Make (www.makerstage.com) is a digital manufacturing platform company in San Francisco. Get 3D printed or CNC machined parts in record fast lead times through our decentralized network of machines across America & Asia.

Make

Make is a conceptually simple build-system, and it is packaged with major Linux distributions. However, Make should rarely be your build-system of choice when starting a new C++ project. That’s a bold claim, so here’s why… Writing a Large, Correct Makefile is Really Hard The dependency graph of a C++ library is actually quite complex. It must be rebuilt every time a linked object-file changes… But an object-file should be rebuilt every time its translation-unit changes, or the compilation flags change, or if any of its included headers change, or if the compiler changes… You can query this information using the compiler and encode all of this in a Makefile, but it’s going to get really verbose. There’s a high chance that you will miss a dependency. If this happens, then you might have stale artefacts after a change. Cue a clean build! No Sandboxing The lack of sandboxing is the root problem. Make has a dependency graph, but it does not actually enforce it. What this means is that a build-rule can read a file even if it is not declared as a dependency in Make! Caches are Not Portable Make does not allow you to share build artefacts across a network, so everyone on your development team will be performing the same build-steps over and over! Yes, you can use CCache, but that is not general (only compiler calls are cached) and it isn’t provided out-of-the-box. No Language Abstractions are Provided Make’s simplicity is a big strength, but it is also a major weakness. Make doesn’t provide any language-specific abstractions, so you have to write them yourself. This can get very tedious, and the odds of making a mistake are high. This also goes for tests, deployments and so on. You need to build everything yourelf. Timestamps, Not Hashes In determining when to rebuild, Make uses the timestamps of build inputs rather than their hashes. This results in many unnecessary rebuilds when a file has been touched, but has not actually changed. If this happens deep in the dependency tree, then the rebuild will take a long time! So When Should You Use Make? It’s not all bad; Make still has its place as a bootstrapping tool. If you have extreme dependency requirements (for example you are building Linux from source), then you need something simple and self-contained. But for most developers this just isn’t the case. We already fetch Clang, GCC and Visual Studio as prebuilt binaries, so why not download a prebuilt build-system? Since You’re Here… We recently announced , a new tool to optimize your C/C++ build scripts. ! BuildInfer Take a look

Why Not Make?

RTV, Indonesia's leading broadcasting network, is thrilled to announce the return of its beloved children's musical theater series, now under the new name "Swish." This highly anticipated revival promises to deliver an enchanting blend of entertainment and education for young viewers nationwide. Originally conceived as a special program to commemorate RTV's third anniversary, "Pesta Sahabat" quickly captured the hearts of audiences with its unique fusion of elements from RTV's cherished children's events, Fun Time and Dubi Dubi Dam. The overwhelming positive response from the community, coupled with the popularity of episodes like "Cinta Indonesia," paved the way for "Friend's Party" to evolve into a full-fledged musical theater series, airing monthly. "Swish" embarked on its musical journey with the debut episode "Ceria Zone," introducing its own theme songs and captivating narratives. From heartwarming tales like "Mother's Love," broadcasted on Mother's Day, to celebratory episodes marking occasions such as Valentine's Day and Kartini Day, the series has resonated deeply with viewers of all ages. In addition to celebrating Indonesia's cultural richness, "Swish" has also paid homage to significant national events, with special episodes commemorating Indonesian Independence Day and National Children's Day. However, the production faced a hiatus in 2020 due to the challenges posed by the COVID-19 pandemic. Now, in response to overwhelming demand, RTV is thrilled to announce the revival of "Swish," promising to reignite the magic for both existing and new audiences. With its vibrant storytelling, catchy tunes, and valuable life lessons, the reimagined series aims to bring joy and inspiration to generations to come. As Indonesia navigates through uncertain times, "Swish" emerges as a symbol of hope and optimism, reaffirming the importance of imagination and creativity in uniting communities. Stay tuned as "Swish" embarks on a new chapter, spreading laughter and learning to every corner of the nation. About RTV: RTV's Musical Theater refers to a theatrical production company or venue that specializes in staging musicals. The acronym "RTV" could stand for a specific theater company or venue, but without more context, it's difficult to determine its exact meaning. However, in the context of the paragraph I provided, "RTV" is simply a placeholder for the name of the theater where "Swish Returns" is being performed. Contact Information:
Email: info@rtv.com
Number: +1 (565) 987-6543 RTV, Indonesia's leading broadcasting network, is thrilled to announce the return of its beloved children's musical theater series, now under the new name "Swish." This highly anticipated revival promises to deliver an enchanting blend of entertainment and education for young viewers nationwide. Originally conceived as a special program to commemorate RTV's third anniversary, "Pesta Sahabat" quickly captured the hearts of audiences with its unique fusion of elements from RTV's cherished children's events, Fun Time and Dubi Dubi Dam. The overwhelming positive response from the community, coupled with the popularity of episodes like "Cinta Indonesia," paved the way for "Friend's Party" to evolve into a full-fledged musical theater series, airing monthly. "Swish" embarked on its musical journey with the debut episode "Ceria Zone," introducing its own theme songs and captivating narratives. From heartwarming tales like "Mother's Love," broadcasted on Mother's Day, to celebratory episodes marking occasions such as Valentine's Day and Kartini Day, the series has resonated deeply with viewers of all ages. In addition to celebrating Indonesia's cultural richness, "Swish" has also paid homage to significant national events, with special episodes commemorating Indonesian Independence Day and National Children's Day. However, the production faced a hiatus in 2020 due to the challenges posed by the COVID-19 pandemic. Now, in response to overwhelming demand, RTV is thrilled to announce the revival of "Swish," promising to reignite the magic for both existing and new audiences. With its vibrant storytelling, catchy tunes, and valuable life lessons, the reimagined series aims to bring joy and inspiration to generations to come. As Indonesia navigates through uncertain times, "Swish" emerges as a symbol of hope and optimism, reaffirming the importance of imagination and creativity in uniting communities. Stay tuned as "Swish" embarks on a new chapter, spreading laughter and learning to every corner of the nation. About RTV: RTV's Musical Theater refers to a theatrical production company or venue that specializes in staging musicals. The acronym "RTV" could stand for a specific theater company or venue, but without more context, it's difficult to determine its exact meaning. However, in the context of the paragraph I provided, "RTV" is simply a placeholder for the name of the theater where "Swish Returns" is being performed. About RTV: Contact Information:
Email: info@rtv.com Number: +1 (565) 987-6543 info@rtv.com

