Later on we're also planning on adding new feature that will allow you to get a full branding done by the AI.
We will also have tools for enterprise and a rapid logo creation process for the internal projects.
Feel free to join the waitlist and be a part of our community.
Thanks!"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"Show HN: Logorax \u2013 AI logo generator for professionals"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"https://logorax.com/?ref=hackernews"}},"_tags":["story","author_markarx","story_36968478","show_hn"],"author":"markarx","created_at":"2023-08-02T08:36:48Z","created_at_i":1690965408,"num_comments":0,"objectID":"36968478","points":5,"story_id":36968478,"story_text":"Hey HN! We're building the best AI logo maker out there. Our goal is to make these logos so good, that it will be a no brainer solution for all MVPs, internal projects and even for white-label design services from agencies.
Later on we're also planning on adding new feature that will allow you to get a full branding done by the AI.
We will also have tools for enterprise and a rapid logo creation process for the internal projects.
Feel free to join the waitlist and be a part of our community.
Thanks!","title":"Show HN: Logorax \u2013 AI logo generator for professionals","updated_at":"2024-09-20T14:49:46Z","url":"https://logorax.com/?ref=hackernews"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"newnix"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"Liora Journal \u2013 Digital Journaling Assistant"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"https://www.liorajournal.com/"}},"_tags":["story","author_newnix","story_44334713"],"author":"newnix","created_at":"2025-06-21T05:12:15Z","created_at_i":1750482735,"num_comments":0,"objectID":"44334713","points":3,"story_id":44334713,"title":"Liora Journal \u2013 Digital Journaling Assistant","updated_at":"2025-06-21T09:17:23Z","url":"https://www.liorajournal.com/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"guitmz"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"Linux.Liora: a Go virus"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"https://guitmz.com/linux-liora/"}},"_tags":["story","author_guitmz","story_14077369"],"author":"guitmz","created_at":"2017-04-10T10:43:38Z","created_at_i":1491821018,"num_comments":0,"objectID":"14077369","points":3,"story_id":14077369,"title":"Linux.Liora: a Go virus","updated_at":"2024-09-20T00:40:04Z","url":"https://guitmz.com/linux-liora/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"organicgrant"},"story_text":{"matchLevel":"none","matchedWords":[],"value":""},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"Logorama - Massive Creativity (video)"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"http://organicgrant.posterous.com/logorama"}},"_tags":["story","author_organicgrant","story_1680594"],"author":"organicgrant","created_at":"2010-09-11T00:05:09Z","created_at_i":1284163509,"num_comments":0,"objectID":"1680594","points":2,"story_id":1680594,"story_text":"","title":"Logorama - Massive Creativity (video)","updated_at":"2024-09-19T17:14:57Z","url":"http://organicgrant.posterous.com/logorama"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"willchennn"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"I built an open source Harvey/Legora in two weeks"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://mikeoss.com"}},"_tags":["story","author_willchennn","story_47949270"],"author":"willchennn","children":[47949271],"created_at":"2026-04-29T14:51:45Z","created_at_i":1777474305,"num_comments":1,"objectID":"47949270","points":1,"story_id":47949270,"title":"I built an open source Harvey/Legora in two weeks","updated_at":"2026-04-30T00:57:20Z","url":"https://mikeoss.com"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"djent"},"story_text":{"matchLevel":"none","matchedWords":[],"value":""},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"Linux.Liora"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"http://vx.thomazi.me/posts/linux-liora"}},"_tags":["story","author_djent","story_9505144"],"author":"djent","created_at":"2015-05-07T14:12:29Z","created_at_i":1431007949,"num_comments":0,"objectID":"9505144","points":1,"story_id":9505144,"story_text":"","title":"Linux.Liora","updated_at":"2023-09-07T02:42:13Z","url":"http://vx.thomazi.me/posts/linux-liora"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"nibab"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"Free alternative to Harvey/Legora's tabular document review"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.usefolio.ai/blog/a-tabular-document-review-companion-for-your-claude-legal-skill"}},"_tags":["story","author_nibab","story_47415806"],"author":"nibab","created_at":"2026-03-17T17:39:39Z","created_at_i":1773769179,"num_comments":0,"objectID":"47415806","points":1,"story_id":47415806,"title":"Free alternative to Harvey/Legora's tabular document review","updated_at":"2026-03-17T17:40:41Z","url":"https://www.usefolio.ai/blog/a-tabular-document-review-companion-for-your-claude-legal-skill"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"jannchie"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"Liora Gallery is a minimal self-hosted gallery for photographers to showcase portfolios. Demo: https://photos.jannchie.com. Source: https://github.com/Jannchie/liora.
