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<div style="display: none; max-height: 0px; overflow: hidden;">Anthropic Interviewer is a tool that uses AI to conduct and analyze large-scale interviews to research AI's role in work across different professions </div>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.algolia.com%2Fresources%2Fasset%2Fwhat-to-know-when-implementing-rag-with-your-search-solution%3Futm_campaign=tldr_global_b2x_ecomm_ecomm_tof_reach%26utm_medium=display%26utm_source=tldr%26utm_content=tldr_global_b2x_ecomm%26utm_term=ebo-implementing-rag-with-your-search-solution%26utm_camp_parent=b2x_ecomm%26utm_2nd_camp=ecomm_tof%26utm_region=global%26utm_goal=reach%26utm_creative_format=prmrynwsl%26utm_model=cpm%26utm_marketing_tactic=reach/1/0100019aeeddf872-87fe5b74-6530-4384-b3b2-3534444c7684-000000/_PIePrtuTyd0xHelnbvxcvANmNkPT1w3QHSsTr0lIj4=434"><img src="https://images.tldr.tech/algolia50.png" valign="middle" style="vertical-align: middle !important; height: 100%;" alt="Algolia"></a></td></tr></tbody></table>
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<h1><strong>TLDR AI <span id="date">2025-12-05</span></strong></h1>
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<strong>The Actually-Useful Guide to RAG: From Chunking Strategies to Production-Ready Pipelines (Sponsor)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
RAG sounds simple in theory: retrieve relevant documents, feed them to an LLM, get grounded answers. In practice, there are <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.algolia.com%2Fresources%2Fasset%2Fwhat-to-know-when-implementing-rag-with-your-search-solution%3Futm_campaign=tldr_global_b2x_ecomm_ecomm_tof_reach%26utm_medium=display%26utm_source=tldr%26utm_content=tldr_global_b2x_ecomm%26utm_term=ebo-implementing-rag-with-your-search-solution%26utm_camp_parent=b2x_ecomm%26utm_2nd_camp=ecomm_tof%26utm_region=global%26utm_goal=reach%26utm_creative_format=prmrynwsl%26utm_model=cpm%26utm_marketing_tactic=reach/3/0100019aeeddf872-87fe5b74-6530-4384-b3b2-3534444c7684-000000/JsM3UQEvw0QziOHZk8ntSt1_ZYsBaiRiUqtTpW6wMkY=434" rel="noopener noreferrer nofollow" target="_blank"><span>dozens of decisions</span></a> that determine whether your system actually works.
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<p><a class="underline" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.algolia.com%2Fresources%2Fasset%2Fwhat-to-know-when-implementing-rag-with-your-search-solution%3Futm_campaign=tldr_global_b2x_ecomm_ecomm_tof_reach%26utm_medium=display%26utm_source=tldr%26utm_content=tldr_global_b2x_ecomm%26utm_term=ebo-implementing-rag-with-your-search-solution%26utm_camp_parent=b2x_ecomm%26utm_2nd_camp=ecomm_tof%26utm_region=global%26utm_goal=reach%26utm_creative_format=prmrynwsl%26utm_model=cpm%26utm_marketing_tactic=reach/4/0100019aeeddf872-87fe5b74-6530-4384-b3b2-3534444c7684-000000/7-aUefxt1XhVfwprZAkKHAVAALCwMwVM66pMlGtCV8g=434" rel="noopener noreferrer nofollow" target="_blank"><span>Algolia's white paper</span></a> covers the full pipeline, including:</p>
<ul>
<li><strong>Chunking:</strong> Why 200-500 word blocks with 10-20% overlap tend to work best for most use cases</li>
<li><strong>Embeddings:</strong> Batch vs. streaming updates, and when to re-embed your entire corpus</li>
<li><strong>Vector stores:</strong> HNSW vs. IVF+PQ indexing, sharding strategies, and how to keep retrieval under 50ms</li>
<li><strong>Prompt assembly:</strong> Structuring system messages, context blocks, and token budgets for reliable outputs</li>
</ul>
<p>The guide includes working code examples using FAISS, LangChain, and Algolia's search client.</p>
<p><a class="underline" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.algolia.com%2Fresources%2Fasset%2Fwhat-to-know-when-implementing-rag-with-your-search-solution%3Futm_campaign=tldr_global_b2x_ecomm_ecomm_tof_reach%26utm_medium=display%26utm_source=tldr%26utm_content=tldr_global_b2x_ecomm%26utm_term=ebo-implementing-rag-with-your-search-solution%26utm_camp_parent=b2x_ecomm%26utm_2nd_camp=ecomm_tof%26utm_region=global%26utm_goal=reach%26utm_creative_format=prmrynwsl%26utm_model=cpm%26utm_marketing_tactic=reach/5/0100019aeeddf872-87fe5b74-6530-4384-b3b2-3534444c7684-000000/O9jLvU0iUYV5nwklVeyvieR7iw4noVFk4Y1sfV-Vs9k=434" rel="noopener noreferrer nofollow" target="_blank"><span>Download the free guide (no form fill required)</span></a>
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<strong>Anthropic Interviewer (29 minute read)</strong>
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Anthropic Interviewer is a tool that uses AI to conduct and analyze large-scale interviews to research AI's role in work across different professions. Initial findings from 1,250 professionals showed optimism toward AI enhancing productivity while highlighting concerns over job displacement and security in creative and scientific fields. Anthropic plans to use this data to improve AI models and influence policy, collaborating with artists, scientists, and educators to align AI development with user needs.