Swish Returns: RTV's Musical Theater Spectacle Makes a Comeback

The buzz surrounding AI chatbots remains strong. Some have already tried using ChatGPT, Copilot, Gemini, ChatOn, or Claude for personal and work purposes, while others keep postponing their encounter with these cutting-edge technologies. If you belong to the latter group, this article will become your helpful guide to effectively using AI in your routine tasks. In the beginning, you should choose a chatbot that will best suit your purposes. Chatbots can be either universal (helping you with any task, from writing a business letter to creating a complaint to an airline) or more specialized ones. For instance, Chatsoinc is focused on content creation and will be most helpful to copywriters and other creative professionals. Another example of a specialized chatbot is Character.ai. It’s designed for those feeling lonely and seeking companionship in online conversations. So, if you're new to chatbots, I suggest starting with either ChatGPT or ChatOn. The first one is convenient for computer use, while ChatOn, built on ChatGPT, was optimized for mobile use. For example, it has a number of additional functions, such as creating summaries of PDF files and YouTube videos. Tip 1. Use Ready-Made Prompts and Be Creative Many chatbots already have pre-made prompts on the main screen, ready for your use. Start with those most relevant to you, but don't limit your creativity. Whether you're crafting a congratulatory message for a colleague's anniversary or composing a bedtime fairy tale for your child, the chatbot is there for you. Even if you’re getting ready for a presentation, you can ask the chatbot to help you create a plan and prepare information for each slide. Moreover, asking the bot for a certain number of tips or examples may be useful. For example, you can write, "Name the 5 most popular destinations for summer trips." Also, you may try limiting the number of words in the bot's responses by entering such a prompt: "Summarize "Thinking, Fast and Slow" by Daniel Kahneman in 100 words." Tip 2. Ask Follow-Up Questions and Give More Details Even while talking with a real person, you should ask clarifying questions to get a complete answer. So, do the same with the chatbot. For example, you ask the bot to help you develop a cover letter for a job application. You write, "Give me some tips on creating a cover letter." But if the answers seem too general, you can tell the bot more about the position you’re applying for, the company, and your background. You may enter this prompt instead: "Write a cover letter for a QA manager position. I'm a 5-year experienced QA manager with strong leadership skills." Tip 3. Ask the Bot to Rewrite the Text If the generated text seems too formal or generic, you can always ask the bot to rewrite it. In a new request, you can specify the desired tone of voice, describe the situation the text will be used for, or add some more details. The prompt might look like this: "Rewrite the text of an email in an optimistic tone, using 100 words. Add greetings with the summer season." Tip 4. Use Additional Chatbot Features Most chatbots allow you to ask a question not only by typing but also by dictating it or by uploading a photo. For example, you get a paper letter from a business partner and want to prepare a response using a chatbot. You don't need to manually type all the text; you can simply take a photo. The optical content recognition (OCR) function will recognize the text and generate a response based on it. Bots like ChatOn or Claude allow you to upload a PDF document and work with it. Some chatbots offer the possibility to immediately generate not only text but also images, which is especially useful for creating presentations or posting on social media. I hope these tips have helped you debunk the myth that using a chatbot is complex and requires special skills. On the contrary, a chatbot is a universal assistant for both personal and professional tasks that’ll streamline your routine activities and save you time. The buzz surrounding AI chatbots remains strong. Some have already tried using ChatGPT, Copilot, Gemini, ChatOn, or Claude for personal and work purposes, while others keep postponing their encounter with these cutting-edge technologies. If you belong to the latter group, this article will become your helpful guide to effectively using AI in your routine tasks. The buzz surrounding AI chatbots remains strong. Some have already tried using ChatGPT, Copilot, Gemini, ChatOn, or Claude for personal and work purposes, while others keep postponing their encounter with these cutting-edge technologies. If you belong to the latter group, this article will become your helpful guide to effectively using AI in your routine tasks. In the beginning, you should choose a chatbot that will best suit your purposes. Chatbots can be either universal (helping you with any task, from writing a business letter to creating a complaint to an airline) or more specialized ones. For instance, Chatsoinc is focused on content creation and will be most helpful to copywriters and other creative professionals. Another example of a specialized chatbot is Character.ai . It’s designed for those feeling lonely and seeking companionship in online conversations. Chatsoinc Character.ai So, if you're new to chatbots, I suggest starting with either ChatGPT or ChatOn . The first one is convenient for computer use, while ChatOn, built on ChatGPT, was optimized for mobile use. For example, it has a number of additional functions, such as creating summaries of PDF files and YouTube videos. ChatGPT ChatOn Tip 1. Use Ready-Made Prompts and Be Creative Tip 1. Use Ready-Made Prompts and Be Creative Many chatbots already have pre-made prompts on the main screen, ready for your use. Start with those most relevant to you, but don't limit your creativity. Whether you're crafting a congratulatory message for a colleague's anniversary or composing a bedtime fairy tale for your child, the chatbot is there for you. Even if you’re getting ready for a presentation, you can ask the chatbot to help you create a plan and prepare information for each slide. Moreover, asking the bot for a certain number of tips or examples may be useful. For example, you can write, "Name the 5 most popular destinations for summer trips." Also, you may try limiting the number of words in the bot's responses by entering such a prompt: "Summarize "Thinking, Fast and Slow" by Daniel Kahneman in 100 words." Tip 2. Ask Follow-Up Questions and Give More Details Tip 2. Ask Follow-Up Questions and Give More Details Even while talking with a real person, you should ask clarifying questions to get a complete answer. So, do the same with the chatbot. For example, you ask the bot to help you develop a cover letter for a job application. You write, "Give me some tips on creating a cover letter." But if the answers seem too general, you can tell the bot more about the position you’re applying for, the company, and your background. You may enter this prompt instead: "Write a cover letter for a QA manager position. I'm a 5-year experienced QA manager with strong leadership skills." Tip 3. Ask the Bot to Rewrite the Text Tip 3. Ask the Bot to Rewrite the Text If the generated text seems too formal or generic, you can always ask the bot to rewrite it. In a new request, you can specify the desired tone of voice, describe the situation the text will be used for, or add some more details. The prompt might look like this: "Rewrite the text of an email in an optimistic tone, using 100 words. Add greetings with the summer season." Tip 4. Use Additional Chatbot Features Tip 4. Use Additional Chatbot Features Most chatbots allow you to ask a question not only by typing but also by dictating it or by uploading a photo. For example, you get a paper letter from a business partner and want to prepare a response using a chatbot. You don't need to manually type all the text; you can simply take a photo. The optical content recognition (OCR) function will recognize the text and generate a response based on it. Bots like ChatOn or Claude allow you to upload a PDF document and work with it. Claude Some chatbots offer the possibility to immediately generate not only text but also images, which is especially useful for creating presentations or posting on social media. I hope these tips have helped you debunk the myth that using a chatbot is complex and requires special skills. On the contrary, a chatbot is a universal assistant for both personal and professional tasks that’ll streamline your routine activities and save you time.

How to Write Effective Prompts and Make the Most Out of Using AI

Of late, there has been quite a lot of interest in increasing/improving the context of LLMs. One good example is the infini-attention paper from Google in early April. While it was more of tweaking the model architecture to improve the context, Microsoft came back with an answer in late April but with a data-driven solution. The paper is titled, “Make your LLM Fully Utilize the Context”. There are mainly two contributions from the paper: A data-driven approach that introduces a novel pipeline to synthesize a novel dataset to train LLMs to cleverly use long contexts. It’s dubbed “Information-Intensive Training” (IN2 in short).


A novel evaluation approach named Various Long-Context (VAL Probing) uses 3 context styles — document, code, and structured data and 3 retrieval patterns — forward, backward, and bi-directional. In this article, let's deep-dive into both the simple ideas followed by some evaluations. Visual Explanation If you are someone who likes audio-visual explanations, here is a video version of this article that explains the paper: https://youtu.be/SA21d_4QQSU?feature=shared&embedable=true IN2 Training The first main idea of the paper is a novel data-driven training approach named IN2 training. There are two types of IN2 training proposed in the paper. Fine-grained Information Awareness. In this case, we first take a raw text dataset and split it into 128 tokens and we call each of this set of 128 tokens a “segment.” We then prompt a powerful LLM model like GPT-4 with an instruction I_f in such a way that the answer a_i to this question q_i requires information in the segment s_i to provide the answer. We then combine the segment s_i with several other segments by shuffling it along with the other segments to create the long context L_i. Figure taken from the paper (figure 2). Integration and Reasoning of Information. There can be situations where the context is located in more than one segment (more than 128 tokens). So we consider a set [s_i] instead of a single s_i. This creates a multi-hop Q&A scenario where the LLM has to do at least 2 passes of QAs. We modify the instruction I_f in the prompt to the LLM accordingly. Figure taken from the paper (figure 2) In both scenarios, the authors vary the long context length L_i from 4K to 32K to prevent length bias in training. During IN2 instruction fine-tuning, the long contexts and questions become input to the LLM, and the answer is shown as the output for supervised training. They fine-tune the Mistral-7B-Instruct-v2 model and call the new model FILl-in-the-Middle(FILM-7B). VAL Probing VArious Long-context Probing or VAL Probing was introduced to overcome some of the problems with the current LLMs with retrieving from long contexts. So how do we even test the long context retrieval capability of any given model? The answer lies with “Needle in a haystack” (NIHS) test. It can be accessed here. Needle in a Haystack NIHS was proposed to pressure test LLMs. It’s a simple analysis to test the in-context retrieval ability of long-context LLMs. The test can be run by following the below 3 steps: Place a random fact or statement (the ‘needle’) in the middle of a long context window (the ‘haystack’).


