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The engineering internet, summarised so you can actually read it.
Supabase ·
Supabase Beta October 2021
Supabase announced its October 2021 beta release, introducing three new authentication providers for Slack, Spotify, and MessageBird phone logins. The platform rolled out database multi-schema support, enabling users to browse different database schemas directly within the Dashboard interface and query them through the PostgREST API after enabling schema access. Documentation and instructional resources were expanded to cover PostgreSQL functions, authentication server-side functions, RedwoodJS quickstarts, self-hosting architectures, and production readiness checklists. In addition to ecosystem releases such as supabase-py v0.0.3, nuxt-supabase v2.2.1, and vue-supabase v2.2.3, the project reached over 21,268 GitHub stars. The team also announced Launch Week III: The Trilogy, set to begin on November 29th on Discord.
Paul Copplestonehuggingface.co ·
Scaling up BERT-like model Inference on modern CPU - Part 2
Scaling transformer inference on modern CPUs requires coordinating hardware capabilities with software optimizations across memory allocation, parallel execution, and mathematical kernel libraries. Intel Ice Lake Xeon processors deliver up to seventy-five percent faster inference across natural language processing tasks compared to Cascade Lake by combining Sunny Cove architecture improvements with dedicated extensions like Intel Extension for PyTorch. Fine-tuning software knobs such as alternative allocators, threading runtimes like Intel OpenMP, and core allocations can involve thousands of possible configuration combinations. Rather than evaluating these combinations through exhaustive brute force, Bayesian optimization via Intel SigOpt achieves comparable latency within roughly twenty trials and reveals experiment-dependent parameter importance. These software tuning strategies enable low-latency production transformer serving without dedicated accelerators.
Ella Charlaix, Jeff Boudier, Morgan Funtowicz, Michael BenayounSupabase ·
Supabase $30m Series A
Supabase announced a $30 million Series A funding round led by Coatue, bringing its total funding to $36 million. The company aims to provide an open source alternative to Firebase while addressing the scalability limitations typical of abstracted database platforms. Rather than building a compatibility layer, Supabase provisions a full PostgreSQL database for every project alongside integrated authentication, storage, RESTful APIs, and realtime data streaming. Platform features rely directly on core PostgreSQL primitives, including Row Level Security policies, database triggers, and logical replication streams. Funding will support long-term development of a cloud-native PostgreSQL platform offering database branching, distributed read-write nodes, scalable storage, and ephemeral compute that scales down to zero.
Paul Copplestonehuggingface.co ·
Course Launch Community Event
Hugging Face scheduled the release of Part 2 of its machine learning course for November 15th alongside a collaborative community event. While Part 1 covered text classification and Model Hub uploads, Part 2 expands into token classification, causal and masked language modeling, translation, summarization, question answering, and ecosystem tools like Datasets and Tokenizers. The event includes two days of technical talks from industry practitioners covering transfer learning, ethics in machine learning development, and workflows across Keras, PyTorch, and TensorFlow. Participants collaborate on team projects to fine-tune natural language processing models and develop interactive portfolio demos, with AWS supplying complimentary compute through Amazon SageMaker. Attendees who successfully complete and demonstrate a project receive a certificate of completion.
Sylvain Guggerhuggingface.co ·
Large Language Models: A New Moore's Law?
Recent advancements in generative artificial intelligence have spurred the rapid growth of large language models like Megatron-Turing NLG 530B, which require vast financial investments and substantial energy consumption. Training these massive parameter architectures demands hundreds of multi-GPU servers and generates significant carbon footprints for relatively modest benchmark gains. Rather than relying on brute-force scaling, engineering teams can adopt smaller, more frugal architectures through knowledge distillation and transfer learning techniques. Practical alternatives include fine-tuning existing pretrained models, deploying on energy-efficient cloud infrastructure, and applying optimizations like pruning, layer fusion, and quantization. These methods deliver low-latency inference and high task accuracy while significantly decreasing hardware requirements, development time, and environmental impact.
