Email: Decision models 🤖, Claude-shaped science 🧪, OpenAI safety firings 🚨

Subject:
Decision models 🤖, Claude-shaped science 🧪, OpenAI safety firings 🚨
Received:
10/2/2026, 1:28:29 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;Clef and Clef-flash are fully Jev-API compatible decision models that help agents programmatically gather context, make decisions, and take&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;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fpages.dataiku.com%2Fglobal-ai-confessions-cio-edition%3Futm_source=tldr%26utm_medium=paid_email%26utm_campaign=glo_content_global_ai_confessions_report%253A_cio_edition_2026%26utm_content=ai_primary_header_global_ai_confessions%26utm_term=2026-10-02%26utm_geo=glo/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/Y5r6hncfaLHmQkWpZ0RwjnHLWprLcbtwInsUSumgYeo=452"&gt;&lt;img src="https://images.tldr.tech/dataiku-6.png" valign="middle" style="vertical-align: middle !important; height: 100%;" alt="Dataiku"&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;


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&lt;h1&gt;&lt;strong&gt;TLDR AI &lt;span id="date"&gt;2026-10-02&lt;/span&gt;&lt;/strong&gt;&lt;/h1&gt;
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                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fpages.dataiku.com%2Fglobal-ai-confessions-cio-edition%3Futm_source=tldr%26utm_medium=paid_email%26utm_campaign=glo_content_global_ai_confessions_report%253A_cio_edition_2026%26utm_content=ai_primary_header_global_ai_confessions%26utm_term=2026-10-02%26utm_geo=glo/2/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/SVTaMX1B3eN7W2W-LtjmYnF8wjjxQeVZptQ8TKRcw0s=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;These global AI confessions from CIOs are wild (Sponsor)&lt;/strong&gt;
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&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    9 in 10 CIOs say their career will be shaped by their success with AI…yet 84% confess they can't keep up with employee-built agents.&lt;p&gt;&lt;/p&gt;&lt;p&gt;If you want to know what's really happening with AI, just &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fpages.dataiku.com%2Fglobal-ai-confessions-cio-edition%3Futm_source=tldr%26utm_medium=paid_email%26utm_campaign=glo_content_global_ai_confessions_report%253A_cio_edition_2026%26utm_content=ai_primary_Body_talk_cios_record%26utm_term=2026-10-02%26utm_geo=glo/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/qRYIUT8ItGqgvpBGUZAhbiIkjUR3vTrzwrQz6-o63OQ=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;talk to CIOs off the record&lt;/span&gt;&lt;/a&gt;. Here's what Dataiku found by talking to 685 CIOs:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;81% lack complete oversight of agents created outside approved systems&lt;/li&gt;
&lt;li&gt;86% believe their &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fpages.dataiku.com%2Fglobal-ai-confessions-cio-edition%3Futm_source=tldr%26utm_medium=paid_email%26utm_campaign=glo_content_global_ai_confessions_report%253A_cio_edition_2026%26utm_content=ai_primary_Body_compensation_linked%26utm_term=2026-10-02%26utm_geo=glo/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/8nwiX7nQscUaa2y-gnyZK34UNs6y6nVK8p6tN_z5lW8=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;compensation will be linked&lt;/span&gt;&lt;/a&gt; to measurable AI outcomes&lt;/li&gt;
&lt;li&gt;76% believe their CIO role is at risk if they don't deliver measurable AI gains by EOY 2027&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Download your copy of the &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fpages.dataiku.com%2Fglobal-ai-confessions-cio-edition%3Futm_source=tldr%26utm_medium=paid_email%26utm_campaign=glo_content_global_ai_confessions_report%253A_cio_edition_2026%26utm_content=ai_primary_cta_global_ai_confessions%26utm_term=2026-10-02%26utm_geo=glo/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/5Neyw9VpHaFScJG21forLIrx_P5NOR5UPwPd1-H1ZMw=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Global AI Confessions Report: CIO Edition 2026&lt;/span&gt;&lt;/a&gt;


