The best Hacker News stories from Show from the past day
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Show HN: Tiny Diffusion – A character-level text diffusion model from scratch
This is a character-level language diffusion model for text generation.<p>The model is a modified version of Nanochat's GPT implementation and is trained on Tiny Shakespeare!<p>It is only 10.7 million parameters, so you can try it out locally.
Show HN: Epstein Files Organized and Searchable
Hey all,<p>Throwaway in case this is assumed to be politcally motivated.<p>I spent some time organizing the Eptstein files to make transparency a little clearer. I need to tighten the data for organizations and people a bit more, but hopeful this is helpful in research in the interim.
Show HN: Epstein Files Organized and Searchable
Hey all,<p>Throwaway in case this is assumed to be politcally motivated.<p>I spent some time organizing the Eptstein files to make transparency a little clearer. I need to tighten the data for organizations and people a bit more, but hopeful this is helpful in research in the interim.
Show HN: Epstein Files Organized and Searchable
Hey all,<p>Throwaway in case this is assumed to be politcally motivated.<p>I spent some time organizing the Eptstein files to make transparency a little clearer. I need to tighten the data for organizations and people a bit more, but hopeful this is helpful in research in the interim.
Show HN: Shadowfax AI – an agentic workhorse to 10x data analysts productivity
Hi HN,<p>We built an AI agent for data analysts that turns the soul crushing spreadsheet & BI tool grind into a fast, verifiable and joyful experience. Early users reported going from hours to minutes on common real-world data wrangling tasks.<p>It's much smarter than an Excel copilot: immutable data steps, a DAG of SQL views, and DuckDB for instant crunching over millions of rows. Our early agent prototype ranked #1 on the Spider2-DBT bench. <a href="https://spider2-sql.github.io" rel="nofollow">https://spider2-sql.github.io</a><p>Try it out and we'd love your feedback!<p>Thanks,
Di Wu & the Shadowfax team<p>P.S. Shadowfax is Gandalf's horse from LOTR. There's a hidden easter egg site with 3 different triggers, see if you can find them.
Show HN: I made an open-source Rust program for memory-efficient genomics
My cofounder and I run a startup in oncology, where we handle cancer genomics data. It occurred to me that, thanks to a recent complexity theory result, there's a clever way to run bioinformatics algorithms using far less RAM. I built this Rust engine for running whole-genome workloads in under 100MB of RAM. Runtime is a little longer as a result - O(TlogT) instead of O(T). But it should enable whole-genome analytics on consumer-grade hardware.
Show HN: SkillGraph – Open-source agentic framework with skills instead of tools
Show HN: AI Bubble Monitor
The AI Bubble Monitor is an analytical tool designed to track and visualize indicators of potential market bubbles in AI-related sectors. It aggregates multiple data sources and metrics to produce a composite "AI Bubble Score" that ranges from 0 to 100.
The tool breaks down the overall score into five sub-indices: Valuation, Capital Flows, Adoption vs Fundamentals, Sentiment & Hype, and Systemic Risk. Each sub-index provides insight into different aspects of market behavior and potential overvaluation.
Show HN: I built a platform where audiences fund debates between public thinkers
Hey HN, I built Logosive because I want to see certain debates between my favorite thinkers (especially in health/wellness, tech, and public policy), but there's no way for regular people to make these happen.<p>With Logosive, you propose a debate topic and debaters. We then handle outreach, ticket sales, and logistics. After the debate, ticket revenue is split between everyone involved, including the person that proposed the debate, the debaters, and the host.<p>Logosive is built with Django, htmx, and Alpine.js. Claude generates the debate launch pages, including suggesting debaters or debate topics, all from a single prompt (but the debates happen between real debaters).<p>I’m now looking for help launching new debates, so if you have any topics or people you really want to see debate, please submit them at <a href="https://logosive.com" rel="nofollow">https://logosive.com</a>.<p>Thanks!
