Loading…
Latest reads
The engineering internet, summarised so you can actually read it.
huggingface.co ·
Director of Machine Learning Insights
Directors of machine learning must balance complex mathematical modeling, system design, and sector-specific business operations across diverse industries. In digital media, practitioners leverage deep learning and Bayesian techniques to power assistive content creation tools and short-term interest recommendations while maintaining user privacy. Pharmaceutical teams employ natural language understanding, computer vision, and reinforcement learning across research and manufacturing, requiring strict GxP validation to navigate regulatory compliance and dataset diversity challenges. In the utility and energy sector, machine learning bridges disparate relational billing records, spatial GIS feeds, and physical grid machinery data to generate operational insights. Across these domains, leaders emphasize that successful integration depends on data quality validation, model monitoring safeguards, and assistive human-in-the-loop collaboration.
Britney Mullerhuggingface.co ·
Getting Started with Transformers on Habana Gaudi
Habana Labs and Hugging Face partnered to accelerate Transformer model training using Habana Gaudi accelerators, which offer up to 40% better price performance than recent GPU-based Amazon EC2 instances. Setting up the environment requires launching an Amazon EC2 dl1.24xlarge instance using the Habana Deep Learning Base AMI on Ubuntu 20.04. Inside an interactive PyTorch Docker container configured for Habana runtimes, users install the Optimum Habana package from source along with necessary dependencies. Fine-tuning the bert-large-uncased-whole-word-masking model on the GLUE MRPC task completes in 2 minutes and 12 seconds with an F1 score of 0.9181. Utilizing EC2 Spot Instances reduces the hourly compute cost by 70%, from $13.11 down to $3.93.
Julien Simonhuggingface.co ·
Introducing Hugging Face for Education 🤗
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.
Violettehuggingface.co ·
Supercharged Customer Service with Machine Learning
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.
Patrick von Platenhuggingface.co ·
CO2 Emissions and the 🤗 Hub: Leading the Charge
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.
Sasha Luccioni, Zachary Mueller, Nate RawSupabase ·
Works With Supabase - announcing our Partner Gallery
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.
Alaister Young, Shane Rice, Thor SchaeffGrab ·
How we reduced our CI YAML files from 1800 lines to 50 lines
Grab's Cauldron Machine Learning Platform team managed continuous delivery across multiple pipelines by using nested GitLab CI configurations, but soon encountered platform limitations such as the 100-include ceiling and bloated 1,800-line YAML files. To address these constraints, the team implemented GitLab Dynamic Child Pipelines to programmatically create execution stages on the fly. They built a command-line utility in Rust that runs git diff against the base branch, extracts pipeline and stage metadata using configurable stop patterns, and applies a template to produce a dynamic CI definition. On the master branch, the tool fetches the diff artifact from the source branch through the GitLab API to execute identical generation logic. This architectural shift reduced the root CI file from 1,800 lines down to a constant 50 lines, allowing configuration size to remain stable regardless of repository growth.
Jia Long LohSupabase ·
Bring the Func Hackathon Winners 2022
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.
Thor SchaeffSupabase ·
Supabase Beta March 2022
Supabase announced several major platform updates for March 2022, introducing global edge deployments for JavaScript and TypeScript functions developed and tested locally. Database querying expanded to support GraphQL through an open-source, portable Postgres extension. Supabase Realtime received support for multiplayer and presence to enable collaborative applications, alongside enterprise and scale capabilities including Point-In-Time-Recovery, SLAs, premium support, a Prometheus endpoint, and an observability dashboard. To enhance security, an integration with GitHub secret scanning automatically notifies developers and rolls exposed service_role keys found in public repositories. Additional updates include the PostgREST 10 pre-release, Next-JS auth helpers, new OAuth providers, and full remote hiring across engineering, marketing, and human resources.
