Guess my RGB
500 by talonx | 126 comments on Hacker News.
Sunday, March 31, 2024
Saturday, March 30, 2024
Friday, March 29, 2024
Thursday, March 28, 2024
Wednesday, March 27, 2024
Tuesday, March 26, 2024
New best story on Hacker News: Launch HN: Aqua Voice (YC W24) – Voice-driven text editor
Launch HN: Aqua Voice (YC W24) – Voice-driven text editor
369 by the_king | 125 comments on Hacker News.
Hey HN! We’re Jack and Finn from Aqua Voice ( https://withaqua.com/ ). Aqua is a voice-native document editor that combines reliable dictation and natural language commands, letting you say things like: “make this a list” or “it’s Erin with an E” or “add an inline citation here for page 86 of this book”. Here is a demo: https://youtu.be/qwSAKg1YafM . Finn, who is big-time dyslexic, has been using dictation software since the sixth grade when his dad set him up on Dragon Dictation. He used it through school to write papers, and has been keeping his own transcription benchmarks since college. All that time, writing with your voice has remained a cumbersome and brittle experience that is riddled with painpoints. Dictation software is still terrible. All the solutions basically compete on accuracy (i.e. speech recognition), but none of them deal with the fundamentally brittle nature of the text that they generate. They don't try to format text correctly and require you to learn a bunch of specialized commands, which often are not worth it. They're not even close to a voice replacement for a keyboard. Even post LLM, you are limited to a set of specific commands and the most accurate models don’t have any commands. Outside of these rules, the models have no sense for what is an instruction and what is content. You can’t say “and format this like an email” or “make the last bullet point shorter”. Aqua solves this. This problem is important to Finn and millions of other people who would write with their voice if they could. Initially, we didn't think of it as a startup project. It was just something we wanted for ourselves. We thought maybe we'd write a novel with it - or something. After friends started asking to use the early versions of Aqua, it occurred to us that, if we didn't build it, maybe nobody would. Aqua Voice is a text editor that you talk to like a person. Depending on the way that you say it and the context in which you're operating, Aqua decides whether to transcribe what you said verbatim, execute a command, or subtly modify what you said into what you meant to write. For example, if you were to dictate: "Gryphons have classic forms resembling shield volcanoes," Aqua would output your text verbatim. But if you stumble over your words or start a sentence over a few times, Aqua is smart enough to figure that out and to only take the last version of the sentence. The vision is not only to provide a more natural dictation experience, but to enable for the first time an AI-writing experience that feels natural and collaborative. This requires moving away from using LLMs for one-off chat requests and towards something that is more like streaming where you are in constant contact with the model. Voice is the natural medium for this. Aqua is actually 6 models working together to transcribe, interpret, and rewrite the document according to your intent. Technically, executing a real-time voice application with a language model at its core requires complex coordination between multiple pieces. We use MoE transcription to outperform what was previously thought possible in terms of real-time accuracy. Then we sync up with a language model to determine what should be on the screen as quickly as possible. The model isn't perfect, but it is ready for early adopters and we’ve already been getting feedback from grateful users. For example, a historian with carpal tunnel sent us an email he wrote using Aqua and said that he is now able to be five times as productive as he was previously. We've heard from other people with disabilities that prevent them from typing. We've also seen good adoption from people who are dyslexic or simply prefer talking to typing. It’s being used for everything from emails to brainstorming to papers to legal briefings. While there is much left to do in terms of latency and robustness, the best experiences with Aqua are beginning to feel magical. We would love for you to try it out and give us feedback, which you can do with no account on https://withaqua.com . If you find it useful, it’s $10/month after a 1000-token free trial. (We want to bump the free trial in the future, but we're a small team, and running this thing isn’t cheap.) We’d love to hear your ideas and comments with voice-to-text!
369 by the_king | 125 comments on Hacker News.
Hey HN! We’re Jack and Finn from Aqua Voice ( https://withaqua.com/ ). Aqua is a voice-native document editor that combines reliable dictation and natural language commands, letting you say things like: “make this a list” or “it’s Erin with an E” or “add an inline citation here for page 86 of this book”. Here is a demo: https://youtu.be/qwSAKg1YafM . Finn, who is big-time dyslexic, has been using dictation software since the sixth grade when his dad set him up on Dragon Dictation. He used it through school to write papers, and has been keeping his own transcription benchmarks since college. All that time, writing with your voice has remained a cumbersome and brittle experience that is riddled with painpoints. Dictation software is still terrible. All the solutions basically compete on accuracy (i.e. speech recognition), but none of them deal with the fundamentally brittle nature of the text that they generate. They don't try to format text correctly and require you to learn a bunch of specialized commands, which often are not worth it. They're not even close to a voice replacement for a keyboard. Even post LLM, you are limited to a set of specific commands and the most accurate models don’t have any commands. Outside of these rules, the models have no sense for what is an instruction and what is content. You can’t say “and format this like an email” or “make the last bullet point shorter”. Aqua solves this. This problem is important to Finn and millions of other people who would write with their voice if they could. Initially, we didn't think of it as a startup project. It was just something we wanted for ourselves. We thought maybe we'd write a novel with it - or something. After friends started asking to use the early versions of Aqua, it occurred to us that, if we didn't build it, maybe nobody would. Aqua Voice is a text editor that you talk to like a person. Depending on the way that you say it and the context in which you're operating, Aqua decides whether to transcribe what you said verbatim, execute a command, or subtly modify what you said into what you meant to write. For example, if you were to dictate: "Gryphons have classic forms resembling shield volcanoes," Aqua would output your text verbatim. But if you stumble over your words or start a sentence over a few times, Aqua is smart enough to figure that out and to only take the last version of the sentence. The vision is not only to provide a more natural dictation experience, but to enable for the first time an AI-writing experience that feels natural and collaborative. This requires moving away from using LLMs for one-off chat requests and towards something that is more like streaming where you are in constant contact with the model. Voice is the natural medium for this. Aqua is actually 6 models working together to transcribe, interpret, and rewrite the document according to your intent. Technically, executing a real-time voice application with a language model at its core requires complex coordination between multiple pieces. We use MoE transcription to outperform what was previously thought possible in terms of real-time accuracy. Then we sync up with a language model to determine what should be on the screen as quickly as possible. The model isn't perfect, but it is ready for early adopters and we’ve already been getting feedback from grateful users. For example, a historian with carpal tunnel sent us an email he wrote using Aqua and said that he is now able to be five times as productive as he was previously. We've heard from other people with disabilities that prevent them from typing. We've also seen good adoption from people who are dyslexic or simply prefer talking to typing. It’s being used for everything from emails to brainstorming to papers to legal briefings. While there is much left to do in terms of latency and robustness, the best experiences with Aqua are beginning to feel magical. We would love for you to try it out and give us feedback, which you can do with no account on https://withaqua.com . If you find it useful, it’s $10/month after a 1000-token free trial. (We want to bump the free trial in the future, but we're a small team, and running this thing isn’t cheap.) We’d love to hear your ideas and comments with voice-to-text!