Julien Simonhuggingface.co ·
Train a Sentence Embedding Model with 1B Training Pairs
Sentence embeddings map sentences into dense vectors to capture semantic meaning for downstream tasks such as clustering, asymmetric question answering, and text search. To train general-purpose models at scale, the team utilized a dataset compiled from multiple sources containing up to one billion sentence pairs. Training leveraged contrastive learning with Multiple Negative Ranking Loss and in-batch negatives, running across seven TPU v3-8 accelerators with JAX and Flax. Batches combined samples from at least two datasets to balance hard negatives within topics against broader cross-topic semantic structure. The project resulted in twenty trained models—including variants of RoBERTa, DistilBERT, and MPNet—alongside eight specialized evaluation datasets and an interactive demo covering sentence similarity, search, and gender bias.
Antoine SIMOULINGrab ·
Designing products and services based on Jobs to be Done
The Jobs to be Done (JTBD) framework helps organizations understand consumer goals by defining a job as the progress a consumer seeks to achieve in a specific context. To prioritize new features, GrabFood applied this framework and force diagrams to investigate consumer motivations and friction points. Interviews revealed that working parents ordering family meals experienced anxiety from navigating excessive individual choices during checkout. To address this obstacle, Grab developed an algorithm within the GrabMerchant application that identified complementary food items and automatically suggested ready-made bundles. Following the release, thousands of merchants adopted one-tap bundle creation, streamlining ordering for families while supporting restaurant growth.
Tim Langehuggingface.co ·
The Age of Machine Learning As Code Has Arrived
Recent findings from the 2021 State of AI Report and Kaggle State of Machine Learning and Data Science Survey indicate that machine learning is expanding into critical infrastructure while Transformers become general-purpose architectures across text, vision, and audio. In response, organizations face questions about scaling infrastructure, team composition, and engineering maturity. Rather than treating machine learning as isolated sandbox experiments or hiring solely data scientists, teams benefit from adopting established software engineering and DevOps principles like versioning, testing, automation, and continuous deployment. Furthermore, the rise of pre-trained Transformer architectures enables practitioners to fine-tune existing off-the-shelf models rather than training from scratch, reducing compute costs and training duration. Tools from platforms such as Hugging Face streamline model deployment, latency optimization, and infrastructure abstraction.
Julien SimonSupabase ·
Replenysh uses Supabase to implement OTP in less than 24-hours
Replenysh builds infrastructure connecting brands with local communities to track, monetize, and recover used packaging and materials. The engineering team previously maintained their own backend infrastructure, pairing custom Twilio and password authentication with a Haskell server and a Postgres database on Heroku. Seeking to accelerate development velocity and minimize ongoing DevOps maintenance during a codebase refactor, Replenysh required an alternative backend platform that preserved relational Postgres and Row Level Security. The team selected Supabase over alternatives like Firebase because it provided native Row Level Security alongside built-in authentication tooling. By adopting Supabase Auth, Replenysh successfully implemented an SMS one-time password system in less than 24 hours before deploying their mobile application to production.
Supabase ·
Hacktoberfest Hackathon Winners 2021
The organizers of the second open-source Hacktoberfest Hackathon announced the top ten projects across five distinct categories, selecting a winner and runner-up for each. Awarded projects include Pickle, a privacy-focused developer analytics service featuring realtime event synchronization, and CourseBuddy, a collaborative study platform for students. Other winning entries encompass the Feedback widget for sentiment collection, a utility management Flutter application, and Spookd, a Halloween-themed game built with Framer and connected to Supabase. Runner-up projects feature tools such as og:supa for generating Open Graph preview images, Relm for community management, Party Parrot as a Service, the TLWR path logger for Flutter, and the uwudaily mood-logging website. All winners receive custom gold medal shirts, runners-up receive silver medal shirts, and every participant receives a limited-edition participation tee.
