Email: Jev’s hidden architecture 🧠, AI-era CI speedups ⚡, Jev for coding agents 🤖

Subject:
Jev’s hidden architecture 🧠, AI-era CI speedups ⚡, Jev for coding agents 🤖
Received:
9/22/2026, 11:12:09 AM
From:
"TLDR Dev" <dan@tldrnewsletter.com>
To:
silk.theater.56@fwdnl.com
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&lt;div style="display: none; max-height: 0px; overflow: hidden;"&gt;Ten thousand API probes suggest Jev is a causal decision model that shares state across isolated questions and emits decision probabilities&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;
&lt;div style="display: none; max-height: 0px; overflow: hidden;"&gt;
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&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fopenrouter.ai%2F%3Futm_source=sponsorship%26utm_medium=newsletter%26utm_campaign=tldr_september22%26utm_content=header_one_api_every%26utm_term=2026-09-22_tldr_dev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/CznZn726woQehYKleFbw8Gq-YQuzvdeDIZsklyvHHXo=452"&gt;&lt;img src="https://images.tldr.tech/openrouter2.png" valign="middle" style="vertical-align: middle !important; height: 100%;" alt="OpenRouter"&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;&nbsp;TLDR Dev &lt;span id="date"&gt;2026-09-22&lt;/span&gt;&lt;/strong&gt;&lt;/h1&gt;
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                                        &lt;strong&gt;One API. Every Model. The best price, speed, and uptime - automatically (Sponsor)&lt;/strong&gt;
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&lt;p&gt;&lt;strong&gt;&gt; Switch models easily:&lt;/strong&gt;&nbsp;Keep your existing OpenAI SDK calls and swap models with a single parameter.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&gt; Route intelligently:&lt;/strong&gt;&nbsp;Use AutoRouter to choose models based on price, speed, and quality.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&gt; Build for reliability:&lt;/strong&gt;&nbsp;Multi-provider failover, transparent pricing, usage analytics, and fine-grained data policies are built in.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fopenrouter.ai%2F%3Futm_source=sponsorship%26utm_medium=newsletter%26utm_campaign=tldr_september22%26utm_content=cta_building%26utm_term=2026-09-22_tldr_dev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/--oEgUY5O4ZZH2Kor8NjDRVyiCEBurYjdEvZU5KFitc=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;&lt;strong&gt;Start building&lt;/strong&gt;&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&gt;&lt;/div&gt;
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&lt;h1&gt;&lt;strong&gt;Articles &amp; Tutorials&lt;/strong&gt;&lt;/h1&gt;
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                                        &lt;strong&gt;Jev's Architecture Unmasked (28 minute read)&lt;/strong&gt;
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&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Ten thousand API probes suggest Jev is a causal decision model that shares state across isolated questions and emits decision probabilities directly instead of generating text. The strongest evidence supports direct readouts, batched question branches, and option interaction, while the sparse mixture-of-experts backbone remains speculative.
                                &lt;/span&gt;
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                                        &lt;strong&gt;AI Coding Has Made CI a Bottleneck, So We Reworked Ours to Keep Up (11 minute read)&lt;/strong&gt;
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                                    Linear cut average pull request wait time from more than six minutes to just over five, even as its test suite nearly quadrupled, while halving runner time per test. The changes combined faster third-party runners, lighter gating jobs, less repeated setup, and more efficient test execution.
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                                    Agentic coding is turning Markdown from documentation into an executable source artifact that should live beside the code it generates. HTMX's proposed workflow derives code and tests from durable Markdown specifications instead of leaving intent trapped in disposable prompt sessions.
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&lt;h1&gt;&lt;strong&gt;Opinions &amp; Advice&lt;/strong&gt;&lt;/h1&gt;
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                                    Current coding agents inherit costly constraints from KV-cache-centric designs, including awkward model routing, upfront tool schemas, compaction, weak subagent coordination, and state corruption. A TypeSafe-centric agent could treat context as dynamic, explicit state, enabling query-aware compression, cost-aware routing, better tool selection, security-aware model policies, and cheaper background parallelism.
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&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    LLMs can be useful and thorough while still feeling untrustworthy because they speak with artificial confidence, invent facts, and imitate a grating human voice. Their behavior reflects the values and incentives of the companies that build them, which makes the discomfort more than a matter of style.