New best story on Hacker News: Launch HN: Aqua Voice (YC W24) – Voice-driven text editor
Launch HN: Aqua Voice (YC W24) – Voice-driven text editor
349 by the_king | 124 comments on Hacker News.
Hey HN! We’re Jack and Finn from Aqua Voice ( https://withaqua.com/ ). Aqua is a voice-native document editor that combines reliable dictation and natural language commands, letting you say things like: “make this a list” or “it’s Erin with an E” or “add an inline citation here for page 86 of this book”. Here is a demo: https://youtu.be/qwSAKg1YafM . Finn, who is big-time dyslexic, has been using dictation software since the sixth grade when his dad set him up on Dragon Dictation. He used it through school to write papers, and has been keeping his own transcription benchmarks since college. All that time, writing with your voice has remained a cumbersome and brittle experience that is riddled with painpoints. Dictation software is still terrible. All the solutions basically compete on accuracy (i.e. speech recognition), but none of them deal with the fundamentally brittle nature of the text that they generate. They don't try to format text correctly and require you to learn a bunch of specialized commands, which often are not worth it. They're not even close to a voice replacement for a keyboard. Even post LLM, you are limited to a set of specific commands and the most accurate models don’t have any commands. Outside of these rules, the models have no sense for what is an instruction and what is content. You can’t say “and format this like an email” or “make the last bullet point shorter”. Aqua solves this. This problem is important to Finn and millions of other people who would write with their voice if they could. Initially, we didn't think of it as a startup project. It was just something we wanted for ourselves. We thought maybe we'd write a novel with it - or something. After friends started asking to use the early versions of Aqua, it occurred to us that, if we didn't build it, maybe nobody would. Aqua Voice is a text editor that you talk to like a person. Depending on the way that you say it and the context in which you're operating, Aqua decides whether to transcribe what you said verbatim, execute a command, or subtly modify what you said into what you meant to write. For example, if you were to dictate: "Gryphons have classic forms resembling shield volcanoes," Aqua would output your text verbatim. But if you stumble over your words or start a sentence over a few times, Aqua is smart enough to figure that out and to only take the last version of the sentence. The vision is not only to provide a more natural dictation experience, but to enable for the first time an AI-writing experience that feels natural and collaborative. This requires moving away from using LLMs for one-off chat requests and towards something that is more like streaming where you are in constant contact with the model. Voice is the natural medium for this. Aqua is actually 6 models working together to transcribe, interpret, and rewrite the document according to your intent. Technically, executing a real-time voice application with a language model at its core requires complex coordination between multiple pieces. We use MoE transcription to outperform what was previously thought possible in terms of real-time accuracy. Then we sync up with a language model to determine what should be on the screen as quickly as possible. The model isn't perfect, but it is ready for early adopters and we’ve already been getting feedback from grateful users. For example, a historian with carpal tunnel sent us an email he wrote using Aqua and said that he is now able to be five times as productive as he was previously. We've heard from other people with disabilities that prevent them from typing. We've also seen good adoption from people who are dyslexic or simply prefer talking to typing. It’s being used for everything from emails to brainstorming to papers to legal briefings. While there is much left to do in terms of latency and robustness, the best experiences with Aqua are beginning to feel magical. We would love for you to try it out and give us feedback, which you can do with no account on https://withaqua.com . If you find it useful, it’s $10/month after a 1000-token free trial. (We want to bump the free trial in the future, but we're a small team, and running this thing isn’t cheap.) We’d love to hear your ideas and comments with voice-to-text!
349 by the_king | 124 comments on Hacker News.