It runs from a single Docker image, stays phone-friendly, and pairs a public waterfall grid with an admin workspace for uploads/metadata. Map view and EXIF autofill keep locations/camera data intact; S3-compatible storage and duplicates detection are built in. Stack: Nuxt 4 + Nuxt UI/Tailwind, Drizzle ORM with SQLite."},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"Show HN: Liora Gallery \u2013 Minimal self-hosted photo portfolio"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://photos.jannchie.com/"}},"_tags":["story","author_jannchie","story_46191782","show_hn"],"author":"jannchie","created_at":"2025-12-08T13:10:36Z","created_at_i":1765199436,"num_comments":0,"objectID":"46191782","points":1,"story_id":46191782,"story_text":"Liora Gallery is a minimal self-hosted gallery for photographers to showcase portfolios. Demo: https://photos.jannchie.com. Source: https://github.com/Jannchie/liora.
It runs from a single Docker image, stays phone-friendly, and pairs a public waterfall grid with an admin workspace for uploads/metadata. Map view and EXIF autofill keep locations/camera data intact; S3-compatible storage and duplicates detection are built in. Stack: Nuxt 4 + Nuxt UI/Tailwind, Drizzle ORM with SQLite.","title":"Show HN: Liora Gallery \u2013 Minimal self-hosted photo portfolio","updated_at":"2026-03-05T23:10:05Z","url":"https://photos.jannchie.com/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"shaanprab"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"I\u2019ve always struggled to retain information from long-form YouTube videos, especially when passively watching tutorials or explainers. I\u2019d finish a 20-minute video and realize I couldn\u2019t recall much of what I just saw.
So I built Lisora which is a minimal tool that takes a YouTube video and automatically inserts short quizzes and reflection prompts at regular intervals to encourage active learning. I also added scores and have future plans to make it competitive.
You don\u2019t need to install anything or sign up just paste a YouTube link and hit go. It works best on educational or lecture-style content.
MVP: https://mvp.lisora.ai\n Vision: https://lisora.ai
It\u2019s still in early MVP stage the quiz logic is simple, and the reflection model is rule-based for now. But I\u2019m planning to:
Make the prompts more intelligent using semantic chunking
Introduce spaced repetition once accounts are enabled
Add retention analytics
Would love any feedback on:
UX flow / onboarding
Technical critiques
Ideas for making learning more interactive
Thanks in advance!"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"Show HN: Lisora \u2013 quizzes/reflection prompts to YouTube to improve retention"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"https://mvp.lisora.ai/"}},"_tags":["story","author_shaanprab","story_44672797","show_hn"],"author":"shaanprab","created_at":"2025-07-24T16:38:21Z","created_at_i":1753375101,"num_comments":0,"objectID":"44672797","points":1,"story_id":44672797,"story_text":"I\u2019ve always struggled to retain information from long-form YouTube videos, especially when passively watching tutorials or explainers. I\u2019d finish a 20-minute video and realize I couldn\u2019t recall much of what I just saw.
So I built Lisora which is a minimal tool that takes a YouTube video and automatically inserts short quizzes and reflection prompts at regular intervals to encourage active learning. I also added scores and have future plans to make it competitive.
You don\u2019t need to install anything or sign up just paste a YouTube link and hit go. It works best on educational or lecture-style content.
