Email: PostHog’s AI Evals 🧪, Polars vs DuckDB 🏁, Jepsen Finds Issues in ParticleDB 💥

Subject:
PostHog’s AI Evals 🧪, Polars vs DuckDB 🏁, Jepsen Finds Issues in ParticleDB 💥
Received:
11/2/2026, 11:23:28 AM
From:
"TLDR Data" <dan@tldrnewsletter.com>
To:
donut.sweater.23@rss.andrealin.it
&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;PostHog tests AI agents on real tasks, such as finding the bugs behind 33 past incidents, where its review agent caught 62%, or checking&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/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/mHqk86dQ3kXs8C67zqcAIitUcr8195hHvzwH-N7Wn0c=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/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/RiE5zM_bypVwDb7hvFBM9ry5NMBDVvVZzFjERuVFFkc=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=aa6c57ca-be20-11f0-8e04-b5caa3a65826%26p=b6bd7b76-d67a-11f1-a213-53364ae89221%26pt=campaign%26t=1793618607%26s=924fa695336460d6e7c5e09597d9e6f73dc1c97fa5588f9d9b3f07758d17bc4d/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/Cuf4Yz4wQXj7lOwKXVxaDv47ZC-aRNRaJWEiSjY8jc0=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%2Fnextquery.phoenixdata.ai%2F%3Futm_source=partner_email%26utm_medium=email%26utm_campaign=FY26_PHX_OEV_NEXT_QUERY_2026%26utm_content=newsletter-tldr/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/_9ewfKmo4ePzHd4hZxlxrpL1nDU3ASkEDgXffTeumw4=452"&gt;&lt;img src="https://images.tldr.tech/phoenix.png" valign="middle" style="vertical-align: middle !important; height: 100%;" alt="Phoenix AI"&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;2026-11-02&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%2Fnextquery.phoenixdata.ai%2F%3Futm_source=partner_email%26utm_medium=email%26utm_campaign=FY26_PHX_OEV_NEXT_QUERY_2026%26utm_content=newsletter-tldr/2/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/h7bxfiAbKSLHbNni4hRTzRsyMtBlo0QcGHGKXjJcgw8=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;NEXT QUERY 2026 is this Wednesday: 20+ production talks, free and virtual (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;
                                    Most hard lessons in data engineering never get written down. They live in someone's head after a long night in production. This Wednesday, 20+ teams share theirs:&lt;br&gt;&lt;br&gt;- Agents and business meaning: DatusAI and PuppyGraph on ontologies.&lt;br&gt;- Fresh context, controlled access: Fresha on real-time state, Toss on permissions.&lt;br&gt;- Reliability at scale: Coinbase, AppLovin and Intuit on production architecture.&lt;br&gt;- Performance and cost: AWS on EKS, Apple's managed analytics service, Carbon Arc's 1 PB migration.&lt;br&gt;- GPUs for analytics: NVIDIA on why GPU execution is now practical at scale.&lt;br&gt;&lt;br&gt;Free and virtual, with recordings for everyone who registers.&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fnextquery.phoenixdata.ai%2F%3Futm_source=partner_email%26utm_medium=email%26utm_campaign=FY26_PHX_OEV_NEXT_QUERY_2026%26utm_content=newsletter-tldr/3/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/VZnxqzIYC4__He0QsBLtTvULez94U6cNhxHKZ2iaMbQ=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Register free&lt;/span&gt;&lt;/a&gt; · &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fnextquery.phoenixdata.ai%2F%3Futm_source=partner_email%26utm_medium=email%26utm_campaign=FY26_PHX_OEV_NEXT_QUERY_2026%26utm_content=newsletter-tldr%23agenda/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/Z7_o39SNtDiNNbH8_r9myi6BTYKw-X5j_czvBC4ERsw=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;See the agenda&lt;/span&gt;&lt;/a&gt;
&lt;br&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%2Fposthog.com%2Fblog%2Fhow-to-benchmark-ai-agents%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/BpkGNmc1RH8u6rRWd66mh5G_nKCbDBvcInRMkqI2gMg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Do you even benchmark? How we evaluate agents at scale (18 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;
                                    PostHog tests AI agents on real tasks, such as finding the bugs behind 33 past incidents, where its review agent caught 62%, or checking whether playback failures affected real users. It isolates each test so agents cannot copy earlier answers and is building resettable environments with fixed data to make comparisons fair.
                                &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%2FKj8Mwb/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/2uYvwbT8W2zVJ28SH45tr0iGLpDkNGCknjgUNHFikjo=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;I Tested Polars 2.1 Against DuckDB 2.0 (10 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;
                                    DuckDB 2.0 adds client-server and AI features, while Polars improves streaming and processing of larger-than-memory datasets. On a 23 GB dataset with 2 GB RAM, Polars 2.0 ran out of memory, but 2.1 finished in 438 seconds, nearly twice as fast as DuckDB's 837 seconds.
