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What's wrong with stickers in Telegram? Deanonymize anonymous channels in two clicks

After reading extensively about the best databases for Rails development, I finally settled on PostgreSQL (mainly because my instructor on YouTube used it). I decided to switch from SQLite to PostgreSQL, and I was very pleased with the results. Following tutorials on how to build with PostgreSQL using the Ruby on Rails framework was smooth, and integrating the PostgreSQL database (DB) was seamless—until I decided to revamp my PC and reinstall Ubuntu Linux. The backstory is that I had a PC running an Ubuntu Linux distribution, but during the OS installation, I had mistakenly installed it on a partition with limited space. I tried to find a workaround to increase the partition size since I still had some free space on the disk and a Windows OS on another partition with plenty of space. As I needed more space for additional software installations, I attempted various mounting and unmounting techniques. Eventually, I figured out a way to expand the /root partition. After weighing my options, I decided that a fresh installation of Ubuntu 24.04 would be the best solution. This way, I could allocate the entire 500GB of space to Ubuntu, and I had also read DHH’s views on transitioning to open-source development tools, moving away from Mac and Windows systems. You can read more about it here. I quickly backed up my important data and created a live USB drive. Before I knew it, I was on track to turn my PC into the best software development tool ever 🤗. Without further ado, I proceeded with the reinstallation. Once completed, I launched my terminal and typed wget -qO- https://omakub.org/install | bash to install the Omakub Ubuntu setup. The process took some time, as it’s an all-in-one suite of software for developers. I won’t go into too much detail about Omakub and its Omakase spirit—I’ll write something nice about it later. After installing Omakub, my PC was fresh and fully equipped with a beautiful UI, along with Ruby, Python, Node, Docker, VS Code, Neovim, and other packages. The Rails framework was also installed with that single command. Just imagine the satisfaction of getting everything set up with a single command. I then connected my external drive and restored all the backed-up data, including my test projects. I launched my terminal, navigated to my directory, then to my test project, started my server with rails server, and clicked on the link to my localhost. Voila! 'ActiveRecord' errors, blah blah. I was confused. The actual error was "unable to connect to the database on port 5432." Maybe I'm a noob here, but I’ve always used default settings for my database during development. I kept at it, trying to figure out what was causing the error. I searched online for solutions, checked Stack Overflow, explored Ubuntu communities, and did other Google searches—I still couldn’t resolve it. Sudo this, su that—nothing worked. I removed and reinstalled PostgreSQL, but the problem persisted. Initially, I thought the Omakub installation was faulty. I was about to remove it when I decided to fork my project using the default database, SQLite. It worked instantly. This vindicated Omakub 😂. But now, I had to make PostgreSQL work by any means necessary. I considered using Docker since it would encapsulate my app, allowing all dependencies to work independently of the host machine. Ahh! Complexities! I reminded myself that I was just trying to get a handle on this framework within 6 weeks before shipping out my products. So, I decided against using Docker for now. I wanted to understand why PostgreSQL wasn’t working on my PC and continued searching for a solution. Yes! I finally found it 😂 The fix was ridiculously simple, but I wasn’t thinking in that direction. I had assumed that Omakub had broken my system. I decided to search for how to set up a profile in PostgreSQL and found a helpful post on Medium—thanks to the writer. I created my PostgreSQL database profile using the Postgres commands, then opened my app files in VS Code, edited the database.yml file to include the username and password, and ran rails db:create. It worked! My app connected to the database, and my server started running. Hurray! 127.0.0.1:3000 finally worked. This story is for anyone who has experienced PostgreSQL port 5432 issues while being a noob at Ruby on Rails. After reading extensively about the best databases for Rails development, I finally settled on PostgreSQL (mainly because my instructor on YouTube used it). I decided to switch from SQLite to PostgreSQL, and I was very pleased with the results. Following tutorials on how to build with PostgreSQL using the Ruby on Rails framework was smooth, and integrating the PostgreSQL database (DB) was seamless—until I decided to revamp my PC and reinstall Ubuntu Linux. The backstory is that I had a PC running an Ubuntu Linux distribution, but during the OS installation, I had mistakenly installed it on a partition with limited space. I tried to find a workaround to increase the partition size since I still had some free space on the disk and a Windows OS on another partition with plenty of space. As I needed more space for additional software installations, I attempted various mounting and unmounting techniques. Eventually, I figured out a way to expand the /root partition. After weighing my options, I decided that a fresh installation of Ubuntu 24.04 would be the best solution. This way, I could allocate the entire 500GB of space to Ubuntu, and I had also read DHH’s views on transitioning to open-source development tools, moving away from Mac and Windows systems. You can read more about it here . I quickly backed up my important data and created a live USB drive. Before I knew it, I was on track to turn my PC into the best software development tool ever 🤗. here Without further ado, I proceeded with the reinstallation. Once completed, I launched my terminal and typed wget -qO- https://omakub.org/install | bash to install the Omakub Ubuntu setup. The process took some time, as it’s an all-in-one suite of software for developers. I won’t go into too much detail about Omakub and its Omakase spirit—I’ll write something nice about it later. wget -qO- https://omakub.org/install | bash After installing Omakub, my PC was fresh and fully equipped with a beautiful UI, along with Ruby, Python, Node, Docker, VS Code, Neovim, and other packages. The Rails framework was also installed with that single command. Just imagine the satisfaction of getting everything set up with a single command. I then connected my external drive and restored all the backed-up data, including my test projects. I launched my terminal, navigated to my directory, then to my test project, started my server with rails server , and clicked on the link to my localhost. rails server Voila! 'ActiveRecord' errors, blah blah. I was confused. The actual error was "unable to connect to the database on port 5432." Maybe I'm a noob here, but I’ve always used default settings for my database during development. I kept at it, trying to figure out what was causing the error. I searched online for solutions, checked Stack Overflow, explored Ubuntu communities, and did other Google searches—I still couldn’t resolve it. Sudo this, su that—nothing worked. I removed and reinstalled PostgreSQL, but the problem persisted. Initially, I thought the Omakub installation was faulty. I was about to remove it when I decided to fork my project using the default database, SQLite. It worked instantly. This vindicated Omakub 😂. But now, I had to make PostgreSQL work by any means necessary. I considered using Docker since it would encapsulate my app, allowing all dependencies to work independently of the host machine. Ahh! Complexities! I reminded myself that I was just trying to get a handle on this framework within 6 weeks before shipping out my products. So, I decided against using Docker for now. I wanted to understand why PostgreSQL wasn’t working on my PC and continued searching for a solution. Yes! I finally found it 😂 The fix was ridiculously simple, but I wasn’t thinking in that direction. I had assumed that Omakub had broken my system. I decided to search for how to set up a profile in PostgreSQL and found a helpful post on Medium —thanks to the writer. Medium I created my PostgreSQL database profile using the Postgres commands, then opened my app files in VS Code, edited the database.yml file to include the username and password, and ran rails db:create . database.yml rails db:create It worked! My app connected to the database, and my server started running. Hurray! 127.0.0.1:3000 finally worked. This story is for anyone who has experienced PostgreSQL port 5432 issues while being a noob at Ruby on Rails.

