RentRedi designed and built software that challenges the history of landlord-tenant software by providing an innovative, mobile software that makes renting easy for both landlords AND tenants. There’s a lot of ways technology is enhancing lives—from ordering food, rideshare services, gaming, streaming, ebooks—almost anything can be done now from a smartphone. We’re looking to bring that ease and peace of mind to renting as well. For landlords that want to self-manage their properties, we’re empowering them by centralizing and streamlining the property management and tenant relationship process—condensing the need for multiple platform visits into one. Everything they need to do to manage their rentals—rent, maintenance, tenant screenings, communication, listings—they can do it from our dashboard. For tenants, we’re putting the power to prequalify and apply to rentals, pay rent, communicate, and submit maintenance requests into their hands—literally. For Landlords: Web App for Desktop, tablets, iPhone, and Android For Renters: Mobile Apps for tablets, iPhone, and Android. Website: www.rentredi.com Web App for Owners: app.rentredi.com iOS App for Tenants: https://itunes.apple.com/us/app/rentredi/id1187683543?mt=8 Android App for Tenants: https://play.google.com/store/apps/details?id=com.RentRedi.RentRedi2

RentRedi

Beware of the possible presence of hidden cameras in hotels and rental homes. Let's go into the details and see how to find out if someone spies on us. Struggling with the various commitments of daily life, starting from family ones up to work ones, sometimes everyone just wants to want to finally unplug and spend some moments, possibly in fabulous places. relaxing At the same time, of course, one cannot help but think about the economic outlay that the holidays bring with them. If all this were not enough, there is another aspect that often raises more than a few concerns, namely that of . privacy In particular, many fear the presence of possible hidden cameras in their hotel room or when they decide to stay, for example, in an apartment rented through Airbnb. Well, precisely in this context, therefore, it will be interesting to know that there are some valid to which you can find out if someone is spying on us. All using only your smartphone. tricks How Is it Possible? So, let's go into the details and see everything there is to know about it. Hidden Cameras in Hotels and Rental Homes; Beware: What You Need to Know Today, we will focus our attention on the possible presence of hidden cameras in hotels or rented houses, for example, through . Airbnb We thank the Russian company , which specializes in security because they recently disclosed some tricks that can be used in order to be able to discover (you can check it in fact, the presence of technological tools through which they spy on us. Kaspersky ) here How to Find Them With Your Smartphone : In order to avoid having to deal with possible prying eyes, all you have to do is use your . smartphone How is it possible? Well, here are some of the useful tricks to be able to discover any hidden cameras: . All you have to do is turn off all the lights in the room or the apartment room and close the windows to remain in the dark. At that point, you have to position yourself near the point in the room where you think there may be a camera and turn on the flashlight of your smartphone together with the camera. Smartphone flashlight If there is a hidden camera, then, we will see a glow coming from it. In this way, we will be able to find out if there are hidden cameras or not. . As an alternative to the flashlight, you can use the camera of your mobile device. Thanks to the latter it is possible to trace the infrared lighting that we would not be able to see with the naked eye. Smartphone camera On the other hand, we invite you to pay attention because, in the event that the camera does not have infrared, unfortunately, we will not be able to identify the device we are looking for. . Finally, we recommend using one of the applications that can detect reflections from a lens or a wireless light. These include several apps, available completely free of charge, for both iOS and Android devices, such as Glint Finder or Hidden Camera Detector. Applications What to do Next In the event that your suspicions are confirmed, and it’s an apartment rented with Airbnb, then it is possible to request a . This is always in the event that the cameras have not previously been reported and inserted, as tools already present inside the house itself. refund If, on the other hand, you find hidden cameras in a hotel room, you have . to contact the management of the structure or the police Being able to check if there are hidden cameras in a hotel room or a rented apartment is possible. All you have to do is use one of the aforementioned tricks and that's it.

Hidden Cameras in Hotels and Rented Houses: How to Find Out if Someone Is Spying on You

#throwback to scrappy 2012… this post is an answer the Quora question: “ What are the challenges of living in rented office space in San Francisco? ” As Bruce Feldman suggested, living in an might be detrimental to your health. But weren’t those high-rise, asbestos insulated, moldy dorms unhealthy during college? office In the hunt for your office space/ , you should look for an office with the following characteristics: apartment 24 Hour Access The office being open 24/7 is crucial. You never want to be trapped in your office at night. Luckily, the majority of offices have 24 hour access. A small percentage of offices have showers; if your office doesn’t have a shower, make sure you have ‘round the clock access and go shower at the nearest gym. Private Room with a Door Here’s why: Close the door, draw your office curtains and turn in. Better than sleeping in common areas or open spaces where you have to stay up until everyone else is gone. Go to sleep when you want without people noticing. Usually focused on common areas and entry ways, cameras are practically never in private rooms. Know the path that avoids the cameras and gets you to the bathroom at night. Otherwise stay in your office until morning . Offices have cameras. (and put a bucket in the corner or something) In order to pull this off, you need to be a minimalist. You can’t own a lot of stuff. You should own 3–4 shirts, a couple pairs of pants, undergarments, a pair of shoes/sandals, your computer, your shower/bathroom kit, one pillow, one sleeping bag (you are camping bud), and a sleeping mat (go to REI) and that’s about it. Storage is important. You won’t be able to store this meager assortment of goods in the small locker that you are renting at your gym… unless you join a higher end gym that actually does laundry and has decent sized lockers (e.g. Equinox). But that’s a lot of extra cost. Storage. Bring “friends” back to the office… ahem, everybody needs private time Relaxed Decorating Standards Get a couple of cardboard boxes for storage. You must be able to close the boxes to hide your clothes and sleeping gear. If anyone ever discovers your gear and asks you about it, just say that you store your Yosemite camping gear at the office. For your desk, save money and buy two saw horses or get concrete blocks. Get an old door and set it on the concrete blocks/sawhorses and lean it up against the wall. These are known as “artist desks” or drafting desks. Should cost you $25–50 total. Find a chair. Done. *Note, when questioned about your spartan office decor, just say “I’m bootstrapping.” They will nod knowingly and complement you on your frugality Here’s an example of desk and sleeping setup: (picture of the apartment, located in Austin at 8th and Brazos, that my co-founder and I shared in 2012… this is the kind of minimalism you should strive for) Clean Air (so you can rest) The air must be clean and breathable. Mold or dust will be bad for your health and disrupt your sleep. An air purifier can help but try to go with an office that has good air. If your office space has these characteristics and you are careful, you will not get caught. Good luck.