Thor Schaeffhuggingface.co ·
Fine tuning CLIP with Remote Sensing (Satellite) images and captions
Pre-trained vision-language models like CLIP generalize well across everyday imagery but struggle with remote sensing domain characteristics. To enable text-based search across satellite imagery, a distributed team fine-tuned the openai/clip-vit-base-patch32 model on TPU hardware using JAX and Flax during the Hugging Face Flax/JAX Community Week. The training pipeline incorporated satellite image and caption pairs from the RSICD, UCM, and Sydney datasets alongside extensive image transforms and backtranslation-based text augmentation. Contrastive optimization using Adam with a learning rate of 5e-6 achieved top-1 retrieval accuracy of 0.883 compared to 0.572 on the baseline model. An interactive demo uses a NMSLib index for text-to-image matching, image-to-image matching, and patch-level feature detection.
Arto, Dev Vidhani, Goutham, Mayank Bhaskar, Ritobrata Ghosh, Sujit Palhuggingface.co ·
Hosting your Models and Datasets on Hugging Face Spaces using Streamlit
Streamlit enables developers to create interactive web demonstrations and visualizations for machine learning models and datasets. In an illustrative text completion interface recreating Write with Transformer, UI components such as text areas, sidebars, number inputs, and continuous sliders collect user text and inference hyperparameters. For data inspection, the streaming feature in Hugging Face Datasets loads large corpora directly into memory without requiring complete local downloads, integrating with pandas, matplotlib, and seaborn for tabular and chart rendering. Deploying these interactive applications to Hugging Face Spaces involves uploading the code files while defining dependencies inside a requirements.txt file. Matching local Streamlit versions ensures consistent runtime execution when hosting models and datasets online.
mervehuggingface.co ·
Showcase Your Projects in Spaces using Gradio
Demonstrating machine learning projects to the community requires accessible hosting and minimal interface boilerplate. Gradio integrates directly with the Hugging Face Hub and its underlying Inference API, enabling developers to load models using repository identifiers and launch interactive interfaces with a few lines of code. Hugging Face Spaces provides a free hosting environment where users select Gradio as the SDK, commit an app.py script, and deploy live web demonstrations for frameworks like Transformers, spaCy, SpeechBrain, and Asteroid. For complex workflows, developers can chain multiple models sequentially via Gradio Series or host custom checkpoints directly when models lack native Inference API support.
merveSupabase ·
Supabase Beta Sept 2021
The September 2021 beta update for Supabase outlines several product enhancements, documentation revamps, and community milestones. Developers using the JavaScript library can now leverage AbortController support to cancel long-running queries. Multiple PostgreSQL capabilities have been surfaced directly within the Supabase Dashboard, allowing users to apply custom column types, configure unique constraints on tables, view and edit columns in bulk, and manage table relationships spanning multiple schemas. In addition, the authentication documentation has been restructured into dedicated sections for Authentication and Authorization alongside an accompanying deep dive series. The update also marks two years since the initial commit to the Realtime Postgres engine, details upcoming events such as a Hacktoberfest hackathon, and announces open hiring for Site Reliability Engineers.
Paul CopplestoneSupabase ·
Supabase Hacktoberfest Hackathon 2021
Supabase announced its virtual open-source hackathon tied to Hacktoberfest 2021, running from October 1st to October 10th. Participants can compete individually or in teams of up to five to build a new open-source project with Supabase or contribute to designated community repositories. Submissions may use any programming language or framework but must be created during the event and made open source with a public code repository. Entries are evaluated on criteria including technical complexity, visual polish, functionality, and Supabase feature adoption across five award categories. Winners and runners-up receive limited edition Supabase swag, while participants retain full intellectual property rights over their submissions.