Ant Wilsonhuggingface.co ·
Machine Learning Experts - Lewis Tunstall
In this interview with Britney Muller, Hugging Face machine learning engineer Lewis Tunstall discusses his background, his book Natural Language Processing with Transformers, and tooling optimizations for deploying transformer models. Tunstall notes that large model parameter sizes often lead to high latency, making off-the-shelf transformers impractical for responsive applications like chatbots. To resolve this issue, he developed single-line export tooling within the transformers library to convert models into the ONNX format. This conversion enables engineers to run PyTorch models in TensorFlow or on specialized hardware, achieving faster latency and higher throughput. Tunstall also recounts co-authoring the O'Reilly book with Leandro von Werra and Thom Wolf, collaborating on the Hugging Face Course, and training a GPT-2 model named code parrot on Python code.
Britney Mullerhuggingface.co ·
Habana Labs and Hugging Face Partner to Accelerate Transformer Model Training
Training transformer models across computer vision, speech, and natural language processing tasks at scale often demands heavy compute resources, incurring significant time and financial expense. To address these bottlenecks, Habana Labs and Hugging Face partnered to integrate the SynapseAI software suite into the Hugging Face Optimum open-source library. This integration allows machine learning practitioners to accelerate transformer training workflows on Habana Gaudi processors using minimal code adjustments. Habana Gaudi hardware, featured in Amazon EC2 DL1 instances and Supermicro X12 servers, incorporates ten 100 Gigabit Ethernet ports per processor to scale from single units to thousands of chips. The combined hardware and software architecture supports TensorFlow and PyTorch while delivering price and performance metrics up to 40% lower than comparable training alternatives.
Susan LansingGrab ·
How Kafka Connect helps move data seamlessly
Grab's real-time data platform team, Coban, implemented a managed Kafka Connect ecosystem on Kubernetes to streamline moving data in and out of Apache Kafka. To resolve dual-write consistency issues and capture pre- and post-change data, the team integrated Debezium connectors to capture MySQL binlog events and accommodate database DDL migrations. For disaster recovery and stream migrations, Coban deployed MirrorMaker2 connectors managed via Terraform to handle message mirroring and consumer offset translation across AWS regions. Additionally, they developed a custom converter utilizing Confluent Schema Registry to transform Protobuf-serialized Kafka records into JSON for ingestion into Azure Event Hubs. This architecture enabled zero-downtime cluster migrations and robust cross-region disaster recovery.
Wenli Wanhuggingface.co ·
~Don't~ Repeat Yourself
Hugging Face's Transformers library intentionally departs from the traditional "Don't repeat yourself" (DRY) software design principle by adopting a single model file policy. This architecture places all code required for a model's forward pass into a dedicated file, avoiding centralized shared modules for components like attention mechanisms. Because machine learning research evolves rapidly and published model architectures remain static, isolating code protects individual models from breaking changes and lowers contribution barriers for the open-source community. To maintain consistency without manually syncing duplicated code across successor models, the library uses automated tooling driven by Copied from comments. While this approach complicates unified API enforcement and component-wide research integration, Transformers validates API consistency by running approximately 20,000 automated tests daily.
Patrick von PlatenSupabase ·
Hackathon: Bring the Func(🕺)
Following the conclusion of its fourth Launch Week, Supabase announced the ten-day community hackathon titled Bring the Func, running through April 10th. The competition highlights newly released platform capabilities, establishing primary prize tracks dedicated to Supabase Edge Functions and GraphQL projects powered by pg_graphql. Participants can compete individually or in teams of up to five people, provided all design and code assets are built during the event and published as open-source repositories. Submissions require linking a repository detailing architecture, hosted demos, and Supabase feature usage across storage, authentication, realtime, or database access layers. Alongside category prizes and community swag, Supabase partnered with the Future Forest Company to plant a tree and issue a certificate for every completed project entry.
Thor SchaeffSupabase ·
Supabase Realtime, with Multiplayer Features
Supabase announced the third major release of Supabase Realtime, evolving the system from a single-tenant PostgreSQL change listener into a multi-tenant, distributed real-time platform. Built using Elixir and the Phoenix framework across a distributed node cluster, the new architecture decouples the database listener into an extension model and opens generic WebSocket functionality. New multiplayer capabilities include Presence, which uses Conflict-free Replicated Data Types (CRDTs) to track user connection states, and Broadcast, which distributes ephemeral messages across topics without writing to disk. The modular extension design allows listening to multiple PostgreSQL instances, external systems like MySQL, and financial or blockchain feeds, as demonstrated on a globally distributed 20-node cluster.