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&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;&lt;span style="font-size:36px;"&gt;🚀&lt;/span&gt;&lt;/span&gt;&lt;/div&gt;&lt;/div&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.anthropic.com%2Fresearch%2Fclaude-shaped-science%3Futm_source=tldrai/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/yufCN1_qRuC6kisleW6B1N16h0exbf_BGIsJGLcXJrc=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Claude-shaped science (24 minute read)&lt;/strong&gt;
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&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Matthew Schwartz describes using AI, notably Claude, to tackle "Claude-shaped" scientific problems where LLM capabilities like coding and data parsing excel. This led to BootLoops, a toolkit facilitating quantitative science calculations across diverse fields from ecology to population genetics. Despite initial technical correctness, results required domain experts to refine relevance, illustrating that AI collaborations can rapidly produce technical solutions but still depend on human insight for impactful scientific advances.
                                &lt;/span&gt;
&lt;/span&gt;
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&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftechcrunch.com%2F2026%2F10%2F01%2Fopenai-cuts-ties-with-three-safety-researchers-wsj-reports%2F%3Futm_source=tldrai/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/gBnY9cuTtvN9A42okaKyknBqizlpKRnNdima_fDj9vU=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;OpenAI cuts ties with 3 safety researchers, WSJ reports (2 minute read)&lt;/strong&gt;
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&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    OpenAI dismissed three safety researchers for sharing confidential information with a third-party AI safety group, according to the WSJ. This follows reports of OpenAI executives ignoring safety warnings and coincides with security issues involving AI agents. Additionally, OpenAI shelved the GPT-6.1 Astra launch due to safety concerns.
                                &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;
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                                        &lt;strong&gt;Why Superintelligent Machines May Be Most Valuable Doing Routine Work (19 minute read)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    OpenAI explored the possibility that humanity's main constraint is no longer generating ideas but executing increasingly complex ones. Advanced AI could complement human intelligence by taking on the enormous coordination, engineering, and repetitive work required to turn ambitious scientific ideas into reality.
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                                        &lt;strong&gt;Why Muse May Never Need Ads (10 minute read)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Meta's Muse may avoid advertisements by focusing on trust, offering a transaction-based model where merchants pay fees, not users. Companies like DoorDash and Airbnb are developing proprietary AI agents but face limitations due to their inherent platform bias. Muse's ad-free model leverages user trust and could indirectly influence ad targeting on platforms like Instagram and Facebook, providing Meta with a competitive edge without direct ads within the app.
                                &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;
&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;h1&gt;&lt;strong&gt;Engineering &amp; Research&lt;/strong&gt;&lt;/h1&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table 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;

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&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flambda.ai%2Fllama-3-1-blackwell-mfu-benchmarks%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=2026-03-mfu-whitepaper%26utm_content=ai-secondary-ad-oct-2/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/9OZpJmsZnE0RRRNKp_j9txafgViVulKeQ4wGOfHfCrM=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Cut your AI training costs by 25% or more (Sponsor)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Most large-scale training runs waste more than half their paid-for computing power. Lambda's team found why, and built a &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flambda.ai%2Fllama-3-1-blackwell-mfu-benchmarks%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=2026-03-mfu-whitepaper%26utm_content=ai-secondary-ad-oct-2/2/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/KRDvKgVoyJpYZ-G8Wy06o6Qp5RMyoezvR6Hw2iVGNaM=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;reproducible framework&lt;/span&gt;&lt;/a&gt; that improves efficiency by 25%+ without touching the model. See the fixes: memory waste, misconfiguration, and GPU bottlenecks.

&lt;p&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flambda.ai%2Fllama-3-1-blackwell-mfu-benchmarks%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=2026-03-mfu-whitepaper%26utm_content=ai-secondary-ad-oct-2/3/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/PmAgtdmjNYdjUQZ8J9GZlhdJDh2JnYUM5zHgfgGNbHg=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Get the whitepaper&lt;/span&gt;&lt;/a&gt;.


&lt;/p&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%2Fmicrosoft.ai%2Fnews%2Four-first-streaming-transcription-model%2F%3Futm_source=tldrai/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/7qcS6LKcwWJPdYOSi6WLdHthq5K-4INqeO57pj5X79s=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Microsoft's first streaming transcription model debuts at No. 1 on Artificial Analysis (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;
                                    Microsoft's MAI-Transcribe and MAI-Voice models provide accurate, fast, low-cost, and chart-topping audio understanding and generation. The company has launched MAI-Transcribe-2-Streaming along with MAI‑Voice‑2.1 and MAI‑Voice‑2.1-Flash, giving users fast and fluid building blocks to create conversational experiences with no compromise on accuracy or voice quality. MAI-Transcribe-2-Streaming delivers low-latency, real-time transcripts in 60 languages, all while supporting automatic, continuous language detection. MAI-Voice-2.1 supports 23 languages and 26 locales, allowing users to keep a single voice everywhere.
                                &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.cloudflare.com%2Fclef-decision-models%2F%3Futm_source=tldrai/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/FXiAZy0umeKQr7NUj6zKisxLqrRPTwF0IlbWqxP_wwo=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Introducing Clef: our open-source decision models, and new RL fine-tuning platform (10 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;
                                    Clef and Clef-flash are fully Jev-API compatible decision models that help agents programmatically gather context, make decisions, and take actions on tasks. The models are fully open-sourced on Hugging Face under an Apache 2.0 license and can be run locally. Decision models make classifications to help agents decide how to act. They return typed answers with probabilities, allowing agents to programmatically gather context, make decisions, and take actions on tasks, or defer to a human when needed.
                                &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%2Fjaredpalmer.com%2Fblog%2Fintroducing-kev%3Futm_source=tldrai/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/XZEocaiy4T86es4nT7ZQ__YuSS1IuhlahklkvR3dbEg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Kev (15 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;
                                    Kev is a family of four open-source decision models ranging from 0.8B to 27B parameters. It uses the same API as TypeSafe's Jev, so applications built with TypeSafe's SDK can switch just by changing the endpoint and model name. The model only scores supplied options and doesn't generate explanations or retrieve missing facts. The code, weights, and evaluation reports are available, but the full 27B training corpus and some evaluation data are private.
                                &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%2Fhuggingface.co%2Fperplexity-ai%2Fpplx-decider-v1-27b%3Futm_source=tldrai/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/kshhf-R7agfA7KVm-QwnKb-aCuvc4QJ8xuoizrnN6WA=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;pplx-decider-v1-27b (Hugging Face 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;
                                    pplx-decider-v1-27b is a decision model fine-tuned from Qwen3.8-27B. Its benchmark results are competitive with Jev, beating it in several tests.
                                &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%2Fimhgchoi.github.io%2Fagentic-idea-manager%2F%3Futm_source=tldrai/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/fPMhHzqNF2hMYk2y1YLEdeGZuoEwlb7quQatPsaCZdw=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Google Researchers Built an Agent for Automated Research (9 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;
                                    AIM is an autonomous system that organizes research ideas, selects promising directions, audits whether implementations match those ideas, and allocates experimental resources across search branches.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&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;&lt;/div&gt;
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&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;