Postgresql and Port 5432 issues on Ubuntu 24.04

As the social media landscape continues to evolve, Clapper, a platform designed for adults seeking a censorship-free space, is making waves with its latest offering: The Clapper Newsroom. Established in 2020, Clapper has built a reputation as a haven for authentic connections, attracting over 2 million monthly active users who appreciate its ad-free environment and commitment to free speech. Now, with the Newsroom, Clapper is taking a bold step to address the growing concern over fake news and biased reporting on social media. The Clapper Newsroom is more than just another news feed; it’s a dedicated space where users can engage with trending news across various topics such as politics, pop culture, sports, and technology. What sets it apart is its commitment to fostering respectful dialogue while maintaining an open platform for diverse opinions. “At Clapper, we believe in the power of free speech, but we also recognize the importance of responsible dialogue,” says Clapper’s CEO, Edison Chen. “The Newsroom is our way of creating a space where people can share their thoughts and perspectives without fear of censorship while also combating the spread of fake or biased news. We want the Newsroom to be a place where users can access reliable information and engage in meaningful conversations without worrying about being judged or disrespected for their beliefs.” The launch of the Newsroom comes at a crucial time, with the presidential debate on the horizon. Clapper is positioning the Newsroom as a hub for all information related to key topics and updates during the election season. This move is part of a broader strategy to make Clapper a go-to platform for those seeking real-time, reliable news without the noise of ads or algorithms skewing the conversation. To ensure the quality and reliability of the content shared in the Newsroom, Clapper has partnered with a diverse group of influencers and journalists. These include news veteran Lisa Remillard, known for her incisive reporting, and viral TikTok sensation Shannon Hill News, who brings a lighter touch to current key events. Lisa Remillard, a veteran broadcast journalist with years of experience in the field and a very large social media footprint, expressed her appreciation for Clapper's initiative goals. "My mission has always been to share the facts first. In a society where misinformation spreads faster than the truth, I appreciate Clapper's mission to not only facilitate the facts but amplify them through the Clapper Newsroom." Debra Lea, a Fox News regular and creator who specializes in conservative content, echoed this sentiment, adding, “Clapper’s commitment to free speech is what drew me to the platform in the first place. The Newsroom is an extension of that, providing a space where different viewpoints can be shared and debated respectfully. It’s important that we have these conversations, especially as we approach such a critical election.” Wally Rashid, a liberal entrepreneur who focuses on the Palestinian liberation cause, highlighted the platform’s balanced approach. “Clapper has managed to create a space where diverse perspectives are not just tolerated but welcomed. The Newsroom is going to be an invaluable resource for those of us who want to stay informed without the bias that’s so prevalent on other platforms.” Clapper’s approach to the Newsroom reflects its broader mission of creating a social media platform where adults can connect authentically and engage in meaningful dialogue. By keeping the platform ad-free and storing all data in the U.S., Clapper is addressing some of the key concerns users have with other social media networks. Shannon Hill, who has gained popularity on TikTok for her engaging takes on the news, is excited to bring her content to Clapper. “The Newsroom on Clapper is a great opportunity to reach an audience that’s looking for something different – a place where they can get the news and talk about it without all the noise and pressure from ads. It’s a refreshing change, and I’m excited to be part of it.” As the Newsroom gains momentum, Clapper is set to become a significant player in the social media landscape, particularly for those who value free speech and unbiased news. With its diverse lineup of creators and journalists and a commitment to fostering respectful dialogue, Clapper’s latest project has the potential to reshape how news is consumed and discussed online. In an era where the line between fact and fiction is increasingly blurred, Clapper’s Newsroom stands out as a beacon for those seeking truth, connection, and community. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. As the social media landscape continues to evolve, Clapper, a platform designed for adults seeking a censorship-free space, is making waves with its latest offering: The Clapper Newsroom. Established in 2020, Clapper has built a reputation as a haven for authentic connections, attracting over 2 million monthly active users who appreciate its ad-free environment and commitment to free speech. Now, with the Newsroom, Clapper is taking a bold step to address the growing concern over fake news and biased reporting on social media. The Clapper Newsroom is more than just another news feed; it’s a dedicated space where users can engage with trending news across various topics such as politics, pop culture, sports, and technology. What sets it apart is its commitment to fostering respectful dialogue while maintaining an open platform for diverse opinions. “At Clapper, we believe in the power of free speech, but we also recognize the importance of responsible dialogue,” says Clapper’s CEO, Edison Chen. “The Newsroom is our way of creating a space where people can share their thoughts and perspectives without fear of censorship while also combating the spread of fake or biased news. We want the Newsroom to be a place where users can access reliable information and engage in meaningful conversations without worrying about being judged or disrespected for their beliefs.” “At Clapper, we believe in the power of free speech, but we also recognize the importance of responsible dialogue,” “The Newsroom is our way of creating a space where people can share their thoughts and perspectives without fear of censorship while also combating the spread of fake or biased news. We want the Newsroom to be a place where users can access reliable information and engage in meaningful conversations without worrying about being judged or disrespected for their beliefs.” The launch of the Newsroom comes at a crucial time, with the presidential debate on the horizon. Clapper is positioning the Newsroom as a hub for all information related to key topics and updates during the election season. This move is part of a broader strategy to make Clapper a go-to platform for those seeking real-time, reliable news without the noise of ads or algorithms skewing the conversation. To ensure the quality and reliability of the content shared in the Newsroom, Clapper has partnered with a diverse group of influencers and journalists. These include news veteran Lisa Remillard, known for her incisive reporting, and viral TikTok sensation Shannon Hill News, who brings a lighter touch to current key events. Lisa Remillard, a veteran broadcast journalist with years of experience in the field and a very large social media footprint, expressed her appreciation for Clapper's initiative goals. "My mission has always been to share the facts first. In a society where misinformation spreads faster than the truth, I appreciate Clapper's mission to not only facilitate the facts but amplify them through the Clapper Newsroom." "My mission has always been to share the facts first. In a society where misinformation spreads faster than the truth, I appreciate Clapper's mission to not only facilitate the facts but amplify them through the Clapper Newsroom." Debra Lea, a Fox News regular and creator who specializes in conservative content, echoed this sentiment, adding, “Clapper’s commitment to free speech is what drew me to the platform in the first place. The Newsroom is an extension of that, providing a space where different viewpoints can be shared and debated respectfully. It’s important that we have these conversations, especially as we approach such a critical election.” “Clapper’s commitment to free speech is what drew me to the platform in the first place. The Newsroom is an extension of that, providing a space where different viewpoints can be shared and debated respectfully. It’s important that we have these conversations, especially as we approach such a critical election.” Wally Rashid, a liberal entrepreneur who focuses on the Palestinian liberation cause, highlighted the platform’s balanced approach. “Clapper has managed to create a space where diverse perspectives are not just tolerated but welcomed. The Newsroom is going to be an invaluable resource for those of us who want to stay informed without the bias that’s so prevalent on other platforms.” “Clapper has managed to create a space where diverse perspectives are not just tolerated but welcomed. The Newsroom is going to be an invaluable resource for those of us who want to stay informed without the bias that’s so prevalent on other platforms.” Clapper’s approach to the Newsroom reflects its broader mission of creating a social media platform where adults can connect authentically and engage in meaningful dialogue. By keeping the platform ad-free and storing all data in the U.S., Clapper is addressing some of the key concerns users have with other social media networks. Shannon Hill, who has gained popularity on TikTok for her engaging takes on the news, is excited to bring her content to Clapper. “The Newsroom on Clapper is a great opportunity to reach an audience that’s looking for something different – a place where they can get the news and talk about it without all the noise and pressure from ads. It’s a refreshing change, and I’m excited to be part of it.” “The Newsroom on Clapper is a great opportunity to reach an audience that’s looking for something different – a place where they can get the news and talk about it without all the noise and pressure from ads. It’s a refreshing change, and I’m excited to be part of it.” As the Newsroom gains momentum, Clapper is set to become a significant player in the social media landscape, particularly for those who value free speech and unbiased news. With its diverse lineup of creators and journalists and a commitment to fostering respectful dialogue, Clapper’s latest project has the potential to reshape how news is consumed and discussed online. In an era where the line between fact and fiction is increasingly blurred, Clapper’s Newsroom stands out as a beacon for those seeking truth, connection, and community. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. Vested Interest Disclosure: business blogging program

