(Bloomberg) -- Stocks closed at all-time highs as traders looked past signals the Federal Reserve is in no rush to cut interest rates.Most Read from BloombergTrump to Halt NY Congestion Pricing by Terminating ApprovalSorry, Kids: Disney’s New York Headquarters Is for Grown-UpsAirbnb Billionaire ...

S&P 500 Hits Record as Traders Brush Off Fed Signs: Markets Wrap

The ESPN hub on Disney+ will bring ... Pat McAfee Show, NBA Today, and First Take, along with live events and on-demand replays, and iconic original programming including ESPN’s library of 30 for 30 films....

Disney+ Expands Entertainment Offering, Joining ESPN Sports Programming In ANZ - B&T

Disney reported quarterly results before the bell on Wednesday. Here's what to know.

Disney price hikes help fuel earnings growth as streaming business posts profit

CEO Bob Iger also took a shot at Netflix, noting that ESPN is not about "one day of football" The post Disney Backed Out of ‘Redundant’ Venu Because of the Rise of Sports Skinny Bundles appeared first on TheWrap.

Disney Backed Out of ‘Redundant’ Venu Because of the Rise of Sports Skinny Bundles

The company's streaming business reported another quarter of profitability despite a 1% decline in subscribers for Disney+.

Disney tops quarterly profit estimates, but starts to lose Disney+ streaming subscribers

Alexandra Canal is a Senior Reporter at Yahoo Finance.

Disney earnings preview: Parks likely to struggle amid hurricanes as investors eye streaming momentum

The House of Mouse is combining its Hulu + Live TV service with the pay-TV streamer Fubo's business. The deal paves the way for a new sports streamer, Venu, to debut in the coming months, with sports content from ESPN, Warner Bros. Discovery, and Fox.

Disney Shakes up Sports-Streaming Strategy With Fubo but Could Confuse - Business Insider

The proposed merger would join two internet TV bundles that together have 6.2 million subscribers.

Walt Disney and FuboTV to combine their online live TV businesses

Disney

Crypto was never meant to be about pleasing the system While meme-stablecoins were busy getting regulatory gold stars, Tether was busy becoming the people's currency. Here's how playing nice became other issuers' biggest strategic mistake. Let's talk about the two paths in the stablecoin revolution: Be the system's perfect student (meme-stablecoins)
Be the market's reliable partner (USDT) But here's what everyone misses: The nice guy doesn't always win. Sometimes, being market-focused is the best strategy. The Nice Guy Strategy Meme-stablecoins' playbook was perfect on paper: Partner with banks
Embrace regulation
Court institutions
Play by the rules
Be the "responsible" one The Alpha Move Meanwhile, Tether chose market reality: Focus on actual adoption
Serve the unbanked
Enable real utility
Build reliable networks
Become too important to ignore Here's the billion-dollar insight: While meme-stablecoins were collecting permits, Tether was collecting users. The Market Verdict The results speak for themselves: Meme-stablecoins: Beloved by regulators, unused by masses
USDT: Trusted by global markets, essential to global trade The Strategic Miscalculation Meme-stablecoins' fundamental error: Thought compliance meant success
Believed institutions would reward loyalty
Expected the system to reciprocate
Forgot who crypto was built for The Power of Market Focus Tether's path to dominance: Focused on real users
Built ground-up adoption
Created genuine utility
Became systemically important But here's where it gets interesting: Tether didn't just win – it became indispensable. Consider the reality: Top 20 holder of U.S. Treasuries
Essential to global dollar access
Backbone of crypto liquidity
Tool of financial inclusion The Ultimate Irony While meme-stablecoins were trying to please everyone: Tether became too strategic to fail
Built real-world utility
Created genuine value
Achieved true adoption The Hidden Power The real lesson isn't about rebellion – it's about understanding market needs: Real power comes from users
True stability comes from utility
Genuine adoption beats approval
Market relevance becomes strength The Future The path forward is clear: Mass adoption > Institutional approval
Real utility > Regulatory gold stars
User trust > Government permits
Market power > Official blessing Welcome to the new financial order. The question isn't who plays nice. The question is: Who's actually useful? Meme-stablecoins tried to join the system. Tether became the system. And that has made all the difference. Remember: In crypto, market relevance isn't just a feature. It's the essential feature. "Meme-stablecoins," a term coined by Tether CEO Paolo Ardoino, refers to stablecoins that prioritize regulatory compliance and institutional approval over actual market utility and user adoption – digital assets that look good on paper but fail to deliver real-world value. Crypto was never meant to be about pleasing the system Crypto was never meant to be about pleasing the system While meme-stablecoins were busy getting regulatory gold stars, Tether was busy becoming the people's currency. Here's how playing nice became other issuers' biggest strategic mistake. Here's how playing nice became other issuers' biggest strategic mistake. Let's talk about the two paths in the stablecoin revolution: Be the system's perfect student (meme-stablecoins) Be the market's reliable partner (USDT) Be the system's perfect student (meme-stablecoins) Be the market's reliable partner (USDT) But here's what everyone misses: The nice guy doesn't always win. Sometimes, being market-focused is the best strategy. But here's what everyone misses: The nice guy doesn't always win. Sometimes, being market-focused is the best strategy. The Nice Guy Strategy Meme-stablecoins' playbook was perfect on paper: Partner with banks Embrace regulation Court institutions Play by the rules Be the "responsible" one Partner with banks Embrace regulation Court institutions Play by the rules Be the "responsible" one The Alpha Move Alpha Meanwhile, Tether chose market reality: Focus on actual adoption Serve the unbanked Enable real utility Build reliable networks Become too important to ignore Focus on actual adoption Serve the unbanked Enable real utility Build reliable networks Become too important to ignore Here's the billion-dollar insight: While meme-stablecoins were collecting permits, Tether was collecting users. Here's the billion-dollar insight: While meme-stablecoins were collecting permits, Tether was collecting users. The Market Verdict The results speak for themselves: Meme-stablecoins: Beloved by regulators, unused by masses USDT: Trusted by global markets, essential to global trade Meme-stablecoins: Beloved by regulators, unused by masses USDT: Trusted by global markets, essential to global trade The Strategic Miscalculation Meme-stablecoins' fundamental error: Thought compliance meant success Believed institutions would reward loyalty Expected the system to reciprocate Forgot who crypto was built for Thought compliance meant success Believed institutions would reward loyalty Expected the system to reciprocate Forgot who crypto was built for The Power of Market Focus Tether's path to dominance: Focused on real users Built ground-up adoption Created genuine utility Became systemically important Focused on real users Built ground-up adoption Created genuine utility Became systemically important But here's where it gets interesting: Tether didn't just win – it became indispensable. But here's where it gets interesting: Tether didn't just win – it became indispensable. Consider the reality: Top 20 holder of U.S. Treasuries Essential to global dollar access Backbone of crypto liquidity Tool of financial inclusion Top 20 holder of U.S. Treasuries Essential to global dollar access Backbone of crypto liquidity Tool of financial inclusion The Ultimate Irony While meme-stablecoins were trying to please everyone: Tether became too strategic to fail Built real-world utility Created genuine value Achieved true adoption Tether became too strategic to fail Built real-world utility Created genuine value Achieved true adoption The Hidden Power The real lesson isn't about rebellion – it's about understanding market needs: Real power comes from users True stability comes from utility Genuine adoption beats approval Market relevance becomes strength Real power comes from users True stability comes from utility Genuine adoption beats approval Market relevance becomes strength The Future The path forward is clear: Mass adoption > Institutional approval Real utility > Regulatory gold stars User trust > Government permits Market power > Official blessing Mass adoption > Institutional approval Real utility > Regulatory gold stars User trust > Government permits Market power > Official blessing Welcome to the new financial order. The question isn't who plays nice. The question is: Who's actually useful? Meme-stablecoins tried to join the system. Tether became the system. And that has made all the difference. Remember: In crypto, market relevance isn't just a feature. It's the essential feature. Remember: "Meme-stablecoins," a term coined by Tether CEO Paolo Ardoino, refers to stablecoins that prioritize regulatory compliance and institutional approval over actual market utility and user adoption – digital assets that look good on paper but fail to deliver real-world value. "Meme-stablecoins,"

How Tether's USDT Became Essential While Meme-Stablecoins Became Compliant

Kunjams Tether ukan USDT ukax wali wakiskirïx Meme-Stablecoins ukax Compliant ukar tukuwayxi

Hvordan Tethers USDT blev essentiel, mens Meme-Stablecoins blev kompatible

Cómo el USDT de Tether se volvió esencial mientras las monedas estables de memes se volvieron compatibles