MVP: https://mvp.lisora.ai\n Vision: https://lisora.ai
It\u2019s still in early MVP stage the quiz logic is simple, and the reflection model is rule-based for now. But I\u2019m planning to:
Make the prompts more intelligent using semantic chunking
Introduce spaced repetition once accounts are enabled
Add retention analytics
Would love any feedback on:
UX flow / onboarding
Technical critiques
Ideas for making learning more interactive
Thanks in advance!","title":"Show HN: Lisora \u2013 quizzes/reflection prompts to YouTube to improve retention","updated_at":"2025-07-24T16:39:17Z","url":"https://mvp.lisora.ai/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"nill0"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"The Ligra Graph Processing Framework"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"http://jshun.github.io/ligra/docs/introduction.html"}},"_tags":["story","author_nill0","story_43297698"],"author":"nill0","created_at":"2025-03-08T05:20:39Z","created_at_i":1741411239,"num_comments":0,"objectID":"43297698","points":1,"story_id":43297698,"title":"The Ligra Graph Processing Framework","updated_at":"2025-03-08T05:24:57Z","url":"http://jshun.github.io/ligra/docs/introduction.html"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"hiroprot"},"story_text":{"matchLevel":"none","matchedWords":[],"value":""},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"Logorama - a video entirely made of Logos"},"url":{"matchLevel":"none","matchedWords":[],"value":"http://www.facebook.com/#!/video/video.php?v=1352585771842&ref=nf"}},"_tags":["story","author_hiroprot","story_1118300"],"author":"hiroprot","created_at":"2010-02-11T18:23:09Z","created_at_i":1265912589,"num_comments":0,"objectID":"1118300","points":1,"story_id":1118300,"story_text":"","title":"Logorama - a video entirely made of Logos","updated_at":"2024-09-19T16:50:16Z","url":"http://www.facebook.com/#!/video/video.php?v=1352585771842&ref=nf"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"mattbgates"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"One time, my supervisor called me into a meeting to tell me that he did not appreciate me using "hate text" for the landing pages I was creating. I actually did not know what he was talking about and I asked him about it. This is what he showed me. He actually printed it out:
<< Lorem ipsum dolorem sit amet, consectetur adipiscing elit. Integer nec odio. Praesent libero. Sed cursus ante dapibus diam. Sed nisi. Nulla quis sem at nibh elementum imperdiet. Duis sagittis ipsum. Praesent mauris. Fusce nec tellus sed augue semper porta. Mauris massa. Vestibulum lacinia arcu eget nulla. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Curabitur sodales ligula in libero. Sed dignissim lacinia nunc.
Curabitur tortor. Pellentesque nibh. Aenean quam. In scelerisque sem at dolor. Maecenas mattis. Sed convallis tristique sem. Proin ut ligula vel nunc egestas porttitor. Morbi lectus risus, iaculis vel, suscipit quis, luctus non, massa. Fusce ac turpis quis ligula lacinia aliquet. Mauris ipsum. Nulla metus metus, ullamcorper vel, tincidunt sed, euismod in, nibh. Quisque volutpat condimentum velit. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Nam nec ante. >>
A few words along with "dolorem" got picked up in the translator as "hate, pain, suffer, sorrow".
The supervisor told me not to use this text, but had scolded me once before for using a "bacon ipsum" and a "cupcake ipsum" generator saying, "We need to provide the client with serious work. Funny text like this is not appropriate." So I went back to using Lorem Ipsum text.
I had come to find out that one of my co-workers "reported me" because her Chrome browser decided to try and translate the Lorem Ipsum text. She thought I was purposely writing that I hated her. And this is why I almost got fired. It took two other co-workers, who had near-identical text, to convince my supervisor that I did not write hate speech."},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Things you got in trouble for at work that were not a big deal?"}},"_tags":["story","author_mattbgates","story_14000336","ask_hn"],"author":"mattbgates","children":[14000477,14000512,14001046,14001253,14001263,14007188,14007747],"created_at":"2017-03-31T00:44:26Z","created_at_i":1490921066,"num_comments":16,"objectID":"14000336","points":12,"story_id":14000336,"story_text":"One time, my supervisor called me into a meeting to tell me that he did not appreciate me using "hate text" for the landing pages I was creating. I actually did not know what he was talking about and I asked him about it. This is what he showed me. He actually printed it out:
<< Lorem ipsum dolorem sit amet, consectetur adipiscing elit. Integer nec odio. Praesent libero. Sed cursus ante dapibus diam. Sed nisi. Nulla quis sem at nibh elementum imperdiet. Duis sagittis ipsum. Praesent mauris. Fusce nec tellus sed augue semper porta. Mauris massa. Vestibulum lacinia arcu eget nulla. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Curabitur sodales ligula in libero. Sed dignissim lacinia nunc.