Clapper Launches “the Newsroom” to Promote Unbiased News and Combat Censorship

ক্ল্যাপার নিরপেক্ষ সংবাদ প্রচার এবং সেন্সরশিপ লড়াইয়ের জন্য "নিউজরুম" চালু করেছে

Clapper startet „Newsroom“, um unvoreingenommene Nachrichten zu fördern und Zensur zu bekämpfen

Clapper lanza “The Newsroom” para promover noticias imparciales y combatir la censura

Clapper lance « The Newsroom » pour promouvoir des informations impartiales et lutter contre la censure

क्लैपर ने निष्पक्ष समाचार को बढ़ावा देने और सेंसरशिप से लड़ने के लिए "न्यूज़रूम" लॉन्च किया

クラッパーは、偏見のないニュースを推進し、検閲と闘うために「ニュースルーム」を立ち上げました

Clapper, 편견 없는 뉴스를 홍보하고 검열에 맞서기 위해 "뉴스룸" 출범

Clapper lança “the Newsroom” para promover notícias imparciais e combater a censura

Клэппер запускает «Newsroom» для продвижения беспристрастных новостей и борьбы с цензурой

Clapper, Tarafsız Haberleri Teşvik Etmek ve Sansüre Karşı Mücadele Etmek İçin “Haber Odası”nı Başlattı