Hey HN! We’re Jack and Finn from Aqua Voice ( https://withaqua.com/ ). Aqua is a voice-native document editor that combines reliable dictation and natural language commands, letting you say things like: “make this a list” or “it’s Erin with an E” or “add an inline citation here for page 86 of this book”. Here is a demo: https://youtu.be/qwSAKg1YafM . Finn, who is big-time dyslexic, has been using dictation software since the sixth grade when his dad set him up on Dragon Dictation. He used it through school to write papers, and has been keeping his own transcription benchmarks since college. All that time, writing with your voice has remained a cumbersome and brittle experience that is riddled with painpoints. Dictation software is still terrible. All the solutions basically compete on accuracy (i.e. speech recognition), but none of them deal with the fundamentally brittle nature of the text that they generate. They don't try to format text correctly and require you to learn a bunch of specialized commands, which often are not worth it. They're not even close to a voice replacement for a keyboard. Even post LLM, you are limited to a set of specific commands and the most accurate models don’t have any commands. Outside of these rules, the models have no sense for what is an instruction and what is content. You can’t say “and format this like an email” or “make the last bullet point shorter”. Aqua solves this. This problem is important to Finn and millions of other people who would write with their voice if they could. Initially, we didn't think of it as a startup project. It was just something we wanted for ourselves. We thought maybe we'd write a novel with it - or something. After friends started asking to use the early versions of Aqua, it occurred to us that, if we didn't build it, maybe nobody would. Aqua Voice is a text editor that you talk to like a person. Depending on the way that you say it and the context in which you're operating, Aqua decides whether to transcribe what you said verbatim, execute a command, or subtly modify what you said into what you meant to write. For example, if you were to dictate: "Gryphons have classic forms resembling shield volcanoes," Aqua would output your text verbatim. But if you stumble over your words or start a sentence over a few times, Aqua is smart enough to figure that out and to only take the last version of the sentence. The vision is not only to provide a more natural dictation experience, but to enable for the first time an AI-writing experience that feels natural and collaborative. This requires moving away from using LLMs for one-off chat requests and towards something that is more like streaming where you are in constant contact with the model. Voice is the natural medium for this. Aqua is actually 6 models working together to transcribe, interpret, and rewrite the document according to your intent. Technically, executing a real-time voice application with a language model at its core requires complex coordination between multiple pieces. We use MoE transcription to outperform what was previously thought possible in terms of real-time accuracy. Then we sync up with a language model to determine what should be on the screen as quickly as possible. The model isn't perfect, but it is ready for early adopters and we’ve already been getting feedback from grateful users. For example, a historian with carpal tunnel sent us an email he wrote using Aqua and said that he is now able to be five times as productive as he was previously. We've heard from other people with disabilities that prevent them from typing. We've also seen good adoption from people who are dyslexic or simply prefer talking to typing. It’s being used for everything from emails to brainstorming to papers to legal briefings. While there is much left to do in terms of latency and robustness, the best experiences with Aqua are beginning to feel magical. We would love for you to try it out and give us feedback, which you can do with no account on https://withaqua.com . If you find it useful, it’s $10/month after a 1000-token free trial. (We want to bump the free trial in the future, but we're a small team, and running this thing isn’t cheap.) We’d love to hear your ideas and comments with voice-to-text!
Monday, March 25, 2024
Sunday, March 24, 2024
Saturday, March 23, 2024
New best story on Hacker News: Game of Life, simulating itself, infinitely zoomable
Game of Life, simulating itself, infinitely zoomable
773 by surprisetalk | 170 comments on Hacker News.
773 by surprisetalk | 170 comments on Hacker News.
New best story on Hacker News: Game of Life, simulating itself, infinitely zoomable
Game of Life, simulating itself, infinitely zoomable
728 by surprisetalk | 159 comments on Hacker News.
728 by surprisetalk | 159 comments on Hacker News.
Friday, March 22, 2024
New best story on Hacker News: Show HN: Memories – FOSS Google Photos alternative built for high performance
Show HN: Memories – FOSS Google Photos alternative built for high performance
686 by radialapps | 201 comments on Hacker News.
Memories is a FOSS Google Photos alternative that you can self-host (it runs as a Nextcloud plugin). Website: https://ift.tt/gwX5FyN GitHub: https://ift.tt/SFEdXLT Demo Server: https://ift.tt/wTRYhib (demo runs in San Francisco on a free-tier cloud vm) Memories has been built ground-up for high performance and is extremely fast when configured correctly. In our testing environment, it can load a timeline view with 100k photos in under 500ms, including query and rendering time! Some features to highlight: * A timeline similar to Google Photos where you can skip to any time in history instantly. * AI-based tagging that runs locally on your server, identifying and tagging people and objects. * Albums and external sharing. * Metadata editing support * A world map of your photos, supported both on mobile and the web * Did I mention it's extremely fast? Would love to hear feedback from the HN community! :)
686 by radialapps | 201 comments on Hacker News.
Memories is a FOSS Google Photos alternative that you can self-host (it runs as a Nextcloud plugin). Website: https://ift.tt/gwX5FyN GitHub: https://ift.tt/SFEdXLT Demo Server: https://ift.tt/wTRYhib (demo runs in San Francisco on a free-tier cloud vm) Memories has been built ground-up for high performance and is extremely fast when configured correctly. In our testing environment, it can load a timeline view with 100k photos in under 500ms, including query and rendering time! Some features to highlight: * A timeline similar to Google Photos where you can skip to any time in history instantly. * AI-based tagging that runs locally on your server, identifying and tagging people and objects. * Albums and external sharing. * Metadata editing support * A world map of your photos, supported both on mobile and the web * Did I mention it's extremely fast? Would love to hear feedback from the HN community! :)
New best story on Hacker News: Show HN: Memories – FOSS Google Photos alternative built for high performance
Show HN: Memories – FOSS Google Photos alternative built for high performance
674 by radialapps | 200 comments on Hacker News.
