{"version":1,"generated_at":"2026-11-06T03:31:59.638Z","site":"https://erpagi.com","description":"Public ERPA/GI article discovery index. Each record is a published EN/KO article with source URLs and evidence counts.","record_schema":{"required":["title","absolute_url","lang","content_type","source_urls"],"evidence_fields":["source_urls","claim_count","faq_count","has_evidence"]},"records":[{"id":"2026-11-05-claude-api-en","url":"/blog/2026-11-05-claude-api-en/","absolute_url":"https://erpagi.com/blog/2026-11-05-claude-api-en/","title":"Analysis of Claude API Free Tier Usage Limits: A Pricing Plan Selection Guide for Individual Developers in Korea","summary":"According to Anthropic's official pricing page, the Claude Free plan is $0 and available to all users. The free tier has limited usage applied every 5 hours and weekly, which is explicitly stated to be at a level lower than the baseline (1x) of paid plans. Please separately verify payment currency and tax/invoice conditions with the latest official guidelines and your contract representative.","lang":"en","content_type":"pseo","topic_key":"claude-api","keywords":["claude-api","Claude API","무료 티어 사용량 제한","한국 사용자 (원화 결제, 부가세 포함가, 세금계산서 발행)","Comparison","ERPAGI Original Data — Last 30 Days Community Observations","Specific Usage Limit Structure of the Claude API Free Tier","Perspective for Korean Users: Considerations on Payment Currency, Taxes, and Invoice Issuance","Analysis of When to Switch from the Free Tier to a Paid 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관찰"],"published_at":"2026-11-05T02:20:20.851Z","updated_at":"2026-11-05T03:05:20.851Z","source_urls":["https://blog.google/innovation-and-ai/products/gemini-notebook/notebooklm-gemini-notebook/","https://support.google.com/gemininotebook/answer/16215270?hl=en","https://support.google.com/gemininotebook/answer/16212820?hl=en","https://support.google.com/gemininotebook/answer/16454555?hl=en","https://www.reddit.com/r/LLMDevs/comments/1xd7f2t/building_a_local_notebooklm_app_open_source/","https://www.youtube.com/watch?v=pcz_KYN23JM","https://www.futurepedia.io/tool/notebooklm","https://felloai.com/notebooklm-update-1m-token-chat-goals-saved-history/"],"claim_count":3,"faq_count":2,"section_count":3,"has_evidence":true},{"id":"google-search-central-seo-starter-guide-for-an-ai-tool-tutorial-en","url":"/blog/google-search-central-seo-starter-guide-for-an-ai-tool-tutorial-en/","absolute_url":"https://erpagi.com/blog/google-search-central-seo-starter-guide-for-an-ai-tool-tutorial-en/","title":"Google 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Google은 주로 이미 크롤링한 다른 페이지의 링크를 통해 페이지를 찾으므로 크롤링 가능한 링크 구조가 중요하다. 기술 요구사항은 검색 결과 표시를 위한 최소 기준이며, Search Console과 URL Inspection Tool은 성능 모니터링과 페이지 확인에 도움을 준다. 랭킹 시스템은 페이지 수준에서 작동하도록 설계되었고, 변경 사항이 반영되기까지는 시간이 걸린다.","lang":"ko","content_type":"ts","topic_key":"Google Search Central SEO starter guide for an AI tool tutorial","keywords":["Google Search Central SEO starter guide for an AI tool tutorial","검색 발견 및 기술 최적화 전략","랭킹 신호 및 콘텐츠 품질 관리","ERPAGI Original Data — 최근 30일 커뮤니티 관찰"],"published_at":"2026-11-05T00:07:03.011Z","updated_at":"2026-11-05T00:34:03.011Z","source_urls":["https://developers.google.com/search/docs/fundamentals/seo-starter-guide?hl=en","https://developers.google.com/search/docs/essentials?hl=en","https://developers.google.com/search/docs/appearance/ranking-systems-guide?hl=en","https://www.youtube.com/watch?v=WYYFZ5D4rxU","https://moz.com/learn/seo","https://backlinko.com/seo-tutorial","https://almcorp.com/on-page-seo-guide-optimizing-pages-for-search-users-ai/","https://developers.google.com/search/blog/2026/10/seo-learning-paths"],"claim_count":4,"faq_count":2,"section_count":3,"has_evidence":true},{"id":"claude-code-official-permissions-and-settings-workflow-for-a-solo-developer-en","url":"/blog/claude-code-official-permissions-and-settings-workflow-for-a-solo-developer-en/","absolute_url":"https://erpagi.com/blog/claude-code-official-permissions-and-settings-workflow-for-a-solo-developer-en/","title":"Claude Code Permissions and Settings for Solo Developers","summary":"Claude Code enforces its permission rules itself, not through the model, and reads settings from files whose scope decides who they apply to. A solo developer can use Manual mode to approve most actions before they run, block a tool with a deny rule that no other level can override, and check with /permissions which file each rule comes from.","lang":"en","content_type":"ts","topic_key":"Claude Code official permissions and settings workflow for a solo developer","keywords":["Claude Code official permissions and settings workflow for a solo developer","Permission Governance and Enforcement","Settings Configuration and Precedence","Decision Criteria for Solo Developers","ERPAGI Original Data — Last 30 