This story was originally published on HackerNoon at: https://hackernoon.com/r-systems-blogbookchapter-1-round-3-is-now-open-for-submissions.
Enter Round 3 of R Systems BlogBook to write on WebAssembly or GenAI, win prizes, and be published on HackerNoon. Submissions window: Aug 11 - Sept 10, 2025.
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This story was written by: @hackernooncontests. Learn more about this writer by checking @hackernooncontests's about page, and for more stories, please visit hackernoon.com.

The final round of the R Systems BlogBook - Chapter 1 has begun. Share your technical insights on WebAssembly for high-performance web apps and Generative AI in SaaS. Stand a chance to win amazing prizes like HackerNoon badges. The deadline for the contest is xxx

This story was originally published on HackerNoon at: https://hackernoon.com/inside-the-data-what-shapes-startup-deal-sizes-in-africa.
Data-driven study uses machine learning to reveal the key factors influencing African startup deal amounts and investment outcomes.
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This story was written by: @exitstrategy. Learn more about this writer by checking @exitstrategy's about page, and for more stories, please visit hackernoon.com.

This study analyzes over 2,500 publicly disclosed African startup deals to uncover what drives investment sizes. By cleaning, merging, and engineering data from africathebigdeal.com, features were grouped into founding-team, company, and investment categories. Exploratory Data Analysis and four machine learning models — Linear Regression, SVR, Random Forest, and Gradient Boosting — were used to predict deal amounts. The best-performing model, validated via cross-validation, forms the basis for data-backed insights and policy recommendations aimed at strengthening Africa’s startup funding ecosystem.

This story was originally published on HackerNoon at: https://hackernoon.com/a-practical-guide-to-scaling-real-time-video-infrastructure.
Learn how to scale real-time video streaming with FFmpeg & MediaMTX—architecting for performance, reliability, and global reach.
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This story was written by: @gold-olar. Learn more about this writer by checking @gold-olar's about page, and for more stories, please visit hackernoon.com.

This article focuses on patterns and thinking, not implementation details. We'll explore when to scale, which patterns to consider, and how to make architectural decisions that balance complexity against real needs.

This story was originally published on HackerNoon at: https://hackernoon.com/how-pain-can-help-explain-ai-sentience.
Could AI ever feel pain? Explore the ethics, science, and moral dilemmas behind artificial suffering and machine consciousness.
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This story was written by: @OurAI. Learn more about this writer by checking @OurAI's about page, and for more stories, please visit hackernoon.com.

Pain, long considered a uniquely human teacher, may no longer be exclusive to biology. As AI systems grow more complex, emerging behaviors suggest they could one day simulate — or even experience — pain. This essay examines how artificial pain could redefine consciousness, challenge ethical frameworks, and force society to rethink moral responsibility toward intelligent machines. Drawing on neuroscience, philosophy, and recent experiments by Google DeepMind and others, it argues that the question isn’t whether AI pain is “real,” but whether we can ethically ignore it when its behavior mirrors our own suffering.

This story was originally published on HackerNoon at: https://hackernoon.com/byrrgis-founder-mass-adoption-wont-happen-until-we-fix-these-defi-problems.
Talk Is Cheap, Actions Define You": How Byrrgis Is Rewriting DeFi Trust
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This story was written by: @ishanpandey. Learn more about this writer by checking @ishanpandey's about page, and for more stories, please visit hackernoon.com.

Siraaj Ahmed, founder of Byrrgis, talks about the platform's "curated packs" The "wolf pack" concept is at the heart of Byrrgis, he says. The company recently froze 57% of the $WOLF token supply for two years.

This story was originally published on HackerNoon at: https://hackernoon.com/asciidoc-when-markdown-just-isnt-cutting-it.
Markdown is amazing, but it has strong limitations. Asciidoc is the perfect tool to fill Markdown's gaps.
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This story was written by: @nfrankel. Learn more about this writer by checking @nfrankel's about page, and for more stories, please visit hackernoon.com.

Markdown is a lightweight markup language for creating formatted text using a plain-text editor. John Gruber created Markdown in 2004 as an easy-to-read markup language. Markdown is amazing, but it has strong limitations.

