---
title: "Open Source"
description: "568 posts about Open Source, summarised, each linking to the original."
---

# Open Source
> 568 posts about Open Source, summarised, each linking to the original.

## Articles

### [Building a Form with Polaris](https://yomu.fyi/post/building-a-form-with-polaris.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Jun 1, 2022
- Type: Tutorial

Shopify’s Polaris design system provides design and content guidelines and reusable React components for Shopify admin and third-party apps. The tutorial builds a basic product form with Title and Description fields, a Save button, and top navigation, while introducing React state, events, and handlers. It uses Form, FormLayout, TextField, Page, and Card, then wraps the application in the required AppProvider after MissingAppProviderError appears; AppProvider shares global settings, including translations, across child components. The form is configured as controlled, with separate useState values and onChange handlers for each field, an onSubmit path connected to the Page component’s onAction prop, and clearing logic after submission.


### [Graphcore and Hugging Face Launch New Lineup of IPU-Ready Transformers](https://yomu.fyi/post/graphcore-and-hugging-face-launch-new-lineup-of-ipu-ready-transformers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Sally Doherty
- Published: May 26, 2022
- Type: Announcement

Graphcore and Hugging Face expanded the range of machine learning modalities and tasks available in Hugging Face Optimum. Developers can now access ten transformer models optimized for Graphcore IPUs across natural language processing, speech, and computer vision. The available architectures include BERT, ViT, GPT-2, RoBERTa, DeBERTa, BART, LXMERT, T5, HuBERT, and Wav2Vec2, complete with IPU configuration files and ready-to-use pre-trained or fine-tuned weights. The integration supports the Bow IPU processor, which uses 3D Wafer-on-Wafer stacking to achieve up to 350 teraFLOPS of AI compute. Optimum also integrates with the Poplar SDK 2.5, enabling compatibility with frameworks such as PyTorch, TensorFlow, Docker, and Kubernetes.


### [Our Favorite Moments From RailsConf 2022](https://yomu.fyi/post/our-favorite-moments-from-railsconf-2022.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: May 24, 2022
- Type: Explainer

RailsConf 2022 returned to an in-person format at Portland’s Oregon Convention Center after a two-year gap, with three days of keynotes, tutorials, and talks on technical and workplace topics. The 16th edition announced three new Rails Core team members, including Shopify’s Jean Boussier, and two new committers, while recurring themes included company investment in Rails, security, Active Records, “build vs buy,” diversity, and burnout. Shopify’s 15 Rails speakers contributed sessions and keynotes, and the recap gathers highlights on open-source staffing, long-term maintenance, developer wellbeing, Rails upgrades, supply-chain attacks, and modularization for “Big Rails.” Specific examples included regexp DOS risks, typosquatting and combosquatting, Rails upgrade automation, Ruby Archaeology, and the history of women in computer science. Because concurrent sessions and hallway conversations limited attendance, a virtual home edition and recordings were scheduled to make more talks available afterward.


### [Efficient Table Pre-training without Real Data: An Introduction to TAPEX](https://yomu.fyi/post/efficient-table-pre-training-without-real-data-an-introduction-to-tape.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Qian Liu
- Published: May 23, 2022
- Type: Problem & solution

Conventional language model pre-training relies on massive natural text corpora and general-purpose objectives like masked language modeling, leaving a substantial gap when applied to structured table tasks. To bridge this gap efficiently, TAPEX (Table Pre-training via Execution) pre-trains a language model, such as BART, by learning a neural SQL executor over a synthetic corpus. The system pairs web tables with systematically sampled executable SQL queries and gathers target execution outputs via an off-the-shelf SQL engine. During downstream fine-tuning, the model consumes natural language questions and flattened tables to predict answers. TAPEX achieved new state-of-the-art results across four benchmark datasets, including WikiTableQuestions and TabFact, while achieving a pre-training speedup of nearly 50 times over TaBERT.


### [An Introduction to Q-Learning Part 2/2](https://yomu.fyi/post/an-introduction-to-q-learning-part-2-2.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Thomas Simonini
- Published: May 20, 2022
- Type: Tutorial

Q-Learning serves as a foundational off-policy reinforcement learning method for finding optimal action-value functions before advancing to deep reinforcement learning. The approach employs a tabular Q-function that updates state-action pairs at each step using temporal difference learning. Action selection balances exploration and exploitation via an epsilon-greedy strategy, where epsilon progressively decays as training proceeds. Updates compute a temporal difference target combining the immediate reward and the discounted maximum value of the subsequent state under a greedy policy. This discrepancy between the exploratory acting policy and the greedy updating policy classifies Q-Learning as an off-policy algorithm, enabling agents to learn optimal policies in environments like Frozen Lake and autonomous taxi navigation.


