At Demand.io, our mission is to make e-commerce work better for everyone. As a small team of product executives who love building world-class products, we create and run platforms that connect people around shared values and towards economic growth. Our products, like Knoji, SimplyCodes and Favely, reach millions of consumers and drive over a billion dollars per year in e-commerce transaction volume.

Demand.io

Aptos Role in the Decentralized Infrastructure The founders of Aptos Labs, Mo Shaikh and Avery Ching, started the company with the goal of making networks easier to use and giving more people access to the benefits of decentralization. The company has gotten a lot of money from trustworthy investors, which shows that it is serious about making a blockchain setting that is easy to use and works well. In order to adapt and improve speed while also offering user protections, Aptos, a next-generation Layer 1 blockchain, makes use of ground-breaking technologies and the Move programming language. This new invention represents a significant step forward in the process of combining blockchain technology with artificial intelligence, with the ultimate goal of redefining the technological environment. Exploring the capabilities of BC8.ai on the Aptos network io.net, a company that specializes in decentralized GPU processing, has partnered with Aptos Labs to expand its operations on Aptos. This alliance aims to decentralize access to the essential components of artificial intelligence (AI) by lowering computing costs and storage requirements, making machine learning more accessible and affordable. As a consequence of this collaboration, io.net's artificial intelligence (AI) products and innovations will benefit from Aptos' safety, speed, and performance capabilities. Currently, the generative artificial intelligence system known as BC8.ai, which is a product of io.net, is operating on the Aptos network and is responsible for managing more than 500,000 transactions each and every day. It is clear from this that the network is able to satisfy the requirements for high-speed and high-volume artificial intelligence. Ahmad Shadid, the Chief Executive Officer of Io.net, has underlined the necessity for a blockchain partner such as Aptos to support the firm's rising scale of innovation. This is especially important as BC8.ai and other future inference products continue to develop. If io.net and Aptos Labs decide to collaborate in the future, they will focus on developing a collection of artificial intelligence models and inference products that are designed to make use of the Aptos network for the purpose of making payments. This will further facilitate the accessibility of technologies that use artificial intelligence. Mo Shaikh, the chief executive officer of Aptos Labs, provided an overview of the goal that both companies have for the future, which is to have artificial intelligence that is decentralized and accessible. For instance, a possible product could be a payment system driven by AI that instantly spots scams and starts safe operations on the Aptos network. This partnership could change the way payments are made and processed around the world by using io.net's skill in AI technology and Aptos Labs' network infrastructure. This new way of using AI in payments would not only make them safer and more efficient, but it would also make these technologies easier for both businesses and customers to get. Vision for Accessible AI The establishment of this partnership represents a significant step towards the incorporation of Web3 technology inside the realm of artificial intelligence. With the goal of making artificial intelligence more responsible, scalable, and accessible to engineers and developers all across the globe, the aim is to be achieved. It is strongly recommended that you go to the website io.net in order to get further information on the ongoing endeavors of io.net and its collaboration with Aptos Labs. The IO Network is a decentralized physical infrastructure network of geo-distributed GPUs, and io.net is responsible for managing GPU clusters that are deployed over the IO Network. When it comes to using cases that need high processing demand and low latency, such as artificial intelligence and machine learning operations, and cloud gaming, this network, which is known as the Internet of GPUs, is aimed to provide companies and engineers with a wider range of options and lower prices. Don't forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Aptos Role in the Decentralized Infrastructure Aptos Role in the Decentralized Infrastructure The founders of Aptos Labs, Mo Shaikh and Avery Ching, started the company with the goal of making networks easier to use and giving more people access to the benefits of decentralization. The company has gotten a lot of money from trustworthy investors, which shows that it is serious about making a blockchain setting that is easy to use and works well. In order to adapt and improve speed while also offering user protections, Aptos, a next-generation Layer 1 blockchain, makes use of ground-breaking technologies and the Move programming language. This new invention represents a significant step forward in the process of combining blockchain technology with artificial intelligence, with the ultimate goal of redefining the technological environment. Exploring the capabilities of BC8.ai on the Aptos network io.net, a company that specializes in decentralized GPU processing, has partnered with Aptos Labs to expand its operations on Aptos. This alliance aims to decentralize access to the essential components of artificial intelligence (AI) by lowering computing costs and storage requirements, making machine learning more accessible and affordable. As a consequence of this collaboration, io.net's artificial intelligence (AI) products and innovations will benefit from Aptos' safety, speed, and performance capabilities. Currently, the generative artificial intelligence system known as BC8.ai, which is a product of io.net, is operating on the Aptos network and is responsible for managing more than 500,000 transactions each and every day. It is clear from this that the network is able to satisfy the requirements for high-speed and high-volume artificial intelligence. Ahmad Shadid, the Chief Executive Officer of Io.net, has underlined the necessity for a blockchain partner such as Aptos to support the firm's rising scale of innovation. This is especially important as BC8.ai and other future inference products continue to develop. If io.net and Aptos Labs decide to collaborate in the future, they will focus on developing a collection of artificial intelligence models and inference products that are designed to make use of the Aptos network for the purpose of making payments. This will further facilitate the accessibility of technologies that use artificial intelligence. Mo Shaikh, the chief executive officer of Aptos Labs, provided an overview of the goal that both companies have for the future, which is to have artificial intelligence that is decentralized and accessible. For instance, a possible product could be a payment system driven by AI that instantly spots scams and starts safe operations on the Aptos network. This partnership could change the way payments are made and processed around the world by using io.net's skill in AI technology and Aptos Labs' network infrastructure. This new way of using AI in payments would not only make them safer and more efficient, but it would also make these technologies easier for both businesses and customers to get. Vision for Accessible AI Vision for Accessible AI The establishment of this partnership represents a significant step towards the incorporation of Web3 technology inside the realm of artificial intelligence. With the goal of making artificial intelligence more responsible, scalable, and accessible to engineers and developers all across the globe, the aim is to be achieved. It is strongly recommended that you go to the website io.net in order to get further information on the ongoing endeavors of io.net and its collaboration with Aptos Labs. The IO Network is a decentralized physical infrastructure network of geo-distributed GPUs, and io.net is responsible for managing GPU clusters that are deployed over the IO Network. When it comes to using cases that need high processing demand and low latency, such as artificial intelligence and machine learning operations, and cloud gaming, this network, which is known as the Internet of GPUs, is aimed to provide companies and engineers with a wider range of options and lower prices. Don't forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program