How to Live in Rented Office Space in San Francisco

How to Implement the Idempotency-Key Specification on Apache APISIX

Apache APISIX-এ আইডেমপোটেন্সি-কী স্পেসিফিকেশন কীভাবে প্রয়োগ করবেন

So implementieren Sie die Idempotency-Key-Spezifikation auf Apache APISIX

Cómo implementar la especificación de clave de idempotencia en Apache APISIX

Comment implémenter la spécification Idempotency-Key sur Apache APISIX

Apache APISIX पर Idempotency-Key विनिर्देश को कैसे लागू करें

Apache APISIX で Idempotency-Key 仕様を実装する方法

Apache APISIX에서 멱등성 키 사양을 구현하는 방법

Como implementar a especificação de chave de idempotência no Apache APISIX

Как реализовать спецификацию идемпотентного ключа в Apache APISIX

Idempotency-Key Belirtimi Apache APISIX'te Nasıl Uygulanır?

Cách triển khai Đặc tả khóa Idempotency trên Apache APISIX

如何在 Apache APISIX 上实现幂等密钥规范

ArrayPool<T>, a mechanism to recycle temporary buffers and optimize performance by reducing garbage collection cycles. Introduction Frequent allocation and deallocation of bigger buffers can impact performance due to the increased work on the garbage collector. The recommendation is to use ArrayPool<T>, a mechanism to recycle temporary buffers and optimize performance by reducing garbage collection cycles. Learning Objectives Understanding the Problem with Traditional Buffer Allocation The Approach Using ArrayPool<T> Best Practices for Using ArrayPool<T> Prerequisites for Developers Basic understanding of C# programming language. 30 Day .Net Challenge Getting Started Understanding the Problem with Traditional Buffer Allocation A common approach developers use involves directly allocating a new buffer // Allocating a new large buffer
byte[] buffer = new byte[4096]; While the aforementioned code snippet looks clean and straightforward, it has significant drawbacks regarding performance, particularly in applications that frequently request large temporary buffers. Each allocation will increase the heap size, leading to more frequent garbage collection. The Approach Using ArrayPool<T> ArrayPool<T> is part of the System.Buffers namespace and provides temporary arrays, thereby reducing the need for frequent memory allocations and GC. // Using ArrayPool<T> to recycle large buffers
var pool = ArrayPool<byte>.Shared;
byte[] buffer = pool.Rent(4096);
try
{
    // Work with the buffer
}
finally
{
    pool.Return(buffer);
} Best Practices for Using ArrayPool<T> While ArrayPool<T> can significantly enhance performance, there are best practices to ensure its effective use: Always return the rented buffers to the pool in a final block to ensure that they are returned even if an exception occurs. Properly Return Buffers: If sensitive data is stored in the buffer, use the overload of the Return method that takes a boolean parameter indicating whether the buffer should be cleared. Clear Buffers When Necessary: Rent buffers for the shortest time necessary to keep the pool efficient and avoid exhausting the pool. Avoid Holding Buffers for Long Periods: Complete Code Create another class named ArrayPoolExample and add the following code snippet. public static class ArrayPoolExample
{
    public static void BadMethod()
    {
        // Allocating a new large buffer
        byte[] buffer = new byte[4096];
        // Simulate work with the buffer
        FillBuffer(buffer, 0xAA); // Example operation
        Console.WriteLine("Buffer used and will be discarded after method execution.");
    }

    public static void GoodMethod()
    {
        var pool = ArrayPool<byte>.Shared;
        byte[] buffer = pool.Rent(4096);
        try
        {
            // Work with the buffer
            FillBuffer(buffer, 0xBB); // Example operation
            Console.WriteLine("Buffer rented from the pool and returned after use.");
        }
        finally
        {
            pool.Return(buffer);
        }
    }

    public static void FillBuffer(byte[] buffer, byte value)
    {
        for (int i = 0; i < buffer.Length; i++)
        {
            buffer[i] = value;
        }
        // Just an example to simulate buffer usage
        Console.WriteLine($"Buffer filled with value: {value}");
    }
} Execute from the main method as follows. #region Day 22: Array Pool
static string ExecuteDay22()
{

    Console.WriteLine("Demonstrating BAD Method:");
    ArrayPoolExample.BadMethod();
    Console.WriteLine("\nDemonstrating GOOD Method:");
    ArrayPoolExample.GoodMethod();
    return "Executed Day 22 successfully..!!";
}

#endregion Console Output Demonstrating BAD Method:
Buffer filled with value: 170
Buffer used and will be discarded after method execution.

Demonstrating GOOD Method:
Buffer filled with value: 187
Buffer rented from the pool and returned after use. Complete Code on GitHub GitHub — ssukhpinder/30DayChallenge.Net C# Programming🚀 Thank you for being a part of the C# community! Before you leave: Follow us: | | | Youtube X LinkedIn Dev.to Visit our other platforms: GitHub More content at C# Programming

The 30-Day .NET Challenge - Day 22: Use Array Pool

Finding the right AI model to build a workflow around is hard. With so many models available across different platforms, it’s impossible to know where to start or how to find the one that best fits your specific needs. That’s the problem I set out to solve with AIModels.fyi, the only search engine for discovering and comparing AI models from across the web. The site has a simple premise: describe your problem in natural language, and get back a list of AI models that can potentially solve it. For example, you might search for “I need a model that can help upscale my images without losing clarity” or “make my cat look like it’s singing”. The goal is to , not just ML engineers, to find useful AI tools for their projects. make it easy for anyone Under the hood, the search works by taking the user’s query and comparing it to a database of over 240,000 AI models from 7,500+ creators across major platforms like Hugging Face, Replicate, Cerebrium, and DeepInfra. When a user types in a search, the query gets embedded into a vector using OpenAI’s text-embedding-ada-002 model. This query vector is then compared to pre-computed vector representations of the model names, descriptions, and use cases in the database. The most similar models by cosine distance are returned as the search results. Here’s a simplified version of the core search logic I’ve been using for about six months: const fetchData = async (query) => {
  // Embed the search query
  const embeddingResponse = await openAi.createEmbedding({
    model: "text-embedding-ada-002",
    input: query,
  });
  
  const embedding = embeddingResponse.data.data[0].embedding;