### [Putting ethical principles at the core of the research lifecycle](https://yomu.fyi/post/putting-ethical-principles-at-the-core-of-the-research-lifecycle.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Lucile Saulnier, Siddharth Karamcheti, Hugo Laurençon, Leo Tronchon, Thomas Wang, Victor Sanh, Amanpreet Singh, Giada Pistilli, Sasha Luccioni, Yacine Jernite, Margaret Mitchell, Douwe Kiela
- Published: May 19, 2022
- Type: Announcement

Machine learning research risks introducing algorithmic biases, privacy violations, and malicious applications when ethical considerations are omitted from project lifecycles. To address these challenges, Hugging Face's multimodal learning group established an ethical charter at the inception of their multimodal project with input from ethics and governance experts. The charter defines content policies barring detrimental activities, the generation of personally identifiable information, and incautious applications in high-risk fields like medicine, law, and finance. Guiding values emphasize transparency, fairness through data and model bias reviews, self-critical curation, open reproducibility, and proper licensing attribution. Because ethical values like open data sharing and personal privacy can conflict, the team plans to iteratively evaluate trade-offs and track document revisions on GitHub.


### [Machine Learning Experts - Sasha Luccioni](https://yomu.fyi/post/machine-learning-experts-sasha-luccioni.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Britney Muller
- Published: May 17, 2022
- Type: Explainer

Research scientist Sasha Luccioni discusses her work on evaluating the environmental, societal, and ethical impacts of machine learning models. Her research includes measuring and reducing artificial intelligence emissions through initiatives such as CodeCarbon, regional grid selection, and the BigScience carbon footprint working group. BigScience aims to make large language models transparent by curating multilingual datasets, assessing manufacturing and compute costs, and analyzing participant workloads. Luccioni also emphasizes expanding model evaluation criteria beyond standard accuracy metrics to encompass efficiency, emissions, and subset-level fairness across demographics and data features. Overall, these efforts aim to democratize artificial intelligence and encourage deliberate trade-offs between model performance and computational resource demands.


### [Announcing the Hugging Face Fellowship Program](https://yomu.fyi/post/announcing-the-hugging-face-fellowship-program.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: merve, Omar Espejel
- Published: May 17, 2022
- Type: Announcement

Hugging Face announced the launch of the Hugging Face Fellowship Program to support and empower prominent contributors across the open-source machine learning ecosystem. The initiative provides selected fellows with personalized resources according to their project goals, including computing power, merchandise, and official recognition from Hugging Face. Candidate selection operates on an ongoing basis through nominations made by Hugging Face team members or existing fellows, evaluated primarily on past contributions toward democratizing machine learning. Eligible open-source activities span model releases on the Hub, documentation translations, library tooling improvements for frameworks like TensorFlow and Keras, and community hackathons. Participation does not constitute employment with Hugging Face, though the program serves as a platform to scale grassroots community impact across diverse machine learning domains.


### [Gradio 3.0 is Out!](https://yomu.fyi/post/gradio-3-0-is-out.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Abubakar Abid
- Published: May 16, 2022
- Type: Announcement

Gradio 3.0 introduces a ground-up redesign of the Gradio library to improve machine learning demo development and browser performance. The frontend has been rebuilt using modern technologies such as Svelte, resulting in smaller payloads, faster page load times, and cleaner visual designs suitable for embedding in blog posts. Component updates include revamping the Dataframe component to support CSV drag-and-drop, introducing new UI elements like Gallery, and adding the TabbedInterface class to group demos under distinct tabs. Developers also gain access to Gradio Blocks, a low-level Python API enabling custom layouts, multi-step interfaces, dynamic component property modifications, and complex data flows. A competition called the Gradio Blocks Party has been organized through the end of May to encourage community adoption.


### [Student Ambassador Program’s call for applications is open!](https://yomu.fyi/post/student-ambassador-program-s-call-for-applications-is-open.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Violette
- Published: May 13, 2022
- Type: Announcement

Hugging Face launched its inaugural Student Ambassador Program to advance its mission of democratizing machine learning globally. The initiative aligns with the organization's overarching goal to teach machine learning to 5 million people by 2023. Selected student ambassadors will receive dedicated workshops, direct team support, access to a peer network, merchandise, and updates on upcoming projects and features. Applicants must be enrolled in an accredited higher education institution, have completed at least one machine learning or data science course, and already use the Hugging Face Hub or libraries. The application window closed on June 13, 2022, with the program operating from June 30, 2022, through December 31, 2022.


