can analyze the quality, cost and latency of LLM apps.<p>When GPT-4 dropped, we started building LLM apps – a lot of them! [1, 2] But they all suffered from the same issue: it’s hard to assure quality in 100% of cases and even to have a clear view of user behavior. Initially, we logged all prompts/completions to our production database to understand what works and what doesn’t. We soon realized we needed more context, more data and better analytics to sustainably improve our apps. So we started building a homegrown tool.<p>Our first task was to track and view what is going on in production: what user input is provided, how prompt templates or vector db requests work, and which steps of an LLM chain fail. We built async SDKs and a slick frontend to render chains in a nested way. It’s a good way to look at LLM logic ‘natively’. Then we added some basic analytics to understand token usage and quality over time for the entire project or single users (pre-built dashboards).<p>Under the hood, we use the T3 stack (Typescript, NextJs, Prisma, tRPC, Tailwind, NextAuth), which allows us to move fast + it means it's easy to contribute to our repo. The SDKs are heavily influenced by the design of the PostHog SDKs [3] for stable implementations of async network requests. It was a surprisingly inconvenient experience to convert OpenAPI specs to boilerplate Python code and we ended up using Fern [4] here. We’re fans of Tailwind + shadcn/ui + tremor.so for speed and flexibility in building tables and dashboards fast.<p>Our SDKs run fully asynchronously and make network requests in the background. We did our best to reduce any impact on application performance to a minimum. We never block the main execution path.<p>We've made two engineering decisions we've felt uncertain about: to use a Postgres database and Looker Studio for the analytics MVP. Supabase performs well at our scale and integrates seamlessly into our tech stack. We will need to move to an OLAP database soon and are debating if we need to start batching ingestion and if we can keep using Vercel. Any experience you could share would be helpful!<p>Integrating Looker Studio got us to first analytics charts in half a day. As it is not open-source and does not work with our UI/UX, we are looking to switch it out for an OSS solution to flexibly generate charts and dashboards. We’ve had a look at Lightdash and would be happy to hear your thoughts.<p>We’re borrowing our OSS business model from Posthog/Supabase who make it easy to self-host with features reserved for enterprise (no plans yet) and a paid version for managed cloud service. Right now all of our code is available under a permissive license (MIT).<p>Next, we’re going deep on analytics. For quality specifically, we will build out model-based evaluations and labeling to be able to cluster traces by scores and use cases.<p>Looking forward to hearing your thoughts and discussion – we’ll be in the comments. Thanks!<p>[1] <a href="https://learn-from-ai.com/" rel="nofollow noreferrer">https://learn-from-ai.com/</a><p>[2] <a href="https://www.loom.com/share/5c044ca77be44ff7821967834dd70cba" rel="nofollow noreferrer">https://www.loom.com/share/5c044ca77be44ff7821967834dd70cba</a><p>[3] <a href="https://posthog.com/docs/libraries">https://posthog.com/docs/libraries</a><p>[4] <a href="https://buildwithfern.com/">https://buildwithfern.com/</a>
Show HN: Customizable terminal UI for monitoring weather, app uptime, and more
Turning websites into animated videos
Would love to try the tool on new apps, feel free to share your website, and we'll make a new video for you
Show HN: I built a website that lets you read classic books as email newsletters
Show HN: I built a website that lets you read classic books as email newsletters
Show HN: MoodMinder – Swift Anger Regulation for Better Emotional Well-Being
Hey Hacker News community! We're excited to showcase MoodMinder, a mental health app MVP that empowers individuals to swiftly regulate anger and enhance emotional well-being. MoodMinder was born out of a desire to provide quick anger regulation solutions for busy individuals.<p>Unique Features:<p>Rapid Mood Identification: Identify anger triggers and tension levels swiftly.<p>Instant Personalized meditations: Receive tailored meditations for immediate anger control.<p>Cognitive Reappraisal: Shift perspectives to defuse triggers in real-time.<p>Quick Interactive Games: Engage in games designed for anger regulation in just minutes.<p>As an MVP, we're seeking insights from the Hacker News community to shape our app's development. Your feedback is pivotal.
