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    Spheron and Heurist Take on AI Innovation

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    Spheron and Heurist Take on AI Innovation


    In this exciting crossover episode of Tech Fusion, we dive deep into the world of AI, GPU, and decentralized computing. This discussion brings together minds from Spheron and Heurist to explore cutting-edge innovations, challenges, and the future of technology in this space. Let’s jump straight into the conversation where our host Prashant (Spheron’s CEO) and JW and Manish from Heurist take center stage.

    If you want to watch this episode, click below or head over to our YouTube channel.

    Introduction to the Tech Fusion Episode

    Host: “Welcome, everyone! This is Episode 9 of Tech Fusion, and we’re in for a treat today. This is our first-ever episode featuring four guests, so it’s a big one! I’m Prashant, and Prakarsh from Spheron is with me today. We have special guests from Heurist Manish and JW. It’s going to be a deep dive into the world of AI, GPUs, decentralized computing, and everything in between. So let’s get started!”

    The Evolution of AI Models and Decentralized Computing

    Prashant: “JW and Manish, let’s start by talking about AI models. We’ve recently seen advancements in AI reasoning capabilities, and it’s clear that decentralized computing is catching up. How long do you think it will take for end-users to fully harness the power of decentralized AI models?”

    JW (Heurist): “Great question. First off, thank you for having us here! It’s always exciting to share thoughts with other innovative teams like Spheron. Now, on AI reasoning—yes, OpenAI has been making waves with its models, and we’ve seen open-source communities attempt to catch up. Generally, I’d say the gap between open-source and closed-source AI models is about six to twelve months. The big companies move faster because they have more resources, but the open-source community has consistently managed to close the gap, especially with models like LLaMA catching up to GPT-4.”

    Challenges in Training and Inference with Decentralized GPUs

    Prashant: “Decentralized computing is a hot topic, especially in how it relates to the scalability of training and inference models. JW, you mentioned some experiments in this space. Could you elaborate?”

    JW: “Absolutely! One exciting development comes from Google’s research into decentralized training. For the first time, we’ve seen large language models (LLMs) trained across distributed GPUs with minimal network bandwidth between nodes. What’s groundbreaking is that they’ve reduced network transmission by over a thousand times. It’s a big leap in showing that decentralized compute isn’t just theoretical—it’s real and can have practical applications.”

    The Role of VRAM and GPU Pricing in AI Models

    Prakarsh (Spheron): “That’s fascinating. Something that I find equally intriguing is the premium we’re paying for VRAM. For instance, an H100 GPU has 80 GB of VRAM, while a A6000 has 48 GB. We’re essentially paying a high premium for that extra VRAM. Do you think we’ll see optimizations that reduce VRAM usage in AI training and inference?”

    Manish (Heurist): “You’re absolutely right about the VRAM costs. Reducing those costs is a huge challenge, and while decentralized computing might help alleviate it in some ways, there’s still a long road ahead. We’re optimistic, though. With technologies evolving, particularly in how models are optimized for different hardware, we may soon see more cost-efficient solutions.”

    Decentralized Compute’s Impact on AI Training and Inference

    Prashant: “So, let’s dig deeper into the training versus inference debate. What’s the biggest difference you’ve seen between these two in terms of cost and resources?”

    JW: “Great question. Based on our data, about 80-90% of compute resources are spent on inference, while only 10% goes to training. That’s why we focus heavily on inference at Heurist. Inference, although less resource-intensive than training, still requires a robust infrastructure. What’s exciting is how decentralized compute could make it more affordable, especially for end-users. A cluster of 8 GPUs, for instance, can handle most open-source models. That’s where we believe the future lies.”

    The Vision for Fizz Node: Decentralized Inferencing

    Prashant: “At Spheron, we’re working on something called Fizz Node, which allows regular computers to participate in decentralized inferencing. Imagine users being able to contribute their GPUs at home to this decentralized network. What do you think of this approach?”

    JW: “Fizz Node sounds incredible! It’s exciting to think of regular users contributing their GPU power to a decentralized network, especially for inference. The idea of offloading lower-compute tasks to smaller machines is particularly interesting. At Heurist, we’ve been considering similar ideas for some time.”

    Technological Challenges of Distributed Compute

    Prakarsh: “One challenge we’ve seen is the efficiency of decentralized nodes. Bandwidth is one thing, but VRAM usage is a critical bottleneck. Do you think models can be trained and deployed on smaller devices effectively?”

    Manish: “It’s possible, but it comes with its own set of complexities. For smaller models or highly optimized tasks, yes, smaller devices can handle them. But for larger models, like 7B or 45B models, it’s tough without at least 24 GB of VRAM. However, we’re optimistic that with the right frameworks, it can become feasible.”

    Prashant: “I noticed Heurist has built several interesting tools like Imagine, Search, and Babel. How did those come about, and what’s the community response been like?”

    JW: “The main goal of our tools is to make AI accessible and easy to use. When we launched Imagine, an AI image generator, the response was overwhelmingly positive. It stood out because we fine-tuned models specifically for our community—things like anime style or 2D art. It really showcased how diverse open-source AI could be. We’ve seen huge adoption in the Web3 space because users don’t need a wallet or even an account to try them. It’s all about creating a seamless user experience.”

    AI-Driven Translation: Bringing Global Communities Together

    Prashant: “Speaking of seamless experiences, I’m intrigued by your Discord translation bot. It sounds like a game-changer for communities with users from all over the world.”

    JW: “It really is! The bot helps our community communicate across languages with ease. We wanted to make sure that AI could bridge language barriers, so now, anyone can send messages in their native language, and they’ll automatically be translated for the rest of the group. It’s been a huge hit, especially with our international users.”

    Exploring Cursor: A Developer’s Dream Tool

    Prakarsh: “Recently, I’ve heard developers rave about Cursor as a coding assistant. Have you integrated Cursor with Heurist?”

    Manish: “Yes, we’ve tested Cursor with our LLM API, and the results have been fantastic. It feels like having multiple interns working for you. With AI-driven development tools like Cursor, it’s becoming much easier to code, even for those who’ve been out of the loop for years.”

    The Future of AI: What’s Next for Spheron and Heurist?

    Prashant: “Looking ahead, what are Heurist’s plans for the next couple of months?”

    JW: “We’re working on some exciting things! First, we’ll be sponsoring DEFCON, and we’re collaborating with an AI partner to promote our Heurist API services. We’re also finalizing our tokenomics for the Heurist network, which we’re really excited about. We’ve been putting a lot of effort into designing a sustainable economic model, one that avoids the pitfalls we’ve seen in other projects.”

    Final Thoughts: AI, GPUs, and Beyond

    Prashant: “Before we wrap up, let’s talk about the episode’s title, AI, GPUs, and Beyond. What do you think the ‘beyond’ part will look like in the next few years?”

    JW: “I believe AI will become so integrated into our daily lives that we won’t even notice it. From how we browse the web to how we work, AI will power much of it without us even being aware of it.”

    Manish: “I agree. AI will blend seamlessly into the background, making everything more efficient. The future is in making these technologies invisible but essential.”

    Conclusion

    This episode of Tech Fusion was a fascinating exploration of how AI, GPUs, and decentralized compute will shape our future. From the challenges of VRAM usage to the exciting potential of Fizz Node and Heurist’s ecosystem, it’s clear that the landscape of technology is rapidly evolving. If you haven’t already, now is the time to dive into the world of decentralized AI and GPU computing!

    FAQs

    1. What is Fizz Node, and how does it work?
    Fizz Node allows regular users to contribute their GPU power to a decentralized network, particularly for AI inferencing tasks. It optimizes small-scale devices to handle lower-compute tasks efficiently.

    2. What is the difference between AI training and inference?
    Training involves teaching the AI model by feeding it data, whereas inference is the process of applying the trained model to new inputs. Inference typically requires fewer resources than training.

    3. How does Heurist’s Imagine tool work?
    Imagine is an AI-driven image generation tool that allows users to create art in different styles, from anime to 3D realistic models, using fine-tuned models developed by the Heurist team.

    4. What makes Heurist’s translation bot unique?
    Heurist’s translation bot enables seamless communication across languages in Discord communities, automatically translating messages into the preferred language of the group.

    5. What’s the future of decentralized GPU computing?
    The future lies in making decentralized computing more accessible, cost-effective, and scalable, potentially competing with centralized giants like AWS. The goal is to decentralize much of the current AI compute load.



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    How the $1.4 billion crypto prediction market industry took off in 2024 – report

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    How the .4 billion crypto prediction market industry took off in 2024 – report


    Receive, Manage & Grow Your Crypto Investments With Brighty

    Prediction markets are experiencing growth, with platforms like Polymarket advancing the sector. Castle Capital reported in its latest deep dive that these markets enable users to bet on future events using crypto, moving traditional gambling into a decentralized domain. This shift allows participants to trade against each other rather than a centralized house, increasing transparency and resistance to manipulation.