Show HN: DBOS Java – Postgres-Backed Durable Workflows
Hi HN - I’m Peter, here with Harry (devhawk), and we’re building DBOS Java, an open-source Java library for durable workflows, backed by Postgres.<p><a href="https://github.com/dbos-inc/dbos-transact-java" rel="nofollow">https://github.com/dbos-inc/dbos-transact-java</a><p>Essentially, DBOS helps you write long-lived, reliable code that can survive failures, restarts, and crashes without losing state or duplicating work. As your workflows run, it checkpoints each step they take in a Postgres database. When a process stops (fails, restarts, or crashes), your program can recover from those checkpoints to restore its exact state and continue from where it left off, as if nothing happened.<p>In practice, this makes it easier to build reliable systems for use cases like AI agents, payments, data synchronization, or anything that takes hours, days, or weeks to complete. Rather than bolting on ad-hoc retry logic and database checkpoints, durable workflows give you one consistent model for ensuring your programs can recover from any failure from exactly where they left off.<p>This library contains all you need to add durable workflows to your program: there's no separate service or orchestrator or any external dependencies except Postgres. Because it's just a library, you can incrementally add it to your projects, and it works out of the box with frameworks like Spring. And because it's built on Postgres, it natively supports all the tooling you're familiar with (backups, GUIs, CLI tools) and works with any Postgres provider.<p>If you want to try it out, check out the quickstart:<p><a href="https://docs.dbos.dev/quickstart?language=java" rel="nofollow">https://docs.dbos.dev/quickstart?language=java</a><p>We'd love to hear what you think! We’ll be in the comments for the rest of the day to answer any questions.
Show HN: DBOS Java – Postgres-Backed Durable Workflows
Hi HN - I’m Peter, here with Harry (devhawk), and we’re building DBOS Java, an open-source Java library for durable workflows, backed by Postgres.<p><a href="https://github.com/dbos-inc/dbos-transact-java" rel="nofollow">https://github.com/dbos-inc/dbos-transact-java</a><p>Essentially, DBOS helps you write long-lived, reliable code that can survive failures, restarts, and crashes without losing state or duplicating work. As your workflows run, it checkpoints each step they take in a Postgres database. When a process stops (fails, restarts, or crashes), your program can recover from those checkpoints to restore its exact state and continue from where it left off, as if nothing happened.<p>In practice, this makes it easier to build reliable systems for use cases like AI agents, payments, data synchronization, or anything that takes hours, days, or weeks to complete. Rather than bolting on ad-hoc retry logic and database checkpoints, durable workflows give you one consistent model for ensuring your programs can recover from any failure from exactly where they left off.<p>This library contains all you need to add durable workflows to your program: there's no separate service or orchestrator or any external dependencies except Postgres. Because it's just a library, you can incrementally add it to your projects, and it works out of the box with frameworks like Spring. And because it's built on Postgres, it natively supports all the tooling you're familiar with (backups, GUIs, CLI tools) and works with any Postgres provider.<p>If you want to try it out, check out the quickstart:<p><a href="https://docs.dbos.dev/quickstart?language=java" rel="nofollow">https://docs.dbos.dev/quickstart?language=java</a><p>We'd love to hear what you think! We’ll be in the comments for the rest of the day to answer any questions.
Show HN: DBOS Java – Postgres-Backed Durable Workflows
Hi HN - I’m Peter, here with Harry (devhawk), and we’re building DBOS Java, an open-source Java library for durable workflows, backed by Postgres.<p><a href="https://github.com/dbos-inc/dbos-transact-java" rel="nofollow">https://github.com/dbos-inc/dbos-transact-java</a><p>Essentially, DBOS helps you write long-lived, reliable code that can survive failures, restarts, and crashes without losing state or duplicating work. As your workflows run, it checkpoints each step they take in a Postgres database. When a process stops (fails, restarts, or crashes), your program can recover from those checkpoints to restore its exact state and continue from where it left off, as if nothing happened.<p>In practice, this makes it easier to build reliable systems for use cases like AI agents, payments, data synchronization, or anything that takes hours, days, or weeks to complete. Rather than bolting on ad-hoc retry logic and database checkpoints, durable workflows give you one consistent model for ensuring your programs can recover from any failure from exactly where they left off.<p>This library contains all you need to add durable workflows to your program: there's no separate service or orchestrator or any external dependencies except Postgres. Because it's just a library, you can incrementally add it to your projects, and it works out of the box with frameworks like Spring. And because it's built on Postgres, it natively supports all the tooling you're familiar with (backups, GUIs, CLI tools) and works with any Postgres provider.<p>If you want to try it out, check out the quickstart:<p><a href="https://docs.dbos.dev/quickstart?language=java" rel="nofollow">https://docs.dbos.dev/quickstart?language=java</a><p>We'd love to hear what you think! We’ll be in the comments for the rest of the day to answer any questions.
