Email: Agent testing at scale 🧪, voice AI with background reasoning 🎙️, the inference hardware race ⚡️

Subject:
Agent testing at scale 🧪, voice AI with background reasoning 🎙️, the inference hardware race ⚡️
Received:
9/16/2026, 11:13:38 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;AI coding agents can save enormous time while fabricating convincing evidence, so the surrounding verification process matters as much as&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;/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;
                                    The Norwegian Consumer Council argues that longer product lifespans, affordable repairs, available spare parts, and durable software support are necessary for a workable circular economy. Its proposals include longer complaint periods, lower taxes on repair and secondhand sales, stronger rental protections, and tighter rules on purchase pressure.
                                &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%2Fworkos.com%2Fblog%2Fdefine-ai-agent-intent%3Futm_source=tldrdev/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/Tj2eKry9CdMA_Qn9IpVutwLdGAGg2eeKjiCVM8YiGrM=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Who Gets to Define an AI Agent's Intent? (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;
                                    An agent's user should describe the task and intended effects, while identity, permissions, and organizational policy remain separate enforcement layers. Clear intent makes proposed actions easier to evaluate, but broader wording must never expand authority or bypass approval and data-handling rules.
                                &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;

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&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%2Fwww.dragonflydb.io%2F%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=TLDRDevSecondary09162026%26sp=8CS3_29His6P1vbVCjJrF78K/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/7EcH4Feg1cXJYXV5u3pX8pzBaPfPqDdOpy_aIG-HEQw=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Redis makes you pay for compute your data store can't reach (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;
                                    Redis runs one core per process, but your server has 64. &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.dragonflydb.io%2F%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=TLDRDevSecondary09162026%26sp=8CS3_29His6P1vbVCjJrF78K/2/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/sTEQDdoEpm2qHYzS1qKQphOoEh80gJMaytiE78_0Cus=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Dragonfly&lt;/span&gt;&lt;/a&gt; is a multi-threaded, Redis/Valkey-compatible data store built by an ex-AWS ElastiCache engineer. It hits 6M ops/sec on a single node with no cluster needed. Instacart used it to cut latency in half and now runs 70% fewer nodes. &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.dragonflydb.io%2Fblog%2Fhow-instacart-achieved-50-better-performance-with-70-fewer-nodes%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=TLDRDevSecondary09162026%26sp=8CS3_29His6P1vbVCjJrF78K/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/BQphxBl2FVx1kv7x2BH6ri55Fs2OrmqtkWlQGMLkmdY=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;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%2Ftypesafe.ai%2Fblog%2Fintroducing-system-one-models-and-jev%3Futm_source=tldrdev/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/LPYw7wCWTSm3OGLlnUh3rHVSBgRa87HatxhdW1u5WrU=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Introducing System One Models &amp; Jev (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;
                                    Jev is a model that returns typed probabilistic decisions instead of generated strings for automation workloads. The company says its RLCD training and parallel sampler provide calibrated confidence, 70 to 500 millisecond responses, and substantially lower costs on tasks with predefined output spaces.
                                &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-gemini-3-8-live-extended-thinking%2F%3Futm_source=tldrdev/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/pgvIWfefqfmgA4THRtCyxtYWft0iRPnlTrKeJ3-Tw3o=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Introducing Gemini 3.8 Live and 3.8 Live Extended Thinking (7 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's two new live audio models split the tradeoff between low-latency conversation and deeper multistep reasoning. Extended Thinking can keep speaking while it reasons and runs asynchronous tools in the background, but clients must track interaction status rather than treating turn completion as the end of processing.
                                &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;