Revolutionizing AI: io.net and Aptos Labs Forge a Path for Decentralized Innovation

বিপ্লবী AI: io.net এবং Aptos Labs বিকেন্দ্রীভূত উদ্ভাবনের জন্য একটি পথ তৈরি করে

Revolutionäre KI: io.net und Aptos Labs ebnen den Weg für dezentrale Innovation

Revolucionando la IA: io.net y Aptos Labs forjan un camino hacia la innovación descentralizada

Révolutionner l'IA : io.net et Aptos Labs ouvrent la voie à l'innovation décentralisée

AI में क्रांतिकारी बदलाव: io.net और Aptos Labs ने विकेंद्रीकृत नवाचार के लिए मार्ग प्रशस्त किया

AI の革命: io.net と Aptos Labs が分散型イノベーションへの道を切り開く

AI 혁명: io.net과 Aptos Labs가 분산형 혁신을 위한 길을 닦습니다.

Revolucionando a IA: io.net e Aptos Labs abrem um caminho para a inovação descentralizada

Революция в области искусственного интеллекта: io.net и Aptos Labs прокладывают путь к децентрализованным инновациям

Yapay Zekada Devrim Yaratıyor: io.net ve Aptos Labs Merkezi Olmayan İnovasyona Yönelik Bir Yol Açıyor

Cách mạng hóa AI: io.net và Aptos Labs tạo ra con đường đổi mới phi tập trung

人工智能革命：io.net 和 Aptos Labs 开辟去中心化创新之路

Economists Agree, Politicians Don't — So Guess Who Loses

অর্থনীতিবিদরা একমত, রাজনীতিবিদরা না - তাই আপনি হারান

Ökonomen sind sich einig, Politiker nicht – also verlieren Sie

Los economistas están de acuerdo, los políticos no, así que usted pierde

Les économistes sont d’accord, les politiciens non – donc vous perdez

अर्थशास्त्री सहमत हैं, राजनेता नहीं - इसलिए आप हारे

経済学者は同意するが、政治家は同意しない — だから負ける

경제학자들은 동의하지만 정치인들은 그렇지 않습니다. 그래서 당신은 패합니다.