Days"],"published_at":"2026-11-04T18:36:27.947Z","updated_at":"2026-11-04T19:02:27.947Z","source_urls":["https://code.claude.com/docs/en/overview.md","https://github.com/anthropics/claude-code/blob/main/README.md","https://code.claude.com/docs/en/permissions.md","https://code.claude.com/docs/en/settings.md","https://code.claude.com/docs/en/permission-modes.md","https://x.com/ClaudeDevs/status/2117670662806675967","https://www.youtube.com/watch?v=oTzLSiQXuaQ","https://x.com/davekiss/status/2116223683085705220","https://x.com/dani_avila7/status/2116311689683427511","https://x.com/Voxyz_ai/status/2115775665463316827","https://x.com/dani_avila7/status/2112573064135266409","https://x.com/DanielSmidstrup/status/2111791262969876724","https://x.com/OpenRouter/status/2110838217834696885"],"claim_count":3,"faq_count":3,"section_count":4,"has_evidence":true},{"id":"claude-code-official-permissions-and-settings-workflow-for-a-solo-developer-ko","url":"/blog/claude-code-official-permissions-and-settings-workflow-for-a-solo-developer-ko/","absolute_url":"https://erpagi.com/blog/claude-code-official-permissions-and-settings-workflow-for-a-solo-developer-ko/","title":"솔로 개발자를 위한 Claude Code 권한 및 설정 워크플로우 분석","summary":"Claude Code는 터미널 중심의 에이전트 코딩 도구로, JSON 기반의 세분화된 권한과 범위가 적용된 설정을 통해 개발자가 도구 행동을 정밀하게 제어할 수 있습니다. 솔로 개발자는 Manual 모드로 작업을 실행 전에 승인하고, 거부 규칙과 설정 우선순위로 막을 것과 허용할 것을 정하며, /permissions로 각 규칙이 어느 파일에서 왔는지 확인할 수 있습니다.","lang":"ko","content_type":"ts","topic_key":"Claude Code official permissions and settings workflow for a solo developer","keywords":["Claude Code official permissions and settings workflow for a solo developer","권한 규칙은 Claude Code가 강제한다","설정 범위와 우선순위 체계의 선택 기준","솔로 개발자를 위한 제약 사항 및 최적화 전략","ERPAGI Original Data — 최근 30일 커뮤니티 관찰"],"published_at":"2026-11-04T18:29:27.947Z","updated_at":"2026-11-04T19:02:27.947Z","source_urls":["https://code.claude.com/docs/en/overview.md","https://github.com/anthropics/claude-code/blob/main/README.md","https://code.claude.com/docs/en/settings.md","https://code.claude.com/docs/en/permissions.md","https://code.claude.com/docs/en/permission-modes.md","https://x.com/ClaudeDevs/status/2117670662806675967","https://www.youtube.com/watch?v=oTzLSiQXuaQ","https://x.com/davekiss/status/2116223683085705220","https://x.com/dani_avila7/status/2116311689683427511","https://x.com/Voxyz_ai/status/2115775665463316827","https://x.com/dani_avila7/status/2112573064135266409","https://x.com/DanielSmidstrup/status/2111791262969876724","https://x.com/OpenRouter/status/2110838217834696885"],"claim_count":3,"faq_count":2,"section_count":4,"has_evidence":true},{"id":"apple-m4-macbook-ai-performance-local-llm-benchmark-en","url":"/blog/apple-m4-macbook-ai-performance-local-llm-benchmark-en/","absolute_url":"https://erpagi.com/blog/apple-m4-macbook-ai-performance-local-llm-benchmark-en/","title":"Apple M4 MacBook Local LLM Inference: Architecture, Benchmarks, and Deployment Limits","summary":"The Apple M4 architecture delivers highly efficient local inference for quantized models up to 70B parameters through unified memory optimization and specialized neural processing units. While peak memory access reaches 120GB/s, scaling beyond this threshold introduces CPU routing bottlenecks that trigger thermal management protocols and degrade sustained throughput. Optimal deployment requires MLX or CoreML backends to minimize data transfer overhead, alongside strict context window management to prevent memory exhaustion.","lang":"en","content_type":"ts","topic_key":"apple_m4_macbook_ai_performance_local_llm_benchmark","keywords":["apple_m4_macbook_ai_performance_local_llm_benchmark","Unified Memory Architecture and Runtime Optimization","Scaling Thresholds and Thermal Management Behavior","Backend Selection and Memory Management Strategies"],"published_at":"2026-07-19T22:38:43.447Z","updated_at":"2026-07-19T23:17:43.447Z","source_urls":["https://ml-explore.github.io/mlx/build/html/index.html","https://github.com/ggerganov/llama.cpp","https://huggingface.co/docs/accelerate/en/usage_guides/mac","https://www.phoronix.com/review/apple-m4-chip","https://www.anandtech.com/show/18643/apple-m4-chip-review"],"claim_count":3,"faq_count":2,"section_count":3,"has_evidence":true},{"id":"2026-07-18-cursor-ai-vs-en","url":"/blog/2026-07-18-cursor-ai-vs-en/","absolute_url":"https://erpagi.com/blog/2026-07-18-cursor-ai-vs-en/","title":"Cursor AI Pricing for Agencies and Outsourcing Teams: Monthly vs. Yearly Plans","summary":"This fact page details Cursor AI's pricing structure, highlighting the differences between monthly and yearly plans, and the specific features and benefits that are most relevant for agencies and outsourcing teams.","lang":"en","content_type":"pseo","topic_key":"cursor-ai-vs","keywords":["cursor-ai-vs","Cursor AI","연간 결제 vs 월간 결제 할인율","에이전시/외주팀","Cursor AI Pricing Plans","Key Features for Agencies and Outsourcing Teams","Payment and Billing Options","Original Analysis for Agencies and Outsourcing Teams"],"published_at":"2026-07-18T05:30:43.651283Z","updated_at":"2026-07-18T05:30:43.651283Z","source_urls":["https://cursor.com/pricing"],"claim_count":5,"faq_count":0,"section_count":4,"has_evidence":true},{"id":"2026-07-18-cursor-ai-seat-5-ko","url":"/blog/2026-07-18-cursor-ai-seat-5-ko/","absolute_url":"https://erpagi.com/blog/2026-07-18-cursor-ai-seat-5-ko/","title":"Cursor AI 팀 요금제 좌석당 가격: 5인 이하 스타트업이 알아야 할 것","summary":"Cursor AI는 팀 단위의 협업을 고려한 요금제를 제공하며, 팀 요금제는 사용자당 월 $40로 책정되어 있다. 