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":["legora"],"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
Looking forward to hearing what people think!","title":"Show HN: An unstructured data workspace for data transformations with LLM","updated_at":"2026-03-30T16:17:22Z","url":"https://www.usefolio.ai/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"ubutler"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["legora"],"value":"Hey HN,\nThis is Isaacus, a legal AI research company that my brother, Abdur-Rahman Butler, and I, Umar Butler, founded last year and have been working full time on ever since.
We make legal AI models. We recently released two state-of-the-art legal information retrieval models, Kanon 2 Embedder and Kanon 2 Reranker. Together, they rank first on Legal RAG Bench and the Massive Legal Embedding Benchmark (MLEB).
We also recently released Kanon 2 Enricher, an entirely new type of AI model capable of transforming lengthy, unstructured legal documents into highly structured knowledge graphs. We had the pleasure of having Harvey, KPMG Law, Alvarez & Marsal, Clifford Chance, Clyde & Co, Carey Olsen, Smokeball, and Moonlit be part of the closed beta for that model.
We're proud supporters of open source and maintain popular legal and AI datasets and libraries like the Open Australian Legal Corpus and the semchunk semantic chunking algorithm, together downloaded over one million times a month.
Our core mission is to solve every common data- and AI-related pain point of the legal tech industry. Uniquely, we're focused primarily on serving the legal tech industry rather than lawyers per se.
We, therefore, view folks like Harvey and Legora as customers instead of competitors.
In the few months since we've been around, we've witnessed massive growth in usage of our models by other new legal tech startups. We think our industry is only going to get bigger, particularly as more accessible legal services make their way into the hands of end consumers and enterprises instead of law firms.
We see ourselves as best placed to serve the needs of our industry given our strong experience and expertise in AI and law. I previously led all national-level AI projects at the Australian Attorney-General's Department as a senior data scientist while also holding an honors degree in law. My brother, in turn, is skilled in economics, data science, and AI.
We're currently working on scaling up our team to meet the growing demand we're seeing as well as help build out the next stage of our roadmap, which includes a first-of-a-kind knowledge graph of laws, decisions, and contracts from around the world, as well as the first legal reasoning model.
If you align with our mission and would like to partner with us or be part of our team, you can reach out at hello@isaacus.com."},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Isaacus \u2013 the legal AI research company"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://isaacus.com/"}},"_tags":["story","author_ubutler","story_47320176","show_hn"],"author":"ubutler","created_at":"2026-03-10T07:43:16Z","created_at_i":1773128596,"num_comments":0,"objectID":"47320176","points":1,"story_id":47320176,"story_text":"Hey HN,\nThis is Isaacus, a legal AI research company that my brother, Abdur-Rahman Butler, and I, Umar Butler, founded last year and have been working full time on ever since.
We make legal AI models. We recently released two state-of-the-art legal information retrieval models, Kanon 2 Embedder and Kanon 2 Reranker. Together, they rank first on Legal RAG Bench and the Massive Legal Embedding Benchmark (MLEB).
We also recently released Kanon 2 Enricher, an entirely new type of AI model capable of transforming lengthy, unstructured legal documents into highly structured knowledge graphs. We had the pleasure of having Harvey, KPMG Law, Alvarez & Marsal, Clifford Chance, Clyde & Co, Carey Olsen, Smokeball, and Moonlit be part of the closed beta for that model.
We're proud supporters of open source and maintain popular legal and AI datasets and libraries like the Open Australian Legal Corpus and the semchunk semantic chunking algorithm, together downloaded over one million times a month.
Our core mission is to solve every common data- and AI-related pain point of the legal tech industry. Uniquely, we're focused primarily on serving the legal tech industry rather than lawyers per se.
We, therefore, view folks like Harvey and Legora as customers instead of competitors.
In the few months since we've been around, we've witnessed massive growth in usage of our models by other new legal tech startups. We think our industry is only going to get bigger, particularly as more accessible legal services make their way into the hands of end consumers and enterprises instead of law firms.
We see ourselves as best placed to serve the needs of our industry given our strong experience and expertise in AI and law. I previously led all national-level AI projects at the Australian Attorney-General's Department as a senior data scientist while also holding an honors degree in law. My brother, in turn, is skilled in economics, data science, and AI.
We're currently working on scaling up our team to meet the growing demand we're seeing as well as help build out the next stage of our roadmap, which includes a first-of-a-kind knowledge graph of laws, decisions, and contracts from around the world, as well as the first legal reasoning model.