Clapper Ra Mắt “The Newsroom” Để Thúc Đẩy Tin Tức Công Bằng Và Chống Kiểm Duyệt

Clapper 推出“新闻编辑室”以促进公正新闻并打击审查制度

I'm excited to tell you about Meta's Llama 3.1, a powerful AI language model you can use for free. In this article, I'll show you how to install and run Llama 3.1 on your computer. This means you can use it without the internet and save money on AI costs. https://youtu.be/UdcmnhmruIs?si=9okWfpSuMgd1AYOZ&embedable=true What is Llama 3.1? Llama 3.1 is Meta’s newest AI model that’s open for anyone to use. It comes in three sizes: A huge 405-billion parameter version
A medium 70 billion parameter version
A smaller 8 billion parameter version Llama 3.1 is special because it works really well. It can even do better than some private AI models. You can read more about it here:https://llama.meta.com/ Why Llama 3.1 is Great It’s free.
Anyone can change and improve it.
It works as well as expensive private models.
It understands many languages.
It can handle long texts. It can work with up to 128,000 words at once, which is great for long writing tasks. Challenges with Llama 3.1 It needs a lot of computer power: The biggest version needs very powerful computers to run.
Might have biases: Like all AI, it might repeat unfair ideas from its training data.
Possible safety issues: Because anyone can see how it works, there might be some safety risks if not used carefully. Where to Try Llama 3.1 Here are some ways you can try out Llama 3.1 for free: Meta.ai Chatbot: Meta’s own platform offers access to Llama 3.1, including the 405B model, along with image generation capabilities. However, this is currently limited to U.S. users with a Facebook or Instagram account.
Perplexity: This AI-powered search tool offers access to Llama 3.1 405B, but it requires a paid subscription.
Cloud Platforms: Various cloud providers offer access to Llama 3.1, including: Amazon Bedrock
Microsoft Azure AI
Cloudflare Workers AI
Snowflake
DataBricks
Nvidia AI Foundry
IBM Cloud In this tutorial, I’ll show you how to use Llama 3.1 for free on your own machine. How to Run Llama 3.1 on Your Computer The big 405 billion version needs a very powerful computer. But I’ll show you how to run the 8-billion version. Here’s what you need: 16 GB of RAM
8-core CPU
20 GB of free space Step 1. Download and Install the Ollama Tool Go to https://ollama.com/ and download the tool for your operating system (OS). For Linux, I’ll use this command:curl -fsSL https://ollama.com/install.sh | sh The command will install Ollama, which is a tool for running and managing large language models (LLMs) locally on your machine. Step 2. Download the Llama 3.1 Model On the same website, click on the link “Models” in the top right corner. Then, find Llama 3.1 in the list. Click on the link and then select the desired model from the drop-down menu. Copy the command and paste it into your terminal. Terminal command: ollama run llama3.1 Wait for it to download (it might take 30 minutes or more). If everything is okay, you can use LLM inside your terminal. Enjoy! :) Using Llama 3.1 Without Internet One cool thing about having Llama 3.1 on your computer is you can use it without the internet. This is great if you need to keep your data private. Things to Remember There are a few downsides to know about: The 8 billion model only knows things up to 2021
It can’t look at files on your computer or websites
It might be slower than online AI services How to Give Llama 3.1 Information The model can’t read your files or websites, but you can: Copy and paste text into it
Use things like Dropbox to share files
Summarize what you want it to know in your question Wrapping Up Running Llama 3.1 on your own computer is a great way to use powerful AI without the internet or ongoing costs. It has some limits, but it’s still amazing to have such a smart AI tool on your own machine. If you like this tutorial, please click like and share. You can follow me on YouTube, join my Telegram, or support me on Patreon. Cheers! :) I'm excited to tell you about Meta's Llama 3.1, a powerful AI language model you can use for free. In this article, I'll show you how to install and run Llama 3.1 on your computer. This means you can use it without the internet and save money on AI costs. https://youtu.be/UdcmnhmruIs?si=9okWfpSuMgd1AYOZ&embedable=true https://youtu.be/UdcmnhmruIs?si=9okWfpSuMgd1AYOZ&embedable=true What is Llama 3.1? Llama 3.1 is Meta’s newest AI model that’s open for anyone to use. It comes in three sizes: A huge 405-billion parameter version A medium 70 billion parameter version A smaller 8 billion parameter version A huge 405-billion parameter version A medium 70 billion parameter version A smaller 8 billion parameter version Llama 3.1 is special because it works really well. It can even do better than some private AI models. You can read more about it here: https://llama.meta.com/ https://llama.meta.com/ https://llama.meta.com/ Why Llama 3.1 is Great It’s free. Anyone can change and improve it. It works as well as expensive private models. It understands many languages. It can handle long texts. It can work with up to 128,000 words at once, which is great for long writing tasks. It’s free. Anyone can change and improve it. It works as well as expensive private models. It understands many languages. It can handle long texts. It can work with up to 128,000 words at once, which is great for long writing tasks. Challenges with Llama 3.1 It needs a lot of computer power: The biggest version needs very powerful computers to run. Might have biases: Like all AI, it might repeat unfair ideas from its training data. Possible safety issues: Because anyone can see how it works, there might be some safety risks if not used carefully. It needs a lot of computer power: The biggest version needs very powerful computers to run. Might have biases: Like all AI, it might repeat unfair ideas from its training data. Possible safety issues: Because anyone can see how it works, there might be some safety risks if not used carefully. Where to Try Llama 3.1 Here are some ways you can try out Llama 3.1 for free: Meta.ai Chatbot: Meta’s own platform offers access to Llama 3.1, including the 405B model, along with image generation capabilities. However, this is currently limited to U.S. users with a Facebook or Instagram account. Perplexity: This AI-powered search tool offers access to Llama 3.1 405B, but it requires a paid subscription. Cloud Platforms: Various cloud providers offer access to Llama 3.1, including: Meta.ai Chatbot: Meta’s own platform offers access to Llama 3.1, including the 405B model, along with image generation capabilities. However, this is currently limited to U.S. users with a Facebook or Instagram account. Perplexity: This AI-powered search tool offers access to Llama 3.1 405B, but it requires a paid subscription. Cloud Platforms: Various cloud providers offer access to Llama 3.1, including: Amazon Bedrock Microsoft Azure AI Cloudflare Workers AI Snowflake DataBricks Nvidia AI Foundry IBM Cloud Amazon Bedrock Microsoft Azure AI Cloudflare Workers AI Snowflake DataBricks Nvidia AI Foundry IBM Cloud In this tutorial, I’ll show you how to use Llama 3.1 for free on your own machine. How to Run Llama 3.1 on Your Computer The big 405 billion version needs a very powerful computer. But I’ll show you how to run the 8-billion version. Here’s what you need: 16 GB of RAM 8-core CPU 20 GB of free space 16 GB of RAM 8-core CPU 20 GB of free space Step 1. Download and Install the Ollama Tool Go to https://ollama.com/ and download the tool for your operating system (OS). https://ollama.com/ https://ollama.com/ For Linux, I’ll use this command: curl -fsSL https://ollama.com/install.sh | sh curl -fsSL https://ollama.com/install.sh | sh The command will install Ollama, which is a tool for running and managing large language models (LLMs) locally on your machine. Step 2. Download the Llama 3.1 Model On the same website, click on the link “Models” in the top right corner. “Models” Then, find Llama 3.1 in the list. Click on the link and then select the desired model from the drop-down menu. Copy the command and paste it into your terminal. Terminal command: ollama run llama3.1 ollama run llama3.1 Wait for it to download (it might take 30 minutes or more). If everything is okay, you can use LLM inside your terminal. Enjoy! :) Using Llama 3.1 Without Internet One cool thing about having Llama 3.1 on your computer is you can use it without the internet. This is great if you need to keep your data private. Things to Remember There are a few downsides to know about: The 8 billion model only knows things up to 2021 It can’t look at files on your computer or websites It might be slower than online AI services The 8 billion model only knows things up to 2021 It can’t look at files on your computer or websites It might be slower than online AI services How to Give Llama 3.1 Information The model can’t read your files or websites, but you can: Copy and paste text into it Use things like Dropbox to share files Summarize what you want it to know in your question Copy and paste text into it Use things like Dropbox to share files Summarize what you want it to know in your question Wrapping Up Running Llama 3.1 on your own computer is a great way to use powerful AI without the internet or ongoing costs. It has some limits, but it’s still amazing to have such a smart AI tool on your own machine. If you like this tutorial, please click like and share. You can follow me on YouTube , join my Telegram , or support me on Patreon . YouTube YouTube Telegram Telegram Patreon Patreon Cheers! :)

