Snowflake breach: Hacker confirms access through infostealer infection
503 by zbangrec | 153 comments on Hacker News.
Friday, May 31, 2024
Thursday, May 30, 2024
New best story on Hacker News: Show HN: ChatGPT UI for rabbit holes
Show HN: ChatGPT UI for rabbit holes
548 by maxkrieger | 134 comments on Hacker News.
I was inspired by the way ChatGPT writes bullet lists, then invites you to "delve" deeper. This is an interface that reifies that rabbit-holing process into a tiling layout. The model is instructed to output hyperlink-prompts when it mentions something you might want to delve into. Lots of features to add (sessions, sharing, navigation, highlight-to-delve, images, ...). Would love to hear other usecases and ideas!
548 by maxkrieger | 134 comments on Hacker News.
I was inspired by the way ChatGPT writes bullet lists, then invites you to "delve" deeper. This is an interface that reifies that rabbit-holing process into a tiling layout. The model is instructed to output hyperlink-prompts when it mentions something you might want to delve into. Lots of features to add (sessions, sharing, navigation, highlight-to-delve, images, ...). Would love to hear other usecases and ideas!
New best story on Hacker News: Show HN: ChatGPT UI for rabbit holes
Show HN: ChatGPT UI for rabbit holes
533 by maxkrieger | 130 comments on Hacker News.
I was inspired by the way ChatGPT writes bullet lists, then invites you to "delve" deeper. This is an interface that reifies that rabbit-holing process into a tiling layout. The model is instructed to output hyperlink-prompts when it mentions something you might want to delve into. Lots of features to add (sessions, sharing, navigation, highlight-to-delve, images, ...). Would love to hear other usecases and ideas!
533 by maxkrieger | 130 comments on Hacker News.
I was inspired by the way ChatGPT writes bullet lists, then invites you to "delve" deeper. This is an interface that reifies that rabbit-holing process into a tiling layout. The model is instructed to output hyperlink-prompts when it mentions something you might want to delve into. Lots of features to add (sessions, sharing, navigation, highlight-to-delve, images, ...). Would love to hear other usecases and ideas!
Wednesday, May 29, 2024
Tuesday, May 28, 2024
Monday, May 27, 2024
Sunday, May 26, 2024
Saturday, May 25, 2024
New best story on Hacker News: Show HN: We open sourced our entire text-to-SQL product
Show HN: We open sourced our entire text-to-SQL product
419 by aazo11 | 137 comments on Hacker News.
Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product. The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset. Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent which uses Chain of Thought (CoT) reasoning and a number of different tools to generate high accuracy SQL from a given user prompt. The engine achieves this by: - Collecting context at configuration from the database and sources such as data dictionaries and unstructured documents which are stored in a data store or a vector DB and injected if relevant - Allowing users to upload sample NL <> SQL pairs (golden SQL) which can be used in few shot prompting or to fine-tune an NL-to-SQL LLM for that specific dataset - Executing the SQL against the DB to get a few sample rows and recover from errors - Using an evaluator to assign a confidence score to the generated SQL The repo includes four services https://ift.tt/EMPtkYc : 1- Engine: The core service which includes the LLM agent, vector stores and DB connectors. 2- Admin Console: a NextJS front-end for configuring the engine and observability. 3- Enterprise Backend: Wraps the core engine, adding authentication, caching, and APIs for the frontend. 4- Slackbot: Integrate Dataherald directly into your Slack workflow for on-the-fly data exploration. Would love to hear from the community on building natural language interfaces to relational data. Anyone live in production without a human in the loop? Thoughts on how to improve performance without spending weeks on model training?
419 by aazo11 | 137 comments on Hacker News.
Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product. The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset. Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent which uses Chain of Thought (CoT) reasoning and a number of different tools to generate high accuracy SQL from a given user prompt. The engine achieves this by: - Collecting context at configuration from the database and sources such as data dictionaries and unstructured documents which are stored in a data store or a vector DB and injected if relevant - Allowing users to upload sample NL <> SQL pairs (golden SQL) which can be used in few shot prompting or to fine-tune an NL-to-SQL LLM for that specific dataset - Executing the SQL against the DB to get a few sample rows and recover from errors - Using an evaluator to assign a confidence score to the generated SQL The repo includes four services https://ift.tt/EMPtkYc : 1- Engine: The core service which includes the LLM agent, vector stores and DB connectors. 2- Admin Console: a NextJS front-end for configuring the engine and observability. 3- Enterprise Backend: Wraps the core engine, adding authentication, caching, and APIs for the frontend. 4- Slackbot: Integrate Dataherald directly into your Slack workflow for on-the-fly data exploration. Would love to hear from the community on building natural language interfaces to relational data. Anyone live in production without a human in the loop? Thoughts on how to improve performance without spending weeks on model training?
New best story on Hacker News: Show HN: We open sourced our entire text-to-SQL product
Show HN: We open sourced our entire text-to-SQL product
415 by aazo11 | 136 comments on Hacker News.
Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product. The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset. Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent which uses Chain of Thought (CoT) reasoning and a number of different tools to generate high accuracy SQL from a given user prompt. The engine achieves this by: - Collecting context at configuration from the database and sources such as data dictionaries and unstructured documents which are stored in a data store or a vector DB and injected if relevant - Allowing users to upload sample NL <> SQL pairs (golden SQL) which can be used in few shot prompting or to fine-tune an NL-to-SQL LLM for that specific dataset - Executing the SQL against the DB to get a few sample rows and recover from errors - Using an evaluator to assign a confidence score to the generated SQL The repo includes four services https://ift.tt/EMPtkYc : 1- Engine: The core service which includes the LLM agent, vector stores and DB connectors. 2- Admin Console: a NextJS front-end for configuring the engine and observability. 3- Enterprise Backend: Wraps the core engine, adding authentication, caching, and APIs for the frontend. 4- Slackbot: Integrate Dataherald directly into your Slack workflow for on-the-fly data exploration. Would love to hear from the community on building natural language interfaces to relational data. Anyone live in production without a human in the loop? Thoughts on how to improve performance without spending weeks on model training?
415 by aazo11 | 136 comments on Hacker News.
Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product. The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset. Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent which uses Chain of Thought (CoT) reasoning and a number of different tools to generate high accuracy SQL from a given user prompt. The engine achieves this by: - Collecting context at configuration from the database and sources such as data dictionaries and unstructured documents which are stored in a data store or a vector DB and injected if relevant - Allowing users to upload sample NL <> SQL pairs (golden SQL) which can be used in few shot prompting or to fine-tune an NL-to-SQL LLM for that specific dataset - Executing the SQL against the DB to get a few sample rows and recover from errors - Using an evaluator to assign a confidence score to the generated SQL The repo includes four services https://ift.tt/EMPtkYc : 1- Engine: The core service which includes the LLM agent, vector stores and DB connectors. 2- Admin Console: a NextJS front-end for configuring the engine and observability. 3- Enterprise Backend: Wraps the core engine, adding authentication, caching, and APIs for the frontend. 4- Slackbot: Integrate Dataherald directly into your Slack workflow for on-the-fly data exploration. Would love to hear from the community on building natural language interfaces to relational data. Anyone live in production without a human in the loop? Thoughts on how to improve performance without spending weeks on model training?
Friday, May 24, 2024
Thursday, May 23, 2024
Wednesday, May 22, 2024
Tuesday, May 21, 2024
New best story on Hacker News: Ask HN: Video streaming is expensive yet YouTube "seems" to do it for free. How?
Ask HN: Video streaming is expensive yet YouTube "seems" to do it for free. How?
399 by pinakinathc | 356 comments on Hacker News.
