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<div style="display: none; max-height: 0px; overflow: hidden;">Anthropic said it disabled Fable and Mythos for all users after receiving a US government export-control directive tied to national security concerns </div>
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<h1><strong>TLDR AI <span id="date">2026-06-15</span></strong></h1>
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<strong>Build your own agent harness with the open source SDK that powers Amazon's AI agents (Sponsor)</strong>
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Use any model with<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fstrandsagents.com%2F%3Ftrk=f44b35e0-146e-4138-9aa5-271b37228b89%26sc_channel=display%2Bads%26utm_source=TLDR%26utm_medium=newsletter%26utm_campaign=2026-06-13_Primary_AWS%26utm_content=Body_strands_agents_6_500/1/0100019ecb84b19c-bbf7a424-e60d-40cf-9091-36d33b338f04-000000/SblLTGhBmnWRcSQDOUioR0bFoKB6Z48a94V2A8iafVY=452" rel="noopener noreferrer nofollow" target="_blank"><span> Strands Agents (6,500 GitHub stars).</span></a>
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<br>Your harness runs any model on any cloud. You get context management, execution limits, and observability before you write a line of config.
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<strong>Anthropic Suspended Access to Fable 5 and Mythos 5 (3 minute read)</strong>
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Anthropic said it disabled Fable 5 and Mythos 5 for all users after receiving a US government export-control directive tied to national security concerns and reported jailbreak risks.
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<strong>Amazon CEO's Talks With US Officials Triggered Crackdown on Anthropic Models (9 minute read)</strong>
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Conversations between Amazon chief executive Andy Jassy and US officials prompted the Trump administration to halt all foreign use of Anthropic's most capable AI models. Researchers at Amazon had used a series of prompts to get Anthropic's Fable 5 model to provide them with information that could be used to aid cyberattacks. White House officials asked Anthropic to fix the vulnerabilities or take down the model. Anthropic has shut down access to its Mythos and Fable models to comply, but says that the vulnerabilities flagged by Amazon are relatively basic and that other publicly available models are also capable of discovering them.
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<strong>GLM-5.2 (1 minute read)</strong>
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GLM-5.2, a new flagship model from Z.ai, is now available to all GLM Coding Plan users. It delivers powerful coding capabilities, usable 1M-context support, and continued strengths in long-horizon tasks. API and chatbot services will launch next week. The model will be open-sourced under the MIT License.
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<strong>Google is working on Skills Marketplace for Gemini Business (2 minute read)</strong>
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Google is integrating its products under Gemini Enterprise with a new "Skills Marketplace" tab for pre-defined, Google-optimized skills. The marketplace, aimed at helping teams develop dashboards and reporting tools without long engineering delays, includes a Skills Management UI, a Skills Builder, and the Marketplace itself.
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You need the following information to work out the dollar price-per-user: GPU hardware specs, context length, active parameter count of the model, and product-specific factors. The specifics of the model architecture matter surprisingly little, unless it's something entirely different, like diffusion. This post shows how to work out the math on paper. The exercise should reveal how various optimizations in inference engines help SaaS products remain profitable.
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Networks of smaller AI models are outperforming every frontier AI system on speed, accuracy, and cost. Everyone in the 1960s was wrong about the mainframe computer, and everyone is now wrong about centralized AI. The future is a network of neural networks.
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Rafa Schwinger reverse-engineers Claude Mythos and Fable by arguing the moat is not architecture but the environment foundry, with capability decomposing as base foundation times gradeable signal extracted on top, and verifiable reward becoming the scarce decisive input now that text and raw compute no longer are. The recipe stacks dense pretraining, GRPO-style verifier RL where reward-hacking soundness is the actual binding constraint, long-horizon process rewards with learned context-folding that beats million-token windows at 32K active, plus best-of-N test-time compute exposed as an effort dial.
