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3월 18일

Connecting Agents and Data: A Guide to A2A and MCP

The provided text outlines two primary frameworks, A2A and MCP, designed to solve the communication challenges inherent in isolated AI agents. A2A (Agent-to-Agent) is an open protocol that facilitates direct interaction between different AI entities, allowing them to exchange tasks and status updates regardless of their underlying vendors. In contrast, the Model Context Protocol (MCP) provides a standardized way for individual agents to access external data sources like databases, file systems, and code repositories. While A2A focuses on the collaborative relationship between multiple agents, MCP serves as a bridge between an agent and its necessary tools or information. Both protocols utilize JSON RPC to ensure structured data exchange, but they serve different roles in the AI ecosystem. Ultimately, the source illustrates how these technologies complement each other to create a fully integrated environment where agents can both work together and interact with existing digital infrastructure.
5월 21일

Don't Build Cascaded Pipelines: Skilling Up Coding Agents for System Observability

[EN IT PDF] https://www.patreon.com/posts/en-it-pdf-dont-158888273?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link
5월 9일

🚀 Scaling LLMs is no longer just about more GPUs—it's about the geometry of the cluster-Stanford's CS336 Lecture 8

🚀 Scaling LLMs is no longer just about more GPUs—it's about the geometry of the cluster. In Stanford's CS336 Lecture 8, we dive deep into the parallelization strategies that make training trillion-parameter models possible. From Zero Redundancy Optimizers (ZeRO) to 4D parallelism, the complexity is staggering. Key Takeaways: 🔹 ZeRO-3 (FSDP) allows sharding parameters "almost for free" on high-speed networks. 🔹 Tensor Parallelism is mandatory for intra-node scaling but relies on massive bandwidth. 🔹 Pipeline Parallelism is the bridge for cross-node training, now improved with "Zero-Bubble" techniques. 🔹 Expert Parallelism (MoE) decouples MLP layers for sparse routing efficiency. The golden rule? Use all sharding methods until the model fits in memory, then scale with Data Parallelism. Check out the full technical summary and transcripts in our repo! All my links: https://linktr.ee/learnbydoingwithsteven #learnbydoingwithsteven #LLM #AIResearch #DistributedComputing #MachineLearning #DeepLearning #StanfordCS336 #GPU #TPU #ModelParallelism #DataParallelism
5월 18일

The One-Person Company (OPC) Paradigm: AI-Driven Industrial Re-Architecture

The One-Person Company (OPC) Paradigm: AI-Driven Industrial Re-Architecture The definition of entrepreneurship is undergoing a fundamental structural transition. Driven by advanced generative AI frameworks and automated operational pipelines, the "One-Person Company" (OPC) is no longer a simple legal designation, but the core building block of the modern digital economy. An objective analysis of the 2026 China OPC landscape reveals critical structural trends: 1️⃣ Scale and Velocity: By mid-2025, China's active one-person limited liability companies exceeded 16 million, representing over 25% of all national business entities. This supply-side explosion is highly concentrated in digital-native sectors like autonomous agent development and specialized digital media. 2️⃣ The Institutional Sandbox: Over 20 major municipalities have established targeted support frameworks, shifting from basic rent-free physical spaces to deep infrastructure provisioning, including computing power vouchers, desk registration protocols, and dedicated micro-seed funding. 3️⃣ Severe Revenue Polarization: Despite ultra-low startup barriers, commercialization remains highly competitive. The revenue profile is sharply pyramidal, with roughly half of exploration-phase founders earning under 7,000 RMB monthly, while a tiny elite of domain-specific, asset-reusable builders achieve multi-million RMB annual run rates. 4️⃣ Organizational Inertia: Large technology corporations are struggling to adapt their enterprise service pipelines to cater to this highly decentralized micro-client market, leaving a critical gap in lightweight API access and distribution support. As the industry enters its secondary phase of development, the priority for solo builders must shift from superficial tool experimentation to the validation of concrete commercial orders, converting individual labor into reusable, long-term digital assets. Source Report: https://www.opcquan.com All my links: https://linktr.ee/learnbydoingwithsteven #OnePersonCompany #ArtificialIntelligence #StartupEcosystem #DigitalEconomy #BusinessInsights #Automation #TechPolicy #AIAgents #Entrepreneurs #learnbydoingwithsteven
5월 21일

Don't Build Cascaded Pipelines: The Rise of Native "Any-to-Any" Multimodal Agents

EN IT PDF https://www.patreon.com/posts/en-it-pdf-dont-158887968?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link
6월 28일