Memories is a FOSS Google Photos alternative that you can self-host (it runs as a Nextcloud plugin). Website: https://ift.tt/gwX5FyN GitHub: https://ift.tt/SFEdXLT Demo Server: https://ift.tt/wTRYhib (demo runs in San Francisco on a free-tier cloud vm) Memories has been built ground-up for high performance and is extremely fast when configured correctly. In our testing environment, it can load a timeline view with 100k photos in under 500ms, including query and rendering time! Some features to highlight: * A timeline similar to Google Photos where you can skip to any time in history instantly. * AI-based tagging that runs locally on your server, identifying and tagging people and objects. * Albums and external sharing. * Metadata editing support * A world map of your photos, supported both on mobile and the web * Did I mention it's extremely fast? Would love to hear feedback from the HN community! :)
674 by radialapps | 200 comments on Hacker News.
Memories is a FOSS Google Photos alternative that you can self-host (it runs as a Nextcloud plugin). Website: https://ift.tt/gwX5FyN GitHub: https://ift.tt/SFEdXLT Demo Server: https://ift.tt/wTRYhib (demo runs in San Francisco on a free-tier cloud vm) Memories has been built ground-up for high performance and is extremely fast when configured correctly. In our testing environment, it can load a timeline view with 100k photos in under 500ms, including query and rendering time! Some features to highlight: * A timeline similar to Google Photos where you can skip to any time in history instantly. * AI-based tagging that runs locally on your server, identifying and tagging people and objects. * Albums and external sharing. * Metadata editing support * A world map of your photos, supported both on mobile and the web * Did I mention it's extremely fast? Would love to hear feedback from the HN community! :)
Thursday, March 21, 2024
New best story on Hacker News: Difftastic, a structural diff tool that understands syntax
Difftastic, a structural diff tool that understands syntax
602 by jiripospisil | 100 comments on Hacker News.
602 by jiripospisil | 100 comments on Hacker News.
New best story on Hacker News: Difftastic, a structural diff tool that understands syntax
Difftastic, a structural diff tool that understands syntax
589 by jiripospisil | 97 comments on Hacker News.
589 by jiripospisil | 97 comments on Hacker News.
Wednesday, March 20, 2024
Tuesday, March 19, 2024
Monday, March 18, 2024
New best story on Hacker News: Fuzzing Ladybird with tools from Google Project Zero
Fuzzing Ladybird with tools from Google Project Zero
511 by awesomekling | 56 comments on Hacker News.
511 by awesomekling | 56 comments on Hacker News.
Sunday, March 17, 2024
Saturday, March 16, 2024
Friday, March 15, 2024
Thursday, March 14, 2024
New best story on Hacker News: Glassdoor updated my profile to add my real name and location
Glassdoor updated my profile to add my real name and location
520 by throwaway_08932 | 231 comments on Hacker News.
520 by throwaway_08932 | 231 comments on Hacker News.
New best story on Hacker News: Glassdoor updated my profile to add my real name and location
Glassdoor updated my profile to add my real name and location
490 by throwaway_08932 | 221 comments on Hacker News.
490 by throwaway_08932 | 221 comments on Hacker News.
Wednesday, March 13, 2024
Tuesday, March 12, 2024
Monday, March 11, 2024
Saturday, March 9, 2024
Friday, March 8, 2024
New best story on Hacker News: Fine tune a 70B language model at home
Fine tune a 70B language model at home
601 by jph00 | 146 comments on Hacker News.
Jeremy from Answer.AI here. This is our first project since launching our new R&D lab at the start of this year. It's the #1 most requested thing I've been hearing from open source model builders: the ability to use multiple GPUs with QLoRA training. So that's why we decided to make it our first project. Huge thanks to Tim Dettmers for helping us get started to this -- and of course for creating QLoRA in the first place! Let me know if you have any questions or thoughts.
601 by jph00 | 146 comments on Hacker News.
Jeremy from Answer.AI here. This is our first project since launching our new R&D lab at the start of this year. It's the #1 most requested thing I've been hearing from open source model builders: the ability to use multiple GPUs with QLoRA training. So that's why we decided to make it our first project. Huge thanks to Tim Dettmers for helping us get started to this -- and of course for creating QLoRA in the first place! Let me know if you have any questions or thoughts.