// Find the most similar models in the vector database 
  const { data: modelsData, error } = await supabase.rpc("search_models", {
    query_embedding: embedding,
    similarity_threshold: 0.75,
    match_count: 10,
  });
  return modelsData;
}; This vector search approach works reasonably well for surfacing relevant models when there’s a direct overlap between the query terms and the model descriptions. However, it struggles with more abstract, complex, or niche queries where the user’s language doesn’t exactly match the technical terminology in the database. For example, I recently wanted to find a model that could animate a portrait image using an audio voice clip to create a talking head effect. I knew there were some good models out there for this, but my searches for things like “talking portrait from image and audio” weren’t turning up the results I expected. The problem was that my query terms didn’t have enough literal matches with the specific words used in the model descriptions, even though the semantic intent was similar. This got me thinking about ways to improve the search experience to better handle those kinds of abstract or complex queries. How could I make the system smarter at understanding the user’s true intent and mapping it to relevant models in the database? That’s when I came across the idea of Hypothetical Document Embeddings (HyDE) from after reading about it in . this research paper this Medium post Meet Mr. HyDE — The query expander! The key insight behind HyDE is to use a large language model (LLM) to expand the user’s short query into a more detailed hypothetical “ideal” document before embedding it for search. So, instead of directly matching the query embedding to the database embeddings, it first translates the query into a richer representation in the same semantic space as the real documents. Here’s how the process works: The user enters a natural language query expressing their needs, like “I want to convert an image into a 3D model.” This generated pseudo-document is then embedded into a dense vector using a model like OpenAI’s text-embedding-ada-002. The resulting embedding is used to query the vector database and find the most similar real model documents. The query is fed into an instruction-tuned LLM like GPT-3 to generate a hypothetical document. The prompt asks the model to imagine an ideal document for answering the query. For example: Query: I want to convert an image into a 3D model Hypothetical Document: There are several AI models available that can convert a 2D image into a 3D model. The process is known as single-view reconstruction or 3D estimation. It typically involves using deep learning to predict the depth map and 3D shape from the 2D input image. Some popular models for this task include Pix2Vox, Pixel2Mesh, AtlasNet, and OccNet. These models are trained on large datasets of 3D objects and can handle complex shapes. To use them, you provide an input image and the model will output a 3D mesh or voxel representation that you can render or edit further in 3D modeling tools… The LLM step helps bridge the gap between the user’s simple query and the more technical language used in the model descriptions. It expands on the initial query to infer additional relevant terms, concepts, and context that can help retrieve better matches. At the same time, using real document embeddings as the final search index keeps the results grounded and prevents hallucination. I implemented a basic version of HyDE on top of the existing AIModels search, and the initial results are promising. In some sample queries I tested, HyDE surfaced relevant models that the original keyword-matching approach missed. For the talking head portrait query, it was able to generate a hypothetical document with key terms like “lip sync”, “facial animation”, and “audio-driven animation”, which helped retrieve some strong model matches that the plain search had overlooked. two to three times more How to implement HyDE in your own search system Let me show you how HyDE can be added in on top of an existing semantic search system to dramatically improve its quality. Let’s start with a simplified code sketch based on my current search flow. Here’s the full file — you can glance through it in totality before we look at the individual parts: const fetchData = async (query) => {
  // Generate a hypothetical document embedding for the query 
  const hypotheticalDocument = await generateHypotheticalDocument(query);
  
  const embeddingResponse = await openAi.createEmbedding({
    model: "text-embedding-ada-002",
    input: hypotheticalDocument,
  });
  
  const embedding = embeddingResponse.data.data[0].embedding;

// Search the real model embeddings using the hypothetical document embedding
  const { data: modelsData, error } = await supabase.rpc("search_models", {
    query_embedding: embedding,
    similarity_threshold: 0.75,
    match_count: 10,
  });
  return modelsData;
};
const generateHypotheticalDocument = async (query) => {
  // Use GPT-3 to expand the query into a hypothetical ideal document
  const prompt = `The user entered the following search query: 
  "${query}"
  
  Please generate a detailed hypothetical document that would be highly relevant to answering this query. The document should use more technical language and expand on the key aspects of the query.
  Hypothetical Document:`;
  const response = await openAi.createCompletion({
    model: "text-davinci-002",
    prompt: prompt,
    max_tokens: 200,
    n: 1,
    stop: null,
    temperature: 0.5,
  });
  const hypotheticalDocument = response.data.choices[0].text.trim();
  return hypotheticalDocument;
} Let’s see how it works. We’ll start with the function, which is the main entry point for the search process. First up: fetchData const hypotheticalDocument = await generateHypotheticalDocument(query); This line calls the function (which we'll explain in more detail later) to generate a hypothetical ideal document based on the user's search query. generateHypotheticalDocument The hypothetical document is a key part of the HyDE approach, as it aims to capture the user’s search intent in a more detailed and technical way than the original query. const embeddingResponse = await openAi.createEmbedding({
  model: "text-embedding-ada-002",
  input: hypotheticalDocument,
});
const embedding = embeddingResponse.data.data[0].embedding; These lines use OpenAI’s model to generate an embedding vector for the hypothetical document. I think it's the best embedding option out there so far. text-embedding-ada-002 Embeddings are a way to represent text as a dense vector of numbers, where similar texts have similar vectors. This allows us to perform semantic similarity searches. By embedding the hypothetical document instead of the original query, we aim to get a vector representation that better captures the expanded search intent. Next up: const { data: modelsData, error } = await supabase.rpc("search_models", {
  query_embedding: embedding,
  similarity_threshold: 0.75,
  match_count: 10,
}); This part performs the actual similarity search in the model database. It is called a remote procedure ( ) on the Supabase database, passing in the embedding of the hypothetical document as the . search_models query_embedding The and parameters control how closely the results need to match and how many to return. similarity_threshold match_count The search looks for model embeddings that are most similar to the query embedding based on cosine similarity. Now let’s look at the function: generateHypotheticalDocument const prompt = `The user entered the following search query: 
"${query}"

