Email: LinkedIn Gives Agents Context 📚, Claude Dashboards 📊, Salesforce Spark Optimization ⚡

Subject:
LinkedIn Gives Agents Context 📚, Claude Dashboards 📊, Salesforce Spark Optimization ⚡
Received:
10/12/2026, 10:12:27 AM
From:
"TLDR Data" <dan@tldrnewsletter.com>
To:
silk.theater.56@fwdnl.com
&lt;!DOCTYPE html&gt;&lt;html lang="en"&gt;&lt;head&gt;&lt;meta http-equiv="Content-Type" content="text/html charset=UTF-8"&gt;&lt;meta charset="UTF-8"&gt;&lt;meta name="viewport" content="width=device-width"&gt;&lt;meta name="x-apple-disable-message-reformatting"&gt;&lt;title&gt;TLDR Data&lt;/title&gt;&lt;meta name="color-scheme" content="light dark"&gt;&lt;meta name="supported-color-schemes" content="light dark"&gt;&lt;style type="text/css"&gt;
			:root {
				color-scheme: light dark; supported-color-schemes: light dark;
			}
			
			*,
			*:after,
			*:before {
				-webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box;
			}
			
			* {
				-ms-text-size-adjust: 100%; -webkit-text-size-adjust: 100%;
			}
			
			html,
			body,
			.document {
				width: 100% !important; height: 100% !important; margin: 0; padding: 0;
			}
			
			body {
				-webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; text-rendering: optimizeLegibility;
			}
			
			div[style*="margin: 16px 0"] {
				margin: 0 !important;
			}
			
			table,
			td {
				mso-table-lspace: 0pt; mso-table-rspace: 0pt;
			}
			
			table {
				border-spacing: 0; border-collapse: collapse; table-layout: fixed; margin: 0 auto;
			}
			
			img {
				-ms-interpolation-mode: bicubic; max-width: 100%; border: 0;
			}
			
			*[x-apple-data-detectors] {
				color: inherit !important; text-decoration: none !important;
			}
			
			.x-gmail-data-detectors,
			.x-gmail-data-detectors *,
			.aBn {
				border-bottom: 0 !important; cursor: default !important;
			}
			
			.btn {
				-webkit-transition: all 200ms ease; transition: all 200ms ease;
			}
			
			.btn:hover {
				background-color: #f67575; border-color: #f67575;
			}
			
			* {
				font-family: Arial, Helvetica, sans-serif; font-size: 18px;
			}
			
			@media screen and (max-width: 600px) {
				.container {
					width: 100%; margin: auto;
				}
				.stack {
					display: block!important; width: 100%!important; max-width: 100%!important;
				}
				.btn {
					display: block; width: 100%; text-align: center;
				}
			}
			
			body,
			p,
			td,
			tr,
			.body,
			table,
			h1,
			h2,
			h3,
			h4,
			h5,
			h6,
			div,
			span {
				background-color: #FEFEFE !important; color: #010101 !important;
			}
			
			@media (prefers-color-scheme: dark) {
				body,
				p,
				td,
				tr,
				.body,
				table,
				h1,
				h2,
				h3,
				h4,
				h5,
				h6,
				div,
				span {
					background-color: #27292D !important; color: #FEFEFE !important;
				}
			}
			
			a {
				color: inherit !important; text-decoration: underline !important;
			}

		&lt;/style&gt;&lt;!--[if mso | ie]&gt;
		&lt;style type="text/css"&gt;
			a {
				background-color: #FEFEFE !important; color: #010101 !important;
			}
			@media (prefers-color-scheme: dark) {
				a {
					background-color: #27292D !important; color: #FEFEFE !important;
				}
			}
	 &lt;/style&gt;
			&lt;![endif]--&gt;&lt;/head&gt;&lt;body class=""&gt;



&lt;div style="display: none; max-height: 0px; overflow: hidden;"&gt;LinkedIn built a shared layer that tells people and AI agents which datasets to trust, which filters and joins are required, and what each row means&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;
&lt;br&gt;
&lt;/div&gt;

&lt;table align="center" class="document"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top"&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" class="container" width="600"&gt;&lt;tbody&gt;&lt;tr class="inner-body"&gt;&lt;td&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr class="header"&gt;&lt;td bgcolor="" class="container"&gt;