Thursday, March 7, 2024
Wednesday, March 6, 2024
เคกॉ. เคช्เคฐเคคिเคฎा เคंเคोเคฒे เคฏांเค्เคฏा เค เคง्เคฏเค्เคทเคคेเคค เคญเคฆ्เคฐाเคตเคคीเคค 17 เคฎाเคฐ्เคเคฒा เคธाเคคเคตे เคธ्เคฎृเคคिเคंเคง เคाเคต्เคฏเคธंเคฎेเคฒเคจ
*เคกॉ. เคช्เคฐเคคिเคฎा เคंเคोเคฒे เคฏांเค्เคฏा เค
เคง्เคฏเค्เคทเคคेเคค เคญเคฆ्เคฐाเคตเคคीเคค 17 เคฎाเคฐ्เคเคฒा เคธाเคคเคตे เคธ्เคฎृเคคिเคंเคง เคाเคต्เคฏเคธंเคฎेเคฒเคจ*
*เคธ्เคต. เคตिเคฃा เคเคกेเคเคฐ เคธ्เคฎृเคคी เคช्เคฐเคคिเคท्เค ाเคจเคे เคเคฏोเคเคจ*
เคธ्เคต. เคตिเคฃा เคเคกेเคเคฐ เคธ्เคฎृเคคी เคช्เคฐเคคिเคท्เค ाเคจ เคญเคฆ्เคฐाเคตเคคीเค्เคฏा เคตเคคीเคจे เคธ्เคฅाเคจीเค เคถ्เคฐी เคฎुเคฐเคฒीเคงเคฐ เคชाเคीเคฒ เคुंเคกाเคตाเคฐ เคธเคญाเคृเคน เคฏेเคฅे เคฐเคตिเคตाเคฐ เคฆिเคจांเค 17 เคฎाเคฐ्เค 2024 เคฒा เคธाเคคเคตे เคธ्เคฎृเคคिเคंเคง เคाเคต्เคฏเคธंเคฎेเคฒเคจ เคธंเคชเคจ्เคจ เคนोเคค เคเคนे. เคจเคตोเคฆीเคค เคต เคช्เคฐเคฅिเคคเคฏเคถ เคाเคต्เคฏเคช्เคฐเคคिเคญेเคा เคธเคจ्เคฎाเคจ เคเคฃि เคฎเคฐाเค ी เคाเคต्เคฏ เคฐเคธिเคांเค्เคฏा เคเคธ्เคตाเคฆเคเคคेเคธ เคช्เคฐोเคค्เคธाเคนเคจ เคฆेเคฃ्เคฏाเคธाเค ी เคน्เคฏा เคธंเคฎेเคฒเคจाเคे เคช्เคฐเคฏोเคเคจ เคเคนे. เคธंเคฎेเคฒเคจाเคง्เคฏเค्เคท เค्เคฏेเคท्เค เคธाเคนिเคค्เคฏिเค เคต เคช्เคฐเคธिเคง्เคฆ เคเคตเคฏिเคค्เคฐी เคกॉ. เคช्เคฐเคคिเคฎा เคंเคोเคฒे เคฆाเคจाเคชूเคฐ (เค
เคोเคฒा), เคฏांเค्เคฏा เค
เคง्เคฏเค्เคทเคคेเคाเคฒी เคช्เคฐเค्เคฏाเคค เคเคตเคฏिเคค्เคฐी เคกॉ. เคธंเคง्เคฏा เคชเคตाเคฐ เคจाเคเคชूเคฐ, เคฏांเค्เคฏा เคถुเคญเคนเคธ्เคคे เคฏा เคจिเคฏोเคीเคค เคธंเคฎेเคฒเคจाเคे เคเคฆ्เคाเคเคจ เคธเคाเคณी เค िเค 10.30 เคตा. เคธंเคชเคจ्เคจ เคนोเคค เคเคนे. เคฏाเคตेเคณी เคช्เคฐเคฎूเค เค
เคคिเคฅी เคฎ्เคนเคฃूเคจ เค्เคฏेเคท्เค เคธเคนिเคค्เคฏिเค เคเคाเคฐ्เคฏ เคจा.เคो.เคฅुเคे เคตเคฐोเคฐा, เคธुเคช्เคฐเคธिเคฆ्เคง เค्เคฐाเคฎीเคฃ เคเคฅाเคाเคฐ เคต เคृเคทी เคคเค्เค्เค เค
เคจंเคค เคญोเคฏเคฐ เคाเคोเคฒ, เคกॉ. เคช्เคฐเคाเคถ เคฎเคนाเคाเคณเคเคฐ เคเคเคถिเค्เคทเคฃाเคงिเคाเคฐी เคช.เคธ. เคญเคฆ्เคฐाเคตเคคी, เค
เคฎीเคค เคुंเคกाเคตाเคฐ เคธाเคฎाเคिเค เคाเคฐ्เคฏเคเคฐ्เคคे เคญเคฆ्เคฐाเคตเคคी, เคเคค्เคฏाเคฆी เคฎाเคจ्เคฏเคตเคฐ เคเคชเคธ्เคฅिเคค เคฐाเคนเคคीเคฒ. เคญเคฆ्เคฐाเคตเคคीเคे เคฎाเคी เคจเคเคฐाเคง्เคฏเค्เคท เค
เคจिเคฒ เคงाเคจोเคฐเคเคฐ เคฏा เคाเคต्เคฏเคธंเคฎेเคฒเคจाเคे เคจिเคฎिเคค्เคคाเคจे เคตिเคถेเคท เค
เคคिเคฅी เคฎ्เคนเคฃूเคจ เคฏाเคตेเคณी เคเคชเคธ्เคฅिเคค เค
เคธเคคीเคฒ.