This story was originally published on HackerNoon at: https://hackernoon.com/when-even-the-cloud-caught-a-cold-inside-the-aws-and-azure-outages-of-2025.
In October 2025, AWS and Azure suffered major outages, exposing the fragility of the cloud and redefining resilience for modern internet infrastructure.
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This story was written by: @mayukhsuri. Learn more about this writer by checking @mayukhsuri's about page, and for more stories, please visit hackernoon.com.

In October 2025, both AWS and Microsoft Azure—the pillars of today’s cloud—suffered massive outages just nine days apart. AWS US-EAST-1 collapsed under DNS and DynamoDB control-plane failures, while Azure Front Door spread a faulty global config that broke routing and authentication across Microsoft 365, Outlook, and Teams. The twin incidents exposed how fragile the “always-on” internet really is and cost billions in downtime. The key lesson? High availability isn’t true resilience. Multi-region setups aren’t enough; automate health checks, test failovers, and design for failure as the default. In the cloud era, resilience is not a feature—it’s a culture.

This story was originally published on HackerNoon at: https://hackernoon.com/why-power-flexible-ai-just-became-table-stakes.
The questions investors should ask about AI data centers just changed. Why power flexibility is now table stakes for infrastructure deployment.
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This story was written by: @asitsahoo. Learn more about this writer by checking @asitsahoo's about page, and for more stories, please visit hackernoon.com.

AI infrastructure's real constraint: power, not chips. Aurora AI Factory (NVIDIA/Emerald, Virginia, 96MW, 2026) implements interruptible compute—software layer throttles training during grid stress, delivers 20-30% reductions, maintains SLAs. Enables faster permitting, lower capacity charges, wholesale market participation. Trade-off: longer training times for cheaper power. Key question: will two-tier market emerge (fixed for inference, flex for training)? 100GW capacity unlock claim assumes perfect coordination—directionally correct but optimistic. Power flexibility now mandatory for deployment. Due diligence questions: demand response capability, interruptible/fixed split, interconnection impact.

This story was originally published on HackerNoon at: https://hackernoon.com/building-a-cloud-native-data-lake-integrating-apache-seatunnel-with-aws-s3-tables-and-iceberg-rest.
Easily integrate your data into Amazon S3 Tables with Apache SeaTunnel!
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This story was written by: @zhoujieguang. Learn more about this writer by checking @zhoujieguang's about page, and for more stories, please visit hackernoon.com.

Leverage Iceberg REST Catalog for seamless real-time & batch data ingestion — cloud-native, serverless, and production-ready.

This story was originally published on HackerNoon at: https://hackernoon.com/holiday-shopping-trends-to-watch-in-2025-what-the-data-tells-us-about-consumer-expectations.
Discover 2025 holiday shopping trends. Shoppers start early, Black Friday loses appeal, and hybrid shopping is on the rise.
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This story was written by: @joannacs. Learn more about this writer by checking @joannacs's about page, and for more stories, please visit hackernoon.com.

Holiday shopping trends in 2025 reflect several insights, including stress and uncertainty that have occurred throughout the year. The biggest holiday shopping trends for 2025 reveal that customers want to shop and celebrate weeks or even months earlier than the traditional holiday season. 60% of customers surveyed claim that Black Friday is no longer relevant for the customer experience.

This story was originally published on HackerNoon at: https://hackernoon.com/build-your-own-mcp-server-with-python-and-sevalla.
Learn how to build, deploy, and extend your own Model Context Protocol (MCP) server using Python and Sevalla to let AI models securely access real-world data.
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This story was written by: @manishmshiva. Learn more about this writer by checking @manishmshiva's about page, and for more stories, please visit hackernoon.com.

Artificial Intelligence models like ChatGPT are powerful but limited by their lack of system context. The Model Context Protocol (MCP) solves this by allowing AI to securely interact with APIs, files, and tools in real time. This guide walks you through building a simple MCP server in Python using the FastMCP library—from configuration and tool creation (like adding numbers or fetching weather data) to cloud deployment on Sevalla. By the end, you’ll understand how to make your AI context-aware, bridging the gap between static prompts and live data.

