Email: iPhone Beats Databricks 📱, Pinterest's Metrics Board 🧮, Airbnb Replays Production Traffic ⏪

Subject:
iPhone Beats Databricks 📱, Pinterest's Metrics Board 🧮, Airbnb Replays Production Traffic ⏪
Received:
10/8/2026, 10:12:58 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;Fivetran CEO George Fraser benchmarked DuckDB on an iPhone 17 Pro against Databricks clusters and found the iPhone was faster on most TPC-&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%2Fclickhouse.com%2Fblog%2Flinkedin-observability-at-scale%3Futm_source=tldr%26utm_medium=paid-newsletter%26utm_campaign=2026-10-08_Primary_Clickhouse%26utm_content=header_linkedin_observability_header_clickhouse_observability_800%26utm_term=2026-10-08%26utm_id=tldr5/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/xOwpSjAnhCFzC8f6--dcQxVwMWCgH1QvUrqckw1HL-8=452"&gt;&lt;img src="https://images.tldr.tech/clickhouse.png" valign="middle" style="vertical-align: middle !important; height: 100%;" alt="ClickHouse"&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;


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&lt;h1&gt;&lt;strong&gt;TLDR Data &lt;span id="date"&gt;2026-10-08&lt;/span&gt;&lt;/strong&gt;&lt;/h1&gt;
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                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fclickhouse.com%2Fblog%2Flinkedin-observability-at-scale%3Futm_source=tldr%26utm_medium=paid-newsletter%26utm_campaign=2026-10-08_Primary_Clickhouse%26utm_content=header_linkedin_observability_header_clickhouse_observability_800%26utm_term=2026-10-08%26utm_id=tldr5/2/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/wUeldpgj5yuy8zdDNTozHCRcnkITGohif_tNj4ifzag=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;ClickHouse for observability: 800 billion spans a day at LinkedIn (Sponsor)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    LinkedIn uses ClickHouse to store and query observability data at scale. Its tracing system handles around &lt;strong&gt;800 billion spans and 200 TB of uncompressed data per day.&lt;/strong&gt;&lt;br&gt;&lt;br&gt;For metric search, it serves &lt;strong&gt;150k+ queries/minute at 68ms average latency across 13B+ metrics.&lt;/strong&gt;&lt;br&gt;&lt;br&gt;You don't need LinkedIn's engineering team to get started. Take a shortcut with &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fclickhouse.com%2Fcloud%2Fclickstack%3Futm_source=tldr%26utm_medium=paid-newsletter%26utm_campaign=2026-10-08_Primary_Clickhouse%26utm_content=body_managed_clickstack_Body_managed_clickstack_clickhouse_cloud%26utm_term=2026-10-08%26utm_id=tldr5/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/oD7sUBTjobK-Han3BRE4D0Kz7c_dDkPV1nsqmQ7kn6w=452" rel="noopener noreferrer" target="_blank"&gt;&lt;span&gt;&lt;strong&gt;Managed ClickStack in ClickHouse Cloud&lt;/strong&gt;&lt;/span&gt;&lt;/a&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fclickhouse.com%2Fcloud%2Fclickstack%3Futm_source=tldr%26utm_medium=paid-newsletter%26utm_campaign=2026-10-08_Primary_Clickhouse%26utm_content=body_managed_clickstack_Body_managed_clickstack_clickhouse_cloud%26utm_term=2026-10-08%26utm_id=tldr5/2/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/0exApLaVZiNz_QR1wlHIUMaEFxthAfLaPkI0Ui9a_zc=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;,&lt;/span&gt;&lt;/a&gt; which handles the infrastructure for you. Send OpenTelemetry logs, metrics and traces, then search and correlate them in one UI.
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&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;strong&gt;Metrics Board: Building an Agent-ready Metrics Layer (10 minute read)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Pinterest built Metrics Board, a central platform that automates metric creation, quality checks, documentation, and publishing, making metrics easier to manage and trust. It now handles over 98% of experimentation metrics, cuts development time from weeks to days, and gives AI agents access to reliable, well-defined metrics for answering data questions.