If you align with our mission and would like to partner with us or be part of our team, you can reach out at hello@isaacus.com.","title":"Show HN: Isaacus \u2013 the legal AI research company","updated_at":"2026-03-10T07:45:26Z","url":"https://isaacus.com/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"carlchenet"},"title":{"matchLevel":"none","matchedWords":[],"value":"French Satirical fake news website Le Gorafi down, announcing \u201cit's over\u201d"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["legora"],"value":"http://www.legorafi.fr/"}},"_tags":["story","author_carlchenet","story_10146023"],"author":"carlchenet","children":[10146171,10146198,10146229,10146260,10146283,10146318],"created_at":"2015-08-31T09:01:38Z","created_at_i":1441011698,"num_comments":28,"objectID":"10146023","points":25,"story_id":10146023,"title":"French Satirical fake news website Le Gorafi down, announcing \u201cit's over\u201d","updated_at":"2024-09-19T22:11:26Z","url":"http://www.legorafi.fr/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"speckx"},"title":{"matchLevel":"none","matchedWords":[],"value":"Everything in Git? 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No Way","updated_at":"2026-06-29T16:35:08Z","url":"https://legoraft.com/posts/everything-git-no-way/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"jpkenobi"},"story_text":{"matchLevel":"none","matchedWords":[],"value":""},"title":{"matchLevel":"none","matchedWords":[],"value":"A quarter of Americans believe that the Earth revolves around the United States"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["legora"],"value":"http://translate.google.com/translate?sl=fr&tl=en&js=n&prev=_t&hl=fr&ie=UTF-8&u=http%3A%2F%2Fwww.legorafi.fr%2F2014%2F03%2F14%2Fun-quart-des-americains-pensent-que-la-terre-tourne-autour-des-etats-unis%2F"}},"_tags":["story","author_jpkenobi","story_7504524"],"author":"jpkenobi","created_at":"2014-03-31T22:03:56Z","created_at_i":1396303436,"num_comments":0,"objectID":"7504524","points":2,"story_id":7504524,"story_text":"","title":"A quarter of Americans believe that the Earth revolves around the United 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servers","updated_at":"2024-09-19T19:25:39Z","url":"http://translate.google.com/translate?sl=fr&tl=en&js=n&prev=_t&hl=en&ie=UTF-8&eotf=1&u=http%3A%2F%2Fwww.legorafi.fr%2F2013%2F03%2F15%2Fle-conseil-national-du-numerique-recommande-decrire-plus-petit-pour-economiser-de-lespace-sur-internet%2F"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"montrealish"},"title":{"matchLevel":"none","matchedWords":[],"value":"A $5.6B valuation and rapid global expansion"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["legora"],"value":"https://fortune.com/2026/06/18/ai-european-scaler-legora-bird-bird-law/"}},"_tags":["story","author_montrealish","story_48592589"],"author":"montrealish","created_at":"2026-06-18T22:38:14Z","created_at_i":1781822294,"num_comments":0,"objectID":"48592589","points":1,"story_id":48592589,"title":"A $5.6B valuation and rapid global 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Canvas","updated_at":"2024-09-20T05:17:15Z","url":"https://legrajs.com/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"rms"},"story_text":{"matchLevel":"none","matchedWords":[],"value":""},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["legora"],"value":"Illegal Art: Logorama (Oscar nominated short film)"},"url":{"matchLevel":"none","matchedWords":[],"value":"http://www.garagetv.be/video-galerij/buzzing_bees/De_kortfilm_der_logo_s.aspx"}},"_tags":["story","author_rms","story_1161280"],"author":"rms","children":[1161805],"created_at":"2010-03-02T13:22:24Z","created_at_i":1267536144,"num_comments":1,"objectID":"1161280","points":13,"story_id":1161280,"story_text":"","title":"Illegal Art: Logorama (Oscar nominated short film)","updated_at":"2023-09-06T19:14:56Z","url":"http://www.garagetv.be/video-galerij/buzzing_bees/De_kortfilm_der_logo_s.aspx"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"nill0"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["legora"],"value":"Legolas \u2013 An Open Source Bipedal Robot"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["legora"],"value":"https://github.com/daviddoo02/Legolas-an-open-source-biped"}},"_tags":["story","author_nill0","story_43139214"],"author":"nill0","children":[43139215],"created_at":"2025-02-22T14:29:56Z","created_at_i":1740234596,"num_comments":0,"objectID":"43139214","points":5,"story_id":43139214,"title":"Legolas \u2013 An Open Source Bipedal Robot","updated_at":"2025-02-26T14:43:23Z","url":"https://github.com/daviddoo02/Legolas-an-open-source-biped"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"markarx"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"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":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["legora"],"value":"Show HN: Logorax \u2013 AI logo generator for professionals"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["legora"],"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.