Os economistas concordam, os políticos não - então você perde

Экономисты согласны, политики — нет, поэтому вы проигрываете

Ekonomistler Kabul Ediyor, Politikacılar Katılmıyor - Yani Kaybediyorsunuz

Các nhà kinh tế đồng ý, các chính trị gia thì không - Vậy là bạn thua

经济学家同意，政客不同意——所以你输了

Strategies for Responsible AI Governance

দায়িত্বশীল এআই গভর্নেন্সের জন্য কৌশল

Strategien für eine verantwortungsvolle KI-Governance

Estrategias para una gobernanza responsable de la IA

Stratégies pour une gouvernance responsable de l’IA

उत्तरदायी एआई शासन के लिए रणनीतियाँ

責任あるAIガバナンスのための戦略

책임 있는 AI 거버넌스를 위한 전략

Estratégias para uma governança responsável da IA

Стратегии ответственного управления ИИ

Sorumlu Yapay Zeka Yönetişimine Yönelik Stratejiler

Chiến lược quản trị AI có trách nhiệm

负责任的人工智能治理策略

Interview with Dr. Yonatan Sompolinsky of Kaspa

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de;
(2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl;
(3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de;
(4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction
Related Work
Methodology
Experimental Design
Experimental Results
Conclusion
Ethics Statement and References VI. CONCLUSION This work addressed text-centric recommender systems in data-poor situations, specifically for the demanding case of book reviewing communities where many users have only few items and exhibit diverse tastes, but post text-rich comments. We presented a Transformer-based framework called CUP, with novel techniques for constructing concise user profiles by selecting informative pieces of user-side text. To mitigate the absence of explicitly negative training points, we employed a technique of picking weighted negative samples from unlabeled data. Our experiments, with both standard evaluation and a search-based mode, show that leveraging user text is beneficial in this data-poor regime, and that our CUP methods clearly outperform state-of-the-art baselines like DeepCoNN, BENEFICT, P5, LLM-Rec and a method based on frozen BERT. Our main goal has been to shed light into the research questions posed in Section I-B). For RQ1, we devised various ways of leveraging LMs, and we compared to recent works on using LLM prompts for item ranking. We found that such an LLM-only approach is not viable, as it cannot cope with long-tail items. In contrast, using LLMs to generate concise user profiles, based on our CUP framework, is a competitive solution. However, they do not outperform the simpler CUP variants, like idf-based sentences, and the simpler methods are low-cost. For RQ2, we limited the number of input tokens for the Transformer to 128, creating a stress-test but also aiming to minimize the computational and energy cost at training time. Our experimental findings confirmed that this tight length for user profiles is indeed viable, yielding good NDCG@5 and P@1 results (relative to the difficulty of this data). For RQ3, we devised various techniques for selecting informative cues from user reviews. A key observation from the experiments is that a moderate degree of sophistication, like idf-selected sentences, is largely sufficient. Our default configuration CUPidf performs on par with more elaborated variants, such as leveraging SentenceBERT or having T5- generated keywords for concise profiles. Moreover, CUPidf mostly outperforms even the profiles that were generated by ChatGPT with the full review texts as input. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction Related Work Methodology Experimental Design Experimental Results Conclusion Ethics Statement and References Abstract and Introduction Abstract and Introduction Related Work Related Work Methodology Methodology Experimental Design Experimental Design Experimental Results Experimental Results Conclusion Conclusion Ethics Statement and References Ethics Statement and References VI. CONCLUSION This work addressed text-centric recommender systems in data-poor situations, specifically for the demanding case of book reviewing communities where many users have only few items and exhibit diverse tastes, but post text-rich comments. We presented a Transformer-based framework called CUP, with novel techniques for constructing concise user profiles by selecting informative pieces of user-side text. To mitigate the absence of explicitly negative training points, we employed a technique of picking weighted negative samples from unlabeled data. Our experiments, with both standard evaluation and a search-based mode, show that leveraging user text is beneficial in this data-poor regime, and that our CUP methods clearly outperform state-of-the-art baselines like DeepCoNN, BENEFICT, P5, LLM-Rec and a method based on frozen BERT. Our main goal has been to shed light into the research questions posed in Section I-B). For RQ1, we devised various ways of leveraging LMs, and we compared to recent works on using LLM prompts for item ranking. We found that such an LLM-only approach is not viable, as it cannot cope with long-tail items. In contrast, using LLMs to generate concise user profiles, based on our CUP framework, is a competitive solution. However, they do not outperform the simpler CUP variants, like idf-based sentences, and the simpler methods are low-cost. For RQ2, we limited the number of input tokens for the Transformer to 128, creating a stress-test but also aiming to minimize the computational and energy cost at training time. Our experimental findings confirmed that this tight length for user profiles is indeed viable, yielding good NDCG@5 and P@1 results (relative to the difficulty of this data). For RQ3, we devised various techniques for selecting informative cues from user reviews. A key observation from the experiments is that a moderate degree of sophistication, like idf-selected sentences, is largely sufficient. Our default configuration CUPidf performs on par with more elaborated variants, such as leveraging SentenceBERT or having T5- generated keywords for concise profiles. Moreover, CUPidf mostly outperforms even the profiles that were generated by ChatGPT with the full review texts as input.