Please generate a detailed hypothetical document that would be highly relevant to answering this query. The document should use more technical language and expand on the key aspects of the query.
Hypothetical Document:`; This part constructs the prompt that will be sent to the language model to generate the hypothetical document. The prompt includes the user’s original query and instructions for the model to generate a detailed, technical document that expands on the query’s key aspects. Crafting an effective prompt is crucial for guiding the language model to generate useful hypothetical documents. Now for the next part: const response = await openAi.createCompletion({
  model: "text-davinci-002",
  prompt: prompt,
  max_tokens: 200,
  n: 1,
  stop: null,
  temperature: 0.5,
}); These lines use OpenAI’s model to generate the hypothetical document based on the prompt. text-davinci-002 The parameter limits the length of the generated document while controls the randomness (higher values make the output more diverse but potentially less focused). Adjusting these parameters can help tune the quality and variety of the generated documents. max_tokens temperature Next, we have: const hypotheticalDocument = response.data.choices[0].text.trim(); This line extracts the generated hypothetical document text from the GPT-3 API response. The generated text is then returned to be used in the embedding step. Then we can just retreive the data as before, this time using the hypothetical document as the query, but getting a lot more relevant results due to the expanded search term and all it’s extra, juicy context! Recap — what did we just build? I hope the code and my explanation demonstrate how the HyDE approach can be implemented on top of an existing semantic search system. It’s not that hard! The key idea is this: By inserting the hypothetical document generation and embedding steps into the search pipeline, we aim to improve the search results by better capturing the user’s intent. To recap, the key parts were: Using a language model to expand the user’s query into a more detailed, technical hypothetical document Embedding the hypothetical document to get a vector representation that aligns with the model database Performing a similarity search between the hypothetical document embedding and the model embeddings to find the most relevant results Of course, this is just a simplified implementation (simplementation?), and there are many details and optimizations to consider in a real implementation. But it illustrates the core flow and components of the HyDE approach. Taking it further Since this is just an initial prototype there’s still a lot of room for experimentation and refinement. Some of the key open questions I’m thinking about: What’s the optimal prompt structure and output length for generating useful hypothetical documents? I’d like to try different prompt templates and output sizes to see how they impact result quality. How can the hypothetical documents be made more interpretable and transparent to users? It could be confusing if users don’t understand why certain terms are being added. I’m considering showing parts of the generated text as a “query interpretation” to provide more insight into the process. To what extent does the result quality depend on the specific LLM and embedding models used? I’m curious to benchmark different model combinations (e.g. GPT-4 vs GPT-3, vs OpenAI embeddings, etc.) to find the best recipe. Contriever How can this approach be efficiently scaled to work over millions of database entries? Inference on large LLMs like GPT-3 is expensive and slow. I’ll need to look into caching, optimizing, or distilling the models to make this viable at scale. I’m excited about the potential for language models to help make search interfaces like AIModels more intuitive and powerful for a wider audience. Bridging the semantic gap between how users naturally express their needs and how AI models are technically described feels like an important piece of the puzzle. Techniques like HyDE point a way toward search experiences that deeply understand the intent and meet users where they are. My long-term goal is to keep refining this interface until anyone can easily find relevant AI models for their needs, no matter their level of technical expertise. I’ll be sharing more details as I continue experimenting with HyDE and other approaches. If you’re curious to learn more about the technical side, I recommend checking out the original paper. And if you have ideas or feedback on the AIModels search experience, I’d love to hear from you! Let me know what you think in the comments. Also published . here

Improve Semantic Search and Enhance AI Model Discovery with Hypothetical Document Embeddings (HyDE)