Ask the model to retrieve this statement.


Iterate over various document depths (where the needle is placed) and context lengths to measure performance. The biggest problem with NIHS these days is that the recent models perform too well making the test less appealing. For example, see below the test result of the proposed FILM-7b model NIHS test on the proposed FILM-7b model The results are near perfect indicating that we really need a new testing approach and so, giving birth to VAL Probing. VAL Probing VAL probing considers 3 data types such as documents, structured data, and code. It also considers three retrieval types — forward, backward, and bi-directional. The below figure from the paper gives examples of all three data types and retrieval directions. Results For evaluation, they used the LongBench evaluation scripts and reported ROGUE-L for summarization and F1-scores for all other tasks. The first problem addressed by the paper is the “Lost-in-the-middle” problem. As shown in the below figure, we can see that there is no “U” shaped dip with the FILM-7b model (in orange) compared to Mistral-7B (green), and even other long-context sota models like LongAlign indicating the strength of FILM-7b. On top of addressing the Lost-in-the-middle problem, FILM-7B seems to outperform other recent models quantitatively too. They show the results of averaging the evaluation metric(Avg) and min-max gap of the evaluation metric (Gap) which is the difference between the minimum and maximum values obtained. In short, the model is comparable to GPT-4’s performance! Summary & Conclusion This paper from Microsoft proposes a simple data-driven solution to addressing the long-context problem in LLMs. On top of proposing a data generation pipeline, it also proposes and tests the usage of the novel VAL Probing that encompasses 3 data types and 3 retrieval methods. Let’s hope this is just the dawn of a new generation of LLMs that are no longer limited by the context length. See you in my next… Of late, there has been quite a lot of interest in increasing/improving the context of LLMs. One good example is the infini-attention paper from Google in early April. While it was more of tweaking the model architecture to improve the context, Microsoft came back with an answer in late April but with a data-driven solution. The paper is titled, “ Make your LLM Fully Utilize the Context ”. infini-attention paper infini-attention paper Make your LLM Fully Utilize the Context Make your LLM Fully Utilize the Context Make your LLM Fully Utilize the Context There are mainly two contributions from the paper: A data-driven approach that introduces a novel pipeline to synthesize a novel dataset to train LLMs to cleverly use long contexts. It’s dubbed “Information-Intensive Training” (IN2 in short). A novel evaluation approach named Various Long-Context (VAL Probing) uses 3 context styles — document, code, and structured data and 3 retrieval patterns — forward, backward, and bi-directional. A data-driven approach that introduces a novel pipeline to synthesize a novel dataset to train LLMs to cleverly use long contexts. It’s dubbed “Information-Intensive Training” (IN2 in short). A data-driven approach that introduces a novel pipeline to synthesize a novel dataset to train LLMs to cleverly use long contexts. It’s dubbed “ Information-Intensive Training ” (IN2 in short). Information-Intensive Training A novel evaluation approach named Various Long-Context (VAL Probing) uses 3 context styles — document, code, and structured data and 3 retrieval patterns — forward, backward, and bi-directional. A novel evaluation approach named Various Long-Context ( VAL Probing ) uses 3 context styles — document, code, and structured data and 3 retrieval patterns — forward, backward, and bi-directional. VAL Probing In this article, let's deep-dive into both the simple ideas followed by some evaluations. Visual Explanation If you are someone who likes audio-visual explanations, here is a video version of this article that explains the paper: https://youtu.be/SA21d_4QQSU?feature=shared&embedable=true https://youtu.be/SA21d_4QQSU?feature=shared&embedable=true IN2 Training The first main idea of the paper is a novel data-driven training approach named IN2 training . There are two types of IN2 training proposed in the paper. data-driven training IN2 training Fine-grained Information Awareness. In this case, we first take a raw text dataset and split it into 128 tokens and we call each of this set of 128 tokens a “segment.” We then prompt a powerful LLM model like GPT-4 with an instruction I_f in such a way that the answer a_i to this question q_i requires information in the segment s_i to provide the answer. We then combine the segment s_i with several other segments by shuffling it along with the other segments to create the long context L_i. Fine-grained Information Awareness. In this case, we first take a raw text dataset and split it into 128 tokens and we call each of this set of 128 tokens a “segment.” We then prompt a powerful LLM model like GPT-4 with an instruction I_f in such a way that the answer a_i to this question q_i requires information in the segment s_i to provide the answer. We then combine the segment s_i with several other segments by shuffling it along with the other segments to create the long context L_i . Fine-grained Information Awareness. I_f a_i q_i s_i s_i L_i Figure taken from the paper (figure 2). Integration and Reasoning of Information. There can be situations where the context is located in more than one segment (more than 128 tokens). So we consider a set [s_i] instead of a single s_i. This creates a multi-hop Q&A scenario where the LLM has to do at least 2 passes of QAs. We modify the instruction I_f in the prompt to the LLM accordingly. Integration and Reasoning of Information. There can be situations where the context is located in more than one segment (more than 128 tokens). So we consider a set [ s_i ] instead of a single s_i. This creates a multi-hop Q&A scenario where the LLM has to do at least 2 passes of QAs. We modify the instruction I_f in the prompt to the LLM accordingly. Integration and Reasoning of Information. s_i s_i. I_f Figure taken from the paper (figure 2) In both scenarios, the authors vary the long context length L_i from 4K to 32K to prevent length bias in training. During IN2 instruction fine-tuning, the long contexts and questions become input to the LLM, and the answer is shown as the output for supervised training. They fine-tune the Mistral-7B-Instruct-v2 model and call the new model FILl-in-the-Middle( FILM-7B ). L_i FILM-7B VAL Probing VArious Long-context Probing or VAL Probing was introduced to overcome some of the problems with the current LLMs with retrieving from long contexts. So how do we even test the long context retrieval capability of any given model? The answer lies with “Needle in a haystack” (NIHS) test. It can be accessed here . here here Needle in a Haystack NIHS was proposed to pressure test LLMs. It’s a simple analysis to test the in-context retrieval ability of long-context LLMs. The test can be run by following the below 3 steps: Place a random fact or statement (the ‘needle’) in the middle of a long context window (the ‘haystack’). Ask the model to retrieve this statement. Iterate over various document depths (where the needle is placed) and context lengths to measure performance. Place a random fact or statement (the ‘needle’) in the middle of a long context window (the ‘haystack’). Place a random fact or statement (the ‘needle’) in the middle of a long context window (the ‘haystack’). Ask the model to retrieve this statement. Ask the model to retrieve this statement. Iterate over various document depths (where the needle is placed) and context lengths to measure performance. Iterate over various document depths (where the needle is placed) and context lengths to measure performance. The biggest problem with NIHS these days is that the recent models perform too well making the test less appealing. For example, see below the test result of the proposed FILM-7b model NIHS test on the proposed FILM-7b model The results are near perfect indicating that we really need a new testing approach and so, giving birth to VAL Probing. VAL Probing VAL probing considers 3 data types such as documents, structured data, and code. It also considers three retrieval types — forward, backward, and bi-directional. The below figure from the paper gives examples of all three data types and retrieval directions. Results For evaluation, they used the LongBench evaluation scripts and reported ROGUE-L for summarization and F1-scores for all other tasks. The first problem addressed by the paper is the “Lost-in-the-middle” problem. As shown in the below figure, we can see that there is no “U” shaped dip with the FILM-7b model (in orange) compared to Mistral-7B (green), and even other long-context sota models like LongAlign indicating the strength of FILM-7b. On top of addressing the Lost-in-the-middle problem, FILM-7B seems to outperform other recent models quantitatively too. They show the results of averaging the evaluation metric(Avg) and min-max gap of the evaluation metric (Gap) which is the difference between the minimum and maximum values obtained. In short, the model is comparable to GPT-4’s performance! Summary & Conclusion This paper from Microsoft proposes a simple data-driven solution to addressing the long-context problem in LLMs. On top of proposing a data generation pipeline, it also proposes and tests the usage of the novel VAL Probing that encompasses 3 data types and 3 retrieval methods. Let’s hope this is just the dawn of a new generation of LLMs that are no longer limited by the context length. See you in my next…

