The best Hacker News stories from Show from the past day
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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.
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: 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!
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!
Show HN: Data Formulator – interactive AI agents for data analysis (Microsoft)
Hi everyone, we are excited to share with you our new release of Data Formulator. Starting from a dataset, you can communicate with AI agents with UI + natural language to explore data and create visualizations to discover new insights. Here's a demo video of the experience: <a href="https://github.com/microsoft/data-formulator/releases/tag/0.5" rel="nofollow">https://github.com/microsoft/data-formulator/releases/tag/0....</a>.<p>This is a build-up from our release a year ago (<a href="https://news.ycombinator.com/item?id=41907719">https://news.ycombinator.com/item?id=41907719</a>). We spent a year exploring how to blend agent mode with interactions to allow you more easily "vibe" with your data but still keeping in control. We don't think the future of data analysis is just "agent to do all for you from a high-level prompt" --- you should still be able to drive the open-ended exploration; but we also don't want you to do everything step-by-step. Thus we worked on this "interactive agent mode" for data analysis with some UI innovations.<p>Our new demo features:<p>* We want to let you import (almost) any data easily to get started exploration — either it's a screenshot of a web table, an unnormalized excel table, table in a chunk of text, a csv file, or a table in database, you should be able to load into the tool easily with a little bit of AI assistance.<p>* We want you to easily choose between agent mode (more automation) vs interactive mode (more fine-grained control) yourself as you explore data. We designed an interface of "data threads": both your and agents' explorations are organized as threads so you can jump into any point to decide how you want to follow-up or revise using UI + NL instruction to provide fine-grained control.<p>* The results should be easily interpretable. Data Formulator now presents "concept" behind the code generated by AI agents alongside code/explanation/data. Plus, you can compose a report easily based on your visualizations to share insights.<p>We are sharing the online demo at <a href="https://data-formulator.ai/" rel="nofollow">https://data-formulator.ai/</a> for you to try! If you want more involvement and customization, checkout our source code <a href="https://github.com/microsoft/data-formulator" rel="nofollow">https://github.com/microsoft/data-formulator</a> and let's build something together as a community!
Show HN: Data Formulator – interactive AI agents for data analysis (Microsoft)
Hi everyone, we are excited to share with you our new release of Data Formulator. Starting from a dataset, you can communicate with AI agents with UI + natural language to explore data and create visualizations to discover new insights. Here's a demo video of the experience: <a href="https://github.com/microsoft/data-formulator/releases/tag/0.5" rel="nofollow">https://github.com/microsoft/data-formulator/releases/tag/0....</a>.<p>This is a build-up from our release a year ago (<a href="https://news.ycombinator.com/item?id=41907719">https://news.ycombinator.com/item?id=41907719</a>). We spent a year exploring how to blend agent mode with interactions to allow you more easily "vibe" with your data but still keeping in control. We don't think the future of data analysis is just "agent to do all for you from a high-level prompt" --- you should still be able to drive the open-ended exploration; but we also don't want you to do everything step-by-step. Thus we worked on this "interactive agent mode" for data analysis with some UI innovations.<p>Our new demo features:<p>* We want to let you import (almost) any data easily to get started exploration — either it's a screenshot of a web table, an unnormalized excel table, table in a chunk of text, a csv file, or a table in database, you should be able to load into the tool easily with a little bit of AI assistance.<p>* We want you to easily choose between agent mode (more automation) vs interactive mode (more fine-grained control) yourself as you explore data. We designed an interface of "data threads": both your and agents' explorations are organized as threads so you can jump into any point to decide how you want to follow-up or revise using UI + NL instruction to provide fine-grained control.<p>* The results should be easily interpretable. Data Formulator now presents "concept" behind the code generated by AI agents alongside code/explanation/data. Plus, you can compose a report easily based on your visualizations to share insights.<p>We are sharing the online demo at <a href="https://data-formulator.ai/" rel="nofollow">https://data-formulator.ai/</a> for you to try! If you want more involvement and customization, checkout our source code <a href="https://github.com/microsoft/data-formulator" rel="nofollow">https://github.com/microsoft/data-formulator</a> and let's build something together as a community!