เคช्เคฐเคธ्เคคूเคค เคाเคต्เคฏ เคธंเคฎेเคฒเคจ เคเคตी เคเคฃि เคเคตिเคคेเคा เคถเคฌ्เคฆ-เคญाเคต-เค
เคจुเคญूเคคीเคा เคเคจंเคฆเคฎेเคณाเค เค
เคธूเคจ, เคฏा เคเคเคฆिเคตเคธीเคฏ เคाเคต्เคฏเคธंเคฎेเคฒเคจाเคค เคฆोเคจ เคเคตिเคธंเคฎेเคฒเคจे เคต เคเคा เคเคเคฒ เคฎैเคซिเคฒीเคे เคเคฏोเคเคจ เคเคฐเคฃ्เคฏाเคค เคเคฒे เคเคนे. เคญเคฐเคเค्เค เคाเคฐ เคธเคค्เคฐांเคฎเคง्เคฏे เคนोเคค เค
เคธเคฒेเคฒ्เคฏा เคฏा เคธाเคนिเคค्เคฏเคธोเคนเคณ्เคฏाเคค เคตाเค्เคฎเคฏीเคจ เคिंเคคเคจाเคธोเคฌเคคเค เคाเคต्เคฏाเคฎृเคคाเคे เคธिंเคเคจ เคนोเคฃाเคฐ เคเคนे.เคธंเคฎेเคฒเคจाเค्เคฏा เคฆुเคธเคฑ्เคฏा เคธเคค्เคฐाเคค เคธंเคฎेเคฒเคจाเคง्เคฏเค्เคท เคกॉ. เคช्เคฐเคคिเคฎा เคंเคोเคฒे เคฏांเค्เคฏा เค
เคง्เคฏเค्เคทเคคेเคाเคฒी เคจिเคฎंเคค्เคฐीเคค เคเคตींเคे เคฌเคนाเคฐเคฆाเคฐ เคเคตिเคธंเคฎेเคฒเคจ เคธंเคชเคจ्เคจ เคนोเคค เคเคนे. เคตिเคฆเคฐ्เคญाเคคीเคฒ เคจाเคฎเคตंเคค เคเคตी เคฏा เคธंเคฎेเคฒเคจाเคฒा เคเคชเคธ्เคฅिเคค เคฐाเคนเคฃाเคฐ เค
เคธूเคจ, เคฎเคฐाเค ी เคाเคต्เคฏเคฐเคธिเคांเคจा เคนी เคเค เคฎोเค ी เคชเคฐ्เคตเคฃीเค เค
เคธเคฃाเคฐ เคเคนे. เคคिเคธเคฑ्เคฏा เคธเคค्เคฐाเคค, เคช्เคฐเค्เคฏाเคค เคเคเคฒเคाเคฐ เคฒोเคเคฐाเคฎ เคถेंเคกे เคฌुเคीเคฌोเคฐी, เคฏांเค्เคฏा เค
เคง्เคฏเค्เคทเคคेเคाเคฒी เคจिเคฎंเคค्เคฐीเคค เคเคตींเคी เคเคเคฒ เคฎैเคซिเคฒ เคฐंเคเคฃाเคฐ เคเคนे. เคฏा เคฎैเคซिเคฒीเคฎเคง्เคฏे เคช्เคฐเคคिเคฅเคฏเคถ เคเคเคฒเคाเคฐांเค्เคฏा เคเคเคฒांเคा เคเคธ्เคตाเคฆ เคเคเคฒ เคฐเคธिเคांเคจा เคेเคคा เคฏेเคฃाเคฐ เคเคนे. เคธंเคฎेเคฒเคจाเค्เคฏा เคौเคฅ्เคฏा เคธเคค्เคฐाเคค เคช्เคฐเค्เคฏाเคค เคเคตเคฏिเคค्เคฐी เคीเคคा เคฆेเคต्เคนाเคฐे-เคฐाเคฏเคชुเคฐे เคंเคฆ्เคฐเคชूเคฐ, เคฏांเค्เคฏा เค
เคง्เคฏเค्เคทเคคेเคाเคฒी เคुเคฒे เคเคตी เคธंเคฎेเคฒเคจ เคธंเคชเคจ्เคจ เคนोเคค เคเคนे. เคुเคฒ्เคฏा เคเคตिเคธंเคฎेเคฒเคจाเคค เคฆेเคीเคฒ เคช्เคฐเคคिเคฅเคฏเคถ เคต เคจเคตोเคฆीเคค เคाเคต्เคฏเคช्เคฐเคคिเคญेเคी เคुเคเคฒเคฌंเคฆी เคाเคต्เคฏเคฐเคธिเคांเคจा เค
เคจुเคญเคตเคคा เคฏेเคฃाเคฐ เคเคนे.
เคธ्เคฎृเคคिเคंเคง เคाเคต्เคฏเคธंเคฎेเคฒเคจाเคे เคธเคฆเคฐ เคเคฏोเคเคจ เคนे เคตिเคฆเคฐ्เคญเคธ्เคคเคฐीเคฏ เค
เคธूเคจ, เคตिเคฆเคฐ्เคญाเคคीเคฒ เคช्เคฐเค्เคฏाเคค เคธाเคนिเคค्เคฏिเค เคต เคเคตींเคा เคฎेเคณा เคฏा เคจिเคฎिเคค्เคคाเคจे เคญเคฆ्เคฐाเคตเคคी เคจเคเคฐीเคฎเคง्เคฏे เคฐंเคเคฃाเคฐ เคเคนे. เคฎाเคฏเคฌोเคฒी เคฎเคฐाเค ी เคต เคฎเคฐाเค ी เคธाเคนिเคค्เคฏाเคตเคฐ เคช्เคฐेเคฎ เคเคฐเคฃाเคฑ्เคฏा เคाเคต्เคฏเคฐเคธिเคांเคจी เคคเคธेเค เคจเคตोเคฆिเคคांเคจी เคฏा เคธंเคฎेเคฒเคจाเคा เคฒाเคญ เค्เคฏाเคตा เค
เคธे เคเคตाเคนเคจ เคธ्เคฎृเคคिเคंเคง เคाเคต्เคฏ เคธंเคฎेเคฒเคจाเคे เคธंเคฏोเคเค เคช्เคฐเคตीเคฃ เคเคกेเคเคฐ เคฏांเคจी เคेเคฒे เคเคนे.