                                &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%2Fbrookslybrand.com%2Fposts%2Fdo-frameworks-matter-anymore%2F%3Futm_source=tldrdev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/9DzXIaM1M7gQx9NN_htCPMFYOXnRi3VlFLpu2kc-jiQ=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Do Frameworks Matter Anymore? (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;
                                    Frameworks still matter in AI-assisted development because they provide reusable abstractions, recognizable structure, and constraints that keep generated code coherent. Without them, coding agents tend to improvise unmaintained frameworks of their own, so the better response is to build stronger frameworks for both humans and agents.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&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;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding-top: 0px; padding-bottom: 0px;"&gt;
&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;

&lt;h1&gt;&lt;strong&gt;Launches &amp; Tools&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;

&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%2Fpages.antithesis.com%2Foreilly-scaling-ai-ebook-tldr%3Futm_medium=Email%26utm_campaign=tldrdev_2026%26utm_source=tldrdev%26utm_content=tldrdev_20260922%26sp=JhYbXsME-jE4Lp5gX11C8s6m/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/RCQPESMq21meGS8SQxQZ3MFKmjYqgaa5kYRGySPtwfE=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Your O'Reilly guide to scaling AI across the enterprise SDLC (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;
                                    AI works great for individuals but stalls for engineering enterprises. In this new O'Reilly book (early release compliments of &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fantithesis.com%2F%3Futm_medium=Email%26utm_campaign=tldrdev_2026%26utm_source=tldrdev%26utm_content=tldrdev_20260922%26sp=JhYbXsME-jE4Lp5gX11C8s6m/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/0sYc_eEGOv3BTSMUML6OISIEXQMD_ROhQOmqPxMaym4=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Antithesis&lt;/span&gt;&lt;/a&gt;), Peter Bell provides a hands-on guide to the real challenges of AI adoption: aligning teams, evolving org structures, adapting hiring strategies, and managing risk. &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fpages.antithesis.com%2Foreilly-scaling-ai-ebook-tldr%3Futm_medium=Email%26utm_campaign=tldrdev_2026%26utm_source=tldrdev%26utm_content=tldrdev_20260922%26sp=JhYbXsME-jE4Lp5gX11C8s6m/2/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/nPqljYGrdg5zBHgpADHP3G4qZucOEV-OfTZKyyUXZMw=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Claim your free copy ›&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%2FDlm33V/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/nyzhK0LrCDd9wv50E5pXN8JE4won8Kf1v0Cssx2P9aE=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Introducing Grok 4.7 (3 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;
                                    Grok 4.7 uses a larger base model and longer reinforcement learning to improve coding, long-running work, self-verification, and professional document tasks.
                                &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%2Fgithub.com%2Fzzet%2Fgortex%3Futm_source=tldrdev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/lIVNfXnFSKe3MhTyh8e0KYzBd9K5mkyVc-3ZZYit-cc=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Gortex (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;
                                    Gortex indexes code into a local graph and exposes cross-repository search, references, call chains, and impact analysis through a CLI, MCP server, API, and web interface. It supports 257 languages and claims up to 50 times fewer tokens per response than naive file reads by returning only the relevant graph context.
                                &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%2Fgithub.com%2Fjaredpalmer%2Fkev%2F%3Futm_source=tldrdev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/PMqPVowHgRZZH1wmfm4KJsXSpTQeaEj19CmlH_C7sfY=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Kev (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;
                                    Kev is a family of small, open-source decision models built on Qwen3.5 that can run locally and expose a TypeSafe System One-compatible API. The repository includes 0.8B, 4B, and 9B checkpoints, training and evaluation code, and support for yes or no, multiple-choice, and rating questions with isolated question context.
                                &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 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%2Ftimdettmers.com%2F2026%2F09%2F21%2Fdlab-open-source-week%2F%3Futm_source=tldrdev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/TOHhz7SISgG5opqI8bmBiYyj_9_c_Ag-haDMeCNSJaA=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;dlab Open Source Week: Frontier AI on Your Own Hardware (19 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 agents have compressed individual research projects from years to weeks or days, making isolated papers a weaker unit of contribution than reusable ecosystems. dlab's open source week packages models, training code, and hardware-conscious tooling to show that university groups can compete by building shared research infrastructure instead of chasing the largest GPU clusters.