How to Make Your LLM Fully Utilize the Context

Auto-Increment Columns in Databases: A Simple Trick That Makes a Big Difference

In this session of our "Behind the Startup" series, we sit down with Zad, the visionary founder of OpenCred, to explore the revolutionary concept of tokenizing trust in the Web3 environment. As a Stanford-certified expert in blockchain and cryptography, Zad discusses the inception of OpenCred, its unique approach to digital identity through eSFT tokens, and its ambitious mission to reshape social and economic interactions online. Ishan Pandey: Hi Zad, great to have you here for our "Innovators in Web3" series. Can you tell us about your background and what led you to create OpenCred? Zad: As a postgraduate in Blockchain Strategy and Stanford-certified cryptography expert, I have spent years conducting cutting-edge research in asset tokenization, DID, and cybersecurity. Trust is actually one of the most difficult things to measure and quantify, no matter “IRL” or online. Now with blockchain and tokens we finally have the tools to tackle this problem in a novel way. I founded OpenCred to bring tokenized trust to the Web3 industry, unleash the hidden potential that lies in social relationships, and eventually enable the establishment of a decentralized society. Ishan Pandey: Could you start by explaining what OpenCred is and what specific gap in the Web3 space it aims to fill? Zad: OpenCred is a decentralized Web3 credit protocol that applies a new token standard called “eSFT” to tokenize trust relationships into tangible and quantifiable economic assets. Our vision is to empower users to monetize their online activity while guaranteeing data privacy and ownership. By providing a blockchain-based solution for establishing and measuring trust relationships, OpenCred fills a crucial gap in the Web3 ecosystem: enabling trustworthy interactions and collaboration without relying on centralized intermediaries or KYC. Ishan Pandey: In the deck, you mention that OpenCred introduces a groundbreaking trust value (credit) quantification for Web3. Can you elaborate on how this works and why it's important? Zad: OpenCred aggregates both on-chain (e.g., DeFi TVL, smart contract interactions) and off-chain data (e.g., social media followers and interactions) to tokenize trust in the form of CRED tokens. This means users can increase their personal trust value by increasing their online activity and footprint. Each CRED is unique to the user and contains a number of fungible Score Tokens (ST) that determine the value of a user’s CRED. By thus providing a measurable and objective way to assess trustworthiness, OpenCred enables secure, traceable, and efficient trust-based interactions in various Web3 applications, such as social media, e-commerce, DeFi, and gaming. This groundbreaking mechanism is crucial for fostering a trustworthy environment in the digital space and enabling countless new use cases in many areas from B2B to B2C to C2B to C2C. Ishan Pandey: The CRED token seems to be a key component of the OpenCred ecosystem. Can you explain its dual nature as a semi-fungible token and how it represents both identity and trustworthiness? And how does this design benefit users of your platform? Zad: CRED tokens are a groundbreaking innovation of OpenCred, creating a new token standard we call embedded semi-fungible token (eSFT) that combines both non-fungible (unique) and fungible (interchangeable) properties. The non-fungible part of the token represents a user's unique identity within the OpenCred protocol, ensuring that each user has a distinct and verifiable presence. On the other hand, the fungible part of the token quantifies the user's trustworthiness, which is determined by their interactions (on-chain & off-chain) and the amount of CRED tokens they receive from others. This design benefits users by empowering them to monetize their online activity and social relationships. Moreover, by tokenizing trust and making it quantifiable and tangible, we are creating a new kind of asset that simply has not existed before, thereby creating huge value. Ishan Pandey: How does sending CRED between users facilitate the building of a trustable and quantifiable social graph in Web3? Zad: When a user sends CRED tokens to another user, they are essentially establishing a trust relationship that is recorded on the blockchain. This transaction signifies that the sender trusts the recipient to a certain degree, based on their interactions, reputation, or other factors. As users continue to interact and send CRED tokens to each other, a dynamic, evolving on-chain social graph emerges. The social graph is quantifiable and traceable. This quantification of trust enables users to make informed decisions when interacting with others, as they can assess the trustworthiness of potential collaborators, partners, or service providers based on their position in the social graph. Ishan Pandey: With OpenCred positioning itself as a provider of "Trust as a Service," how do you ensure the reliability and security of the trust metrics generated by your protocol? Zad: There is a clear friction between incentivizing users on the one hand and ensuring the authenticity of trust relationships on the other hand. We are aware of this and will tackle this problem in multiple ways. First of all, we will connect the sending of CRED with some actions such as “follow”, “like”, etc., so that the transfer of CRED corresponds with the establishment of a meaningful trust relationship. Secondly, we will implement punishments for sybil attackers and other malicious behavior. Additionally, OpenCred implements a multi-layered governance model that involves community participation and decentralized decision-making. This approach helps to maintain the integrity and fairness of the trust metrics, as the community can collectively monitor and flag any suspicious activities or attempts to manipulate trust scores. To further enhance the reliability of trust metrics, OpenCred incorporates a reputation staking mechanism, where users are required to stake a portion of our governance tokens to participate in trust-building activities. OpenCred places a strong emphasis on user education and awareness. By providing clear guidelines, tutorials, and best practices for using the trust protocol. Ishan Pandey: How do you plan to drive adoption and grow the OpenCred ecosystem, particularly in terms of attracting both users and developers? Zad: OpenCred actively seeks partnerships and collaborations with established Web3 projects across various sectors, such as social media, e-commerce, gaming, and decentralized finance (DeFi). We currently have established partnerships with over 10 Web3 projects with close to 1 million daily active users ready to onboard our protocol once it is live. To attract developers, OpenCred will provide a range of tools, SDKs, and documentation to facilitate the integration of its trust protocol into various applications. The protocol will offer developer grants, hackathons, and bounty programs to incentivize the creation of innovative trust-based solutions. By fostering a vibrant developer community and providing the necessary resources and support, OpenCred aims to accelerate the development of a diverse range of dApps that leverage its trust infrastructure. OpenCred's tokenomics model is designed to incentivize user participation and ecosystem growth. The governance token serves as the primary means of interaction within the OpenCred ecosystem, used for establishing trust relationships, staking, and accessing trust-based services. The protocol will implement a reward system that incentivizes users to actively participate in trust-building activities, such as providing accurate trust assessments, staking tokens, and contributing to the network's security. These incentives will drive user adoption and encourage long-term engagement with the OpenCred ecosystem. Don’t forget to like and share the story! 