    Castle Capital outlined how prediction markets were historically centralized, limiting user participation and flexibility. The introduction of blockchain technology has allowed these markets to become decentralized, allowing users to create their own markets and conditions. Since the launch of another prediction market, Augur, in 2015, prediction markets have been recognized as a prominent application of blockchain technology, although mainstream attention has only recently intensified.

    The sector’s total value locked has reached $162 million, significantly increasing user engagement and transaction volumes. Platforms like Azuro and Polymarket have facilitated this growth by offering different approaches. Polymarket, based on Polygon, operates using an order book model, focusing on major political and news-related events. It has processed over $1.4 billion in volume, becoming a key platform for betting on events like the US presidential elections.

    Prediction Markets Volume (Castle Capital)
    Prediction Markets Volume (Castle Capital)

    Castle Capital explained that Azuro utilizes a peer-to-pool design, allowing users to provide liquidity to pools that serve multiple markets. This model diversifies risk and improves capital efficiency, catering primarily to sports betting. Azuro has handled over $200 million in prediction volume, attracting users who engage in recurring bets across various sports events.

    Both platforms aim to expand their market offerings. Polymarket seeks to reduce its reliance on political events by adding more diverse markets, while Azuro reportedly plans to include political and news markets alongside sports. The growth of these platforms highlights the increasing interest in decentralized prediction markets as tools for gauging public sentiment.

    Castle Capital outlined the challenges that remain for mainstream adoption, including liquidity issues, regulatory uncertainties, and the need for improved user experiences. Ensuring reliable oracles and data accuracy is crucial, as is addressing scalability concerns on blockchain networks. Overcoming these obstacles requires innovation and engagement with regulatory bodies.

    As Castle Capital noted, prediction markets have the potential to provide accurate public sentiment on various topics, moving beyond seasonal hype to become integral tools for decision-making. Integrating artificial intelligence and expanded market offerings may enhance their utility and appeal. Prediction markets could offer news outlets decentralized sentiment data and influence political discourse.

    The future of prediction markets appears promising, with platforms like Azuro and Polymarket at the forefront. Their continued growth and adaptation may solidify their position in the crypto landscape, offering valuable insights and opportunities for users forecasting future events.

    According to Castle Capital’s report, the evolution of prediction markets reflects a broader trend of increasing adoption of decentralized applications. However, whether these platforms can sustain their momentum and navigate the challenges ahead to achieve mainstream acceptance remains to be seen.

    Castle Capital’s complete deep dive report is available as part of its Castle Chronicles series.

    Mentioned in this article



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    How Much GPU Memory is Required to Run a Large Language Model?

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    How Much GPU Memory is Required to Run a Large Language Model?


    With the growing importance of LLMs in AI-driven applications, developers and companies are deploying models like GPT-4, LLaMA, and OPT-175B in real-world scenarios. However, one of the most overlooked aspects of deploying these models is understanding how much GPU memory is needed to serve them effectively. Miscalculating memory requirements can cost you significantly more in hardware or cause downtime due to insufficient resources.

    In this article, we’ll explore the key components contributing to GPU memory usage during LLM inference and how you can accurately estimate your GPU memory requirements. We’ll also discuss advanced techniques to reduce memory wastage and optimize performance. Let’s dive in!

    Understanding GPU Memory Requirements for LLMs

    LLMs rely heavily on GPU resources for inference. GPU memory consumption for serving LLMs can be broken down into four key components:

    Model Parameters (Weights)

    Key-Value (KV) Cache Memory

    Activations and Temporary Buffers

    Memory Overheads

    Let’s examine each of these in more detail and see how they contribute to the total memory footprint.

    Model Parameters (Weights)

    Model parameters are the neural network’s learned weights. These weights are stored in GPU memory during inference, and their size is directly proportional to the number of parameters in the model.

    How Model Size Impacts Memory

    A typical inference setup uses each parameter’s FP16 (half-precision) format to save memory while maintaining acceptable precision. Each parameter requires 2 bytes in FP16 format.

    For example:

    A small LLM with 345 million parameters would require:

    345 million × 2 bytes = 690 MB of GPU memory.

    A larger model like LLaMA 13B (13 billion parameters) would require:

    13 billion × 2 bytes = 26 GB of GPU memory.

    For massive models like GPT-3, which has 175 billion parameters, the memory requirement becomes:

    175 billion × 2 bytes = 350 GB.

    Clearly, larger models demand significantly more memory, and distributing the model across multiple GPUs becomes necessary for serving these larger models.

    Key-Value (KV) Cache Memory

    The KV cache stores the intermediate key and value vectors generated during the model’s inference process. This is essential for maintaining the context of the sequence being generated. As the model generates new tokens, the KV cache stores previous tokens, allowing the model to reference them without re-calculating their representations.

    How Sequence Length and Concurrent Requests Impact KV Cache

    Sequence Length: Longer sequences require more tokens, leading to a larger KV cache.

    Concurrent Users: Multiple users increase the number of generated sequences, which multiplies the required KV cache memory.

    Calculating KV Cache Memory

    Here’s a simplified way to calculate the KV cache memory:

    For each token, a key and value vector are stored.

    The number of vectors per token is equal to the number of layers in the model (L), and the size of each vector is the hidden size (H).

    For example, consider a LLaMA 13B model with:

    L = 40 layers

    H = 5120 dimensions

    The KV cache per token is calculated as:

    Key Vector: 40 × 5120 = 204,800 elements

    FP16 requires 204,800 × 2 bytes = 400 KB per key vector.

    The value vector needs the same memory, so the total KV cache memory per token is 800 KB.

    For a sequence of 2000 tokens:

    2000 tokens × 800 KB = 1.6 GB per sequence.

    If the system serves 10 concurrent users, the total KV cache memory becomes:

    1.6 GB × 10 = 16 GB of GPU memory for KV cache alone.

    Activations and Temporary Buffers

    Activations are the outputs of the neural network layers during inference. Temporary buffers store intermediate results during matrix multiplications and other computations.

    While activations and buffers usually consume less memory than model weights and KV cache, they still account for approximately 5-10% of the total memory.

    Memory Overheads and Fragmentation

    Memory overheads come from how memory is allocated. Fragmentation can occur when memory blocks are not fully utilized, leaving gaps that cannot be used efficiently.

    Internal Fragmentation: This occurs when memory blocks are not filled.

    External Fragmentation: This happens when free memory is split into non-contiguous blocks, making it difficult to allocate large chunks of memory when needed.

    Inefficient memory allocation can waste 20-30% of total memory, reducing performance and limiting scalability.

    Calculating Total GPU Memory

    Now that we understand the components, we can calculate the total GPU memory required for serving an LLM.

    For example, let’s calculate the total memory needed for a LLaMA 13B model with the following assumptions:

    The total memory required would be:

    26 GB + 16 GB + 9.2 GB (for activations and overheads) = 101.2 GB.

    Thus, under this scenario, you would need at least 3 A100 GPUs (each with 40 GB of memory) to serve an LLaMA 13B model.

    Challenges in GPU Memory Optimization

    Over-allocating memory for the key-value (KV) cache, or experiencing fragmentation within the memory, can significantly reduce the capacity of a system to handle a large number of requests. These issues often arise in systems dealing with complex tasks, especially in natural language processing (NLP) models or other AI-based frameworks that rely on efficient memory management. Furthermore, when advanced decoding algorithms, such as beam search or parallel sampling, are used, the memory demands grow exponentially. This is because each sequence being processed requires a dedicated KV cache, resulting in even greater pressure on the system’s memory resources. Consequently, both over-allocation and fragmentation can lead to performance bottlenecks, restricting scalability and reducing efficiency.

    Memory Optimization Techniques

    PagedAttention: Reducing Memory Fragmentation with Paging

    PagedAttention is a sophisticated memory management technique inspired by how operating systems handle virtual memory. When we think of computer memory, it’s easy to imagine it as one big block where data is stored in a neat, continuous fashion. However, when dealing with large-scale tasks, especially in machine learning or AI models, allocating such large chunks of memory can be inefficient and lead to memory fragmentation.

    What is Memory Fragmentation?

    Fragmentation happens when memory is allocated in a way that leaves small, unusable gaps between different data blocks. Over time, these gaps can build up, making it harder for the system to find large, continuous memory spaces for new data. This leads to inefficient memory use and can slow down the system, limiting its ability to process large numbers of requests or handle complex tasks.

    How Does PagedAttention Work?

    PagedAttention solves this by breaking down the key-value (KV) cache—used for storing intermediate information in attention mechanisms—into smaller, non-contiguous blocks of memory. Rather than requiring one large, continuous block of memory, it pages the cache, similar to how an operating system uses virtual memory to manage data in pages.

    Dynamically Allocated: The KV cache is broken into smaller pieces that can be spread across different parts of memory, making better use of available space.

