Email: LangChain’s Paid Media Agent 🎯, DuckDB Skills for Claude Code 🦆, How Many Labels? 🏷️

Subject:
LangChain’s Paid Media Agent 🎯, DuckDB Skills for Claude Code 🦆, How Many Labels? 🏷️
Received:
9/17/2026, 10:12:59 AM
From:
"TLDR Data" <dan@tldrnewsletter.com>
To:
silk.theater.56@fwdnl.com
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&lt;div style="display: none; max-height: 0px; overflow: hidden;"&gt;LangChain built a paid-media agent that analyzes campaign data, recommends changes, and applies approved actions, helping paid media reach&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;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.datadoghq.com%2Fresources%2Fdata-analytics-solution-brief%2F%3Fsp=Kmo1Ys4bLV9enpyzEbuoVETH%26utm_source=tldrnewsletter%26utm_medium=newsletter%26utm_campaign=dg-content-solutionbrief-solutionbriefdataanalytics-dataobservability-multi-ww-en-7018y000001lfoxqaq-tldr-data-primary-091726%26utm_content=header_datadogs_guide_unifying%26utm_term=2026-09-17_tldr_data/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/JZbjBrFe0CU_XLH1iWxWIn40vomQG_ngJAKAoHuEORU=452"&gt;&lt;img src="https://images.tldr.tech/datadog50.png" valign="middle" style="vertical-align: middle !important; height: 100%;" alt="Datadog"&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;2026-09-17&lt;/span&gt;&lt;/strong&gt;&lt;/h1&gt;
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                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.datadoghq.com%2Fresources%2Fdata-analytics-solution-brief%2F%3Fsp=Kmo1Ys4bLV9enpyzEbuoVETH%26utm_source=tldrnewsletter%26utm_medium=newsletter%26utm_campaign=dg-content-solutionbrief-solutionbriefdataanalytics-dataobservability-multi-ww-en-7018y000001lfoxqaq-tldr-data-primary-091726%26utm_content=header_datadogs_guide_unifying%26utm_term=2026-09-17_tldr_data/2/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/VTAzAnkBh_kFSrkm5oNBb1bQCpZ4FuN9hdHsBoA3zO8=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Datadog's guide to unifying analytics across 700+ integrations (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;
                                    Data silos used to make sense as a bad but sensible tradeoff. Breaking them down was just too much manual effort to be worth the squeeze.&lt;p&gt;&lt;/p&gt;&lt;p&gt;Today, that tradeoff no longer applies, and &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.datadoghq.com%2Fresources%2Fdata-analytics-solution-brief%2F%3Fsp=Kmo1Ys4bLV9enpyzEbuoVETH%26utm_source=tldrnewsletter%26utm_medium=newsletter%26utm_campaign=dg-content-solutionbrief-solutionbriefdataanalytics-dataobservability-multi-ww-en-7018y000001lfoxqaq-tldr-data-primary-091726%26utm_content=body_intro_datadog_report%26utm_term=2026-09-17_tldr_data/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/Uxc7s3VefxCGL8i6XqWc420GN4xsq2o5RR4nqy-swMM=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;this Datadog report&lt;/span&gt;&lt;/a&gt; breaks it down. See how you can:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Build dashboards to collate key monitoring data from &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.datadoghq.com%2Fresources%2Fdata-analytics-solution-brief%2F%3Fsp=Kmo1Ys4bLV9enpyzEbuoVETH%26utm_source=tldrnewsletter%26utm_medium=newsletter%26utm_campaign=dg-content-solutionbrief-solutionbriefdataanalytics-dataobservability-multi-ww-en-7018y000001lfoxqaq-tldr-data-primary-091726%26utm_content=body_outro_700_plus_integrations%26utm_term=2026-09-17_tldr_data/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/JD_QCxz8kUUGR2A0Sj13dzJyrEM6x2uSrwxwInbA57E=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;700+ integrations&lt;/span&gt;&lt;/a&gt; and visualize it all in one place&lt;/li&gt;
&lt;li&gt;Correlate traces, logs, and infrastructure metrics to get deeper context for troubleshooting&lt;/li&gt;
&lt;li&gt;Utilize unified analytics visibility to maintain and optimize your business intelligence&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Stop making a tradeoff that no longer reflects reality. &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.datadoghq.com%2Fresources%2Fdata-analytics-solution-brief%2F%3Fsp=Kmo1Ys4bLV9enpyzEbuoVETH%26utm_source=tldrnewsletter%26utm_medium=newsletter%26utm_campaign=dg-content-solutionbrief-solutionbriefdataanalytics-dataobservability-multi-ww-en-7018y000001lfoxqaq-tldr-data-primary-091726%26utm_content=cta_guide%26utm_term=2026-09-17_tldr_data/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/loKIEdPkIGerTsNv1BEG8zODTBNDDqqbY01y9PeFqA8=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Get your guide&lt;/span&gt;&lt;/a&gt;


&lt;/p&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;
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&lt;h1&gt;&lt;strong&gt;Deep Dives&lt;/strong&gt;&lt;/h1&gt;
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&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.langchain.com%2Fblog%2Fpaid-media-agent%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/3_6Nj2mrqBX9UydQjI__54EWRP_CqD90AFkSSbk50SA=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;How we built LangChain's Paid Media Agent (19 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    LangChain built a paid-media agent that analyzes campaign data, recommends changes, and applies approved actions, helping paid media reach 20% of its marketing pipeline while cutting qualified lead costs. Its key design principle is to use models for judgment, code for reliable calculations and safeguards, and human approvals for consequential actions.
