Email: ChatGPT Pro Max 🤖, Muse realtime avatar 🎭, DeepSeek $1B ARR 💰

Subject:
ChatGPT Pro Max 🤖, Muse realtime avatar 🎭, DeepSeek $1B ARR 💰
Received:
9/25/2026, 1:30:07 PM
From:
"TLDR AI" <dan@tldrnewsletter.com>
To:
silk.theater.56@fwdnl.com
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&lt;div style="display: none; max-height: 0px; overflow: hidden;"&gt;OpenAI appears to be preparing a new ChatGPT Pro Max subscription priced at $500 per month. It is unknown whether the company plans to justify&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;&lt;/div&gt;
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&lt;h1&gt;&lt;strong&gt;TLDR AI &lt;span id="date"&gt;2026-09-25&lt;/span&gt;&lt;/strong&gt;&lt;/h1&gt;
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                                        &lt;strong&gt;Take control of your AI costs with AMD (Sponsor)&lt;/strong&gt;
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                                    As AI agents take on more work, cloud inference costs can grow with every task. &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2F6ZrG6l%3Futm_source=TLDR%26utm_medium=newsletter%26utm_campaign=2026-09-25_tldr_ai_primary_amd%26utm_content=body_intro_amd_tokenomics_calculator%26utm_term=2026-09-25_tldr_ai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/cEswFll25GbxZVzZWbtpgei2Wf7XYCCb_L0tg1fpXlw=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;&lt;strong&gt;The AMD Tokenomics Calculator&lt;/strong&gt;&lt;/span&gt;&lt;/a&gt; helps you understand the economics of your AI workloads and &lt;strong&gt;optimize token spend.&lt;/strong&gt;

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&lt;ul&gt;
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&lt;li&gt;&lt;strong&gt;Reduce recurring AI costs.&lt;/strong&gt; High-performance agentic PCs featuring AMD processors run multiple agents locally, &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FJgbNUc%3Futm_source=TLDR%26utm_medium=newsletter%26utm_campaign=2026-09-25_tldr_ai_primary_amd%26utm_content=body_outro_completing_tasks_up_6x%26utm_term=2026-09-25_tldr_ai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/epgoPVxaBgJjwLoelyD54UjxZND5SC_TA8iS8IOgSbQ=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;completing tasks up to &lt;strong&gt;6x faster&lt;/strong&gt;&lt;/span&gt;&lt;/a&gt; while reducing reliance on cloud inference.&lt;/li&gt;
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&lt;p&gt;👉 &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2F6ZrG6l%3Futm_source=TLDR%26utm_medium=newsletter%26utm_campaign=2026-09-25_tldr_ai_primary_amd%26utm_content=cta_calculate_potential_savings%26utm_term=2026-09-25_tldr_ai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/rlJo3mJfRmGa2IwuZtgjxZroeSP94WKx0OD6h5WKX7E=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;&lt;strong&gt;Calculate your potential savings now&lt;/strong&gt;&lt;/span&gt;&lt;/a&gt;