Curabitur tortor. Pellentesque nibh. Aenean quam. In scelerisque sem at dolor. Maecenas mattis. Sed convallis tristique sem. Proin ut ligula vel nunc egestas porttitor. Morbi lectus risus, iaculis vel, suscipit quis, luctus non, massa. Fusce ac turpis quis ligula lacinia aliquet. Mauris ipsum. Nulla metus metus, ullamcorper vel, tincidunt sed, euismod in, nibh. Quisque volutpat condimentum velit. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Nam nec ante. >>
A few words along with "dolorem" got picked up in the translator as "hate, pain, suffer, sorrow".
The supervisor told me not to use this text, but had scolded me once before for using a "bacon ipsum" and a "cupcake ipsum" generator saying, "We need to provide the client with serious work. Funny text like this is not appropriate." So I went back to using Lorem Ipsum text.
I had come to find out that one of my co-workers "reported me" because her Chrome browser decided to try and translate the Lorem Ipsum text. She thought I was purposely writing that I hated her. And this is why I almost got fired. It took two other co-workers, who had near-identical text, to convince my supervisor that I did not write hate speech.","title":"Ask HN: Things you got in trouble for at work that were not a big deal?","updated_at":"2024-09-20T00:31:48Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"afistfullof"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"I spent the past couple of weekends building an open-source alternative to Harvey/Legora's popular tabular review application for lawyers.
The project was sparked by a viral LinkedIn post from lawyer Joshua Upin, who described being shown a hallucinated citation by Harvey that was falsely attributed to one of Harvey\u2019s competitors. Seeing such a basic failure emerge from their architecture made me ask a simple question: could I recreate a similar product of theirs without using a single generative model, and in doing so make hallucinations architecturally impossible?
As it turns out, quite a lot.
In building the app, I did not use a single external or generative model. The entire system uses models my organisation trained and owns. More specifically, it uses a combination of Kanon 2 Enricher, Kanon 2 Embedder, and Kanon Answer Extractor. All three are encoder-based, and there are no generative models anywhere in the stack.
That means hallucinations are architecturally impossible. It also means the system can retrieve, classify, extract, and link information in a much more structured and interactive way than products that lean heavily on generation.
At its core, the app turns contracts into a wiki-style, interconnected knowledge graph: a network of entities, annotations, spans, and relations that users can explore interactively. Key features like parties, locations, dates, signatures, and terms are extracted on the first pass. From there, users can define custom spans and relations, extending the graph as they go.
The end result is a tabular review system that matches the core experience offered by the market and, in several meaningful respects, goes beyond it.
I embedded a static version of the app at the top of the linked page so people can try it directly. The static version has real public contracts processed using the application. These contracts relate to public figures like Mark Zuckerberg, Elon Musk, and Jensen Huang, making it easy to verify the accuracy of the stack. The linked page also works as a step-by-step guide for anyone who wants to build something similar themselves."},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: I built a Harvey-style tabular review app, then open sourced the code"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://isaacus.com/blog/hallucination-free-tabular-review-from-scratch"}},"_tags":["story","author_afistfullof","story_47701142","show_hn"],"author":"afistfullof","created_at":"2026-04-09T09:16:22Z","created_at_i":1775726182,"num_comments":0,"objectID":"47701142","points":4,"story_id":47701142,"story_text":"I spent the past couple of weekends building an open-source alternative to Harvey/Legora's popular tabular review application for lawyers.
The project was sparked by a viral LinkedIn post from lawyer Joshua Upin, who described being shown a hallucinated citation by Harvey that was falsely attributed to one of Harvey\u2019s competitors. Seeing such a basic failure emerge from their architecture made me ask a simple question: could I recreate a similar product of theirs without using a single generative model, and in doing so make hallucinations architecturally impossible?