This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Tomáš Jakl, Czech Academy of Sciences and Czech Technical University; (2) Dan Marsden, School of Computer Science University of Nottingham; (3) Nihil Shah, Department of Computer Science University of Oxford. Table of Links Abstract & Introduction Prelimenaries FVM Theorems for Positive Existential Fragments FVM Theorems for Counting Logic FVM Theorems for The Full Logic Abstract FVM Theorems for Products Adding Equality and Other Enrichment Conclusions, Acknowledgments & References Appendix A FVM theorems for coproducts Appendix B Proofs Omitted from Section III Appendix C Proofs Omitted from Section IV Appendix D Proofs Omitted from Section V Appendix E Proofs Omitted from Section VI Appendix F Proofs Omitted from Section VII VIII. CONCLUSION We presented a categorical approach to the composition method, and specifically Feferman–Vaught–Mostowski theorems. We exploit game comonads to encapsulate the logics and their model comparison games. Surprising connections to classical constructions in category theory, and especially the monad theory of bilinear maps emerged from this approach, cf. Section V-A. For finite model theorists, our work provides a novel highlevel account of many FVM theorems, abstracting away from individual logics and constructions. Furthermore, concrete instances of these theorems are verified purely semantically, by finding a suitable collection homomorphisms forming a Kleisli law satisfying (S2’), instead of the usual delicate verification that strategies of model comparison games compose. For game comonads, we provide a much needed tool that enables us to handle logical relationships between structures as they are transformed or viewed in terms of different logics. The FVM results in this paper, combined with judicious use of the techniques described in Section VII can be pushed significantly further. FVM theorems and the compositional method are of particular significance in the setting of monadic second-order (MSO) logic [49]. Exploiting the results we have presented for the composition method, a follow up paper will give a comonadic semantics for MSO, and develop a semantic account of Courcelle’s algorithmic meta-theorems [5]. The original theorem of Feferman–Vaught [3] is very flexible, including incorporating structure on the indices of families of models to be combined by an operation, as well as the models themselves. This aspect is currently outside the scope of our comonadic methods. An ad-hoc adaptation of our approach also gives a proof of the FVM theorem for free amalgamations, but this does not follow directly from our theorems. Developing these extensions is left to future work. The present work bears some resemblance to Turi and Plotkin’s bialgebraic semantics [50], a categorical model of structural operational semantics (SOS) [51]. Turi and Plotkin noticed that the SOS rules assigning behaviour to syntax could be abstracted as a certain distributive law λ. An algebra encodes the composition operations of the syntax, and a coalgebra the behaviour. If this pair is suitably compatible with λ, forming a so-called λ-bialgebra, then crucially bisimulation is a congruence with respect to the composition operations. Both bialgebraic semantics and our work presented in this paper give a categorical account of well-behaved composition operations, with the interaction between composition and observable behaviour mediated by some form of distributive law. There are also essential differences: bialgebraic semantics typically focuses on assigning behaviour to syntax, whereas FVM theorems encompass operations on models. Our FVM theorems are parametric in a choice of logic or observable behaviour, while the notion of bisimulation in bialgebraic semantics is fixed by the coalgebra signature functor. The natural presence of positive existential and counting quantifier variants of FVM theorems, and the incorporation of resource parameters, do not seem to have a direct analogue in bialgebraic semantics. The similarities are intriguing, and exploring the relationship between bialgebraic semantics and our approach to FVM theorems is left to future work. We would like to thank Clemens Berger for suggesting the connection with parametric adjoints and Nathanael Arkor for his suggestions on terminology. We are also grateful to the members of the EPSRC project “Resources and co-resources” for their feedback. Acknowledgements: REFERENCES [1] L. Libkin, Elements of finite model theory. Springer, 2004, vol. 41. [2] A. Mostowski, “On direct products of theories,” The Journal of Symbolic Logic, vol. 17, no. 1, pp. 1–31, 1952. [3] S. Feferman and R. L. Vaught, “The first-order properties of algebraic systems,” Fundamenta Mathematicae, vol. 47, 1959. [4] J. A. Makowsky, “Algorithmic uses of the Feferman–Vaught theorem,” Annals of Pure and Applied Logic, vol. 126, no. 1-3, pp. 159–213, 2004. [5] B. Courcelle and J. Engelfriet, Graph structure and monadic secondorder logic: a language-theoretic approach. Cambridge University Press, 2012, vol. 138. [6] A. Ehrenfeucht, “An application of games to the completeness problem for formalized theories,” Fundamenta mathematicae, vol. 49, no. 129- 141, p. 13, 1960. [7] R. Fraïssé, “Sur quelques classifications des relations, basées sur des isomorphismes restreints,” Publications Scientifiques de l’Université d’Alger. Série A (mathématiques), vol. 2, pp. 273–295, 1955. [8] P. G. Kolaitis and M. Y. Vardi, “Infinitary logics and 0–1 laws,” Information and computation, vol. 98, no. 2, pp. 258–294, 1992. [9] M. Hennessy and R. Milner, “On observing nondeterminism and concurrency,” in International Colloquium on Automata, Languages, and Programming. Springer, 1980, pp. 299–309. [10] S. Abramsky, A. Dawar, and P. Wang, “The pebbling comonad in finite model theory,” in 32nd Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2017, Reykjavik, Iceland, June 20-23, 2017. IEEE Computer Society, 2017, pp. 1–12. [11] S. Abramsky and N. Shah, “Relating structure and power: Comonadic semantics for computational resources,” Journal of Logic and Computation, vol. 31, no. 6, pp. 1390–1428, 2021. [12] E. Moggi, “Computational lambda-calculus and monads,” in Proceedings of the Fourth Annual Symposium on Logic in Computer Science (LICS ’89), Pacific Grove, California, USA, June 5-8, 1989. IEEE Computer Society, 1989, pp. 14–23. [13] ——, “Notions of computation and monads,” Information and Computation, vol. 93, no. 1, pp. 55–92, 1991. [14] E. G. Manes, Algebraic theories. Springer Science, 1976, vol. 26. [15] S. Abramsky and D. Marsden, “Comonadic semantics for guarded fragments,” in 36th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2021, Rome, Italy, June 29 - July 2, 2021. IEEE, 2021, pp. 1–13. [16] A. Ó. Conghaile and A. Dawar, “Game comonads & generalised quantifiers,” in 29th EACSL Annual Conference on Computer Science Logic, CSL 2021, January 25-28, 2021, Ljubljana, Slovenia (Virtual Conference), ser. LIPIcs, C. Baier and J. Goubault-Larrecq, Eds., vol. 183. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021, pp. 16:1–16:17. [17] Y. Montacute and N. Shah, “The pebble-relation comonad in finite model theory,” in LICS ’22: 37th Annual ACM/IEEE Symposium on Logic in Computer Science, Haifa, Israel, August 2 - 5, 2022, C. Baier and D. Fisman, Eds. ACM, 2022, pp. 13:1–13:11. [Online]. Available: https://doi.org/10.1145/3531130.3533335 [18] T. Paine, “A pebbling comonad for finite rank and variable logic, and an application to the equirank-variable homomorphism preservation theorem,” in Proceedings of the 36th Conference on the Mathematical Foundations of Programming Semantics, MFPS 2020, Online, October 1, 2020, ser. Electronic Notes in Theoretical Computer Science, P. Johann, Ed., vol. 352. Elsevier, 2020, pp. 191–209. [19] A. Dawar, T. Jakl, and L. Reggio, “Lovász-type theorems and game comonads,” in 36th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2021, Rome, Italy, June 29 - July 2, 2021. IEEE, 2021, pp. 1–13. [20] S. Abramsky and D. Marsden, “Comonadic semantics for hybrid logic,” in 47th International Symposium on Mathematical Foundations of Computer Science, MFCS 2022, August 22-26, 2022, Vienna, Austria, ser. LIPIcs, S. Szeider, R. Ganian, and A. Silva, Eds., vol. 241. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, pp. 7:1–7:14. [21] L. Reggio, “Polyadic sets and homomorphism counting,” Advances in Mathematics, vol. 410, p. 108712, 2022. [22] S. Abramsky, “Structure and power: an emerging landscape,” Fundam. Informaticae, vol. 186, no. 1-4, pp. 1–26, 2022. [23] S. Abramsky and L. Reggio, “Arboreal categories and resources,” in 48th International Colloquium on Automata, Languages, and Programming, ICALP 2021, July 12-16, 2021, Glasgow, Scotland (Virtual Conference), ser. LIPIcs, N. Bansal, E. Merelli, and J. Worrell, Eds., vol. 198. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021, pp. 115:1–115:20. [24] H. Ebbinghaus and J. Flum, Finite model theory, ser. Perspectives in Mathematical Logic. Springer, 1995. [25] E. G. Manes and P. Mulry, “Monad compositions I: general constructions and recursive distributive laws,” Theory and Applications of Categories, vol. 18, no. 7, pp. 172–208, 2007. [26] A. Kock, “Bilinearity and cartesian closed monads,” Mathematica Scandinavica, vol. 29, no. 2, pp. 161–174, 1971. [27] T. Jakl, D. Marsden, and N. Shah, “Generalizations of bilinear maps,” 2022, technical report, arXiv preprint. [Online]. Available: https://arxiv.org/abs/2205.05382 [28] A. Dawar, S. Severini, and O. Zapata, “Pebble games and cospectral graphs,” Electronic Notes in Discrete Mathematics, vol. 61, pp. 323– 329, 2017. [29] S. Abramsky and N. Tzevelekos, “Introduction to categories and categorical logic,” in New structures for physics. Springer, 2010, pp. 3–94. [30] S. Awodey, Category theory. Oxford University Press, 2010. [31] C. Stirling, Modal and temporal properties of processes. Springer Science & Business Media, 2001. [32] A. W. Roscoe, Understanding concurrent systems. Springer Science & Business Media, 2010. [33] H. Kleisli, “Every standard construction is induced by a pair of adjoint functors,” Proceedings of the American Mathematical Society, vol. 16, no. 3, pp. 544–546, 1965. [34] B. Jacobs, “Semantics of weakening and contraction,” Annals of Pure and Applied Logic, vol. 69, no. 1, pp. 73–106, 1994. [35] S. Abramsky, T. Jakl, and T. Paine, “Discrete density comonads and graph parameters,” in Coalgebraic Methods in Computer Science - 16th IFIP WG 1.3 International Workshop, CMCS 2022, Colocated with ETAPS 2022, Munich, Germany, April 2-3, 2022, Proceedings, ser. Lecture Notes in Computer Science, H. H. Hansen and F. Zanasi, Eds., vol. 13225. Springer, 2022, pp. 23–44. [Online]. Available: https://doi.org/10.1007/978-3-031-10736-8_2 [36] P. T. Johnstone, “Adjoint lifting theorems for categories of algebras,” Bulletin of the London Mathematical Society, vol. 7, no. 3, pp. 294– 297, 1975. [37] A. Kock, “Closed categories generated by commutative monads,” Journal of the Australian Mathematical Society, vol. 12, no. 4, pp. 405–424, 1971. [38] G. Seal, “Tensors, monads and actions,” Theory and Applications of Categories, vol. 28, no. 15, pp. 403–434, 2013. [39] F. E. Linton, “Coequalizers in categories of algebras,” in Seminar on triples and categorical homology theory. Springer, 1969, pp. 75–90. [40] R. Street, “The petit topos of globular sets,” Journal of Pure and Applied Algebra, vol. 154, no. 1-3, pp. 299–315, 2000. [41] M. Weber, “Generic morphisms, parametric representations and weakly cartesian monads,” Theory and Applications of Categories, vol. 13, no. 14, pp. 191–234, 2004. [42] ——, “Familial 2-functors and parametric right adjoints,” Theory and Applications of Categories, vol. 18, no. 22, pp. 665–732, 2007. [43] C. Berger, P.-A. Mellies, and M. Weber, “Monads with arities and their associated theories,” Journal of Pure and Applied Algebra, vol. 216, no. 8-9, pp. 2029–2048, 2012. [44] T. Altenkirch, J. Chapman, and T. Uustalu, “Monads need not be endofunctors,” in International Conference on Foundations of Software Science and Computational Structures. Springer, 2010, pp. 297–311. [45] P. J. Freyd and G. M. Kelly, “Categories of continuous functors, I,” Journal of Pure and Applied Algebra, vol. 2, no. 3, pp. 169–191, 1972. [46] B. Bednarczyk and M. Urbanczyk, “Comonadic semantics for description logics games,” in Proceedings of the 35th International Workshop on Description Logics (DL 2022) co-located with Federated Logic Conference (FLoC 2022), Haifa, Israel, August 7th to 10th, 2022, ser. CEUR Workshop Proceedings, O. Arieli, M. Homola, J. C. Jung, and M. Mugnier, Eds., vol. 3263. CEUR-WS.org, 2022. [Online]. Available: http://ceur-ws.org/Vol-3263/paper-5.pdf [47] M. Bojanczyk, “Separator logic and star-free expressions ´ for graphs,” 2021, arXiv preprint. [Online]. Available: https://arxiv.org/abs/2107.13953 [48] N. Schirrmacher, S. Siebertz, and A. Vigny, “First-Order Logic with Connectivity Operators,” in 30th EACSL Annual Conference on Computer Science Logic (CSL 2022), ser. Leibniz International Proceedings in Informatics (LIPIcs), F. Manea and A. Simpson, Eds., vol. 216. Dagstuhl, Germany: Schloss Dagstuhl – LeibnizZentrum für Informatik, 2022, pp. 34:1–34:17. [Online]. Available: https://drops.dagstuhl.de/opus/volltexte/2022/15754 [49] Y. Gurevich, “Monadic second-order theories,” in Model-theoretic logics, J. Barwise and S. Feferman, Eds. Cambridge University Press, 1985, pp. 479–506. [50] D. Turi and G. Plotkin, “Towards a mathematical operational semantics,” in Proceedings of Twelfth Annual IEEE Symposium on Logic in Computer Science. IEEE, 1997, pp. 280–291. [51] G.D.Plotkin, “A structural approach to operational semantics,” Aarhus University, Tech. Rep. DAIMI FN-19, 1981. [52] J. Adámek, J. Adamek, J. Rosicky et al., Locally presentable and accessible categories. Cambridge University Press, 1994, vol. 189.