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. #DYOR. In this session of our "Behind the Startup" series, we sit down with Zad, the visionary founder of OpenCred, to explore the revolutionary concept of tokenizing trust in the Web3 environment. As a Stanford-certified expert in blockchain and cryptography, Zad discusses the inception of OpenCred, its unique approach to digital identity through eSFT tokens, and its ambitious mission to reshape social and economic interactions online. Ishan Pandey: Hi Zad, great to have you here for our "Innovators in Web3" series. Can you tell us about your background and what led you to create OpenCred? Ishan Pandey: Zad: As a postgraduate in Blockchain Strategy and Stanford-certified cryptography expert, I have spent years conducting cutting-edge research in asset tokenization, DID, and cybersecurity. Trust is actually one of the most difficult things to measure and quantify, no matter “IRL” or online. Now with blockchain and tokens we finally have the tools to tackle this problem in a novel way. I founded OpenCred to bring tokenized trust to the Web3 industry, unleash the hidden potential that lies in social relationships, and eventually enable the establishment of a decentralized society. Zad: Ishan Pandey: Could you start by explaining what OpenCred is and what specific gap in the Web3 space it aims to fill? Ishan Pandey: Zad: OpenCred is a decentralized Web3 credit protocol that applies a new token standard called “eSFT” to tokenize trust relationships into tangible and quantifiable economic assets. Our vision is to empower users to monetize their online activity while guaranteeing data privacy and ownership. By providing a blockchain-based solution for establishing and measuring trust relationships, OpenCred fills a crucial gap in the Web3 ecosystem: enabling trustworthy interactions and collaboration without relying on centralized intermediaries or KYC. Zad: Ishan Pandey: In the deck, you mention that OpenCred introduces a groundbreaking trust value (credit) quantification for Web3. Can you elaborate on how this works and why it's important? Ishan Pandey: Zad: OpenCred aggregates both on-chain (e.g., DeFi TVL, smart contract interactions) and off-chain data (e.g., social media followers and interactions) to tokenize trust in the form of CRED tokens. This means users can increase their personal trust value by increasing their online activity and footprint. Each CRED is unique to the user and contains a number of fungible Score Tokens (ST) that determine the value of a user’s CRED. By thus providing a measurable and objective way to assess trustworthiness, OpenCred enables secure, traceable, and efficient trust-based interactions in various Web3 applications, such as social media, e-commerce, DeFi, and gaming. This groundbreaking mechanism is crucial for fostering a trustworthy environment in the digital space and enabling countless new use cases in many areas from B2B to B2C to C2B to C2C. Zad: Ishan Pandey: The CRED token seems to be a key component of the OpenCred ecosystem. Can you explain its dual nature as a semi-fungible token and how it represents both identity and trustworthiness? And how does this design benefit users of your platform? Ishan Pandey: Zad: CRED tokens are a groundbreaking innovation of OpenCred, creating a new token standard we call embedded semi-fungible token (eSFT) that combines both non-fungible (unique) and fungible (interchangeable) properties. The non-fungible part of the token represents a user's unique identity within the OpenCred protocol, ensuring that each user has a distinct and verifiable presence. On the other hand, the fungible part of the token quantifies the user's trustworthiness, which is determined by their interactions (on-chain & off-chain) and the amount of CRED tokens they receive from others. This design benefits users by empowering them to monetize their online activity and social relationships. Moreover, by tokenizing trust and making it quantifiable and tangible, we are creating a new kind of asset that simply has not existed before, thereby creating huge value. Zad: Ishan Pandey: How does sending CRED between users facilitate the building of a trustable and quantifiable social graph in Web3? Ishan Pandey: Zad: When a user sends CRED tokens to another user, they are essentially establishing a trust relationship that is recorded on the blockchain. This transaction signifies that the sender trusts the recipient to a certain degree, based on their interactions, reputation, or other factors. As users continue to interact and send CRED tokens to each other, a dynamic, evolving on-chain social graph emerges. The social graph is quantifiable and traceable. This quantification of trust enables users to make informed decisions when interacting with others, as they can assess the trustworthiness of potential collaborators, partners, or service providers based on their position in the social graph. Zad: Ishan Pandey: With OpenCred positioning itself as a provider of "Trust as a Service," how do you ensure the reliability and security of the trust metrics generated by your protocol? Ishan Pandey: Zad: There is a clear friction between incentivizing users on the one hand and ensuring the authenticity of trust relationships on the other hand. We are aware of this and will tackle this problem in multiple ways. First of all, we will connect the sending of CRED with some actions such as “follow”, “like”, etc., so that the transfer of CRED corresponds with the establishment of a meaningful trust relationship. Secondly, we will implement punishments for sybil attackers and other malicious behavior. Additionally, OpenCred implements a multi-layered governance model that involves community participation and decentralized decision-making. This approach helps to maintain the integrity and fairness of the trust metrics, as the community can collectively monitor and flag any suspicious activities or attempts to manipulate trust scores. To further enhance the reliability of trust metrics, OpenCred incorporates a reputation staking mechanism, where users are required to stake a portion of our governance tokens to participate in trust-building activities. OpenCred places a strong emphasis on user education and awareness. By providing clear guidelines, tutorials, and best practices for using the trust protocol. Zad: Ishan Pandey: How do you plan to drive adoption and grow the OpenCred ecosystem, particularly in terms of attracting both users and developers? Ishan Pandey: Zad: OpenCred actively seeks partnerships and collaborations with established Web3 projects across various sectors, such as social media, e-commerce, gaming, and decentralized finance (DeFi). We currently have established partnerships with over 10 Web3 projects with close to 1 million daily active users ready to onboard our protocol once it is live. To attract developers, OpenCred will provide a range of tools, SDKs, and documentation to facilitate the integration of its trust protocol into various applications. The protocol will offer developer grants, hackathons, and bounty programs to incentivize the creation of innovative trust-based solutions. Zad: By fostering a vibrant developer community and providing the necessary resources and support, OpenCred aims to accelerate the development of a diverse range of dApps that leverage its trust infrastructure. OpenCred's tokenomics model is designed to incentivize user participation and ecosystem growth. The governance token serves as the primary means of interaction within the OpenCred ecosystem, used for establishing trust relationships, staking, and accessing trust-based services. The protocol will implement a reward system that incentivizes users to actively participate in trust-building activities, such as providing accurate trust assessments, staking tokens, and contributing to the network's security. These incentives will drive user adoption and encourage long-term engagement with the OpenCred ecosystem. Don’t forget to like and share the story! 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. #DYOR. 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. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

