The best Hacker News stories from Show from the past day
Latest posts:
Show HN: A tool to analyze Hacker News sentiment on any term in seconds
Hi everyone, we developed a tool that can easily tell you the overall sentiment of a message based on a word. For now it’s hacker news only but we think this thing has potential.<p>Whether you’re a startup, solopreneur or product manager, you can track trends with it. We are also planning to add predictive tools and real time analysis. Operationally this tool is a lot cheaper than Sprout Social or other similar solutions on the market.<p>No sign-up required. Just type and see results.<p>I'd love your feedback on the tool's usefulness and any ideas for improvement.
Show HN: A tool to analyze Hacker News sentiment on any term in seconds
Hi everyone, we developed a tool that can easily tell you the overall sentiment of a message based on a word. For now it’s hacker news only but we think this thing has potential.<p>Whether you’re a startup, solopreneur or product manager, you can track trends with it. We are also planning to add predictive tools and real time analysis. Operationally this tool is a lot cheaper than Sprout Social or other similar solutions on the market.<p>No sign-up required. Just type and see results.<p>I'd love your feedback on the tool's usefulness and any ideas for improvement.
Show HN: A tool to analyze Hacker News sentiment on any term in seconds
Hi everyone, we developed a tool that can easily tell you the overall sentiment of a message based on a word. For now it’s hacker news only but we think this thing has potential.<p>Whether you’re a startup, solopreneur or product manager, you can track trends with it. We are also planning to add predictive tools and real time analysis. Operationally this tool is a lot cheaper than Sprout Social or other similar solutions on the market.<p>No sign-up required. Just type and see results.<p>I'd love your feedback on the tool's usefulness and any ideas for improvement.
Show HN: PDF to MD by LLMs – Extract Text/Tables/Image Descriptives by GPT4o
I've developed a Python API service that uses GPT-4o for OCR on PDFs. It features parallel processing and batch handling for improved performance. Not only does it convert PDF to markdown, but it also describes the images within the PDF using captions like `[Image: This picture shows 4 people waving]`.<p>In testing with NASA's Apollo 17 flight documents, it successfully converted complex, multi-oriented pages into well-structured Markdown.<p>The project is open-source and available on GitHub. Feedback is welcome.
Show HN: PDF to MD by LLMs – Extract Text/Tables/Image Descriptives by GPT4o
I've developed a Python API service that uses GPT-4o for OCR on PDFs. It features parallel processing and batch handling for improved performance. Not only does it convert PDF to markdown, but it also describes the images within the PDF using captions like `[Image: This picture shows 4 people waving]`.<p>In testing with NASA's Apollo 17 flight documents, it successfully converted complex, multi-oriented pages into well-structured Markdown.<p>The project is open-source and available on GitHub. Feedback is welcome.
Show HN: Time Flies
A visualization of the passage of time using flies. Written in JavaScript with some HTML & CSS in one index.html.
Show HN: Time Flies
A visualization of the passage of time using flies. Written in JavaScript with some HTML & CSS in one index.html.
Show HN: Time Flies
A visualization of the passage of time using flies. Written in JavaScript with some HTML & CSS in one index.html.