                                &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%2Farchestra.ai%2Fblog%2Fwe-tested-jev-on-100-real-agent-calls%3Futm_source=tldrdev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/cH1lrGoBSfcP69GVrj8EU2i-CaxIWgshUnHq7qAH8bc=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;We Tested Jev on 100 Real Agent Calls. How Easy Is It to Beat a Constant? (11 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;
                                    Tests on 100 production agent tool calls found that 79% were benign, so a classifier had to beat a constant benign baseline before its accuracy was useful. Jev's errors, class imbalance, and benchmark design show why evaluations need deployment-aware baselines and why a strong-looking score can still lose to simple code.
                                &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%2Frobocurve.org%2Froboharm%2F%3Futm_source=tldrdev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/xEkcQTCHARGUu7Z495mEXEME7-1cElDRJd0UlvTji28=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;RoboHarm: Do Frontier Robot Policies Refuse Unsafe Instructions? (Website)&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;
                                    RoboHarm ran 300 trials across three robot policies and five hazardous instructions, but recorded only 22 safety refusals. The benchmark separates refusal from failure to complete a task and warns that its narrow setup uses one wording per instruction, 20 trials per condition, and five tabletop scenes.
                                &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;
&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;
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&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.acquia.com%2Fcampaign%2Fdrupal-seriously-reimagined%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=fy26q3-global-drupal-new%26utm_content=newsletter-tldrdev-quicklinks-22sep%26utm_term=publisher-list-701Pb00004WPZcRIAX/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/jD5Ew3MBw4l7qlAKgSDNkGewXD8hpiJ2j43Q0wfBmT4=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;A seriously future-proof CMS (Sponsor)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Acquia-powered Drupal delivers API-first scale, security, and integration with 48+ AI providers - without vendor lock-in. &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.acquia.com%2Fcampaign%2Fdrupal-seriously-reimagined%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=fy26q3-global-drupal-new%26utm_content=newsletter-tldrdev-quicklinks-22sep%26utm_term=publisher-list-701Pb00004WPZcRIAX/2/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/xsy5lOo0wJzVUTJC1ABnMBXJW708q4p9meGffBwmqDY=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Explore the free trial&lt;/span&gt;&lt;/a&gt;.
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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fdata.agentic-system.org%2F%3Futm_source=tldrdev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/6M8oD2ebICkh6gLUFzdXPowanKt4Sl6utRiB9RNYC2k=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Open Data: A Dataset Hub for LLM Serving Research (Website)&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;
                                    This hub publishes production-scale request traces, GPU telemetry, and agent-serving datasets for studying batching, scheduling, cache reuse, sessions, and infrastructure behavior.
                                &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%2Fdevfra.me%2Fposts%2Fpluggable-extensible-playful-devtools%3Futm_source=tldrdev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/iXNdB5c95ZS4Pj-ZNnOx5tnS97_HdNs_E53J9pRdUPw=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Pluggable, Extensible, and Playful DevTools (16 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;
                                    A proposed universal DevTools ecosystem decomposes debugging capabilities into reusable, framework-agnostic modules that hosts such as Nuxt, React, Svelte, and Astro can compose while retaining framework-specific tools.
                                &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%2Flemire.me%2Fblog%2F2026%2F09%2F22%2Fa-summer-of-ai-optimization%2F%3Futm_source=tldrdev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/3EGAPAV9B7NtycGae-h7i7ruBinQQ62fqmiiYh_UPEo=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;A Summer of AI Optimization (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;
                                    Benchmarks across six widely used open-source libraries show recent AI-assisted optimization gains ranging from 17% faster Base64 decoding to 4.9 times faster bitmap cardinality calculations, largely because cheap experimentation made more ideas worth testing.
                                &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%2Fconfident-ai%2Fdeepteam%3Futm_source=tldrdev/1/010001a0c8d0e74b-bfc145fb-0ccc-4522-823a-10d680da5dc7-000000/EajeqpfMxEttSQ3sCFbmseEtEe3HoWiTIZQaS_2VmsE=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;DeepTeam (GitHub Repo)&lt;/strong&gt;
                                    &lt;/span&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    DeepTeam is a local, open-source red teaming framework that simulates prompt injection, jailbreaking, multi-turn exploitation, and more than 50 vulnerability classes across LLM applications, agents, RAG pipelines, and chatbots.
                                &lt;/span&gt;
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