Ինչպե՞ս է Tether-ի USDT-ն դարձել էական, մինչդեռ Meme-Stablecoins-ը դարձել է համապատասխան

ミームステーブルコインが準拠する一方で、テザーの USDT が不可欠なものになった経緯

Tether의 USDT가 필수가 된 반면 Meme-Stablecoin은 규정을 준수하게 됨

Kaip „Tether“ USDT tapo būtinas, o „Meme-Stablecoins“ tapo suderinamas

Bagaimana USDT Tether Menjadi Penting Sementara Meme-Stablecoins Menjadi Patuh

Hoe Tether's USDT essentieel werd terwijl Meme-Stablecoins compatibel werden

Како је Тетхер-ов УСДТ постао неопходан док су Меме-Стаблецоинс постали усклађени

Hur Tethers USDT blev väsentligt medan Meme-Stablecoins blev kompatibla

USDT của Tether đã trở nên thiết yếu như thế nào trong khi các loại tiền ổn định meme đã trở nên tuân thủ

Inside the Incredible Potential of Quantum Computing in Drug Development

The #blockchain Writing Contest, presented by Aleph Cloud and HackerNoon, is putting decentralized technologies front and center. Answer the following questions to win up to $2000. Note: This template is a guide, not a constraint—answer the title question however you see fit. If it doesn’t work for you, choose another from our full list of writing prompts here. What Are the Benefits of Decentralized Cloud Computing Over Traditional Cloud Services? 1. Introduction Define cloud computing, and explain how traditional cloud services work.
Discuss the risks of centralized cloud models.
How is decentralized cloud computing different? 2. Advantages of Decentralized Cloud Computing Enumerate and explain the advantages of decentralized cloud computing. 3. Decentralized Computing in the Wild Which projects are leading the way in decentralized cloud computing?
How are businesses and individuals benefiting from them? 4. Challenges Facing Decentralized Computing What are the technical and adoption barriers for decentralized cloud services?
What solutions can help overcome these barriers to foster mainstream adoption? 5. Conclusion Summarize your thoughts and end your article. Ready to get Started? Start a draft or use this template to enter! Submissions close on May 7, 2025. If you’d like to participate in the Blockchain Writing Contest but feel this template isn’t right for you, feel free to explore any of the other contest tags:

decentralized-ai
dePIN The #blockchain Writing Contest, presented by Aleph Cloud and HackerNoon, is putting decentralized technologies front and center. Answer the following questions to win up to $2000. The #blockchain Writing Contest , presented by Aleph Cloud and HackerNoon, is putting decentralized technologies front and center. Answer the following questions to win up to $2000. #blockchain Writing Contest #blockchain Writing Contest win up to $2000. Note: This template is a guide, not a constraint—answer the title question however you see fit. If it doesn’t work for you, choose another from our full list of writing prompts here. Note: This template is a guide, not a constraint—answer the title question however you see fit. If it doesn’t work for you, choose another from our full list of writing prompts here . Note: here here here What Are the Benefits of Decentralized Cloud Computing Over Traditional Cloud Services? 1. Introduction Define cloud computing, and explain how traditional cloud services work.
Discuss the risks of centralized cloud models.
How is decentralized cloud computing different? Define cloud computing, and explain how traditional cloud services work. Discuss the risks of centralized cloud models. How is decentralized cloud computing different? Define cloud computing, and explain how traditional cloud services work. Discuss the risks of centralized cloud models. How is decentralized cloud computing different? 2. Advantages of Decentralized Cloud Computing Enumerate and explain the advantages of decentralized cloud computing. Enumerate and explain the advantages of decentralized cloud computing. Enumerate and explain the advantages of decentralized cloud computing. 3. Decentralized Computing in the Wild Which projects are leading the way in decentralized cloud computing?
How are businesses and individuals benefiting from them? Which projects are leading the way in decentralized cloud computing? How are businesses and individuals benefiting from them? Which projects are leading the way in decentralized cloud computing? How are businesses and individuals benefiting from them? 4. Challenges Facing Decentralized Computing What are the technical and adoption barriers for decentralized cloud services?
What solutions can help overcome these barriers to foster mainstream adoption? What are the technical and adoption barriers for decentralized cloud services? What solutions can help overcome these barriers to foster mainstream adoption? What are the technical and adoption barriers for decentralized cloud services? What solutions can help overcome these barriers to foster mainstream adoption? 5. Conclusion Summarize your thoughts and end your article. Summarize your thoughts and end your article. Ready to get Started? Start a draft or use this template to enter! Submissions close on May 7, 2025. Start a draft or use this template to enter! Submissions close on May 7, 2025. Start a draft or use this template to enter! Submissions close on May 7, 2025. draft draft template template If you’d like to participate in the Blockchain Writing Contest but feel this template isn’t right for you, feel free to explore any of the other contest tags: decentralized-ai
dePIN If you’d like to participate in the Blockchain Writing Contest but feel this template isn’t right for you, feel free to explore any of the other contest tags: decentralized-ai dePIN decentralized-ai decentralized-ai decentralized-ai dePIN dePIN dePIN

Answer To Win from $2000: How Does Decentralized Cloud Compare to Traditional Cloud Services?

2000 dollardan qazanmağa cavab: Mərkəzləşdirilməmiş Bulud Ənənəvi Bulud Xidmətləri ilə necə müqayisə olunur?

Respuesta para ganar desde $2000: ¿Cómo se compara la nube descentralizada con los servicios de nube tradicionales?

Odgovor za pobjedu od 2000 USD: Kako se decentralizirani oblak može usporediti s tradicionalnim uslugama u oblaku?

Válasz a nyerésre 2000 dollártól: Hogyan viszonyul a decentralizált felhő a hagyományos felhőszolgáltatásokhoz?

Jawaban untuk Menang dari $2000: Bagaimana Cloud Terdesentralisasi Dibandingkan dengan Layanan Cloud Tradisional?

2,000 ドルを獲得するための回答: 分散型クラウドは従来のクラウド サービスとどう違うのか?

2000 დოლარიდან მოგების პასუხი: როგორ ადარებს დეცენტრალიზებული ღრუბელი ტრადიციულ ღრუბლოვან სერვისებს?

ຄໍາຕອບທີ່ຈະຊະນະຈາກ $ 2000: ຟັງແບບ Decentralized ປຽບທຽບກັບການບໍລິການຄລາວແບບດັ້ງເດີມແນວໃດ?

Одговор за победа од 2000 долари: Како децентрализираниот облак се споредува со традиционалните услуги на облак?

Sagot Upang Manalo mula sa $2000: Paano Inihahambing ang Desentralisadong Cloud sa Mga Tradisyunal na Serbisyo ng Cloud?

$2000 سے جیتنے کا جواب: ڈی سینٹرلائزڈ کلاؤڈ کا روایتی کلاؤڈ سروسز سے موازنہ کیسے ہوتا ہے؟

Câu trả lời để giành giải thưởng 2000 đô la: Đám mây phi tập trung so với dịch vụ đám mây truyền thống như thế nào?

