VC Astasia Myers’ perspectives on machine learning, cloud infrastructure, developer tools, open source, and security. Sign up here.
CodeSee visualizes your codebase and services, and automates your workflows. It’s there to help as you dive into your codebase, plan your feature or refactor, write your code, and complete a code review — making every step easier, and you better at it.
Why does this matter? Codebases are becoming increasingly complex. Codebases are larger than ever before and with the movement to microservices teams need to be considerate about how their changes affect other systems. Teams need help understanding code when onboarding to teams, doing system design, and debugging. The way we understand code hasn’t evolved in decades. Codesee achieved 710 upvotes on ProductHunt for 2.0.
Meta’s Velox: An Open Source Unified Execution Engine
Velox is an open source unified execution engine aimed at accelerating data management systems and streamlining their development. Velox unifies the common data-intensive components of data computation engines while still being extensible and adaptable to different computation engines. It democratizes optimizations that were previously implemented only in individual engines, providing a framework in which consistent semantics can be implemented. This reduces work duplication, promotes reusability, and improves overall efficiency and consistency.
Velox is under active development, but it’s already in various stages of integration with more than a dozen data systems at Meta, including Presto, Spark, and PyTorch (the latter through a data preprocessing library called TorchArrow), as well as other internal stream processing platforms, transactional engines, data ingestion systems and infrastructure, ML systems for feature engineering, and others.
Why does this matter? The past few years have seen the rise of specialized execution engines and the fragmentation of the execution market. Teams pick the best engine for their use case and needs. This is exemplified by the numerous options like Spark, Presto, Dask, Ray, DuckDB, etc. This fragmentation has made maintaining and enhancing them difficult, especially considering that as workloads evolve, the hardware that executes these workloads also changes. Ultimately, this fragmentation results in systems with different feature sets and inconsistent semantics — reducing the productivity of data users that need to interact with multiple engines to finish tasks.
Velox demonstrates it is possible to make data computation systems more adaptable by consolidating their execution engines into a single unified library. Since the dataframe libraries can represent the execution plan as the Velox plan it could be possible to have a unified execution engine for both dataframe, SQL, and Python workloads. Bridging the gap between the SQL and Python users and workloads is a large trend.
Stable Diffusion Public Release
Stable Diffusion is a text-to-image model released by Stability AI. It is similar to DALL·E but completely open source. It can be used online or downloaded and run on a computer.
Why does this matter? Generative AI is a growing field. Stable Diffusion stands out because it was released as an open source project versus closed source like DALL·E and Midjourney. We expect more foundational models to be released open source. We are already seeing generative content could have a impact on the creative fields.
“Where Can Heroku Free Tier Users Go?”
Salesforce Heroku announced that it will cease to support the free tier for Heroku Dynos, Postgres and Data for Redis. The company also laid out its product roadmap plans, which some skeptics viewed as lackluster and a sign that Salesforce is sunsetting Heroku.
Why does this matter? Heroku is a platform as a service (PaaS) offering, which is a complete development and deployment environment in the cloud. PaaS are often the first-place startups start building. Ending the free tier suggests it no longer became effective to support the long tail of free users. It also presented an opportunity for emerging PaaS vendors like Railway.
“Developer Experience Infrastructure (DXI)” by Kenneth Auchenberg
Auchenberg states Developer Experience (DX) as “the holistic experience offered to developers throughout the lifecycle they interact with your product or service.” He underscores there are certain functionality developers expect in 2022 including documentation and content, great attention to detail on error messages, API references, baseline API infrastructure, debugging tools, and more. He discusses the cost of poor DX and the transition from DX to DXI as an emerging trend.
Why does this matter? Given the scarcity of developers, they have leverage of where they work, which can be influenced by the processes and tools. Developers want tooling that makes their lives easier while management wants to make them as productive as possible. In Auchenberg’s DXI market map he includes Speakeasy that offers SDKs and an API operations platform that we previously discussed.
“Research: Quantifying Github Copilot’s Impact on Developer Productivity and Happiness”
In 2021 GitHub Copilot launched in preview and early users reported that it improved developer productivity. After early observations and interviews with users, GitHub surveyed more than 2,000 developers to learn at scale about their experience using GitHub Copilot. Between 60–75% of users reported they feel more fulfilled with their job, feel less frustrated when coding, and are able to focus on more satisfying work when using GitHub Copilot.
Additionally, Github recruited 95 professional developers, split them randomly into two groups, and timed how long it took them to write an HTTP server in JavaScript. One group used GitHub Copilot to complete the task, and the other one didn’t. The group that used GitHub Copilot had a higher rate of completing the task (78%, compared to 70% in the group without Copilot).
Why does this matter? Generative AI is a large trend, and Copilot applies it to the code generation use case. Early results show that Copilot makes developers not only more satisfied but more productive. We expect generative AI to be applied to other aspects of the developer workflow including testing, code review, refactoring, etc.
⭐️Speakeasy — Founding UX Lead
⭐️Humanitec — Backend Software Engineer (fully remote)
⭐️Omni — Full Stack Engineer (fully remote)