Thor Schaeffhuggingface.co ·
Summer at Hugging Face
Hugging Face released several major updates across its ecosystem, expanding its public Hub repository to over 16,000 models. Platform additions include Spaces for deploying Gradio and Streamlit demo applications, automatic TensorBoard instances, and evaluation metric tracking integrated with Papers With Code leaderboards. The Transformers library gained JAX/Flax support across more than 5,000 models, improved TensorFlow implementations, and introduced the transformers.onnx export module for model conversion. In research, the BigScience project completed large-scale training of a 13-billion-parameter English decoder model on Jean Zay, while the DeDLOC method enabled training the sahajBERT Bengali model without HPC infrastructure. Accepted conference papers detailed the Datasets library, prompt data-point equivalence, prompt-based fine-tuning heuristics, and block pruning techniques that reduced BERT size by 74 percent.
systemhuggingface.co ·
Hugging Face and Graphcore partner for IPU-optimized Transformers
Hugging Face launched its Hardware Partner Program at the 2021 AI Hardware Summit to offer device-optimized models and software integrations. As a founding member of the program, Graphcore collaborated with Hugging Face to optimize Transformer models for its Intelligence Processing Unit (IPU). Graphcore IPUs use a massively parallel MIMD architecture with on-die ultra-high bandwidth memory designed for AI workloads featuring fine-grained parallelism, low precision arithmetic, and sparsity. Through Hugging Face's open-source Optimum toolkit, developers will receive plug-and-play access to certified, hardware-optimized models across vision, speech, translation, and text generation. Graphcore's Poplar SDK integrates with PyTorch, TensorFlow, Docker, and Kubernetes, simplifying the process of porting workloads to IPU-POD datacenter compute systems.
Sally Dohertyhuggingface.co ·
Introducing Optimum: The Optimization Toolkit for Transformers at Scale
Running Transformer models fast and efficiently at production scale presents a significant challenge due to the complex compatibility between model architectures, acceleration techniques like quantization or sparsity, and specific silicon hardware features. Hugging Face introduced Optimum, an open-source library built to abstract hardware acceleration complexity and optimize training and inference on target devices through direct collaborations with hardware partners. Using quantization as a primary example, engineering teams often struggle with eager-mode model graph modifications, kernel compatibility checks, calibration parameter tuning, and acceptable accuracy loss trade-offs. Optimum addresses these challenges by integrating tools such as Intel Neural Compressor, which supports post-training quantization, quantization-aware training, and dynamic quantization driven by user-defined YAML configurations. Through these hardware-tailored integrations, the library aims to democratize production performance and reduce the aggregate energy consumed by machine learning workloads.
Morgan Funtowicz, Ella Charlaix, Michael Benayoun, Jeff BoudierSupabase ·
Supabase Beta August 2021
Supabase announced a thirty million dollar Series A funding round alongside multiple product updates and platform improvements during August 2021. The team opened an RFC for WALRUS, an initiative aimed at bringing Row Level Security to Postgres subscriptions via Write Ahead Log Realtime Unified Security. Auth updates included customizable SMS login messages, HCaptcha integration for passwordless authentication, and a revised email change flow that notifies both old and new addresses. Platform enhancements introduced a twelfth hosting region in Seoul, improved CSV importing for large files, and Dart reference documentation covering CRUD, Auth, and Realtime operations. Supabase reiterated an ongoing engineering focus on security, performance, and reliability to prepare the platform for production readiness across all workloads.
Paul CopplestoneGrab ·
Automating Multi-Armed Bandit testing during feature rollout
Traditional feature rollouts and Multi-Armed Bandit testing operate as separate workflows that often depend on delayed offline analysis. To eliminate manual intervention, the Multi-Armed Bandit Optimiser automates testing concurrently during feature rollouts by responding to minute-level feedback metrics. The architecture connects Kafka Streams data processing, a metrics server with Spark jobs, and an adaptive rollout module updating online experimentation configurations. Candidate models are evaluated via Thompson Sampling on Beta distributions, with Monte Carlo simulations determining traffic allocation across user entities. In production for the GrabFood recommendation widget, the system optimizes the Effective Conversion Rate over a 30-minute window and includes fallback distribution logic.
Weicheng Zhu