Can anyone help me understand the economics of video streaming platforms? Streaming, encoding, and storage demands enormous costs -- especially at scale (e.g., on average each 4k video with close to 1 million views). Yet YouTube seems to charge no money for it. I know advertisements are a thing for YT, but is it enough? If tomorrow I want to start a platform that is supported with Advert revenues, I know I will likely fail. However, maybe at YT scale (or more specifically Google Advert scale) the economics works? ps: I would like this discussion to focus on the absolute necessary elements (e.g., storing, encoding, streaming) and not on other factors contributing to latency/cost like running view count algorithms.
399 by pinakinathc | 356 comments on Hacker News.
Can anyone help me understand the economics of video streaming platforms? Streaming, encoding, and storage demands enormous costs -- especially at scale (e.g., on average each 4k video with close to 1 million views). Yet YouTube seems to charge no money for it. I know advertisements are a thing for YT, but is it enough? If tomorrow I want to start a platform that is supported with Advert revenues, I know I will likely fail. However, maybe at YT scale (or more specifically Google Advert scale) the economics works? ps: I would like this discussion to focus on the absolute necessary elements (e.g., storing, encoding, streaming) and not on other factors contributing to latency/cost like running view count algorithms.
Monday, May 20, 2024
Sunday, May 19, 2024
Friday, May 17, 2024
Thursday, May 16, 2024
Wednesday, May 15, 2024
Tuesday, May 14, 2024
Monday, May 13, 2024
Sunday, May 12, 2024
Friday, May 10, 2024
Thursday, May 9, 2024
Wednesday, May 8, 2024
Tuesday, May 7, 2024
Monday, May 6, 2024
New best story on Hacker News: Show HN: Dillo 3.1.0 released after 9 years
Show HN: Dillo 3.1.0 released after 9 years
420 by rodarima | 104 comments on Hacker News.
As commented before[1], I've been working on the past months to get the Dillo back to life and today I'm happy to release the 3.1.0 version, after almost 9 years since the last one. [1]: https://ift.tt/OP39XCD During this time: - A new mailing list was created[2] which is beginning to get some messages and patches. It is available in gmane via NNTP at gmane.comp.web.dillo.devel. [2]: https://ift.tt/d5jGsaZ... - A LiberaPay page[3] which received the first donations (thanks!). [3]: https://ift.tt/bsngJov - Some more bugs where fixed and new features where added (details in the release page and/or changelog). Thanks to all the people that contributed with patches and tests. Now let's see if we can make it land in some distros!
420 by rodarima | 104 comments on Hacker News.
As commented before[1], I've been working on the past months to get the Dillo back to life and today I'm happy to release the 3.1.0 version, after almost 9 years since the last one. [1]: https://ift.tt/OP39XCD During this time: - A new mailing list was created[2] which is beginning to get some messages and patches. It is available in gmane via NNTP at gmane.comp.web.dillo.devel. [2]: https://ift.tt/d5jGsaZ... - A LiberaPay page[3] which received the first donations (thanks!). [3]: https://ift.tt/bsngJov - Some more bugs where fixed and new features where added (details in the release page and/or changelog). Thanks to all the people that contributed with patches and tests. Now let's see if we can make it land in some distros!
Sunday, May 5, 2024
Saturday, May 4, 2024
Friday, May 3, 2024
Thursday, May 2, 2024
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рдкрд╛рд╡рдирд╛ (рд░реИ)рдпреЗрдереЗ рдЧреЛрдВрдбреА рдзрд░реНрдо рдкреНрд░рдмреЛрдзрди рдореЗрд│рд╛рд╡рд╛. рдЧреЛрдВрдбреА рдкрд╛рд░рдВрдкрд░рд┐рдХ рдиреВрддреНрдп рддрдерд╛ рдЧреЛрдВрдбреА рд░реЗрдХреЙрд░реНрдбрд┐рдВрдЧ рдбрд╛рдБрдиреНрд╕ рдиреВрддреНрдп рд╕реНрдкрд░реНрдзреЗрдЪреЗ рдЖрдпреЛрдЬрди. рднрджреНрд░рд╛рд╡рддреА(рджрд┐ .3...