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<div style="display: none; max-height: 0px; overflow: hidden;">Surprisingly, sloppy code isn't due to incompetent engineers, but rather the common practice of assigning engineers to unfamiliar codebases. </div>
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<h1><strong> TLDR Dev <span id="date">2025-12-01</span></strong></h1>
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TanStack Router achieved a 20,000× performance improvement in route matching by switching from a flat list iteration approach to a segment trie data structure. This changed the algorithmic complexity from O(N), based on the number of routes, to O(M), based on pathname segments. It also added backwards stack processing, bitmasking for optional segments, and reusing typed arrays for parsing to further boost performance across both small apps (60× faster) and large apps (10,000× faster).
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CLAUDE.md, a file for onboarding Claude to a codebase, provides important context about the project's purpose, technology stack, and operational instructions. The CLAUDE.md should be kept concise and universally applicable. Instead of stuffing everything into CLAUDE.md, progressive disclosure should be used by pointing to separate, task-specific markdown files when needed and relying on deterministic tools like linters for code styling.
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Surprisingly, sloppy code at big tech companies isn't due to incompetent engineers, but rather the common practice of assigning engineers to unfamiliar codebases. Engineers often work outside their expertise, rotate frequently, and are incentivized to change jobs, resulting in a lack of long-term ownership and expertise in specific systems.
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<strong>It's Always the Process, Stupid! (3 minute read)</strong>
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Implementing AI on top of broken business processes will only accelerate the generation of inefficiencies. Instead of viewing AI as a magical solution, enterprises should focus on optimizing their own processes first, especially when dealing with unstructured data.
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Many companies struggle with retaining engineers because of poor information flow within their hierarchical structure, leading to executives being unaware of critical issues until it's too late. Companies need to prioritize open communication channels like skip-level meetings and act on the feedback they receive to show that engineers' input is valued.
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AI agent development breaks the rules of traditional software development. Learn from teams at Canva, Cisco, Snowflake and more, on how to establish an evaluation framework so you can assess your agent across accuracy, latency, cost, safety, and user experience and more. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwandb.ai%2Fsite%2Fresources%2Fwhitepapers%2Fevaluating-ai-agent-applications%2F%3Futm_source=tldr-dev%26utm_medium=cpc%26utm_campaign=WP-EvalAIAgents%26utm_content=weave%26utm_term=newsletter/2/0100019ad9d0dc9a-9e5cd904-6917-4a77-a185-b58f6109f116-000000/wvN0jI69Bfl27dBHqo880v6aoujVqzSolfWfaozq8Kk=433" rel="noopener noreferrer nofollow" target="_blank"><span>Get a practical five-step recipe for effective evaluations in this guide from Weights & Biases</span></a>.
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Devs should resist the pressure to integrate AI into every feature, and instead use deterministic code for simple, well-defined tasks. Using LLMs for straightforward problems like email validation introduces unnecessary costs, latency, and reliability issues compared to traditional code solutions. AI should be reserved for genuinely complex problems involving unstructured data, natural language understanding, or creative generation.
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<h1><strong>Quick Links</strong></h1>
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Both animal and LLM intelligences are forms of optimization.
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