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                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fairbnb.tech%2Finfrastructure%2Fbeyond-synthetic-testing-capturing-and-replaying-real-database-workloads-at-airbnb%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/8tcXa9-Rl2YnWFZ_gI5hYzGfs9-b9pHzgmkvUEZsLuw=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Beyond synthetic testing: Capturing and replaying real database workloads at Airbnb (14 minute read)&lt;/strong&gt;
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&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Airbnb built a database traffic replay system to test real MySQL workloads instead of relying on synthetic benchmarks. By capturing production queries with low overhead and replaying them while preserving ordering and timing, it can validate upgrades, migrations, and capacity plans. Replay surfaced severe regressions that ordinary testing would likely have missed.
                                &lt;/span&gt;
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                                    &lt;span&gt;
                                        &lt;strong&gt;CDC at WHOOP: Self-Service Replication for Hundreds of Postgres Tables (16 minute read)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    WHOOP built a self-service CDC pipeline so application teams can replicate Postgres tables into the warehouse. The design separates fast access to raw changes from slower, costlier table materialization, while automating schema evolution and ownership. The key lesson is that CDC scales better when onboarding is publication-driven and operational responsibility stays aligned with the source service.
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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;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.fivetran.com%2Fblog%2Fi-benchmarked-databricks-against-my-iphone%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/ePExVM3Um83L1oQ5IIOxDLMSooNQhn7_N9CwOyR5uyk=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;I benchmarked Databricks against my iPhone (7 minute read)&lt;/strong&gt;
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&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Fivetran CEO George Fraser benchmarked DuckDB on an iPhone 17 Pro against Databricks clusters and found the iPhone was faster on most TPC-H workloads, even with far fewer CPUs and less memory. The results suggest many business analytics workloads don't need expensive distributed systems and could run more cheaply on single machines using engines like DuckDB or Polars.
                                &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%2Ficeberglakehouse.com%2Fposts%2Fdata-agent-golden-question-benchmark%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/vcgjd0yDp2AfAqWzsJal1BicNFgQWmT75WDf3N9k9kI=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;How to Build a Golden-Question Benchmark for Your Data Agents (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;
                                    Data agents need regression testing, not demo-style evaluation, because models, prompts, schemas, and metric definitions keep drifting. A strong pattern is a golden-question suite tied to frozen data snapshots and scored on actual execution results, then enforced in CI. Treat agent changes like software releases, with stored runs, category-level thresholds, and hard safety gates.
                                &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%2Fmotherduck.com%2Fblog%2Fjev-analytics-use-cases%2F%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/TnqI2PbopPldXLCeGCQKOb2q_HFu9pAdLppuyXwQBTo=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;5 Jev use cases for analytics, with real queries and datasets (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;
                                    MotherDuck shows five practical ways to use its Jev AI model to classify text directly in SQL, including evaluating AI agents, analyzing sentiment, scoring sales calls, and categorizing job postings. Jev returns predefined answers with confidence scores and can be around 20 times faster than standard LLM calls, making large-scale text analysis cheaper and easier.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&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;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%2Fpages.dataiku.com%2Fagent-management%3Futm_source=tldr%26utm_medium=paid_email%26utm_campaign=glo_content_pmm_agent_management_fy27%26utm_content=data_secondary%26utm_geo=glo/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/4NCI3DNlweC9NzsTwX8-TQ-GitIUQjrkTIZBanPwbDY=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;1 in 4 of the world's top companies control their agent portfolio with this (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;
                                    Want to know what your agents are actually doing? See how &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fpages.dataiku.com%2Fagent-management%3Futm_source=tldr%26utm_medium=paid_email%26utm_campaign=glo_content_pmm_agent_management_fy27%26utm_content=data_secondary%26utm_geo=glo/2/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/8j7JUPZKS2ttZqOyUfEz8-xQb6Xr0tn4KruPic5Y2cw=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;Dataiku Agent Management&lt;/span&gt;&lt;/a&gt; can help you track performance, detect drift, and confirm every agent is operating as intended. Brands like LVMH, Toyota, and GE use it to keep agents aligned with their business goals. &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fpages.dataiku.com%2Fagent-management%3Futm_source=tldr%26utm_medium=paid_email%26utm_campaign=glo_content_pmm_agent_management_fy27%26utm_content=data_secondary%26utm_geo=glo/3/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/-fW0S6LIXp7YYTxsThPn3ZF7O0gjfin_-vK6MsW1ifg=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;See it in action&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%2Fpola.rs%2Fposts%2Frelease-polars-2%2F%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/RHGmzNlT3nyww6pZir5lx1l2-X6KmynC9nwmcyjeKNc=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Release of Polars 2.0 (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;
                                    Polars 2.0 brings faster queries, first-class SQL support, a new Map data type, and streaming with spill-to-disk enabled by default to handle larger datasets without running out of memory. Polars also reports beating DuckDB and DataFusion in most of its SQL benchmarks, while stricter type checking helps developers and AI agents catch errors earlier.