Recommendations by Concise User Profiles from Review Text: Conclusion

Recommendations by Concise User Profiles from Review Text: Experimental Results

The AI industry is dominated by discussions of massive LLMs like GPT-4, Claude 3, and Gemini. But there are equally big issues with relying on these closed-source LLMs. For one, you have minimal control. The models often worsen over time and exhibit biases that you can’t fix. Plus, the firms gatekeeping the models, from OpenAI to Anthropic to Google, can throttle your usage, increase your prices, or simply ban your account. If you run a business, you’re risking everything by using closed-source models. Another issue is their sheer size. They’re far too big and slow to be useful for hardware devices like portable assistants - something Rabbit and Humane AI learned only after scathing reviews. That’s where open-source, tiny LLM models come into play. The most accurate of these in the <2B parameter category is H2O.ai’s Danube 2. They’re becoming increasingly popular for deployment on edge devices, as traditional large language models are too computationally demanding to run efficiently on resource-constrained hardware. Tiny LLMs for phones Danube 2 is a “tiny LLM” that packs a surprising punch for its size. With just 1.8 billion parameters, Danube is lightweight compared to giants like GPT-4, but it achieves highly competitive performance across a wide range of natural language tasks. Trained on a carefully curated dataset of 2 trillion tokens from diverse web sources, Danube leverages techniques refined from models like Mistral to maximize its efficiency. Basically, you can download this model to your device and run it wherever you like — whether that’s new portable consumer hardware, a security camera, an IoT sensor, or anywhere else. One of the key advantages of Danube is its accessibility. With an Apache 2.0 license, it’s freely available for commercial use, lowering the barriers to entry for developers and businesses looking to incorporate AI into their mobile applications. H2O.ai has also released a fine-tuned version, Danube2–1.8B-Chat, specifically optimized for conversational AI. This could spur a wave of more natural and responsive chatbots and virtual assistants on devices from smartphones to smart home hubs. Tiny AI in healthcare The potential applications for small AI extend far beyond consumer-facing chatbots or phones. In the healthcare sector, companies like HeartSciences are leveraging compact machine learning models to supercharge the electrocardiogram (ECG). Heart disease is the leading cause of death globally, claiming around 51,000 lives each day, and ECGs are one of the primary tools for diagnosing cardiac issues. However, interpreting these readings requires specialized expertise that many healthcare providers lack, particularly in resource-constrained settings. HeartSciences’ MyoVista Wavelet ECG system aims to bridge this gap by incorporating sophisticated AI algorithms capable of spotting subtle patterns that human eyes might miss. Trained on vast datasets, these models act as an early warning system for cardiac dysfunction. Crucially, the machine learning behind MyoVista is designed to run efficiently on the kind of hardware you’d find in a typical doctor’s office or clinic, not a high-performance computing cluster. This is the essence of small AI — bringing the power of cutting-edge algorithms to the point of care, where they can have the greatest impact on patient outcomes. When minutes can mean the difference between life and death, having AI-powered insights available immediately is a game-changer. And with hundreds of millions of ECGs administered every year, the scalability of lightweight models like those used in MyoVista is key to making a real dent in the global burden of heart disease. Tiny AI for security Another promising application for small AI is in the realm of smart security systems. Traditional security cameras rely on motion detection and human monitoring to identify potential threats, leading to high rates of false alarms and missed incidents. However, by incorporating compact machine learning models, these systems can become much more intelligent and autonomous. Imagine a security camera equipped with an AI model trained to recognize not just motion but specific types of behaviors and objects. Such a system could distinguish between a person walking their dog and someone attempting to break into a building, alerting human operators only when there’s a genuine threat. By running these models directly on the camera hardware rather than streaming video to the cloud for analysis, smart security systems can operate more efficiently and protect sensitive data from potential breaches. Of course, developing effective AI models for smart security presents its own set of challenges. The models must be able to operate in real-time, processing video feeds as they come in while also being robust to variations in lighting, weather, and other environmental factors. They must also be trained on diverse datasets to avoid bias and ensure fair performance across different demographic groups. Despite these challenges, the potential benefits of AI-powered smart security are significant. By reducing false alarms and enabling proactive threat detection, these systems can help keep people and property safe while also reducing the workload on human operators. And as the underlying hardware continues to become more powerful and energy-efficient, the possibilities for edge-based AI in security applications will only continue to grow. Takeaways As the AI community continues to push the boundaries of what’s possible with large-scale models and infrastructure, it’s important not to lose sight of the transformative potential of small AI. By bringing powerful capabilities to the devices and contexts where they’re needed most, compact models have the potential to democratize access to AI’s benefits and touch billions of lives in meaningful ways. From the smartphones in our pockets to the medical devices in our clinics to the security cameras watching over our streets, small AI is poised to make a big impact. As we work to overcome the challenges around data, privacy, and bias, we must also recognize and celebrate the quiet revolution that’s happening at the edge. The future of AI may be small, but its potential