    Reduced Fragmentation: By using smaller blocks, it reduces the number of memory gaps, leading to better memory utilization. This helps prevent fragmentation, as there’s no need to find large, continuous blocks of memory for new tasks.

    Improved Performance: Since memory is allocated more efficiently, the system can handle more requests simultaneously without running into memory bottlenecks.

    vLLM: A Near-Zero Memory Waste Solution

    Building on the concept of PagedAttention, vLLM is a more advanced technique designed to optimize GPU memory usage even further. Modern machine learning models, especially those that run on GPUs (Graphics Processing Units), are incredibly memory-intensive. Inefficient memory allocation can quickly become a bottleneck, limiting the number of requests a system can process or the size of batches it can handle.

    What Does vLLM Do?

    vLLM is designed to minimize memory waste to nearly zero, allowing systems to handle more data, larger batches, and more requests with fewer resources. It achieves this by making memory allocation more flexible and reducing the amount of memory that goes unused during processing.

    Key Features of vLLM:

    Dynamic Memory Allocation:Unlike traditional systems that allocate a fixed amount of memory regardless of the actual need, vLLM uses a dynamic memory allocation strategy. It allocates memory only when it’s needed and adjusts the allocation based on the system’s current workload. This prevents memory from sitting idle and ensures that no memory is wasted on tasks that don’t require it.

    Cache Sharing Across Tasks:vLLM introduces the ability to share the KV cache across multiple tasks or requests. Instead of creating separate caches for each task, which can be memory-intensive, vLLM allows the same cache to be reused by different tasks. This reduces the overall memory footprint while still ensuring that tasks can run in parallel without performance degradation.

    Handling Larger Batches:With efficient memory allocation and cache sharing, vLLM allows systems to process much larger batches of data at once. This is particularly useful in scenarios where processing speed and the ability to handle many requests at the same time are crucial, such as in large-scale AI systems or services that handle millions of user queries simultaneously.

    Minimal Memory Waste:The combination of dynamic allocation and cache sharing means that vLLM can handle more tasks with less memory. It optimizes every bit of available memory, ensuring that almost none of it goes to waste. This results in near-zero memory wastage, which significantly improves system efficiency and performance.

    Managing Limited Memory

    When working with deep learning models, especially those that require significant memory for operations, you may encounter situations where GPU memory becomes insufficient. Two common techniques can be employed to address this issue: swapping and recomputation. Both methods allow for memory optimization, though they come with latency and computation time trade-offs.

    1. Swapping

    Swapping refers to the process of offloading less frequently used data from GPU memory to CPU memory when GPU resources are fully occupied. A common use case for swapping in neural networks is the KV cache (key-value cache), which stores intermediate results during computations.

    When the GPU memory is exhausted, the system can transfer KV cache data from the GPU to the CPU, freeing up space for more immediate GPU tasks. However, this process comes at the cost of increased latency. Since the CPU memory is slower compared to GPU memory, accessing the swapped-out data requires additional time, leading to a performance bottleneck, especially when the data needs to be frequently swapped back and forth.

    Advantages:

    Saves GPU memory by offloading less essential data.

    Prevents out-of-memory errors, allowing larger models or batch sizes.

    Drawbacks:

    2. Recomputation

    Recomputation is another technique that helps conserve memory by reusing previously discarded data. Instead of storing intermediate activations (results from earlier layers of the model) during forward propagation, recomputation discards these activations and recomputes them on-demand during backpropagation. This reduces memory consumption but increases the overall computation time.

    For instance, during the training process, the model might discard activations from earlier layers after they are used in forward propagation. When backpropagation starts, the model recalculates the discarded activations as needed to update the weights, which saves memory but requires additional computation.

    Advantages:

    Drawbacks:

    Increases computation time since activations are recalculated.

    May slow down the training process, especially for large and deep networks.

    Conclusion

    Determining the GPU memory requirements for serving LLMs can be challenging due to various factors such as model size, sequence length, and concurrent users. However, by understanding the different components of memory consumption—model parameters, KV cache, activations, and overheads—you can accurately estimate your needs.

    Techniques like PagedAttention and vLLM are game-changers in optimizing GPU memory, while strategies like swapping and recomputation can help when facing limited memory.

    FAQs

    What is KV Cache in LLM inference?

    The KV cache stores intermediate key-value pairs needed for generating tokens during sequence generation, helping models maintain context.

    How does PagedAttention optimize GPU memory?

    PagedAttention dynamically allocates memory in smaller, non-contiguous blocks, reducing fragmentation and improving memory utilization.

    How much GPU memory do I need for a GPT-3 model?

    GPT-3, with 175 billion parameters, requires around 350 GB of memory for weights alone, making it necessary to distribute the model across multiple GPUs.

    What are the benefits of using vLLM?

    vLLM reduces memory waste by dynamically managing GPU memory and enabling cache sharing between requests, increasing throughput and scalability.

    How can I manage memory if I don’t have enough GPU capacity?

    You can use swapping to offload data to CPU memory or recomputation to reduce stored activations, though both techniques increase latency.



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    Nextrope on Economic Forum 2024: Insights from the Event – Nextrope – Your Trusted Partner for Blockchain Development and Advisory Services

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    Nextrope on Economic Forum 2024: Insights from the Event – Nextrope – Your Trusted Partner for Blockchain Development and Advisory Services


    Behavioral economics is a field that explores the effects of psychological factors on economic decision-making. This branch of study is especially pertinent while designing a token since user perception can significantly impact a token’s adoption.

    We will delve into how token design choices, such as staking yields, token inflation, and lock-up periods, influence consumer behavior. Research studies reveal that the most significant factor for a token’s attractiveness isn’t its functionality, but its past price performance. This underscores the impact of speculative factors. Tokens that have shown previous price increases are preferred over those with more beneficial economic features.

    Understanding Behavioral Tokenomics

    Understanding User Motivations

    The design of a cryptocurrency token can significantly influence user behavior by leveraging common cognitive biases and decision-making processes. For instance, the concept of “scarcity” can create a perceived value increase, prompting users to buy or hold a token in anticipation of future gains. Similarly, “loss aversion,” a foundational principle of behavioral economics, suggests that the pain of losing is psychologically more impactful than the pleasure of an equivalent gain. In token design, mechanisms that minimize perceived losses (e.g. anti-dumping measures) can encourage long-term holding.

    Incentives and Rewards

    Behavioral economics also provides insight into how incentives can be structured to maximize user participation. Cryptocurrencies often use tokens as a form of reward for various behaviors, including mining, staking, or participating in governance through voting. The way these rewards are framed and distributed can greatly affect their effectiveness. For example, offering tokens as rewards for achieving certain milestones can tap into the ‘endowment effect,’ where people ascribe more value to things simply because they own them.

    Social Proof and Network Effects

    Social proof, where individuals copy the behavior of others, plays a crucial role in the adoption of tokens. Tokens that are seen being used and promoted by influential figures within the community can quickly gain traction, as new users emulate successful investors. The network effect further amplifies this, where the value of a token increases as more people start using it. This can be seen in the rapid growth of tokens like Ethereum, where the broad adoption of its smart contract functionality created a snowball effect, attracting even more developers and users.

    Token Utility and Behavioral Levers

    The utility of a token—what it can be used for—is also crucial. Tokens designed to offer real-world applications beyond mere financial speculation can provide more stable value retention. Integrating behavioral economics into utility design involves creating tokens that not only serve practical purposes but also resonate on an emotional level with users, encouraging engagement and investment. For example, tokens that offer governance rights might appeal to users’ desire for control and influence within a platform, encouraging them to hold rather than sell.

    Understanding Behavioral Tokenomics

    Intersection of Behavioral Economics and Tokenomics

    Behavioral economics examines how psychological influences, various biases, and the way in which information is framed affect individual decisions. In tokenomics, these factors can significantly impact the success or failure of a cryptocurrency by influencing user behavior towards investment

    Influence of Psychological Factors on Token Attraction

    A recent study observed that the attractiveness of a token often hinges more on its historical price performance than on intrinsic benefits like yield returns or innovative economic models. This emphasizes the fact that the cryptocurrency sector is still young, and therefore subject to speculative behaviors. 

    The Effect of Presentation and Context

    Another interesting finding from the study is the impact of how tokens are presented. In scenarios where tokens are evaluated separately, the influence of their economic attributes on consumer decisions is minimal. However, when tokens are assessed side by side, these attributes become significantly more persuasive. This highlights the importance of context in economic decision-making—a core principle of behavioral economics. It’s easy to translate this into real-life example – just think about the concept of staking yields. When told that the yield on e.g. Cardano is 5% you might not think much of it. But, if you were simultaneously told that Anchor’s yield is 19%, then that 5% seems like a tragic deal.