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เคฐाเค्เคฏ เคฎเคนिเคฒा เคเคฏोเค เคธเคฆเคธ्เคฏा เคธंเคीเคคा เคเคต्เคนाเคฃ เคฏांเคी เคถिเคตเคธेเคจेเคे เคฎเคง्เคฏเคตเคฐ्เคคी เคाเคฐ्เคฏाเคฒเคฏ “เคถिเคตाเคฒเคฏ” เคฏेเคฅे เคธเคฆिเค्เคा เคญेเค
*เคฐाเค्เคฏ เคฎเคนिเคฒा เคเคฏोเค เคธเคฆเคธ्เคฏा เคธंเคीเคคा เคเคต्เคนाเคฃ เคฏांเคी เคถिเคตเคธेเคจेเคे เคฎเคง्เคฏเคตเคฐ्เคคी เคाเคฐ्เคฏाเคฒเคฏ “เคถिเคตाเคฒเคฏ” เคฏेเคฅे เคธเคฆिเค्เคा เคญेเค*
*เคเคฐ्เคจाเคเค เคเคฎ्เคा เคต เคेเคชीเคธिเคเคฒ เคंเคชเคจी เคตिเคฐोเคงाเคค เคเคชोเคทเคฃเคเคฐ्เคคी เคฎเคนिเคฒांเคจा เคญेเค เคฆेเคฃाเคฐ*
เคตเคฐोเคฐा :
เคฐाเค्เคฏ เคฎเคนिเคฒा เคเคฏोเค เคธเคฆเคธ्เคฏा เฅฒเคก. เคธंเคीเคคा เคเคต्เคนाเคฃ เคฏा เคंเคฆ्เคฐเคชूเคฐ เคिเคฒ्เคน्เคฏाเค्เคฏा เคฆौเคฑ्เคฏाเคตเคฐ เค
เคธเคคांเคจा เคเค เคฆि. 02 เคฒा “เคถिเคตाเคฒเคฏ” เคถिเคตเคธेเคจा เคฎเคง्เคฏเคตเคฐ्เคคी เคाเคฐ्เคฏाเคฒเคฏ เคตเคฐोเคฐा เคฏेเคฅीเคฒ เคाเคฐ्เคฏाเคฒเคฏाเคธ เคธเคฆिเค्เคा เคญेเค เคฆिเคฒी. เคฏाเคช्เคฐเคธंเคी เคฎौเคा เคฌเคฐांเค เคฎोเคाเคธा เคฏेเคฅे เคเคฐ्เคจाเคเค เคเคฎ्เคा เคंเคชเคจी เคต เคेเคชीเคธिเคเคฒ เคंเคชเคจी เคจिเคฏเคฎเคฌाเคน्เคฏ เคต เคฌेเคाเคฏเคฆेเคถिเคฐ เคुเค เคฒाเคนी เคคाเคฌा เคจ เคेเคคा เค
เคตैเคง เคเคค्เคเคจเคจ เคเคฐीเคค เค
เคธเคฒ्เคฏाเคฎुเคณे เคคเคธेเค เคाเคตเคเคฑ्เคฏांเคตเคฐ เค
เคจ्เคฏाเคฏ เค
เคค्เคฏाเคाเคฐ เคเคฐीเคค เค
เคธूเคจ เคค्เคฏांเคจा เคจ्เคฏाเคฏ เคต เคนเค्เค เคฎिเคณเคฃ्เคฏाเคฌाเคฌเคค เคคเคธेเค เคฏाเคธंเคฌเคงाเคจे เคฌเคฐांเค เคฎोเคाเคธा เคฏेเคฅीเคฒ เคฎเคนिเคฒांเคे เคธुเคฐू เค
เคธเคฃाเคฐे เคเคชोเคทเคฃाเคฌाเคฌเคค เคฎाเคนिเคคी เคฆेเคฃ्เคฏाเคค เคเคฒी.
“เคถिเคตाเคฒเคฏ” เคธเคฆिเค्เคा เคญेเคीเคช्เคฐเคธंเคी เคค्เคฏांเคจा เคฎเคนाเคฐाเคท्เค्เคฐाเคे เคเคฐाเคง्เคฏ เคฆैเคตเคค เคเคค्เคฐเคชเคคी เคถिเคตाเคी เคฎเคนाเคฐाเค เคฏांเคे เคฎाเคจเคिเคจ्เคน, เคถॉเคฒ เคต เคชुเคท्เคชเคुเค्เค เคฆेเคตुเคจ เคंเคฆ्เคฐเคชूเคฐ เคिเคฒ्เคนा เคธंเคเคीเคा เคจเคฐ्เคฎเคฆा เคฌोเคฐेเคเคฐ เคฏांเค्เคฏा เคนเคธ्เคคे เคธเคค्เคाเคฐ เคเคฐเคฃ्เคฏाเคค เคเคฒा. เคค्เคฏांเค्เคฏाเคธोเคฌเคค เค
เคธเคฒेเคฒ्เคฏा เคถिเคตเคธेเคจा เคฎเคนिเคฒा เคिเคฒ्เคนा เคธंเคเคीเคा เคเคกเคिเคฐोเคฒी เคाเคฏा เคुंเคญाเคฐे, เคंเคฆ्เคฐเคชूเคฐ เคฎाเคी เคिเคฒ्เคนा เคธंเคเคीเคा เคจिเคฒीเคฎा เคถिंเคฆे, เคंเคฆ्เคฐเคชूเคฐ เคिเคฒ्เคนा เคธंเคเคीเคा เคเค्เคตเคฒा เคจเคฒเคे เคฏांเคाเคธुเคง्เคฆा เคเคค्เคฐเคชเคคी เคถिเคตाเคी เคฎเคนाเคฐाเค เคฏांเคे เคฎाเคจเคिเคจ्เคน, เคถॉเคฒ เคต เคชुเคท्เคชเคुเค्เค เคฆेเคตुเคจ เคธเคจ्เคฎाเคจ เคเคฐเคฃ्เคฏाเคค เคเคฒा.