### [Accelerated Inference with Optimum and Transformers Pipelines](https://yomu.fyi/post/accelerated-inference-with-optimum-and-transformers-pipelines.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Philipp Schmid
- Published: May 10, 2022
- Type: Announcement

Transformer-based models are moving into production for large-scale workloads, but default implementations remain slow and resource-intensive compared to traditional machine learning algorithms. Hugging Face Optimum addresses this issue by integrating performance optimization tools and accelerated runtimes like ONNX Runtime directly into standard Transformers pipelines. Developers replace standard model classes with Optimum equivalents to convert models to ONNX, run graph optimizations such as operator fusion, and apply quantization. In an evaluation on an AWS EC2 m5.xlarge CPU instance, optimizing and quantizing a RoBERTa question-answering model cut average latency from 117.61 ms to 64.94 ms while retaining 99.61 percent accuracy. Known limitations include a 2GB remote model size limit, lack of seq2seq support, omitted past key values in causal language models, and no local caching for optimized ONNX models.


### [We Raised $100 Million for Open & Collaborative Machine Learning 🚀](https://yomu.fyi/post/we-raised-100-million-for-open-collaborative-machine-learning.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: system
- Published: May 9, 2022
- Type: Announcement

Hugging Face announced a 100 million dollar Series C funding round led by Lux Capital, alongside participants including Sequoia, Coatue, and several angel investors. The platform hosts over 100,000 pre-trained models and 10,000 datasets spanning natural language processing, computer vision, speech, and other scientific domains for more than 10,000 customer organizations. The company plans to use the capital to expand research, open-source initiatives, tooling, and efforts focused on mitigating machine learning limitations like biases, privacy risks, and energy consumption. In addition, the organization is directing the BigScience research workshop with over 1,000 participants to train a massive open-source multilingual language model. Following organizational growth from 30 to more than 120 employees in twelve months, the remote-friendly company is actively hiring across all positions and seniority levels.


### [Talk Nerdy to Me: 10 Tech Podcast Recommendations from Shopify Engineering](https://yomu.fyi/post/talk-nerdy-to-me-10-tech-podcast-recommendations-from-shopify-engineer.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: May 6, 2022
- Type: Announcement

Shopify Engineering curated ten podcast recommendations from conversations with its technology employees, extending an earlier roundup of book recommendations. The list spans software engineering, front-end development, JavaScript, career growth, cybersecurity, data science, and broader technology topics, with selections ranging from quick, technical episodes to witty, conversational shows. Several entries emphasize specific formats or practices: Software Engineering Daily conducts researched interviews and focuses on open source, Soft Skills Engineering answers two listener-submitted questions in 30 minutes, and Darknet Diaries covers hacking, cybercrime, and data breaches. The recommendations also include podcasts valued for approachable explanations, collaborative software work, diverse guests, and perspectives on leadership and professional development, while some contributors identify particular episodes or hosts as reasons they keep listening.


### [Welcome fastai to the Hugging Face Hub](https://yomu.fyi/post/welcome-fastai-to-the-hugging-face-hub.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Omar Espejel
- Published: May 6, 2022
- Type: Tutorial

Hugging Face Hub has integrated with the fastai deep learning library, allowing practitioners to upload and download models directly through the huggingface\_hub Python client. Fastai Learner objects, which bundle model architectures, data loaders, and loss functions, can be published to a designated repository namespace using push\_to\_hub\_fastai. Uploaded models automatically receive Git-based version control, Git LFS support for large files, and generated model cards. Practitioners can retrieve and instantiate published models locally for inference or transfer learning via from\_pretrained\_fastai. The integration also supports Blurr, an ecosystem library that combines fastai with Hugging Face Transformers for sequence classification and related text modeling tasks.


### [React Native Skia—For Us, For You, and For Fun](https://yomu.fyi/post/react-native-skia-for-us-for-you-and-for-fun.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Apr 29, 2022
- Type: Announcement

React Native Skia brings Skia’s cross-platform 2D drawing primitives to React Native, with the stated goals of improving its limited graphical capabilities and aligning designers and developers around capabilities comparable to Figma. The integration uses React Native’s JavaScript Interface (JSI), enabling direct JavaScript-to-native communication through C++ rather than asynchronous messages, and exposes an API described as virtually 100% compatible with Flutter’s. On top of the imperative API, a declarative layer uses the React Reconciler to diff internal representation states and send changes to the Skia engine, supporting composable drawings and declarative optimizations. The library includes image filters, shaders, SVG, path operations, vertices, text layouts, mesh gradients, shadows, backdrop blur, and animations, with a direct connector to Reanimated 2; the authors expect it to address a major React Native pain point and describe the work as just beginning.