A Categorical Account of
Composition Methods in Logic: Conclusions, Acknowledgments & References

It’s no secret that the death of the internet as we know it is no longer greatly exaggerated. Between the big tech giants and generative AI the death spiral of the web has accelerated, like some digital ouroboros, the internet has begun to feed on itself and shit out the worse parts for the rest of us to consume. Web3 failed to deliver anything but more similar in the form of cryptocurrencies, NFTs and ultimately a market-fuelled distraction cobbled together on top of blockchains which is hardly that venture capitalists are determined to push as the correct rhetoric — one that is self-serving because of their investment into get-rich-quick meme coins rather than the projects themselves. If we look back at the web it began with one man’s PC sitting on his desk with a post-it note warning people not to turn it off. It then took 2 years of fucking around with DNS, TCP/IP and HTML before the first 50 websites emerged and then things took off nicely; it was under control of no one and it was decentralised. enshittification the new internet Sadly it wasn’t to last. What began as something owned by everyone on the planet became something owned by few and rented out to everyone for a price, that invoice was either paid financially or through data which the latter we are now beginning to understand the full extent of because it’s being put to good use by the generative AI startups with no compensation. And that’s coming back to bite back hard so very quickly. It’s poetic that Facebook will die choking on its own vomit soon. AI-derived content has taken over the internet and it’s unstoppable and almost unrecognisable as something an algorithm generated to the untrained eye. Those in the know are a privileged few, the vast majority of people wouldn't know where to look to spot a faked article or image, recognise telltale signs, or even copy into an AI detector to confirm its source and origin. The long term consequences are such that pretty much nothing on the internet will be human created or curated, nor even read and digested by people either. We’re being pushed to adopt personal AI assistants to consume the content they gave birth to then regurgitate it back to us as a synopsis for us to use elsewhere or even take action on either personally or in a business context. This is the antithesis of what Tim Berners-Lee wanted to see out of the Semantic Web (or Web 3.0) back in the late 90's— an internet where users kept their data sovereignty, instructed AI agents to perform tasks for them but the web itself was very much human. Web3 startups claim that they have the answer, that blockchain is somehow a magical silver bullet for data provenance and to train new types of AI on, and another excuse for another round of meme coins and but this house of cards is rapidly falling down. Like a stack of dominoes one by one very public and in your face Web3 projects like Nike’s RTFKT and are quietly being abandoned, with claims that intelligent NFTs Starbucks’s Odyssey it was a proof of concept and we’ll do better next time. Only there won’t be a next time for them. They overestimated how interested their existing customer base would be, underestimated how much the hype would be driven by the same circle of Discord lurkers shouting WAGMI and wen moon, and grossly underestimated and understood just how apathetic the general public and those who just want to drink a bloody cup of coffee would be to collecting for a glorified Pannini sticker album. What I will say is that blockchain isn’t the answer, it never was. It’s a distributed ledger you can do stuff with but it’s not an internet protocol or replacement for the web itself. The fact that you need to create and burn magic internet money to make it work invites speculative behaviour. Web3 turned the internet into a different type of stock market, trading made-up asset classes and foreign currencies and completely abandoning why it existed in the first place. After the death of the first internet, lots of tiny new internets will grow up in its place. Philip Rosedale, creator of Second Life There’s an interesting turn of events emerging though, a strange confluence of ideas mixed with a societal need for nostalgia. We’re seeing a shift across generations that is railing against the relentless push towards subscriptions and digital ownership People are buying physical media in all forms, old and current formats are enjoying a revival as subscriptions become ever more expensive, overlap each other and the choice that we thought was a convenience is now strangling us. because the reality is that you own nothing under either model. The digital feudal system has robbed us all of what we held dear before and web3 failed to give us control back. The internet and how it runs now is dead or drawing its last breath. But it won’t be replaced by one single, new internet nor will it be replaced by blockchains. It’ll be replaced by networks. We have some examples already like and but these rely on blockchains and ultimately are no good both longer term but also from a decentralised control perspective. Another example is , a small group that’s been working for nearly two decades on building a distributed network protocol that works with ordinary kit you’d find in the home (recently announced rebrand as now) IPFS Arweave SAFE Autonomi Much like the distributed systems that allowed BitTorrent (distributed file sharing) and BOINC (distributed computing like SETI@Home and Fold@Home) to function on consumer-grade hardware, there will be a re-emergence of the same movement where people will power the next iteration of the internet from their own devices — not from centralised cloud stacks owned by the same 3–4 major corporations. The internet itself was build as a decentralised and distributed network before capitalism found a way to make it work to its advantages. You can see how this could come about after recently watching the NVIDIA GTC keynote and understanding Sam Altman’s own drive to create a new silicon empire — they’re scared there is not enough compute power that they can build and control for their own ambitions, and control is the key point to pay attention to here because there is already enough compute and storage out there sitting on billions of devices in the world but they just won’t control any of it. When robotics start taking central stage over and above incorporeal AI systems then you’ll begin to understand that every robot in itself is a distributed node on a new type of compute network that won’t rely on centralisation for decision making, but as a collective hive mind can improve and self-learn at an exponential rate. But this is an example of a distributed network under centralised control. And it doesn’t stop at being just one internet, the revolution will be that there will be many under this model, all owned by nobody but powered by everyone in those particular communities it supports. What we need for the internet to be remade is not only a new set of distributed networks and designed to operate in a decentralised manner but also that the network, storage and compute are owned by nobody but powered by everybody. operating systems It was a reason I believed that Web3 DAOs (decentralised autonomous organisations) could have taken back control from local authorities as community driven projects, wholly owned and run by the people in those local communities, empowering them to make decisions that made a difference rather than line an MPs pockets. When folks like Peter Diamandis claim that AI will usher in an Age of Abundance what they’re really calling for is a rewriting of economic systems beyond the capitalist one we use today, and that starts by rewriting the infrastructure we rely on so much to power it — the internet. The internet is dead. Long live networks. Also appears . here

The Internet Is Dead. Long Live The Internet.

ইন্টারনেট ইজ ডেড দীর্ঘজীবী ইন্টারনেট।

Das Internet ist tot. Es lebe das Internet.

Internet está muerta. Larga vida a Internet.

Internet est mort. Vive Internet.

इंटरनेट ख़त्म हो गया है. इंटरनेट लंबे समय तक जीवित रहे।

インターネットは死んだ。インターネット万歳。

인터넷은 죽었습니다. 인터넷 만세.

A Internet está morta. Viva a Internet.

Интернет мертв. Да здравствует Интернет.

İnternet Öldü. Yaşasın İnternet.

Internet đã chết. Internet sống lâu.