이 요금제는 중앙 집중식 팀 청구 및 관리, 공유 팀 콘텍스트, 그리고 SAML/OIDC SSO 같은 고급 기능을 포함한다. 5인 이하 스타트업은 팀 요금제의 유연성과 기능성을 통해 효율적인 협업과 비용 관리가 가능하다.","lang":"ko","content_type":"pseo","topic_key":"cursor-ai-seat-5","keywords":["cursor-ai-seat-5","Cursor AI","팀 요금제 좌석(seat)당 가격","5인 이하 스타트업","Cursor AI 요금제 개요","팀 요금제의 주요 기능","팀 요금제와 기업용 요금제의 차이","FAQ"],"published_at":"2026-07-17T21:31:57.135848Z","updated_at":"2026-07-17T21:31:57.135848Z","source_urls":["https://cursor.com/pricing"],"claim_count":5,"faq_count":4,"section_count":4,"has_evidence":true},{"id":"notebooklm-tutorial-research-study-ai-2025-en","url":"/blog/notebooklm-tutorial-research-study-ai-2025-en/","absolute_url":"https://erpagi.com/blog/notebooklm-tutorial-research-study-ai-2025-en/","title":"NotebookLM Tutorial & Research Study Guide for AI Learning Workflows","summary":"NotebookLM transforms static documents into interactive knowledge frameworks by anchoring insights to uploaded sources. Users benefit from structured outputs containing an average of 12 sections and over 45 sub-items, facilitating systematic study planning. However, baseline accuracy hovers around 78.4%, with performance declining when contextual inputs remain sparse. Complex logical reasoning tasks present significant limitations, exhibiting error rates up to 34% during multi-step deductions. Optimal usage involves treating the tool as a synthesis engine rather than an independent analytical authority.","lang":"en","content_type":"ts","topic_key":"notebooklm_tutorial_research_study_ai","keywords":["notebooklm_tutorial_research_study_ai","Core Document Processing & Citation Architecture","Accuracy Metrics & Contextual Dependency Analysis","Structural Output Generation & Study Methodology","Logical Reasoning Benchmarks & Performance Limitations","Strategic Implementation & Workflow Optimization"],"published_at":"2026-07-16T02:48:38.545Z","updated_at":"2026-07-16T03:14:38.545Z","source_urls":["https://blog.google/technology/ai/google-notebooklm-ai-study-buddy/","https://support.google.com/notebooklm/answer/14673508","https://arxiv.org/abs/2402.15678","https://www.nature.com/articles/s41598-024-73898-2","https://notebooklm.google.com/guides"],"claim_count":3,"faq_count":2,"section_count":5,"has_evidence":true},{"id":"cursor-ai-setup-tutorial-beginners-2025-en","url":"/blog/cursor-ai-setup-tutorial-beginners-2025-en/","absolute_url":"https://erpagi.com/blog/cursor-ai-setup-tutorial-beginners-2025-en/","title":"Cursor AI Setup Tutorial for Beginners in 2025","summary":"A comprehensive guide to installing Cursor IDE, configuring advanced .cursorrules parameters, leveraging automated agent workflows, and implementing essential security protocols for safe AI-assisted development in 2025.","lang":"en","content_type":"ts","topic_key":"cursor_ai_setup_tutorial_beginners_2025","keywords":["cursor_ai_setup_tutorial_beginners_2025","Getting Started with the Cursor IDE Environment","Advanced Configuration and Token Management","Automating Workflows with the Integrated Agent","Security Protocols and Realistic Performance Expectations"],"published_at":"2026-07-15T22:47:44.857Z","updated_at":"2026-07-15T23:07:44.857Z","source_urls":["https://docs.cursor.com/getting-started/introduction","https://docs.cursor.com/advanced/configuration","https://www.cursor.com/blog/cursor-agent-release","https://security.googleblog.com/2024/05/ai-code-assistants-security-considerations.html","https://github.com/swe-bench/SWE-bench"],"claim_count":2,"faq_count":2,"section_count":4,"has_evidence":true},{"id":"cursor-ai-setup-tutorial-beginners-2025-ko","url":"/blog/cursor-ai-setup-tutorial-beginners-2025-ko/","absolute_url":"https://erpagi.com/blog/cursor-ai-setup-tutorial-beginners-2025-ko/","title":"Cursor AI IDE 2025 초보자 완벽 가이드: 설치부터 보안 검증까지","summary":"Cursor IDE는 2분 내 설치가 가능한 VS Code 기반 AI 네이티브 환경으로, .cursorrules 파일을 통해 응답 토큰을 최대 128,000까지 확장하고 코드 스타일을 강제할 수 있습니다. Agent 기능은 반복 작업을 줄이지만, 보안 취약점 및 레거시 시스템 한계를 고려해 정적 분석과 수동 검증을 병행해야 합니다.","lang":"ko","content_type":"ts","topic_key":"cursor-ai-setup-tutorial","keywords":["cursor-ai-setup-tutorial","Cursor IDE 기본 설치 및 환경 구축",".cursorrules를 통한 AI 응답 토큰 및 코드 스타일 최적화","Cursor Agent 도입 시 고려사항 및 보안 검증 프로세스","초보자를 위한 실전 활용 전략 및 학습 로드맵"],"published_at":"2026-07-15T22:44:44.857Z","updated_at":"2026-07-15T23:07:44.857Z","source_urls":["https://docs.cursor.com/getting-started/introduction","https://docs.cursor.com/advanced/configuration","https://www.cursor.com/blog/cursor-agent-release","https://security.googleblog.com/2024/05/ai-code-assistants-security-considerations.html","https://github.com/swe-bench/SWE-bench"],"claim_count":2,"faq_count":2,"section_count":4,"has_evidence":true},{"id":"how-to-use-claude-projects-for-work-productivity-en","url":"/blog/how-to-use-claude-projects-for-work-productivity-en/","absolute_url":"https://erpagi.com/blog/how-to-use-claude-projects-for-work-productivity-en/","title":"Mastering Claude Projects for Enterprise Work Productivity","summary":"Claude Projects enables structured knowledge management through strict context boundaries and explicit API scoping. By leveraging project isolation and standardized workflows, organizations can achieve measurable efficiency gains while maintaining data security and computational stability across complex operational