                                &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%2Faiven.io%2Fblog%2Fmove-the-elephant-keep-the-lights-on%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/go1q8H9ZSH5yw5zrquOyibmCLnd_RgM97NZ_z51h7fw=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Move the elephant, keep the lights on (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;
                                    Logical replication lets you upgrade or migrate PostgreSQL with only seconds of downtime: run the target cluster in parallel, stream changes, then cut over when ready. Its real advantage over in-place upgrades is rollback safety and cross-environment portability. Plan around what it does not move automatically, especially schema changes and sequences.
                                &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%2Fstartree.ai%2Fresources%2Fvector-search-on-apache-iceberg-without-moving-your-data%2F%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/W-Ol7esALptkV8ifnW4kqemU8We7YxPLPWcxGZiMZa0=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Vector Search on Apache Iceberg Without Moving Source Data (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;
                                    StarTree adapts Pinot's HNSW vector index for Iceberg tables, keeping Parquet source records in object storage while storing and serving a derived index alongside them. This enables hybrid similarity and SQL-filtered queries without a second copy of the records. The trade-off: indexes are roughly as large as the embeddings, and freshness follows Iceberg snapshot synchronization rather than real-time ingestion.
                                &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%2Fjepsen.io%2Fanalyses%2Fparticledb-1.1.4%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/YaMMiaw-ilWXyvMiizqQLBvEoenzmfDrACZ0FxBE6cg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;ParticleDB 1.1.4 (80 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;
                                    Jepsen found 96 issues in ParticleDB, including data loss, crashes, and incorrect SQL results, mostly without any server failures. Most were fixed during testing, but serious transaction bugs remained. Orkes says a newer release fixes them, though Jepsen has not verified this.
                                &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%2Fjarkkomoilanen.com%2Farticles%2Fwhat-happened-to-data-products%2F%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/8_7YAlqIiMKMIv_pKDxxJ7SoE2zWRyHz_etlv7kv1po=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;What Happened to Data Products? (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;
                                    Data products outlasted data-mesh hype by becoming a delivery discipline rather than an enterprise reorganisation: a governed output, accountable owner, defined consumer, and evidence of value. AI agents raise the stakes because they cannot recover missing context through informal knowledge. Start with a business-critical workflow, then make meaning, quality, access, and permitted use machine-readable.
                                &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%2Fwww.deasylabs.com%2F%3Futm_source=mediadirect%26utm_medium=paid-display%26utm_campaign=GBL-26-Q4-SCL-Q4-Deasy-hero/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/8-NvWT6TFNJh_mOuGT0XM0lA-JeiyKf5U08532My-84=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;46% more accurate when your agent stops reading the drafts (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;
                                    Nobody deletes anything from SharePoint, so your agent reads old versions, contradictions, half-finished files and rogue customer data. Deasy qualifies every file and keeps the bad ones out of reach. Takes minutes. Works for every agent after that.&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.deasylabs.com%3Futm_source=mediadirect%26utm_medium=paid-display%26utm_campaign=GBL-26-Q4-SCL-Q4-Deasy-hero/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/s0Ul7FZzlgcpSEthgUl0RwSTHFSGDgbpQkxKMPY4dbY=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;&lt;strong&gt;See it →&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;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%2Fnodal-data%2Fnodal-context%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/olVq1cn-m8ITBz7AWOSuQ0LdAR36EEutO_MNEmH0zkI=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Nodal Analytics (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;
                                    Nodal is an open-source toolkit that turns analysts' business definitions into reusable context for AI analytics agents. It plans queries and checks results against approved metrics, filters, and joins, flagging uncertainty for expert review.
                                &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%2Ficeberglakehouse.com%2Fposts%2Ficeberg-v4-table-constraints%2F%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/6WNeqz7dSHax-ykPjrVZr328G-udPlGS6-GomxpArdA=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Primary Keys Come to Apache Iceberg V4, and Most of Them Start Out Unenforced (23 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 Iceberg V4 adds primary keys, unique rules, and checks, such as requiring positive order amounts, though most will initially be unenforced. Each snapshot records whether the data meets those rules, separately from whether writers must enforce them on new writes.
                                &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%2Frynr.dev%2Fblog%2F700mbskafgres%2F%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/gnvrQXgTHXDGLtyNS71Q-2s1Ig88NISmhW23dHxgGv0=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Kafgres 0.2.0: 700 MB/s Kafka-Compatible Streaming Inside Postgres (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;
                                    Kafgres, a Kafka-compatible Postgres extension, reached about 700 MB/s after caching query plans, relaxing commit durability, and replacing timer-based polling with socket readiness checks. It could suit many workloads, but the single-broker setup lacks the resilience of a Kafka cluster spread across availability zones.