&lt;table width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container"&gt;



&lt;table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" style="margin-top: 0px;" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td style="padding: 0px;"&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: 15px 15px;"&gt;
&lt;div style="text-align: center;"&gt;
&lt;span style="margin-right: 0px;"&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftldr.tech%2Fdata%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/GOKaPWUmA1l0NgbW-enLBvu_RlhrJgC3iNGc11zsesM=452" rel="noopener noreferrer" target="_blank"&gt;&lt;span&gt;Sign Up&lt;/span&gt;&lt;/a&gt;
|&lt;span style="margin-right: 2px; margin-left: 2px;"&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fadvertise.tldr.tech%3Futm_source=tldrdata%26utm_medium=newsletter%26utm_campaign=advertisetopnav/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/cVrfEN2fuEwD7Rx4RsvMl8reyhB7_HnPQAV_l0XXLsc=452" rel="noopener noreferrer" target="_blank"&gt;&lt;span&gt;Advertise&lt;/span&gt;&lt;/a&gt;&lt;/span&gt;|&lt;span style="margin-left: 2px;"&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fa.tldrnewsletter.com%2Fweb-version%3Fep=1%26lc=1670a604-84b7-11f0-bcf5-55fc1d40139c%26p=add32f7c-c614-11f1-89d3-a7bc31e86e00%26pt=campaign%26t=1791799946%26s=16f947e6247c42a0f003572b35f69c8d96388d076dff7f19a18a7a9413cfc0fe/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/wefAAkNTNUKm61MTIhx7o3elzA6dmQI1Wdjmbj-cLrs=452"&gt;&lt;span&gt;View Online&lt;/span&gt;&lt;/a&gt;&lt;/span&gt;
&lt;br&gt;
&lt;/span&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="text-align: center;"&gt;&lt;span data-darkreader-inline-color="" style="--darkreader-inline-color:#3db3ff; color: rgb(51, 175, 255) !important; font-size: 30px;"&gt;T&lt;/span&gt;&lt;span style="font-size: 30px;"&gt;&lt;span data-darkreader-inline-color="" style="color: rgb(232, 192, 96) !important; --darkreader-inline-color:#e8c163; font-size:30px;"&gt;L&lt;/span&gt;&lt;span data-darkreader-inline-color="" style="color: rgb(101, 195, 173) !important; --darkreader-inline-color:#6ec7b2; font-size:30px;"&gt;D&lt;/span&gt;&lt;/span&gt;&lt;span data-darkreader-inline-color="" style="--darkreader-inline-color:#dd6e6e; color: rgb(220, 107, 107) !important; font-size: 30px;"&gt;R&lt;/span&gt;
&lt;br&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;br&gt;

&lt;table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr id="together-with"&gt;&lt;td align="center" height="20" style="vertical-align:middle !important;" valign="middle" width="100%"&gt;&lt;strong style="vertical-align:middle !important; height: 100%;"&gt;Together With &lt;/strong&gt;
&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcubecloud.dev%2Fauth%2Fsignup%3Futm_source=tldrdata%26utm_medium=newsletter%26utm_campaign=Primary10122026%26utm_content=primary-ad_headline/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/La3uPIhYwO_MhyqLDYtBDJm5hTgFebAZ-aHDZxl9pmA=452"&gt;&lt;img src="https://images.tldr.tech/cube2.png" valign="middle" style="vertical-align: middle !important; height: 100%;" alt="Cube"&gt;&lt;/a&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;"&gt;
&lt;div style="text-align: center;"&gt;