    Implications for Token Designers

    The application of behavioral economics to the design of cryptocurrency tokens involves leveraging human psychology to encourage desired behaviors. Here are several core principles of behavioral economics and how they can be effectively utilized in token design:

    Leveraging Price Performance

    Studies show clearly: “price going up” tends to attract users more than most other token attributes. This finding implies that token designers need to focus on strategies that can showcase their economic effects in the form of price increases. This means that e.g. it would be more beneficial to conduct a buy-back program than to conduct an airdrop.

    Scarcity and Perceived Value

    Scarcity triggers a sense of urgency and increases perceived value. Cryptocurrency tokens can be designed to have a limited supply, mimicking the scarcity of resources like gold. This not only boosts the perceived rarity and value of the tokens but also drives demand due to the “fear of missing out” (FOMO). By setting a cap on the total number of tokens, developers can create a natural scarcity that may encourage early adoption and long-term holding.

    Initial Supply Considerations

    The initial supply represents the number of tokens that are available in circulation immediately following the token’s launch. The chosen number can influence early market perceptions. For instance, a large initial supply might suggest a lower value per token, which could attract speculators. Data shows that tokens with low nominal value are highly volatile and generally underperform. Understanding how the initial supply can influence investor behavior is important for ensuring the token’s stability.

    Managing Maximum Supply and Inflation

    A finite maximum supply can safeguard the token against inflation, potentially enhancing its value by ensuring scarcity. On the other hand, the inflation rate, which defines the pace at which new tokens are introduced, influences the token’s value and user trust.

    Investors in cryptocurrency markets show a notable aversion to deflationary tokenomics. Participants are less likely to invest in tokens with a deflationary framework, viewing them as riskier and potentially less profitable. Research suggests that while moderate inflation can be perceived neutrally or even positively, high inflation does not enhance attractiveness, and deflation is distinctly unfavorable.

    Source: Behavioral Tokenomics: Consumer Perceptions of Cryptocurrency Token Design

    These findings suggest that token designers should avoid high deflation rates, which could deter investment and user engagement. Instead, a balanced approach to inflation, avoiding extremes, appears to be preferred among cryptocurrency investors.

    Loss Aversion

    People tend to prefer avoiding losses to acquiring equivalent gains; this is known as loss aversion. In token design, this can be leveraged by introducing mechanisms that protect against losses, such as staking rewards that offer consistent returns or features that minimize price volatility. Additionally, creating tokens that users can “earn” through participation or contribution to the network can tap into this principle by making users feel they are safeguarding an investment or adding protective layers to their holdings.

    Social Proof

    Social proof is a powerful motivator in user adoption and engagement. When potential users see others adopting a token, especially influential figures or peers, they are more likely to perceive it as valuable and trustworthy. Integrating social proof into token marketing strategies, such as showcasing high-profile endorsements or community support, can significantly enhance user acquisition and retention.

    Mental Accounting

    Mental accounting involves how people categorize and treat money differently depending on its source or intended use. Tokens can be designed to encourage specific spending behaviors by being categorized for certain types of transactions—like tokens that are specifically for governance, others for staking, and others still for transaction fees. By distinguishing tokens in this way, users can more easily rationalize holding or spending them based on their designated purposes.

    Endowment Effect

    The endowment effect occurs when people value something more highly simply because they own it. For tokenomics, creating opportunities for users to feel ownership can increase attachment and perceived value. This can be done through mechanisms that reward users with tokens for participation or contribution, thus making them more reluctant to part with their holdings because they value them more highly.

    Conclusion

    By considering how behavioral factors influence market perception, token engineers can create much more effective ecosystems. Ensuring high demand for the token, means ensuring proper funding for the project in general.

    If you’re looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

    FAQ

    How does the initial supply of a token influence its market perception?

    The initial supply sets the perceived value of a token; a larger supply might suggest a lower per-token value.

    Why is the maximum supply important in token design?

    A finite maximum supply signals scarcity, helping protect against inflation and enhance long-term value.

    How do investors perceive inflation and deflation in cryptocurrencies?

    Investors generally dislike deflationary tokens and view them as risky. Moderate inflation is seen neutrally or positively, while high inflation is not favored.



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    OpenAI Launches GPT-01 ‘Strawberry’: A Breakthrough in Advanced Reason

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    OpenAI Launches GPT-01 ‘Strawberry’: A Breakthrough in Advanced Reason


    On September 12, 2024, OpenAI introduced GPT-01, also known as “Strawberry.” This release is the first of a planned series of reasoning models aimed at solving complex tasks more effectively. While previous models, like GPT-4, excelled at generating human-like text and responses, GPT-01 brings something new: advanced reasoning capabilities to tackle multi-step problems, such as complicated math, logic, and coding tasks.

    What Is Advanced Reasoning in AI?

    Advanced reasoning refers to the AI’s ability to process information logically and step-by-steply, similar to how a human would tackle a problem. While earlier models mainly focused on pattern recognition and data-based predictions, GPT-01 can break down tasks into smaller steps, analyze them, and provide a coherent solution.

    This leap in reasoning power makes GPT-01 particularly effective in areas where multi-step logic is essential, such as coding, mathematical proofs, and strategic planning.

    GPT-01 represents a significant milestone in AI research, particularly in reasoning. By pushing the boundaries of what AI can achieve, this model sets the stage for future innovations in AI development, bringing us closer to creating autonomous systems capable of complex decision-making.

    The introduction of advanced reasoning in GPT-01 paves the way for more sophisticated AI applications, particularly in fields that require logical thinking and problem-solving. As models evolve, AI may take on more significant roles in decision-making processes, from healthcare to engineering.

    Why Is the Model Called “Strawberry”?

    GPT -01 was nicknamed “Strawberry” to reflect its user-friendliness and adaptability. According to OpenAI, the model was designed with human-like interaction in mind, making it a more intuitive and collaborative tool.

    Key Features of GPT-01 “Strawberry”

    1. Coding: Thoroughly Analyzing Each Instruction

    It’s fascinating how GPT-01 generates code compared to GPT-4. The GPT-01 preview version takes time, carefully considering the prompt. In programming, we often provide detailed instructions, and GPT-4 tends to miss or overlook some aspects, much like how we might feel when juggling too many tasks simultaneously. However, GPT-01 meticulously processes all the information, analyzing every requirement slowly and thoroughly.

    In a demonstration, they used the following coding prompt:

    2. Reasoning: Understanding Context and Surroundings

    GPT-01 is designed to tackle common-sense reasoning, where most large language models (LLMs) struggle. It can make decisions in complex situations, such as identifying relationships between objects and their physical context.

    In the demonstration, they used this prompt:

    3. Mathematics: Tackling Complex Problems

    In the demonstration, GPT-01 handled math problems of medium difficulty easily. It efficiently processed tasks involving logical sequences, groupings, and trends.

    The math prompt presented in the video was:

    Comparing GPT-01 to GPT-4

    Although GPT-01 is slower and more expensive to use than GPT-4, it excels in complex reasoning tasks. In a test against the International Mathematics Olympiad’s qualifying exam, GPT-01 correctly solved 83% of the problems, compared to GPT-4’s 13%.

    Here’s a comparison chart highlighting the key differences between GPT-01 (Strawberry) and GPT-4:

    FeatureGPT-01 (Strawberry)GPT-4

    Release DateSeptember 2024March 2023

    Core FocusAdvanced reasoning and multi-step problem-solvingGeneral-purpose language generation

    Reasoning CapabilitiesSuperior in complex tasks like coding, math, and logical reasoningModerate reasoning skills

    SpeedSlower, takes more time to process multi-step tasksFaster response time for general queries

    Cost (per 1M input tokens)$15$5

    Cost (per 1M output tokens)$60$15

    AccuracyHigher accuracy in reasoning tasks, fewer hallucinationsAccurate for general text generation but prone to more hallucinations in complex tasks

    Use CasesBest suited for math, coding, logic, and strategic tasksIdeal for text generation, creative writing, and casual Q&A

    Complex Problem SolvingExcels in multi-step reasoning, performs well on math exams and programming tasksLimited, struggles with advanced multi-step problems

    Conversational Context RetentionRetains context over extended dialogues effectivelyAdequate but can lose context in long conversations

    Factual KnowledgeLess proficient at factual world knowledgeStronger at handling factual information

    Browsing and File ProcessingDoes not support browsing or file/image processingCan browse the web (with plugins) and process files (with plugins)

    Target AudienceDevelopers, engineers, educators, researchersGeneral users, content creators, casual inquiries

    Training ApproachTrained with reinforcement learning for reasoning tasksTrained on large datasets for language prediction

    AvailabilityCurrently available to ChatGPT Plus and Team usersWidely available for both free and paid users

    Use Cases for GPT-01

    Coding and Programming

    GPT-01 significantly outperforms its predecessors when it comes to programming tasks. It can process complicated code, understand step-by-step instructions, and produce real-time error-free outputs, making it ideal for developers and engineers.