Authors:
(1) Wanyun Cui, Shanghai University of Finance and Economics, with equal contribution;
(2) Qianle Wang, Shanghai University of Finance and Economics, with equal contribution. Table of Links Abstract and 1 Introduction 2 Related Work 3 Quantifying the Impact of Parameters on Model Performance & 4. Unified Mixed-Precision Training 5 Prevalence of Parameter Heterogeneity in LLMs 6 Quantization Experiments and 6.1 Implementation Details 6.2 Effect of Base LLM Quantization 6.3 Effect of Chat LLM Quantization 6.4 Comparison of Parameter Selection Criteria, Conclusion, & References 6.4 Comparison of Parameter Selection Criteria To evaluate the effectiveness of our proposed impact-based parameter selection criterion, we conduct experiments comparing it with the commonly used magnitude-based criterion [18]. Table 5 presents the perplexity of LLaMA2-7B-3bit and LLaMA2-13B-3bit models, using both criteria for cherry parameter selection. From the results, it is evident that the impact-based criterion consistently outperforms the magnitude-based criterion across all settings. These results demonstrate that our proposed impact-based criterion is a more effective measure of parameter importance compared to the magnitude-based criterion. The impacts identify and preserve the most critical parameters during the quantization process. We think this justify the heterogeneity of parameter impacts against parameter magnitudes as we highlighted in § 5. The extensive experimental results presented in this section clearly demonstrate the superiority of CherryQ compared to existing quantization methods. By effectively identifying the critical cherry parameters and unifying the mixed-precision parameter optimization, CherryQ achieves state-of-theart performance for both base LLMs and chat LLMs. 7. Conclusion In this paper, we investigated the parameter heterogeneity phenomenon in LLMs. Our experiments on LLaMA2, Mistral, Gemma, and Vicuna models, consistently demonstrated that a small subset of parameters plays a crucial role in maintaining the model’s performance, while the majority of parameters can be quantized to ultra-low precision without significant degradation. This finding highlights the potential for efficient model compression and quantization techniques that take into account the heterogeneous nature of parameter importance. Motivated by this observation, we proposed a novel impact-based parameter selection criterion for quantization. Our method effectively identifies and preserves the most critical cherry parameters during the quantization process. We use a QAT framework for unified optimization of both cherry parameters and normal parameters. Extensive experiments demonstrate that CherryQ outperforms the commonly used magnitude-based criterion, achieving significantly lower perplexity scores and better downstream performance. The heterogeneity and proposed approach pave the way for more efficient deployment of LLMs in resource-constrained environments. A Effect of Chat LLM Quantization on MMLU We further evaluate the performance of CherryQ on the MMLU benchmark by quantizing the Vicuna1.5 model. As shown in Table 6, CherryQ outperforms both QAT and GPTQ in terms of average accuracy across almost all categories. References [1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. [2] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. [3] Yoshua Bengio, Nicholas L´eonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013. [4] Yelysei Bondarenko, Markus Nagel, and Tijmen Blankevoort. Understanding and overcoming the challenges of efficient transformer quantization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7947–7969, 2021. [5] Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march 2023. URL https://lmsys. org/blog/2023-03- 30-vicuna, 3(5), 2023. [6] Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale. Advances in Neural Information Processing Systems, 35:30318–30332, 2022. [7] Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. Qlora: Efficient finetuning of quantized llms. Advances in Neural Information Processing Systems, 36, 2024. [8] Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Gptq: Accurate post-training quantization for generative pre-trained transformers. In The Eleventh International Conference on Learning Representations, 2023. [9] Han Guo, Philip Greengard, Eric P Xing, and Yoon Kim. Lq-lora: Low-rank plus quantized matrix decomposition for efficient language model finetuning. arXiv preprint arXiv:2311.12023, 2023. [10] Babak Hassibi, David G Stork, and Gregory J Wolff. Optimal brain surgeon and general network pruning. In IEEE international conference on neural networks, pages 293–299. IEEE, 1993. [11] John A Hertz. Introduction to the theory of neural computation. 2018. [12] Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. Mistral 7b. arXiv preprint arXiv:2310.06825, 2023. [13] Sehoon Kim, Coleman Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W Mahoney, and Kurt Keutzer. Squeezellm: Dense-and-sparse quantization. arXiv preprint arXiv:2306.07629, 2023. [14] Raghuraman Krishnamoorthi. Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342, 2018. [15] Yann LeCun, John Denker, and Sara Solla. Optimal brain damage. Advances in neural information processing systems, 2, 1989. [16] Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu. Brecq: Pushing the limit of post-training quantization by block reconstruction. In International Conference on Learning Representations, 2020. [17] Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han. Awq: Activationaware weight quantization for llm compression and acceleration. arXiv preprint arXiv:2306.00978, 2023. [18] Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, and Vikas Chandra. Llm-qat: Data-free quantization aware training for large language models. arXiv preprint arXiv:2305.17888, 2023. [19] Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. arXiv preprint arXiv:1609.07843, 2016. [20] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1–67, 2020. [21] Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo. Omniquant: Omnidirectionally calibrated quantization for large language models. In The Twelfth International Conference on Learning Representations, 2023. [22] Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivi`ere, Mihir Sanjay Kale, Juliette Love, et al. Gemma: Open models based on gemini research and technology. arXiv preprint arXiv:2403.08295, 2024. [23] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. [24] Xiuying Wei, Yunchen Zhang, Xiangguo Zhang, Ruihao Gong, Shanghang Zhang, Qi Zhang, Fengwei Yu, and Xianglong Liu. Outlier suppression: Pushing the limit of low-bit transformer language models. Advances in Neural Information Processing Systems, 35:17402–17414, 2022. [25] Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models. In International Conference on Machine Learning, pages 38087–38099. PMLR, 2023. [26] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems, 36, 2024. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Wanyun Cui, Shanghai University of Finance and Economics, with equal contribution; (2) Qianle Wang, Shanghai University of Finance and Economics, with equal contribution. Authors: Authors: (1) Wanyun Cui, Shanghai University of Finance and Economics, with equal contribution; (2) Qianle Wang, Shanghai University of Finance and Economics, with equal contribution. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Quantifying the Impact of Parameters on Model Performance & 4. Unified Mixed-Precision Training 3 Quantifying the Impact of Parameters on Model Performance & 4. Unified Mixed-Precision Training 5 Prevalence of Parameter Heterogeneity in LLMs 5 Prevalence of Parameter Heterogeneity in LLMs 6 Quantization Experiments and 6.1 Implementation Details 6 Quantization Experiments and 6.1 Implementation Details 6.2 Effect of Base LLM Quantization 6.2 Effect of Base LLM Quantization 6.3 Effect of Chat LLM Quantization 6.3 Effect of Chat LLM Quantization 6.4 Comparison of Parameter Selection Criteria, Conclusion, & References 6.4 Comparison of Parameter Selection Criteria, Conclusion, & References 6.4 Comparison of Parameter Selection Criteria To evaluate the effectiveness of our proposed impact-based parameter selection criterion, we conduct experiments comparing it with the commonly used magnitude-based criterion [18]. Table 5 presents the perplexity of LLaMA2-7B-3bit and LLaMA2-13B-3bit models, using both criteria for cherry parameter selection. From the results, it is evident that the impact-based criterion consistently outperforms the magnitude-based criterion across all settings. These results demonstrate that our proposed impact-based criterion is a more effective measure of parameter importance compared to the magnitude-based criterion. The impacts identify and preserve the most critical parameters during the quantization process. We think this justify the heterogeneity of parameter impacts against parameter magnitudes as we highlighted in § 5. The extensive experimental results presented in this section clearly demonstrate the superiority of CherryQ compared to existing quantization methods. By effectively identifying the critical cherry parameters and unifying the mixed-precision parameter optimization, CherryQ achieves state-of-theart performance for both base LLMs and chat LLMs. 7. Conclusion In this paper, we investigated the parameter heterogeneity phenomenon in LLMs. Our experiments on LLaMA2, Mistral, Gemma, and Vicuna models, consistently demonstrated that a small subset of parameters plays a crucial role in maintaining the model’s performance, while the majority of parameters can be quantized to ultra-low precision without significant degradation. This finding highlights the potential for efficient model compression and quantization techniques that take into account the heterogeneous nature of parameter importance. Motivated by this observation, we proposed a novel impact-based parameter selection criterion for quantization. Our method effectively identifies and preserves the most critical cherry parameters during the quantization process. We use a QAT framework for unified optimization of both cherry parameters and normal parameters. Extensive experiments demonstrate that CherryQ outperforms the commonly used magnitude-based criterion, achieving significantly lower perplexity scores and better downstream performance. The heterogeneity and proposed approach pave the way for more efficient deployment of LLMs in resource-constrained environments. A Effect of Chat LLM Quantization on MMLU We further evaluate the performance of CherryQ on the MMLU benchmark by quantizing the Vicuna1.5 model. As shown in Table 6, CherryQ outperforms both QAT and GPTQ in terms of average accuracy across almost all categories. References [1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. [2] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. [3] Yoshua Bengio, Nicholas L´eonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013. [4] Yelysei Bondarenko, Markus Nagel, and Tijmen Blankevoort. Understanding and overcoming the challenges of efficient transformer quantization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7947–7969, 2021. [5] Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march 2023. URL https://lmsys. org/blog/2023-03- 30-vicuna, 3(5), 2023. [6] Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale. Advances in Neural Information Processing Systems, 35:30318–30332, 2022. [7] Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. Qlora: Efficient finetuning of quantized llms. Advances in Neural Information Processing Systems, 36, 2024. [8] Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Gptq: Accurate post-training quantization for generative pre-trained transformers. In The Eleventh International Conference on Learning Representations, 2023. [9] Han Guo, Philip Greengard, Eric P Xing, and Yoon Kim. Lq-lora: Low-rank plus quantized matrix decomposition for efficient language model finetuning. arXiv preprint arXiv:2311.12023, 2023. [10] Babak Hassibi, David G Stork, and Gregory J Wolff. Optimal brain surgeon and general network pruning. In IEEE international conference on neural networks, pages 293–299. IEEE, 1993. [11] John A Hertz. Introduction to the theory of neural computation. 2018. [12] Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. Mistral 7b. arXiv preprint arXiv:2310.06825, 2023. [13] Sehoon Kim, Coleman Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W Mahoney, and Kurt Keutzer. Squeezellm: Dense-and-sparse quantization. arXiv preprint arXiv:2306.07629, 2023. [14] Raghuraman Krishnamoorthi. Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342, 2018. [15] Yann LeCun, John Denker, and Sara Solla. Optimal brain damage. Advances in neural information processing systems, 2, 1989. [16] Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu. Brecq: Pushing the limit of post-training quantization by block reconstruction. In International Conference on Learning Representations, 2020. [17] Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han. Awq: Activationaware weight quantization for llm compression and acceleration. arXiv preprint arXiv:2306.00978, 2023. [18] Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, and Vikas Chandra. Llm-qat: Data-free quantization aware training for large language models. arXiv preprint arXiv:2305.17888, 2023. [19] Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. arXiv preprint arXiv:1609.07843, 2016. [20] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1–67, 2020. [21] Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo. Omniquant: Omnidirectionally calibrated quantization for large language models. In The Twelfth International Conference on Learning Representations, 2023. [22] Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivi`ere, Mihir Sanjay Kale, Juliette Love, et al. Gemma: Open models based on gemini research and technology. arXiv preprint arXiv:2403.08295, 2024. [23] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. [24] Xiuying Wei, Yunchen Zhang, Xiangguo Zhang, Ruihao Gong, Shanghang Zhang, Qi Zhang, Fengwei Yu, and Xianglong Liu. Outlier suppression: Pushing the limit of low-bit transformer language models. Advances in Neural Information Processing Systems, 35:17402–17414, 2022. [25] Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models. In International Conference on Machine Learning, pages 38087–38099. PMLR, 2023. [26] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems, 36, 2024. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

