Email: Agents Rewire Robot Policies 🤖, Making AI-SQL Much Faster ⚡, Safe Not Safe 🔍

Subject:
Agents Rewire Robot Policies 🤖, Making AI-SQL Much Faster ⚡, Safe Not Safe 🔍
Received:
9/28/2026, 10:13:08 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;Robotics Harness Optimization used coding agents to evolve a Graph-as-Policy robot controller, boosting simulated throughput 5.27x and success&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;h1&gt;&lt;strong&gt;TLDR Data &lt;span id="date"&gt;2026-09-28&lt;/span&gt;&lt;/strong&gt;&lt;/h1&gt;
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                                    Robotics Harness Optimization used coding agents to evolve a Graph-as-Policy robot controller, boosting simulated throughput 5.27x and success from 67% to 96%. The strongest version only rewired existing skills, without editing them or using human demonstrations.
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                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fboringsql.com%2Fposts%2Frepack-concurrently-costs%2F%3Futm_source=tldrdata/1/010001a0e7810a40-fd10699d-ac3a-4512-8206-b85fba56a031-000000/xeCVPlWZA3rsbtK8c5toFP9f3ZAN_NIoSoeMMLBkGl0=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;What REPACK (CONCURRENTLY) costs while it runs (15 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 adds built-in REPACK (CONCURRENTLY) for online table rewrites, avoiding extensions while generally running faster and generating less WAL than pg_repack or pg_squeeze. The tradeoffs are significant: it can delay VACUUM across the cluster, briefly block writes during the final swap, and currently has a ceiling of approximately 105 million updates/deletes per run.
                                &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%2Ftapoueh.org%2Fblog%2F2026%2F09%2Ften-years-of-postgres-logical-replication%2F%3Futm_source=tldrdata/1/010001a0e7810a40-fd10699d-ac3a-4512-8206-b85fba56a031-000000/qdY8LLZq7EfA5mSAi8chUWc8ZBf1oRbUTs6YbShlHCU=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Ten years of Postgres logical replication (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;
                                    Postgres logical replication has steadily replaced custom data-movement plumbing with core SQL features: row filters, column lists, partitioned targets, streaming apply, parallel apply, and safer loop prevention. This retrospective connects those releases to practical migration and hub-and-worker scaling patterns. It is a useful checklist for teams revisiting replication slots, failover, write routing, and schema evolution.
                                &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/1/010001a0e7810a40-fd10699d-ac3a-4512-8206-b85fba56a031-000000/PgEG6a0zueUiCkb4TNiCiFXITnd54B4934VzXq7DCyI=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/2/010001a0e7810a40-fd10699d-ac3a-4512-8206-b85fba56a031-000000/RMt4stb0Ge2o4ANljs4YAMpVve8M142s0fzsJw0ikH8=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%2Ffsdatalab.github.io%2Fblog%2Fintroducing-quail%2F%3Futm_source=tldrdata/1/010001a0e7810a40-fd10699d-ac3a-4512-8206-b85fba56a031-000000/nKEOJf7VEdW_sxDMCiNNIWUOBEQ4pzna1zQBihEntlA=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Building an Ultra-High Throughput AI-SQL Engine (13 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;
                                    Quail is an open-source AI-SQL engine that jointly optimizes query planning and LLM inference, running 1.84x faster than tuned vLLM on average and up to 14x faster on some workloads. It reduces wasted KV-cache work and GPU scheduling overhead, making large-scale LLM filtering and joins much cheaper.
                                &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%2Fsafenotsafe.dev%2F%3Futm_source=tldrdata/1/010001a0e7810a40-fd10699d-ac3a-4512-8206-b85fba56a031-000000/vbavVxqsalxRyPQHWHAduT8wzocqyt2OnEpBAK0Xrck=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Safe Not Safe (Tool)&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;
                                    Safe Not Safe is a browser-based PostgreSQL migration checker that flags risky DDL patterns like blocking locks, rewrites, and unsafe constraint rollouts. It runs entirely locally in the browser, with no API, uploads, logs, or account required.
                                &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.dremio.com%2Fblog%2Fapache-iceberg-views-portable-view-metadata-across-sql-engines%2F%3Futm_source=tldrdata/1/010001a0e7810a40-fd10699d-ac3a-4512-8206-b85fba56a031-000000/n6GoAlJLRwbvV0y_bZaedA4jYoYbKgo7QaSLo0e59YQ=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Apache Iceberg Views: Portable View Metadata Across SQL Engines (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;
                                    Iceberg's View Spec makes metadata portable by versioning schemas, dependencies, properties, and dialect-tagged SQL text, but it does not standardize execution or authorization. Production portability still depends on REST-catalog support and compatible SQL dialects. Teams should test dependency integrity, concurrent replacement, and cross-engine result consistency before treating views as shared control-plane assets.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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

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&lt;div 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%2Fparquet.apache.org%2Fblog%2F2026%2F09%2F22%2Falp-adaptive-lossless-floating-point-encoding-in-apache-parquet%2F%3Futm_source=tldrdata/1/010001a0e7810a40-fd10699d-ac3a-4512-8206-b85fba56a031-000000/roRxHNegWnnPMyKaFCYkqnFoJJSDwcI2aurVySBZYRY=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;ALP: Adaptive Lossless Floating-Point Encoding in Apache Parquet (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;
                                    Apache Parquet added ALP, a new lossless encoding for floating-point data that delivers compression similar to ZSTD while decoding around 10x faster. It also enables much faster random access and parallel decoding, making it especially useful for prices, coordinates, and scientific measurements.
                                &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%2Fnetflixtechblog.com%2Ftrading-a-cloud-identity-for-your-own-workload-attestation-on-managed-compute-516d5a29b252%3Futm_source=tldrdata/1/010001a0e7810a40-fd10699d-ac3a-4512-8206-b85fba56a031-000000/uMj2-7VwFBN6cZ3jJjbIAaOOgXf_Fzwz3AkSdBPI0xk=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Trading a Cloud Identity for Your Own: Workload Attestation on Managed Compute (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;
                                    Netflix describes how Spark jobs on managed compute exchange a cloud execution role for an internally trusted workload identity. A signed metadata payload and AWS identity proof are verified before short-lived certificates are issued for mutual TLS. The pattern separates cloud-provider authorization from application trust, giving the platform stronger workload identity and more auditable access boundaries.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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



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

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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;

&lt;h1&gt;&lt;strong&gt;Quick Links&lt;/strong&gt;&lt;/h1&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&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;

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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fhex.tech%2Fdatabench%2F%3Futm_source=tldrdata/1/010001a0e7810a40-fd10699d-ac3a-4512-8206-b85fba56a031-000000/kDb04WBQvr4cIwFAE0d_b2bmaR0WoqSt8Y8d0CSKZAA=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;DataBench (Tool)&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;
                                    Opus 5.5 leads Hex's DataBench, scoring 70.5% at Max reasoning and 67.0% at XHigh, but at relatively high cost and 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%2Fdataopsleadership.substack.com%2Fp%2Fthe-context-gap-how-to-build-data%3Futm_source=tldrdata/1/010001a0e7810a40-fd10699d-ac3a-4512-8206-b85fba56a031-000000/EOhkYhrjAxFXRdVH4MAmzQLHbe1JZWnBbB6hLDtatqQ=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;The Context Gap | How to build Data Architecture like Open AI (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;
                                    OpenAI's AI data stack centralizes rich metadata to close the context gap, while a Rust rewrite reportedly cut Airflow scheduling latency 35x at 70,000+ concurrent tasks.
                                &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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