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As background, we have run a naming contest on both squadhelp.com and namingforce.com. We have gone through the list of names generated from both contests to create the following shortlist of - what we think are - the best names.
We are looking for feedback from other people to help us make a decision on which name to choose. Feel free to pick your favourite, your top 3 favourites, your worst 3 favourites, or whichever feedback format you prefer. We will be incorporating the company in Australia on July 1st, 2014 so fast feedback is sought, if possible.
Thanks in advance for those offering their opinions. Your feedback will be very much appreciated and well received.
(Who are we? We will operate as an online bookkeeping service provider in Australia. We will provide bookkeeping, accounting & tax services to small businesses. To understand more about what an online bookkeeping service provider is - in case anybody doesn't know - please visit http://www.bench.co. Bench Accounting are a USA version of what we will do in Australia. Our service will be targetted to small companies with expenses of $75,000 or less per month. It is a B2B / Business to Business market).
SHORTLIST OF NAMES in alphabetical order:
Accountaroo. \nAccountiva. \nAccrutiva. \nBlue Ledger. \nBookanomics. \nBookaroo. \nBookeepia. \nBookinetics. \nBookkeepia. \nBookopolis. \nClerkis. \nCountify. \nDatascopic. \nDatorama. \nEquinova. \nEquitus. \nLedgaroo. \nLedgerama. \nLedgerbase. \nLedgerforce. \nLedgerise. \nLedgerly. \nLedgerMate. \nLedgeroo. \nLedgorama. \nNumbaroo. \nOrphis. \nPerfecto. \nProficia. \nQualisum. \nQualitax. \nSumstar. \nSumtastic. \nTaxella. \nTaxPal. \nTempleblue. \nTigertax. \nTrustica.
Kind Regards,
Gary Leech."},"title":{"matchLevel":"none","matchedWords":[],"value":"Which of the following company names do you think is best?"},"url":{"matchLevel":"none","matchedWords":[],"value":""}},"_tags":["story","author_garyjames","story_7909090","ask_hn"],"author":"garyjames","children":[7909268,7909294],"created_at":"2014-06-18T08:53:33Z","created_at_i":1403081613,"num_comments":4,"objectID":"7909090","points":2,"story_id":7909090,"story_text":"In short, we would love feedback from the community on which of the following company names is the best to choose for our Start Up Company?
As background, we have run a naming contest on both squadhelp.com and namingforce.com. We have gone through the list of names generated from both contests to create the following shortlist of - what we think are - the best names.
We are looking for feedback from other people to help us make a decision on which name to choose. Feel free to pick your favourite, your top 3 favourites, your worst 3 favourites, or whichever feedback format you prefer. We will be incorporating the company in Australia on July 1st, 2014 so fast feedback is sought, if possible.
Thanks in advance for those offering their opinions. Your feedback will be very much appreciated and well received.
(Who are we? We will operate as an online bookkeeping service provider in Australia. We will provide bookkeeping, accounting & tax services to small businesses. To understand more about what an online bookkeeping service provider is - in case anybody doesn't know - please visit http://www.bench.co. Bench Accounting are a USA version of what we will do in Australia. Our service will be targetted to small companies with expenses of $75,000 or less per month. It is a B2B / Business to Business market).
SHORTLIST OF NAMES in alphabetical order:
Accountaroo. \nAccountiva. \nAccrutiva. \nBlue Ledger. \nBookanomics. \nBookaroo. \nBookeepia. \nBookinetics. \nBookkeepia. \nBookopolis. \nClerkis. \nCountify. \nDatascopic. \nDatorama. \nEquinova. \nEquitus. \nLedgaroo. \nLedgerama. \nLedgerbase. \nLedgerforce. \nLedgerise. \nLedgerly. \nLedgerMate. \nLedgeroo. \nLedgorama. \nNumbaroo. \nOrphis. \nPerfecto. \nProficia. \nQualisum. \nQualitax. \nSumstar. \nSumtastic. \nTaxella. \nTaxPal. \nTempleblue. \nTigertax. \nTrustica.