🚀 Stanford CS336 Lecture 13: The Evolution of Language Model Data. -Notebooklm Summary

Stanford CS336 Lecture 13 focuses on the critical evolution of language model training data. While model architectures are widely disclosed, dataset details remain highly proprietary due to commercial competition and copyright considerations. The lifecycle of language model training spans pre-training, mid-training, and post-training. The mid-training phase curates high-quality datasets to enhance specific capabilities like coding, mathematics, and long-context reasoning. Ultimately, data curation shifts from high-volume, low-quality web crawls to low-volume, high-quality specialized datasets. Data filtering methodologies have evolved from basic rule-based heuristics and language identification to advanced model-based approaches. Modern curation pipelines leverage large language models to assess the educational value of documents, rewrite low-quality texts, and synthesize high-quality QA pairs to scale data efficiently. Legal and compliance challenges, including copyright and fair use, remain central to data acquisition. As models risk memorizing training text, developers navigate the balance between direct commercial licensing and fair use arguments. Key Takeaways: Mid-training acts as a crucial bridge, refining models for targeted reasoning tasks.Advanced LLM-driven filtering and synthesis scale high-quality data while avoiding rule-based bias.Copyright compliance and memorization concerns limit public dataset disclosures.All my links: https://linktr.ee/learnbydoingwithsteven #learnbydoingwithsteven #AI #DeepLearning #Research #TechSummary #MachineLearning #LLM #DataScience #DataCuration #Copyright #ArtificialIntelligence
7월 3일

The Production AI Playbook: Five Pillars for Enterprise Scaling

✅ Transitioning AI from prototype to production requires closing three critical gaps: observability, evaluation, and governance. ✅ The "Week 7 Rule" advises building the evaluation layer and data foundation before choosing a specific model. ✅ Enterprise evaluation requires a three-layered defense: deterministic checks, semantic judges, and behavioral decision tracing. ✅ A bifurcated data strategy separating question data from tracking logs is essential to prevent agent hallucinations. All my links: https://linktr.ee/learnbydoingwithsteven #AI #SoftwareEngineering #AIEngineer #AIAgents #MultiAgentOrchestration #EnterpriseAI #TokenEfficiency #SystemSecurity #LLMs #StevenDataTalk #learnbydoingwithsteven
7월 4일

The Agentic Architecture: Five Essential AI Terms Explained

✅ Recently, the evolution of Artificial Intelligence from conversational models to autonomous agents is driven by an instruction layer wrapped around Large Language Models (LLMs). ✅ The internal behavioral framework of an agent is defined by project-specific rules in the agents. ✅ While project rules are governed by agents. ✅ Connectivity and interoperability are crucial for autonomous agents to interact with external environments. All my links: https://linktr.ee/learnbydoingwithsteven Website: https://learnbydoingwithsteven.github.io #AIAgents #AgenticAI #SoftwareEngineering #LLMs #ModelContextProtocol #SystemSecurity #Microservices #AIAgentsOrchestration #learnbydoingwithsteven
7월 8일

AI-Driven Multi-Document Correlation for Financial Compliance

✅ Transition from reactive validation to proactive, cross-document intelligence. ✅ Entity Correlation Engine built on graph database to reveal hidden relationships. ✅ Adaptive Probabilistic Risk Model combining multiple signals to compute confidence-based risk scores. ✅ Cross-Jurisdictional Normalization Layer to standardize data across countries. All my links: https://linktr.ee/learnbydoingwithsteven #learnbydoingwithsteven #AI #LLM #TechTrends #FinancialCompliance #GraphDatabase #EntityCorrelation #ProbabilisticRisk #ComplianceEngineering #FinTech
7월 3일

Bridging the LLM Data Gap with Web Access Platforms

✅ LLMs often prioritize answering over admitting failure, leading to up to 60% of web citations resulting in 404 errors. ✅ When blocked by CAPTCHAs or IP blocks, agents enter the "invisible failure group" and fail silently. ✅ Websites employ "AI Labyrinths" to trap crawling bots and feed them fake data to corrupt LLM outputs. ✅ Some MCP offers 66 tools, mimicking human mouse movements and typing to bypass blocks. ✅ Generating dedicated parser scripts with LLMs instead of raw parsing saves up to 99% of token costs. ✅ Compliance is maintained by focusing strictly on public, login-free data to avoid legal liabilities. All my links: https://linktr.ee/learnbydoingwithsteven #AI #SoftwareEngineering #AIEngineer #AIAgents #WebScraping #ModelContextProtocol #TokenEfficiency #SystemSecurity #LLMs #StevenDataTalk #learnbydoingwithsteven

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