No More AI Costs: How to Run Meta Llama 3.1 Locally

Is Your Reporting Software WCAG Compliant? Make Data Accessible to Everyone with Practical Steps

Authors:
(1) Renan F. F. da Silva, Institute of Computing, University of Campinas;
(2) Yulle G. F. Borges, Institute of Computing, University of Campinas;
(3) Rafael C. S. Schouery, Institute of Computing, University of Campinas. 6 Variable Neighborhood Search 6.1 Objective function Our objective function f has two levels: the primary objective that seeks to minimize the number of used bins and the secondary objective that seeks the lowest lexicographic order in the residual vector. The residual vector consists of the residual capacity of used bins in non-decreasing order. 6.2 Neighborhoods The VNS performs local searches in neighborhoods, as will be explained below. One relevant factor is the heuristic used in the local search. In our VNS, we use the Best Improvement heuristic, which analyzes all neighbors of a solution and changes to the best neighbor (in terms of the objective function). Our VNS has neighborhoods with the moves: Move-Item, Swap-Items, Swap-and-Move, and MoveTwo-to-One. The number of items changed in one move is constant for these neighborhoods, which just change a constant number of bins and elements in the residual vector (of the second objective function). As we can compare two neighborhoods using their variations in the objective function, it can be done in O(1). Next, we present our neighborhoods, showing optimizations to obtain fast algorithms even for large instances. 6.2.1 Move-Item Neighborhood For the CBPP, one possible algorithm for this neighborhood is to adapt the algorithm used by Fleszar and Hindi [19] for the BPP, that have time complexity O(n2). In this, for each item i, we iterate in the bins given in non-ascending order of residual capacity. Then, we check if the move (i, Br) is valid, where Br is the current bin, and evaluate it if so. Besides, we stop the search when we find the first bin Br such that wi > res(r), since the ordering guarantee that the bins forward also do not have space for the item i. However, it is not necessary to evaluate all moves to find the best one. Applying the Auxiliary Algorithm using the current solution B, it returns the best move for each item i ∈ I, so the best one is the best move between them. It gives us an implementation of the Move-Item Neighborhood with time complexity Θ(n). 6.2.2 Swap-Items Neighborhood Fleszar and Hindi [19] propose for the BPP a Swap-Items Neighborhood with complexity O(n+M), where M is the number of valid moves, which can be very fast in some solutions with little free space. We present the pseudocode of this neighborhood in the Algorithm 4. Although the time complexity of this algorithm is O(n log n + M), in our tests, it performed better than Fleszar and Hindi’s algorithm, especially in large instances. Algorithm 5 presents the pseudocode of this algorithm. 6.2.3 Move-Two-to-One Neighborhood 6.2.4 Swap-and-Move Neighborhood The Swap-and-Move Neighborhood selects three items i, j, k from different bins, then swaps i and j, and moves k to B(i) (its bin before the move). This neighborhood has complexity O(n3) when implemented in the completed way, which offers a low cost-benefit, so we use a restricted version from this one. Instead of testing all the possibilities of k, we test only the heaviest item that can be moved to B(i). The first candidate for k is the heaviest item that fits in B(i), and we can use a binary search to find it. 6.3 Shake Algorithm The Shake Algorithm is a function that makes random moves, seeking to escape from local optimal. In particular, our Shake chooses to execute one of two subroutines with equal probability. In the first subroutine, we performed 20 moves with the constraint that each bin belongs to a maximum of one move. The possible moves are to move one item or swap two items. In the second subroutine, we select two bins and then repack their items in a random order using the Best Fit heuristic. Note that we use a very aggressive Shake since its moves can make the solution much worse. Our Shake is based on the one proposed by Fleszar and Hindi [19] for the BPP, which is equal to the first subroutine without the constraint that bins must be distinct in different moves. The second routine was added because the perturbation generated by the first one can be small in instances with little free space. 6.4 Algorithm 6.5 Neighborhood Ordering This paper is available on arxiv under CC 4.0 license. Authors: (1) Renan F. F. da Silva, Institute of Computing, University of Campinas; (2) Yulle G. F. Borges, Institute of Computing, University of Campinas; (3) Rafael C. S. Schouery, Institute of Computing, University of Campinas. Authors: (1) Renan F. F. da Silva, Institute of Computing, University of Campinas; (2) Yulle G. F. Borges, Institute of Computing, University of Campinas; (3) Rafael C. S. Schouery, Institute of Computing, University of Campinas. 6 Variable Neighborhood Search 6.1 Objective function Our objective function f has two levels: the primary objective that seeks to minimize the number of used bins and the secondary objective that seeks the lowest lexicographic order in the residual vector. The residual vector consists of the residual capacity of used bins in non-decreasing order. 6.2 Neighborhoods The VNS performs local searches in neighborhoods, as will be explained below. One relevant factor is the heuristic used in the local search. In our VNS, we use the Best Improvement heuristic, which analyzes all neighbors of a solution and changes to the best neighbor (in terms of the objective function). Our VNS has neighborhoods with the moves: Move-Item, Swap-Items, Swap-and-Move, and MoveTwo-to-One. The number of items changed in one move is constant for these neighborhoods, which just change a constant number of bins and elements in the residual vector (of the second objective function). As we can compare two neighborhoods using their variations in the objective function, it can be done in O(1). Next, we present our neighborhoods, showing optimizations to obtain fast algorithms even for large instances. 6.2.1 Move-Item Neighborhood For the CBPP, one possible algorithm for this neighborhood is to adapt the algorithm used by Fleszar and Hindi [19] for the BPP, that have time complexity O(n2). In this, for each item i, we iterate in the bins given in non-ascending order of residual capacity. Then, we check if the move (i, Br) is valid, where Br is the current bin, and evaluate it if so. Besides, we stop the search when we find the first bin Br such that wi > res(r), since the ordering guarantee that the bins forward also do not have space for the item i. However, it is not necessary to evaluate all moves to find the best one. Applying the Auxiliary Algorithm using the current solution B, it returns the best move for each item i ∈ I, so the best one is the best move between them. It gives us an implementation of the Move-Item Neighborhood with time complexity Θ(n). 6.2.2 Swap-Items Neighborhood Fleszar and Hindi [19] propose for the BPP a Swap-Items Neighborhood with complexity O(n+M), where M is the number of valid moves, which can be very fast in some solutions with little free space. We present the pseudocode of this neighborhood in the Algorithm 4. Although the time complexity of this algorithm is O(n log n + M), in our tests, it performed better than Fleszar and Hindi’s algorithm, especially in large instances. Algorithm 5 presents the pseudocode of this algorithm. 6.2.3 Move-Two-to-One Neighborhood 6.2.4 Swap-and-Move Neighborhood The Swap-and-Move Neighborhood selects three items i, j, k from different bins, then swaps i and j, and moves k to B(i) (its bin before the move). This neighborhood has complexity O(n3) when implemented in the completed way, which offers a low cost-benefit, so we use a restricted version from this one. Instead of testing all the possibilities of k, we test only the heaviest item that can be moved to B(i). The first candidate for k is the heaviest item that fits in B(i), and we can use a binary search to find it. 6.3 Shake Algorithm The Shake Algorithm is a function that makes random moves, seeking to escape from local optimal. In particular, our Shake chooses to execute one of two subroutines with equal probability. In the first subroutine, we performed 20 moves with the constraint that each bin belongs to a maximum of one move. The possible moves are to move one item or swap two items. In the second subroutine, we select two bins and then repack their items in a random order using the Best Fit heuristic. Note that we use a very aggressive Shake since its moves can make the solution much worse. Our Shake is based on the one proposed by Fleszar and Hindi [19] for the BPP, which is equal to the first subroutine without the constraint that bins must be distinct in different moves. The second routine was added because the perturbation generated by the first one can be small in instances with little free space. 6.4 Algorithm 6.5 Neighborhood Ordering This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Understanding Variable Neighborhood Search