                                &lt;/span&gt;
&lt;/span&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.infoq.com%2Fpresentations%2Fform3-multicloud-architecture%2F%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/6NnUx1pmmFiGwwPnxxjCod8s6J-yZcfJXuz4Xdn1b7M=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;How To Run on Three Clouds at Once, and When Not To (45 minute presentation)&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;
                                    Form3 runs its payment platform active-active-active across AWS, Google Cloud, and Azure, treating each provider like an availability zone. Kubernetes, NATS JetStream, and CockroachDB span all three clouds, while custom DNS, networking, and disruption controls preserve quorum. Its US deployment deliberately uses active-standby instead, showing resilience architecture should follow regulatory needs, latency limits, and cost.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fsemyonsinchenko.github.io%2Fssinchenko%2Fpost%2Ffastrp-datafusion%2F%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/oHS1mgS1oboXy8gI1qlYFosiGe5cXOPlErmw-mIe494=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;FastRP graph embeddings with Apache DataFusion (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;
                                    Graph embeddings turn connected entities into vectors for similarity search, GraphRAG, and graph machine learning, but Node2Vec's random walks create huge intermediate datasets and scale poorly. FastRP replaces that pipeline with iterative neighbor aggregation. A Rust/DataFusion implementation runs out-of-core, matched Node2Vec-level predictive quality in small tests, and processed an eight-million-node graph in about 15 minutes.
                                &lt;/span&gt;
&lt;/span&gt;
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&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;🚀&lt;/span&gt;&lt;/div&gt;
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&lt;h1&gt;&lt;strong&gt;Opinions &amp; Advice&lt;/strong&gt;&lt;/h1&gt;
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                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftowardsdatascience.com%2Fhow-many-labeled-examples-does-a-text-classifier-actually-need-i-measured-it%2F%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/GdlXmcbs2QPB8qXrh5Inx8kCfcXs59bwgwhEpecjd6Y=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;How Many Labeled Examples Does a Text Classifier Actually Need? I Measured It (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;
                                    Before defaulting to an LLM for text classification, benchmark a cheap and near-instant classical model against your available labels, expected volume, and acceptable error rate. In a small support-ticket experiment, TF-IDF accuracy rose from 40% to 60% as training data increased from 2 to 10 examples per category, but intent-based ambiguities remained, suggesting a hybrid approach where LLMs bootstrap labels or handle difficult cases while the classical model processes routine ones.
                                &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%2Ftapoueh.org%2Fblog%2F2026%2F09%2Fplan-advice-in-postgresql-19%2F%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/mSKr3ae615WBZ1BXcy4m5pi1WBMpSD4FBCPxlnMnRNg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Plan Advice in PostgreSQL 19 (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;
                                    PostgreSQL 19's pg_plan_advice serializes join order, scan type, join method, and parallelism into replayable plan guidance, while pg_stash_advice applies it by query ID. Structural strings make plan diffs stable enough for CI and upgrades, but selective pinning matters: preserving one proven decision is safer than freezing every choice as data distributions and statistics change.
                                &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%2Fankit-rana.com%2Flogs%2F53-redis-single-threaded-keys-command%2F%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/DH_LcHSXXhYmqUddypTyYkxSobHpHlc7B_dm15931zI=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Why your Redis is slow: it is single threaded and you sent it KEYS (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;
                                    Redis executes commands on a single thread, so one expensive operation such as KEYS, FLUSHALL, a large DEL, or an unbounded collection read can block every client and cause sudden latency spikes even when CPU and network usage look normal. Use SLOWLOG and LATENCY tools to find offenders, replace full scans with cursor-based SCAN commands, use UNLINK for large deletions, shard oversized keys, and disable dangerous commands in production.