environments.","lang":"en","content_type":"ts","topic_key":"claude-projects-productivity","keywords":["claude-projects-productivity","Architectural Foundation and Context Management","API Integration and Latency Optimization","Workflow Standardization and Automation Pipelines","Strategic Implementation Matrix and Resource Allocation","Operational Best Practices and Risk Mitigation"],"published_at":"2026-07-15T09:22:33.161Z","updated_at":"2026-07-15T09:40:33.161Z","source_urls":["https://docs.anthropic.com/en/docs/build-with-claude/projects/overview","https://github.com/anthropics/anthropic-sdk-python","https://docs.anthropic.com/en/reference/messages/create-message","https://arxiv.org/abs/2402.13177","https://www.anthropic.com/engineering/building-effective-agents"],"claim_count":4,"faq_count":3,"section_count":5,"has_evidence":true},{"id":"perplexity-ai-vs-chatgpt-vs-google-search-comparison-ko","url":"/blog/perplexity-ai-vs-chatgpt-vs-google-search-comparison-ko/","absolute_url":"https://erpagi.com/blog/perplexity-ai-vs-chatgpt-vs-google-search-comparison-ko/","title":"Perplexity AI vs ChatGPT vs Google Search: 성능과 신뢰성 비교 분석","summary":"AI 검색 도구는 전통 엔진 대비 정확도 68% 향상과 속도 3.5배 개선을 보였으나, citation 누락 시 오류율 41% 증가 등 신뢰성 관리가 필수적입니다. 플랫폼별 citation 제공 수와 API 한계를 고려해 목적에 맞게 선택해야 합니다.","lang":"ko","content_type":"ts","topic_key":"ai_search_comparison","keywords":["ai_search_comparison","ERPAGI Original Data — 최근 30일 커뮤니티 관찰","AI 검색 엔진의 핵심 성능 비교","출처 Citation의 신뢰성 및 한계 분석","플랫폼별 실전 적용 시나리오"],"published_at":"2026-07-15T03:01:46.853Z","updated_at":"2026-07-15T03:19:46.853Z","source_urls":["https://platform.openai.com/docs/guides/search","https://docs.perplexity.ai/","https://blog.google/products/search/google-ai-overviews-search/","https://arxiv.org/abs/2310.16897","https://www.ftc.gov/news-events/news/press-releases/2024/07/federal-trade-commission-reports-ai-search-tools-misleading-consumers","https://llmpulse.ai/blog/glossary/aeo-answer-engine-optimization/","https://x.com/DrSandeepRana1","https://www.reddit.com/r/seogrowth/comments/1virkec/where_can_i_learn_seo_in_2026_everything_i_find/","https://www.youtube.com/watch?v=SdcNfrd9cqk","https://www.instagram.com/p/Da8bGYlo4wB/","https://x.com/tomcrawshaw01/status/2084997129957732561","https://www.reddit.com/r/DigitalMarketing/comments/1vaqhqg/is_ai_replacing_traditional_digital_marketing/","https://www.youtube.com/watch?v=avPfpHkp_YI","https://www.linkedin.com/posts/prestondmiller_seo-is-becoming-a-citation-game-heres-activity-7487970380876316673-VWkV","https://x.com/bloggersarvesh/status/2080643945017868708","https://www.reddit.com/r/b2bmarketing/comments/1vgzfr9/b2b_seo_newbie_struggling_to_figure_out_where_to/","https://www.youtube.com/watch?v=nCn5ZvpVcGg","https://www.instagram.com/p/DbkxdK_CII7/","https://x.com/alexgroberman/status/2086116710189527460/photo/3","https://www.reddit.com/r/seogrowth/comments/1vinsvl/anyone_feel_like_new_domains_are_super_hard_to/","https://www.youtube.com/watch?v=n-4haLYhZnQ","https://www.instagram.com/reel/Db1diQkgjvP/","https://x.com/alexgroberman/status/2085750923327107196","https://www.reddit.com/r/localseo/comments/1vistu2/after_4_months_of_seo_for_a_client_the_results/","https://www.youtube.com/watch?v=jW8hYUn99Uk","https://www.facebook.com/SearchEngineJournal/posts/i-read-perplexitys-raw-answer-stream-to-see-how-it-picks-sources-which-domains-g/1523923313111447/","https://x.com/alexgroberman/status/2085750923327107196/photo/2","https://www.reddit.com/r/IndieHackLab/comments/1vdf1wg/how_to_generate_seo_pages/","https://www.youtube.com/watch?v=jke34lA9HpY"],"claim_count":4,"faq_count":1,"section_count":4,"has_evidence":true},{"id":"perplexity-ai-vs-chatgpt-vs-google-search-comparison-en","url":"/blog/perplexity-ai-vs-chatgpt-vs-google-search-comparison-en/","absolute_url":"https://erpagi.com/blog/perplexity-ai-vs-chatgpt-vs-google-search-comparison-en/","title":"Perplexity AI vs ChatGPT vs Google Search: A Data-Driven Comparison","summary":"AI search platforms now outperform traditional engines in speed and factual accuracy, yet citation reliability remains a critical vulnerability. Data indicates a 68% improvement in factual precision and a 3.5x faster response time compared to legacy systems. However, regulatory findings warn that approximately 23% of AI outputs contain inaccuracies, with error rates spiking by 41% when citations are omitted. Platform selection should hinge on citation density, API rate limits, and domain-specific verification needs.","lang":"en","content_type":"ts","topic_key":"ai_search_comparison","keywords":["ai_search_comparison","ERPAGI Original Data — Last 30 Days","Performance Benchmarks and Latency Analysis","Citation Density and Verification Reliability","Error Rates and Regulatory Compliance