is enormous. The AI industry is dominated by discussions of massive LLMs like GPT-4, Claude 3, and Gemini. But there are equally big issues with relying on these closed-source LLMs. For one, you have minimal control. The models often worsen over time and exhibit biases that you can’t fix. worsen over time exhibit biases Plus, the firms gatekeeping the models, from OpenAI to Anthropic to Google, can throttle your usage, increase your prices, or simply ban your account. If you run a business, you’re risking everything by using closed-source models. Another issue is their sheer size. They’re far too big and slow to be useful for hardware devices like portable assistants - something Rabbit and Humane AI learned only after scathing reviews . scathing reviews That’s where open-source, tiny LLM models come into play. The most accurate of these in the <2B parameter category is H2O.ai’s Danube 2 . They’re becoming increasingly popular for deployment on edge devices, as traditional large language models are too computationally demanding to run efficiently on resource-constrained hardware. H2O.ai’s Danube 2 Tiny LLMs for phones Danube 2 is a “tiny LLM” that packs a surprising punch for its size. With just 1.8 billion parameters, Danube is lightweight compared to giants like GPT-4, but it achieves highly competitive performance across a wide range of natural language tasks. Trained on a carefully curated dataset of 2 trillion tokens from diverse web sources, Danube leverages techniques refined from models like Mistral to maximize its efficiency. Basically, you can download this model to your device and run it wherever you like — whether that’s new portable consumer hardware, a security camera, an IoT sensor, or anywhere else. One of the key advantages of Danube is its accessibility. With an Apache 2.0 license, it’s freely available for commercial use, lowering the barriers to entry for developers and businesses looking to incorporate AI into their mobile applications. H2O.ai has also released a fine-tuned version, Danube2–1.8B-Chat , specifically optimized for conversational AI. This could spur a wave of more natural and responsive chatbots and virtual assistants on devices from smartphones to smart home hubs. Danube2–1.8B-Chat Tiny AI in healthcare The potential applications for small AI extend far beyond consumer-facing chatbots or phones. In the healthcare sector, companies like HeartSciences are leveraging compact machine learning models to supercharge the electrocardiogram (ECG). HeartSciences Heart disease is the leading cause of death globally, claiming around 51,000 lives each day, and ECGs are one of the primary tools for diagnosing cardiac issues. However, interpreting these readings requires specialized expertise that many healthcare providers lack, particularly in resource-constrained settings. HeartSciences’ MyoVista Wavelet ECG system aims to bridge this gap by incorporating sophisticated AI algorithms capable of spotting subtle patterns that human eyes might miss. Trained on vast datasets, these models act as an early warning system for cardiac dysfunction. Crucially, the machine learning behind MyoVista is designed to run efficiently on the kind of hardware you’d find in a typical doctor’s office or clinic, not a high-performance computing cluster. This is the essence of small AI — bringing the power of cutting-edge algorithms to the point of care, where they can have the greatest impact on patient outcomes. When minutes can mean the difference between life and death, having AI-powered insights available immediately is a game-changer. And with hundreds of millions of ECGs administered every year, the scalability of lightweight models like those used in MyoVista is key to making a real dent in the global burden of heart disease. Tiny AI for security Another promising application for small AI is in the realm of smart security systems . Traditional security cameras rely on motion detection and human monitoring to identify potential threats, leading to high rates of false alarms and missed incidents. However, by incorporating compact machine learning models, these systems can become much more intelligent and autonomous. smart security systems Imagine a security camera equipped with an AI model trained to recognize not just motion but specific types of behaviors and objects. Such a system could distinguish between a person walking their dog and someone attempting to break into a building, alerting human operators only when there’s a genuine threat. By running these models directly on the camera hardware rather than streaming video to the cloud for analysis, smart security systems can operate more efficiently and protect sensitive data from potential breaches. Of course, developing effective AI models for smart security presents its own set of challenges. The models must be able to operate in real-time, processing video feeds as they come in while also being robust to variations in lighting, weather, and other environmental factors. They must also be trained on diverse datasets to avoid bias and ensure fair performance across different demographic groups. Despite these challenges, the potential benefits of AI-powered smart security are significant. By reducing false alarms and enabling proactive threat detection, these systems can help keep people and property safe while also reducing the workload on human operators. And as the underlying hardware continues to become more powerful and energy-efficient, the possibilities for edge-based AI in security applications will only continue to grow. Takeaways As the AI community continues to push the boundaries of what’s possible with large-scale models and infrastructure, it’s important not to lose sight of the transformative potential of small AI. By bringing powerful capabilities to the devices and contexts where they’re needed most, compact models have the potential to democratize access to AI’s benefits and touch billions of lives in meaningful ways. From the smartphones in our pockets to the medical devices in our clinics to the security cameras watching over our streets, small AI is poised to make a big impact. As we work to overcome the challenges around data, privacy, and bias, we must also recognize and celebrate the quiet revolution that’s happening at the edge. The future of AI may be small, but its potential is enormous.