How OpenCred Makes Trust Tangible via Tokenization

কিভাবে ওপেনক্রেড অনলাইনে বিশ্বাস তৈরি করতে টোকেনাইজেশন ব্যবহার করে

Wie OpenCred Tokenisierung nutzt, um Online-Vertrauen aufzubauen

Cómo OpenCred utiliza la tokenización para generar confianza en línea

Comment OpenCred utilise la tokenisation pour renforcer la confiance en ligne

ओपनक्रेड ऑनलाइन विश्वास बनाने के लिए टोकनाइजेशन का उपयोग कैसे करता है

OpenCred がトークン化を利用してオンラインで信頼を築く方法

OpenCred가 토큰화를 사용하여 온라인 신뢰를 구축하는 방법

Como o OpenCred usa tokenização para construir confiança online

Как OpenCred использует токенизацию для укрепления доверия в Интернете

OpenCred Çevrimiçi Güven Oluşturmak İçin Tokenizasyonu Nasıl Kullanıyor?

Cách OpenCred sử dụng mã thông báo để xây dựng niềm tin trực tuyến

OpenCred 如何使用代币化在线建立信任

I always wondered how free apps make money. I did some research on this and found a lot of different avenues to follow. Let's jump into the ins and outs of how to earn money with free apps Background I always encountered those exciting, sometimes annoying ads and pop-ups while browsing the web. YouTube has certainly made their ads unescapable, robbing me of two minutes of desired view time. Have you thought about why they appear anyway? I certainly have. Those are some of the different monetization strategies for free apps. There are multiple other ways to monetize free apps. “Free App” Monetization Monetization is the ability to earn from your creations. App monetization is the strategy of making money off an application. Some monetization styles perform better than others. Let's learn the monetization method that works best in your situation. How to make money from an app: Different monetization strategies There are countless ways to profit from free apps. You can apply one or several monetization methods depending on your industry or the type of app. Let's get into the details: In-app advertising Do you know how YouTube makes money? Discover what is in-app advertising and how it occurs below: Apps like YouTube display banner ads via content uploaded on its platform. Despite being a popular monetization style, you may need high engagement rates to make good advertising income. Video ads You have most likely come across those ad videos that appear when viewing certain content. Video ads are more visually appealing and could attract the audience better than display ads. At the same time, you must ensure the ads do not ruin user experience and affect your monetization. There’s no way to tell where this line is but if YouTube is anything to go by then I can guess the line is far. Native advertising In-app advertising can also work by promoting other businesses, products, or services. Native advertising may also include sponsored content mixed into the app. This method may be popular if you do not want to distract your audience through other ads. In-app purchases How does Amazon make money? In-app purchases are one of them through the products or services listed on the platform. The benefits of in-app purchases are low costs, and you do not need to own the products or services. In-app purchasing is popular with e-commerce sites, dating apps, gaming platforms, and fitness apps. Monetization through this method can occur in the following ways: Paid content Paid content is information on your app, whether video, text, or downloadable that others must pay to access. Examples like e-books and courses can be offered as paid-for content despite your app being free for other services. Subscriptions Free apps may not be free forever. Free apps provide trial periods where users can use them at no cost. The aim is to lure them into paying a subscription fee once they are used to the product or service offered. Freemium model Freemium apps make money by charging users for premium features. Users have unrestricted access to the basic free version. They have to pay for advanced features. An example of this is ChatGPT, which you can access for free, but you will be charged $20 monthly for ChatGPT Plus. Data monetization Have you ever wondered why companies like Google and Facebook can provide relevant content tailored to your needs? It's because they have mastered the art of data monetization. Below are some valuable data monetization methods. User data Do you want to know how to monetize user data? If you hold a directory of people visiting your app, you can monetize it by licensing or selling it to third parties that need it. This monetization may require user approval; as unauthorized sharing can cause privacy violations. You may also use the user data to tailor your in-app products or services for maximum value creation. Location data Think of navigation apps like Waze. These apps collect crucial geo data. Businesses needing to place location-related ads rely on such apps for data collection. If your app is one of these categories, this could be a suitable monetization strategy, although you may need user consent. Targeted advertising Once you understand the type of data you hold, including age profiles, sex, preferences, and user traits, you can use it for targeted advertising. You can sell your own or a third-party product or service through targeted ads. How to choose the best free app monetization strategy There are numerous ways to monetize free apps. However, to succeed in-app monetization, you must apply the right strategy. There are plenty of tips to use when choosing the app monetization strategy. What is your business goal? It must be clear what you want in your app monetization. A monetization method like a subscription could work best if you already have a client base. If you are relatively new, you may consider an advertising revenue model. What's your target audience? Know your customers and what they want. Are they able and willing to pay for a product or service, and what functionality do they want with the app? It would be prudent to run a rewarded video ad for gamers since it addresses their needs. Knowing your audience and offering tailored ads ensures better response rates and engagements. How about the competition? Chances are that a rival app is using your considered monetization method. You may use the same strategy if you can cut a niche that suits your business. However, you may evaluate the competitors' strengths and weaknesses and use a unique monetization method. Does it make economic sense? As much as you may prefer a particular monetization, it must make sense regarding the potential number of users, signups, average spending, etc. The numbers must add up either now or in a predictive model. Your last wish is to select a monetization method that will make your business struggle to break even. Summary I don’t think there is a specific rule for monetization for your free app. Your choice method needs to make economic sense and not impact user experience. My rule of thumb is to choose a monetization method that aligns with the business goal and users and carries a unique value proposition. I always wondered how free apps make money. I did some research on this and found a lot of different avenues to follow. Let's jump into the ins and outs of how to earn money with free apps Background I always encountered those exciting, sometimes annoying ads and pop-ups while browsing the web. YouTube has certainly made their ads unescapable, robbing me of two minutes of desired view time. YouTube Have you thought about why they appear anyway? I certainly have. Those are some of the different monetization strategies for free apps. There are multiple other ways to monetize free apps. “Free App” Monetization Monetization is the ability to earn from your creations. App monetization is the strategy of making money off an application. Some monetization styles perform better than others. Let's learn the monetization method that works best in your situation. How to make money from an app: Different monetization strategies There are countless ways to profit from free apps. You can apply one or several monetization methods depending on your industry or the type of app. Let's get into the details: In-app advertising Do you know how YouTube makes money? Discover what is in-app advertising and how it occurs below: Apps like YouTube display banner ads via content uploaded on its platform. Despite being a popular monetization style, you may need high engagement rates to make good advertising income. Video ads You have most likely come across those ad videos that appear when viewing certain content. Video ads are more visually appealing and could attract the audience better than display ads. At the same time, you must ensure the ads do not ruin user experience and affect your monetization. There’s no way to tell where this line is but if YouTube is anything to go by then I can guess the line is far. Native advertising In-app advertising can also work by promoting other businesses, products, or services. Native advertising may also include sponsored content mixed into the app. This method may be popular if you do not want to distract your audience through other ads. In-app purchases How does Amazon make money? In-app purchases are one of them through the products or services listed on the platform. The benefits of in-app purchases are low costs, and you do not need to own the products or services. In-app purchasing is popular with e-commerce sites, dating apps, gaming platforms, and fitness apps. Monetization through this method can occur in the following ways: Paid content Paid content is information on your app, whether video, text, or downloadable that others must pay to access. Examples like e-books and courses can be offered as paid-for content despite your app being free for other services. Subscriptions Free apps may not be free forever. Free apps provide trial periods where users can use them at no cost. The aim is to lure them into paying a subscription fee once they are used to the product or service offered. Freemium model Freemium apps make money by charging users for premium features. Users have unrestricted access to the basic free version. They have to pay for advanced features. An example of this is ChatGPT, which you can access for free, but you will be charged $20 monthly for ChatGPT Plus. Data monetization Have you ever wondered why companies like Google and Facebook can provide relevant content tailored to your needs? It's because they have mastered the art of data monetization. Below are some valuable data monetization methods. User data Do you want to know how to monetize user data? If you hold a directory of people visiting your app, you can monetize it by licensing or selling it to third parties that need it. This monetization may require user approval; as unauthorized sharing can cause privacy violations. You may also use the user data to tailor your in-app products or services for maximum value creation. Location data Think of navigation apps like Waze. These apps collect crucial geo data. Businesses needing to place location-related ads rely on such apps for data collection. If your app is one of these categories, this could be a suitable monetization strategy, although you may need user consent. Targeted advertising Once you understand the type of data you hold, including age profiles, sex, preferences, and user traits, you can use it for targeted advertising. You can sell your own or a third-party product or service through targeted ads. How to choose the best free app monetization strategy There are numerous ways to monetize free apps. However, to succeed in-app monetization, you must apply the right strategy. There are plenty of tips to use when choosing the app monetization strategy. What is your business goal? It must be clear what you want in your app monetization. A monetization method like a subscription could work best if you already have a client base. If you are relatively new, you may consider an advertising revenue model. What's your target audience? Know your customers and what they want. Are they able and willing to pay for a product or service, and what functionality do they want with the app? It would be prudent to run a rewarded video ad for gamers since it addresses their needs. Knowing your audience and offering tailored ads ensures better response rates and engagements. How about the competition? Chances are that a rival app is using your considered monetization method. You may use the same strategy if you can cut a niche that suits your business. However, you may evaluate the competitors' strengths and weaknesses and use a unique monetization method. Does it make economic sense? As much as you may prefer a particular monetization, it must make sense regarding the potential number of users, signups, average spending, etc. The numbers must add up either now or in a predictive model. Your last wish is to select a monetization method that will make your business struggle to break even. Summary I don’t think there is a specific rule for monetization for your free app. Your choice method needs to make economic sense and not impact user experience. My rule of thumb is to choose a monetization method that aligns with the business goal and users and carries a unique value proposition.

