--- title: Best Towards Data Science posts — March 2026 | daily.dev description: The most upvoted Towards Data Science posts from March 2026, curated by the daily.dev community. canonical: https://daily.dev/sources/tds/best-of/2026/03 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/sources/tds/best-of/2026/03 og:type: website og:site_name: daily.dev og:title: Best Towards Data Science posts — March 2026 | daily.dev og:description: The most upvoted Towards Data Science posts from March 2026, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of Towards Data Science — March 2026 1. 1 [](https://daily.dev/posts/the-black-box-problem-why-ai-generated-code-stops-being-maintainable-7ovjc2yg0 "The Black Box Problem: Why AI-Generated Code Stops Being Maintainable") Article Towards Data Science · 26w The Black Box Problem: Why AI-Generated Code Stops Being Maintainable AI-generated code tends to become unmaintainable due to structural problems: monolithic outputs, implicit circular dependencies, missing contracts, and implementation-only documentation. The core issue is that AI tools generate code without real-time structural feedback, producing what the author calls 'black boxes.' The solution isn't better prompting but enforcing architectural constraints during generation itself — explicit component boundaries, validated dependency graphs, isolated testability, and typed interfaces. Practical advice covers how to prompt with architectural intent, audit existing generated code for implicit coupling, and evaluate AI tools by their post-generation reviewability and structural enforcement. 189 15 2. 2 [](https://daily.dev/posts/i-quit-my-130-000-ml-engineer-job-after-learning-4-lessons-hb3msn94w "I Quit My $130,000 ML Engineer Job After Learning 4 Lessons") Article Towards Data Science · 27w I Quit My $130,000 ML Engineer Job After Learning 4 Lessons A machine learning engineer shares why they quit a $130k Big Tech job despite good pay and perks. The four key reasons were: slow pace and bureaucracy at large companies, feeling like a small cog with little purpose, reliance on internal tooling that limits transferable skill development, and a narrow scope for meaningful impact on mature products. The author, 26, chose to join a startup as the sixth hire, prioritizing growth, ownership, and entrepreneurial risk over comfort and stability. 69 17 3. 3 [](https://daily.dev/posts/machine-learning-at-scale-managing-more-than-one-model-in-production-r2wbzp29a "Machine Learning at Scale: Managing More Than One Model in Production") Article Towards Data Science · 26w Machine Learning at Scale: Managing More Than One Model in Production Managing a portfolio of ML models in production requires a fundamentally different mindset than single-model deployments. Key challenges include prioritizing availability over perfection (using safe fallbacks when models fail), the limitations of traditional accuracy metrics at scale, infrastructure decisions around cloud vs. device and tiered GPU/CPU strategies, and the near-invisible risk of label leakage across complex data pipelines. Practical safeguards include feature latency monitoring, shadow deployments, and human-in-the-loop auditing for high-stakes models. 19 4. 4 [](https://daily.dev/posts/vibe-coding-with-ai-best-practices-for-human-ai-collaboration-in-software-development-qfeh8tjhn "Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development") Article Towards Data Science · 24w Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development Explores best practices for human-AI collaboration in software development using vibe coding tools. Key risks identified include garbage-in-garbage-out prompting, poor prompt quality burning through model limits, and AI tendency to over-engineer solutions. Using a RAG system over news articles as a practical example, the author demonstrates a workflow: define clear requirements with test queries, generate architecture before code, validate and stress-test the design with edge cases, have the AI self-critique, and push back on unnecessary complexity. The central principle is a human-in-the-loop cycle where AI accelerates but humans remain the final arbiter on trade-offs, maintainability, and production readiness. 24 3 5. 5 [](https://daily.dev/posts/the-basics-of-vibe-engineering-ljipaqa3r "The Basics of Vibe Engineering") Article Towards Data Science · 24w The Basics of Vibe Engineering Vibe engineering — building software by directing AI agents rather than writing code manually — is explored through the lens of what still requires human input. Planning, architecture, and system thinking remain essential; AI handles implementation. Claude Code has emerged as the dominant AI coding tool, surpassing Cursor and Codex in community mindshare. Key risks include hallucinated outputs (fabricated API endpoints, accidental data deletion), model drift outside user control, and potential skill atrophy from reduced hands-on coding. The author argues engineering is shifting up a layer of abstraction toward system judgment rather than disappearing entirely. 13 1 [See all Towards Data Science archives](/sources/tds/best-of) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@graph":[{"@type":"CollectionPage","@id":"https://daily.dev/sources/tds/best-of/2026/03#page","url":"https://daily.dev/sources/tds/best-of/2026/03","name":"Best Towards Data Science Posts — March 2026","description":"The most upvoted Towards Data Science posts from March 2026, curated by the daily.dev community.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/sources/tds/best-of/2026/03#items","numberOfItems":5,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/the-black-box-problem-why-ai-generated-code-stops-being-maintainable-7ovjc2yg0","name":"The Black Box Problem: Why AI-Generated Code Stops Being Maintainable"},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/i-quit-my-130-000-ml-engineer-job-after-learning-4-lessons-hb3msn94w","name":"I Quit My $130,000 ML Engineer Job After Learning 4 Lessons"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/machine-learning-at-scale-managing-more-than-one-model-in-production-r2wbzp29a","name":"Machine Learning at Scale: Managing More Than One Model in Production"},{"@type":"ListItem","position":4,"url":"https://daily.dev/posts/vibe-coding-with-ai-best-practices-for-human-ai-collaboration-in-software-development-qfeh8tjhn","name":"Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development"},{"@type":"ListItem","position":5,"url":"https://daily.dev/posts/the-basics-of-vibe-engineering-ljipaqa3r","name":"The Basics of Vibe Engineering"}]},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Sources","item":"https://daily.dev/sources"},{"@type":"ListItem","position":3,"name":"Towards Data Science","item":"https://daily.dev/sources/tds"},{"@type":"ListItem","position":4,"name":"Best of","item":"https://daily.dev/sources/tds/best-of"},{"@type":"ListItem","position":5,"name":"March 2026"}]}]} ```