Danube-2: The Tiny AI Model Leading the Open LLM Leaderboard

Stack: Older developers and engineers might remember the LAMP stack💡  and although it’s reengineered, if we strip down everything to the basics what we find and need is: an operating system


a web server


a database


a programming language What I’m going to talk to you today is how I reimagined the LAMP stack in an alternative format: Linux
Apache
MySql
Php
to:
Ubuntu
Apache
Sqlite3
Python What Does It Take? Nowadays, all that I hear all too often is: “I want to become a developer how do I get started?”, “I want to become a developer what laptop do I need?” or “Do I need to buy the latest MacBook to become a developer?” and to be honest, it’s starting to get to me. No kids, you don’t need the latest fancy tech or a rocket🚀 to become a developer, what you need is an operating system that is capable of installing programming packages and can compile them; you can even use your grandma’s old laptop. And now you ask: “How can this be?!” Let’s take a short trip down memory lane; almost 3 and a half years ago, I started tinkering with an SBC - single board computer that starts at under 50E to buy, and on it right now, I have a web server, a database, am performing hundreds of web-scrapes per hour, and on top of it, I’m working on developing an API endpoint. This SBC is not even the latest in its range and has: 8GB of LPDDR4  internal memory


1.5GHz quad-core ARM Cortex-A72 CPU (launched in 2015) - that you can easily and safely overclock at 2GHz (+fan extension & heatsink) Here’s what GPT has to say about these specs:
The specifications you provided are decent for certain types of computing tasks, but they may not be sufficient for others. Here's an evaluation: 8GB LPDDR4 internal memory:
LPDDR4 is a relatively fast and power-efficient type of RAM commonly used in mobile devices and some laptops.
8GB is a moderate amount of memory that should be sufficient for everyday tasks like web browsing, office work, and light multitasking. It can also handle moderate gaming and content creation applications.
However, for more demanding tasks like video editing, graphic design, or running virtual machines, 8GB may be limiting, and you might experience performance bottlenecks.
1.5GHz Quad-Core ARM Cortex-A72 CPU:


The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices.


A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently.


The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications.


However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. Trust me, in the beginning, light to moderate is more than sufficient, so with a Pi board, you can easily have a working desktop environment in which you can learn how to code, and not only frontend but even backend programming languages such as Python, c++ (which is even more energy efficient), or javascript. What do I use? On my Raspberry Pi, I am developing code with: Ubuntu Desktop - courtesy of desktopify:

Which I had to secure and in doing so I learned about:


web server vulnerabilities


firewalls


antivirus


intrusion prevention software IPS


networking


DNS


strong password management


ssh keys


SSL certificates






VSCode remotely connecting to the server


Python3 scripts which scrape the web and build HTML webpages embodied by CSS


requests


selenium


pandas


parallel processing - concurrent.futures




Javascript coupled with PHP here and there.


Python3 scripts that automatically write in my Sqlite3 database in JSON format.


Flask app, a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python.


And lastly, a Javascript piece of code that calls the Flask endpoint - a RESTful API endpoint
For close to 4 years at a definitely amortized cost of 120E and < 4E per month in electricity🔌. To summarize everything, here’s the BerryNews system architecture: The key factors here are Efficient Resource Utilization + Resource Utilization Constraints. Why splurge when you can write efficient code? Conclusion: If we were to sum up everything, we’d end up with a table such as this on a 100-dollar computer: Category

Keywords



Programming languages

Python3, Javascript, PHP, CSS, HTML, SQL



Data formats

JSON



Frameworks

flask, selenium, concurrent.futures



Databases

sqlite3



Networking

router config, DNS



Cybersecurity

UFW (firewall), ClamAV (antivirus), Fail2ban (IPS), password management, SSH Keys, Let’s Encrypt (SSL certificate)



Linux

Raspberry Pi, Ubuntu Desktop, VSCode So, tell me, do you still think you need the latest laptop specs to become a developer? Stack: Stack: Older developers and engineers might remember the LAMP stack💡  and although it’s reengineered, if we strip down everything to the basics what we find and need is: strip down everything to basics an operating system a web server a database a programming language an operating system an operating system a web server a web server a database a database a programming language a programming language What I’m going to talk to you today is how I reimagined the LAMP stack in an alternative format : the LAMP stack in an alternative format Linux Apache MySql Php
to: Ubuntu Apache Sqlite3 Python Linux Apache MySql Php to: to: Ubuntu Apache Sqlite3 Python What Does It Take? What Does It Take? Nowadays, all that I hear all too often is: “I want to become a developer how do I get started?”, “I want to become a developer what laptop do I need?” or “Do I need to buy the latest MacBook to become a developer?” and to be honest, it’s starting to get to me. No kids , you don’t need the latest fancy tech or a rocket🚀 to become a developer, what you need is an operating system that is capable of installing programming packages and can compile them; you can even use your grandma’s old laptop. And now you ask: “How can this be?!” No kids Let’s take a short trip down memory lane; almost 3 and a half years ago, I started tinkering with an SBC - single board compute r that starts at under 50E to buy, and on it right now, I have a web server, a database, am performing hundreds of web-scrapes per hour, and on top of it, I’m working on developing an API endpoint. SBC single board compute This SBC is not even the latest in its range and has: SBC 8GB of LPDDR4  internal memory 1.5GHz quad-core ARM Cortex-A72 CPU (launched in 2015) - that you can easily and safely overclock at 2GHz (+fan extension & heatsink) 8GB of LPDDR4  internal memory 8GB of LPDDR4  internal memory 1.5GHz quad-core ARM Cortex-A72 CPU (launched in 2015) - that you can easily and safely overclock at 2GHz (+fan extension & heatsink) 1.5GHz quad-core ARM Cortex-A72 CPU (launched in 2015) - that you can easily and safely overclock at 2GHz (+fan extension & heatsink) Here’s what GPT has to say about these specs:
The specifications you provided are decent for certain types of computing tasks, but they may not be sufficient for others. Here's an evaluation: 8GB LPDDR4 internal memory: LPDDR4 is a relatively fast and power-efficient type of RAM commonly used in mobile devices and some laptops. 8GB is a moderate amount of memory that should be sufficient for everyday tasks like web browsing, office work, and light multitasking. It can also handle moderate gaming and content creation applications. However, for more demanding tasks like video editing, graphic design, or running virtual machines, 8GB may be limiting, and you might experience performance bottlenecks. 1.5GHz Quad-Core ARM Cortex-A72 CPU:


The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices.


A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently.


The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications.


However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. 8GB LPDDR4 internal memory: LPDDR4 is a relatively fast and power-efficient type of RAM commonly used in mobile devices and some laptops. 8GB is a moderate amount of memory that should be sufficient for everyday tasks like web browsing, office work, and light multitasking. It can also handle moderate gaming and content creation applications. However, for more demanding tasks like video editing, graphic design, or running virtual machines, 8GB may be limiting, and you might experience performance bottlenecks. 1.5GHz Quad-Core ARM Cortex-A72 CPU: The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices.


A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently.


The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications.


However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices. A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently. The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications. However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices. The ARM Cortex-A72 is a capable processor design commonly found in smartphones, tablets, and other mobile devices. A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently. A quad-core configuration means it has four cores, allowing it to handle multiple tasks simultaneously more efficiently. The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications. The 1.5GHz clock speed is decent for general-purpose computing tasks, including web browsing, media consumption, and productivity applications. However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. However, for more demanding tasks such as gaming, video editing, or running complex software, a higher clock speed and possibly more cores would provide better performance.
In summary, these specifications are suitable for light to moderate computing tasks, such as everyday productivity, web browsing, and multimedia consumption. However, for more demanding tasks or future-proofing, you may want to consider upgrading to a system with more RAM and a faster CPU. Trust me, in the beginning, light to moderate is more than sufficient, so with a Pi board, you can easily have a working desktop environment in which you can learn how to code, and not only frontend but even backend programming languages such as Python, c++ (which is even more energy efficient), or javascript. desktop What do I use? What do I use? On my Raspberry Pi, I am developing code with: Ubuntu Desktop - courtesy of desktopify:

Which I had to secure and in doing so I learned about:


web server vulnerabilities


firewalls


antivirus


intrusion prevention software IPS


networking


DNS


strong password management


ssh keys


SSL certificates VSCode remotely connecting to the server Python3 scripts which scrape the web and build HTML webpages embodied by CSS


requests


selenium


pandas


parallel processing - concurrent.futures Javascript coupled with PHP here and there. Python3 scripts that automatically write in my Sqlite3 database in JSON format. Flask app, a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python. And lastly, a Javascript piece of code that calls the Flask endpoint - a RESTful API endpoint
For close to 4 years at a definitely amortized cost of 120E and < 4E per month in electricity🔌. Ubuntu Desktop - courtesy of desktopify: Which I had to secure and in doing so I learned about:


web server vulnerabilities


firewalls


antivirus


intrusion prevention software IPS


networking


DNS


strong password management


ssh keys


SSL certificates Ubuntu Desktop - courtesy of desktopify : Ubuntu desktopify Which I had to secure and in doing so I learned about:


web server vulnerabilities


firewalls


antivirus


intrusion prevention software IPS


networking


DNS


strong password management


ssh keys


SSL certificates Which I had to secure and in doing so I learned about: web server vulnerabilities


firewalls


antivirus


intrusion prevention software IPS


networking


DNS


strong password management


ssh keys


SSL certificates secure web server vulnerabilities firewalls antivirus intrusion prevention software IPS networking DNS strong password management ssh keys SSL certificates web server vulnerabilities web server vulnerabilities web server vulnerabilities firewalls firewalls antivirus antivirus intrusion prevention software IPS intrusion prevention software IPS networking networking DNS DNS DNS strong password management strong password management ssh keys ssh keys ssh keys SSL certificates SSL certificates SSL certificates VSCode remotely connecting to the server VSCode remotely connecting to the server Python3 scripts which scrape the web and build HTML webpages embodied by CSS requests


