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<div style="display: none; max-height: 0px; overflow: hidden;">DuckDB has emerged as the go-to solution for production-scale data processing, outperforming Polars with robust, and developer-focused support </div>
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<h1><strong>TLDR Data <span id="date">2026-01-01</span></strong></h1>
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<h1><strong>Deep Dives</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fragaboutit.com%2Fthe-knowledge-decay-problem-how-to-build-rag-systems-that-stay-fresh-at-scale%2F%3Futm_source=tldrdata/1/0100019b793d139e-0a4974e9-7868-4271-9804-a3d80a348b6c-000000/kmgF4k_e39jS1vhqnXQS6QlvfQukPM7nTkk50740PPM=438">
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<strong>The Knowledge Decay Problem: How to Build RAG Systems That Stay Fresh at Scale (10 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Enterprise RAG systems often fail at scale due to stale knowledge, not weak retrieval or generation. Effective designs prioritize real-time ingestion, freshness-aware retrieval, and continuous staleness monitoring to prevent outdated data from becoming a systemic risk.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fblog.gdeltproject.org%2Fplanetary-scale-deep-reasoning-building-our-final-presidential-daily-brief-prompt-comparing-gemini-3-2-5-pro-flash-asr-toc%2F%3Futm_source=tldrdata/1/0100019b793d139e-0a4974e9-7868-4271-9804-a3d80a348b6c-000000/888qqqbTrHw9QkikOd6KJQpYZUHS_evFtJfXG06zaGs=438">
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<strong>Planetary-Scale Deep Reasoning: Building Our Final Presidential Daily Brief Prompt & Comparing Gemini 3/2.5 Pro/Flash ASR/TOC (18 minute read)</strong>
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GDELT uses a tightly structured prompt to have Gemini analyze a full day of TV news into PDB-style insights, not summaries. Prompt structure narrows model quality gaps, but cost savings from TOC shortcuts are limited, and hallucinations still occur.
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<strong>PostgreSQL Recovery Internals (8 minute read)</strong>
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PostgreSQL's recovery relies on Write-Ahead Logging (WAL), replaying records from the last checkpoint's REDO point during startup to ensure consistency, supporting crash recovery (to WAL end), Point-in-Time Recovery (via targets like time or LSN), and standby replication with hot standby. The core redo loop in PerformWalRecovery, triggered by control/signal files, applies records via resource managers with prefetching and delays, ending at a consistent state.
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<h1><strong>Opinions & Advice</strong></h1>
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<strong>10 Predictions for Data Infrastructure in 2026 (7 minute read)</strong>
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In 2026, data infrastructure progress will come from better foundations, not new tools. Open standards are becoming core application infrastructure, shifting the hard problems to interoperability, sustainability, and maintenance. The biggest leverage is now in the boring plumbing that makes everything work together at scale.
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<strong>The Next Data Bottleneck (7 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
As analytics agents remove friction, most people still use data mainly to pull facts, not to ask big strategic questions. This reveals the real bottleneck: knowing when data is actually useful and how to turn it into clarity, not just access. The lasting value of data work is problem framing and sense-making, not data fetching.
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<strong>How AI Will Change Software Engineering (110 minute video)</strong>
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LLMs are a once-in-a-career shift like assembly to high-level languages, but bigger in one way: software becomes non-deterministic (probabilistic outputs), forcing new engineering habits. AI is great for fast prototyping, navigating unfamiliar stacks, and understanding legacy code, but unsafe for blind “vibe coding,” which breaks the learning loop. Treat AI output like a PR from a dodgy but productive teammate: review hard, test relentlessly, and refactor constantly.
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<strong>DuckDB Beats Polars for 1TB of Data (3 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
DuckDB has emerged as the go-to solution for production-scale data processing, outperforming Polars with robust, developer-focused support, extensive integrations, and streaming execution designed for large datasets with disk spill capabilities. In a real-world 1TB Parquet aggregation test on a 64GB instance, DuckDB completed the task in 19 minutes without memory issues, while Polars consistently ran out of memory.
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<strong>Squirreling: A New SQL Engine for the Web (5 minute read)</strong>
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Squirreling is a tiny, browser-first SQL engine for fast, interactive queries. It achieves this through async streaming execution and late materialization.
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<strong>Unfreezing The Data Lake: The Future-Proof File Format (1 hour podcast)</strong>
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The Future Proof File Format (F3) is a next-gen, self-describing file format built to handle wide tables, multimodal data, and ML workloads that strain Parquet and ORC. It separates file and table formats and uses embedded WebAssembly for safe extensibility, aiming to better support evolving analytics and AI pipelines via Arrow-native integration.
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<strong>Exploring TabPFN: A Foundation Model Built for Tabular Data (7 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
TabPFN-2.5 brings a transformer-based foundation model to tabular data, handling up to 100,000 rows and 2,000 features for classification with low-latency, zero-shot inference. Pretrained via in-context learning on 130 million synthetic datasets, it eliminates retraining and seamlessly integrates with scikit-learn pipelines, supporting missing values and mixed types. Built-in SHAP-based interpretability and GPU support further enhance its practical value, making it a compelling alternative to traditional tree-based methods.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fblog.ecapuano.com%2Fp%2Fhunting-mongobleed-cve-2025-14847%3Futm_source=tldrdata/1/0100019b793d139e-0a4974e9-7868-4271-9804-a3d80a348b6c-000000/epD5ujegoz3kYgb6d5-YA2rgvRcA0T1amnp9xLkYYOI=438">
<span>
<strong>Hunting MongoBleed (CVE-2025-14847) (6 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
CVE-2025-14847 (“MongoBleed”) exposes MongoDB instances using zlib compression to unauthenticated memory disclosure, leaking credentials and PII—impacting all major versions before their respective fixes (e.g., 8.2.3, 8.0.17, 7.0.28, 6.0.27). A new Velociraptor artifact enables high-confidence detection by analyzing log event patterns: massive connection bursts lacking client metadata. Validation showed attack velocities exceeding 100,000 connections/minute versus legitimate traffic's ≤3.2 connections/minute. Patch immediately and use the linked artifact to retrospectively identify exploitation from existing logs.
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