Show HN: Parse your Postgres queries into a fully-typed AST in TypeScript
Hey all, I'm the creator of @pg-nano/pg-parser. I'm using it in pg-nano[1] to statically analyze Postgres schemas spread across multiple SQL files for a couple of reasons:<p>1. Each CREATE statement needs to be in topological order, so pg-nano's dev command can execute them without issue.<p>2. pg-nano has a plugin system like Vite that allows SQL generation based on the parsed schema.<p>Probably to the surprise of no one, working with an untyped AST feels like you're back in the days of JavaScript, because well... you are. Most of you know by now just how great TypeScript and static types in general are, especially if you appreciate SQL.<p>So why is this project worth sharing with you?<p>Well, writing the AST type definitions by hand would have taken me way too much time. It would also be a bear to keep up-to-date as Postgres continues to evolve.<p>To my surprise, I discovered that libpg_query, the C library used under-the-hood, includes JSON definitions in their /srcdata/ folder. I figured I could use them to <i>generate</i> the type definitions. Genius, right? Okay... maybe not <i>genius</i>, but still cool, I think.<p>You see, those JSON definitions provided by libpg_query? They don't exactly contain the TypeScript definitions (was that obvious?). No, no. I had to <i>translate</i> them into TypeScript definitions. (I'm sure <i>you</i> could have done it, yes yes. But did you? No siree bob)<p>It was pain-staking, but overall really not too hard. Time-consuming? Yes, but not as much as writing the type definitions by hand. So... was it worth it? Only time will tell. I hope you find it as useful as I do. And that's all I've got, so thanks for reading.<p>P.S. The build for Windows is broken, so if anyone could lend a hand, you would be a true hero.<p>[1]: <a href="https://github.com/pg-nano/pg-nano">https://github.com/pg-nano/pg-nano</a> (not ready for production use)
Show HN: Selectable – mobile-friendly Postgres client
Show HN: CNC Microscopy for Fun
Show HN: CNC Microscopy for Fun
Show HN: CNC Microscopy for Fun
Show HN: Inngest 1.0 – Open-source durable workflows on every platform
Hi HN! I’m Tony, one of the co-founders of Inngest (<a href="https://inngest.com/" rel="nofollow">https://inngest.com/</a>)<p>Inngest is an open-source durable workflow platform that works on any cloud. Durable workflows are stateful, long running step functions written in code, which automatically retry on failure. It abstracts everything about queues, event streams and state for you, letting you focus on code. Some examples of uses: managing stateful AI chained step functions; managing search/rag indexes and data pipelines; integrations and webhooks; billing and payment flows.<p>Technical details: unlike other solutions, we put lots of effort into designing our SDK’s step.run APIs to make them extremely easy to use — developer experience is the most important thing for us.<p>We had to design and build our own queueing system to work with multi-tenancy, batching, and debouncing, and we’re iterating on this as we move to FoundationDB. It’s largely all Go in the backend, with a bunch of caching, clickhouse, event streams, and coordination on our behalf. Workers are shared nothing, and run based off of the queue and execution state.<p>We did a post last year as we iterated on our TS SDK. The product has changed a lot since then and wanted to show the community what’s changed as we reach 1.0:<p><pre><code> * Golang, Java, and Python SDKs with cross-language function invocation (across clouds, too)
* Multi-tenant aware flow control (concurrency, throttling, debounce)
* Batching, grouping many events into a single function call
* Much improved dashboard, with tracing and metrics built in
* Advanced recovery tools like function replay, temporary pausing, bulk cancellation (with optional expressions). No more dead letter queues!
* Branch deploys built in, with staging env support out of the box
* Full local testing with production parity
</code></pre>
There's a ton on the roadmap, with more launching next week. We’re hiring systems & infra engineers, too — it’s a fun job with lots of challenges!<p>Wanted to say thank you to the HN community for feedback so far! Happy Friday :)
Show HN: Inngest 1.0 – Open-source durable workflows on every platform
Hi HN! I’m Tony, one of the co-founders of Inngest (<a href="https://inngest.com/" rel="nofollow">https://inngest.com/</a>)<p>Inngest is an open-source durable workflow platform that works on any cloud. Durable workflows are stateful, long running step functions written in code, which automatically retry on failure. It abstracts everything about queues, event streams and state for you, letting you focus on code. Some examples of uses: managing stateful AI chained step functions; managing search/rag indexes and data pipelines; integrations and webhooks; billing and payment flows.<p>Technical details: unlike other solutions, we put lots of effort into designing our SDK’s step.run APIs to make them extremely easy to use — developer experience is the most important thing for us.<p>We had to design and build our own queueing system to work with multi-tenancy, batching, and debouncing, and we’re iterating on this as we move to FoundationDB. It’s largely all Go in the backend, with a bunch of caching, clickhouse, event streams, and coordination on our behalf. Workers are shared nothing, and run based off of the queue and execution state.<p>We did a post last year as we iterated on our TS SDK. The product has changed a lot since then and wanted to show the community what’s changed as we reach 1.0:<p><pre><code> * Golang, Java, and Python SDKs with cross-language function invocation (across clouds, too)
* Multi-tenant aware flow control (concurrency, throttling, debounce)
* Batching, grouping many events into a single function call
* Much improved dashboard, with tracing and metrics built in
* Advanced recovery tools like function replay, temporary pausing, bulk cancellation (with optional expressions). No more dead letter queues!