Show HN: A free Instagram story viewer that lets you watch anonymously
Show HN: Venturu – Zillow for the market of local businesses
Hey HN, Joel here, co-founder of Venturu.<p>Imagine trying to buy a house before Zillow. That’s what buying a local business is like today. It's a massive market, but it's completely fragmented and stuck in the 90s.<p>My co-founder, Luis, discovered this firsthand by knocking on doors to buy six of his own businesses. I saw it at industry conferences, where at 29, I’m usually the youngest person in the room. The system is built on gatekept information and a wall of fees designed to keep people out.<p>For a small business owner, it starts with a gut punch: you have to pay thousands of dollars just to get an idea of what your life's work is worth. Then, you face thousands more in listing fees just to get it seen on an outdated platform.<p>This broken model forces brokers to be gatekeepers. The high costs mean they can only list a fraction of their clients' businesses, hiding the rest on thousands of separate, clunky websites.<p>We’re trying to fix this by building the single, open, and free platform this market needs. We got rid of the scary upfront fees by offering free, instant valuations, and unlocked the hidden market by making all listings free.<p>It’s one place for owners, buyers, and brokers to finally connect efficiently.<p>It seems to be working. We’ve welcomed over 1,300 brokers who have listed 3,800+ businesses across all 50 states.<p>It's still early days, but our goal is to build this into the definitive marketplace for local businesses, creating the first real source of truth for valuations and making the entire process, from discovery to closing, more straightforward.<p>We’re building in the open and would love your feedback.
Ask us anything.
Show HN: Venturu – Zillow for the market of local businesses
Hey HN, Joel here, co-founder of Venturu.<p>Imagine trying to buy a house before Zillow. That’s what buying a local business is like today. It's a massive market, but it's completely fragmented and stuck in the 90s.<p>My co-founder, Luis, discovered this firsthand by knocking on doors to buy six of his own businesses. I saw it at industry conferences, where at 29, I’m usually the youngest person in the room. The system is built on gatekept information and a wall of fees designed to keep people out.<p>For a small business owner, it starts with a gut punch: you have to pay thousands of dollars just to get an idea of what your life's work is worth. Then, you face thousands more in listing fees just to get it seen on an outdated platform.<p>This broken model forces brokers to be gatekeepers. The high costs mean they can only list a fraction of their clients' businesses, hiding the rest on thousands of separate, clunky websites.<p>We’re trying to fix this by building the single, open, and free platform this market needs. We got rid of the scary upfront fees by offering free, instant valuations, and unlocked the hidden market by making all listings free.<p>It’s one place for owners, buyers, and brokers to finally connect efficiently.<p>It seems to be working. We’ve welcomed over 1,300 brokers who have listed 3,800+ businesses across all 50 states.<p>It's still early days, but our goal is to build this into the definitive marketplace for local businesses, creating the first real source of truth for valuations and making the entire process, from discovery to closing, more straightforward.<p>We’re building in the open and would love your feedback.
Ask us anything.
Show HN: Tusk Drift – Open-source tool for automating API tests
Hey HN, I'm Marcel from Tusk. We’re launching Tusk Drift, an open source tool that generates a full API test suite by recording and replaying live traffic.<p>How it works:<p>1. Records traces from live traffic (what gets captured)<p>2. Replays traces as API tests with mocked responses (how replay works)<p>3. Detects deviations between actual vs. expected output (what you get)<p>Unlike traditional mocking libraries, which require you to manually emulate how dependencies behave, Tusk Drift automatically records what these dependencies respond with based on actual user behavior and maintains recordings over time. The reason we built this is because of painful past experiences with brittle API test suites and regressions that would only be caught in prod.<p>Our SDK instruments your Node service, similar to OpenTelemetry. It captures all inbound requests and outbound calls like database queries, HTTP requests, and auth token generation. When Drift is triggered, it replays the inbound API call while intercepting outbound requests and serving them from recorded data. Drift’s tests are therefore idempotent, side-effect free, and fast (typically <100 ms per test). Think of it as a unit test but for your API.