selenium


pandas


parallel processing - concurrent.futures Python3 scripts which scrape the web and build HTML webpages embodied by CSS Python3 HTML CSS requests selenium pandas parallel processing - concurrent.futures requests requests selenium selenium pandas pandas parallel processing - concurrent.futures parallel processing - concurrent.futures Javascript coupled with PHP here and there. Javascript coupled with PHP here and there. Javascript PHP Python3 scripts that automatically write in my Sqlite3 database in JSON format. Python3 scripts that automatically write in my Sqlite3 database in JSON format. Sqlite3 JSON Flask app, a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python. Flask app, a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python. Flask web framework And lastly, a Javascript piece of code that calls the Flask endpoint - a RESTful API endpoint For close to 4 years at a definitely amortized cost of 120E and < 4E per month in electricity🔌. And lastly, a Javascript piece of code that calls the Flask endpoint - a RESTful API endpoint endpoint RESTful For close to 4 years at a definitely amortized cost of 120E and < 4E per month in electricity🔌. To summarize everything, here’s the BerryNews system architecture: The key factors here are Efficient Resource Utilization + Resource Utilization Constraints . Efficient Resource Utilization Resource Utilization Constraints Why splurge when you can write efficient code? Conclusion: If we were to sum up everything, we’d end up with a table such as this on a 100-dollar computer: Category

Keywords



Programming languages

Python3, Javascript, PHP, CSS, HTML, SQL



Data formats

JSON



Frameworks

flask, selenium, concurrent.futures



Databases

sqlite3



Networking

router config, DNS



Cybersecurity

UFW (firewall), ClamAV (antivirus), Fail2ban (IPS), password management, SSH Keys, Let’s Encrypt (SSL certificate)



Linux

Raspberry Pi, Ubuntu Desktop, VSCode Category

Keywords Programming languages

Python3, Javascript, PHP, CSS, HTML, SQL Data formats

JSON Frameworks

flask, selenium, concurrent.futures Databases

sqlite3 Networking

router config, DNS Cybersecurity

UFW (firewall), ClamAV (antivirus), Fail2ban (IPS), password management, SSH Keys, Let’s Encrypt (SSL certificate) Linux

Raspberry Pi, Ubuntu Desktop, VSCode Category Keywords Category Category Category Keywords Keywords Keywords Programming languages Python3, Javascript, PHP, CSS, HTML, SQL Programming languages Programming languages Python3, Javascript, PHP, CSS, HTML, SQL Python3, Javascript, PHP, CSS, HTML, SQL Data formats JSON Data formats Data formats JSON JSON Frameworks flask, selenium, concurrent.futures Frameworks Frameworks flask, selenium, concurrent.futures flask, selenium, concurrent.futures Databases sqlite3 Databases Databases sqlite3 sqlite3 Networking router config, DNS Networking Networking router config, DNS router config, DNS Cybersecurity UFW (firewall), ClamAV (antivirus), Fail2ban (IPS), password management, SSH Keys, Let’s Encrypt (SSL certificate) Cybersecurity Cybersecurity UFW (firewall), ClamAV (antivirus), Fail2ban (IPS), password management, SSH Keys, Let’s Encrypt (SSL certificate) UFW ( firewall ), ClamAV ( antivirus ), Fail2ban ( IPS ), password management, SSH Keys, Let’s Encrypt ( SSL certificate ) firewall antivirus IPS SSL certificate Linux Raspberry Pi, Ubuntu Desktop, VSCode Linux Linux Raspberry Pi, Ubuntu Desktop, VSCode Raspberry Pi, Ubuntu Desktop, VSCode So, tell me, do you still think you need the latest laptop specs to become a developer? So, tell me, do you still think you need the latest laptop specs to become a developer?

RaspberryPi: Technology Overload - LAMP🕯 to UASP🐝

রাস্পবেরিপি: প্রযুক্তি ওভারলোড - LAMP🕯 থেকে UASP🐝

RaspberryPi: Technologie-Überlastung – LAMP🕯 zu UASP🐝

RaspberryPi: Sobrecarga tecnológica: LAMP🕯 a UASP🐝

RaspberryPi : surcharge technologique - LAMP🕯 vers UASP🐝

रास्पबेरीपाई: प्रौद्योगिकी अधिभार - LAMP🕯 से UASP🐝 तक

RaspberryPi: テクノロジーの過剰 - LAMP🕯からUASP🐝まで

RaspberryPi: 기술 과부하 - LAMP🕯에서 UASP🐝까지

RaspberryPi: sobrecarga de tecnologia - LAMP🕯 para UASP🐝

RaspberryPi: технологическая перегрузка — от LAMP🕯 до UASP🐝

RaspberryPi: Aşırı Teknoloji Yükü - LAMP🕯'den UASP🐝'ye

RaspberryPi: Quá tải công nghệ - LAMP🕯 đến UASP🐝

RaspberryPi：技术超载 - 从 LAMP🕯 到 UASP🐝

DEMAND.IO

demand.io

11-50 employees

Since 2009

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DEMAND.IO

demand.io

11-50 employees

Since 2009

COMPANY RANKING

DEMAND.IO

EVERGREEN INDEX #3071

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STORIES ABOUT DEMAND.IO

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