The Future of AI Compression: Smarter Quantization Strategies

Table of Links Abstract and 1 Introduction 1.1 State of the art 1.2 Some remarks on dynamics and initial condition 1.3 Outline of the paper 1.4 List of notations 2 Large Deviation Principle 2.1 Establishing the LDP for the SID 2.2 Results related to the LDP 2.3 Compactness results 3 Exit-time 3.1 Auxiliary results 3.2 Proof of the main theorem 3.3 Proofs of auxiliary lemmas 4 Generalization and References 2.3 Compactness results This completes the proof. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors:
(1) Ashot Aleksian, Université Jean Monnet, Institut Camille Jordan, 23, rue du docteur Paul Michelon, CS 82301, 42023 Saint-Étienne Cedex 2, France;
(2) Aline Kurtzmann, Université de Lorraine, CNRS, Institut Elie Cartan de Lorraine UMR 7502, Vandoeuvre-lès-Nancy, F-54506, France;
(3) Julian Tugaut, Université Jean Monnet, Institut Camille Jordan, 23, rue du docteur Paul Michelon, CS 82301, 42023 Saint-Étienne Cedex 2, France. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 State of the art 1.1 State of the art 1.2 Some remarks on dynamics and initial condition 1.2 Some remarks on dynamics and initial condition 1.3 Outline of the paper 1.3 Outline of the paper 1.4 List of notations 1.4 List of notations 2 Large Deviation Principle 2 Large Deviation Principle 2.1 Establishing the LDP for the SID 2.1 Establishing the LDP for the SID 2.2 Results related to the LDP 2.2 Results related to the LDP 2.3 Compactness results 2.3 Compactness results 3 Exit-time 3 Exit-time 3.1 Auxiliary results 3.1 Auxiliary results 3.2 Proof of the main theorem 3.2 Proof of the main theorem 3.3 Proofs of auxiliary lemmas 3.3 Proofs of auxiliary lemmas 4 Generalization and References 4 Generalization and References 2.3 Compactness results This completes the proof. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv Authors: (1) Ashot Aleksian, Université Jean Monnet, Institut Camille Jordan, 23, rue du docteur Paul Michelon, CS 82301, 42023 Saint-Étienne Cedex 2, France; (2) Aline Kurtzmann, Université de Lorraine, CNRS, Institut Elie Cartan de Lorraine UMR 7502, Vandoeuvre-lès-Nancy, F-54506, France; (3) Julian Tugaut, Université Jean Monnet, Institut Camille Jordan, 23, rue du docteur Paul Michelon, CS 82301, 42023 Saint-Étienne Cedex 2, France. Authors: Authors: (1) Ashot Aleksian, Université Jean Monnet, Institut Camille Jordan, 23, rue du docteur Paul Michelon, CS 82301, 42023 Saint-Étienne Cedex 2, France; (2) Aline Kurtzmann, Université de Lorraine, CNRS, Institut Elie Cartan de Lorraine UMR 7502, Vandoeuvre-lès-Nancy, F-54506, France; (3) Julian Tugaut, Université Jean Monnet, Institut Camille Jordan, 23, rue du docteur Paul Michelon, CS 82301, 42023 Saint-Étienne Cedex 2, France.