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix VII. CONCLUSION In this work, we have carried out a full-blown analysis and experimental implementation of a Slice Reconciliation scheme applied to a specific CV QKD protocol (with post-selection) under simulated channel conditions anticipated for satelliteto-Earth channels. We have provided the optimal solution for the classical reconciliation process for this CV-QKD protocol in the context of massive parallelisation under the finite key regime. More specifically, we have identified the optimal block length when a large-code block is to be subdivided so as to improve the final secure key rate in bits per second. Although our results were based on a specific CV-QKD protocol and a specific GPU architecture, the type of analysis we have introduced here will apply in general terms a large suite of CV-QKD protocols run over any form of architecture that offers massive parallelisation. Our results, therefore, pave the way to optimal reconciliation system design for a wide range of practical CV-QKD systems that operate in the finite key regime. As the demand on the finite key size grows (better security thresholds), and technology advances lead to larger quantum signalling rates, the importance of optimised multithreaded CV-QKD reconciliation will grow. REFERENCES [1] N. Hosseinidehaj, Z. Babar, R. Malaney, S. X. Ng, and L. 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Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: Conclusion & References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix Abstract Designing a practical Continuous Variable (CV) Quantum Key Distribution (QKD) system requires an estimation of the quantum channel characteristics and the extraction of secure key bits based on a large number of distributed quantum signals. Meeting this requirement in short timescales is difficult. On standard processors, it can take several hours to reconcile the required number of quantum signals. This problem is exacerbated in the context of Low Earth Orbit (LEO) satellite CV-QKD, in which the satellite flyover time is constrained to be less than a few minutes. A potential solution to this problem is massive parallelisation of the classical reconciliation process in which a large-code block is subdivided into many shorter blocks for individual decoding. However, the penalty of this procedure on the important final secured key rate is non-trivial to determine and hitherto has not been formally analysed. Ideally, a determination of the optimal reduced block size, maximising the final key rate, would be forthcoming in such an analysis. In this work, we fill this important knowledge gap via detailed analyses and experimental verification of a CV-QKD sliced reconciliation protocol that uses large block-length low-density parity-check decoders. Our new solution results in a significant increase in the final key rate relative to non-optimised reconciliation. In addition, it allows for the acquisition of quantum secured messages between terrestrial stations and LEO satellites within a flyover timescale even using off-the-shelf processors. Our work points the way to optimised global quantum networks secured via fundamental physics. I. INTRODUCTION Continuous Variable (CV) Quantum Key Distribution (QKD) has been intensively studied and significant breakthroughs have been achieved in both theory and experiment (see [1] for review). Compared to Discrete Variable (DV) QKD [2]–[5], CV-QKD can be implemented with well-developed technologies (e.g., homodyne detectors) in commercial fibreoptic networks [6], [7] and free-space optical communications [8], [9], providing it a potential advantage in practical deployments [10]–[14]. Considering the finite-key security of CV-QKD and DV-QKD, there are three critical parameters. These are, No, the number of original quantum signals sent by the transmitter (Alice) that are collected by the receiver (Bob); Ne, the number of quantum signals from which the protocol parameters are estimated;[1] and, ǫ, the probability that a QKD protocol fails to generate secret keys [15], [16]. To satisfy an upper limit on the failure probability of parameter estimation, Alice and Bob set Ne to a large value, which in turn implies a larger No. Despite the advantages in deployment, CV-QKD systems tend to demand a larger No to reach the same ǫ relative to DVQKD protocols. For example, to achieve a final key rate of 0.1 bits per pulse with ǫ = 10−9 , a CV-QKD protocol studied in [17] required No ≈ 109 signals. However, to achieve the same final key rate with ǫ = 10−14, the DV-QKD protocol in [18] required No ≈ 104 signals. This higher number of required signals in CV-QKD can render the classical post-processing (i.e. key reconciliation and privacy amplification[2] ) slow - possibly failing to meet target timescales for reconciliation. The end-users of a CV-QKD system expect the system to deliver two identical and secure keys under a limited time interval. For example, for satellite-based deployments, we would hope that the reconciliation is completed while maintaining a line-of-sight connection with the ground station. For a CV-QKD-enabled satellite with orbital parameters similar to Micius [20], this would mean the reconciliation should be completed in less than a few minutes. For the protocol we use in this work (see later), and for ǫ = 10−9 , this, in turn, would require the data rate of reconciliation to be at least 3.6 × 106 bits per second. For real-time reconciliation (say in subsecond timescales), two orders of magnitude increases in the reconciliation rates would be required. Demands for smaller ǫ will exacerbate the issue. Ideally, the rate of reconciliation should always be faster than the rate of quantum signalling. This all raises the question as to whether current CV-QKD reconciliation schemes are optimised for the highest possible key rates in bits per second. As we show here, this is not the case. Further optimisation is possible on all current schemes. To understand the issue better, we define reconciliation in the context of CV-QKD as a two-step scheme where the inputs to the reconciliation are non-identical N = 2No − 2Ne quadrature values[3] held by Alice and Bob (after parameter estimation), and the output is an identical bit string held by Alice and Bob [21]–[23]. Assuming a reverse reconciliation scheme, Bob first converts the quadrature values encoded by Alice in each signal to m bits. Alice, after converting each of her encoded real numbers also to m bits, then initiates some discrepancy-correction algorithms based on pre-defined errorcorrection codes to ensure her mN bits are identical to Bob’s. In this work, we will adopt Low-Density Parity-Check (LDPC) codes for the error correction. However, as alluded to above, reconciling mN bits within a limited time frame can be challenging. State-of-the-art LDPCbased reconciliation schemes for CV-QKD systems involve parallelised computation on a Graphics Processing Unit (GPU) [13], [24] or Field-Programmable Gate Arrays (FPGAs) [25], [26]. Reconciliation schemes implemented on FPGAs offer more programmable flexibility, but sometimes at the cost of reduced memory access relative to GPUs. For our purposes, both hardware architectures are useful - both offer massive parallelisation opportunities. These parallelisation solutions generally take the following two-step approach: 1) The mN bits are organised as m N-bit blocks to be reconciled. Each N-bit block is divided into multiple shorter blocks of size, say, NR. This is usually just set to a block size that can be processed within some timescale. 2) Then the m NR-bit blocks are reconciled in parallel (via independent processors) using optimally-designed LDPC decoders. However, what is missing in this approach is a proper optimisation analysis as to what the optimal value of NR is. As we show below, simply reducing NR at the cost of additional processing units is not an optimal solution. It transpires that in QKD the “penalty” cost of reducing the code rate (implicit in the use of small block lengths) significantly influences the bit per second final key rate. A more sophisticated analysis is required to determine the optimal reduced block length. Such an analysis is the key contribution of this work. Although we will adopt a specific CV-QKD protocol for our analysis, the key steps of our scheme will apply to any CV-QKD protocol. Our reconciliation scheme will deliver the highest reconciliation rate for a given processor speed - thus allowing for the optimal solution to CV-QKD reconciliation. [1] More precisely, in a CV-QKD protocol, Alice and Bob randomly select a Ne-signal subset from the No signals to estimate the parameters. [2] In this work, we focus on the key reconciliation step because it is the more time-consuming part in the post-processing steps while the privacy amplification involving only bit-wise operations can be easily implemented faster than the reconciliation [19]. [3] No and Ne are multiplied by 2 since Alice and Bob utilise both quadratures from heterodyne detection - the detection process we assume in this work.

Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: Abstract & Introduction

Life happily carries on and we don’t even have to look at our own darkness. Perhaps the real problem with modern life is the benign micro-systems that foster the covert conspiracy- we agree to blame the world for things that are, in fact, within us all. Implicitly we agree to live in a superficial dystopia of masks and make-up, where the wounds of psyche and identity stay hidden below the surface. The very comforts that our system affords us, also engage us in a conspiracy of denial, where we avoid the discomfiting process of taking a deep look within, and consequently, we allow the demons free space in our psyche. That’s why the unacknowledged, negative parts of us don’t simply lay dormant; they affect our thinking and our behavior, both conscious and unconscious, in ways that can be insidious and even destructive. Recognizing our shadows is a positive expression of our autonomy, an alternative to the easy way of least resistance, it is a refusal to ignore parts of ourselves, and a positive act of contact with that essential but compromised figure we all carry around inside our heads in the form of ‘I’. Recognition, then, is neither masochism nor resigned submission to this feature of our psychology. It is a positive move towards genuine self-awareness, which – by integrating in this way the previously separate shadow, by bringing that shadow into the full light of consciousness – is then powerless to continue the task it set itself, i.e, to deceive us into projecting onto other people the pain or injustice it embodies. No longer can we be unsuspecting hosts for the behaviors that are put to use by the shadow with such vicious selectivity. It takes courage and defiance to engage in this process of recognition – both externally and inwardly – to challenge how confinement insulates us from the truth of our ambivalence, how confinement requires people to live in a suspended way, aware of only part of themselves. If we allow ourselves to acknowledge the full range of our humanity, we open ourselves to a greater capacity for change. There is power in the acknowledgment of our demons, which changes their ability to function in the dark; it will not make us immune to them, but it should let us manage them better. Knowing that we have a dark side expands our compassion towards ourselves and others. We all are fighting a battle within, and this makes us vulnerable to each other. But awareness can transform vulnerability into a source of solidarity. In fact, this recognition opens the door for a more equitable and empathetic society. In owning our inner conflict, we reduce the risk of projecting it onto others. This creates space for a culture of accountability and a compassionate conversation with our shadow as an integral part of the work of being in this world. It transforms the myth of ‘what was done’ to ‘what I’ve done’, and perhaps becomes a lifelong conversation with our courageous and compassionate inner voice: ‘This is what was done to me. Now, what can be done with it?’ To opt out of a system that not only permits but welcomes this denial of our demons is an act of profound individual and societal autonomy. It serves as an affirmation that we’re no longer willing to be accomplices to lying to ourselves and others; and, perhaps more vitally, that we decline to be complicit in the injustice of the status quo. Then, armed with this existential awareness of who we really are, we can begin to chart a course for a different kind of beings. We can let our actions become less reactive and more reflective, our behaviors less governed by dark assumptions, and conscious biases – and more the results of a holistic sense of self. In this emerging reality, autonomy and identity are no longer ideals we strive to achieve but realities we actively embody. They are a commitment to being human with grace, intentionality, and an intense dedication to becoming more fully human as individuals and a larger society. If we and create systems that nurture our deepest values, we unshackle ourselves from the painful inescapability of separate selves, and liberate ourselves to co-create a world in which human beings live their differences together in safety and dignity. pay attention to our demons,

Can Humanity's Systems Serve as Material Enablers for Evasion?

