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<div style="display: none; max-height: 0px; overflow: hidden;">Meta has unveiled agreements that will make it an anchor customer for nuclear power. Financial details of the deal have not been disclosed. </div>
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<h1><strong>TLDR AI <span id="date">2026-01-12</span></strong></h1>
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<strong>Stop bots from abusing free trials (Sponsor)</strong>
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Free trials help AI apps grow, but <a class="in-cell-link" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fworkos.com%2Fblog%2Fwhat-is-free-trial-abuse%3Futm_source=tldrai%26utm_medium=newsletter%26utm_campaign=q12026/1/0100019bb2b095db-dee0444d-9c9d-449b-90ed-a28eae8e6ffb-000000/f0QWhJmAyouZn04yo9IOk7fbLsit9vIXlbxjsX3Kl1c=439" rel="noopener noreferrer nofollow" target="_blank"><span>bots and fake accounts exploit them</span></a>. They steal tokens, burn compute, and disrupt real users.
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<br>Cursor, the fast-growing AI code assistant, uses <a class="in-cell-link" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fworkos.com%2Fradar%3Futm_source=tldrai%26utm_medium=newsletter%26utm_campaign=q12026/3/0100019bb2b095db-dee0444d-9c9d-449b-90ed-a28eae8e6ffb-000000/QqeyAjlTBmFK7AvQ9TDE0oOZLKP-OKjpTmeiShi_ZxQ=439" rel="noopener noreferrer nofollow" target="_blank"><span>WorkOS Radar</span></a> to detect and stop abuse in real time. With device fingerprinting and behavioral signals, Radar blocks fraud before it reaches your app.
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<strong>Anthropic cracks down on unauthorized Claude usage by third-party harnesses and rivals (9 minute read)</strong>
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Anthropic has implemented strict new technical safeguards to prevent third-party applications from spoofing Claude Code to access more favorable pricing and limits. Ensuring model integrity now requires routing all automated agents through the official Commercial API or the Claude Code client. The company has also restricted the usage of its AI models by rival labs. Security directors should audit internal tool chains to ensure they aren't violating commercial terms and that all automated workflows are authenticated via proper enterprise keys.
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<strong>Meta Unveils Sweeping Nuclear-Power Plan to Fuel Its AI Ambitions (6 minute read)</strong>
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Meta has unveiled a series of agreements that will make it an anchor customer for new and existing nuclear power in the US. The company will back new reactor projects with TerraPower and Oklo and has struck a deal with the power producer Vistra to purchase and expand the generation output of three existing nuclear plants. The first new reactors are expected to be delivered as early as 2030 and 2032. Financial details of the deal have not been disclosed.
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<strong>Best Practices for Coding with Agents (18 minute read)</strong>
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Tips for maximizing productivity with coding agents, including how to structure problems, guide multi-file changes, and ensure agents iterate effectively on code until tests pass.
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<strong>Global AI computing capacity is doubling every 7 months (1 minute read)</strong>
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Total available capacity from AI chips across all major designers has grown approximately 3.3 times per year since 2022. This has enabled larger-scale model development and consumer adoption. Nvidia AI chips currently account for over 60% of total compute. Google and Amazon make up much of the remainder.
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<strong>Use multiple models (7 minute read)</strong>
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Problems with AI models can often be solved by passing the same query to a peer model. This suggests that models are actually quite close to being able to solve many tasks, but they're just not there yet. For switching to regularly solve tasks, each model must have a fairly high probability of success. The next generation of models will be even more capable.
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<strong>Agents that don't suck (Sponsor)</strong>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FgDoRDp/1/0100019bb2b095db-dee0444d-9c9d-449b-90ed-a28eae8e6ffb-000000/IKgTI0ltkde7eBJFYMqd1IW0qG30YAViT2l6tHk82zY=439" rel="noopener noreferrer nofollow" target="_blank"><span>Agent Bricks</span></a> helps you build, evaluate and optimize AI agents grounded in your unique data. It evaluates automatically, scores outputs against your goals and improves with human feedback — giving you a clearer path to production. Build agents that work in the real world.
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<p><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FgDoRDp/2/0100019bb2b095db-dee0444d-9c9d-449b-90ed-a28eae8e6ffb-000000/3E4bC3TOKWMK8LxzyjZB6E-9OPab4G27wyGUEB_0MYg=439" rel="noopener noreferrer nofollow" target="_blank"><span>See why it's worth your time</span></a>
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<strong>Under the Hood: Universal Commerce Protocol (UCP) (19 minute read)</strong>
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The Universal Commerce Protocol (UCP) is an open-source standard that enables seamless commerce journeys between customer surfaces, businesses, and payment providers. It works with existing retail infrastructure and is compatible with the Agents Payments Protocol. The UCP provides businesses with flexible ways to integrate via APIs, Agent2Agent, and the Model Context Protocol. It was developed by Google in collaboration with industry leaders.
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<strong>Agent design patterns (12 minute read)</strong>
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We are getting closer to long-running autonomous agents. However, models get worse as context grows. Effective agent design largely boils down to context management. This post explores common design patterns across agents.
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<strong>Evaluating AI Agents in Production (28 minute read)</strong>
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Anthropic's practical approaches to agent evaluation emphasize pre-deployment tests that simulate real-world conditions and reduce failures in complex, multi-turn agent systems.
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<strong>Deep Think with Confidence (23 minute read)</strong>
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Meta AI researchers developed DeepThink with Confidence (DeepConf), a technique that uses internal confidence signals to cut LLM reasoning overhead by up to 84.7% while maintaining accuracy. The method monitors token-level confidence to terminate low-quality reasoning traces early, particularly when models generate uncertainty markers like "wait" or "think again."
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<strong>Agent-Native Architectures and the Shift Beyond Code (3 minute read)</strong>
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Agent-native architectures replace step-by-step code with agents that decide how to achieve outcomes, while developers define only the desired results through prompts. This model makes software faster to build and adapt, since behavior changes come from modifying language instead of rewriting logic. The shift trades predictability for flexibility, pushing software design toward continuous pruning and observation rather than rigid control.
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<strong>In AI Agents, Traces Are the Source of Truth (4 minute read)</strong>
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In AI agents, the code only orchestrates models and tools, while real decision-making happens inside the model at runtime and cannot be understood by reading the code alone. Traces capture the actual reasoning steps, tool calls, errors, costs, and outcomes, making them the primary artifact for debugging, testing, optimization, and monitoring.
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<strong>Anthropic cuts off xAI's access to its models for coding (1 minute read)</strong>
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Anthropic has cut access to xAI developers who were using its Claude models for coding via the Cursor AI coding tool.
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Indonesia and Malaysia have temporarily blocked access to xAI's chatbot Grok after it generated non-consensual deepfakes, including content depicting minors and violence.
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Experts predict significant economic and technological impacts.
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<strong>The Coming AI Compute Crunch (9 minute read)</strong>
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Demand for AI compute is outstripping supply due to soaring token consumption and a surge in LLM users.
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<strong>Strategy in four worlds (10 minute read)</strong>
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Deep Future is an AI stress-testing tool for strategies that simulates thousands of futures using a scenario generation engine.
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