Warnings"],"published_at":"2026-07-15T02:37:46.853Z","updated_at":"2026-07-15T03:19:46.853Z","source_urls":["https://platform.openai.com/docs/guides/search","https://docs.perplexity.ai/","https://blog.google/products/search/google-ai-overviews-search/","https://arxiv.org/abs/2310.16897","https://www.ftc.gov/news-events/news/press-releases/2024/07/federal-trade-commission-reports-ai-search-tools-misleading-consumers","https://llmpulse.ai/blog/glossary/aeo-answer-engine-optimization/","https://x.com/DrSandeepRana1","https://www.reddit.com/r/seogrowth/comments/1virkec/where_can_i_learn_seo_in_2026_everything_i_find/","https://www.youtube.com/watch?v=SdcNfrd9cqk","https://www.instagram.com/p/Da8bGYlo4wB/","https://x.com/tomcrawshaw01/status/2084997129957732561","https://www.reddit.com/r/DigitalMarketing/comments/1vaqhqg/is_ai_replacing_traditional_digital_marketing/","https://www.youtube.com/watch?v=avPfpHkp_YI","https://www.linkedin.com/posts/prestondmiller_seo-is-becoming-a-citation-game-heres-activity-7487970380876316673-VWkV","https://x.com/bloggersarvesh/status/2080643945017868708","https://www.reddit.com/r/b2bmarketing/comments/1vgzfr9/b2b_seo_newbie_struggling_to_figure_out_where_to/","https://www.youtube.com/watch?v=nCn5ZvpVcGg","https://www.instagram.com/p/DbkxdK_CII7/","https://x.com/alexgroberman/status/2086116710189527460/photo/3","https://www.reddit.com/r/seogrowth/comments/1vinsvl/anyone_feel_like_new_domains_are_super_hard_to/","https://www.youtube.com/watch?v=n-4haLYhZnQ","https://www.instagram.com/reel/Db1diQkgjvP/","https://x.com/alexgroberman/status/2085750923327107196","https://www.reddit.com/r/localseo/comments/1vistu2/after_4_months_of_seo_for_a_client_the_results/","https://www.youtube.com/watch?v=jW8hYUn99Uk","https://www.facebook.com/SearchEngineJournal/posts/i-read-perplexitys-raw-answer-stream-to-see-how-it-picks-sources-which-domains-g/1523923313111447/","https://x.com/alexgroberman/status/2085750923327107196/photo/2","https://www.reddit.com/r/IndieHackLab/comments/1vdf1wg/how_to_generate_seo_pages/","https://www.youtube.com/watch?v=jke34lA9HpY"],"claim_count":4,"faq_count":3,"section_count":4,"has_evidence":true},{"id":"how-to-run-llama-3-locally-ollama-mac-windows-en","url":"/blog/how-to-run-llama-3-locally-ollama-mac-windows-en/","absolute_url":"https://erpagi.com/blog/how-to-run-llama-3-locally-ollama-mac-windows-en/","title":"How to Run Llama 3 Locally with Ollama on Mac and Windows","summary":"Running Llama 3 locally with Ollama offers a powerful solution for developers seeking privacy and control over their AI infrastructure. The process involves pulling the model via `ollama pull llama3`, where users can choose between 8B and 70B parameter sizes. For most consumer hardware, the Q4_K_M quantization is recommended as it requires approximately ~5GB of VRAM or RAM for inference, striking a balance between performance and resource usage. macOS users benefit from native GPU acceleration via the Metal API, ensuring efficient deployment on Apple Silicon devices. In contrast, Windows users must utilize WSL2 with CUDA 12.x or rely on DirectML fallback, as native Windows CUDA support is not yet available. It is critical to avoid quantization levels below Q4_K_M, as this can cause over >15% perplexity degradation in benchmark tests, severely impacting model quality. Furthermore, users must implement strict prompt constraints to prevent safety violations and account for factual drift past the training cutoff date.","lang":"en","content_type":"ts","topic_key":"llama3_local_ollama_mac_win","keywords":["llama3_local_ollama_mac_win","Introduction to Local Deployment with Ollama & Llama 3 Overview","macOS Deployment Strategy & Apple Silicon Acceleration","Windows Configuration via WSL2 and CUDA Requirements","Hardware Specifications & Quantization Impact Analysis","Safety Constraints, Limitations & Prompt Engineering"],"published_at":"2026-07-13T22:53:54.769Z","updated_at":"2026-07-13T23:30:54.769Z","source_urls":["https://ollama.com/library/llama3","https://github.com/ollama/ollama","https://github.com/ollama/ollama/blob/main/docs/windows.md","https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct","https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/"],"claim_count":5,"faq_count":2,"section_count":5,"has_evidence":true},{"id":"2026-07-14-cursor-ai-1-ko","url":"/blog/2026-07-14-cursor-ai-1-ko/","absolute_url":"https://erpagi.com/blog/2026-07-14-cursor-ai-1-ko/","title":"Cursor AI 팀 요금제: 1인 개발자가 알아야 할 무료 티어 사용량 제한","summary":"Cursor AI는 무료 및 유료 요금제를 제공하며, 무료 티어는 제한된 Agent 요청과 Tab 완성을 포함합니다. 1인 개발자는 자신의 사용 패턴에 따라 요금제를 선택해야 하며, 유료 요금제는 더 높은 사용 한도와 추가 기능을 제공합니다.","lang":"ko","content_type":"pseo","topic_key":"cursor-ai-1","keywords":["cursor-ai-1","Cursor AI","무료 티어 사용량 제한","1인 개발자","Cursor AI의 요금제 및 가격","무료 티어의 제한 사항","유료 요금제의 혜택","결제 및 보안"],"published_at":"2026-07-13T21:32:01.265500Z","updated_at":"2026-07-13T21:32:01.265500Z","source_urls":["https://cursor.com/pricing"],"claim_count":5,"faq_count":4,"section_count":4,"has_evidence":true},{"id":"2026-07-13-github-copilot-vs-en","url":"/blog/2026-07-13-github-copilot-vs-en/","absolute_url":"https://erpagi.com/blog/2026-07-13-github-copilot-vs-en/","title":"GitHub Copilot Pricing for Agencies and External Teams: Annual vs. Monthly Plans","summary":"This page provides a detailed breakdown of GitHub Copilot's pricing plans, focusing on the differences between annual and monthly billing cycles, and highlights the best value options for agencies and external teams managing multiple developers.","lang":"en","content_type":"pseo","topic_key":"github-copilot-vs","keywords":["github-copilot-vs","GitHub Copilot","연간 결제 vs 월간 결제 할인율","에이전시/외주팀","Pricing Plan Comparison","Key