<p>Our Cloud platform does the following automatically:<p>- Updates the test suite of recorded traces to maintain freshness<p>- Matches relevant Drift tests to your PR’s changes when running tests in CI<p>- Surfaces unintended deviations, does root cause analysis, and suggests code fixes<p>We’re excited to see this use case finally unlocked. The release of Claude Sonnet 4.5 and similar coding models have made it possible to go from failing test to root cause reliably. Also, the ability to do accurate test matching and deviation classification means running a tool like this in CI no longer contributes to poor DevEx (imagine the time otherwise spent reviewing test results).<p>Limitations:<p>- You can specify PII redaction rules but there is no default mode for this at the moment. I recommend first enabling Drift on dev/staging, adding transforms (<a href="https://docs.usetusk.ai/api-tests/pii-redaction/basic-concepts">https://docs.usetusk.ai/api-tests/pii-redaction/basic-concep...</a>), and monitoring for a week before enabling on prod.<p>- Expect a 1-2% throughput overhead. Transforms result in a 1.0% increase in tail latency when a small number of transforms are registered; its impact scales linearly with the number of transforms registered.<p>- Currently only supports Node backends. Python SDK is coming next.<p>- Instrumentation limited to the following packages (more to come): <a href="https://github.com/Use-Tusk/drift-node-sdk?tab=readme-ov-file#requirements" rel="nofollow">https://github.com/Use-Tusk/drift-node-sdk?tab=readme-ov-fil...</a><p>Let me know if you have questions or feedback.<p>Demo repo: <a href="https://github.com/Use-Tusk/drift-node-demo" rel="nofollow">https://github.com/Use-Tusk/drift-node-demo</a>
Show HN: Tusk Drift – Open-source tool for automating API tests
Hey HN, I'm Marcel from Tusk. We’re launching Tusk Drift, an open source tool that generates a full API test suite by recording and replaying live traffic.<p>How it works:<p>1. Records traces from live traffic (what gets captured)<p>2. Replays traces as API tests with mocked responses (how replay works)<p>3. Detects deviations between actual vs. expected output (what you get)<p>Unlike traditional mocking libraries, which require you to manually emulate how dependencies behave, Tusk Drift automatically records what these dependencies respond with based on actual user behavior and maintains recordings over time. The reason we built this is because of painful past experiences with brittle API test suites and regressions that would only be caught in prod.<p>Our SDK instruments your Node service, similar to OpenTelemetry. It captures all inbound requests and outbound calls like database queries, HTTP requests, and auth token generation. When Drift is triggered, it replays the inbound API call while intercepting outbound requests and serving them from recorded data. Drift’s tests are therefore idempotent, side-effect free, and fast (typically <100 ms per test). Think of it as a unit test but for your API.<p>Our Cloud platform does the following automatically:<p>- Updates the test suite of recorded traces to maintain freshness<p>- Matches relevant Drift tests to your PR’s changes when running tests in CI<p>- Surfaces unintended deviations, does root cause analysis, and suggests code fixes<p>We’re excited to see this use case finally unlocked. The release of Claude Sonnet 4.5 and similar coding models have made it possible to go from failing test to root cause reliably. Also, the ability to do accurate test matching and deviation classification means running a tool like this in CI no longer contributes to poor DevEx (imagine the time otherwise spent reviewing test results).<p>Limitations:<p>- You can specify PII redaction rules but there is no default mode for this at the moment. I recommend first enabling Drift on dev/staging, adding transforms (<a href="https://docs.usetusk.ai/api-tests/pii-redaction/basic-concepts">https://docs.usetusk.ai/api-tests/pii-redaction/basic-concep...</a>), and monitoring for a week before enabling on prod.<p>- Expect a 1-2% throughput overhead. Transforms result in a 1.0% increase in tail latency when a small number of transforms are registered; its impact scales linearly with the number of transforms registered.<p>- Currently only supports Node backends. Python SDK is coming next.<p>- Instrumentation limited to the following packages (more to come): <a href="https://github.com/Use-Tusk/drift-node-sdk?tab=readme-ov-file#requirements" rel="nofollow">https://github.com/Use-Tusk/drift-node-sdk?tab=readme-ov-fil...</a><p>Let me know if you have questions or feedback.<p>Demo repo: <a href="https://github.com/Use-Tusk/drift-node-demo" rel="nofollow">https://github.com/Use-Tusk/drift-node-demo</a>