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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.
Building an App Clip with React Native
The Shop team built an iOS App Clip to reduce the drop-off between a buyer’s checkout thank-you page and opening the full Shop app, where App Store download and onboarding added friction. App Clips are invoked through actions such as QR scans, links, or Smart App Banners and must stay below 10MB, while the team wanted React Native support for order tracking and related functionality. A Hello World build initially measured 28MB; explicitly selecting native dependencies in the Podfile reduced the baseline to 4.3MB, and a scrappy feature build reached 6.5MB. The team used early testing, invocation URL handling through React Native Linking, and shared data containers for app upgrades, ultimately releasing an App Clip at 9.1MB; extensive code sharing proved too complex and some code was copied.
Five Tips for Growing Your Engineering Career
Early-career engineers may struggle to learn and grow while adapting to a new job, especially when time and energy are limited. The guidance recommends identifying knowledge gaps, pairing with teammates, and learning by contributing to unfamiliar project areas, then taking ownership of a defined component through completion, technical decisions, and communication. It also emphasizes teaching and documenting what was learned, tracking accomplishments in a “brag document,” sharing progress with leads and colleagues, and mentoring interns or others to build leadership skills. The author frames growth as intentional “own your own development,” supported by a professional development plan, and gives personal examples including closing gaps in React, Redux, and GraphQL, contributing to technical designs, and giving talks.
Using real-world patterns to improve matching in theory and practice
Continuous ride-hailing assignment relies on solving the minimum weight bipartite matching problem between passengers and driver-partners. While traditional implementations assume a precalculated cost matrix, computing shortest-path travel times across large road networks dominates total execution time. Researchers introduced an Incremental Kuhn-Munkres algorithm that leverages the spatial locality of optimal matches to compute edge costs on demand. The approach integrates priority queues and lower-bounding techniques with refinement rules to avoid evaluating distant pairs while guaranteeing the same optimal assignment. Evaluated on Singapore road network data and real Grab production workloads, the incremental techniques reduced exact cost calculations and decreased assignment running times by over an order of magnitude.
Accelerating PyTorch distributed fine-tuning with Intel technologies
Transfer learning makes CPU-based clusters a viable, cost-effective option for shorter deep learning fine-tuning jobs. To accelerate PyTorch workloads, clusters can leverage Intel Ice Lake architecture featuring AVX-512 and Vector Neural Network Instructions alongside specialized software optimizations. Implementing the Intel Extension for PyTorch and the oneAPI Collective Communications Library (oneCCL) addresses communication bottlenecks across distributed nodes. In demonstrated tests fine-tuning transformer models on GLUE tasks across Amazon EC2 c6i.16xlarge instances, two-node jobs delivered roughly a 1.7x speedup, while four-node setups achieved a 3x speedup. These performance libraries and multi-node configurations maintain consistent scaling across both smaller datasets like MRPC and larger benchmarks like QQP.
Launching Livebook using LiveView
Fly.io’s Livebook launcher uses a single LiveView process to deploy a Livebook instance, but temporarily held deployment secrets could be lost when a user refreshed the browser or the server restarted. The secrets, including liveview_password, were sent to deployment and stored in Vault, so the values could not be retrieved; keeping them in a database or Redis also conflicted with avoiding persistent server-side storage. The implementation encrypts the private state with Phoenix.Token.encrypt/4, stores the resulting blob in browser SessionStorage, and uses LiveView hooks and websocket events to restore it through Phoenix.Token.decrypt/4. After recovery, the UI resumes tracking deployment progress, then clears the cached data when deployment succeeds or fails, while closing the tab also clears SessionStorage.
Bootstrapping a UI component library
Ryu is a component library created at a small startup to separate design concerns from feature development and business logic while product and conventions continue changing. It mirrors Figma's design system through shared colors, sizes, fonts, line heights, media breakpoints, z-indices, and components such as inputs, buttons, tooltips, menus, tables, and drawers. The article argues for a hard boundary between the library and main codebase, using a separate repository and an npm package with documented interfaces, hand-crafted changelogs, versioned releases, and extensive TypeScript. It emphasizes resisting narrowly tailored settings, postponing unresolved use cases, and prioritizing adoption over completeness, including preventing new code from using legacy components without good reason. Building on mature existing components while simplifying their interfaces, the library aims to improve maintainability, consistency, accessibility, theming, contribution, and coordination across applications.
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.
Using Propensity Score Matching to Uncover Shopify Capital’s Effect on Business Growth
Shopify’s data team sought to estimate whether accepting Shopify Capital for the first time increased merchants’ subsequent sales, without randomly denying funding in an A/B test. They compared US first-time adopters in January 2019 with Canadian shops that later pre-qualified for Capital, using propensity scores built from Shopify sales and performance characteristics and matching them with a caliper-based greedy nearest-neighbor algorithm. After matching about 600 treated shops against roughly 8,000 controls, they evaluated log-transformed cumulative GMV from February through July 2019 with a binary regression and checked balance using standardized mean differences, visual comparisons, and variance ratios. The estimated geometric-average GMV was 36% higher for US merchants, with a 95% confidence interval of 13% to 65%; robustness checks supported a positive treatment effect.