&lt;h1&gt;&lt;strong&gt;TLDR Data &lt;span id="date"&gt;2026-10-12&lt;/span&gt;&lt;/strong&gt;&lt;/h1&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 id="sponsy-copy"&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%2Fcubecloud.dev%2Fauth%2Fsignup%3Futm_source=tldrdata%26utm_medium=newsletter%26utm_campaign=Primary10122026%26utm_content=primary-ad_headline/2/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/ufcqXxHjdRbnovvZXuvH57rIUbTh7aX99AubDtlrVKk=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Your agents sound confident. Cube makes them correct (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;
                                    As agents get more autonomous, they act on your numbers without waiting for a person. An LLM pointed at raw tables re-derives your metric logic on every prompt. Whatever it guesses becomes your source of truth. 🤦‍♂️&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcube.dev%2F%3Futm_source=tldrdata%26utm_medium=newsletter%26utm_campaign=Primary10122026%26utm_content=primary-ad_body_brand/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/YWbRCrP2E5hdVdWyexWqeflbGBcwgUklTT5eQIsSz14=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;&lt;strong&gt;Cube&lt;/strong&gt;&lt;/span&gt;&lt;/a&gt; is the agentic analytics and BI platform, built on a semantic layer. Your definitions and permissions are enforced deterministically — in dashboards, embedded apps, and Claude, ChatGPT or Cursor over MCP.&lt;/p&gt;

&lt;p&gt;👀 Brex chose Cube over dbt Semantic Layer and LookML. Budget answers that took two days now take 2 seconds, for 35,000+ customers.&lt;/p&gt;

&lt;p&gt;🚀 &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcubecloud.dev%2Fauth%2Fsignup%3Futm_source=tldrdata%26utm_medium=newsletter%26utm_campaign=Primary10122026%26utm_content=primary-ad_cta_outro_start-free/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/oBDuJv8f0DnIKeu1MToR-_D8OuYeXYg6PGpV1IpjWyA=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;&lt;strong&gt;Start free — free-forever tier with Chat and MCP →&lt;/strong&gt;&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;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;
&lt;/td&gt;&lt;/tr&gt;
&lt;tr bgcolor=""&gt;&lt;td class="container"&gt;

&lt;table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td style="padding: 0px;"&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;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;Deep Dives&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.linkedin.com%2Fblog%2Fengineering%2Finfrastructure%2Finside-linkedins-data-context-layer-curating-trustworthy-context-for-humans-and-agents%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/s31Gj36bMKxY0dHbsmaJ1P-dsePI2Jzp4A9d9giamEs=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Inside LinkedIn's data context layer: curating trustworthy context for humans and agents (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;
                                    LinkedIn built a shared layer that tells people and AI agents which datasets to trust, which filters and joins are required, and what each row means. It combines profiles that an LLM writes from schemas, query logs, and code with notes that teams write and review in Git like code. After LinkedIn's analytics agent was connected to it, the share of acceptable answers in one test rose from 25.9% to 79.5%, and median time to answer fell from 10.7 to 4.7 minutes.
                                &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%2Fengineering.salesforce.com%2Fapache-spark-resource-optimization-lessons-from-eight-million-jobs-a-day%2F%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/Qpp4078RqS83cVP-WP6WfppMOnLcm1SZnkJVS6RjFH0=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Apache Spark Resource Optimization: Lessons From Eight Million Jobs a Day (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;
                                    Salesforce runs about 8 million Spark jobs a day and found some workloads left executors idle more than 90% of the time. Sizing targeted workloads to measured usage reduced weekly compute costs by 31%, while runtime, failures, and retries remained within acceptable levels.
                                &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%2FolX0hu/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/xBGAs6gCdCqRCBUODNcs7hrqYLDqcr7-gyQRT0UoYPg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;From Activity to Intent: Generating User Journeys with LLMs (14 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;
                                    Pinterest replaces a fragile keyword-clustering pipeline with a fine-tuned 4B LLM that converts a user's activity history into ranked, localized “journeys” (active goals). Teacher-model distillation makes it affordable at scale. The system improves notification engagement while applying strict output validation, safety filters, and feedback loops.
                                &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%2Fipolyzos.substack.com%2Fp%2Fwhat-defines-the-streamhouse-architecture%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/loorsgQVHJhxH5FDk2f0ZeWWRMIQ1eoy9N_fkj1C7Ag=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;What Defines The Streamhouse Architecture? (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;
                                    Apache Fluss' “Streamhouse” concept proposes one maintained logical table for streaming, serving, and analytics instead of separate synchronized systems. The key idea is to unify fresh stream data and historical lakehouse data behind shared metadata and tiering, so teams stop rebuilding state in multiple places. This can cut duplicate ingestion while still allowing specialized caches or replicas for specific access patterns.
                                &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;

