Tech Stories Tech Brief By HackerNoon

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. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #mcp-server, #python-mcp-tutorial, #ai-agent, #fastmcp, #ai-integration, #mcp-server-setup, #context-aware-ai, #how-to-build-mcp-servers, and more. 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. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #supercomputer, #amd-ai-chips, #doe-and-amd-partnership, #lux-supercomputer, #fusion-energy-research, #cancer-treatment-research, #amd-supercomputers, #hackernoon-top-story, and more. 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 Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #hyperbolic-deep-learning, #riemannian-optimization, #lorentz-manifold, #metric-learning, #curvature-learning, #computer-vision-architectures, #hyperbolic-neural-networks, #lorentz-space-neural-networks, and more. 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. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #microsoft-ai, #linear-time-complexity, #state-space-models, #mamba-hybrid-model, #language-model-scaling, #efficient-llm-design, #long-context-learning-ai, #hackernoon-top-story, and more. 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. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #llm-applications, #llm-cost-optimization, #how-to-build-an-llm-app, #rag, #mcp-agent-to-agent, #chain-of-thought-agents, #reranking-semantic-search, #scaling-ai-applications, and more. This story was written by: @hackerclwsnc87900003b7ik3g3neqg. Learn more about this writer by checking @hackerclwsnc87900003b7ik3g3neqg's about page, and for more stories, please visit hackernoon.com. 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. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #conscious-ai, #machine-consciousness, #neurorobotics, #brain-based-devices, #neural-darwinism, #thalamo-cortical-system, #dynamic-core-theory, #computational-neuroscience, and more. 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. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #hot-tech-takes, #atlas, #ai-browser, #ai-internet-browser, #browser-wars, #chromium, #chatgpt, #hackernoon-top-story, and more. 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. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #bitcoin-transaction-fees, #mempool-management, #fee-rate-analysis, #bitcoin-fee-estimation, #blockchain-ai, #mempool-analysis, #btcflow, #bitcoin-transaction-feerate, and more. 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. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #bitcoin-transaction-fees, #mempool-management, #fee-rate-analysis, #bitcoin-fee-estimation, #blockchain-ai, #mempool-analysis, #btcflow, #hackernoon-top-story, and more. 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. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #ai-testing, #llm-security, #red-teaming, #prompt-injection, #ai-safety, #ai-fuzzing, #ml-engineering, #good-company, and more. 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.

This story was originally published on HackerNoon at: https://hackernoon.com/what-quantum-machine-learning-means-for-the-future-of-ai. Open up AI's black box with Quantum Computing. This article explains how quantum kernels and QNLP enhance machine learning explainability and traceability. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #quantum-machine-learning, #quantum-computing, #quantum-nlp, #ai-explainability, #future-of-ai, #qnlp, #explainable-ai, #variational-quantum-circuits, and more. This story was written by: @hacker76882811. Learn more about this writer by checking @hacker76882811's about page, and for more stories, please visit hackernoon.com. Explore Quantum Computing's impact on AI Explainability. Learn about qubits, superposition, QML, and QNLP for transparent, grammar-driven AI models.

This story was originally published on HackerNoon at: https://hackernoon.com/neo-and-spoonos-offer-$100k-to-solve-the-problem-centralized-ai-cannot-fix. Neo and SpoonOS launch $100K Scoop AI Hackathon across 8 cities to unite AI and blockchain developers building the sentient economy Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #spoonos, #spoonos-news, #neo, #web3, #blockchain, #cryptocurrency, #ai, #good-company, and more. 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. Neo and SpoonOS launch $100K Scoop AI Hackathon across 8 cities to unite AI and blockchain developers building the sentient economy

This story was originally published on HackerNoon at: https://hackernoon.com/building-a-data-driven-ranching-assistant-with-python-and-a-government-weather-api. A Python-powered agri-tech tool that scrapes feed prices and pulls NOAA weather data to help ranchers cut costs and plan smarter. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #ai-in-agriculture, #agritech, #precision-agriculture, #python-web-scraping, #noaa-api, #weather-data-api, #smart-farming-technology, #sustainable-livestock-farming, and more. This story was written by: @knightbat2040. Learn more about this writer by checking @knightbat2040's about page, and for more stories, please visit hackernoon.com. This article explores how I combined web scraping, agricultural formulas, and NOAA’s weather API to build a Python-based tool that helps ranchers optimize cattle feed costs. The project features two modules—an economic engine that calculates feed requirements and scrapes real-time prices, and an environmental monitor that uses weather data to predict heat stress. By integrating these systems, the tool bridges agricultural science and data automation, offering a glimpse into how developers can create real-world value for traditional industries.

This story was originally published on HackerNoon at: https://hackernoon.com/learning-about-gans-showed-me-why-ai-needs-more-local-data. A generative adversarial network is a type of machine learning model that is trained on some sets of data, like images or texts, to make them look real. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #gans, #generative-adversarial-network, #training-gans, #cyclegan, #stylegan2, #ai-image-generation, #african-ai-projects, #lagosgan, and more. This story was written by: @theelvace. Learn more about this writer by checking @theelvace's about page, and for more stories, please visit hackernoon.com. Generative Adversarial Networks (GANs) are a type of machine learning model. GANs are used to generate images of entirely new cats using a probability distribution of the data it has.