    Mathematical Problem Solving

    The model’s ability to solve complex math problems is a notable advancement. For example, GPT-01 can tackle multistep word problems and logical puzzles, making it a valuable tool for anyone studying or working in math-intensive fields.

    Business Applications

    In business, GPT-01 can assist with data analysis, risk assessment, and long-term strategic planning by logically processing incomplete data and offering suggestions based on trends and predictions.

    Education and Tutoring

    With its ability to break down complex problems and offer step-by-step reasoning, GPT-01 can act as a student tutor. Whether in math, coding, or philosophy, this model can offer detailed explanations and help learners understand difficult concepts.

    Challenges with GPT-01

    Despite its advancements, GPT-01 does have limitations. The model is slower than GPT-4 and requires more computational power, which increases its cost. It also struggles with factual knowledge and cannot browse the web or process files and images, limiting its use in certain scenarios.

    What’s Next for OpenAI?

    OpenAI has indicated that GPT-01 is just the beginning of a new series of reasoning models. The company also focuses on improving the model’s speed, cost-efficiency, and factual accuracy in future iterations. As these models evolve, they will likely take on more advanced tasks, moving closer to human-like intelligence.

    Conclusion

    OpenAI’s GPT-01 “Strawberry” is a groundbreaking development in AI, marking the first model with advanced reasoning capabilities. While slower and more expensive than previous models, GPT-01 excels at solving complex problems like coding, math, and logical reasoning. As AI continues to evolve, GPT-01 paves the way for smarter, more collaborative AI systems that can assist humans in more nuanced and sophisticated ways.

    FAQs

    What makes GPT-01 different from GPT-4? GPT-01 is designed for complex reasoning tasks, such as coding and mathematical problem-solving, while GPT-4 is faster and better suited for general text generation.

    Why is GPT-01 called “Strawberry”? The name reflects its user-friendly nature and adaptability, emphasizing its collaborative potential.

    How much does GPT-01 cost to use? GPT-01-preview costs $15 per 1 million input tokens and $60 per 1 million output tokens, making it more expensive than GPT-4.

    Can GPT-01 browse the web? No, GPT-01 does not currently have browsing capabilities or the ability to process files and images.

    What industries will benefit most from GPT-01? Industries like coding, education, business analysis, and healthcare will see the most immediate benefits from GPT-01’s reasoning abilities.



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    Can Machine Learning Models Learn from Each Other?

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    Can Machine Learning Models Learn from Each Other?


    Artificial Intelligence (AI) is evolving rapidly, particularly in how machine learning (ML) models learn and interact with one another. Traditional methods heavily depend on vast human-generated datasets, but recent advancements explore the idea of models teaching each other, resembling human learning dynamics. This approach, which uses synthetic data and novel learning paradigms, has significant implications for overcoming data scarcity, enhancing accessibility, and democratizing AI.

    The Rise of Collaborative Learning in AI

    Artificial intelligence has reached new heights with the development of collaborative learning techniques, where machine learning (ML) models learn from one another, mimicking human-like interactions. This approach moves away from traditional reliance on vast amounts of human-generated data, which is becoming harder to obtain and more expensive. Instead, AI models now generate synthetic data and use it to improve themselves and others in an efficient, iterative process. This shift could democratize AI, making advanced technology more accessible to small companies and individuals with limited resources.

    The Role of Synthetic Data in Transfer Learning

    At the core of collaborative learning is synthetic data generation, which allows models to create their own datasets rather than relying solely on human-generated data. This synthetic data is a crucial component of transfer learning, where a larger, more capable model acts as a teacher to a smaller, less powerful model. This teacher-student dynamic enables the smaller model to gain insights from the larger one without extensive retraining on expensive and scarce datasets.

    Transfer learning with synthetic data transforms how models are trained, making it possible for even small-scale AI projects to benefit from the knowledge embedded in larger models. Two key projects, Self-Instruct and Alpaca, have demonstrated the immense potential of this approach.

    Self-Instruct: Enhancing Models with Self-Generated Tasks

    Self-Instruct is an innovative project that enhances a model’s instruction-following capabilities by allowing it to train on synthetic tasks it creates. The process, called iterative refinement, allows the model to generate instructions, perform tasks, and then evaluate and refine its own outputs.

    In the Self-Instruct project, a pre-trained language model generates tasks that consist of instructions, contextual inputs, and expected outputs. These tasks are not manually crafted by humans but produced by the model itself. As the model continues to work through these tasks, it improves its ability to follow instructions in new, unseen scenarios. Notably, a quality filter ensures that only the best-generated tasks are used for further training, eliminating low-quality or irrelevant data.

    The iterative nature of this refinement allows the model to continuously improve, making it increasingly adept at understanding and executing instructions with minimal human intervention. By reducing dependency on human-generated data, Self-Instruct offers a way to create highly specialized models that can perform well across various tasks without requiring vast datasets or retraining.

    Alpaca: Efficient AI with Teacher-Student Dynamics

    While Self-Instruct focuses on self-generated learning, Alpaca introduces a hierarchical approach, where a more advanced model teaches a smaller, less capable one. Alpaca leverages OpenAI’s text-davinci-003, a highly advanced language model, to create instruction-following tasks for LLaMA 7B, a smaller and less resource-intensive model developed by Meta.

    The Alpaca project demonstrates that it is possible to train smaller models to follow complex instructions effectively, even with limited resources. This teacher-student setup replicates a real-world classroom scenario, where the more knowledgeable teacher provides guidance and tasks that help the student model learning. The student model, in turn, benefits from the teacher’s advanced capabilities, achieving a high level of performance without requiring the same level of computational power or data as the teacher.

    This method reduces the cost of training AI models and allows smaller entities to participate in AI development. Companies and individual researchers with limited budgets can still produce AI models that perform well on specific tasks, such as natural language understanding or instruction-following.

    How Collaborative Learning is Democratizing AI

    The success of projects like Self-Instruct and Alpaca highlights the potential for collaborative learning to democratize access to advanced AI technology. Previously, training large models required immense computational resources and datasets, making it the domain of large tech companies with deep pockets. However, with collaborative learning techniques, smaller models can tap into the knowledge of larger ones, drastically reducing the need for resources.

    Critical Benefits of Collaborative Learning:

    Resource Efficiency: Smaller models can achieve high performance by learning from larger models, reducing the need for extensive computational power and large datasets.

    Cost Reduction: Companies no longer need to invest heavily in hardware or data acquisition. Models like Alpaca prove that effective instruction-following models can be built on a budget.

    Scalability: The teacher-student framework can be applied across industries, allowing for scalable AI development even in resource-constrained environments.

    Increased Accessibility: Collaborative learning lowers the barriers to AI development, ensuring that smaller companies and individual researchers can contribute to the AI landscape.

    The Future of Collaborative AI

    As AI continues to evolve, the future will likely see an increasing reliance on collaborative learning methods. These techniques solve the pressing issue of data scarcity and introduce a more sustainable way to train and refine models. By utilizing teacher-student setups and synthetic data generation, the AI field is set to become more inclusive, with even small players able to develop powerful models for various applications.

    The combination of transfer learning, iterative refinement, and hierarchical teaching structures presents an exciting future for AI development. Whether in healthcare, finance, customer service, or other industries, collaborative learning will enable more entities to harness the power of AI, regardless of their size or resources.

    In essence, collaborative learning redefines how AI models are trained and deployed, making them more efficient, cost-effective, and accessible. This paradigm shift not only accelerates the pace of innovation in AI but also ensures that the benefits of these advancements are widely shared, empowering businesses and individuals alike to explore new frontiers in machine learning.

    Learning Paradigms: Beyond Traditional Fine-Tuning

    Machine learning has come a long way since the early days of simple algorithms and data processing. Today, researchers are exploring advanced learning paradigms that push the boundaries of what AI can achieve. These new methods go beyond traditional fine-tuning, offering innovative ways for models to learn, adapt, and improve without the typical limitations of human-generated data and extensive retraining. Let’s dive into three key approaches—iterative Refinement, Knowledge Distillation, and Learning by Teaching—that are reshaping the landscape of collaborative machine learning.

    1. Iterative Refinement: Self-Improvement Through Continuous Feedback

    Iterative Refinement is a process where models enhance their capabilities by generating and learning from their outputs. Unlike conventional training, which relies heavily on external datasets, Iterative Refinement creates a feedback loop where the model continuously assesses and improves itself.

    Here’s how it works: the model generates synthetic data—often tasks or scenarios similar to what it’s already been trained on—and then uses these outputs as new training data. By iterating on this process, the model identifies errors, adjusts its parameters, and refines its performance over time. This approach is similar to a student reworking practice problems to master a concept, constantly learning from mistakes and refining their understanding.

    Benefits:

    Data Efficiency: Models can continue to learn without needing vast amounts of new, human-generated data.