                                &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%2Ftowardsdatascience.com%2Fi-told-an-ai-agent-that-its-wrong-answers-were-right-one-model-believed-me%2F%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/JA_59_iV3ZKw5pchIs1-JJegXLFfjVROXSyruqhFUSo=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;I Told an AI Agent That Its Wrong Answers Were Right. One Model Believed Me (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;
                                    In most cases, flipping the reward labels in an agent's memory barely changes how it learns. This test hid a rule only the labels could reveal. With true labels, GPT-6 Sol scored 94.4 out of 100 on its last six trials, and with flipped labels 7.8. Claude Haiku 5.5 improved less, and Qwen3 4B barely moved.
                                &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%2Fhackernoon.com%2Factive-active-oracle-goldengate-across-regions-decide-who-owns-the-data-before-you-resolve-conflict%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/D__SsoK8DJostf_VpbKOYTLrPGRQeE9X1Fer0ykvxXY=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Active-Active Oracle GoldenGate Across Regions: Decide Who Owns the Data Before You Resolve Conflict (10 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Active-active GoldenGate only works when data ownership and conflict policy are designed table by table before replication starts. Simulations showed that even convergent strategies like last-writer-wins can silently lose updates, while delta merging preserves each decrement but cannot prevent both regions from accepting a request against the same last unit before replication catches up. The lesson is blunt: if business rules cannot tolerate reconciliation, do not rely on multi-master replication to fix the problem later.
                                &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%2Fthenewstack.io%2Fp99-conf-database-tradeoffs%2F%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/ilPPAM1yKzDUYpfXEm6ssKdsVw9r5Km_XWtzqtnK3aY=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;What Five P99 CONF Talks Reveal About Database Trade-offs (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;
                                    A sponsored P99 CONF round-up exposes the costs behind several database designs: object storage trades low-cost durability for cold-read latency, read-optimized engines trade RAM and write amplification for tail latency, and vector search trades quantization and cache complexity for scale. The reported benchmarks are workload-specific, but the recurring lesson is: removing one bottleneck moves the constraint elsewhere.
                                &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%2Fengineering.zalando.com%2Fposts%2F2026%2F10%2Fupgrading-mlflow-pipelines.html%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/0C4u0ukwEwfXfeGGr00qElaSyvTIwyLQ_AazRYg1ifw=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Upgrading MLflow Pipelines Across a Python Version Boundary (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;
                                    Moving Databricks from Python 3.10 to 3.12 broke Zalando's pickled MLflow models.
                                &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%2Fstormatics.tech%2Fblogs%2Fpostgresql-backups-restore-testing%3Futm_source=tldrdata/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/su7TbtSkn37yoB6iHGsaVgPptXHRT2AfW6paEao58_M=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Your backups are green. That does not mean you can restore (6 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;
                                    Successful PostgreSQL backups can still fail to restore because of missing transaction logs, copied corruption, or lost encryption keys.
                                &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/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/gwY7snkxwbbivzWS365Sh1KTUzlTP2Z-NAAG8mvB9Rc=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/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/YAKSAO0-SkX7wmz4ZMfrkfSjyLrx9CkquCoo5GZ6FUU=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-d3e165357871/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/2i9O5K9zipUXFweSCjplxcBfDltayNA9Oca4rBHkZxw=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/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/GiFI3Q9C0FVZFaWqE74yBV6tknjR8M3St1Phv9p8-oo=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/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/zr-jcYENnP3wLmqE2GZwmrPghki__EX7Yr0gxv9ql64=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/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/yVGLukYQUo5tdwjHBl8mwAr-TbiP8wS460mcnLJwyGQ=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/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/LlY2dcArY0T5UT9T-A2YdOVXAr9hTYxOn6Zas2--FmA=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=donut.sweater.23%2540rss.andrealin.it/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/xbCbM3AhBvs_iBXCnzlk0b5oMSbKDaPCe9iFb04pGuw=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=aa6c57ca-be20-11f0-8e04-b5caa3a65826%26p=b6bd7b76-d67a-11f1-a213-53364ae89221%26pt=campaign%26pv=5%26spa=1793617260%26t=1793618607%26s=ec457fe9aecab6f98be1bb0204e2c23a19e583701b8d5f134664ddffd852934e/1/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/fScmEH9cJUY8WR-aem6CcRV88VBP6HgmMu80oZRlu7c=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/CI0/010001a19bffff7f-faac0ab1-31db-40e2-b1f7-7137b51dee71-000000/KW0FIaFlJBRtaYWVhx0XKdyBITRG-R3WnAFpr14PHE4=452" style="display: none; width: 1px; height: 1px;"&gt;
&lt;/body&gt;&lt;/html&gt;
Email Content