Show HN: DBOS Java – Postgres-Backed Durable Workflows
Hi HN - I’m Peter, here with Harry (devhawk), and we’re building DBOS Java, an open-source Java library for durable workflows, backed by Postgres.<p><a href="https://github.com/dbos-inc/dbos-transact-java" rel="nofollow">https://github.com/dbos-inc/dbos-transact-java</a><p>Essentially, DBOS helps you write long-lived, reliable code that can survive failures, restarts, and crashes without losing state or duplicating work. As your workflows run, it checkpoints each step they take in a Postgres database. When a process stops (fails, restarts, or crashes), your program can recover from those checkpoints to restore its exact state and continue from where it left off, as if nothing happened.<p>In practice, this makes it easier to build reliable systems for use cases like AI agents, payments, data synchronization, or anything that takes hours, days, or weeks to complete. Rather than bolting on ad-hoc retry logic and database checkpoints, durable workflows give you one consistent model for ensuring your programs can recover from any failure from exactly where they left off.<p>This library contains all you need to add durable workflows to your program: there's no separate service or orchestrator or any external dependencies except Postgres. Because it's just a library, you can incrementally add it to your projects, and it works out of the box with frameworks like Spring. And because it's built on Postgres, it natively supports all the tooling you're familiar with (backups, GUIs, CLI tools) and works with any Postgres provider.<p>If you want to try it out, check out the quickstart:<p><a href="https://docs.dbos.dev/quickstart?language=java" rel="nofollow">https://docs.dbos.dev/quickstart?language=java</a><p>We'd love to hear what you think! We’ll be in the comments for the rest of the day to answer any questions.
Kratos - Cloud native Auth0 open-source alternative (self-hosted)
Show HN: Lexical - How I learned 3 languages in 3 years
Hey HN, I'm Taylor and today I'm launching Lexical, a language learning app that actually works by teaching the 5,000 most common words in the target language.<p>Spaced repetition is how I've learned Spanish, French, and Italian in the last 3 years and I couldn't find tools that have the languages that I want to learn in the future, so I built Lexical.<p>The link goes to my White-Paper describing the language learning philosophy behind Lexical if you just want to try it out you can click "Get Started" at the top of the page. To really experience Lexical at it's best you'll need an account (sorry guys) but I've tried to make it as easy as possible.<p>I'm a big fan of Hacker News and am looking forward to your comments and hopefully even some beta users.<p>Here are some old HN posts that inspired my app and white-paper.<p>Learning is remembering
<a href="https://news.ycombinator.com/item?id=32982513">https://news.ycombinator.com/item?id=32982513</a><p>Kanji only requires 777 words
<a href="https://news.ycombinator.com/item?id=20721736">https://news.ycombinator.com/item?id=20721736</a><p>Most of language learning is temporary heuristics
<a href="https://news.ycombinator.com/item?id=44907531">https://news.ycombinator.com/item?id=44907531</a><p>Duolingo sucks
<a href="https://news.ycombinator.com/item?id=45425061">https://news.ycombinator.com/item?id=45425061</a>
Show HN: Creavi Macropad – Built a wireless macropad with a display
Hey HN,<p>We built a wireless, low-profile macropad with a display called the Creavi Macropad. It lasts at least 1 month on a single charge.
We also put together a browser-based tool that lets you update macros in real time and even push OTA updates over BLE.
Since we're software engineers by day, we had to figure out the hardware, mechanics, and industrial design as we went (and somehow make it all work together).
This post covers the build process, and the final result.<p>Hope you enjoy!
Show HN: Gerbil – an open source desktop app for running LLMs locally
Gerbil is an open source app that I've been working on for the last couple of months. The development now is largely done and I'm unlikely to add anymore major features. Instead I'm focusing on any bug fixes, small QoL features and dependency upgrades.<p>Under the hood it runs llama.cpp (via koboldcpp) backends and allows easy integration with the popular modern frontends like Open WebUI, SillyTavern, ComfyUI, StableUI (built-in) and KoboldAI Lite (built-in).<p>Why did I create this? I wanted an all-in-one solution for simple text and image-gen local LLMs. I got fed up with needing to manage multiple tools for the various LLM backends and frontends. In addition, as a Linux Wayland user I needed something that would work and look great on my system.