As it turns out, quite a lot.
In building the app, I did not use a single external or generative model. The entire system uses models my organisation trained and owns. More specifically, it uses a combination of Kanon 2 Enricher, Kanon 2 Embedder, and Kanon Answer Extractor. All three are encoder-based, and there are no generative models anywhere in the stack.
That means hallucinations are architecturally impossible. It also means the system can retrieve, classify, extract, and link information in a much more structured and interactive way than products that lean heavily on generation.
At its core, the app turns contracts into a wiki-style, interconnected knowledge graph: a network of entities, annotations, spans, and relations that users can explore interactively. Key features like parties, locations, dates, signatures, and terms are extracted on the first pass. From there, users can define custom spans and relations, extending the graph as they go.
The end result is a tabular review system that matches the core experience offered by the market and, in several meaningful respects, goes beyond it.
I embedded a static version of the app at the top of the linked page so people can try it directly. The static version has real public contracts processed using the application. These contracts relate to public figures like Mark Zuckerberg, Elon Musk, and Jensen Huang, making it easy to verify the accuracy of the stack. The linked page also works as a step-by-step guide for anyone who wants to build something similar themselves.","title":"Show HN: I built a Harvey-style tabular review app, then open sourced the code","updated_at":"2026-04-21T10:53:48Z","url":"https://isaacus.com/blog/hallucination-free-tabular-review-from-scratch"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"nibab"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["ligora"],"value":"hi HN!
a couple of months ago I had to analyze a few thousand audio recordings to help identify issues with customer support. i was able to get some raw high-level initial results with python scripts invoking LLM APIs, but they were too general and unhelpful. writing basic prompts is easy, but tuning them and making them specific enough to ensure no faint signal is missed is hard. you need to iterate through the data with an initial prompt, segment the data into different buckets, chain another prompt for each bucket etc. Then you need to constantly review the raw data to tweak the prompts just the right way to get the desired results.
There are no good user-facing tools for scaling to thousands of rows of unstructured data analysis with LLMs. Claude Cowork / agents with access to filesystems are scratching the surface, but having a text-only UI is challenging, especially when you want to go back and adjust your research pipeline, narrow down deterministically to a specific subset of your data with SQL-like filters, or do any cost management. Scaling past 100 files is not well supported. Deep research is difficult to steer and verify.
I needed a mini-data warehouse that could help me get insights out of my data, optimize costs with bulk LLM operations (via cost estimation and model choice), and let me browse and verify the data in a user-friendly way, without requiring me to set up something like Databricks. So, I built folio.
Folio is a free, local, macOS app for analyzing your unstructured data with LLMs. It's a UI wrapper around a minimal data warehouse that lets users (and agents) do LLM-based transformations on big unstructured datasets. All you need to get started is an AI API key and an account with modal.com
Users bring their files into Folio which then get loaded into a tabl, where each row contains a markdown representation of the file contents. Users can then run LLM operations in bulk on those files and use sql filters to create views and narrow down the scope of the transformations. Agents are a first-class citizen and they can plug into folio to do most of the work for you. To take load off the desktop for OCR/Audio Transcription as well as the thousands of http requests to AI APIs we integrate with modal.com as the execution engine. A local orchestrator fans out jobs to modal and then fans them in once complete. Data is never stored anywhere, and only moves in transit through AI API provider and the user's own modal infrastructure.
folio workspaces are multi-modal (you can load different data types in the same workspace and move it through the same analysis pipeline) and they can support thousands of files.
People use folio today to:\n- review customer support tickets/emails: bucket issue into different categories, narrow in on categories of interest, and then action that data by generating a response.\n- extract detailed data from financial documents: load all data that can be found on a particular company, extract structured data like revenue numbers and projections.\n- do literature reviews: there are lots of agents that help you load data from research paper repositories. once that data is loaded into folio, users can do a steerable deep research over those files.\n- perform criteria-based search: generate yes/no criteria like "document contains data on XYZ", "document mentions ABC", "documented cites XYZ".
