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<div style="display: none; max-height: 0px; overflow: hidden;">Google DeepMind has introduced the Gemini 2.5 Computer Use model, which allows agents to interact directly with user interfaces </div>
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<h1><strong> TLDR Dev <span id="date">2025-10-08</span></strong></h1>
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<strong>Build high performance RAG apps with Redis and AWS (Sponsor)</strong>
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Microlandia is a city builder game inspired by SimCity with a focus on realistic simulation using real-world data. It was initially built in Go, but the project faced challenges in creating a 3D view and a full-featured UI. To overcome these hurdles, the game is being ported to Deno, taking advantage of its cross-platform capabilities and integration with web technologies like ThreeJS and React.
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A developer optimized Node.js performance for processing a 14.8GB weather data file containing 1 billion temperature measurements to achieve a 78% speedup. The main breakthrough involved bypassing JavaScript's string encoding overhead by parsing raw bytes directly, storing temperatures as integers (e.g., 25.3°C as 253 tenths), and implementing a hash-first approach that only decoded station names when absolutely necessary.
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Google DeepMind has introduced the Gemini 2.5 Computer Use model, which allows agents to interact directly with user interfaces. This model, available via the Gemini API, can navigate web pages and applications by clicking, typing, and scrolling, outperforming existing solutions with lower latency. The model analyzes user requests and screenshots to generate UI actions, operating within a loop for iterative task completion while prioritizing safety through built-in features and developer controls.
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Arc is a high-performance time-series data warehouse built on DuckDB, Parquet, and MinIO. It supports high-speed data ingestion and fast analytical queries with SQL.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>What Makes 5% of AI Agents Actually Work in Production? (12 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Successful AI agents in production depend on good context engineering, governance, memory architecture, and multi-model orchestration. Most AI deployments fail due to bad scaffolding around the models. Also, trust in AI is built through human-in-the-loop design and consistent, explainable behavior, positioning AI as an assistant rather than an autonomous decision maker.
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<strong>Vibe engineering (7 minute read)</strong>
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Vibe Engineering is a responsible and stronger approach to software development using AI, whereas "vibe coding" is more irresponsible. Vibe engineering has experienced engineers using AI tools like coding agents to speed up their work while still having high standards. This approach requires strong software engineering practices such as automated testing, planning, documentation, and code review, while also relying on QA, research, and careful management of AI assistants.
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<div style="text-align: center;"><span style="font-size: 36px;">⚡</span></div></div>
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<h1><strong>Quick Links</strong></h1>
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<strong>Who needs git when you have 1M context windows? (3 minute read)</strong>
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An AI developer accidentally deleted working code but was able to recover it using an LLM with a 1 million token context window, showing an unexpected benefit of LLMs remembering past interactions.
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<strong>Pdoc (Website)</strong>
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Pdoc is a tool that automatically generates API documentation for Python projects with features like type annotation support, cross-linking, a built-in web server, and compatibility with various docstring styles.
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<strong>Become Unbannable from Your Email/Gmail (5 minute read)</strong>
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Email is a crucial digital asset, and you can gain control over it and become "unbannable" by using your own domain with a trusted email provider and backing up all emails to a local server.
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<strong>Part I: How does gradient descent work? (23 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
This is a mathematical guide to gradient descent during deep learning, which is an optimization algorithm used to train machine learning models by finding the minimum of a cost function.
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