    Self-Sufficiency: The model independently identifies areas of improvement, leading to continuous growth.

    Customization: Models can adapt to specific tasks, making them highly specialized without needing extensive retraining.

    Challenges:

    Quality Control: It is crucial to ensure that the synthetic data generated is of high quality; poor-quality data can lead to suboptimal learning.

    Computational Resources: Iterative processes can be computationally expensive, requiring significant processing power and time.

    2. Knowledge Distillation: Transferring Wisdom from Teacher to Student

    Knowledge Distillation is inspired by the traditional educational model, where a knowledgeable teacher guides students through complex subjects. In this machine-learning context, a large, powerful model (the teacher) transfers its expertise to a smaller, less capable model (the student). This transfer allows the student model to perform highly without extensive retraining on large datasets.

    The process involves the teacher model providing output predictions, which the student model then learns to replicate. Through this method, the student model not only learns the task but also picks up the teacher’s nuanced decision-making processes, resulting in a highly efficient and specialized model that performs well with fewer resources.

    Benefits:

    Resource Efficiency: The student model is much smaller and requires fewer computational resources, making it suitable for deployment in resource-constrained environments.

    Performance Enhancement: The student model can perform at levels comparable to the larger teacher model, making high-quality AI accessible even without powerful hardware.

    Scalability: This approach allows AI capabilities to be scaled down effectively, enabling practical applications in various industries.

    Challenges:

    Loss of Detail: The student model may lose some of the finer points of the teacher’s knowledge, potentially leading to reduced performance in highly complex tasks.

    Dependency on Teaching Quality: This approach’s effectiveness depends heavily on the quality of the teacher model and the training process.

    3. Learning by Teaching: A Two-Way Feedback Loop for Continuous Improvement

    Learning by Teaching draws inspiration from a well-known human learning strategy: teaching others. This approach introduces a unique feedback loop where a student model doesn’t just learn passively but actively contributes to the teacher model’s improvement. Here’s how it works: after the teacher model instructs the student, the student’s performance is assessed, and this feedback is returned to the teacher. The teacher model then uses this performance data to adjust its teaching strategy, creating a dynamic and reciprocal learning environment.

    This method mirrors how a tutor learns to refine their explanations based on how well their student understands and applies the material. In machine learning, this approach allows models to enhance their teaching methods, adapting and evolving to improve.

    Benefits:

    Enhanced Learning Efficiency: The feedback loop ensures that the teacher and student models continuously improve, leading to more effective learning outcomes.

    Adaptive Teaching: The teacher model can adjust its approach based on real-time student performance data, leading to a more personalized learning experience.

    Innovation in Learning Paradigms: This method introduces interactivity and adaptability beyond traditional model training techniques.

    Challenges:

    Complex Implementation: Setting up a feedback loop between teacher and student models requires sophisticated programming and robust evaluation metrics.

    Risk of Overfitting: The teacher model might overly tailor its teaching to the student’s current performance, potentially limiting the broader applicability of the learned knowledge.

    The potential applications of models learning from each other are vast and varied. From creating specialized models that perform specific tasks without needing large, general-purpose datasets to address privacy and copyright concerns, these techniques can redefine AI training.

    However, challenges remain. For example, fine-tuning can lead to “catastrophic forgetting,” where a model loses its general abilities in favor of specialized skills. This trade-off raises questions about the broader applicability of collaborative learning methods beyond specific use cases.

    Philosophical Implications: Machines Learning from Machines

    The concept of machines teaching machines carries philosophical intrigue. It parallels human learning, where teaching often solidifies one’s understanding. Richard Feynman, a physicist known for his teaching techniques, highlighted the power of learning by teaching—a principle that now finds resonance in machine learning. The emerging AI techniques reflect a creative and experimental spirit, pushing the boundaries of how models can evolve autonomously.

    Final Thoughts

    Collaborative learning through synthetic data and model interaction is a promising avenue in AI research. It challenges traditional data-centric paradigms, offering new ways to train models efficiently and make AI accessible to a wider audience. While the full potential of these methods is still unfolding, they represent a significant step towards more flexible, resource-efficient, and innovative AI systems.

    This exploration of machine models learning from each other underscores a pivotal shift in AI development. It embraces the creativity and ingenuity of human-inspired learning techniques, making the future of AI both exciting and accessible.



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    Heurist: A Comprehensive Overview

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    Heurist: A Comprehensive Overview



    <![CDATA[

    Heurist is a Layer 2 network designed for AI model hosting and inference, built on the ZK Stack. Its mission is to be the HuggingFace of Web3. By leveraging a decentralized computing resource network, Heurist provides serverless access to open-source AI models, utilizing blockchain technology to democratize AI and ensure accessibility and unbiased innovation.

    Vision and Values

    Heurist envisions AI to be instrumental in onboarding the next billion users to crypto. The project is grounded in the shared values of open-source AI and crypto, such as transparency, data ownership, and diversity. Heurist aims to foster a two-way onboarding process: empowering crypto enthusiasts to engage with AI through a permissionless mining network using their home PCs and enabling AI model developers to access increased compute resources, publicize their models, and earn revenue share.

    By supporting the development of Web3-native AI applications, Heurist allows individuals to access AI services at low or no cost, showcasing the practical benefits of decentralized technologies. The Heurist ecosystem promotes innovation, collaboration, and inclusivity, uniting the strengths of AI and crypto to create a future where advanced technology is accessible to everyone.

    Name and Inspiration

    The name Heurist is derived from the concept of heuristics, which refers to mental shortcuts that help humans quickly arrive at reasonable solutions to complex problems, reflecting the projects goal to make sophisticated AI accessible and approachable for all users.

    How Heurist was Founded

    Jiewen Wang and Frank is the founder of Heurist. The journey began with an AI agent Heurist founders built to engage in crypto discussions on friend.tech. Through this project, they encountered several common challenges in AI Application development: high computational costs, censorship by major platforms like OpenAI, and a lack of AI model control.

    To address these, they launched Heurist as a decentralized protocol to cut AI costs while maximizing customizability and sovereignty. With their teams extensive experience in Web2 and deep roots in crypto, the team is dedicated to democratizing technology and making an AI ecosystem safe, accessible, and resistant to central controls.

    Heurist vision is to empower developers and users worldwide to harness the potential of AI without the limitations imposed by traditional centralized platforms.

    The Heurist Team

    Here’s how Heurist works🤔

    Heurist protocol intricately connects various participants, each playing a vital role in maintaining a healthy decentralized ecosystem. These participants include Model Users (Consumers), Miners (Model Hosts), Model Creators, Application Integrators, and Validators.

    To ensure economic security and the integrity of data returned from AI models, we will launch a utility token, Heurist Token (HUE). Participants can spend, stake and/or earn HUE based on the rules defined in smart contracts.

    Roles in Heurist Ecosystem

    Model Users: Users interact with the Heurist protocol to run inference tasks, such as text generation and image generation. They can choose from a variety of hosted AI models and pay for the computational resources on a pay-as-you-go basis.

    Miners: Individuals with GPU resources can mine Heurist Token by hosting AI models. They run models on their own hardware and are compensated with user payments and Heurist Token emissions when consumers execute inference tasks. Miners are required to stake a certain amount of tokens as a commitment to providing quality services.

    Model Creators: AI model creators drive the vibrancy of Heurist ecosystem. They upload AI models to the model registry of Heurist network and, in return, receive a portion of the payments made by users. This incentivizes creators to develop more advanced models to satisfy user’s growing demand.

    Application Integrators: Application integrators provide interfaces to end users that use Heurists AI models under the hood. Such interfaces might include chatbots, AI agents, and image generation tools hosted as web applications, and might also include SDK that can be integrated in web services. Application integrators earn a fraction of fees in Heurist Token whenever consumers make a payment through the application.

    Validators: Validators maintains the integrity and trustworthiness of the Heurist network. They periodically check the correctness of the data produced by miners. If a miner is found to be producing invalid or fake data, their stake is slashed, with a portion awarded to the validator who identified the discrepancy.

    Dynamic Resource Allocation

    As the network grows, an increasing number of AI models and compute resources (mainly GPUs) will join it. We use token voting to allocate compute resources to every model.

    The heurist protocol allocates more powerful GPUs to those models with (1) higher demand and (2) high token voting. An AI model creator can acquire more compute resources in two ways: by encouraging organic usage or by voting with their own tokens. Token holders are incentivized to vote for higher-quality models with higher demand as they will receive a larger revenue share from those models.

    The Current Project Update of Heurist

    August has been a month of exciting launches, milestones, and community-driven initiatives. Lets dive into the highlights. You can refer to the blog Heurists August Highlights for full details.

    zkImagine Launches on ZKsync: Heurist’s AI-powered NFT collection, zkImagine, allows users to create and mint unique artworks with zero gas fees, thanks to the Paymaster feature. Explore zkImagine.