Show HN: Gerbil – an open source desktop app for running LLMs locally
Gerbil is an open source app that I've been working on for the last couple of months. The development now is largely done and I'm unlikely to add anymore major features. Instead I'm focusing on any bug fixes, small QoL features and dependency upgrades.<p>Under the hood it runs llama.cpp (via koboldcpp) backends and allows easy integration with the popular modern frontends like Open WebUI, SillyTavern, ComfyUI, StableUI (built-in) and KoboldAI Lite (built-in).<p>Why did I create this? I wanted an all-in-one solution for simple text and image-gen local LLMs. I got fed up with needing to manage multiple tools for the various LLM backends and frontends. In addition, as a Linux Wayland user I needed something that would work and look great on my system.
Show HN: Gerbil – an open source desktop app for running LLMs locally
Gerbil is an open source app that I've been working on for the last couple of months. The development now is largely done and I'm unlikely to add anymore major features. Instead I'm focusing on any bug fixes, small QoL features and dependency upgrades.<p>Under the hood it runs llama.cpp (via koboldcpp) backends and allows easy integration with the popular modern frontends like Open WebUI, SillyTavern, ComfyUI, StableUI (built-in) and KoboldAI Lite (built-in).<p>Why did I create this? I wanted an all-in-one solution for simple text and image-gen local LLMs. I got fed up with needing to manage multiple tools for the various LLM backends and frontends. In addition, as a Linux Wayland user I needed something that would work and look great on my system.
Show HN: Cancer diagnosis makes for an interesting RL environment for LLMs
Hey HN, this is David from Aluna (YC S24). We work with diagnostic labs to build datasets and evals for oncology tasks.<p>I wanted to share a simple RL environment I built that gave frontier LLMs a set of tools that lets it zoom and pan across a digitized pathology slide to find the relevant regions to make a diagnosis.
Here are some videos of the LLM performing diagnosis on a few slides:<p>(<a href="https://www.youtube.com/watch?v=k7ixTWswT5c" rel="nofollow">https://www.youtube.com/watch?v=k7ixTWswT5c</a>): traces of an LLM choosing different regions to view before making a diagnosis on a case of small-cell carcinoma of the lung<p>(<a href="https://youtube.com/watch?v=0cMbqLnKkGU" rel="nofollow">https://youtube.com/watch?v=0cMbqLnKkGU</a>): traces of an LLM choosing different regions to view before making a diagnosis on a case of benign fibroadenoma of the breast<p>Why I built this:<p>Pathology slides are the backbone of modern cancer diagnosis. Tissue from a biopsy is sliced, stained, and mounted on glass for a pathologist to examine abnormalities.<p>Today, many of these slides are digitized into whole-slide images (WSIs)in TIF or SVS format and are several gigabytes in size.<p>While there exists several pathology-focused AI models, I was curious to test whether frontier LLMs can perform well on pathology-based tasks. The main challenge is that WSIs are too large to fit into an LLM’s context window. The standard workaround, splitting them into thousands of smaller tiles, is inefficient for large frontier LLMs.<p>Inspired by how pathologists zoom and pan under a microscope, I built a set of tools that let LLMs control magnification and coordinates, viewing small regions at a time and deciding where to look next.<p>This ended up resulting in some interesting behaviors, and actually seemed to yield pretty good results with prompt engineering:<p>- GPT 5: explored up to ~30 regions before deciding (concurred with an expert pathologist on 4 out of 6 cancer subtyping tasks and 3 out of 5 IHC scoring tasks)<p>- Claude 4.5: Typically used 10–15 views but similar accuracy as GPT-5 (concurred with the pathologist on 3 out of 6 cancer subtyping tasks and 4 out of 5 IHC scoring tasks)<p>- Smaller models (GPT 4o, Claude 3.5 Haiku): examined ~8 frames and were less accurate overall (1 out of 6 cancer subtytping tasks and 1 out of 5 IHC scoring tasks)<p>Obviously, this was a small sample set, so we are working on creating a larger benchmark suite with more cases and types of tasks, but I thought this was cool that it even worked so I wanted to share with HN!