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency Coherent interconversion between microwave and optical frequencies can serve as both classical and quantum interfaces for computing, communication, and sensing. Here, we present a compact microwave-optical transducer based on monolithic integration of piezoelectric actuators atop silicon nitride photonic circuits. Such an actuator directly couples microwave signals to a high-overtone bulk acoustic resonator defined by the suspended silica cladding of the optical waveguide core, which leads to enhanced electromechanical and optomechanical couplings. At room temperature, this triply resonant piezo-optomechanical transducer achieves an off-chip photon number conversion efficiency of 1.6 × 10−5 over a bandwidth of 25 MHz at an input pump power of 21 dBm. The approach is scalable in manufacturing and, unlike existing electro-optic transducers, does not rely on superconducting resonators. As the transduction process is bidirectional, we further demonstrate synthesis of microwave pulses from a purely optical input. Combined with the capability of leveraging multiple acoustic modes for transduction, the present platform offers prospects for building frequency-multiplexed qubit interconnects and for microwave photonics at large. I. INTRODUCTION Modern data centers have seen rapidly increasing traffic that motivates an overhaul of existing network infrastructures. As optical fibers support nearly lossless transport and high bandwidths, twisted-pair copper deployment has shrunk in favor of optical interconnects. Energy-efficient optical transceivers [1] and optical network architectures [2] are being explored in parallel to accommodate emerging data- and resource-intensive applications. In an analogous fashion, processing quantum information in superconducting circuits and networking via photonic interconnects has been envisioned as an effective strategy to address the scalability challenges in advancing quantum technologies [3–5]. The scheme, featuring the full universal set of microwave quantum gates [6] with vanishing thermal occupancy and loss of the optical channels, calls for the development of microwaveoptical transducers to bridge the two energy scales that differ by more than four orders of magnitude. Aside from facilitating the networking of remote quantum processors, these transducers may enable fully optical control and readout of microwave qubits [7, 8]. The subsequent replacement of coaxial lines bridging room temperature and cryogenic environments by optical fibers is expected to significantly ease the space and heat load constraints in dilution refrigerators, opening up a path toward upscaling processor units housed in a single fridge. Efficient frequency conversion requires a nonlinear interaction stronger than the coupling to loss channels. The highest conversion efficiency to date has been achieved using an electro-optomechanical approach [9]. There, a silicon nitride membrane interacts with an optical mode of a free-space Fabry-Pérot cavity via radiation pressure and simultaneously serves as the top plate of a capacitor, parametrically coupling the mechanics to the microwave resonator. By virtue of the high resonator quality factors and pump power handling capability, 47% of input photons can be interconverted between optical and microwave domains [10], approaching the 50% efficiency required for attaining finite quantum capacity [11]. This highly efficient transducer has enabled optical dispersive qubit readout with negligible excess backaction [12]. The low-frequency (MHz) mechanical intermediary nevertheless limits the transduction bandwidth and leads to appreciable added noise even at dilution refrigerator temperature. To address these drawbacks, piezo-optomechanical transducers based on optomechanical crystals (OMC) have been developed [13–22], where a tightly confined high-frequency (GHz) mechanical mode and a co-localized optical mode can interact at a vacuum optomechanical coupling rate g0 ∼ 2π × 500 kHz. The trade-off lies instead in the sophistication required for microwave-phonon wave matching that significantly increases design complexity, as well as thermo-optic instability that constrains the intra-cavity photon number. An on-chip efficiency of 5% has recently been reported on such a platform [20]. However, low thermal conductance of these suspended quasi-one-dimensional structures hinders correlated microwave-optical photon pair generation at a practical rate. Cavity electro-optic modulators constitute a conceptually simpler approach where Pockels effect directly mediates microwave-optical interaction [23–25]. On-chip realizations employing planar superconducting microwave resonators, benefited from the deep sub-wavelength mode volume of the vacuum electric field, to reach single photon electro-optic coupling rates g0 ∼ 2π × 1 kHz [26– 29]. However, the material science of χ (2) crystals poses additional challenges. Photorefractive effects—observed, for instance, in LiNbO3 thin films [30]—hamper optical power handling, while piezoelectric loss and scattered optical photons degrade the quality factor of superconducting resonators [31, 32]. The difficulty of producing smooth sidewall surfaces in the workhorse Pockels material, LiNbO3 , through dry etching results in propagation losses an order of magnitude above the absorption limit [33]. As a result, the maximum on-chip efficiency achieved with integrated electro-optic transducers exceeds just 2% [26]. In the spirit of Ref. [9], a bulk transducer comprising a mm-size mechanically polished LiNbO3 whispering gallery mode (WGM) resonator coupled to a 3D superconducting microwave cavity has proved competitive, trading g0 for improved power handling and quality factors [34, 35]. Taking one step further with pulsed optical pumping, the system has demonstrated not only a hallmark 14.4% total efficiency but also electro-optic dynamical backaction [36, 37] and microwave-optical quadrature entanglement [38]. With the aforementioned design trade-offs in mind, we present a new piezo-optomechanical transducer based solely on wafer-scale, CMOS-compatible fabrication processes. We utilize a low-loss silicon nitride (Si3N4 ) photonic molecule, as well as multiple GHz high-overtone bulk acoustic resonances (HBAR) parametrically coupled to the optical modes. Endowed with power handling capabilities superior to existing integrated transducers, the device fits compactly within a 100 µm-by-50 µm footprint, in contrast to state-of-the-art bulk designs [10, 36]. In addition, the transduction HBAR modes are more readily coupled to the microwave signal, unlike OMC transducers. We demonstrate bidirectional microwave-optical transduction with a bandwidth of 25 MHz and total efficiency up to 1.6 × 10−5 by pumping with 21 dBm of off-chip optical power in continuous-wave (CW) operation. The device is also characterized with a pulsed optical pump, which constitutes the first step toward optical control and readout of qubits, as well as heralded microwave-optical photon pair generation. The simple design, ease of fabrication, robust operation, and compact form factor anticipate wide applicability in quantum technologies and microwave photonics at large.

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