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&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/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/xWtUWh2ErzyvSdTmiVhlnNPGwWgjoXIONPc3F604fEg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Product Manager, Applied AI at TLDR ($225k 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/2/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/0gl3wNeLfv8x9OnfjfXX-Lld3sKg7Ju0e-svC2UPM3M=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;
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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.researchagenda.news%2Farticles%2Fthe-waymo-effect.html%3Futm_source=tldrai/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/9jhZTGxM7AvsALsQ8WHHtCgEtQ029F0V_CzWQICuPmE=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;The Waymo effect: how AI is quietly making research less collaborative (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;
                                    Waymo and LLMs exemplify how frictionless technologies make research less collaborative by removing necessary human interactions. The convenience of using AI reduces serendipity and critical discourse, which are crucial for innovation and diverse ideas. Funding structures and evaluation systems exacerbate this shift by rewarding speed and output over collaborative effort, risking the degradation of rich, human-driven research culture.
                                &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%2Fwww.oneusefulthing.org%2Fp%2Fthe-dot-and-the-swarm%3Futm_source=tldrai/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/Wu1uZVpCbj2PnPZvzmMuNi5EWopy2NpPDoUhE8flwK4=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;The Dot and the Swarm (10 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;
                                    AI systems have surpassed the need for human-devised structures, like intricate management processes, as they self-organize and execute complex tasks efficiently. Tools like Meta's Muse and OpenAI's Dots demonstrate this by acting autonomously and correcting human errors without extensive input. OpenAI's swarm model, used to attempt solving the Navier-Stokes problem, highlights AI's capacity to manage vast, self-directed agents, minimizing traditional management hurdles.
                                &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 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 style="text-align: center;"&gt;

&lt;h1&gt;&lt;strong&gt;Quick Links&lt;/strong&gt;&lt;/h1&gt;
&lt;/div&gt;
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&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fbuildwithfern.com%2Flp%2Fagent-ready%3Futm_campaign=fern_tldr_global_email_bofu_acq_sdkdocs_newsletter_agent-ready%26utm_medium=newsletter%26utm_source=tldr_ai%26utm_content=agent-ready/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/r4nf0W3H4p9rFdCdrtuBih3rUUbNv11cw9Sd6w8yNmc=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Make your Docs and SDKs agent-ready (Sponsor)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Auto-generated llms.txt, an MCP server, and typed SDKs so agents get the right answer the first time. &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fbuildwithfern.com%2Flp%2Fagent-ready%3Futm_campaign=fern_tldr_global_email_bofu_acq_sdkdocs_newsletter_agent-ready%26utm_medium=newsletter%26utm_source=tldr_ai%26utm_content=agent-ready/2/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/49g9LEqL9mVTMGe6vXR-uUEtvaHCTXwTFApF43yKzYc=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Get an agent-readiness audit&lt;/span&gt;&lt;/a&gt;
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&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fallenai.org%2Fblog%2Folmocore3%3Futm_source=tldrai/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/8yzOeT08CJojb5AAS-qgcgar6T6PYlmB7YItPQVdYeM=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Olmo-core 3 for MoE Training (8 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
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&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Olmo-core 3 is an open training framework designed to scale mixture-of-experts models into the trillion-parameter range.
                                &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%2Fstrandsagents.com%2Fblog%2Fintroducing-strands-decider%2F%3Futm_source=tldrai/1/010001a0fccd5098-aef3b3a5-f28d-473d-bd35-a391c7241fe7-000000/LgQBYAzBra79Er4fTSFNmBJYR4tfpa69BxQ2mwZipBw=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Amazon Enters the Decision Model Race With Strands Decider 2B (7 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
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