Companies like v7labs, hebbia, Legora, Harvey have similar "Tabular Document Review" features, but they are not scalable or compatible with outside agents like Claude Code. Additionally they require expensive enterprise contracts.
I see folio moving beyond data analysis into the perfect companion for agentic tasks that require a human-facing UI/UX, cost management and actioning on data in bulk.
Website: https://www.usefolio.ai\nGithub: https://github.com/usefolio/folio\nX: https://x.com/usefolio_ai
Looking forward to hearing what people think!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: An unstructured data workspace for data transformations with LLM"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.usefolio.ai/"}},"_tags":["story","author_nibab","story_47532301","show_hn"],"author":"nibab","children":[47574918],"created_at":"2026-03-26T16:12:26Z","created_at_i":1774541546,"num_comments":0,"objectID":"47532301","points":4,"story_id":47532301,"story_text":"hi HN!
a couple of months ago I had to analyze a few thousand audio recordings to help identify issues with customer support. i was able to get some raw high-level initial results with python scripts invoking LLM APIs, but they were too general and unhelpful. writing basic prompts is easy, but tuning them and making them specific enough to ensure no faint signal is missed is hard. you need to iterate through the data with an initial prompt, segment the data into different buckets, chain another prompt for each bucket etc. Then you need to constantly review the raw data to tweak the prompts just the right way to get the desired results.
There are no good user-facing tools for scaling to thousands of rows of unstructured data analysis with LLMs. Claude Cowork / agents with access to filesystems are scratching the surface, but having a text-only UI is challenging, especially when you want to go back and adjust your research pipeline, narrow down deterministically to a specific subset of your data with SQL-like filters, or do any cost management. Scaling past 100 files is not well supported. Deep research is difficult to steer and verify.
I needed a mini-data warehouse that could help me get insights out of my data, optimize costs with bulk LLM operations (via cost estimation and model choice), and let me browse and verify the data in a user-friendly way, without requiring me to set up something like Databricks. So, I built folio.
Folio is a free, local, macOS app for analyzing your unstructured data with LLMs. It's a UI wrapper around a minimal data warehouse that lets users (and agents) do LLM-based transformations on big unstructured datasets. All you need to get started is an AI API key and an account with modal.com
Users bring their files into Folio which then get loaded into a tabl, where each row contains a markdown representation of the file contents. Users can then run LLM operations in bulk on those files and use sql filters to create views and narrow down the scope of the transformations. Agents are a first-class citizen and they can plug into folio to do most of the work for you. To take load off the desktop for OCR/Audio Transcription as well as the thousands of http requests to AI APIs we integrate with modal.com as the execution engine. A local orchestrator fans out jobs to modal and then fans them in once complete. Data is never stored anywhere, and only moves in transit through AI API provider and the user's own modal infrastructure.
folio workspaces are multi-modal (you can load different data types in the same workspace and move it through the same analysis pipeline) and they can support thousands of files.
People use folio today to:\n- review customer support tickets/emails: bucket issue into different categories, narrow in on categories of interest, and then action that data by generating a response.\n- extract detailed data from financial documents: load all data that can be found on a particular company, extract structured data like revenue numbers and projections.\n- do literature reviews: there are lots of agents that help you load data from research paper repositories. once that data is loaded into folio, users can do a steerable deep research over those files.\n- perform criteria-based search: generate yes/no criteria like "document contains data on XYZ", "document mentions ABC", "documented cites XYZ".
Companies like v7labs, hebbia, Legora, Harvey have similar "Tabular Document Review" features, but they are not scalable or compatible with outside agents like Claude Code. Additionally they require expensive enterprise contracts.
I see folio moving beyond data analysis into the perfect companion for agentic tasks that require a human-facing UI/UX, cost management and actioning on data in bulk.
Website: https://www.usefolio.ai\nGithub: https://github.com/usefolio/folio\nX: https://x.com/usefolio_ai
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---
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- 97.90% do not know which loans accumulate interest in-school or during deferment
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- 6.10% know their repayment terms
- 58.90% thought the total amount of outstanding student loan debt was in the millions (not billions or trillions)
- 96.02% did not know that student loan refinancing was an option after graduation
---
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