Kind Regards,
Gary Leech.","title":"Which of the following company names do you think is best?","updated_at":"2024-09-19T20:52:35Z","url":""},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"jclevy"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["legora"],"value":"Seven 5th grade girls in Austin creating an ingenious solution for flash floods, \nneed to have 100 comments by Friday night on their blog page:
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Our robotics team, The Highlighters, made up of seven 5th grade girls, will be competing in the FIRST \u00aeLEGO\u00ae LEAGUE invitational tournament this Saturday. In addition to designing and programming a robot made out of Legos to complete missions on a game field, we were asked to design an innovative solution to a real world problem with this year\u2019s theme NATURE\u2019S FURY\u00ae.
We chose to focus on Flash Flooding as our topic of study after we saw the terrible flooding that occurred in Colorado shortly after the challenge began this Fall. Soon after that, Austin was hit with heavy rains and we saw the effects of flooding and the unfortunate tragedies from flash floods right in our own town.
Despite the current signs and barricades used to warn us of flooded roads, Central Texas still leads the nation in flood related deaths and 75% are vehicle-related at low water crossings. This inspired us to design an automatic flood warning system that would be a great improvement over existing signs and barricades.
We took first place in the research portion of the FLL\u00ae competition at our Qualifier in December. We recently learned to make blogs in school so in order to get our ideas out to the community and get feedback that may improve our design, the team has created a blog describing our invention, the Flash FLare FLood FLoat (FL4).
We would be very grateful if you would take a few minutes to look over our blog and leave a comment with feedback on our ideas and design. The judges really like to see that the teams have reached out to the community. Our goal is to have 100 comments by Friday night to print and show the judges on Saturday morning so feel free to forward this to anyone you think may like to help us."},"title":{"matchLevel":"none","matchedWords":[],"value":"Support 5th grade girls creating an ingenious solution for flash floods"},"url":{"matchLevel":"none","matchedWords":[],"value":""}},"_tags":["story","author_jclevy","story_7354040","ask_hn"],"author":"jclevy","created_at":"2014-03-06T15:07:59Z","created_at_i":1394118479,"num_comments":0,"objectID":"7354040","points":2,"story_id":7354040,"story_text":"Seven 5th grade girls in Austin creating an ingenious solution for flash floods, \nneed to have 100 comments by Friday night on their blog page:
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Our robotics team, The Highlighters, made up of seven 5th grade girls, will be competing in the FIRST \u00aeLEGO\u00ae LEAGUE invitational tournament this Saturday. In addition to designing and programming a robot made out of Legos to complete missions on a game field, we were asked to design an innovative solution to a real world problem with this year\u2019s theme NATURE\u2019S FURY\u00ae.
We chose to focus on Flash Flooding as our topic of study after we saw the terrible flooding that occurred in Colorado shortly after the challenge began this Fall. Soon after that, Austin was hit with heavy rains and we saw the effects of flooding and the unfortunate tragedies from flash floods right in our own town.
Despite the current signs and barricades used to warn us of flooded roads, Central Texas still leads the nation in flood related deaths and 75% are vehicle-related at low water crossings. This inspired us to design an automatic flood warning system that would be a great improvement over existing signs and barricades.
We took first place in the research portion of the FLL\u00ae competition at our Qualifier in December. We recently learned to make blogs in school so in order to get our ideas out to the community and get feedback that may improve our design, the team has created a blog describing our invention, the Flash FLare FLood FLoat (FL4).
We would be very grateful if you would take a few minutes to look over our blog and leave a comment with feedback on our ideas and design. The judges really like to see that the teams have reached out to the community. Our goal is to have 100 comments by Friday night to print and show the judges on Saturday morning so feel free to forward this to anyone you think may like to help us.","title":"Support 5th grade girls creating an ingenious solution for flash floods","updated_at":"2024-09-19T20:35:47Z","url":""},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Gostai"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["legora"],"value":"Gostai, the editor of innovative software for robotics & AI, is pleased to announce that its core Urbi technology is opening its code base to the open source community.\nUrbi is an advanced robotics operating system, already available for a large number of robots like Aldebaran\u00ae Nao, Segway\u00ae RMP or Lego\u00ae Mindstorm, among 15 other different robots. One of its main innovations lies in a new orchestration script language called urbiscript, which natively integrates parallelism and event-based programming. Next to Urbi, Gostai also offers the Gostai Studio graphical programming tools, and compatibility with various simulators, making the Urbi framework one of the most advanced and complete solution for robot and complex system programming available today.\nRemarkably, Urbi is also very simple to use, even for beginners. It is perfectly suitable for advanced industrial or academics applications, as well as for the educational market.\nThe source code is covered by an Affero GNU GPL v3 license and is available for immediate download and use from Gostai\u2019s website. 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