The Compactness Results of Our Non-Markov Processes With Path Dependency Paper

Have you ever wondered what it takes to make a great comic series? It’s a grueling and painstakingly lengthy process involving writers, artists, and editors who toil for months to come up with a few episodes that leave fans yearning for more. Every frame requires minute attention to detail to portray the characters and narrate the story. After all, the essence of comics is in the finer details. At least, that was the case before the introduction of generative artificial intelligence (AI) to the public, and while this introduction caused much change overnight, something was still missing. Initially, AI could either just work as a helping hand for research and ideation or generate cookie-cutter images with none of an artist’s personal touch. The comic industry needed much more than that — a set of AI tools designed for comic creation — and this Indian startup has provided it. Pocket Entertainment, the parent company behind the globally successful audio series platforms Pocket FM and Pocket Novel, has developed Blaze! — a proprietary AI studio that’s flipping the script on comic creation. With its next-gen image generation, pose guidance, storyboard transformation, and smart AI-powered quality checks, Blaze! is rewriting the playbook for comic creation. Powered by Blaze!, Pocket Entertainment has just launched Pocket Toons, its new platform for serialized comics. Blaze! is a co-pilot for artists that supercharges their workflows, ramping production up approximately 20 times over and cutting costs by nearly two-thirds, unlocking creative possibilities never seen before. It uses diffusion models fine-tuned with the personalized style of artists’ comic titles served on a cost-effective infrastructure without compromising the speed of inference. With Blaze! as a co-pilot, artists can focus on storytelling while AI assists with coloring, backgrounds, and such, enhancing efficiency without diluting creative intent. Streamlining Comics Production with AI: The Blaze! Advantage One of the key advantages of Blaze! is its capability to transform rough storyboards into appealing, high-quality visuals that reflect each comic’s specific style with minimal human efforts for refinement. It achieves this by leveraging advanced conditioning techniques to generate appealing visuals aligned with the storyboards. While doing this, Blaze! ensures structured, production-ready panels while preserving the artist’s intent. The storyboard conditioning mechanism of Blaze! takes rough sketches and refines them into crisp, well-defined outlines, setting a clear structural foundation for AI interpretation. Paired with text-based prompts, the model fills in intricate artistic details based on the art style on which it was trained and fine-tuned while simultaneously ensuring that character design, lighting, and scene composition remain consistent across panels. However, Blaze! is not only about automation but also about preserving style and character fidelity. That’s why the AI studio integrates modular fine-tuning, so artists can seamlessly adopt new drawing styles or character variations while maintaining consistency throughout the entire production run of a comic title. Scalable Infrastructure: Powering AI-Assisted Comics Creation AI-powered image generation demands serious computing power. And Blaze! has what it takes to change the game. Instead of relying on pricey server-grade GPUs, Blaze! democratizes AI-assisted comics creation by using consumer-grade RTX 4090 GPUs — a cost-effective yet powerful alternative. This ensures independent artists and studios alike can generate high-resolution, production-quality artwork without investing in expensive cloud computing resources. Blaze! balances this while keeping generation times low using dynamic scaling infrastructure that auto-adjusts resources based on demand. By leveraging serverless GPU clusters with optimal choices for model loading and memory use, Blaze! ensures near-instant image generation. In other words, the days of artists having to wait hours for an image to render are over. AI-Powered Quality Control: The End of Inconsistencies Consistency in how a character is designed and drawn — on every panel and in a way that covers the entire spectrum of human emotions — is key to creating high-quality comics. This consistency helps readers follow the story and is non-negotiable for delivering a great reading experience. Traditionally, quality control (QC) is manual, subjective, and quite reactive. Blaze! changes that by integrating an AI-powered QC system that automates facial consistency checks early on before they become production nightmares, which it achieves in two parts: Deep Face Analysis: Blaze! uses a proprietary model to ensure characters maintain their distinct facial features across every panel.
Pose & Proportion Assessment: Blaze! can detect unnatural limb positions, structural inconsistencies, and perspective errors. Finally, while even the best AI models have a tendency to “hallucinate,” Blaze! ensures no quality defects are introduced due to this. By embedding real-time AI-driven quality control mechanisms into the creative pipeline, Blaze! minimizes rejections and reduces manual revisions. Artists get instant, objective feedback — allowing them to focus on storytelling instead of fixing mistakes. Intelligent Image Repository: Search, Reference, Create In comics creation, reference materials are the secret sauce behind consistent storytelling. Whether it’s keeping character designs cohesive or aligning a new scene with past panels, having easy access to past work is critical. Blaze! integrates an intelligent image repository powered by AI-driven search and retrieval, allowing artists to: Search previously-made panels using text-based queries to quickly find similar scenes.
Retrieve visually similar images to maintain continuity in style and character designs or to serve as an inspiration. This improves collaboration between writers, storyboard artists, and illustrators, making production more efficient and cohesive than ever. The truth is, Blaze! isn’t just another AI tool. It's a game changer that is democratizing comics creation. With AI-powered image generation, real-time quality control, scalable infrastructure, and intelligent search tools, it’s redefining how artists bring stories to life with less friction, more creativity, and faster workflows — which, in turn, creates better comics. The future of comics isn’t coming. It’s already here, with Blaze! Have you ever wondered what it takes to make a great comic series? It’s a grueling and painstakingly lengthy process involving writers, artists, and editors who toil for months to come up with a few episodes that leave fans yearning for more. Every frame requires minute attention to detail to portray the characters and narrate the story. After all, the essence of comics is in the finer details. At least, that was the case before the introduction of generative artificial intelligence (AI) to the public, and while this introduction caused much change overnight, something was still missing. Initially, AI could either just work as a helping hand for research and ideation or generate cookie-cutter images with none of an artist’s personal touch. The comic industry needed much more than that — a set of AI tools designed for comic creation — and this Indian startup has provided it. Pocket Entertainment , the parent company behind the globally successful audio series platforms Pocket FM and Pocket Novel, has developed Blaze! — a proprietary AI studio that’s flipping the script on comic creation. With its next-gen image generation, pose guidance, storyboard transformation, and smart AI-powered quality checks, Blaze! is rewriting the playbook for comic creation. Pocket Entertainment Pocket Entertainment Blaze! Blaze! Powered by Blaze!, Pocket Entertainment has just launched Pocket Toons, its new platform for serialized comics. Blaze! is a co-pilot for artists that supercharges their workflows, ramping production up approximately 20 times over and cutting costs by nearly two-thirds, unlocking creative possibilities never seen before. It uses diffusion models fine-tuned with the personalized style of artists’ comic titles served on a cost-effective infrastructure without compromising the speed of inference. With Blaze! as a co-pilot, artists can focus on storytelling while AI assists with coloring, backgrounds, and such, enhancing efficiency without diluting creative intent. Streamlining Comics Production with AI: The Blaze! Advantage One of the key advantages of Blaze! is its capability to transform rough storyboards into appealing, high-quality visuals that reflect each comic’s specific style with minimal human efforts for refinement. It achieves this by leveraging advanced conditioning techniques to generate appealing visuals aligned with the storyboards. While doing this, Blaze! ensures structured, production-ready panels while preserving the artist’s intent. The storyboard conditioning mechanism of Blaze! takes rough sketches and refines them into crisp, well-defined outlines, setting a clear structural foundation for AI interpretation. Paired with text-based prompts, the model fills in intricate artistic details based on the art style on which it was trained and fine-tuned while simultaneously ensuring that character design, lighting, and scene composition remain consistent across panels. However, Blaze! is not only about automation but also about preserving style and character fidelity. That’s why the AI studio integrates modular fine-tuning, so artists can seamlessly adopt new drawing styles or character variations while maintaining consistency throughout the entire production run of a comic title. Scalable Infrastructure: Powering AI-Assisted Comics Creation AI-powered image generation demands serious computing power. And Blaze! has what it takes to change the game. Instead of relying on pricey server-grade GPUs, Blaze! democratizes AI-assisted comics creation by using consumer-grade RTX 4090 GPUs — a cost-effective yet powerful alternative. This ensures independent artists and studios alike can generate high-resolution, production-quality artwork without investing in expensive cloud computing resources. Blaze! balances this while keeping generation times low using dynamic scaling infrastructure that auto-adjusts resources based on demand. By leveraging serverless GPU clusters with optimal choices for model loading and memory use, Blaze! ensures near-instant image generation. In other words, the days of artists having to wait hours for an image to render are over. AI-Powered Quality Control: The End of Inconsistencies Consistency in how a character is designed and drawn — on every panel and in a way that covers the entire spectrum of human emotions — is key to creating high-quality comics. This consistency helps readers follow the story and is non-negotiable for delivering a great reading experience. Traditionally, quality control (QC) is manual, subjective, and quite reactive. Blaze! changes that by integrating an AI-powered QC system that automates facial consistency checks early on before they become production nightmares, which it achieves in two parts: Deep Face Analysis: Blaze! uses a proprietary model to ensure characters maintain their distinct facial features across every panel. Pose & Proportion Assessment: Blaze! can detect unnatural limb positions, structural inconsistencies, and perspective errors. Deep Face Analysis : Blaze! uses a proprietary model to ensure characters maintain their distinct facial features across every panel. Deep Face Analysis Pose & Proportion Assessment : Blaze! can detect unnatural limb positions, structural inconsistencies, and perspective errors. Pose & Proportion Assessment Finally, while even the best AI models have a tendency to “ hallucinate ,” Blaze! ensures no quality defects are introduced due to this. hallucinate hallucinate By embedding real-time AI-driven quality control mechanisms into the creative pipeline, Blaze! minimizes rejections and reduces manual revisions. Artists get instant, objective feedback — allowing them to focus on storytelling instead of fixing mistakes. Intelligent Image Repository: Search, Reference, Create In comics creation, reference materials are the secret sauce behind consistent storytelling. Whether it’s keeping character designs cohesive or aligning a new scene with past panels, having easy access to past work is critical. Blaze! integrates an intelligent image repository powered by AI-driven search and retrieval, allowing artists to: Search previously-made panels using text-based queries to quickly find similar scenes. Retrieve visually similar images to maintain continuity in style and character designs or to serve as an inspiration. Search previously-made panels using text-based queries to quickly find similar scenes. Retrieve visually similar images to maintain continuity in style and character designs or to serve as an inspiration. This improves collaboration between writers, storyboard artists, and illustrators, making production more efficient and cohesive than ever. The truth is, Blaze! isn’t just another AI tool. It's a game changer that is democratizing comics creation. With AI-powered image generation, real-time quality control, scalable infrastructure, and intelligent search tools, it’s redefining how artists bring stories to life with less friction, more creativity, and faster workflows — which, in turn, creates better comics. The future of comics isn’t coming. It’s already here, with Blaze!