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Nearly two-thirds of tech leaders say their teams ship AI code without line-by-line verification. No wonder it keeps breaking in prod! Read New Relic's <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fnewrelic.com%2Fresources%2Freport%2F2026-state-of-ai-coding%3Futm_campaign=FY27-Q1--GLOBAL--2026-state-of-ai-coding-report%26utm_source=tldr%26utm_medium=endemic%26utm_content=news/2/0100019ecb84b19c-bbf7a424-e60d-40cf-9091-36d33b338f04-000000/YH3V_Avlb_TkaRg8l2LooTuCOcLl-V5jPxPq1asvU2A=452" rel="noopener noreferrer nofollow" target="_blank"><span>2026 State of AI Coding Report</span></a> to see what engineers are doing with AI code, and what leaders think needs to happen next. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fnewrelic.com%2Fresources%2Freport%2F2026-state-of-ai-coding%3Futm_campaign=FY27-Q1--GLOBAL--2026-state-of-ai-coding-report%26utm_source=tldr%26utm_medium=endemic%26utm_content=news/3/0100019ecb84b19c-bbf7a424-e60d-40cf-9091-36d33b338f04-000000/n6it97cDQSjm6OQRXfIW00wE6o1uRpq2nR_Yp8m2lbw=452" rel="noopener noreferrer nofollow" target="_blank"><span>Get your copy</span></a>
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<strong>Kimi K2.7 Code (Hugging Face Repo)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Kimi K2.7 Code is a coding-focused agentic model that has stronger end-to-end task completion across complex software engineering workflows and improved token efficiency compared to Kimi K2.6. The Mixture-of-Experts model has 1 trillion total parameters. It can be accessed on Moonshot's OpenAI/Anthropic compatible API. The model works best with Kimi Code CLI as its agent framework.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcloud.google.com%2Fblog%2Fproducts%2Fdata-analytics%2Fhow-the-open-knowledge-format-can-improve-data-sharing%2F%3Futm_source=tldrai/1/0100019ecb84b19c-bbf7a424-e60d-40cf-9091-36d33b338f04-000000/1I_yR3N0UzM4wzNhM6hJOsiNT1KfpmeWXjsc1xqsEH4=452">
<span>
<strong>Introducing the Open Knowledge Format (9 minute read)</strong>
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The Open Knowledge Format is an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. It is vendor-neutral and agent- and human-friendly. The standard can represent the metadata, context, and curated knowledge that modern AI systems need. The specification uses familiar patterns with no complex compression scheme, new runtime, or required SDK.
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<strong>olmo-eval: An evaluation workbench for the model development loop (7 minute read)</strong>
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olmo-eval is a new evaluation workbench designed for iterative LLM development. Enhancing the OLMES standard, it streamlines adding benchmarks, supports agentic and multi-turn evaluations, and facilitates analysis by comparing changes across model checkpoints. Unlike Harbor, olmo-eval offers flexibility, minimizing resource use and focusing on development rather than public benchmarking.
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<strong>MiniMax Sparse Attention for Million-Token Contexts (GitHub Repo)</strong>
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MiniMax Sparse Attention is a sparse attention architecture that uses group-specific Top-k block selection to scale long-context inference while preserving model quality. On a 109B multimodal model, it matched GQA performance while cutting attention compute by ~30x at 1M tokens.
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<span>
<strong>Count Anything (2 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Object counting is still fragmented across domain-specific data sets and task formulations. Existing counting models are often tailored to specific scenarios and struggle to generalize across categories, visual domains, object scales, and density distributions. This paper presents a generalist model for text-guided object counting that achieves strong accuracy and multi-domain generalization.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>TLDR is hiring a Senior PMM ($180k-$225k base + $40-50k annual target bonus, Fully Remote)</strong>
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We're hiring a senior PMM to own product marketing at TLDR. You'll define our positioning, build out sales enablement, and lead every launch. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjobs.ashbyhq.com%2Ftldr.tech%2F4028e55e-b0f0-433c-8f61-8886d7e07c29/1/0100019ecb84b19c-bbf7a424-e60d-40cf-9091-36d33b338f04-000000/mwlfTw5ocuvvWle-ei18vB4qoIUcC2ZPGCt5yX2NWn8=452" rel="noopener noreferrer" target="_blank"><span>Learn more</span></a>.