This story was originally published on HackerNoon at: https://hackernoon.com/building-decentralized-prediction-markets-across-three-blockchains-with-myriad-protocol. Myriad Protocol operates as an EVM-based prediction market infrastructure deployed across Abstract, Linea, and Celo chains. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #myriad-protocol, #web3, #blockchain, #cryptocurrency, #prediction-markets, #good-company, #defi, #myriad-protocol-news, and more. 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. Myriad Protocol operates as an EVM-based prediction market infrastructure deployed across Abstract, Linea, and Celo chains. The protocol uses Polkamarkets smart contracts and provides both REST API and JavaScript SDK for developers. Each market supports multiple ERC-20 tokens with liquidity pools, outcome shares, and resolution mechanisms. The architecture includes versioned smart contracts (v3.2-3.4) with separate querier contracts for read operations, treasury fee mechanisms, and support for both standard and referral-based trading.

This story was originally published on HackerNoon at: https://hackernoon.com/microsoft-365-recovers-after-widespread-outage. Microsoft resolves a major 365 outage after a North America network misconfiguration left 17,000 users unable to access Teams and Exchange Online. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #microsoft-365, #mircosoft-365-issues, #exchange-online-issues, #microsoft-365-network-issues, #mircosoft-365-issues-resolved, #microsoft-365-downtime, #microsoft-service-status, #microsoft-outage-fix, and more. 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. Microsoft confirmed that a widespread Microsoft 365 outage, which affected about 17,000 users across North America, has been fully resolved. The disruption was traced to a misconfigured part of the company’s network infrastructure that temporarily blocked access to services like Teams and Exchange Online. Reports of downtime peaked on Downdetector before sharply declining once Microsoft implemented a fix.

This story was originally published on HackerNoon at: https://hackernoon.com/windsurf-mcp-how-i-stopped-context-switching-and-started-actually-coding. Integrate Windsurf with GitHub & Linear through MCP to automate PRs, issues, and project tracking.3x faster feature development with this 30-minute setup guide Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #ai-coding-tools, #developer-productivity, #windsurf, #linear, #github, #windsurf-+-mcp, #context-switching, #context-engineering, and more. This story was written by: @pnadagoud. Learn more about this writer by checking @pnadagoud's about page, and for more stories, please visit hackernoon.com. MCP servers let Windsurf’s AI assistant interact directly with GitHub and Linear, eliminating constant context switching. This guide covers setup (30 minutes), configuration, real productivity gains (3x faster feature development), and troubleshooting. If you’re tired of copying data between tools, this integration is worth the setup time.

This story was originally published on HackerNoon at: https://hackernoon.com/the-lost-art-of-web3-marketing. Explore why Web3 marketing feels like a lost art—and how projects can move beyond hype to build real, sustainable growth, community, and clarity in messaging. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #fixing-web3-marketing, #web3-marketing, #web3-marketing-problems, #web3, #utility-driven-marketing, #marketing, #hack-marketing-tips, #hackernoon-top-story, and more. This story was written by: @hackmarketing. Learn more about this writer by checking @hackmarketing's about page, and for more stories, please visit hackernoon.com. Web3 marketing often falls short because it leans too heavily on hype, airdrops, and engagement metrics rather than fundamentals. The article argues that to succeed, Web3 projects must prioritize clarity in messaging, real utility, retention strategies, and authentic community over superficial metrics and short-term growth hacks.

This story was originally published on HackerNoon at: https://hackernoon.com/ai-just-got-better-at-counting-trees. Deep learning meets forestry: TreeLearn improves tree segmentation accuracy across diverse forest types using multi-domain training. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #domain-adaptation-ai, #lidar-forest-mapping, #ai-environmental-monitoring, #3d-forest-reconstruction, #uav-laser-scanning, #lidar-point-clouds, #treelearn-model, #instance-segmentation, and more. This story was written by: @instancing. Learn more about this writer by checking @instancing's about page, and for more stories, please visit hackernoon.com. This study evaluates TreeLearn, a deep-learning-based tree segmentation model trained on multi-domain forest point clouds. Results show that fine-tuning the model with both high- and low-resolution datasets (MLS, TLS, UAV) significantly improves instance segmentation performance and generalization across forest types. The findings highlight the importance of diverse, labeled training data to develop AI models capable of accurately mapping trees in varying environments—laying groundwork for scalable, data-driven forest monitoring and management.

This story was originally published on HackerNoon at: https://hackernoon.com/why-ml-can-predict-the-weather-but-not-financial-markets. Why machine learning models fail in finance: noisy data, scarce samples, and chaotic markets make prediction nearly impossible. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #ai-in-finance, #ai-trading, #financial-markets, #trading-algorithms, #market-prediction-ai, #financial-data-noise, #synthetic-financial-data, #hackernoon-top-story, and more. This story was written by: @hacker47950068. Learn more about this writer by checking @hacker47950068's about page, and for more stories, please visit hackernoon.com. Financial data is just harder to work with than data in other domains, mainly for three reasons: Too much noise, not enough data, and constantly changing markets. Grigory Heron: The problem is that they only work in isolation. Nobody has managed to put them all into a single trading machine.