BLAZE: The AI Revolution That’s Changing Comic Creation Forever

BLAZE: Die KI-revolusie wat die komiese skepping vir altyd verander

BLAZE: ثورة الذكاء الاصطناعي التي ستغير صناعة القصص المصورة إلى الأبد

BLAZE: AI revolucija koja zauvijek mijenja stvaranje stripova

BLAZE: La revolución de la inteligencia artificial que está cambiando la creación de cómics para siempre

BLAZE: AI-vallankumous, joka muuttaa sarjakuvan luomisen ikuisesti

BLAZE: Revolisyon AI ki chanje kreyasyon komik pou tout tan

BLAZE: 漫画創作を永遠に変える AI 革命

BLAZE: күлкілі туындыларды мәңгілікке өзгертетін AI революциясы

BLAZE: Комикс жаратуусун түбөлүккө өзгөрткөн AI революциясы

BLAZE: Phetogelo ya AI yeo e Fetošago Tlholo ya Metlae ka mo go sa Felego

BLAZE: революція ШІ, яка назавжди змінює створення коміксів

I-BLAZE: I-AI Revolution Eshintsha Indalo Yamahlaya Phakade

I get a lot of people in business who seem distant and have not engaged with AI as they seem to think that AI is a novelty and consequently they have not engaged much with it. In response I have used the Captain Kirk analogy. For decades, science fiction has inspired technological advancements, serving as a blueprint for what could one day be possible. One of the most iconic examples of this is how Captain Kirk, Picard, and other Starfleet officers interacted with the computer aboard the USS Enterprise in Star Trek. At the time, the idea of speaking to a machine and receiving intelligent, relevant responses seemed like a futuristic fantasy. However, we now live in a world where AI-powered systems like ChatGPT, Siri, and Alexa have turned that fiction into reality. The Evolution of AI: From Fiction to Function 1. Natural Language Processing: Understanding Human Speech In Star Trek, the ship’s computer effortlessly understood complex commands and responded with relevant information. Today, advancements in natural language processing (NLP) allow AI to interpret and process human speech with remarkable accuracy. Modern AI models can analyze context, intent, and even tone, enabling fluid conversations much like the ones depicted in science fiction. 2. Voice Assistants: The Starfleet Computer at Home Voice-activated assistants such as Alexa, Google Assistant, and Siri have become commonplace. Just as Captain Picard would say, “Computer, initiate scan for lifeforms,” today we can say, “Alexa, set a reminder,” or “Hey Google, what’s the weather like?” The interaction may not be as sophisticated as Starfleet’s computer, but it’s a clear step toward that vision. 3. Problem-Solving and Decision-Making AI In Star Trek, the computer was not merely a data repository—it actively assisted in decision-making, analyzing potential outcomes, and aiding in strategic planning. While today’s AI is not yet at that level, it is already being used in fields like healthcare, finance, and engineering to analyze vast amounts of data and provide recommendations. For example, AI-driven systems assist doctors in diagnosing diseases by recognizing patterns in medical scans. Financial institutions use AI for risk analysis, predicting market trends much like how the Enterprise’s computer would analyze potential threats in space. 4. Conversational AI: A Step Toward Starfleet-Level Intelligence Modern AI models, including ChatGPT, are becoming increasingly adept at engaging in meaningful, ongoing conversations. While they lack full sentience, they can provide research insights, draft reports, and even help troubleshoot complex problems. This mirrors how Starfleet officers relied on their computer for brainstorming and problem-solving. 5. Predictive and Proactive Assistance One of the most fascinating aspects of the Star Trek computer was its ability to anticipate needs. It could provide alerts about approaching danger, suggest optimal travel routes, or monitor vital signs. Today’s AI is advancing in a similar direction with predictive analytics and automation. AI in smart homes can adjust lighting and temperature based on user habits, and AI-driven customer service bots can predict user inquiries before they are fully articulated. 6. Real-World Applications: AI for Business Monitoring Imagine that you are traveling and away from the office. You want to know how your factory is running while you are away. Instead of calling managers or checking multiple dashboards, you simply ask your AI assistant, "Tell me how the status is with the manufacture of my biscuits today?" Within seconds, the AI provides an update: "Production is running at 95% efficiency. 10,000 biscuits have been completed so far today, with an estimated 15,000 by end of shift. There was a minor delay due to an equipment recalibration, but it has been resolved. No further issues detected." This level of seamless, instant access to real-time data is what AI is making possible. Whether in manufacturing, logistics, or customer service, AI is transforming how we monitor and manage businesses remotely, making operations more efficient and responsive. What’s Next? The Future of AI and Star Trek’s Vision While AI has come a long way, we are still far from achieving the level of sophistication seen in Star Trek. The ship’s computer functioned more like an artificial general intelligence (AGI), capable of reasoning, adapting, and handling virtually any query with precision. However, ongoing research in AGI aims to develop AI that can think, learn, and reason across multiple domains, just like Data, the android officer from Star Trek: The Next Generation. In the near future, AI may become even more proactive, seamlessly integrating into daily life and business operations, making interactions as natural and intuitive as those seen in science fiction. Final Thoughts: Science Fiction as a Technological Blueprint The way Captain Kirk and Picard interacted with their ship’s computer was once the stuff of dreams. Today, those dreams have materialized in the form of AI-driven systems that assist with communication, problem-solving, and predictive analytics. While we may not have a fully sentient AI onboard a starship just yet, it’s clear that technology is rapidly catching up with science fiction. As AI continues to evolve, it’s worth considering: If Star Trek imagined today’s technology decades ago, what other innovations from science fiction might we see in the years to come? One thing is certain—the journey of AI is just beginning, and the future promises to be just as fascinating as the stories that first inspired it. So, folks, isn't it about time you engaged with ChatGPT, PaLM, Claude, LLaMA, or BLOOM? If not, ask your kids—they're already part of the future. This reminds me of how adults first reacted to the Internet in its early days. It's not a toy or a game it's a business reality! I get a lot of people in business who seem distant and have not engaged with AI as they seem to think that AI is a novelty and consequently they have not engaged much with it. In response I have used the Captain Kirk analogy. For decades, science fiction has inspired technological advancements, serving as a blueprint for what could one day be possible. One of the most iconic examples of this is how Captain Kirk, Picard, and other Starfleet officers interacted with the computer aboard the USS Enterprise in Star Trek. At the time, the idea of speaking to a machine and receiving intelligent, relevant responses seemed like a futuristic fantasy. However, we now live in a world where AI-powered systems like ChatGPT, Siri, and Alexa have turned that fiction into reality. The Evolution of AI: From Fiction to Function 1. Natural Language Processing: Understanding Human Speech In Star Trek, the ship’s computer effortlessly understood complex commands and responded with relevant information. Today, advancements in natural language processing (NLP) allow AI to interpret and process human speech with remarkable accuracy. Modern AI models can analyze context, intent, and even tone, enabling fluid conversations much like the ones depicted in science fiction. 2. Voice Assistants: The Starfleet Computer at Home Voice-activated assistants such as Alexa, Google Assistant, and Siri have become commonplace. Just as Captain Picard would say, “Computer, initiate scan for lifeforms,” today we can say, “Alexa, set a reminder,” or “Hey Google, what’s the weather like?” The interaction may not be as sophisticated as Starfleet’s computer, but it’s a clear step toward that vision. 3. Problem-Solving and Decision-Making AI In Star Trek, the computer was not merely a data repository—it actively assisted in decision-making, analyzing potential outcomes, and aiding in strategic planning. While today’s AI is not yet at that level, it is already being used in fields like healthcare, finance, and engineering to analyze vast amounts of data and provide recommendations. For example, AI-driven systems assist doctors in diagnosing diseases by recognizing patterns in medical scans. Financial institutions use AI for risk analysis, predicting market trends much like how the Enterprise’s computer would analyze potential threats in space. 4. Conversational AI: A Step Toward Starfleet-Level Intelligence Modern AI models, including ChatGPT, are becoming increasingly adept at engaging in meaningful, ongoing conversations. While they lack full sentience, they can provide research insights, draft reports, and even help troubleshoot complex problems. This mirrors how Starfleet officers relied on their computer for brainstorming and problem-solving. 5. Predictive and Proactive Assistance One of the most fascinating aspects of the Star Trek computer was its ability to anticipate needs. It could provide alerts about approaching danger, suggest optimal travel routes, or monitor vital signs. Today’s AI is advancing in a similar direction with predictive analytics and automation. AI in smart homes can adjust lighting and temperature based on user habits, and AI-driven customer service bots can predict user inquiries before they are fully articulated. 6. Real-World Applications: AI for Business Monitoring Imagine that you are traveling and away from the office. You want to know how your factory is running while you are away. Instead of calling managers or checking multiple dashboards, you simply ask your AI assistant, "Tell me how the status is with the manufacture of my biscuits today?" Within seconds, the AI provides an update: "Production is running at 95% efficiency. 10,000 biscuits have been completed so far today, with an estimated 15,000 by end of shift. There was a minor delay due to an equipment recalibration, but it has been resolved. No further issues detected." This level of seamless, instant access to real-time data is what AI is making possible. Whether in manufacturing, logistics, or customer service, AI is transforming how we monitor and manage businesses remotely, making operations more efficient and responsive. What’s Next? The Future of AI and Star Trek’s Vision While AI has come a long way, we are still far from achieving the level of sophistication seen in Star Trek. The ship’s computer functioned more like an artificial general intelligence (AGI), capable of reasoning, adapting, and handling virtually any query with precision. However, ongoing research in AGI aims to develop AI that can think, learn, and reason across multiple domains, just like Data, the android officer from Star Trek: The Next Generation. In the near future, AI may become even more proactive, seamlessly integrating into daily life and business operations, making interactions as natural and intuitive as those seen in science fiction. Final Thoughts: Science Fiction as a Technological Blueprint The way Captain Kirk and Picard interacted with their ship’s computer was once the stuff of dreams. Today, those dreams have materialized in the form of AI-driven systems that assist with communication, problem-solving, and predictive analytics. While we may not have a fully sentient AI onboard a starship just yet, it’s clear that technology is rapidly catching up with science fiction. As AI continues to evolve, it’s worth considering: If Star Trek imagined today’s technology decades ago, what other innovations from science fiction might we see in the years to come? One thing is certain—the journey of AI is just beginning, and the future promises to be just as fascinating as the stories that first inspired it. So, folks, isn't it about time you engaged with ChatGPT, PaLM, Claude, LLaMA, or BLOOM? If not, ask your kids—they're already part of the future. This reminds me of how adults first reacted to the Internet in its early days. It's not a toy or a game it's a business reality!

