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<div style="display: none; max-height: 0px; overflow: hidden;">OpenAI launched Agent Builder, which is a visual drag-and-drop canvas called the Canva for building agents and ChatKit for embedding chat interfaces </div>
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<h1><strong>TLDR AI <span id="date">2025-10-07</span></strong></h1>
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<strong>Introducing AgentKit (2 minute read)</strong>
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OpenAI launched Agent Builder, which is a visual drag-and-drop canvas called the "Canva for building agents", ChatKit for embedding chat interfaces, evaluation tools including trace grading and automated prompt optimization, and a connector registry for linking internal tools.
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<strong>AMD stock skyrockets 23% as OpenAI looks to take stake in AI chipmaker (3 minute read)</strong>
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OpenAI will deploy 6 gigawatts of AMD Instinct GPUs over multiple years, starting with 1 gigawatt in late 2026. If the deal is fully exercised, OpenAI would acquire 10% of AMD. Two weeks ago, OpenAI agreed to an agreement with Nvidia for $100 billion, with OpenAI's data center spending commitments now totalling over roughly $1 trillion over the coming years.
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<strong>OpenAI launches apps inside of ChatGPT (2 minute read)</strong>
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Users can interact with a set of limited apps directly within ChatGPT using MCP connectors. The company demoed searching hotels with Booking.com, researching houses with Zillow, and designing presentations with Canva. Some believe this is a step towards monetizing OpenAI's massive free user base, potentially by earning a finder's fee by referring users to certain products and platforms.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.bloomberg.com%2Fopinion%2Fnewsletters%2F2025-10-06%2Fopenai-is-good-at-deals%3FaccessToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzb3VyY2UiOiJTdWJzY3JpYmVyR2lmdGVkQXJ0aWNsZSIsImlhdCI6MTc1OTgxMzk5NywiZXhwIjoxNzYwNDE4Nzk3LCJhcnRpY2xlSWQiOiJUM1EwSFpHUEw0R0EwMCIsImJjb25uZWN0SWQiOiI2NTc1NjkyN0UwMkM0N0MwQkQ0MDNEQTJGMEUyNzIyMyJ9.gXyol5FFBAd1hXETehRW5k9CCDqlE3gSUuuidIyPcXQ%26utm_source=tldrai/1/01000199bece5d58-a0218d7a-3304-409e-afd8-afbf01fd73a2-000000/q0xtr0XUWBrO0GdamPKZMIElrfJXZIQsUjr2RJyRog8=426">
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<strong>OpenAI Is Good at Deals (24 minute read)</strong>
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Whenever OpenAI announces a big partnership with a public company, that company's stock will go up. OpenAI is using that stock value to subsidize deals. Its deal with AMD involves OpenAI receiving warrants for up to 160 million AMD shares at 1 cent per share, awarded in phases if OpenAI hits certain milestones for deployment. AMD's stock price also has to increase for the warrants to be exercised. While OpenAI is not publicly traded, it is increasingly making waves in the stock market.
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<strong>Why do LLMs freak out over the seahorse emoji? (14 minute read)</strong>
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Large language models are very confident that there is a seahorse emoji. The emoji has never existed, despite some Redditors claiming it did. This post takes a look at why models might do this. Models just don't know that their belief about a seahorse emoji existing is wrong - they can only assume that 'seahorse + emoji' will produce the tokens it wants.
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<strong>Sakana AI (7 minute read)</strong>
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ShinkaEvolve, an open-source framework from Sakana AI, uses LLMs to achieve state-of-the-art sample efficiency in evolving programs. It significantly improves solutions in areas like circle packing, math competition problem-solving, competitive programming, and LLM training design. The framework's innovative methodologies include exploration-exploitation balancing, novelty-based program rejection, and dynamic LLM prioritization.
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<strong>Google's CodeMender (4 minute read)</strong>
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Google's CodeMender is an AI agent that automatically detects and patches code vulnerabilities using Gemini models. Alongside it, it launched the AI Vulnerability Reward Program and SAIF 2.0 to define secure-by-design principles for autonomous agents and boost AI-driven cybersecurity.
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<strong>Why Can't Transformers Learn Multiplication? Reverse-Engineering Reveals Long-Range Dependency Pitfalls (28 minute read)</strong>
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Researchers reverse-engineered why language models fail at simple 4×4 multiplication and found successful models naturally built tree-like attention patterns that "cache" intermediate calculations then retrieve them later, like jotting down a thought on scratch paper. Standard training fails because models learn the easy outer digits first, but get stuck on the middle digits. By adding a simple auxiliary loss that forces the model to predict running totals at each step, models dramatically improve.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>GPT-5 Pro found a counterexample to the NICD-with-erasures majority optimality (1 minute read)</strong>
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A group of scientists released a collection of open problems in computer science in 2014. One of these was the Non-Interactive Correlation Distillation with Erasures. GPT-5 Pro found a counterexample to the problem. A screenshot of its answer is available.
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<strong>Cerebras CEO explains IPO withdrawal, says AI chipmaker still intends to go public (2 minute read)</strong>
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Cerebras withdrew its IPO filing because it wants to come out with updated financial and strategy information. Some of the company's new investors weren't mentioned in last year's filing. The company still intends to go public. It will refile again with updated financials and strategy information, including its approach to the rapidly changing AI landscape.
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<strong>Replit launches Connectors for seamless tool integration (2 minute read)</strong>
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<strong>OpenAI offered $500 million to buy Medal, a site where gamers upload gameplay videos (1 minute read)</strong>
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The deal fell through, so Medal is now creating an AI lab called General Intuition to build AI models using its gaming videos.
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