    1 Billion Inference Requests Processed: Heurist has processed over 1 billion inference requests, maintaining a strong performance with over 1,000 active GPUs. Mining emissions are stabilized at an annualized rate of 2.1%.

    Heurist Heroes Game: A 50,000 HEU token grant has been approved for the Heurist Heroes Game, an engaging 2D endless shooter with AI quizzes and reward incentives.

    %[https://vimeo.com/1005038193]

    Create-to-Earn Campaign: Sprint 0 of the Create-to-Earn campaign is complete, refining a reward mechanism for users who create and mint NFTs with zkImagine.

    Art Contest with Moni Talks: Over 100 images were submitted for the Russian-speaking communitys art contest, showcasing Heurist Imagines creative potential.

    Heurist Roadmap

    Heurist token (HUE)

    HUE is a utility token with a dynamic supply, influenced by both emission and burning mechanisms. The maximum supply is capped at 1,000,000,000.

    Emission Mechanisms

    1. Mining

    Process: Users can mine HEU tokens by hosting AI models on their GPUs.

    Staking Requirement: A minimum of 10,000 esHEU tokens must be staked for a mining node to be active. Below this threshold, no rewards are generated.

    Reward: Mining yields HEU tokens. Reward rate depends on GPU efficiency, availability (uptime), the type of AI model being run, and the total amount staked in a miner node.

    Boosted POW Mining: For stakes between 10,000 to 100,000 HEU tokens, mining efficiency increases proportionally with the amount staked.

    2. Staking

    Process: Any user can stake esHEU tokens in mining nodes.

    Reward: Staking yields are given in HEU token. Staking esHEU produces higher yields than HEU.

    Vesting: esHEU rewards can be vested into HEU over a one-year period with linear vesting.

    Transfer of Stake: Users can instantly transfer their HEU or esHEU stake from one miner to another. This promotes flexibility and competition among miners.

    Burning Mechanisms

    Similar to the EIP-1559 model in Ethereum, Heurist implements a token burn mechanism. When users pay for AI inference, a portion of the HEU payment is permanently removed from circulation.

    The balance between token creation and burning is closely tied to network activity. During periods of high usage, the rate of token burning can exceed the rate of new token creation. This will potentially lead the Heurist network into a deflationary phase. This mechanism helps in regulating the token supply, and aligns the token value with the actual demand within the network.

    Bribes and (3,3)

    Bribes were a term first coined by users of DeFi, more specifically, by those of Curve Finance. These bribes are actually gamified incentivizes that help direct rewards for liquidity pools, in exchange for an incentive. We draw inspiration from the bribing mechanism of Curve, and apply it in boosted POW mining in Heurist.

    Miners have the option to set a specific percentage of their mining rewards as a bribe to attract stakers. A staker may choose the miner with the highest bribe, but other factors should also affect the staker’s choice such as miner’s hardware performance and uptime.

    Miners are incentivized to bribe because a higher stake in the miner node will result in higher mining efficiency. It creates a competitive yet cooperative environment where miners and stakers are aligned to provide better service to the network.

    With this game theory-inspired (3,3) mechanism, we create an alignment between miners and token holders who take actions to achieve the best collective outcomes.

    Proposal Page

    Heurist Stats

    Top 10 GPUs on Heurist Ecosystem

    Heurist Unique Features and Advantages

    Heurist offers many unique advantages and features:

    Decentralized AI Network: Heurist is a decentralized protocol for AI model hosting and inference, built on the ZK Stack Layer 2 network. This decentralized approach aims to democratize AI and ensure accessibility, transparency, and unbiased innovation.

    Incentivized Ecosystem: Heurist has a utility token (HUE) that facilitates an incentivized ecosystem. Participants can earn, stake, or spend HUE tokens based on their roles, such as miners (model hosts), model creators, application integrators, and validators.

    Dynamic Resource Allocation: Heurist uses token voting to dynamically allocate compute resources (GPUs) to AI models based on demand and token voting. Models with higher demand and more token votes receive more powerful GPU resources.

    Open-Source AI Models: Heurist emphasizes the use of open-source AI models, which are transparent, customizable, and often comparable in performance to closed-source models like ChatGPT or DALL-E.

    Pay-as-you-go Model Access: Users can access and run inference tasks on a variety of hosted AI models, paying for computational resources on a pay-as-you-go basis, potentially at lower costs than closed-source alternatives.

    Incentivized Testnet: Heurist has an incentivized testnet where participants can earn testnet rewards (Llama Points and Waifu Points) for hosting AI models on their GPUs. These points will be claimable as liquid HUE tokens at the mainnet launch.

    Developer APIs: Heurist provides REST APIs for developers to integrate AI models, like Stable Diffusion for image generation and Mistral 8x7b (comparable to ChatGPT 3.5) for language models, into their applications.

    Bribes and (3,3) Mechanism: Heurist implements a “bribes” mechanism inspired by DeFi, where miners can set a percentage of their mining rewards as a bribe to attract stakers, creating a competitive yet cooperative environment aligned with providing better services to the network.

    Token Burning Mechanism: Heurist has a token burn mechanism similar to EIP-1559, where a portion of user payments for AI inference is permanently removed from circulation, potentially leading to a deflationary phase and aligning token value with network demand.

    Anti-Cheat System: Heurist has an asynchronous monitoring system to track input and output of compute jobs, allowing for the slashing of testnet rewards for miners demonstrating malicious activities.

    Node Rental Service: Heurist provides a managed GPU node rental service, sourcing deals from various data center partners, making it easier for participants without GPU hardware to join the network.

    Community-Driven Innovation: Heurist aims to foster a culture of innovation, collaboration, and inclusivity, bringing together the AI and crypto communities to create an ecosystem where cutting-edge technology is accessible to all

    Spheron X Heurist: Unleashing the Power of Decentralized AI

    Heurist and Spheron are joining forces to redefine the landscape of AI and decentralized compute, empowering a community-driven future free from traditional barriers. By integrating Spherons cutting-edge, scalable compute solutions, Heurist will provide seamless access to open-source AI models for training and inference, significantly lowering costs and democratizing access to powerful AI tools.

    In return, Spheron will leverage Heurists infrastructure to develop innovative, open-source use cases, showcasing the power of decentralized AI and GPU rental solutions. This partnership will also introduce a dedicated “GPU Rental” tab on Heurist’s platform, prominently featuring Spheron as a key partner, creating a unique synergy that accelerates the adoption of open, censorship-free AI technologies.

    Heurist and Spheron are not just building tools; they are creating a decentralized, community-led ecosystem that prioritizes accessibility, transparency, and innovation in AI and compute.

    Conclusion

    In conclusion, Heurist is positioning itself at the forefront of a transformative movement in the AI and blockchain space. By leveraging decentralized computing and the ZK Stack Layer 2 network, Heurist aims to democratize AI access and foster innovation without the constraints of traditional centralized platforms.

    The protocols unique structurewhere users, miners, developers, and validators collaborateensures a fair, incentivized, and transparent ecosystem that balances performance and community-driven governance. The vision of combining AI with crypto opens the door for billions to engage with advanced technologies in a cost-effective, unbiased, and censorship-free environment.

    Through initiatives like zkImagine, the Heurist Heroes Game, and strategic partnerships with companies like Spheron, Heurist is advancing the technology and building a future where AI innovation is genuinely decentralized and accessible to everyone. As Heurist continues to grow, it is clear that the projects values of openness, collaboration, and inclusivity will be crucial to its long-term success.

    FAQs

    What is Heurist? Heurist is a decentralized Layer 2 network designed for AI model hosting and inference. It leverages blockchain technology to democratize AI and provide serverless access to open-source models.

    How does Heurist work? Heurist connects various participantsmodel users, miners, creators, application integrators, and validatorsin a decentralized ecosystem where computational resources are allocated dynamically through token voting and incentives.

    What role does the Heurist Token (HUE) play? HUE is the utility token of the Heurist network. It facilitates economic interactions like mining, staking, and payments for AI model usage, as well as governance through token voting.

    What makes Heurist unique? Heurist offers decentralized AI services focusing on open-source models, pay-as-you-go access, a bribe mechanism for staking, and a token-burning system that can make the network deflationary during periods of high demand.

    How does Heurist promote decentralized AI? Heurist decentralizes the hosting and usage of AI models, allowing individuals and developers to engage with AI services without relying on centralized platforms. Thus, it enhances accessibility, innovation, and fairness in the AI landscape.

    ]]>



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    Launchpad: The Future of Token Distribution

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    Launchpad: The Future of Token Distribution


    We’re redefining how entire crypto ecosystems are built, launched, and scaled. Introducing the Layer3 Launchpad for exclusive token launches and ecosystem campaigns. This is tokenized attention at scale. Buckle up.

