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<div style="display: none; max-height: 0px; overflow: hidden;">Bun, a JavaScript runtime, underwent a huge rewrite from Zig to Rust to address persistent memory-related bugs that were problematic in Zig β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β </div>
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<h1><strong> TLDR Dev <span id="date">2026-07-17</span></strong></h1>
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<div style="text-align: center;"><span style="font-size: 36px;">π§βπ»</span></div>
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<h1><strong>Articles & Tutorials</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fblog.pragmaticengineer.com%2Fthe-pulse-what-can-we-learn-from-buns-rapid-rust-rewrite-with-ai%2F%3Futm_source=tldrdev/1/0100019f6fc646f6-48b9a5e6-d629-42db-8664-507ae94207fa-000000/d0lGFziuDwNSCeMfnVHjONmTysBKCC9ZUFuh8U2ufBA=452">
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<strong>What can we learn from Bun's rapid Rust rewrite with AI? (13 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Bun, a JavaScript runtime, underwent a huge rewrite from Zig to Rust to address persistent memory-related bugs that were problematic in Zig, a language not designed for memory safety. Using Claude Fable, the rewrite was done over 11 days with the help of 64 AI agents, resulting in the migration of over 535,000 lines of code and the resolution of 1,600 compiler errors.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Farshad.fyi%2Fwritings%2Fengineering-high-performance-parsers%3Futm_source=tldrdev/1/0100019f6fc646f6-48b9a5e6-d629-42db-8664-507ae94207fa-000000/FZ2toPpM-YzZay9yIHZrAZmihL7W1d1NQ4DArS6vgXo=452">
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<strong>Engineering High-Performance Parsers with Data-Oriented Design (10 minute read)</strong>
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Yuku is a JavaScript/TypeScript parser in Zig that runs 3β10x faster than npm alternatives. Its speed comes not from the grammar, but from representing the AST as flat arrays of u32 indices instead of a pointer tree. That one decision cascades into everything else: strings become source offsets, variable-length children live in a shared array, token metadata is packed into tag bits, Unicode tables are deduplicated to stay cache-resident, and serialization to JavaScript is just a memcpy since the pointerless tree is already position-independent bytes.
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<strong>Shipyard: How We Built Slack's Next-Generation EC2 Platform (15 minute read)</strong>
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Shipyard is a next-generation EC2 platform that replaces the old model of continuously patching long-lived instances with immutable, deployable artifacts, bringing modern practices like progressive rollouts and automated rollback directly to EC2 workloads that can't move to containers. It's built on a hardened βgoldenβ base image (slack-zero) that service teams layer their own AMIs on top of, with a heavy bake phase and lightweight boot-time provisioning so instances start fast and fleets update through replacement rather than in-place changes.
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<div style="text-align: center;"><span style="font-size: 36px;">π§ </span></div>
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<strong>Contract-Driven Development: Write the Truth Once (15 minute read)</strong>
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Contract-driven development focuses on maintaining a single, authoritative source of truth in software engineering to prevent discrepancies and bugs caused by duplicated definitions. By having all relevant information within a machine-readable contract, developers can automate code generation and ensure consistency across systems.
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<strong>How do you stay familiar with the code when it's written by an LLM? (10 minute read)</strong>
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As reliance on LLMs for coding increases, developers often lose familiarity with the code they produce, reducing their understanding of underlying decisions and structures. However, this understanding is still necessary to debug code and maintain the codebase as it grows. Some ways to maintain understanding are to sometimes type code personally, question design choices often, and explore context beyond the changes by asking the LLM questions about the codebase.
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<strong>The LLM Critics Are Right. I Use LLMs Anyway (20 minute read)</strong>
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Despite acknowledging the valid criticisms of LLMs, this dev continues to use them extensively. LLMs can amplify human thought and improve the quality of output as long as they are used with care.
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<div style="text-align: center;"><span style="font-size: 36px;">π</span></div></div>
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<h1><strong>Launches & Tools</strong></h1>
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<strong>agent-device (GitHub Repo)</strong>
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Agent-device is a device automation CLI that allows AI agents to interact with and verify mobile applications across various platforms, including iOS, Android, and desktop environments. It helps with tasks such as capturing evidence, debugging, and replays of workflows.
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<strong>Hermes Studio (GitHub Repo)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Hermes Studio is a desktop app and web console designed for managing and interacting with Hermes Agents, enabling functionalities like real-time chat, automation, model management, and session tracking from a unified dashboard. It supports multiple platforms and has features such as voice input/output, a customizable environment for coding agents, and tools for file management.
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<div style="text-align: center;"><span style="font-size: 36px;">π</span></div></div>
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>Use cases for aria-expanded (13 minute read)</strong>
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The `aria-expanded` attribute is relevant for users using Assistive Technology on the web. This article categorizes collapsible widgets into two main types: collapsible sections and interactive elements. It shows how to implement these patterns effectively while making sure they comply with accessibility standards.
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<strong>Guide to data tools landscape for developers (48 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
This article provides an overview of the data tools landscape. It explains the data lifecycle, various types of data professions, and the processes through which data is stored, processed, and consumed. Additionally, it goes over concepts, tools, and methodologies relevant to data handling, such as ETL processes, data warehouses, data lakes, and the integration of data into applications for operational analytics and reporting.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.photoroom.com%2Finside-photoroom%2Fthe-insights-factory-how-we-run-deep-data-investigations-with-llm-agents%3Futm_source=tldrdev/1/0100019f6fc646f6-48b9a5e6-d629-42db-8664-507ae94207fa-000000/L70t1MSgkQeICQ35ZxSfBc28ex4uwgjhA814niibUpI=452">
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<strong>The Insights Factory: how we run deep data investigations with LLM agents (16 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
The Insights Factory turns complex data inquiries into small, manageable, and auditable tasks executed by agents with human oversight. The system uses a structured markdown file to document each investigation, framing questions and tracking progress while maintaining a single source of truth to prevent errors from poor data. By effectively splitting tasks and using reusable skills, the methodology increases efficiency, allowing for detailed investigations to be much faster than before.
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<div style="text-align: center;"><span style="font-size: 36px;">β‘</span></div></div>
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