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&lt;h1&gt;&lt;strong&gt;Headlines &amp; Launches&lt;/strong&gt;&lt;/h1&gt;
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&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.testingcatalog.com%2Fopenai-prepares-new-500-month-pro-max-plan-for-chatgpt%2F%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/WuRfnJIir1umJRfV48QQayyStewUYzTZYzcdrt2VqoA=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;OpenAI prepares new $500/month Pro Max plan for ChatGPT (2 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    OpenAI appears to be preparing a new ChatGPT Pro Max subscription priced at $500 per month. It is unknown whether the company plans to justify the $500 price through speed, larger usage allowances, longer-running Work sessions, or a combination of the three. The timing of the leak is notable as OpenAI's DevDay will take place on September 29. The company is expected to discuss APIs, developer tooling, new subscription tiers, and new products at the event.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fresearch.meta.ai%2Fblog%2Fbringing-your-muse-to-life%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/D0a_IreFSed4J0MUoTnkhq7UsQNdhfMfPuhVpdGzw-E=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Bringing Your Muse to Life (5 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Meta has introduced Muse Realtime Avatar, a technology that transforms conversations into expressive, interactive avatars in real time. Using a seamless integration of voice and visual performance, it synchronizes speech and avatar expressions accurately in live interactions. Muse Realtime Avatar outperforms competitors with improved visual quality and real-time responsiveness, supported by Meta's optimized AI inference stack.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fblog.google%2Finnovation-and-ai%2Fmodels-and-research%2Fgemini-models%2Fgemini-3-8-live-with-live-avatar%2F%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/Tf7YriZ_shDawI3PDYCXRA2NXGrrDVD0D6myNhY6G0I=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Gemini 3.8 Live with Live Avatar (1 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Google launched Gemini 3.8 Live featuring a Live Avatar, enhancing user interaction. This update offers real-time responses and more personalized user experiences. It positions Google to compete more aggressively with other AI platforms.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fthenextweb.com%2Fnews%2Fdeepseek-revenue-run-rate-1bn%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/AVUcRxW54r3de-MCmhEoz_aXE35Zb7nrTyEoqqderWQ=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;DeepSeek allegedly doubled its revenue run rate to $1 billion (1 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    DeepSeek allegedly doubled its annualized revenue run rate to $1 billion after raising API prices by 2.3 to 4.5 times. The Information says developer demand held up despite the increase.
                                &lt;/span&gt;
&lt;/span&gt;
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&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;🧠&lt;/span&gt;&lt;/div&gt;
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&lt;h1&gt;&lt;strong&gt;Deep Dives &amp; Analysis&lt;/strong&gt;&lt;/h1&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Finvertedpassion.substack.com%2Fp%2Fmodern-llms-have-tiny-gpts-hidden%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/sOtRzlbyM11r13gqZGi3Da4W6DeP5YIg_P66UC0Wsks=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Modern LLMs have tiny GPTs hidden inside them (8 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    It's likely that modern LLMs have tiny self-models of LLMs inside of them that allow them to better predict the next token generated by LLMs. Models approximating LLMs within themselves would open up all sorts of metacognitive abilities. It would open doors for a self-model within LLMs similar to a model humans have about themselves they call a 'self'.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.astralcodexten.com%2Fp%2Fthe-specter-of-neuralese%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/Msi-BR3O2j9gINzqZAeeCBp9UtW9W--RDUxRS-OlvlI=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;AI may learn to reason in representations people cannot read (20 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Models may reason through internal states that never appear in their written chain of thought, then give a plausible explanation after the fact. That would weaken safety checks that rely on reading the visible reasoning.
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&lt;/span&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Finferact.ai%2Fblog%2Ftpu-megakernels%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/LNnt2Qnpy56rc4mn7nit1A6TgjKPOy47R-rb7UuOtfY=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;700 TPS on Kimi K3: A Case for TPU Megakernels (20 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    inferact/tpu-megakernels is a collection of megakernels for TPU v7. Its Kimi K3 implementation delivers over 700 tokens per second with speculative decoding. Without speculative decoding, its megakernels for K3 and Qwen 3.8 27B deliver roughly 1.4 to 2x the decode throughput of the GB200 baseline at batch sizes 1 through 8. This post explains the design and why megakernels and TPUs are a good fit.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.backblaze.com%2Fblog%2Fwhy-the-best-ai-clouds-dont-run-on-flash-alone%2F%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/HNKwwB-vu0UlwA4EzmMqu7C2H9ApDsRnCIy56JCDWQU=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Why the Best AI Clouds Don't Run on Flash Alone (8 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Flash storage is fast and reliable, but much of the storage work that surrounds active training doesn't need flash-grade speed or prices. A lot of the infrastructure neoclouds have is built around flash, so for storage that doesn't need flash, they often point customers to hyperscaler object storage solutions instead. The more data you have stored somewhere, the harder it becomes to leave. While the neocloud keeps getting paid for GPU, the hyperscaler quietly takes over the broader account.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;🧑‍💻&lt;/span&gt;&lt;/div&gt;
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&lt;div style="text-align: center;"&gt;