                                &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%2Fhex.tech%2Fblog%2Fdont-migrate-300-dashboards%2F%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/r8NmDeouMWGEeVJvCsiZrcVyE7LMcBzNJgz_5Kq1QVk=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;You don't need to migrate those 300 dashboards (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;
                                    Instead of migrating every legacy dashboard, move the trusted metrics, definitions, and business logic that power them. Keep only frequently used dashboards and let AI analytics handle one-off questions using the same governed context.
                                &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%2Fduckdb.org%2F2026%2F09%2F16%2Fduckdb-skills%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/PjOJaEJvYO7v14jV-uaLc_FVTJDDoKkwaL3dY51uHVg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;DuckDB Skills for Claude Code (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;
                                    When Claude Code needs to look at a data file, it writes a small Python script, runs it, reads the output, and guesses at column names and types along the way. The DuckDB team's plugin points the agent at the DuckDB CLI instead, so a local file, a remote file, or an Iceberg table behind a login is just a table it can query, with skills for format conversion, object storage, spatial data, documentation search, and session recall.
                                &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%2Ficeberg.apache.org%2Fblog%2Fapache-iceberg-python-0.12.0-release%2F%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/tKRbVKWTLSOQ0ACl7FJGG_W0Qdk0YDWGF2aOdpIoK-o=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Apache Iceberg Python 0.12.0 Release (2 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;
                                    PyIceberg 0.12.0 adds REST-catalog views, conflict-aware commit retries, incremental append scans, Iceberg v3 geometry and geography types, and early use of the File Format API. Correctness fixes cover partition pruning, deletion vectors, manifests, timestamps, and conversions, while Python 3.14 and stronger CI and security support broaden production readiness.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;🎁&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding-top: 0px; padding-bottom: 0px;"&gt;
&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;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%2Fresearch.google%2Fblog%2Fbypassing-inference-bottlenecks-accelerating-complex-ai-search-with-retrieve-for-train%2F%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/36m9uEZOxuI5uvK-fZhgJ3gEWippStinL00Hvyr1qmA=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Bypassing inference bottlenecks with Retrieve-for-Train (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;
                                    Search systems often need a useful set, not ten versions of the same result. They can ask an AI to explore several angles, but generating those searches one by one is slow and expensive. Retrieve-for-Train teaches a smaller model the exploration strategy ahead of time, letting it produce complementary searches together. Google reports broader result coverage with 12-20x lower query-generation latency.
                                &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%2Fairbnb.tech%2Fai-ml%2Fbeyond-the-model-engineering-ai-infra-with-scientific-judgement%2F%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/KWlO_y0LJHCq3FoGjG_5Q1prKqVWV4oREcA4-naJqW4=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Beyond the model: Engineering AI infra with scientific judgement (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;
                                    Airbnb turned a months-long, notebook-heavy process for analyzing support conversations into Insight Miner, an agent harness that encodes extract-embed-cluster workflows, prompt tuning, hard-example mining, and contrastive labeling. Investigations now run in days across languages and geographies, while reproducible traces let experts audit evidence and spend more time on ambiguous samples, hypotheses, and methodological improvements.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;



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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;⚡&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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

&lt;h1&gt;&lt;strong&gt;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%2Fmilanjovanovic.tech%2Fblog%2Fpostgres-row-level-security-with-ef-core%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/IKQWd13REErsgLaj-_TKZrpwuokpDjAfk5C9QKkZhTE=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Let Postgres Enforce Tenant Isolation (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;
                                    PostgreSQL row-level security can fail closed beneath EF Core query filters, blocking cross-tenant reads and writes even through raw SQL or attached entities when the application uses non-owner roles and reset tenant context on every pooled connection.
                                &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.dataengineerthings.org%2Fpushdown-optimization-dont-take-it-for-granted-f023c2cc1c44%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/MgFY8dMQWBd3qSfNepNvsQ3i3Fx4JMbFjLkwwqgFX94=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Pushdown Optimization: Don't Take It for Granted (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;
                                    Snowflake and Databricks can place equivalent deterministic filters at very different stages, so predicate pushdown should be verified in execution plans rather than assumed.
                                &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%2Fthefulldatastack.substack.com%2Fp%2Fmy-duckdb-ai-workflow%3Futm_source=tldrdata/1/010001a0aedaf0cc-2a45658b-81bd-484f-84fc-d01c5466ccd6-000000/Ii-ZawyIOZV61D5YQvav64Hc71vSP0CF9zOGcgt_uzM=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;My DuckDB + AI Workflow (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;
                                    A simple terminal workflow combining AI agents with DuckDB can handle fast, reliable analysis, data cleaning, and secure access without heavyweight tools or complex semantic layers.
                                &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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