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<strong>Google partners with Replit, in vibe-coding push (3 minute read)</strong>
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Google Cloud has partnered with AI coding startup Replit to enhance enterprise vibe-coding using Google models. Replit will use Google Cloud services to expand its platform, supporting AI coding for enterprise clients. This collaboration aims to drive Google Cloud adoption and extend AI reach beyond traditional engineers.
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<strong>Gemini 3 Deep Think is now available in the Gemini app (2 minute read)</strong>
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Gemini 3 Deep Think uses parallel reasoning to explore multiple hypotheses simultaneously, building on the Gemini 2.5 Deep Think variants that won a gold medal on the International Mathematical Olympiad.
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<h1><strong>Deep Dives & Analysis</strong></h1>
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<strong>State of AI (90 minute read)</strong>
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This year was a turning point in the real-world use of large language models. The field shifted from single-pass pattern generation to multi-step deliberation inference. The shift up folded so fast that our understanding of how these models have been used in practice has lagged behind. This study leverages the OpenRouter platform to analyze over 100 trillion tokens of real-world AI interactions to see how the technology is being used in the real world. The way developers and end-users have been engaging with AI is complex and multifaceted. The study shows how a data-driven understanding of usage can inform better design and deployment.
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<strong>We Got Claude to Fine-Tune an Open Source LLM (15 minute read)</strong>
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Hugging Face Skills gives Claude the ability to fine-tune language models. It can submit jobs to cloud GPUs, monitor progress, and push finished models to the Hugging Face Hub. This tutorial teaches readers how it works and how to use it. The tool allows users to train models from 0.5B to 70B parameters, convert them to GGUF for local deployment, and run multi-stage pipelines that combine different techniques.
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OpenAI has outlined how to get optimal results from GPT-5.1-Codex-Max, highlighting its faster token efficiency, long-running autonomy, and improved compaction for extended reasoning.
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Experience a new AI-driven development that our developers report cut time on selected tasks by an average of 70%. Preview <a class="c-link c-link--underline" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.ibm.com%2Fproducts%2Fbob%3Futm_content=WACWW%26p1=Display%26p2=427889318%26p3=227599223%26utm_term=30A06/1/0100019aeeddf872-87fe5b74-6530-4384-b3b2-3534444c7684-000000/iw8EE8Bpb0wGNvFr52uEVCdn9bIZFtanjRUo0SJGXjo=434" rel="noopener noreferrer" target="_blank"><span><strong><em>IBM® Project Bob</em></strong></span></a>, the latest AI tech designed to accelerate coding, testing and modernization for enterprises and their mission-critical systems, in action at the Technology Summit.
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<strong>Building with Cursor (public) (Website)</strong>
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This is the public-facing version of an internal onboarding guide at Cursor. It walks through how to get started from scratch to a built-out and deployed project. It covers how to set up and use Cursor, how to build and customize projects, and how to deploy using Vercel.
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<strong>Context Engineering for AI Agents (7 minute read)</strong>
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The field of context engineering is moving fast. However, the biggest performance gains nowadays come from removing complexity. As models get stronger, we should be getting out of the model's way. Context engineering is about minding the minimal effective context required for the next step, not about adding more context.
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<strong>Architecting efficient context-aware multi-agent framework for production (17 minute read)</strong>
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The landscape of AI agent development is shifting fast. Organizations are now deploying sophisticated, autonomous agents to handle long-horizon tasks. However, this ambition is being bottlenecked by context. The context stack in Google Agent Development Kit was developed to support context engineering. The open-source, multi-agent-native framework is built to make active context engineering achievable in real systems.
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<strong>The Math Legend Who Just Left Academia—for an AI Startup Run by a 24-Year-Old (10 minute read)</strong>
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Ken Ono is one of the most prominent mathematicians in the world. He recently joined Axiom Math to revolutionize math with AI. The company was founded by one of his former students. He joined because he couldn't resist the opportunity to put his mark on something other than a chalkboard.
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<strong>Power Overwhelming (17 minute read)</strong>
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AI capex is driving US GDP growth, yet a $1.5 trillion AI revenue shortfall looms compared to invested capital. OpenAI's infrastructure spend and emerging AI application revenues like ChatGPT's $20B by 2025 reveal a disconnect between projected and necessary earnings to justify current investments. The uncertain AI cloud business model, characterized by rapid hardware obsolescence, suggests heavy reliance on the Magnificent Seven's internal workloads to prevent an impending AI bubble and excessive market overbuild.
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<h1><strong>Quick Links</strong></h1>
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<strong>NVIDIA and AWS Expand Full-Stack Partnership, Providing the Secure, High-Performance Compute Platform Vital for Future Innovation (7 minute read)</strong>
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NVIDIA and AWS expanded their partnership to integrate NVIDIA NVLink Fusion with AWS' Trainium4, Graviton CPUs, and Nitro System, enhancing cloud-scale AI capabilities.
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<strong>For AI at work, start with something small (4 minute read)</strong>
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AI tools excel at automating repetitive, time-consuming tasks in the "messy middle," such as summarizing notes or categorizing feedback.
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<strong>Anthropic Interviewer (Hugging Face Repo)</strong>
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Anthropic Interviewer is a tool for conducting AI-powered qualitative research interviews at scale.
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<strong>AI #145: You've Got Soul (102 minute read)</strong>
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The cycle of language model releases is now complete.
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<strong>The first research article in theoretical physics in which the main idea came from an AI (4 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
The paper derives new operator integrability conditions required for foliation independence.
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