### [Introducing Hugging Face for Education 🤗](https://yomu.fyi/post/introducing-hugging-face-for-education.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Violette
- Published: Apr 25, 2022
- Type: Announcement

Hugging Face established an initiative to teach machine learning to 5 million people by the end of 2023. As machine learning expands across software development and impacts non-technical users, adapting workforce skills and assisting educators with ethical considerations become critical challenges. The educational framework provides browser-based model testing widgets, documentation on model biases, and free courses covering Natural Language Processing, Deep Reinforcement Learning, and interactive demo creation. Instructors receive free infrastructure to create Hub classrooms, alongside a multilingual toolkit spanning topics like Transformers and Gradio. Through ongoing global tours and free compute events, the organization supports collaborative machine learning education across diverse experience levels.


### [Supercharged Customer Service with Machine Learning](https://yomu.fyi/post/supercharged-customer-service-with-machine-learning.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Patrick von Platen
- Published: Apr 25, 2022
- Type: Tutorial

Customer support teams often receive high volumes of messages that cannot all be answered manually. To prioritize urgent inquiries, support workflows can be modeled as a text classification task to identify the most unsatisfied customers. Using the Hugging Face ecosystem, an NLP pipeline is established by selecting the Amazon reviews multi dataset and fine-tuning a DeBERTa model for sentiment classification across five granular categories. Evaluation on test data shows that the model identifies roughly 95% of unsatisfied messages with an 11.7% false-positive rate on satisfied messages, potentially reducing human triage workload by 83%. For production deployment, performance can be optimized through hardware acceleration, lower precision arithmetic, open-source libraries like Optimum and ONNX Runtime, and inference servers.


### [CO2 Emissions and the 🤗 Hub: Leading the Charge](https://yomu.fyi/post/co2-emissions-and-the-hub-leading-the-charge.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Sasha Luccioni, Zachary Mueller, Nate Raw
- Published: Apr 22, 2022
- Type: Tutorial

Training and deploying machine learning models produces greenhouse gas emissions determined by infrastructure runtime, hardware type, and energy grid carbon intensity. Hugging Face introduced mechanisms to measure, report, and filter models based on their carbon footprint to improve ecological transparency. Using the huggingface\_hub library, practitioners can query HfApi.list\_models with an emissions\_thresholds parameter to filter models by maximum or minimum carbon emissions measured in grams. In addition, installing the codecarbon package enables the Transformers Trainer to automatically attach CodeCarbonCallback and log training emissions to an emissions.csv file. Users can then publish these measured values directly into model card metadata under co2\_eq\_emissions for public tracking.


### [Works With Supabase - announcing our Partner Gallery](https://yomu.fyi/post/works-with-supabase-announcing-our-partner-gallery.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: Alaister Young, Shane Rice, Thor Schaeff
- Published: Apr 20, 2022
- Type: Announcement

Supabase launched its Partner Gallery to showcase integration partners and certified expert agencies assisting developers on the platform. The open-source gallery is built using the Supabase stack, featuring Postgres Full Text Search, next-image with Supabase Storage, and email triggers via Supabase Edge Functions. Initial agency partners include Morrow, a UK app development agency specializing in React Native and Supabase, alongside Quinence, a Singapore-based product development studio. Integrated tooling covers authentication systems like Clerk and Stytch, database and developer utilities like Prisma, Snaplet, Vercel, and pgMustard, and low-code builders including Appsmith, Draftbit, Plasmic, and Teta. These ecosystem integrations allow developers to manage Postgres workflows, secure user authorization, and build applications across web and mobile platforms.


### [Bring the Func Hackathon Winners 2022](https://yomu.fyi/post/bring-the-func-hackathon-winners-2022.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: Thor Schaeff
- Published: Apr 18, 2022
- Type: Announcement

Supabase announced the winners and runners-up of the 2022 Bring the Func Hackathon following their recent launch week. The event featured multiple project categories, showcasing applications built by community developers with Supabase tools. In the main showcase, mintbaseXsupabase won for an Instagram-like NFT browsing platform, while Pgm Dictionary served as runner-up with an UrbanDictionary clone built on Next.js, Mantine, and Supabase. The Edge Functions category highlighted Wen NFT for managing NFT community access and runner-up DocuPool for token-unlocked document sharing. Finally, AbileneX won the Most Fun or Visually Pleasing category for gathering feedback on ideas, alongside project management tool Kardow as runner-up.


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