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Open Source
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Community Day
Supabase kicked off its Launch Week by showcasing integrations, ecosystem updates, and community contributions across its open-source stack. The platform announced that all hosted projects are upgrading to PostgREST 9, introducing inner joins, functions with unnamed parameters, and PostgreSQL 14 compatibility. Through community contributions, Supabase expanded authentication options to include Slack, Spotify, and MessageBird alongside existing providers. Updates to the Python ecosystem include package renaming to supabase, synchronous operation support in postgrest-py, and a rewritten gotrue-py matching JavaScript feature parity. Additionally, Supabase partnered with GitGuardian to automate secret detection and remediation across development lifecycles, protecting sensitive database service role keys.
Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets
Hugging Face released an open-source Python library and no-code interface named the Data Measurements Tool to calculate metrics for responsible dataset curation. In AI development, scraped data often enters training pipelines without detailed analysis of representation or downstream model influence, partly because querying raw datasets typically requires complex coding. Built using Hugging Face Dataset and Spaces Hubs alongside Streamlit, the tool provides descriptive, distributional, and comparison statistics. It computes missing values, vocabulary sizes, duplicate counts, label distributions, and Zipf's law adherence to identify unnatural language patterns or markup artifacts. Additionally, it offers embedding clusters via a Sentence-Transformer model and normalized pointwise mutual information metrics across gender and sexual orientation identity terms.
New in PostgreSQL 14: What every developer should know
PostgreSQL 14 introduces several enhancements to improve application performance, data manipulation, query troubleshooting, and database security. Developers can traverse nested JSON data structures using subscripting and store noncontiguous values using multirange data types in single operations. Applications relying on libpq can leverage client-side query pipelining to send multiple queries across high-latency networks without waiting for individual responses, while parallel execution now supports foreign data wrappers, parallel sequential scans, and materialized view refreshes. For observability, query IDs are integrated into live execution tracking via pg_stat_activity, EXPLAIN VERBOSE outputs, and log files. Security upgrades include predefined read-only roles such as pg_read_all_data alongside SCRAM-SHA-256 enabled as the default authentication mechanism.
PostgREST 9
PostgREST 9 introduces several core updates to the tool that turns PostgreSQL databases into RESTful APIs. Developers can now perform inner joins when embedding tables using the !inner keyword, allowing top-level table rows to be filtered directly by embedded table attributes across multiple operators. The release also adds support for POST requests to functions containing a single unnamed parameter, which simplifies handling raw JSON payloads sent by external webhooks. To ensure compatibility with PostgreSQL 14, custom authentication functions accessing HTTP context headers and JWT claims must be updated to parse JSON properties from consolidated settings. Additional enhancements included in this version provide support for partitioned tables alongside documentation improvements and bug fixes.
How we launch at Supabase
Following their participation in YCombinator, Supabase sought a structured method to maintain product momentum and recreate the urgency of an accelerator internally. The team established Launch Week, a quarterly shipping sprint using a fixed-timeline and flexible-scope model where a major feature or announcement releases each day for a week. Each cycle begins with a broad distributed planning session that maps high-level goals and generates ideas, followed by execution and finely scheduled launch days covering coordinated social media, blog posts, and community engagement. Following the launch, team members transition into maintenance mode and downtime to handle support, bug fixes, and open-source contributions. Finally, a structured retrospective evaluates successes, failures, and process relevance before carrying lessons into the next planning meeting.
Supabase Launch Week III: Holiday Special
Supabase organized Launch Week III: Holiday Special from November 29 to December 3, 2021, delivering daily product releases, tooling improvements, and ecosystem announcements. The week began with Community Day to showcase external contributions and open source tooling utilized by the team. On Tuesday, Supabase released Supabase Studio, allowing engineers to utilize the official platform management dashboard in self-hosted configurations and local development workflows. Midweek releases included the rollout of Realtime Row Level Security on Wednesday, followed immediately by Thursday's announcement detailing the acquisition of Logflare. The five-day launch series concluded on Friday with a final batch of five distinct feature announcements across the Supabase platform.
Fine-Tune XLSR-Wav2Vec2 for low-resource ASR with 🤗 Transformers
Fine-tuning the multilingual XLS-R speech model adapts pretrained cross-lingual audio representations to automatic speech recognition tasks with limited labeled data. The demonstration configures a Wav2Vec2-XLS-R-300M checkpoint using Connectionist Temporal Classification on roughly four hours of validated Turkish audio from Common Voice. Building the pipeline requires pairing a Wav2Vec2FeatureExtractor for audio signals with a custom Wav2Vec2CTCTokenizer derived from dataset transcriptions. Training with Hugging Face Trainer over 3,200 steps decreases the validation word error rate from 0.7000 down to 0.3195. While the resulting transcription demonstrates recognizable phonetic alignment, output quality can be further improved by extending training schedules, refining preprocessing, and adding language model decoding.
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.
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.
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.
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.
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.
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.
YJIT: Building a New JIT Compiler for CRuby
YJIT is a new just-in-time compiler being built inside CRuby to improve Ruby performance on modern hardware, where single-core speed gains are limited and energy efficiency matters. Unlike MJIT’s weaker results on large real-world applications, YJIT uses a data-driven focus on performance hotspots in Rails and Shopify Core and is based on Basic Block Versioning (BBV). After about one year, it reports speedups over the CRuby interpreter of 20% on railsbench, 39% on liquid template rendering, and 37% on activerecord, with near-peak performance after one benchmark iteration. YJIT passes CRuby’s roughly 30,000-test suite, Shopify Core and GitHub backend CI, while running on a small percentage of Shopify production servers; its team plans further CRuby changes and broader JIT coverage before and after its intended Ruby 3.1 integration.
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.
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.
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.
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.
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.
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.
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