Building Blocks of High Performance Hydrogen-powered Storefronts
Hydrogen is a React-based framework for custom Shopify storefronts, designed to balance fast loading, personalized and dynamic commerce, and developer experience. The central trade-off is that static generation and edge delivery can speed loading but defer personalization, while conventional server-side rendering can slow time-to-first-byte as data is fetched. Hydrogen addresses this with React 18 alpha streaming SSR powered by Suspense, which streams the page shell before data resolves and progressively hydrates components without extra client round trips. React Server Components keep server-only code out of bundles, support server-side data access, and enable component-level updates that preserve client state. Built-in fetching and configurable caching, including stale-while-revalidate, work with Oxygen’s colocated runtime, while developers can mix server rendering, client fetching, and edge delivery at page or component level.
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.
The Vitality of Core Web Vitals
Core Web Vitals were introduced by Google in 2020 as unified guidance for web UX, and their loading, interactivity, and visual stability metrics became one factor in ranking systems after rollout from mid-June through the end of August 2021. The article explains LCP, FID, and CLS, including how LCP identifies the largest visible element, FID measures delay before the browser responds to a first interaction, and CLS sums unexpected layout shifts. It gives good and poor thresholds of 2.5 seconds and 4 seconds for LCP, 100 milliseconds and 300 milliseconds for FID, and 0.1 and 0.25 for CLS, with intermediate scores needing improvement. Recommended improvements include updating dependencies and tree-shaking unused library code, preloading critical assets, improving server response time, lazy-loading below-the-fold content, compressing images, and reserving image space to prevent shifts.
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.
Always on Calibration with a Quarterly Summary
Annual or infrequent performance reviews can create surprises, incomplete records of contributions, recency bias, and missed opportunities for timely feedback, correction, and career growth. The proposed alternative is “always-on” calibration, which treats individual development like continuous deployment: managers and team members repeatedly align expectations, discuss impact, and identify just-in-time opportunities. Each quarter, team members create a living document with intended outcomes, top and other accomplishments, upcoming opportunities, coworker feedback, and a quarterly review connecting impact to organizational expectations for role and level. They align on outcomes at the quarter’s first one-on-one and update accomplishments and feedback weekly before subsequent one-on-ones. Quarterly rollups provide smaller contribution snapshots, enable performance ratings and earlier course correction, and distinguish missed personal-development outcomes from missed role or level expectations.
Lessons from my internship at Ramp
The author reflects on a three-month software engineering internship at Ramp from March to June, describing lessons about execution, company choice, and location. Ramp’s charge card appeared to be a commodity, but the author credits its bias for shipping quickly, technical and product strength, differentiated technology, and favorable capital secured through finance expertise with turning it into a distinctive product. Compared with more formal, lower-stakes internships, Ramp offered flexibility, mentorship, and exposure to a fast-growing organization, although its pace and limited documentation could be challenging. The experience also shaped the author’s view that working on large problems at such a company can magnify impact, while New York provided experiences and a tech community distinct from San Francisco’s.
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.
The AWARE Development Plan
The AWARE Development Plan presents a structured way to pursue long-term goals in software development, education, dance, or other areas of interest. David Ward developed it after seeing team members struggle to define their growth paths and describes using a similar process for his own goals. AWARE stands for Aim, Work Back, Achieve, Reflect, and Encore: define the desired outcome, work backward into smaller aims, execute measurable objectives, learn from the result, and maintain the plan. Short- and medium-term aims act as checkpoints, allowing people to identify missing experience, seek opportunities, and course-correct. Ward recommends reviewing the plan with a lead or manager, including during weekly one-on-ones, to select next steps, connect opportunities to aims, and ensure completed aims are reflected on.
Shopify's Path to a Faster Trino Query Execution: Custom Verification, Benchmarking, and Profiling Tooling
Shopify needed safer ways to evaluate Trino configuration changes and software upgrades as its forked, hundreds-node cluster handled more data and data scientists depended on fast queries. The Data Reliability team built a lightweight Python library that centralizes Trino query orchestration, then exposed it through PyTest fixtures for repeatable verification, including correctness checks and candidate-versus-control comparisons. The broader framework also supports benchmarking and profiling concepts such as replaying historical queries, reproducing traffic patterns, and testing autoscaling, although the project ultimately focused most of its performance-testing scope on verification. Extensive correctness tests helped the Data Foundations team complete a successful Trino upgrade, while p95 query execution time remained stable over the reported period; the library was designed to leave room for more advanced interfaces later.
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