&lt;h1&gt;&lt;strong&gt;Engineering &amp; Research&lt;/strong&gt;&lt;/h1&gt;
&lt;/div&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fnewrelic.com%2Fresources%2Freport%2Fobservability-forecast%2F2026%3Futm_campaign=FY27-Q2--GLOBAL--observability-forecast-2026-launch%26utm_source=tldr%26utm_medium=endemic%26utm_content=news%26sp=S0my-Pj5lPhhAPEafySFCGGA/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/Kz_5N0Pc7u90cqDPHCXStiBbTYqmaxRRc8CnADpH474=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Are businesses closing the AI observability gap? (Sponsor)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    1 in 4 AI agents still deploy without runtime monitoring, per New Relic's &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fnewrelic.com%2Fresources%2Freport%2Fobservability-forecast%2F2026%3Futm_campaign=FY27-Q2--GLOBAL--observability-forecast-2026-launch%26utm_source=tldr%26utm_medium=endemic%26utm_content=news%26sp=S0my-Pj5lPhhAPEafySFCGGA/2/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/4h-ND6auNQhN1LwTFLB1eUVjpvXjo81ab_BczeyQgKA=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Observability Forecast&lt;/span&gt;&lt;/a&gt; survey of 2.5k IT and engineering pros. And as organizations scramble to close the gap, tool sprawl is creeping back in. To see what sets leaders apart from those playing catch-up, &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fnewrelic.com%2Fresources%2Freport%2Fobservability-forecast%2F2026%3Futm_campaign=FY27-Q2--GLOBAL--observability-forecast-2026-launch%26utm_source=tldr%26utm_medium=endemic%26utm_content=news%26sp=S0my-Pj5lPhhAPEafySFCGGA/3/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/SquuDL9tFquAdw7j_m3xEh5sccpDFzyRN3G0UvWexLY=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;browse the report&lt;/span&gt;&lt;/a&gt;
&lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcontrastive-lm.notion.site%2F%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/zFa6XnVrrzq62BMEU_9t3HhN8mFou2zmRhkzX14Qb_M=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Contrastive Language Models (8 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Contrastive Language Models (CLMs) are a new class of System One model trained with a contrastive learning objective that connects states and actions. CLM-8B delivers performance comparable to Jev across computer-use, gaming, and tool-calling tasks while achieving up to 9x lower latency. It also sets a new state-of-the-art on changing agentic coding benchmarks. The model is pre-trained on 60M Nemotron Q&amp;A pairs, mid-trained on 30M synthetic hard negatives, and post-trained on 1M agentic trajectories.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.langchain.com%2Fblog%2Flangsmith-managed-deep-agents-whats-new%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/EGYQPFbNl0ZtJbaY04xu8x671_JJ8usLnIadFtD8xOI=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Managed Deep Agents v0.8: new auth, memory, and channels (12 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Managed Deep Agents combines the Deep Agents harness with all of the infrastructure required to run agents in production. It is the simplest way to build, deploy, and run mission-critical agents in production. Managed Deep Agents 0.8 adds support for user-owned credentials, user-level memory, HTTP channels, and file transfer in Slack. It also adds a pre-built tool for web search powered by Parallel.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;🎁&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;strong&gt;&lt;h1&gt;Miscellaneous&lt;/h1&gt;&lt;/strong&gt;&lt;/div&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table bgcolor="" style="table-layout: fixed; width: 100%;" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td style="padding:0;border-collapse:collapse;border-spacing:0;margin:0;" valign="top"&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjobs.ashbyhq.com%2Ftldr.tech%2F897f4391-66ab-44ca-a8eb-65dd9809200d%3Futm_source=tldr/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/BzswH_ap2ZapT4J49gLT-iYFJM4lRH_R7pRG_3XdgQs=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Product Manager, Applied AI at TLDR ($200k base + $60k bonus, Fully Remote)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    TLDR is hiring its first PM to help build the agent-first operating layer used across the company. We're looking for a builder who has shipped real products/systems with LLMs. &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjobs.ashbyhq.com%2Ftldr.tech%2F897f4391-66ab-44ca-a8eb-65dd9809200d%3Futm_source=tldr/2/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/03aBFxH65jgY9YnL9eQcPe4QGNwN7rqbifAiKa2kCEQ=452" rel="noopener noreferrer" target="_blank"&gt;&lt;span&gt;Click here to learn more&lt;/span&gt;&lt;/a&gt;.
&lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FCYmygt/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/lOJi-xSKQ4F9rMoD32oXfgLbEN5lYp2j6F0vD_IHHRE=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;What we learned from being the first company to disclose an agent cyberattack (4 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Hugging Face says its autonomous-agent cyberattack exposed three priorities: stronger incident transparency, reducing capability asymmetries between attackers and defenders, and preserving open-source access for defense. AI created new attack risks but also helped investigate, mitigate, and harden systems.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FlkF1YA/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/Bh_KcaO6cNOPsqiA-pIJpnQS5Q6Vdyx47SEu9rmDD-A=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Are you ready for superintelligence (13 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Recent frontier models are rapidly saturating old benchmarks and moving into harder real-world, scientific, and agentic tasks. The pace of capability gains, alongside soaring AI usage and revenue, is shifting debate from workplace augmentation toward recursive self-improvement and superintelligence.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;


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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;⚡&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div style="text-align: center;"&gt;

&lt;h1&gt;&lt;strong&gt;Quick Links&lt;/strong&gt;&lt;/h1&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.anthropic.com%2Fresearch%2Fproject-swap%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/j3HfKpHWIpacLJDWsBcfnAaF5iF0fg8O_iV6uQuqoS8=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Anthropic tested what happens when agents bargain for people (38 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    After five-minute interviews, Claude agents traded books for employees, and their preference rankings matched the humans' on 61% of pairs.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2Fwbopan%2Ftastebench%3Futm_source=tldrai/1/010001a0d8c24c31-c79fcdab-db38-46ca-a2a0-a87d4596b50c-000000/iC78UprcFA0YDOBJBhpeJiL75ZYDhAthLSiQYyDhtVg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;How Good Are LLMs at Decision Forking? (GitHub Repo)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Taste-Bench evaluates whether an LLM agent can choose the better next step at consequential forks in long-horizon tasks.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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