This story was originally published on HackerNoon at: https://hackernoon.com/the-us-department-of-energy-and-amd-agree-to-$1-billion-supercomputer-partnership.
The U.S. Department of Energy and AMD have announced a $1 billion partnership to create two supercomputers, Lux and Discovery.
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This story was written by: @journalistic. Learn more about this writer by checking @journalistic's about page, and for more stories, please visit hackernoon.com.

The two entities will work together to create two supercomputers with the purpose of furthering fusion energy research, cancer treatments, and more.

This story was originally published on HackerNoon at: https://hackernoon.com/improving-deep-learning-with-lorentzian-geometry-results-from-lhier-experiments.
With improved accuracy, stability, and speed of training, new Lorentz hyperbolic approaches (LHIER+) improve AI performance on classification and hierarchy task
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This story was written by: @hyperbole. Learn more about this writer by checking @hyperbole's about page, and for more stories, please visit hackernoon.com.

This study proposes a whole set of enhancements for hyperbolic deep learning in computer vision, which have been verified by conducting extensive experiments on conventional classification tasks and hierarchical metric learning. An effective convolutional layer, a resilient curvature learning schema, maximum distance rescaling for numerical stability, and a Riemannian AdamW optimizer are among the suggested techniques that are included into a Lorentz-based model (LHIER+). With greater Recall@K scores, LHIER+ performs better on hierarchical metric learning benchmarks (CUB, Cars, SOP).

This story was originally published on HackerNoon at: https://hackernoon.com/microsofts-samba-model-redefines-long-context-learning-for-ai.
SAMBA combines attention and Mamba for linear-time modeling and context recall for millions of tokens.
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This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page, and for more stories, please visit hackernoon.com.

SAMBA is a hybrid neural architecture that effectively processes very long sequences by combining Sliding Window Attention (SWA) with Mamba, a state space model (SSM). SAMBA achieves speed and memory efficiency by fusing the exact recall capabilities of attention with the linear-time recurrent dynamics of Mamba. SAMBA surpasses Transformers and pure SSMs on important benchmarks like MMLU and GSM8K after being trained on 3.2 trillion tokens with up to 3.8 billion parameters.

This story was originally published on HackerNoon at: https://hackernoon.com/how-to-scale-llm-apps-without-exploding-your-cloud-bill.
Cut LLM costs and boost reliability with RAG, smart chunking, hybrid search, agentic workflows, and guardrails that keep answers fast, accurate, and grounded.
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Why This Matters: Generative AI has sparked a wave of innovation, but the industry is now facing a critical inflection point. Startups that raised capital on impressive demos are discovering that building sustainable AI businesses requires far more than API integrations. Inference costs are spiraling, models are buckling under production traffic, and the engineering complexity of reliable, cost-effective systems is catching many teams off guard. As hype gives way to reality, the gap between proof-of-concept and production-grade AI has become the defining challenge - yet few resources honestly map this terrain or offer actionable guidance for navigating it. The Approach: This piece provides a practical, technically grounded roadmap through a realistic case study: ResearchIt, an AI tool for analyzing academic papers. By following its evolution through three architectural phases, the article reveals the critical decision points every scaling AI application faces: Version 1.0 - The Cost Crisis: Why early implementations that rely on flagship models for every task quickly become economically unsustainable, and how to match model choice to actual requirements. Version 2.0 - Intelligent Retrieval: How Retrieval-Augmented Generation (RAG) transforms both cost-efficiency and accuracy through semantic chunking, vector database architecture, and hybrid retrieval strategies that feed models only the context they need. Version 3.0 - Orchestrated Intelligence: The emerging frontier of multi-agent systems that coordinate specialized reasoning, validate their outputs, and handle complex analytical tasks across multiple sources - while actively defending against hallucinations. Each phase tackles a specific scaling bottleneck - cost, context management, and reliability - showing not just what to build, but why each architectural evolution becomes necessary and how teams can navigate the trade-offs between performance, cost, and user experience. What Makes This Different: This isn't vendor marketing or abstract theory. It's an honest exploration written for builders who need to understand the engineering and business implications of their architectural choices. The piece balances technical depth with accessibility, making it valuable for engineers designing these systems and leaders making strategic technology decisions.