&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;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;Opinions &amp; Advice&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%2Fthedatavist.substack.com%2Fp%2Fclaude-dashboards-and-the-shift-from%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/TEZod8npVQZkclbSt2ZluNIs4ezih6j7RC0k4mNkTGU=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Claude Dashboards and the shift from chart builder to chart specifier (14 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;
                                    Anthropic's new Claude Dashboards beta can build a dashboard without making a single chart manually. You can trace every number back to its source, but there is no row-level security, refresh, or audit log yet. The advice to BI developers: spend less time on tool tricks and more on data modelling, SQL, and design.
                                &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%2Fexplainmeasured.com%2Fposts%2Fqueries-doing-extra-work%2F%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/4-LpYWmgLcPJASfJZyU_z4Pt_SVD5uXlzyyMmgYse0o=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Five PostgreSQL queries that did more work than I asked for (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;
                                    Five PostgreSQL benchmarks show how unnecessary deduplication, CTE materialization, random sorting, extra column retrieval, and unindexed expressions can dramatically slow queries, with small SQL changes or a matching index delivering major speedups.
                                &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%2Fcontextandchaos.substack.com%2Fp%2Fyour-ontology-records-permission%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/J9OS41Bqpn52SbTd9emAN_wPo888UV8dQKe6itXnrSA=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Your Ontology Records Permission. It Does Not Resolve Authority (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;
                                    Enterprise agents can enforce permissions yet make bad decisions when formal records omit the people whose judgment shapes outcomes. The Authority Resolution Framework separates documented rights, system permissions, recorded approvals, informal influence, and an agent's mandate, then treats disagreement among them as a governance signal. Audit authority gaps before automation turns organizational blind spots into confident actions.
                                &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;

&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;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%2Faws.amazon.com%2Fmarketplace%2Fbuild-learn%2Fai-agent-learning-series%2Fdata-pipelines-and-lineage%3Ftrk=b0663ee7-1444-430d-adf2-28368ccfee31%26sc_channel=el/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/j9BFJMZ7Rdbx3HT2ijlJY2dlC0rs0UzlsTdC73vt9Xg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Data pipelines and lineage for AI agents on AWS (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;
                                    Your AI agent's accuracy comes down to the data pipeline behind it. Data Pipelines and Lineage is a guide to engineering that pipeline: source-to-response lineage, data quality checks, and pipeline observability. &lt;a class="TypographyPresentation_31345133_root LinkThemeablePresentation LinkThemeablePresentation_450120641_root Surface_311631053_color--selected-text Surface_311631053_color-hover--selected-text-hover Surface_311631053_color-active--selected-text-hover HighlightSol_1625403506_highlightSol HighlightSol_1625403506_buildingBlock HighlightSol_1625403506_highlightSol HighlightSol_1625403506_core HighlightSol_1625403506_buildingBlock" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Faws.amazon.com%2Fmarketplace%2Fbuild-learn%2Fai-agent-learning-series%2Fdata-pipelines-and-lineage%3Ftrk=b0663ee7-1444-430d-adf2-28368ccfee31%26sc_channel=el/2/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/tSvBbSj3TQUbcXQGaYRPPsx0oiCBAJvkpeF8ByycVJg=452" rel="noreferrer noopener" target="_blank"&gt;&lt;span&gt;Read the guide in AWS Marketplace&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%2Fgithub.com%2Felstongun%2Fleviathan%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/fA-RKgsMO_yFNrUMDms5TGn2jXpdT3rO41B-iXXA5K4=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Leviathan: Deep memory for agents over large datasets (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;
                                    Leviathan is a Rust tool that gives AI agents compact, cited search results from large datasets using SQLite full-text indexing, with optional persistent memory and CLI, MCP, or remote access.
                                &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%2F6aNwWm/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/uKIHSAunSU-wCgpVXXJpS0VDpk2JWIaEuVGtyie1jJ0=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;I'm now convinced single-node DuckDB is the best engine at any realistic scale (Reddit Thread)&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 Databricks user found that Spark, Trino, and DuckDB all spent most of their time waiting on S3, with CPUs about 90% idle. After running four times as many read threads as CPU cores, one DuckDB machine beat Trino on the same hardware by 1.7 to 2.1 times on 10 TB queries. Against a Trino cluster with 5 to 13 nodes, it was 2 to 3 times slower but about half the cost.
                                &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%2Fduckdb.org%2F2026%2F10%2F09%2Fagent-mode%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/KNdl4_a2GGqVGylsoY6z3Ib_qrVHydxU4ryuibp_Ekk=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Agent Mode in the DuckDB CLI (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;
                                    The DuckDB 2.0 command line now detects when an AI agent is using it. It then switches to compact output, flags cut-off results, stops runaway queries, and returns errors as JSON. Across 132 TPC-H runs, agents read back about 60% fewer tokens and still got every answer right.
                                &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;