Learn How Free Apps Make Money

After digging deep and scouring the internet, I'm excited to share my findings on how to harness the power of Llama-3 without breaking the bank! As a large language model, Llama-3 has the potential to revolutionize the way we work and create, but accessing its capabilities can come with a hefty price tag. Determined to find a way to make it accessible to everyone, I've uncovered seven hidden gems that allow you to tap into Llama-3's potential without spending a single penny. From lesser-known platforms to creative workarounds, I'll  show you how to get started with Llama-3 for free. Whether you're a developer, writer, or simply curious about AI, get ready to unlock the possibilities of this cutting-edge technology without incurring costs! Meta AI Web
Meta AI is available in a wide range of countries across the globe, including Australia, Canada, Ghana, Jamaica, Malawi, New Zealand, Nigeria, Pakistan, Singapore, South Africa, Uganda, Zambia, and Zimbabwe. This cutting-edge artificial intelligence platform can be accessed in English through various Meta services such as Facebook, Instagram, WhatsApp, Messenger, and the Meta AI website.Accessing Meta's platform directly is the best way to experience Llama in its full, official capacity. Unfortunately, as a user based in Hong Kong without a premium VPN subscription, I'm unable to showcase the full interface. AnakinAI AnakinAI offers the Meta Llama-3 70B and 8B models on its AI tools platform and it can be used for free. The platform boasts of hundreds of other AI apps and you can also build apps yourself. You can use the 7B and 8B models in English and Japanese as shown below. LLAMA 3 8B English Ver
LLAMA 3 8B Japanese Ver
LLAMA 3 70B English Ver
LLAMA 3 70B Japanese Ver Perplexity Labs
Perplexity Labs, a part of Perplexity AI, provides a user-friendly platform for developers to explore and experiment with large language models, including Llama 3. With a simple and intuitive interface, you can easily select either the llama-3-70b-instruct or llama-3-8b-instruct model and start interacting right away.
One of the notable advantages of Perplexity Labs is its generous token limits, allowing you to extensively test Llama 3's capabilities without restrictions.
For the llama-3–70b-instruct model, you can make up to 20 requests per 5 seconds, 60 requests per minute, and 600 requests per hour, with a token rate limit of 40,000 tokens per minute and 160,000 tokens per 10 minutes. The llama-3–8b-instruct model has similar limits, with a token rate limit of 16,000 tokens per 10 seconds, 160,000 tokens per minute, and 512,000 tokens per 10 minutes. Perplexity Labs is part of Perplexity AI, a search engine powered by OpenAI's GPT models, and is definitely worth exploring for its impressive capabilities and user-friendly interface.

HuggingChat
HuggingChat, an open-source interface developed by HuggingFace, allows users to engage with large language models, including Llama 3.
Simply log in or create a free account, select the "Meta-Llama-3–70B-Instruct" model, and start conversing. The intuitive interface makes it easy to interact with Llama 3, although the free token amount is not clearly specified.

Replicate
Replicate Playground allows users to experiment with Llama 3 models without creating an account. Access the models through meta-llama-3–70b and meta-llama-3–8b. The simple interface makes it easy to start testing and comparing Llama 3 models. Vercel Chat
Vercel Chat offers free testing of Llama 3 models, excluding "llama-3–70b-instruct". Compare response quality and token usage by chatting with two or more models side-by-side. This feature provides valuable insights into the strengths, weaknesses, and cost efficiency of different models.



Run llama Chatbot on Your Local Machine
If your system has the necessary hardware capabilities, you can set up and run llama Chatbot on your local machine. To do this:
Visit the Meta AI website to download the required weights (files containing the model's parameters)
Head to the llamma Chatbot repository on GitHub, where you'll find step-by-step instructions to install and set up the chatbot