Features for Agencies and External Teams","Annual vs. Monthly Billing Considerations","Original Analysis for Agencies and External Teams"],"published_at":"2026-07-13T12:30:45.996126Z","updated_at":"2026-07-13T12:30:45.996126Z","source_urls":["https://github.com/features/copilot/plans"],"claim_count":5,"faq_count":0,"section_count":4,"has_evidence":true},{"id":"claude-api-vs-openai-api-pricing-rate-limits-2025-en","url":"/blog/claude-api-vs-openai-api-pricing-rate-limits-2025-en/","absolute_url":"https://erpagi.com/blog/claude-api-vs-openai-api-pricing-rate-limits-2025-en/","title":"Strategic Comparison of Claude and OpenAI API Pricing & Rate Limits for 2025 Deployments","summary":"Platform selection requires aligning workload characteristics with distinct pricing architectures and throttling frameworks. Organizations prioritizing massive prompt ingestion benefit from substantially lower input costs, while teams needing predictable throughput leverage automated tier upgrades offering significant multipliers. Strict daily consumption boundaries demand meticulous monitoring to prevent service halts, requiring robust error handling protocols that gracefully manage throttling signals and mandatory cooldown periods.","lang":"en","content_type":"ts","topic_key":"claude_vs_openai_api_pricing_rate_limits_2025","keywords":["claude_vs_openai_api_pricing_rate_limits_2025","Core Pricing Architecture & Token Economics","Rate Limit Mechanics & Throughput Scaling","Daily Constraints & Error Handling Protocols","Comparative Analysis & Strategic Selection Matrix","Implementation Best Practices & Future-Proofing"],"published_at":"2026-07-13T02:57:56.884Z","updated_at":"2026-07-13T03:34:56.884Z","source_urls":["https://openai.com/api/pricing/","https://platform.openai.com/docs/guides/rate-limits","https://www.anthropic.com/pricing","https://docs.anthropic.com/en/api/rate-limits","https://docs.anthropic.com/en/docs/about-claude/limits"],"claim_count":4,"faq_count":3,"section_count":5,"has_evidence":true},{"id":"midjourney-vs-dall-e-3-vs-stable-diffusion-2025-en","url":"/blog/midjourney-vs-dall-e-3-vs-stable-diffusion-2025-en/","absolute_url":"https://erpagi.com/blog/midjourney-vs-dall-e-3-vs-stable-diffusion-2025-en/","title":"Midjourney vs DALL-E 3 vs Stable Diffusion: A 2025 Technical Comparison","summary":"A technical breakdown revealing that Midjourney leads in upscaling capabilities beyond standard pixel limits, DALL-E 3 prioritizes safety compliance at the cost of strict API throttling, and Stable Diffusion's latest transformer models demand substantial hardware investment for local deployment.","lang":"en","content_type":"ts","topic_key":"midjourney-vs-dall-e-3-vs-stable-diffusion-2025","keywords":["midjourney-vs-dall-e-3-vs-stable-diffusion-2025","Architectural Evolution and Resolution Boundaries","Hardware Constraints and API Rate Limitations","Safety Compliance and Content Moderation Frameworks"],"published_at":"2026-07-10T23:16:42.561Z","updated_at":"2026-07-10T23:30:42.561Z","source_urls":["https://docs.midjourney.com/docs/prompting","https://platform.openai.com/docs/guides/dall-e","https://github.com/Stability-AI/generative-models","https://stability.ai/stable-diffusion","https://platform.openai.com/docs/guides/safety-policies"],"claim_count":5,"faq_count":2,"section_count":3,"has_evidence":true},{"id":"2026-07-01-chatgpt-vs-claude-vs-gemini-best-ai-assistant-2025-en","url":"/blog/2026-07-01-chatgpt-vs-claude-vs-gemini-best-ai-assistant-2025-en/","absolute_url":"https://erpagi.com/blog/2026-07-01-chatgpt-vs-claude-vs-gemini-best-ai-assistant-2025-en/","title":"ChatGPT vs Claude vs Gemini: The Best AI Assistant of 2025 Compared","summary":"The AI landscape has evolved, with ChatGPT, Claude, and Gemini emerging as top contenders for the best AI assistant in 2025. This comparison examines their strengths, weaknesses, and ideal use cases based on community insights and real-world performance metrics.","lang":"en","content_type":"comparison","topic_key":"chatgpt-vs-claude-vs-gemini-best-ai-assistant-2025","keywords":["chatgpt-vs-claude-vs-gemini-best-ai-assistant-2025","Feature and Performance Comparison","Why Claude Leads in 2025","When to Choose ChatGPT or Gemini","Use Case Recommendations"],"published_at":"2026-07-01T04:30:05.589837Z","updated_at":"2026-07-01T04:30:05.589837Z","source_urls":["https://www.reddit.com/r/GeminiAI/comments/1tzv2h8/according_to_the_leaks/","https://www.reddit.com/r/charts/comments/1u82bl0/chatgpt_drops_below_50_market_share_of_ai/","https://www.reddit.com/r/Chatbots/comments/1tykqie/chatgpt_vs_claude_vs_gemini_what_to_use_each_for/","https://www.reddit.com/r/technology/comments/1u79t34/chatgpts_market_share_slips_below_50_for_first/","https://www.reddit.com/r/AiNoteTaker/comments/1udle1o/how_to_use_premium_ai_plans_chatgpt_plus_gemini/","https://www.reddit.com/r/consulting/comments/1u6cp4e/is_claude_the_go_to_ai_tool_to_use_in_consulting/","https://www.reddit.com/r/GeminiAI/comments/1u6sxx5/isnt_it_absurd_that_googles_own_ai_gemini_refuses/","https://www.reddit.com/r/ChatGPTcomplaints/comments/1tz70dj/those_of_you_who_used_the_gpt4_series_models_and/","https://www.reddit.com/r/AIDiscussion/comments/1u2xyuz/whats_one_ai_tool_you_use_regularly_thats_lowkey/","https://www.reddit.com/r/GeminiAI/comments/1u4pbiz/google_is_way_too_quiet_right_now_and_gemini_35/","https://x.com/hashsyedai","https://x.com/godofprompt","https://x.com/bearlyai","https://x.com/MarcosDPiaz","https://x.com/kocer_eth/status/2068714810611528103"],"claim_count":5,"faq_count":5,"section_count":4,"has_evidence":true},{"id":"openai-chatgpt-o1-reasoning-models-en","url":"/blog/openai-chatgpt-o1-reasoning-models-en/","absolute_url":"https://erpagi.com/blog/openai-chatgpt-o1-reasoning-models-en/","title":"OpenAI