&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;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;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.infoq.com%2Fpresentations%2Fnetflix-observability-aiops-ontology-scale%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/V5KumxcJw2FdMmdsG3AoxQHJh0_Yyu06LLGXMevqiYk=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Ontology‐Driven Observability: Building the E2E Knowledge Graph at Netflix Scale (51 minute video)&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;
                                    Netflix is using an ontology-driven knowledge graph to make observability data, incident chatter, and service topology queryable as one system. This delivers faster automated triage and root-cause analysis at large scale, with AI helping turn messy human signals into structured facts. The key takeaway: better incident response may come from modeling operational meaning, not just collecting more telemetry.
                                &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%2Fboringsql.com%2Fposts%2Ffiles-forks-and-relfilenodes%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/zkPAwWKljHXtYZirvJgRDRfCkvcdStWguKndHPN7o48=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Postgres relation files, filed under base/ (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;
                                    Postgres stores each relation as physical files identified by tablespace, database, and relfilenode, with extra forks and TOAST files appearing as needed. Logical objects and physical files are loosely coupled: operations like VACUUM FULL or REINDEX can rewrite storage and swap relfilenodes while the table's identity stays the same. Large relations also continue transparently across segmented files.
                                &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;



&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;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;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;

&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%2Fpeter.eisentraut.org%2Fblog%2F2026%2F10%2F09%2Fthe-reverts-will-continue-until-morale-improves%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/aaAIdIEdE0CJ80VjwAgeQ5sqPiRud8PjNq649fIl00o=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;The reverts will continue until morale improves (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;
                                    Eight to ten major features have been pulled from Postgres 19 during beta, some very late.
                                &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.elastic.co%2Fsearch-labs%2Fblog%2Fcontext-engineering-for-agents%3Futm_source=tldrdata/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/m4ot7ShXobU2SU7tErenftpbFewtaJ3Uhg5Nh0Ia9cE=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;How we're building a context engine to cut agent token use by 71% (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;
                                    Agents waste most of their tokens working out what tables and fields mean, then do it again on the next question.
                                &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;






&lt;table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td align="left" style="word-break: break-word; vertical-align: top; padding: 5px 10px;"&gt;

&lt;p style="padding: 0; margin: 0; font-size: 22px; color: #000000; line-height: 1.6; font-weight: bold;"&gt;
Want to advertise in TLDR? 📰
&lt;/p&gt;
&lt;div class="text-block" style="margin-top: 10px;"&gt;
If your company is interested in reaching an audience of data engineering professionals and decision makers, you may want to &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fadvertise.tldr.tech%2F%3Futm_source=tldrdata%26utm_medium=newsletter%26utm_campaign=advertisecta/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/C51eQoNNXq5aHnC03nx1EbxHBMD89FdhADGNthLypSc=452"&gt;&lt;strong&gt;&lt;span&gt;advertise with us&lt;/span&gt;&lt;/strong&gt;&lt;/a&gt;.
&lt;/div&gt;
&lt;br&gt;

&lt;!-- New "Want to work at TLDR?" section --&gt;