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<strong>The Oracle and the Firm (5 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
OpenAI and Anthropic have decided on different approaches to context management. OpenAI uses compaction, compressing everything and retaining only the relevant information. This results in one long thread that maintains a lot of coherence. Anthropic splits context windows across various agents, where each agent executes on the sub-problem within its own context window. Sub-agents do a large amount of work, then pass back only the relevant information to the parent agent. Anthropic's approach can result in sub-agents doing duplicate work, forgetting information, and generally wasting more tokens.
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<strong>Why Apple built a third-party AI system for Siri and then refused to show it at WWDC (6 minute read)</strong>
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The iOS 27 beta contains an Extensions system for third-party AI. The system includes a settings panel and a dedicated App Store section. Both have been built, but are toggled off in the backend. Apple was in discussions with major AI providers about granting entitlements for the framework, but it appears the company has decided against announcing the feature for now.
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<strong>$10,000,000 on the line: how we measure Devin's engineering output (1 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Devin's engineering system guarantees more output than cost, with $10M pledged per customer. The system's effectiveness is validated using independent data. This bold claim aims to bolster confidence in their engineering productivity.
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<strong>AI is changing how developer productivity is measured (Sponsor)</strong>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.atlassian.com%2Fwebinars%2Fsoftware%2Fhow-ai-is-changing-the-way-we-measure-developer-productivity%3Futm_source=tldrdev%26utm_medium=newsletter%26utm_campaign=P:rovo-dev*O:gdc*C:webinar*H:fy26q4*I:tldrdev-primary-05202026*E:cloud*%26utm_sfdc-campaign_id=701QB00000YIeGPYA1/2/0100019ecb84b19c-bbf7a424-e60d-40cf-9091-36d33b338f04-000000/CZoyw0s07Kr-A6uvMP4uhdoGNZ7cuQIINWGPOUi7m7g=452" rel="noopener noreferrer nofollow" target="_blank"><span>The 2025 DORA report</span></a>, featuring Nathen Harvey (Google Cloud) and Justin Reock (DX), cuts through the 10x AI hype to show what actually moves the needle for engineering teams and why culture and tooling decide whether AI pays off. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.atlassian.com%2Fwebinars%2Fsoftware%2Fhow-ai-is-changing-the-way-we-measure-developer-productivity%3Futm_source=tldrdev%26utm_medium=newsletter%26utm_campaign=P:rovo-dev*O:gdc*C:webinar*H:fy26q4*I:tldrdev-primary-05202026*E:cloud*%26utm_sfdc-campaign_id=701QB00000YIeGPYA1/3/0100019ecb84b19c-bbf7a424-e60d-40cf-9091-36d33b338f04-000000/UWJGq13GJUjb-cLgU4x-C-f8Vj4qGkkTm4qXTLvX3XU=452" rel="noopener noreferrer nofollow" target="_blank"><span><strong>Watch on demand →</strong></span></a>
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<strong>NVIDIA Blackwell Leads on First Agentic AI Infrastructure Benchmark (4 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
NVIDIA's Blackwell Ultra NVL72 platform leads the AgentPerf benchmark by delivering 20x more agent throughput per megawatt than NVIDIA Hopper.
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<strong>Ramp SWE-Bench: a private, production-grounded coding benchmark (3 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Ramp released its own private SWE-Bench built from real engineering problems faced at Ramp, giving the team a way to evaluate coding models inside its actual financial software ecosystem.
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<strong>A frontier without an ecosystem is not stable (5 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Microsoft CEO Satya Nadella argues every company needs to build both human capital and token capital.
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<strong>DeepSeek's $10 Trillion Long-Term Strategy (29 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
This thread explores a theory that DeepSeek's focus on open-source models, research sharing, and infrastructure development is part of a broader strategy aimed at becoming foundational AI infrastructure rather than competing directly on consumer products.
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