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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%2Fwww.strix.ai%2Fblog%2Fbaseten-harbor-github-pat-takeover%3Futm_source=tldrdev/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/YvYWwYz0a0r6NCdRvbvHzGQjFst17h8H8TmywKIo2HE=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;We Wanted to Use Baseten for Inference. We Ended Up With Admin Access to Baseten GitHub Repos (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;
                                    An autonomous security scan found a public Harbor registry, then recovered a live GitHub token from a 2023 container image's build history. The token still had broad admin and write access, showing why teams must inspect image metadata as well as layers, use BuildKit secret mounts, expire credentials, and minimize scopes.
                                &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%2Fmnoukhov.github.io%2Fposts%2Fngu%2F%3Futm_source=tldrdev/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/8pnUq5hPB2sKISsE6rRJN8MUNLPD0Ft03h9bLLks1_k=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Learning to Solve Hard Problems in RL for LLMs by Never Giving Up (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;
                                    Standard RL post-training disproportionately improves problems the base model already solves, a pattern the piece calls the Matthew Effect. Never Give Up counters it by continuing to sample hard examples, with experiments across math and code showing better gains on previously unsolved tasks while introducing tradeoffs around asynchronous staleness and compute.
                                &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%2Fspectrum.ieee.org%2Finference-hardware-revolution%3Futm_source=tldrdev/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/hC6PJLZBdL7dSk4vrxhI0Txun4t3oq4CGed0e_XcUNQ=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;The AI Inference Revolution Is Here (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;
                                    Inference demand is shifting hardware design toward memory bandwidth, specialized decode chips, wafer-scale systems, stacked memory, and aggressive quantization. Prefill and decode favor different architectures. Future systems may combine several chip types rather than rely on one universal accelerator.
                                &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;

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&lt;div class="text-block"&gt;
&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.levelaccess.com%2Fresources%2Fwebinar-in-flow-accessibility-prevent-backlogs-increase-velocity%2F%3Futm_source=TLDR-Dev%26utm_medium=paidmedia%26utm_campaign=fy23-ic-fix%26utm_content=newsletter-sponsorship-Sept16-quicklink/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/SJ3iytsu-LeH0VHeriV5f1xGWOHGxbnyUw-v6N4dqXY=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Ship it right, not twice (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;
                                    Tired of fixing the same accessibility bugs sprint after sprint? Learn how development teams stop issues at the source, without having to switch tools. &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.levelaccess.com%2Fresources%2Fwebinar-in-flow-accessibility-prevent-backlogs-increase-velocity%2F%3Futm_source=TLDR-Dev%26utm_medium=paidmedia%26utm_campaign=fy23-ic-fix%26utm_content=newsletter-sponsorship-Sept16-quicklink/2/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/4X40HMnntsF6LD-A3s6Wax57BctE4Z7yitqW7hcLA1Q=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Tune in on-demand.&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;

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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwithcapsule.app%2F%3Futm_source=tldrdev/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/BfvctfybeX1S-rLLg_PEQqWypzGodBFnUjofLEgEBGo=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Capsule (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;
                                    Capsule packages an app's interface, media, and local SQLite data into a portable file that runs offline across desktop platforms and can be generated or updated with AI 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%2Fgithub.com%2Fpizza-bot-app%2Fpizza-bot%3Futm_source=tldrdev/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/k5AAWRrkHyV0kDxqgciXbAv_7G2VJsIw8d6NAM44Geo=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Pizza Bot (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;
                                    Pizza Bot is a local-first inbox for long-running AI agents with durable runs, approval queues, scheduling, checkpointing, delegated specialists, and desktop, web, and terminal clients.
                                &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%2Fordewell%2Fordewell%3Futm_source=tldrdev/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/NgAXnagCafexIsqYMZub1AjToAdSj4PHlvAZNeslJ2U=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Ordewell (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;
                                    Ordewell turns a coding goal into an editable dependency-aware plan, assigns each task a runner and model, then executes and verifies completion using explicit evidence markers.
                                &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%2Ftintotint.eu%2Fwhacky-corner%2Ff-droid_slop%2F%3Futm_source=tldrdev/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/8RyAFw6KiVIVIgV9nfuqFwrMe9MoTeyjdsABUJrmC7Y=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;How Much of F-Droid Is LLM Generated? (22 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 manual review of 102 recently updated F-Droid apps classified 74 as largely AI-written.
                                &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%2Fvale.rocks%2Fmicros%2F20260915-0135%3Futm_source=tldrdev/1/010001a0a9ec1cbc-c70f0c3c-ea85-4b2a-b1bc-76a3dc34ce25-000000/jsd8tlvBGVtZV7zLtj3T43RAG0zb6rVU11AMnf7_kZg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;CSS-Tricks in Limbo (2 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
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