Star Trek’s Fictional Computer Has Became Our Present-Day Technology

San Francisco, California, March 3rd, 2025/CyberNewsWire/--With the growing importance of security compliance for startups, more companies are seeking to achieve and maintain compliance with frameworks like SOC 2, ISO 27001 & GDPR. Bubba AI, Inc. is building a comprehensive solution for these organizations to easily integrate compliance workflows and build their own customized processes through an open-source alternative to existing GRC (Governance, Risk, and Compliance) automation platforms. The company is positioning itself to address the compliance needs of organizations ranging from early-stage startups to established enterprises. Bubba AI's flagship product, Comp AI, offers a built-in risk register, and policies required for frameworks while also allowing companies to build their compliance workflows using building blocks provided by the platform. Introducing Comp AI Comp AI is an open-source alternative to GRC automation platforms like Vanta and Drata. The platform includes several key features designed to automate compliance with frameworks such as SOC 2: A built-in risk register to help companies identify, document, and assess potential security risks
Out-of-the-box security policies for modern companies, complete with an AI-powered editor for customization
A comprehensive vendor management suite for tracking, assessing, and identifying third-party vendors
Automated evidence-collection tools that reduce the manual burden of compliance documentation The open source nature of Comp AI differentiates it from existing solutions in the market, allowing for greater community involvement, customization, and cost savings for companies on their compliance journey. The Value of Open Source Compliance Solutions Bubba AI was founded in late 2024 by Lewis Carhart. Carhart recognized a significant gap in the market for affordable, flexible compliance automation tools that could serve the needs of a wide range of companies. "While building at previous companies, I experienced firsthand how painful and resource-intensive the compliance process can be, especially for smaller organizations. The existing solutions were either prohibitively expensive or lacked the flexibility we needed. I wanted to create an open source platform that democratizes access to compliance automation", Lewis Carhart commented. This experience led Carhart to develop Comp AI as an open source alternative that could help organizations of all sizes achieve SOC 2 compliance without breaking the bank or getting locked into proprietary systems. The Ambitious Goal Bubba AI has set an ambitious target: helping 100,000 companies achieve compliance with cyber security frameworks like SOC 2, ISO 27001 & GDPR by 2032. This goal reflects the growing importance of security certifications as businesses increasingly handle sensitive customer data and face stricter regulatory requirements. "We believe that strong security practices shouldn't be a luxury that only well-funded companies can afford. By providing an open source solution, we're removing barriers to entry and empowering organizations to build robust security programs regardless of their size or resources", said Lewis Carhart. The company plans to build a community around its open-source platform, encouraging contributions and extensions that can benefit the broader business ecosystem. About Bubba AI Bubba AI, Inc. was founded at the end of 2024. Its mission is clear: help 100,000 companies get compliant with common cyber security frameworks by 2032. To do this, Bubba AI, Inc. is launching its first product - Comp AI, an open-source alternative to Vanta & Drata. Contact Founder Lewis Carhart Bubba AI, Inc. hello@trycomp.ai This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here San Francisco, California, March 3rd, 2025/CyberNewsWire/--With the growing importance of security compliance for startups, more companies are seeking to achieve and maintain compliance with frameworks like SOC 2, ISO 27001 & GDPR. Bubba AI, Inc. is building a comprehensive solution for these organizations to easily integrate compliance workflows and build their own customized processes through an open-source alternative to existing GRC (Governance, Risk, and Compliance) automation platforms. The company is positioning itself to address the compliance needs of organizations ranging from early-stage startups to established enterprises. Bubba AI's flagship product, Comp AI , offers a built-in risk register, and policies required for frameworks while also allowing companies to build their compliance workflows using building blocks provided by the platform. Comp AI Comp AI Introducing Comp AI Comp AI is an open-source alternative to GRC automation platforms like Vanta and Drata. The platform includes several key features designed to automate compliance with frameworks such as SOC 2: A built-in risk register to help companies identify, document, and assess potential security risks Out-of-the-box security policies for modern companies, complete with an AI-powered editor for customization A comprehensive vendor management suite for tracking, assessing, and identifying third-party vendors Automated evidence-collection tools that reduce the manual burden of compliance documentation A built-in risk register to help companies identify, document, and assess potential security risks Out-of-the-box security policies for modern companies, complete with an AI-powered editor for customization A comprehensive vendor management suite for tracking, assessing, and identifying third-party vendors Automated evidence-collection tools that reduce the manual burden of compliance documentation The open source nature of Comp AI differentiates it from existing solutions in the market, allowing for greater community involvement, customization, and cost savings for companies on their compliance journey. The Value of Open Source Compliance Solutions Bubba AI was founded in late 2024 by Lewis Carhart. Carhart recognized a significant gap in the market for affordable, flexible compliance automation tools that could serve the needs of a wide range of companies. "While building at previous companies, I experienced firsthand how painful and resource-intensive the compliance process can be, especially for smaller organizations. The existing solutions were either prohibitively expensive or lacked the flexibility we needed. I wanted to create an open source platform that democratizes access to compliance automation", Lewis Carhart commented. "While building at previous companies, I experienced firsthand how painful and resource-intensive the compliance process can be, especially for smaller organizations. The existing solutions were either prohibitively expensive or lacked the flexibility we needed. I wanted to create an open source platform that democratizes access to compliance automation", Lewis Carhart commented. This experience led Carhart to develop Comp AI as an open source alternative that could help organizations of all sizes achieve SOC 2 compliance without breaking the bank or getting locked into proprietary systems. Comp AI as an open source alternative Comp AI as an open source alternative The Ambitious Goal Bubba AI has set an ambitious target: helping 100,000 companies achieve compliance with cyber security frameworks like SOC 2 , ISO 27001 & GDPR by 2032. This goal reflects the growing importance of security certifications as businesses increasingly handle sensitive customer data and face stricter regulatory requirements. SOC 2 SOC 2 "We believe that strong security practices shouldn't be a luxury that only well-funded companies can afford. By providing an open source solution, we're removing barriers to entry and empowering organizations to build robust security programs regardless of their size or resources", said Lewis Carhart. "We believe that strong security practices shouldn't be a luxury that only well-funded companies can afford. By providing an open source solution, we're removing barriers to entry and empowering organizations to build robust security programs regardless of their size or resources", said Lewis Carhart. The company plans to build a community around its open-source platform, encouraging contributions and extensions that can benefit the broader business ecosystem. About Bubba AI Bubba AI , Inc. was founded at the end of 2024. Its mission is clear: help 100,000 companies get compliant with common cyber security frameworks by 2032. To do this, Bubba AI, Inc. is launching its first product - Comp AI, an open-source alternative to Vanta & Drata. Bubba AI Bubba AI Contact Founder Lewis Carhart Bubba AI, Inc. hello@trycomp.ai This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Bubba AI, Inc. Is launching Comp AI To Help 100,000 Startups Get SOC 2 Compliant By 2032

Bubba AI, Inc. llança Comp AI per ajudar 100.000 startups a complir amb SOC 2 el 2032

Bubba AI, Inc. lanza Comp AI para ayudar a 100.000 empresas emergentes a cumplir con la norma SOC 2 para 2032

Bubba AI، Inc. در حال راه اندازی Comp AI برای کمک به 100000 استارتاپ است که تا سال 2032 با SOC 2 سازگار شوند.