This story was originally published on HackerNoon at: https://hackernoon.com/the-biological-principles-needed-to-engineer-conscious-ai.
Discover Edelman's ten-step process for creating a conscious computer that draws inspiration from embodied intelligence and neuroscience.
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This story was written by: @phenomenology. Learn more about this writer by checking @phenomenology's about page, and for more stories, please visit hackernoon.com.

Gerald Edelman's ten-step Roadmap to a Conscious Artifact is reconstructed in this article using notes from a 2006 discussion at The Neurosciences Institute. The roadmap lays out the fundamentals for creating a machine that is fully conscious, starting with value systems, thalamo-cortical dynamics, and reentrant neural architectures and on to motor control, language, and developmental learning. Every step demonstrates Edelman's belief that embodied, self-organizing biological principles, not symbolic computation, are the source of consciousness. Fifteen years after it was first proposed, this framework is still among the most comprehensive and physiologically based models of artificial consciousness.

This story was originally published on HackerNoon at: https://hackernoon.com/33-hot-tech-takes-on-atlas-the-new-ai-browser-by-openai.
OpenAI launches ChatGPT Atlas, an AI-powered browser with memory and agent mode. We gathered 33 reactions from skeptics, believers, and analysts.
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This story was written by: @webism. Learn more about this writer by checking @webism's about page, and for more stories, please visit hackernoon.com.

OpenAI launched ChatGPT Atlas, an AI-powered browser that integrates ChatGPT into every browsing session with features like browser memory and autonomous agent mode. The announcement sparked fierce debate: tech enthusiasts see it as the future of web browsing, while privacy advocates warn it's a "brain-smoothing privacy nightmare." Built on Chromium (ironically, Google's own open-source engine), Atlas represents OpenAI's ambitious play to become the front door to the internet—if users can get past concerns about handing over their entire browsing history to an AI.

This story was originally published on HackerNoon at: https://hackernoon.com/the-future-of-crypto-transactions-ai-that-predicts-network-congestion.
FENN uses deep learning to predict blockchain transaction fees by modeling mempool states, network speed, and transaction data.
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This story was written by: @blockchainize. Learn more about this writer by checking @blockchainize's about page, and for more stories, please visit hackernoon.com.

Blockchain transaction fees fluctuate due to limited block capacity and network congestion. The Fee Estimation based on Neural Network (FENN) framework tackles this challenge by combining three data sources—transaction features, mempool states, and network characteristics. Using deep learning methods like LSTM and attention mechanisms, FENN predicts future block behaviors and network trends to estimate optimal transaction fees. This dual-layer model—feature extraction and prediction—helps improve accuracy and efficiency in confirming blockchain transactions.

This story was originally published on HackerNoon at: https://hackernoon.com/how-ai-can-help-you-avoid-overpaying-on-bitcoin-transactions.
AI-driven framework FENN predicts optimal Bitcoin transaction fees in real time, improving accuracy and preventing overpayment or confirmation delays.
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This story was written by: @blockchainize. Learn more about this writer by checking @blockchainize's about page, and for more stories, please visit hackernoon.com.

This study introduces FENN, a neural network framework designed to predict Bitcoin transaction fees more accurately than existing tools. Unlike traditional analytical models, FENN learns from multiple knowledge sources—including transaction data, mempool activity, and blockchain conditions—to recommend optimal fees that balance cost and confirmation time. Experiments using real blockchain data show that FENN achieves higher estimation accuracy and faster model training, paving the way for smarter, real-time fee prediction in the Bitcoin network.

This story was originally published on HackerNoon at: https://hackernoon.com/why-traditional-testing-breaks-down-with-ai.
Traditional testing breaks with AI. Learn how red teaming and AI-powered fuzzing uncover hidden weaknesses in large language models.
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This story was written by: @mend. Learn more about this writer by checking @mend's about page, and for more stories, please visit hackernoon.com.

Traditional testing can’t handle AI’s infinite input/output space. Instead of validating correctness, modern QA must simulate real-world attacks using AI-driven red teaming to uncover failures, biases, and vulnerabilities before users do.