                                &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%2Fgithub.com%2Fstatelyai%2Fgraph%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/-bt4RvjxSBSdb4cepMxllqAHyYGWoclPlQ1BeYYJH8k=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Stately Graph (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;
                                    Stately released an open-source TypeScript library for building, analyzing, and visualizing graphs using simple JSON data, with support for common algorithms and 14 graph formats. It outperforms popular alternatives in most benchmarks, including building 100,000-node graphs 9-15 times faster than Graphology.
                                &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%2Fdatafusion.apache.org%2Fblog%2F2026%2F10%2F01%2Fdatafusion-comet-1.1.0%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/hvI7Foucc_Juu_IEz1QSoUE0B_mDbqGz9OES8VjEftY=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Apache DataFusion Comet 1.1.0 Release (9 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;
                                    Comet 1.1.0 pushes more Spark execution into native code, most notably with experimental Iceberg writes and native shuffle via Celeborn. The release also targets a common operational failure mode: opaque off-heap memory pressure. Alongside broader native coverage, it adds memory accounting and logging to reduce container kills.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&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;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%2Fgithub.com%2Fopenai%2Fmath%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/z4trqjjn_7E-p_cyshXGNE6DocylojdS7iSftGoeVg4=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;OpenAI Math (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;
                                    OpenAI published 722 AI-generated mathematical manuscripts covering 372 research families, produced by an unreleased internal model tested on roughly 4,000 problems. Many results include formally verified proofs using Lean, but OpenAI cautions that some unverified findings may contain errors.
                                &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%2Fontologist.substack.com%2Fp%2Fturtle-is-not-json%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/of4tyZ1KeQi22N9tsct3l3sF-1q7bk9lHzXz13zVTiE=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Turtle Is Not JSON (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;
                                    Time-series data often needs more than values and timestamps: units, provenance, derivations, and exceptions matter too. JSON-LD and RDF make that context explicit and queryable, which improves validation and interoperability. Keep bulk measurements in efficient tabular or time-series storage, and use a graph layer for metadata, semantics, and anomaly handling.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.biorxiv.org%2Fcontent%2F10.64898%2F2026.10.01.756017v1%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/k623mn8rjVeIsB8mf-JvcP4eL2N_mH3DLyn1JDsalzQ=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Designing enzymes for new-to-nature chemistry and non-natural substrates with AlphaProtein Novo (22 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Google DeepMind's AlphaProtein Novo uses AI to design new enzymes from scratch, including ones that produce drug ingredients and break down plastic pollutants. Some outperform natural enzymes on specific tasks, opening new possibilities for medicine, chemistry, and environmental cleanup.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;⚡&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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

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

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&lt;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%2Fprateek-arora.github.io%2F2026%2F10%2Ftimed-179-index-recommendations%2F%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/B9N8CPidSB8GNZrLJz6U3d9djMy-zGAyxvqUZUKy-ss=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;I timed 179 index recommendations on real data. 18% made the query slower (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;
                                    A developer tested 179 PostgreSQL index recommendations and found that 72% made queries at least 15% faster, but 18% actually made them slower, sometimes by more than 2x.
                                &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%2Fthreadreaderapp.com%2Fthread%2F2107644837407404340.html%3Futm_source=tldrdata/1/010001a11b0079d0-6df617c7-0074-4fbd-835b-57e03363cb9a-000000/9us2IMaRI0G1wm-hYTSAPrJDQjZUt0qqI9MI-v1DJaI=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;I benchmarked "Jev-killer" OpenAI Decisions API against Jev (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;
                                    HiringCafe found Jev outperformed OpenAI's new Decisions API and Gemini 3.1 Flash-Lite at matching jobs to searches and resumes.
                                &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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