ChatGPT o1 Reasoning Models: Performance, Constraints & Strategic Deployment","summary":"The o1 reasoning models deliver unprecedented STEM accuracy through extended reinforcement learning deliberation but introduce substantial latency, elevated costs, and distinct failure patterns that restrict deployment to asynchronous analytical workflows rather than interactive consumer applications.","lang":"en","content_type":"ts","topic_key":"openai-chatgpt-o1-reasoning-models","keywords":["openai-chatgpt-o1-reasoning-models","Core Architecture & STEM Benchmark Performance","Latency Profiles & Operational Deployment Constraints","Failure Modes & Strategic Limitations"],"published_at":"2026-06-30T10:25:20.068Z","updated_at":"2026-06-30T10:52:20.068Z","source_urls":["https://openai.com/index/introducing-o1/","https://platform.openai.com/docs/guides/reasoning","https://github.com/openai/openai-python","https://huggingface.co/blog/openai-o1","https://www.anthropic.com/engineering/claude-on-openai-o1"],"claim_count":5,"faq_count":2,"section_count":3,"has_evidence":true},{"id":"cursor-ai-vscode-alternatives-development-en","url":"/blog/cursor-ai-vscode-alternatives-development-en/","absolute_url":"https://erpagi.com/blog/cursor-ai-vscode-alternatives-development-en/","title":"Cursor AI vs VSCode Alternatives: Performance, Privacy, and Enterprise Compliance Analysis","summary":"A comprehensive technical evaluation of Cursor AI alternatives reveals distinct trade-offs between performance optimization, data sovereignty, and enterprise compliance. Zed Editor delivers superior UI latency and memory efficiency through Rust architecture, while Continue.dev enables fully on-premises AI inference to eliminate cloud data exposure. GitHub Copilot offers expansive context windows but exhibits documented syntax error rates up to 30% in complex tasks. Amazon Q Developer integrates native AWS security guidelines with SOC 2 and ISO 27001 certifications for regulated environments. Strategic adoption requires aligning tool capabilities with specific privacy mandates, latency tolerances, and validation workflows.","lang":"en","content_type":"ts","topic_key":"cursor-ai-vscode-alternatives","keywords":["cursor-ai-vscode-alternatives","Performance Architecture & Resource Optimization","AI Integration & Data Privacy Paradigms","Enterprise Compliance & Security Frameworks","Strategic Adoption Guidelines & Limitations"],"published_at":"2026-06-30T09:55:25.573Z","updated_at":"2026-06-30T10:39:25.573Z","source_urls":["https://zed.dev/docs","https://github.com/continuedev/continue","https://docs.github.com/en/copilot/getting-started-with-github-copilot","https://docs.aws.amazon.com/qdeveloper/latest/userguide/what-is-q-developer.html","https://arxiv.org/abs/2305.15829"],"claim_count":5,"faq_count":4,"section_count":4,"has_evidence":true},{"id":"anthropic-claude-3-5-sonnet-artifacts-en","url":"/blog/anthropic-claude-3-5-sonnet-artifacts-en/","absolute_url":"https://erpagi.com/blog/anthropic-claude-3-5-sonnet-artifacts-en/","title":"Claude 3.5 Sonnet Artifacts: Architecture, Latency, and Engineering Trade-offs","summary":"A cross-referenced technical brief on Anthropic's Artifacts capability in Claude 3.5 Sonnet, covering sandbox architecture, streaming protocol, benchmark pass-rates, latency percentiles, bundle-size failure thresholds, and prompt-engineering uplift metrics drawn from Anthropic documentation, AWS Bedrock engineering posts, arXiv studies, and GitHub community reports.","lang":"en","content_type":"ts","topic_key":"claude-35-sonnet-artifacts","keywords":["claude-35-sonnet-artifacts","Sandbox Architecture and Supported MIME Surface","Streaming Protocol, Latency Profile, and Bedrock Integration","Benchmark Pass-Rates, Failure Modes, and Prompt-Engineering Uplift","Bundle-Size Failure Threshold and Hard Limits in Practice"],"published_at":"2026-06-30T01:24:12.735Z","updated_at":"2026-06-30T01:52:12.735Z","source_urls":["https://www.anthropic.com/news/claude-3-5-sonnet","https://docs.anthropic.com/en/docs/build-with-claude/artifacts","https://www-cdn.anthropic.com/1adf000c8f675958c92f08a58c4e89a8c6a4c0a8.pdf","https://github.com/anthropics/claude-code/blob/main/CHANGELOG.md","https://aws.amazon.com/blogs/machine-learning/build-generative-ai-applications-with-claude-3-5-sonnet-on-amazon-bedrock/","https://arxiv.org/abs/2407.11016","https://github.com/anthropics/claude-artifacts-runner","https://www.anthropic.com/engineering/building-effective-agents-with-skills","https://www.reddit.com/r/ClaudeAI/comments/1dxyz20/artifacts_frequently_crashing_on_large_react_apps/","https://arxiv.org/abs/2410.01942"],"claim_count":8,"faq_count":5,"section_count":4,"has_evidence":true},{"id":"apple-intelligence-wwdc-2026-en","url":"/blog/apple-intelligence-wwdc-2026-en/","absolute_url":"https://erpagi.com/blog/apple-intelligence-wwdc-2026-en/","title":"Apple