&lt;p style="padding: 0; margin: 0; font-size: 22px; color: #000000; line-height: 1.6; font-weight: bold;"&gt;
Want to work at TLDR? 💼
&lt;/p&gt;
&lt;div class="text-block" style="margin-top: 10px;"&gt;
&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjobs.ashbyhq.com%2Ftldr.tech/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/DIgTxxdp5711twdI9U6biUfrxBnM7gIUjL482s5zLEk=452" rel="noopener noreferrer" style="color: #0000EE; text-decoration: underline;" target="_blank"&gt;&lt;strong&gt;Apply here&lt;/strong&gt;&lt;/a&gt;,
&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjobs.ashbyhq.com%2Ftldr.tech%2Fc227b917-a6a4-40ce-8950-d3e165357871/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/DOLPQO4o3X2sP1hAwI6klbrVLN46j_inmtb1WhHdUzo=452" rel="noopener noreferrer" style="color: #0000EE; text-decoration: underline;" target="_blank"&gt;&lt;strong&gt;create your own role&lt;/strong&gt;&lt;/a&gt; or send a friend's resume to &lt;a href="mailto:jobs@tldr.tech" style="color: #0000EE; text-decoration: underline;"&gt;jobs@tldr.tech&lt;/a&gt; and get $1k if we hire them! TLDR is one of &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Ffeed%2Fupdate%2Furn:li:activity:7401699691039830016%2F/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/QbkOYgFx_LI1Ft1o4Y7HYpqoGptvwh_1-KFCS7GIzyI=452" rel="noopener noreferrer" style="color: #0000EE; text-decoration: underline;" target="_blank"&gt;&lt;strong&gt;Inc.'s Best Bootstrapped businesses&lt;/strong&gt;&lt;/a&gt; of 2025.
&lt;/div&gt;
&lt;br&gt;

&lt;div class="text-block"&gt;
If you have any comments or feedback, just respond to this email!
&lt;br&gt;
&lt;br&gt; Thanks for reading,
&lt;br&gt;
&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Fin%2Fjoelvanveluwen%2F/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/CFoWOhsR8wpDG-CJICpbO0VU6D9vR8C2RTtMEGfWV4I=452"&gt;&lt;span&gt;Joel Van Veluwen&lt;/span&gt;&lt;/a&gt;, &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Fin%2Fjennytzurueyching%2F/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/0b5apuyFFueTMdlgsbF1HozfqhFYvYBQzIcpy77VkYE=452"&gt;&lt;span&gt;Tzu-Ruey Ching&lt;/span&gt;&lt;/a&gt; &amp; &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Fin%2Fremi-turpaud%2F/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/Ac4Su_DS774s7ROLzI9x6SbeEw1mnVnvFsPgP1jQw4Y=452"&gt;&lt;span&gt;Remi Turpaud&lt;/span&gt;&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;/div&gt;
&lt;br&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: 15px 15px;"&gt;
&lt;div class="text-block" id="testing-id"&gt;
&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftldr.tech%2Fdata%2Fmanage%3Femail=silk.theater.56%2540fwdnl.com/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/robGAwe2pV5QCS90Rd7Z-Z3_TuH1jTdf4gjS1O7XjV8=452"&gt;Manage your subscriptions&lt;/a&gt; to our other newsletters on tech, startups, and programming. Or if TLDR Data isn't for you, please &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fa.tldrnewsletter.com%2Funsubscribe%3Fep=1%26l=037ede50-92cc-11ee-b0f2-b761aa2217ad%26lc=1670a604-84b7-11f0-bcf5-55fc1d40139c%26p=add32f7c-c614-11f1-89d3-a7bc31e86e00%26pt=campaign%26pv=5%26spa=1791799265%26t=1791799946%26s=a4df1091bcaaac87c9c1df42693ff2cdfb24de9a6cf1d6288107f71797f5a9d2/1/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/1zArH0SaI8A9-dZtcmQraWUj5a5IGI0uJhFtRNIoeeE=452"&gt;unsubscribe&lt;/a&gt;.
&lt;br&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;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&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;


&lt;img alt="" src="http://tracking.tldrnewsletter.com/CI0/010001a12f996f14-3de485c5-7aa6-469b-b0ba-1504b1896e60-000000/HJ9PEIvZJAcRapzyjXqQF09F_5ddDkrXNshDkXAGE_A=452" style="display: none; width: 1px; height: 1px;"&gt;
&lt;/body&gt;&lt;/html&gt;
Email Content