By following these steps, you'll be able to run the chatbot on your own machine, experiment with it, and explore its features.
With the seven free methods outlined in this article, anyone can now tap into the power of this revolutionary language model. From exploring user-friendly platforms to leveraging open-source implementations, the possibilities are endless. Whether you're looking to gen Note: This post is not sponsored or affiliated with any of the mentioned platforms. I'm sharing this information purely to benefit readers who are interested in exploring Llama 3 models and large language technology. My goal is to provide helpful resources and insights, and I hope you find this information useful! After digging deep and scouring the internet, I'm excited to share my findings on how to harness the power of Llama-3 without breaking the bank! As a large language model, Llama-3 has the potential to revolutionize the way we work and create, but accessing its capabilities can come with a hefty price tag. Determined to find a way to make it accessible to everyone, I've uncovered seven hidden gems that allow you to tap into Llama-3's potential without spending a single penny. From lesser-known platforms to creative workarounds, I'll  show you how to get started with Llama-3 for free. Whether you're a developer, writer, or simply curious about AI, get ready to unlock the possibilities of this cutting-edge technology without incurring costs! Meta AI Web
Meta AI is available in a wide range of countries across the globe, including Australia, Canada, Ghana, Jamaica, Malawi, New Zealand, Nigeria, Pakistan, Singapore, South Africa, Uganda, Zambia, and Zimbabwe. This cutting-edge artificial intelligence platform can be accessed in English through various Meta services such as Facebook, Instagram, WhatsApp, Messenger, and the Meta AI website.Accessing Meta's platform directly is the best way to experience Llama in its full, official capacity. Unfortunately, as a user based in Hong Kong without a premium VPN subscription, I'm unable to showcase the full interface. Meta AI Web Meta AI is available in a wide range of countries across the globe, including Australia, Canada, Ghana, Jamaica, Malawi, New Zealand, Nigeria, Pakistan, Singapore, South Africa, Uganda, Zambia, and Zimbabwe. This cutting-edge artificial intelligence platform can be accessed in English through various Meta services such as Facebook, Instagram, WhatsApp, Messenger, and the Meta AI website. Accessing Meta's platform directly is the best way to experience Llama in its full, official capacity. Unfortunately, as a user based in Hong Kong without a premium VPN subscription, I'm unable to showcase the full interface. Meta AI Web Meta AI Web Meta A I is available in a wide range of countries across the globe, including Australia, Canada, Ghana, Jamaica, Malawi, New Zealand, Nigeria, Pakistan, Singapore, South Africa, Uganda, Zambia, and Zimbabwe. This cutting-edge artificial intelligence platform can be accessed in English through various Meta services such as Facebook, Instagram, WhatsApp, Messenger, and the Meta AI website. Meta A Accessing Meta's platform directly is the best way to experience Llama in its full, official capacity. Unfortunately, as a user based in Hong Kong without a premium VPN subscription, I'm unable to showcase the full interface. AnakinAI AnakinAI AnakinAI AnakinAI offers the Meta Llama-3 70B and 8B models on its AI tools platform and it can be used for free. The platform boasts of hundreds of other AI apps and you can also build apps yourself. AnakinAI You can use the 7B and 8B models in English and Japanese as shown below. LLAMA 3 8B English Ver LLAMA 3 8B Japanese Ver LLAMA 3 70B English Ver LLAMA 3 70B Japanese Ver LLAMA 3 8B English Ver LLAMA 3 8B English Ver LLAMA 3 8B Japanese Ver LLAMA 3 8B Japanese Ver LLAMA 3 70B English Ver LLAMA 3 70B English Ver LLAMA 3 70B Japanese Ver LLAMA 3 70B Japanese Ver Perplexity Labs
Perplexity Labs, a part of Perplexity AI, provides a user-friendly platform for developers to explore and experiment with large language models, including Llama 3. With a simple and intuitive interface, you can easily select either the llama-3-70b-instruct or llama-3-8b-instruct model and start interacting right away.
One of the notable advantages of Perplexity Labs is its generous token limits, allowing you to extensively test Llama 3's capabilities without restrictions.
For the llama-3–70b-instruct model, you can make up to 20 requests per 5 seconds, 60 requests per minute, and 600 requests per hour, with a token rate limit of 40,000 tokens per minute and 160,000 tokens per 10 minutes. The llama-3–8b-instruct model has similar limits, with a token rate limit of 16,000 tokens per 10 seconds, 160,000 tokens per minute, and 512,000 tokens per 10 minutes. Perplexity Labs is part of Perplexity AI, a search engine powered by OpenAI's GPT models, and is definitely worth exploring for its impressive capabilities and user-friendly interface. HuggingChat
HuggingChat, an open-source interface developed by HuggingFace, allows users to engage with large language models, including Llama 3.
Simply log in or create a free account, select the "Meta-Llama-3–70B-Instruct" model, and start conversing. The intuitive interface makes it easy to interact with Llama 3, although the free token amount is not clearly specified. Replicate
Replicate Playground allows users to experiment with Llama 3 models without creating an account. Access the models through meta-llama-3–70b and meta-llama-3–8b. The simple interface makes it easy to start testing and comparing Llama 3 models. Perplexity Labs Perplexity Labs, a part of Perplexity AI, provides a user-friendly platform for developers to explore and experiment with large language models, including Llama 3. With a simple and intuitive interface, you can easily select either the llama-3-70b-instruct or llama-3-8b-instruct model and start interacting right away.
One of the notable advantages of Perplexity Labs is its generous token limits, allowing you to extensively test Llama 3's capabilities without restrictions. For the llama-3–70b-instruct model, you can make up to 20 requests per 5 seconds, 60 requests per minute, and 600 requests per hour, with a token rate limit of 40,000 tokens per minute and 160,000 tokens per 10 minutes. The llama-3–8b-instruct model has similar limits, with a token rate limit of 16,000 tokens per 10 seconds, 160,000 tokens per minute, and 512,000 tokens per 10 minutes. Perplexity Labs is part of Perplexity AI, a search engine powered by OpenAI's GPT models, and is definitely worth exploring for its impressive capabilities and user-friendly interface. Perplexity Labs Perplexity Labs Perplexity Labs , a part of Perplexity AI, provides a user-friendly platform for developers to explore and experiment with large language models, including Llama 3. With a simple and intuitive interface, you can easily select either the llama-3-70b-instruct or llama-3-8b-instruct model and start interacting right away.
One of the notable advantages of Perplexity Labs is its generous token limits, allowing you to extensively test Llama 3's capabilities without restrictions. Perplexity Labs For the llama-3–70b-instruct model, you can make up to 20 requests per 5 seconds, 60 requests per minute, and 600 requests per hour, with a token rate limit of 40,000 tokens per minute and 160,000 tokens per 10 minutes. The llama-3–8b-instruct model has similar limits, with a token rate limit of 16,000 tokens per 10 seconds, 160,000 tokens per minute, and 512,000 tokens per 10 minutes. Perplexity Labs is part of Perplexity AI, a search engine powered by OpenAI's GPT models, and is definitely worth exploring for its impressive capabilities and user-friendly interface. HuggingChat
HuggingChat, an open-source interface developed by HuggingFace, allows users to engage with large language models, including Llama 3. Simply log in or create a free account, select the "Meta-Llama-3–70B-Instruct" model, and start conversing. The intuitive interface makes it easy to interact with Llama 3, although the free token amount is not clearly specified. HuggingChat HuggingChat , an open-source interface developed by HuggingFace, allows users to engage with large language models, including Llama 3. HuggingChat HuggingChat Simply log in or create a free account, select the "Meta-Llama-3–70B-Instruct" model, and start conversing. The intuitive interface makes it easy to interact with Llama 3, although the free token amount is not clearly specified. Replicate
Replicate Playground allows users to experiment with Llama 3 models without creating an account. Access the models through meta-llama-3–70b and meta-llama-3–8b. The simple interface makes it easy to start testing and comparing Llama 3 models. Replicate Replicate Playground allows users to experiment with Llama 3 models without creating an account. Access the models through meta-llama-3–70b and meta-llama-3–8b . The simple interface makes it easy to start testing and comparing Llama 3 models. Replicate Replicate Playground meta-llama-3–70b meta-llama-3–8b Vercel Chat
Vercel Chat offers free testing of Llama 3 models, excluding "llama-3–70b-instruct". Compare response quality and token usage by chatting with two or more models side-by-side. This feature provides valuable insights into the strengths, weaknesses, and cost efficiency of different models. Run llama Chatbot on Your Local Machine
If your system has the necessary hardware capabilities, you can set up and run llama Chatbot on your local machine. To do this:
Visit the Meta AI website to download the required weights (files containing the model's parameters)
Head to the llamma Chatbot repository on GitHub, where you'll find step-by-step instructions to install and set up the chatbot

By following these steps, you'll be able to run the chatbot on your own machine, experiment with it, and explore its features.
With the seven free methods outlined in this article, anyone can now tap into the power of this revolutionary language model. From exploring user-friendly platforms to leveraging open-source implementations, the possibilities are endless. Whether you're looking to gen Vercel Chat Vercel Chat offers free testing of Llama 3 models, excluding "llama-3–70b-instruct". Compare response quality and token usage by chatting with two or more models side-by-side. This feature provides valuable insights into the strengths, weaknesses, and cost efficiency of different models. Vercel Chat Vercel Chat Vercel Chat offers free testing of Llama 3 models, excluding "llama-3–70b-instruct". Compare response quality and token usage by chatting with two or more models side-by-side. This feature provides valuable insights into the strengths, weaknesses, and cost efficiency of different models. Run llama Chatbot on Your Local Machine If your system has the necessary hardware capabilities, you can set up and run llama Chatbot on your local machine. To do this: Visit the Meta AI website to download the required weights (files containing the model's parameters)
Head to the llamma Chatbot repository on GitHub, where you'll find step-by-step instructions to install and set up the chatbot By following these steps, you'll be able to run the chatbot on your own machine, experiment with it, and explore its features. With the seven free methods outlined in this article, anyone can now tap into the power of this revolutionary language model. From exploring user-friendly platforms to leveraging open-source implementations, the possibilities are endless. Whether you're looking to gen Run llama Chatbot on Your Local Machine Run llama Chatbot on Your Local Machine If your system has the necessary hardware capabilities, you can set up and run llama Chatbot on your local machine. To do this: llama Visit the Meta AI website to download the required weights (files containing the model's parameters) Head to the llamma Chatbot repository on GitHub, where you'll find step-by-step instructions to install and set up the chatbot Visit the Meta AI website to download the required weights (files containing the model's parameters) Meta AI website Head to the llamma Chatbot repository on GitHub, where you'll find step-by-step instructions to install and set up the chatbot llamma Chatbot repository By following these steps, you'll be able to run the chatbot on your own machine, experiment with it, and explore its features. With the seven free methods outlined in this article, anyone can now tap into the power of this revolutionary language model. From exploring user-friendly platforms to leveraging open-source implementations, the possibilities are endless. Whether you're looking to gen Note: This post is not sponsored or affiliated with any of the mentioned platforms. I'm sharing this information purely to benefit readers who are interested in exploring Llama 3 models and large language technology. My goal is to provide helpful resources and insights, and I hope you find this information useful!

MAKE

makerstage.com

1-10 employees

Since 2017

COMPANY RANKING

MAKE

EVERGREEN INDEX #6701

Representing this company? Click here to claim and customize this page!

Make WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

MAKE

makerstage.com

1-10 employees

Since 2017

COMPANY RANKING

MAKE

EVERGREEN INDEX #6701

RELATED COMPANIESRANK

STORIES ABOUT MAKE

Representing this company? Click here to claim and customize this page!

Make WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!