เคเคชोเคทเคฃเคเคฐ्เคค्เคฏा เคฎเคนिเคฒांเคจा เคฏाเคตेเคณी เคเคชोเคทเคฃाเคธंเคฆเคฐ्เคญाเคจे เคฏा เคช्เคฐเคเคฐเคฃाเคธंเคฌंเคงाเคจे เคธंเคชुเคฐ्เคฃ เคाเคเคฆเคชเคค्เคฐाเคी เคฎाเคเคฃी เคेเคฒी เคคเคธेเค เคคेเคฅीเคฒ เคเคชोเคทเคฃเค्เคฐเคธ्เคค เคฎเคนिเคฒांเคจा เคญेเค เคฆेเคฃाเคฐ เค
เคธเคฒ्เคฏाเคे เคฏाเคตेเคณी เคฐाเค्เคฏ เคฎเคนिเคฒा เคเคฏोเค เคธเคฆเคธ्เคฏा เคธंเคीเคคा เคเคต्เคนाเคฃ เคฌोเคฒเคฒ्เคฏा. เคค्เคฏांเคจी เคเคชเคฒ्เคฏा เคฎाเคฐ्เคเคฆเคฐ्เคถเคชเคฐ เคญाเคทเคฃाเคค เคฐाเค्เคฏ เคฎเคนिเคฒा เคเคฏोเคाเคฌाเคฌเคค เคเคชเคธ्เคฅिเคค เคฎंเคกเคณीเคจा เคฎाเคนिเคคी เคฆिเคฒी เคคเคธेเค เคฎเคนिเคฒांเคे เคธเค्เคทเคฎीเคเคฐเคฃ เคต เคฎเคนिเคฒांเคเคฐीเคคा เคฎเคนिเคฒा เคเคฏोเค เคाเคฏ เคญुเคฎिเคा เคฌเคाเคตเคคो เคฏाเคตเคฐ เคฏोเค्เคฏ เคฎाเคฐ्เคเคฆเคฐ्เคถเคจ เคเคฐीเคค, เคฎเคนिเคฒांเคจा เคोเคฃเคคीเคนी เคฎเคฆเคค เคฒाเคเคฒ्เคฏाเคธ เคฎเคฆเคค เคเคฐเคฃ्เคฏाเคे เคเคถ्เคตाเคธเคจ เคฆिเคฒे.
เคฏाเคตेเคณी เคंเคฆ्เคฐเคชुเคฐ เคिเคฒ्เคนा เคธंเคเคीเคा เคจเคฐ्เคฎเคฆा เคฌोเคฐेเคเคฐ, เคตเคฐोเคฐा เคตिเคงाเคจเคธเคญा เคช्เคฐเคฎुเค เคฐเคตिंเคฆ्เคฐ เคถ्เคฐीเคจिเคตाเคธเคฐाเคต เคถिंเคฆे, เคตเคฐोเคฐा เคคाเคฒुเคा เคช्เคฐเคฎुเค เคคเคฅा เคธंเคाเคฒเค เคृ.เค.เคฌा.เคธ.เคตเคฐोเคฐा เคฆเคค्เคคा เคฌोเคฐेเคเคฐ, เคเคชเคคाเคฒुเคा เคช्เคฐเคฎुเค เคคเคฅा เคตเคฐोเคฐा เคृ.เค.เคฌा.เคธ. เคธंเคाเคฒเค เค
เคญिเคीเคค เคชाเคตเคกे, เคंเคฆ्เคฐเคชुเคฐ เคिเคฒ्เคนा เคฏुเคตเคคी เคธेเคจा เค
เคงिเคाเคฐी เคช्เคฐเคคिเคญा เคฎांเคกเคตเคเคฐ, เคตเคฐोเคฐा เคคाเคฒुเคा เคธंเคเคीเคा เคธเคฐเคฒा เคฎाเคฒोเคเคฐ, เคตเคฐोเคฐा เคถเคนเคฐ เคธंเคเคीเคा เคถुเคญांเคी เค
เคนिเคฐเคเคฐ, เคเคชเคคाเคฒुเคा เคธंเคเคीเคा เคธ्เคฎीเคคा เคคाเคेเคตाเคฐ, เคเคชเคถเคนเคฐ เคช्เคฐเคฎुเค เค
เคจिเคฒ เคธिंเค, เค
เคงिเคตเค्เคคा เคตिเคจोเคฆ เคोเคฌ्เคฐाเคเคกे, เคฆुเคฐ्เคा เคเคจเคे, เคฎुเค्เคคा เคนเคाเคฐे, เคฒเคคा เคฆाเคคे, เคธुเคช्เคฐเคญा เคฎเคธ्เคी, เคธाเค्เคทी เคฆौเคฒเคคเคเคฐ เคต เค
เคจेเค เคฎเคนिเคฒा เคต เคชुเคฐूเคท เคชเคฆाเคงिเคाเคฐी เคเคชเคธ्เคฅिเคค เคนोเคคे.
Friday, March 1, 2024
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