互联网已死。互联网万岁。

or a server with a with professional-grade or cards. are also available. Rent GPU servers with instant deployment custom configuration NVIDIA Tesla H100 / H100 80Gb RTX A5000 / A4000 GPU servers with game RTX4090 cards Sporting events today are rife with advertising, from commercials on screens to static company logos on stadium billboards. The airwaves span multiple countries with different brands and advertising laws. Thanks to breakthroughs in AI and AR technology, it is possible to customize the information on display for each audience directly during the live broadcast of a match. Advertising in sports arenas was initially static and intended for the people attending a particular event in a particular city. Later, LED screens appeared, which showed alternating adverts. The next evolution was to insert a green screen into the spots on the billboards for a fraction of a second, allowing the AI to define the space and insert a unique text. Nowadays, you can replace anything with anything. The challenge is to make sure that the replacement is done discreetly and realistically. The major players in this market use special cameras. From the photos, we can conclude that there are specialized sensors that discern positioning and alignment information. That is, popular solutions still have hardwired crutches. All this requires enormous computing power, as the inputs and the original broadcast are processed by specialized software online. That is, servers for computing are connected to the cameras and sensors. This equipment is expensive, the market is closed, and the technology is closed, too. This is where AI and cloud GPU servers come to the rescue. Expensive, static, and not for everyone HOSTKEY has a client, a startup called , which has developed its own technology for implementing virtual advertising and integrating digital content (like augmented reality) in a multi-regional mode. And their solution promises seamless (and boneless) integration of adverts directly into the video stream. PTF Lab Some time ago, they saw information about HOSTKEY in the Open Data Science (ODS) community and reached out to us when they needed cloud capacity, appreciating our service, pricing, and a flexible lineup of available options. The startup's goals are noble, understandable, and quite achievable: To get away from expensive proprietary equipment and complex setup, shifting the task of advertising placement and frame construction to artificial intelligence, which takes into account overlapping people and objects in the frame. By doing so, they can cover relatively small events (such as arena fights, etc.) and bring the technology to the masses. Ultimately, sports advertising should be made accessible and relatively inexpensive. Moreover, the startup has set an ambitious goal of surpassing the solutions offered by monopoly giants in terms of flexibility. For example, it can display "virtual adverts" during replays and use angles from any camera, not just from certain angles. How does it work? The video signal from a sporting event venue can be processed using computing power not only at the venue itself but also in the cloud. This allows for flexible load distribution and the choice of when to apply adverts: before or during broadcasting, taking into account different markets. Also, working with cloud services allows you to use advertising in locations where it is impossible to bring a server (and in principle, it is more convenient). Object segmentation is based on the neural network architecture from U-Net. Neural networks are responsible for the location of objects and for detecting and comparing key points. However, the task is non-trivial, so all the solutions and neural networks had to be reworked and trained for use. It is especially difficult in martial arts broadcasts, where literally everything is unpredictable from the point of view of the picture: light sources, shadows, camera angles, the grid overlapping sponsor logos, and the bodies of the fighters and referees. Neural networks are not used everywhere. Sometimes, to solve a problem, it is enough to show ingenuity and use simple algorithms. For example, tracking algorithms combine neural network methods and systems of linear and nonlinear equations. A significant part of the GPU is taken up by segmentation. The better the detection of people and objects in the frame and their separation by plans and type is implemented, the more natural and attractive the frame will look after the advertising overlay. A separate task is related to lighting and shadows, which must be taken into account in augmented reality when rendering a scene. The realism of shadows is a key element in assessing the "believability" of the picture. Traditionally, sports neural networks are trained on real broadcasts using human markings and synthetic models. Here, Blender comes to the rescue. The company builds 3D models of the ring, fighters, and referees and gets both rendered real footage from the right angles and the segmentation mask, or the position of objects and cameras needed for training. Markup of real data is time-consuming and expensive but of high quality for specific venues or types of competitions. Synthetic data with less realism provides more data for training. The main difficulty is that the venues can vary. In one case, it will be a boxing ring with ropes, and in another case, it will be an arena with mesh walls, each of which creates difficulties for segmentation. Camera tracking and advert position are determined by comparing the point cloud from the venue's 3D model with its actual position in the frame. This allows them to determine the position, even for manual cameras with chaotic movement. After reconstruction of the 3D frame from 2D (the determination of the required angle), the direct rendering of the advertisement in the 3D engine is performed and combined with the video broadcast frame. A 3D scene has to be built before we start work, and, in fact, we have a virtual copy of the venue in the frame, into which we fit real people and objects through render masks. It sounds complicated, but with the right power and optimized neural networks, it is possible to perform these somersaults instantly and seamlessly. Why does the project need GPU computing and server rentals? PTF Lab has its own servers (and the possibility of using them on-site at the competition venue was mentioned earlier), but it is more convenient to use remote resources, as the service provider's engineers are responsible for equipment availability, and the company allocates fewer resources to this. And the client votes with his wallet and cost-effective options are always preferable. Also, the capacity required by the company is constantly growing. If necessary, it can be easily scaled up just by renting more (up to and including changing the server configuration to suit the company's needs). In the future, a startup may need a lot of cloud capacity. It is easier to rent them than to buy and sell the physical servers when the demand for their services rises and falls. The leased servers and GPU capacity are now being used in the following areas: Training of neural networks on GPUs (segmentation of people and other objects in sports broadcasts; 3D virtual camera tracking). Data backup (video from events, datasets, etc.). The direct process of working with video-testing cloud production, when the company's software is deployed on remote servers and video signals flow through it: input - without graphics, output - one or more signals with graphics. In addition to our own computing power at HOSTKEY, the startup is currently leveraging the following GPU configurations: AMD Ryzen 9 5950X 3.4GHz (16 cores)/128Gb/1Tb NVMe SSD+12Tb HDD/2xRTX 3090+PSU] Xeon E3-1230 3.2GHz (4 cores)/16Gb/2x12Tb HDD/PSU] AMD Ryzen 9 5950X 3.4GHz (16 cores)/128Gb/1Tb NVMe SSD+12Tb HDD/2xRTX 3090+PSU+HDMI emulator] AMD Ryzen 9 5950X 3.4GHz (16 cores)/128Gb/1Tb NVMe SSD+12Tb HDD/2xRTX 3090+PSU] AMD EPYC 7402P 2.8GHz (24 cores)/384Gb/2x1.92Tb U3 NVMe SSD/4xRTX 4090+2xPSU] As you can see, most GPU solutions are rented based on the RTX4090/3090, but as the computing power requirements increase, the startup wishes to have the option for servers with more powerful, professional cards that offer better stability when running in continuous 24/7 operation. In the case of the 3090, renting the same A5000 might even be cheaper. PTF Lab is at the beginning of its journey, but its results are already promising, and we at HOSTKEY wish them success and growth, especially in terms of overcoming the monopoly of sports augmented reality. The witty David always defeats the clumsy proprietary Goliath. If you have any questions for our colleagues from PTF Lab, please feel free to ask them in the comments. or a server with a with professional-grade or cards. are also available. Rent GPU servers with instant deployment custom configuration NVIDIA Tesla H100 / H100 80Gb RTX A5000 / A4000 GPU servers with game RTX4090 cards

How AI Is Fighting Monopolies in Sports Advertising With GPUs and Servers

কিভাবে AI GPU এবং সার্ভারের সাথে ক্রীড়া বিজ্ঞাপনে একচেটিয়া লড়াই করছে

Wie KI Monopole in der Sportwerbung mit GPUs und Servern bekämpft

Cómo la IA lucha contra los monopolios en la publicidad deportiva con GPU y servidores

Comment l'IA combat les monopoles dans la publicité sportive avec des GPU et des serveurs

एआई कैसे जीपीयू और सर्वर के साथ खेल विज्ञापन में एकाधिकार से लड़ रहा है

AI が GPU とサーバーを使用してスポーツ広告の独占とどのように戦うか

AI가 GPU와 서버를 사용해 스포츠 광고 독점에 맞서 싸우는 방법

Como a IA está combatendo os monopólios na publicidade esportiva com GPUs e servidores

Как ИИ борется с монополиями в спортивной рекламе с помощью графических процессоров и серверов

Yapay Zeka, GPU'lar ve Sunucularla Spor Reklamcılığında Tekellerle Nasıl Mücadele Ediyor?

AI đang chống lại sự độc quyền trong quảng cáo thể thao bằng GPU và máy chủ như thế nào

人工智能如何利用 GPU 和服务器对抗体育广告领域的垄断

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