    For explorers, Launchpad means:

    Exclusive access to pre-token protocols

    Incentives from established ecosystems

    High-quality, interactive experiences

    For projects, Launchpad is new rocket fuel:

    Exposure to a massive, targeted user base

    Onchain engagement plus flexible incentives

    Token distribution with powerful Sybil filters

    Launchpad isn’t just another platform. It’s the future of how crypto projects launch, grow, and align with their communities. We’re bridging the gap between emerging protocols and millions of engaged users, creating a new standard for token distribution and ecosystem expansion.

    Launchpad on Layer3

    Launching Now: Caldera 🌋🚀

    Meet the Metalayer — the unifying layer of Ethereum rollups in our exclusive Launchpad campaign. Easily connect to new protocols via the Caldera Metalayer including Plume, B3, & Manta.

    Featuring $100K in exclusive incentives for discovering Caldera’s rollup ecosystem.

    Meet the Metalayer

    L3 Token Staking

    By staking L3, your loyalty unlocks expanded utility:

    Boosted rewards on all campaigns

    Early and extended access

    Tiered benefits that scale with your stake

    This is how you align with the future of crypto.



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    Bitcoin Layer 2s: Explained & Examples | Chainlink

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    Bitcoin Layer 2s: Explained & Examples | Chainlink


    A Bitcoin layer 2 is any offchain network, system, or technology built on top of the Bitcoin blockchain to help extend its capabilities.

    Since its inception in 2008, Bitcoin has become the focal point of the verifiable web as the first decentralized cryptocurrency, remaining the largest one by market capitalization. However, Bitcoin’s growth has faced challenges due to the network’s limited scalability, often resulting in high transaction fees and network congestion. 

    The recent introduction of ordinals, BRC-20 tokens, and other Bitcoin-native onchain applications has further exacerbated these issues, where—especially during peak times—Bitcoin can become impractical for everyday use. These ongoing scalability challenges highlight the immediate need for Bitcoin scaling solutions such as layer-2 networks.

    In this post, we discuss what Bitcoin layer 2s are, how they work, and how they can benefit from industry-standard oracle services.

    How Do Bitcoin Layer 2s Work?

    The Bitcoin network takes about 10 minutes to finalize a single block of transactions—only seven transactions per second (TPS) on average. Scaling the Bitcoin blockchain directly isn’t an option, as it would require compromising either security or decentralization per the blockchain scalability trilemma.

    The blockchain scalability trilemma posits that a tradeoff has to be made when attempting to maximize scalability, security, and decentralization.

    It’s worth noting that the limited core functionality (i.e., a global, censorship-resistant decentralized currency) helps make the Bitcoin network so tamper-proof and robust, and a significant technological breakthrough of the 21st century. However, this narrow focus limits the network’s usability in everyday scenarios and limits developers’ ability to launch new applications on Bitcoin. Enter layer 2s.

    A Bitcoin layer 2 is any offchain network, system, or technology built on top of the Bitcoin blockchain that helps extend its capabilities. Layer-2 networks can introduce improvements such as greater transaction throughput, reduced fees, and programmability through smart contracts. A key requirement for a network to be considered a layer 2 is that it must inherit the security of the blockchain it is built on—in this case, Bitcoin. In the case of Bitcoin layer 2s, this means that transaction data is verified and confirmed by the Bitcoin blockchain rather than a separate set of nodes.

    Layer-2 networks can vary considerably in how they achieve this increased scalability, but a common denominator between layer-2 environments is that when looking to settle on the base chain, they must provide some kind of cryptographic proof to the blockchain on the integrity of the proposed state change, either preemptively or retroactively.

    If you’d like a deep dive into how layer 2s work, read What Is Layer 2?.

    Types of Bitcoin Layer 2s

    There are several types of Bitcoin scalability solutions that can be categorized as layer 2s, though some exist in a gray area regarding their classification as true layer-2 solutions. Note that Bitcoin scalability is an ongoing area of research, and new solutions and technologies may emerge in the future to address some of the limitations of current Bitcoin layer-2 technologies.

    State Channels

    State channels enable users to bypass high transaction fees by moving transactions offchain, where two parties lock a certain amount of bitcoin into a multisig to send and receive payments. These channels then maintain records of all transactions that occur within them until they are closed. When the parties are done transacting, they sign and broadcast the final state of the channel to the Bitcoin blockchain.

    State channels keep all transactions within them offchain, only reporting the opening and closing balance of participants to the Bitcoin network. This allows participants to make transactions without having to pay Bitcoin mainnet fees for each transaction. 

    State channels are similar to payment channels in the Bitcoin Lightning Network, but they also support more complex transactions other than micropayments.

    Bitcoin Lightning Network payment channels diagram
    State and payment channels enable users to move transactions offchain.

    Sidechains

    A sidechain is an independent blockchain with its own consensus mechanism that connects to Bitcoin via a two-way peg, allowing the transfer of assets or balances between the two chains. While sidechains often use bitcoin as their native currency, they can also issue their own native tokens.

    Operating as separate blockchains, sidechains offer faster transactions and additional features like smart contracts. Because sidechains have their own validator set, they are not always considered true layer-2 solutions, as transactions aren’t necessarily ultimately verified by the Bitcoin network. However, some sidechains may tap into Bitcoin’s security or periodically settle on the main chain.

    Rollups

    Bitcoin layer-2 rollups move transaction execution and data off the main Bitcoin blockchain to a separate rollup chain or layer while still anchoring to Bitcoin for data availability and consensus.

    Rollup technology involves executing transactions on the rollup chain, compressing data, and anchoring to the Bitcoin mainnet. Users submit transactions to be executed on the rollup chain rather than directly on the Bitcoin blockchain. The rollup chain then processes these transactions and updates account balances accordingly. 

    After processing a batch of transactions offchain, the rollup compresses the transaction data into a compact cryptographic proof or commitment, representing the net effect of all those transactions. This compressed proof is periodically submitted to the Bitcoin blockchain as a single transaction, and some kind of verification mechanism on Bitcoin then validates and applies the changes represented by the rollup proof.

    Rollup transaction bundling
    Rollups bundle transactions into batches that are executed offchain and verified onchain using some kind of proof.

    Benefits of Bitcoin Layer 2s

    Bitcoin layer 2s offer several benefits:

    Greater scalability—Bitcoin layer 2s effectively increase the transaction throughput and speed of Bitcoin by processing transactions offchain and then settling them on the main chain, depending on the solution.
    Reduced fees—Bitcoin layer 2s enable lower transaction costs, unlocking use cases secured by Bitcoin that wouldn’t be feasible on the main Bitcoin network, such as micropayments.
    Programmable smart contracts—Although Bitcoin was not initially designed to support smart contracts, layer-2 solutions can introduce this functionality, enabling the creation of complex decentralized applications and novel programmable financial instruments built on Bitcoin.
    Deeper liquidity—Bitcoin layer 2s can improve liquidity and access to Bitcoin, unlocking DeFi on Bitcoin with enhanced liquidity, capital efficiency, and increased access.

    Bitcoin Layer 2 vs. Ethereum Layer 2

    The utility of layer-2 solutions lies in how they tap into the security of the main chain while increasing its scalability. In this way, Bitcoin and Ethereum layer 2s are quite similar—both seek to introduce enhanced scalability without making changes to the base layer. However, Bitcoin and Ethereum layer 2s differ significantly in their technical implementation, as Bitcoin and Ethereum themselves are designed for distinct purposes.

    Accelerating Bitcoin Layer 2 Adoption

    State channels, sidechains, rollups, and other layer-2 solutions are all methods of approaching the blockchain scalability problem in different ways—supporting both the growing adoption of onchain applications and enhanced use cases and user experiences.

    The Chainlink platform has underpinned the DeFi ecosystem since its inception, fulfilling the need for high-quality data, compute, and interoperability services that help developers create fully-fledged decentralized applications. In many cases, integrating Chainlink has helped layer-1 and layer-2 ecosystems bootstrap their growth with battle-tested Chainlink infrastructure, attracting both new developers and users. With the emerging ecosystem of Bitcoin layer-2 scalability solutions, the need for high-quality data and other oracle services will also grow in the Bitcoin ecosystem.

    Chainlink Effect
    Ecosystems that integrate high-quality Chainlnik services are better equipped to help bootstrap their ecosystem.

    A significant advantage of adopting the Chainlink platform is that once an app uses a single Chainlink service, there are little-to-no additional trust assumptions for using other Chainlink services since they are all built upon the same time-tested oracle infrastructure. And every new service built on top of Chainlink adds more value for all existing users. This is why blockchains are increasingly joining the Chainlink Scale program as a way to accelerate the growth of their application ecosystem. An integration with Chainlink can bring multiple services to a single blockchain and subsequently drive a surge in developer activity.

    To learn more about Chainlink, visit chain.link, subscribe to the Chainlink newsletter, and follow Chainlink on Twitter, YouTube, and Reddit.



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