# Dropbox > A place where all your team’s content comes together. Where you can use the tools you love. Where we help you cut through the clutter and bring to the surface what matters most. ## Articles ### [Improving infrastructure efficiency for growing demand in the age of AI](https://yomu.fyi/post/improving-infrastructure-efficiency-for-growing-demand-in-the-age-of-a.md) - Company: [Dropbox](https://yomu.fyi/company/dropbox.md) - Author: Ilya Yakovlev - Published: Aug 18, 2026 Dropbox manages growing infrastructure demands from AI and standard workloads by maximizing the efficiency of its existing data centers rather than just building more capacity. Through a system-level approach spanning software, hardware, and physical facility design, the company dynamically manages power and balances workloads. These optimizations, combined with higher storage density and data-driven hardware lifecycle management, have significantly reduced energy use per unit of storage. ### [How our universal content processing platform Riviera evolved for AI and beyond](https://yomu.fyi/post/how-our-universal-content-processing-platform-riviera-evolved-for-ai-a.md) - Company: [Dropbox](https://yomu.fyi/company/dropbox.md) - Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash - Published: Jul 20, 2026 Dropbox developed Riviera, a centralized content processing platform that evolved from an internal file preview generator into a shared infrastructure supporting over 300 file formats. By decomposing complex conversion tasks into modular, reusable transformations, the system now powers search indexing, media transcoding, and AI context extraction across Dropbox products. ### [How we used DSPy to turn AI evaluations into better responses in Dash chat](https://yomu.fyi/post/how-we-used-dspy-to-turn-ai-evaluations-into-better-responses-in-dash.md) - Company: [Dropbox](https://yomu.fyi/company/dropbox.md) - Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon - Published: Jun 25, 2026 Dropbox improved its Dash chat agent by establishing an automated optimization loop powered by DSPy and LLM-as-judge evaluations. Engineers first calibrated their LLM judges against human-annotated interaction traces, then used those judges to systematically optimize the agent's system prompts via offline counterfactual replay. This automated workflow reduced incomplete responses by 26% while decreasing overall token consumption. ### [How Dropbox uses MCP and Dash to close the design-to-code security gap](https://yomu.fyi/post/how-dropbox-uses-mcp-and-dash-to-close-the-design-to-code-security-gap.md) - Company: [Dropbox](https://yomu.fyi/company/dropbox.md) - Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra - Published: Jun 12, 2026 Dropbox developed a system using the Model Context Protocol (MCP) and Dash's semantic search to bridge the gap between security threat models and code implementation. By retrieving original security documents during pull requests, an LLM agent automatically evaluates whether the proposed code adheres to previously agreed-upon security requirements. This approach surfaces design regressions and missing controls that traditional static analysis tools miss. ### [Beyond code generation: rethinking engineering productivity in the age of AI agents](https://yomu.fyi/post/beyond-code-generation-rethinking-engineering-productivity-in-the-age.md) - Company: [Dropbox](https://yomu.fyi/company/dropbox.md) - Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura - Published: May 28, 2026 Dropbox shares how widespread AI code generation shifts software development bottlenecks downstream into code review, CI infrastructure, and validation pipelines. To adapt, they built Nova, an internal coding agent platform that safely automates scoped tasks such as migrations and flaky test remediation. They also evolved their developer productivity framework to measure end-to-end customer impact and code quality rather than simple pull request throughput. ### [Introducing Nova, our internal platform for coding agents](https://yomu.fyi/post/introducing-nova-our-internal-platform-for-coding-agents.md) - Company: [Dropbox](https://yomu.fyi/company/dropbox.md) - Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler - Published: May 21, 2026 Dropbox developed Nova, an internal platform that runs AI coding agents in isolated cloud environments integrated with their Bazel monorepo. The platform supports both interactive developer workflows and autonomous background tasks, such as automated CI debugging, flaky test remediation, and codebase-wide migrations. By pairing code generation with automated validation and strict execution guardrails, Nova ensures generated fixes are tested and reproducible. ### [Improving storage efficiency in Magic Pocket, our immutable blob store](https://yomu.fyi/post/improving-storage-efficiency-in-magic-pocket-our-immutable-blob-store.md) - Company: [Dropbox](https://yomu.fyi/company/dropbox.md) - Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel - Published: Apr 2, 2026 After introducing a direct erasure-coding service, Dropbox encountered severe storage fragmentation in its immutable blob store, Magic Pocket, due to a large number of under-filled volumes. Because their legacy compaction strategy was only designed to top off near-full volumes, the team created new multi-strategy compaction methods to aggressively consolidate sparse volumes. These approaches leverage dynamic programming and streaming pipelines to rapidly reclaim unused disk capacity at exabyte scale. ### [Reducing our monorepo size to improve developer velocity](https://yomu.fyi/post/reducing-our-monorepo-size-to-improve-developer-velocity.md) - Company: [Dropbox](https://yomu.fyi/company/dropbox.md) - Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel,Ishan Mishra - Published: Mar 25, 2026 Dropbox reduced its core server monorepo size from 87GB to 20GB after approaching GitHub's 100GB limit and suffering from hour-long clone times. The excessive bloat was traced to Git's delta compression heuristics interacting poorly with internationalization directory paths, generating inefficient diffs across different languages. By collaborating with GitHub to run an aggressive server-side repack with tuned window and depth settings, Dropbox cut clone times to under 15 minutes. ### [How we optimized Dash's relevance judge with DSPy](https://yomu.fyi/post/how-we-optimized-dash-s-relevance-judge-with-dspy.md) - Company: [Dropbox](https://yomu.fyi/company/dropbox.md) - Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel,Ishan Mishra,Eric Wang,Dmitriy Meyerzon - Published: Mar 17, 2026 Dropbox Dash optimized its LLM-as-a-judge relevance scoring system using DSPy to migrate from expensive proprietary models to cheaper open-weight alternatives. By establishing automated feedback loops based on human agreement and strict JSON format validation, the team systematically generated robust prompts for new models. This reduced human-score disagreement by 45% and slashed model adaptation time from weeks to days while enabling 10x to 100x more data labeling. ### [Using LLMs to amplify human labeling and improve Dash search relevance](https://yomu.fyi/post/using-llms-to-amplify-human-labeling-and-improve-dash-search-relevance.md) - Company: [Dropbox](https://yomu.fyi/company/dropbox.md) - Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel,Ishan Mishra,Eric Wang,Dmitriy Meyerzon,Dmitriy Meyerzon - Published: Feb 26, 2026 Dropbox Dash uses large language models (LLMs) to amplify human labeling efforts for training its search relevance and ranking models. By validating and optimizing LLM evaluators against a small set of human-labeled internal data, Dropbox creates massive, high-quality training datasets offline for production rankers like XGBoost without incurring high latency or latency costs at query time.