Wen Bo XieSupabase ·
Supabrew - Never Code Thirsty
Supabase presented SupaBrew, a mock non-alcoholic beverage engineered specifically to address developer thirst during extended coding sessions and large pull request reviews. Developed following a survey of 80,000 registered developers, the recipe relies entirely on three ingredients consisting of barley, hops, and water in strict adherence to German beer purity laws. The mock packaging design features bottles manufactured from recycled cathode-ray tube monitors and labels repurposed from leftover Ubuntu conference stickers. The fictitious rollout introduces tiered pricing models, including free access for indie developers, Pay-as-you-Drink consumption billing, and dedicated enterprise tiers. Supabase concluded the announcement by confirming that the product release is an April Fools joke.
Ant WilsonGrab ·
Supporting large campaigns at scale
Grab developed a batch job service within its Trident automation engine to execute multi-step marketing campaigns for millions of users simultaneously. The system replaces sequential, single-server execution with a distributed architecture powered by Apache Kafka, which distributes batches of 100 users across server clusters using hashed partition keys. To reduce network overhead and queries per second, downstream reward and messaging services introduced batch endpoints backed by bulk database queries, decreasing API latency by up to 85%. Grab further optimized performance by sharding Kafka topics by country and action type to prevent long-running reward tasks from blocking time-sensitive messaging workloads. Additionally, making terminal messaging calls asynchronous allows subsequent batch processing to proceed without waiting for message delivery confirmations.
Jie ZhangSupabase ·
Edge Functions are now available in Supabase
Supabase launched Edge Functions to execute TypeScript code close to end-users across more than 30 data centers. Built on the secure-by-default Deno runtime and hosted via Deno Deploy, the feature offers fast start-ups, scale-to-zero pricing, and native support for npm modules and Node built-in APIs. Incoming requests first reach an API gateway called Relay, which authenticates JSON Web Tokens, handles rate-limiting, and forwards requests using a Deployment ID to Deno Deploy. Observability is handled through Logflare to feed structured logs and metrics directly into the Supabase Dashboard. To mitigate abuse risks experienced during earlier product rollouts, invocations are currently limited to POST requests rather than general website hosting.
Inian ParameshwaranSupabase ·
Introducing Supabase Enterprise
Supabase announced the launch of its Enterprise Plan alongside new features integrated across existing tiers. The Pro Plan now merges pay-as-you-go billing with a default twenty-five dollar monthly spend cap, and users gain access to self-serve database add-ons for on-demand scaling. A new Log Explorer powered by the Logflare acquisition allows querying logs directly with SQL, providing ninety days of retention on the Enterprise tier. Dedicated enterprise capabilities include Point-in-Time Recovery powered by the open-source tool WAL-G, custom Prometheus scraping endpoints for observability, and guaranteed support service level agreements. Additionally, Supabase achieved SOC2 Type 1 compliance and introduced Elixir Livebooks to monitor PgBouncer connection pools.
Rory Wilding, Paul CopplestoneSupabase ·
GraphQL is now available in Supabase
Supabase announced the general availability of GraphQL support on its platform via pg_graphql, an open-source PostgreSQL extension that reflects SQL schemas into GraphQL types, fields, and relationships. Accessible through SQL functions or HTTP endpoints, the extension enforces PostgreSQL row-level security and filters introspection schemas according to the SQL roles assigned to request JWTs. To optimize performance, pg_graphql transpiles every GraphQL query into a single SQL query that aggregates results into JSON, avoiding the N+1 query problem and eliminating redundant join data payloads. Transpiled queries are cached as prepared statements to bypass planning overhead, and all generated mutations and queries support bulk operations. On Supabase Free Plan hardware, these optimizations achieve mean throughputs of 377.4 requests per second over the API and 656.2 queries per second over SQL connections.
Oliver Rice, David Thyresson