Thank you for being part of our journey!
Show HN: MuscleWiki Advanced Bodymap – A More Granular Exercise Finder
We are currently working on moving our site over to a react front end and adding more features to the site. I wanted to highlight one we are quite proud of - The advanced bodymap. Click the advanced button and get specific exercises that work that muscle. We are hoping to have an even bigger breakdown in the future when we build our "recovery" section of the site.<p>Happy to answer any questions and take feature requests.
Show HN: MuscleWiki Advanced Bodymap – A More Granular Exercise Finder
We are currently working on moving our site over to a react front end and adding more features to the site. I wanted to highlight one we are quite proud of - The advanced bodymap. Click the advanced button and get specific exercises that work that muscle. We are hoping to have an even bigger breakdown in the future when we build our "recovery" section of the site.<p>Happy to answer any questions and take feature requests.
Show HN: Faking SIMD to Search and Sort Strings 5x Faster
I want to share a really dumb, but very practical project I have packaged this summer, to perform operations on strings much faster. I was using Python to work with a multi-terabyte newline-delimited file. Reading, splitting, and shuffling it was a nightmare. So, I wrapped a trivial hardware-friendly heuristic I've been using for the last few years into a CPython library.<p>The part I enjoyed the most is implementing SIMD behavior without SIMD instructions... Using 64-bit words to work at 8-bit granularity. Unlike conventional SIMD, the code would remain the same for ~~almost~~ any hardware. Let this library be a reminder of how awesome bit-level hacks are! Feel free to use it when working with CommonCrawl or any other sizeable textual dataset.
Show HN: Investor radar, the ultimate database of investors for your startup
Show HN: Investor radar, the ultimate database of investors for your startup
Show HN: Graphweaver – Instant GraphQL API on Postgres, MySQL, SQLite and More
Graphweaver is an open-source GraphQL API Server that can connect many data sources to create a single API. Create a headless CMS, API Gateway, BaaS or use it as a BFF.
Show HN: Graphweaver – Instant GraphQL API on Postgres, MySQL, SQLite and More
Graphweaver is an open-source GraphQL API Server that can connect many data sources to create a single API. Create a headless CMS, API Gateway, BaaS or use it as a BFF.
Show HN: Graphweaver – Instant GraphQL API on Postgres, MySQL, SQLite and More
Graphweaver is an open-source GraphQL API Server that can connect many data sources to create a single API. Create a headless CMS, API Gateway, BaaS or use it as a BFF.
Show HN: Web App with GUI for AutoML on Tabular Data
Show HN: Going into freshman year, figured I should build an interpreter
Hi all!<p>I'm going into my freshman year, and figured that the best way to prepare for the intro to programming Racket course would be to implement my own garbage-collected, dynamically typed, functional programming language in C ;)<p>Anyways... here's the repo:
https://github.com/liam-ilan/crumb<p>I started learning C over the summer, so I still have a whole lot to learn... Any feedback would be greatly appreciated! :D
Show HN: Going into freshman year, figured I should build an interpreter
Hi all!<p>I'm going into my freshman year, and figured that the best way to prepare for the intro to programming Racket course would be to implement my own garbage-collected, dynamically typed, functional programming language in C ;)<p>Anyways... here's the repo:
https://github.com/liam-ilan/crumb<p>I started learning C over the summer, so I still have a whole lot to learn... Any feedback would be greatly appreciated! :D
Show HN: Going into freshman year, figured I should build an interpreter
Hi all!<p>I'm going into my freshman year, and figured that the best way to prepare for the intro to programming Racket course would be to implement my own garbage-collected, dynamically typed, functional programming language in C ;)<p>Anyways... here's the repo:
https://github.com/liam-ilan/crumb<p>I started learning C over the summer, so I still have a whole lot to learn... Any feedback would be greatly appreciated! :D