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<div style="display: none; max-height: 0px; overflow: hidden;">OpenAI can't beat Google at consumer AI as long as it is still in the chatbot paradigm. Recent OpenAI launches haven't done well </div>
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<h1><strong>TLDR AI <span id="date">2025-11-21</span></strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.algolia.com%2Fresources%2Fasset%2Febook-genai%3Futm_campaign=tldr_global_b2x_ecomm_ecomm_tof_reach%26utm_medium=display%26utm_source=tldr%26utm_content=tldr_global_b2x_ecomm%26utm_term=ebo-genai%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/2/0100019aa6c47c8d-0aa7ff0f-a982-4ade-82cd-a66a1a2b4609-000000/CTvEmPG0jiHuWOA555Suky5YTaMDVwKblYEeo4ZnjfQ=432">
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<strong>How genAI and LLMs are driving revenue growth in ecommerce (Sponsor)</strong>
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Ecommerce teams are experimenting with AI, but most are still figuring out how to move from proof-of-concept to production.<p></p><p><a class="underline" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.algolia.com%2Fresources%2Fasset%2Febook-genai%3Futm_campaign=tldr_global_b2x_ecomm_ecomm_tof_reach%26utm_medium=display%26utm_source=tldr%26utm_content=tldr_global_b2x_ecomm%26utm_term=ebo-genai%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/3/0100019aa6c47c8d-0aa7ff0f-a982-4ade-82cd-a66a1a2b4609-000000/7rcBOTEFt40HYTZZYS006jCzoWAIxrEh6FyRKZmkq3A=432" rel="noopener noreferrer nofollow" target="_blank"><span>Algolia's ebook</span></a> breaks down how generative AI and LLMs are being deployed in ecommerce today - and driving 3-15% revenue uplift with:</p>
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<li>AI-generated product descriptions and marketing content at scale</li>
<li>Personalized recommendations using LLMs trained on customer behavior</li>
<li>Virtual try-ons and product customization</li>
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<strong>Gemini 3 Pro Image (5 minute read)</strong>
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Nano Banana Pro, aka Gemini 3 Pro Image, claims studio-quality image gen and editing. It can render text (a historic struggle), consistent multi-character scenes, and real-world knowledge integration for creating infographics.
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<strong>A new way to collaborate in ChatGPT (4 minute read)</strong>
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Group chats are now rolling out globally to ChatGPT Free, Go, Plus, and Pro plans. Users can now invite up to 20 other people into a shared conversation with ChatGPT. Group chats are optional and separate from private conversations. Personal ChatGPT memory isn't shared in group chats, and no new memories are created from them. Users can only join group chats via invitation, and they can choose to leave or remove members from the chat at any time.
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<strong>Some thoughts on AI (7 minute read)</strong>
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Gemini 3 shows that scaling laws for pretraining are intact. This means that Blackwell models are likely to show a significant increase in performance when they come out next year. GPT-5 was not evidence of a slowdown in scaling laws - the model was designed to be cheaper to inference, not better. While Gemini 3 was trained on TPUs, coherent FLOPs is what matters for pre-training, so the results should transfer to Blackwell chips fine.
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<strong>OpenAI can't beat Google in consumer AI (5 minute read)</strong>
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OpenAI can't beat Google at consumer AI as long as it is still in the chatbot paradigm. Recent OpenAI launches haven't done well. OpenAI needs new form factors to drive incremental growth. Google's data advantage is really showing, and its little advantages from its wide variety of apps add up. Microsoft and Amazon are at risk of losing the largest AI workloads.
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<strong>The Scaling Wall Was A Mirage (3 minute read)</strong>
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Gemini 3 showed that models can still improve by adding more compute during scaling. It has the same parameter count as Gemini 2.5 but with massive performance improvements. Google credits the improvements to pre-training and post-training and says that there is still no limit in sight. The model release is the strongest evidence since OpenAI's o1 that pre-training scaling still works when algorithmic improvements meet better compute.
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<strong>We're the AI company that doesn't have churn (Sponsor)</strong>
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We actually have a <strong>127% Net Revenue Retention </strong>(our voice agents are pretty good). They sound human, run 24/7, and never ask for PTO. <strong>If you want to see for yourself, </strong>we're offering to build you a custom agent for your business for free. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fbland.ai%2Fcustomagent%3Futm_source=TLDRAI%26utm_medium=newsletter%26utm_campaign=nov21%26utm_id=TLDRAInov21/1/0100019aa6c47c8d-0aa7ff0f-a982-4ade-82cd-a66a1a2b4609-000000/xnw1nYjUR1k6z_0mErlIdtAxilBqMobo4WcffnPZUto=432" rel="noopener noreferrer nofollow" target="_blank"><span><strong>Get it here</strong></span></a><strong>.</strong>
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<strong>Parallel Extract (2 minute read)</strong>
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Parallel Extract is an API that fetches content from URLs and returns it as Markdown. It is built on the same proprietary web index and retrieval infrastructure that powers Parallel's Search, Task, FindAll, and Monitor APIs. Parallel Extract can reliably extract information from the most challenging sites. Developers can try Extract for free in the Parallel Developer Platform.
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<strong>Introducing cline-bench: A Real-World, Open Source Benchmark for Agentic Coding (11 minute read)</strong>
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cline-bench is a new initiative focused on creating high fidelity benchmarks and reinforcement learning environments derived from real open source development scenarios. AI models have advanced significantly, but the field still lacks a rigorous open source benchmark that represents real engineering work. Model labs require evals that expose real breakdowns. cline-bench supports the next stage of AI research and development by creating research-grade environments that capture actual engineering constraints.
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<strong>Autocomp (20 minute read)</strong>
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Autocomp is a framework for optimizing tensor accelerator code. It helps hardware designers extract the full performance of tensor accelerators. Autocomp outperforms human expert kernel writers by up to 17x on AWS Trainium. It is highly portable and easy to use.
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<strong>ACT-1 (3 minute read)</strong>
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ACT-1 is a frontier robot foundation model trained on zero robot data. It was trained with special Skill Capture Gloves, which allow for data collection from anywhere without having to move robots around. The glove-based data collection produces higher quality data than teleoperations on contact-rich tasks. Videos of Sunday Robotics' Memo robot performing tasks using the ACT-1 model are available in the thread.
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<strong>OpenAI's dominance is unlike anything Silicon Valley has ever seen (7 minute read)</strong>
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OpenAI dominates the AI landscape with secretive financials and a rapid expansion strategy across data centers, applications, and devices. CEO Sam Altman's aggressive partnerships with Nvidia, Broadcom, Oracle, and AMD underscore a $500 billion growth trajectory. Despite concerns about competition, industry leaders see OpenAI's rapid pace as both a challenge and an asset, sparking a "gold rush mentality" in AI investments.
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Google has integrated SynthID-based AI image verification into the Gemini app, allowing users to check whether an image was generated or edited by Google AI directly through simple queries.
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<strong>Early experiments in accelerating science with GPT-5 (12 minute read)</strong>
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OpenAI published case studies from collaborations with institutions, including Oxford, Berkeley, and Lawrence Livermore, showing GPT-5 helping researchers solve problems that took months in minutes.
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<strong>Foxconn, OpenAI partner on AI hardware manufacturing (2 minute read)</strong>
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In the latest in a string of high-profile deals, OpenAI will work with Foxconn on the design of data center racks and AI hardware components.
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<strong>AI2's Olmo3 Technical Report (95 minute read)</strong>
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Olmo 3 is a new suite of fully open models at 7B and 32B sizes.
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