* Branch deploys built in, with staging env support out of the box
* Full local testing with production parity
</code></pre>
There's a ton on the roadmap, with more launching next week. We’re hiring systems & infra engineers, too — it’s a fun job with lots of challenges!<p>Wanted to say thank you to the HN community for feedback so far! Happy Friday :)
Show HN: Inngest 1.0 – Open-source durable workflows on every platform
Hi HN! I’m Tony, one of the co-founders of Inngest (<a href="https://inngest.com/" rel="nofollow">https://inngest.com/</a>)<p>Inngest is an open-source durable workflow platform that works on any cloud. Durable workflows are stateful, long running step functions written in code, which automatically retry on failure. It abstracts everything about queues, event streams and state for you, letting you focus on code. Some examples of uses: managing stateful AI chained step functions; managing search/rag indexes and data pipelines; integrations and webhooks; billing and payment flows.<p>Technical details: unlike other solutions, we put lots of effort into designing our SDK’s step.run APIs to make them extremely easy to use — developer experience is the most important thing for us.<p>We had to design and build our own queueing system to work with multi-tenancy, batching, and debouncing, and we’re iterating on this as we move to FoundationDB. It’s largely all Go in the backend, with a bunch of caching, clickhouse, event streams, and coordination on our behalf. Workers are shared nothing, and run based off of the queue and execution state.<p>We did a post last year as we iterated on our TS SDK. The product has changed a lot since then and wanted to show the community what’s changed as we reach 1.0:<p><pre><code> * Golang, Java, and Python SDKs with cross-language function invocation (across clouds, too)
* Multi-tenant aware flow control (concurrency, throttling, debounce)
* Batching, grouping many events into a single function call
* Much improved dashboard, with tracing and metrics built in
* Advanced recovery tools like function replay, temporary pausing, bulk cancellation (with optional expressions). No more dead letter queues!
* Branch deploys built in, with staging env support out of the box
* Full local testing with production parity
</code></pre>
There's a ton on the roadmap, with more launching next week. We’re hiring systems & infra engineers, too — it’s a fun job with lots of challenges!<p>Wanted to say thank you to the HN community for feedback so far! Happy Friday :)
Show HN: Put this touch sensor on a robot and learn super precise tasks
We just released a very excited touch sensor that finally simplifies touch sensing for robotics.<p>Our most exciting result: Learned visuotactile policies for precise tasks like inserting USBs and credit card swiping, that work out-of-the-box when you replace skins! To the best of our knowledge, this has never been shown before with any existing tactile sensor.<p>Why is this important? For the first time, you could now collect data and train models on one sensor and expect them to generalize to new copies of the sensor -- opening the door to the kind of large foundation models that have revolutionized vision and language reasoning.<p>Would love to hear the community's questions, thoughts and comments!
Show HN: Put this touch sensor on a robot and learn super precise tasks
We just released a very excited touch sensor that finally simplifies touch sensing for robotics.<p>Our most exciting result: Learned visuotactile policies for precise tasks like inserting USBs and credit card swiping, that work out-of-the-box when you replace skins! To the best of our knowledge, this has never been shown before with any existing tactile sensor.<p>Why is this important? For the first time, you could now collect data and train models on one sensor and expect them to generalize to new copies of the sensor -- opening the door to the kind of large foundation models that have revolutionized vision and language reasoning.<p>Would love to hear the community's questions, thoughts and comments!
Show HN: Put this touch sensor on a robot and learn super precise tasks
We just released a very excited touch sensor that finally simplifies touch sensing for robotics.<p>Our most exciting result: Learned visuotactile policies for precise tasks like inserting USBs and credit card swiping, that work out-of-the-box when you replace skins! To the best of our knowledge, this has never been shown before with any existing tactile sensor.<p>Why is this important? For the first time, you could now collect data and train models on one sensor and expect them to generalize to new copies of the sensor -- opening the door to the kind of large foundation models that have revolutionized vision and language reasoning.<p>Would love to hear the community's questions, thoughts and comments!
Show HN: Put this touch sensor on a robot and learn super precise tasks
We just released a very excited touch sensor that finally simplifies touch sensing for robotics.<p>Our most exciting result: Learned visuotactile policies for precise tasks like inserting USBs and credit card swiping, that work out-of-the-box when you replace skins! To the best of our knowledge, this has never been shown before with any existing tactile sensor.<p>Why is this important? For the first time, you could now collect data and train models on one sensor and expect them to generalize to new copies of the sensor -- opening the door to the kind of large foundation models that have revolutionized vision and language reasoning.<p>Would love to hear the community's questions, thoughts and comments!