Bubba AI, Inc. משיקה את Comp AI כדי לעזור ל-100,000 סטארט-אפים לקבל תאימות ל-SOC 2 עד 2032

Bubba AI, Inc. lancia Comp AI per aiutare 100.000 startup a ottenere la conformità SOC 2 entro il 2032

Bubba AI, Inc. は、2032 年までに 10 万社のスタートアップが SOC 2 に準拠できるよう支援する Comp AI を立ち上げます。

Bubba AI, Inc. გამოუშვებს Comp AI-ს, რათა დაეხმაროს 100 000 სტარტაპს მიიღონ SOC 2-ის შესაბამისობა 2032 წლისთვის

Bubba AI, Inc កំពុងបើកដំណើរការ Comp AI ដើម្បីជួយដល់ការចាប់ផ្តើមអាជីវកម្ម 100,000 ទទួលបាន SOC 2 Compliant នៅឆ្នាំ 2032

„Bubba AI, Inc.“ pristato „Comp AI“, kad padėtų 100 000 naujų įmonių iki 2032 m. atitikti SOC 2 reikalavimus

Bubba AI, Inc нь 100,000 гарааны бизнес эрхлэгчдэд 2032 он гэхэд SOC 2-т нийцүүлэхэд нь туслах зорилгоор Comp AI-г эхлүүлж байна.

Bubba AI, Inc. запускает Comp AI, чтобы помочь 100 000 стартапов соответствовать требованиям SOC 2 к 2032 году

Bubba AI, Inc. Po lançon Comp AI për të ndihmuar 100,000 startups që të jenë në përputhje me SOC 2 deri në vitin 2032

Meet Spacecoin: HackerNoon Company of the Week

Hedera (HBAR) has long positioned itself as a leading blockchain for institutional adoption, thanks to its governance council model featuring companies like Google, IBM, and Boeing. While this structure offers stability and enterprise credibility, it has also raised concerns about decentralization, with critics arguing that decision-making is concentrated in the hands of corporate giants. In contrast, Coldware’s Web3-native approach is attracting investors who prioritize decentralization and open governance. Coldware is designed to be fully community-driven, enabling token holders to actively shape the network’s development and governance policies. As a result, HBAR whales are beginning to shift toward Coldware (COLD), recognizing its long-term potential in DePIN and PayFi applications. Hedera (HBAR) recently saw a 10.9% surge following news of a partnership with Snoop Dogg’s Tune.fm, reinforcing Hedera’s position in the Web3 music and entertainment space. However, institutional investors are looking beyond entertainment applications, with DePIN and PayFi emerging as key sectors for blockchain adoption. Coldware (COLD) is positioning itself as the go-to blockchain for real-world infrastructure and decentralized finance, making it a compelling alternative for HBAR investors. With Coldware’s presale already surpassing $1.25M, the momentum is shifting toward next-generation blockchains that offer greater flexibility, scalability, and decentralization. Hedera’s Centralized Council vs. Coldware’s Decentralized Network \One of the biggest criticisms of Hedera is its governing council, which limits community participation in decision-making. While this model is ideal for large-scale enterprise adoption, it contradicts the decentralized ethos of blockchain technology. Coldware (COLD), on the other hand, empowers its community, ensuring that protocol upgrades, network governance, and staking rewards are controlled by token holders rather than corporate entities. This fully decentralized model is attracting Web3 investors and institutional players who value transparency and community governance. Coldware’s PayFi Model Challenges HBAR’s Institutional Focus \Hedera’s (HBAR) focus on enterprise partnerships has helped it gain credibility among traditional financial institutions. However, Coldware’s PayFi model offers a more inclusive, community-driven alternative that appeals to both institutions and retail users. By facilitating fast, low-cost transactions for businesses and individuals, Coldware is setting itself apart as a blockchain that prioritizes both financial inclusion and institutional-grade security. This dual focus is why HBAR whales are increasingly backing Coldware’s presale, seeing it as a long-term investment opportunity in the growing PayFi sector. Will Coldware Become the Next Institutional-Grade Blockchain? While Hedera (HBAR) remains a strong player in blockchain infrastructure, its corporate-heavy governance model may limit its Web3 adoption potential. Coldware (COLD), with its community-first approach and focus on DePIN and PayFi, is positioning itself as a true alternative for institutions looking for decentralized financial solutions.\ With Coldware’s presale continuing to gain traction, it’s clear that HBAR investors are taking notice. As the battle between centralized and decentralized governance models unfolds, Coldware’s Web3 approach is quickly gaining momentum—proving that blockchain’s future lies in decentralization, not corporate control. For more information on the Coldware (COLD) Presale: Visit Coldware (COLD) Join and become a community member: https://t.me/coldwarenetwork https://x.com/ColdwareNetwork This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. Hedera (HBAR) has long positioned itself as a leading blockchain for institutional adoption, thanks to its governance council model featuring companies like Google, IBM, and Boeing. While this structure offers stability and enterprise credibility, it has also raised concerns about decentralization, with critics arguing that decision-making is concentrated in the hands of corporate giants. In contrast, Coldware’s Web3-native approach is attracting investors who prioritize decentralization and open governance. Coldware is designed to be fully community-driven, enabling token holders to actively shape the network’s development and governance policies. As a result, HBAR whales are beginning to shift toward Coldware (COLD) , recognizing its long-term potential in DePIN and PayFi applications. Coldware (COLD) Coldware (COLD) Hedera (HBAR) recently saw a 10.9% surge following news of a partnership with Snoop Dogg’s Tune.fm, reinforcing Hedera’s position in the Web3 music and entertainment space. However, institutional investors are looking beyond entertainment applications, with DePIN and PayFi emerging as key sectors for blockchain adoption. Coldware (COLD) is positioning itself as the go-to blockchain for real-world infrastructure and decentralized finance, making it a compelling alternative for HBAR investors. With Coldware’s presale already surpassing $1.25M, the momentum is shifting toward next-generation blockchains that offer greater flexibility, scalability, and decentralization. Coldware (COLD) Coldware (COLD) Hedera’s Centralized Council vs. Coldware’s Decentralized Network \One of the biggest criticisms of Hedera is its governing council, which limits community participation in decision-making. While this model is ideal for large-scale enterprise adoption, it contradicts the decentralized ethos of blockchain technology. Coldware (COLD) , on the other hand, empowers its community, ensuring that protocol upgrades, network governance, and staking rewards are controlled by token holders rather than corporate entities. This fully decentralized model is attracting Web3 investors and institutional players who value transparency and community governance. Coldware (COLD) Coldware (COLD) Coldware’s PayFi Model Challenges HBAR’s Institutional Focus \Hedera’s (HBAR) focus on enterprise partnerships has helped it gain credibility among traditional financial institutions. However, Coldware’s PayFi model offers a more inclusive, community-driven alternative that appeals to both institutions and retail users. By facilitating fast, low-cost transactions for businesses and individuals, Coldware is setting itself apart as a blockchain that prioritizes both financial inclusion and institutional-grade security. This dual focus is why HBAR whales are increasingly backing Coldware’s presale, seeing it as a long-term investment opportunity in the growing PayFi sector. Will Coldware Become the Next Institutional-Grade Blockchain? While Hedera (HBAR) remains a strong player in blockchain infrastructure, its corporate-heavy governance model may limit its Web3 adoption potential. Coldware (COLD) , with its community-first approach and focus on DePIN and PayFi, is positioning itself as a true alternative for institutions looking for decentralized financial solutions.\ Coldware (COLD) Coldware (COLD) With Coldware’s presale continuing to gain traction, it’s clear that HBAR investors are taking notice. As the battle between centralized and decentralized governance models unfolds, Coldware’s Web3 approach is quickly gaining momentum—proving that blockchain’s future lies in decentralization, not corporate control. For more information on the Coldware (COLD) Presale: Visit Coldware (COLD) Coldware (COLD) Coldware (COLD) Join and become a community member: https://t.me/coldwarenetwork https://t.me/coldwarenetwork https://t.me/coldwarenetwork https://x.com/ColdwareNetwork https://x.com/ColdwareNetwork https://x.com/ColdwareNetwork This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. Business Blogging Business Blogging

DePIN and PayFi: Coldware’s Web3 Community Approach

DePIN እና PayFi፡ Coldware Web3 Community Approach

DePIN i PayFi: Coldwareov Web3 pristup zajednici

DePIN und PayFi: Coldwares Web3-Community-Ansatz

DePIN y PayFi: el enfoque comunitario Web3 de Coldware

DePIN ja PayFi: Coldwaren Web3 Community Approach

DePIN と PayFi: Coldware の Web3 コミュニティ アプローチ

DePIN និង PayFi៖ វិធីសាស្រ្តសហគមន៍ Web3 របស់ Coldware

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