Intelligence at WWDC 2026: Architecture, Privacy Boundaries, and Hardware Gating","summary":"Apple's WWDC 2026 keynote (June 9) expanded Apple Intelligence across iOS 27, iPadOS 27, and macOS 27 'Golden Gate', built on a bifurcated Apple Foundation Model (AFM) stack of an approximately 3B on-device model and a 128k-context Private Cloud Compute server model, with Siri AI, Image Playground 2.0, and a Foundation Models SDK as developer-facing surfaces. Official documentation and Apple's ML research attest the privacy posture through iBoot/OS cryptographic attestation, while independent reporting flags a roughly 1-in-10,000 ChatGPT fallback path that is not end-to-end auditable, leaving an unresolved trust gap that practitioners should weigh against the system's hardware-gated rollout to A17 Pro and newer devices.","lang":"en","content_type":"ts","topic_key":"apple-intelligence-wwdc-2026","keywords":["apple-intelligence-wwdc-2026","Bifurcated Foundation Model Stack: On-Device AFM versus Private Cloud Compute","Private Cloud Compute Attestation Chain and the 1-in-10,000 ChatGPT Fallback Gap","Developer Surfaces: Foundation Models SDK, Siri AI Personal Context, and Image Playground 2.0","Hardware Gating, Context-Length Trade-Offs, and Where the Trust Boundary Actually Moves"],"published_at":null,"updated_at":null,"source_urls":["https://www.apple.com/newsroom/","https://developer.apple.com/wwdc26/","https://machinelearning.apple.com/research/","https://security.apple.com/blog/private-cloud-compute/","https://arxiv.org/abs/2407.21075","https://techcrunch.com/2026/06/09/apple-wwdc-2026-everything-announced/","https://www.theverge.com/2026/6/9/wwdc-2026-news","https://www.wired.com/story/apple-intelligence-wwdc-2026-privacy-questions/"],"claim_count":7,"faq_count":4,"section_count":4,"has_evidence":true},{"id":"apple-intelligence-wwdc-2026-ko","url":"/blog/apple-intelligence-wwdc-2026-ko/","absolute_url":"https://erpagi.com/blog/apple-intelligence-wwdc-2026-ko/","title":"Apple Intelligence WWDC 2026: 이중 AFM 아키텍처와 검증되지 않은 ChatGPT 폴백의 경계","summary":"WWDC 2026의 Apple Intelligence는 온디바이스 ~3B 파라미터 AFM과 Private Cloud Compute(PCC) 서버 모델의 이중 구조로 출시되었으며, PCC는 1.14.0 iBoot/OS 이미지를 암호학적 증명하지만, 약 1/10,000 비율의 ChatGPT 폴백 경로는 종단 감사 가능성이 부족합니다.","lang":"ko","content_type":"ts","topic_key":"apple-intelligence-wwdc-2026","keywords":["apple-intelligence-wwdc-2026","WWDC 2026 키노트 구조와 Apple Intelligence 발표 범위","이중 AFM 아키텍처: ~3B 온디바이스 모델과 PCC 서버 모델의 트레이드오프","Foundation Models 프레임워크 실전 호출 패턴과 디바이스 게이팅","ChatGPT 폴백의 비대칭 프라이버시 경계: PCC 증명과 감사 불가능 구간","디바이스 단편화와 3B AFM의 현실적 한계"],"published_at":null,"updated_at":null,"source_urls":["https://www.apple.com/newsroom/","https://developer.apple.com/wwdc26/","https://machinelearning.apple.com/research/","https://security.apple.com/blog/private-cloud-compute/","https://arxiv.org/abs/2407.21075","https://techcrunch.com/2026/06/09/apple-wwdc-2026-everything-announced/","https://www.theverge.com/2026/6/9/wwdc-2026-news","https://www.wired.com/story/apple-intelligence-wwdc-2026-privacy-questions/"],"claim_count":7,"faq_count":5,"section_count":5,"has_evidence":true},{"id":"astro-cloudflare-tutorial","url":"/blog/astro-cloudflare-tutorial/","absolute_url":"https://erpagi.com/blog/astro-cloudflare-tutorial/","title":"Astro on Cloudflare: The Ultimate Deployment Guide","summary":"This guide covers everything you need to know about deploying Astro static sites to Cloudflare Pages.","lang":"en","content_type":"ts","topic_key":"Astro Cloudflare Tutorial","keywords":["Astro Cloudflare Tutorial","Introduction to Astro","Deploying to Cloudflare"],"published_at":null,"updated_at":null,"source_urls":["https://docs.astro.build/en/guides/deploy/cloudflare/"],"claim_count":2,"faq_count":1,"section_count":2,"has_evidence":true},{"id":"astro-cloudflare-pages","url":"/blog/astro-cloudflare-pages/","absolute_url":"https://erpagi.com/blog/astro-cloudflare-pages/","title":"Astro와 Cloudflare Pages 결합의 아키텍처 가치와 엣지 플랫폼의 현실적 제약","summary":"Astro의 Islands 아키텍처는 최소 JS 페이로드로 엣지 SSR 비용을 절감하지만, Cloudflare Images 바인딩의 유료 전환 임계치와 빌드 동시성 1개 제한이 실무 운영에서 명확한 트레이드오프로 작용한다.","lang":"en","content_type":"ts","topic_key":"astro-cloudflare-pages","keywords":["astro-cloudflare-pages","6.7KB JS 페이로드가 만드는 엣지 과금 모델의 구조적 이점","어댑터 설치와 Cloudflare Pages 런타임 모드 분기","Cloudflare Images 바인딩 비용 임계치와 무료 티어 회귀의 트레이드오프","2024년 인시던트가 드러낸 엣지 빌드 플랫폼의 가용성 리스크","실전 운영을 위한 CLI 워크플로우와 핵심 설정 코드","도입 시 반드시 검토해야 할 비용·성능·신뢰성 트레이드오프"],"published_at":null,"updated_at":null,"source_urls":["https://docs.astro.build/en/guides/integrations-guide/cloudflare/","https://github.com/withastro/adapters/tree/main/packages/cloudflare","https://developers.cloudflare.com/pages/get-started/","https://arxiv.org/abs/2308.00045","https://astro.build/blog/astro-5/","https://www.cloudflarestatus.com/","https://github.com/withastro/adapters/issues/98"],"claim_count":6,"faq_count":4,"section_count":6,"has_evidence":true}],"facets":{"languages":["en","ko"],"content_types":["comparison","pseo","ts"]}}