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Best-in-Class Developer Experience with Vite and Hydrogen
Hydrogen, Shopify’s framework for custom React storefronts, chooses Vite to improve developer experience while targeting performance for end users and evergreen browsers. Traditional bundling with tools such as Webpack can become complex and slow during development because each change may require processing more project files; Vite instead serves user code through native ES Modules, while pre-bundling and caching third-party dependencies. Vite combines an HMR development server with sensible defaults, ESBuild compilation, Rollup’s plugin system, CommonJS-to-ESM conversion, and SSR support, including transformations that differ between browser and server environments. Hydrogen integrates Vite through four plugins covering Hydrogen configuration, React Server Components, SSR and RSC middleware, and React fast refresh, enabling server components, streaming requests, and caching; the text concludes that Vite is a suitable choice, while acknowledging Webpack remains appropriate for some advanced use cases.
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.
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.
A Foolish Consistency: Consul at Fly.io
Fly.io’s global application platform uses Consul as a service-discovery catalog, but the post argues that a single globally consistent store is a poor fit for every part of the system. Consul Servers run Raft, while lightweight agents register events; fly-proxy consumes generated JSON describing service instances, and separate data tracks network distance. Because load and health are volatile, Fly.io moved load gossip to NATS and lets fly-proxy generate and distribute health hints, while Consul remains useful for orchestration and status. Strong consistency remains important for DNS, where stale or missing addresses can break ordinary applications; the proposed remedy is optional internal Anycast, routing internal traffic through fly-proxy. The conclusion is qualified: Consul is easy to deploy and valuable for infrastructure configuration, but Fly.io’s trajectory is away from using it as the backing store for a global app platform.
10 Books Shopify’s Tech Talent Think You Should Read
Shopify presents ten books recommended by its developers and engineers for people working in technology, spanning technical foundations, software design, learning habits, feedback, leadership, documentation, and focus. The recommendations were gathered from ten technical employees, while Shopify says its book bar stocks favorite titles and makes requested copies available to employees. Several entries address software systems directly: The Elements of Computing Systems guides readers through hardware, assembly language, programming languages, and operating systems, while A Philosophy of Software Design examines managing complexity and distinguishing tactical from strategic coding. Other selections argue for deep practice, smarter questioning, effective feedback reception, incremental knowledge transmission, decentralized leadership, critical reflection on the technology industry, and uninterrupted work blocks, with the contributors describing how particular books changed their studying, systems thinking, or approach to growth.
Community Day
Supabase initiated its Launch Week with Community Day, announcing a series of ecosystem updates, security integrations, and open-source contributions. Through a partnership with GitHub, secret scanning now detects exposed Supabase service role API keys and automatically revokes them to prevent unauthorized database access. The pre-release of PostgREST version 10 introduces arrow operator queries for composite types and array elements, structured PostgreSQL-compliant error messages, and automated recovery from Linux EMFILE conditions. Authentication features expanded with four community-contributed OAuth providers, two phone providers, email one-time passwords via verifyOTP, and framework-specific supabase-auth-helpers starting with Next.js and Nuxt. Finally, Supabase launched a partner gallery powered by Postgres Full Text Search alongside educational courses and an upcoming ten-day virtual hackathon.
Introducing Decision Transformers on Hugging Face 🤗
Hugging Face has integrated the Decision Transformer architecture into its transformers library and Hub, expanding support for offline reinforcement learning. Unlike standard reinforcement learning methods that maximize returns by learning value functions or policies through active environment interaction, Decision Transformers model trajectories as conditional sequence problems. The model feeds past states, actions, and target returns-to-go over a context window into a GPT-2 autoregressive backbone to generate future actions. Evaluating pre-trained continuous control checkpoints requires standardizing observations with training set statistics and iteratively updating the target return based on received rewards. This sequence modeling paradigm enables practitioners to adjust agent behaviors and skill levels directly by specifying different target returns at runtime.
Should I Open Source my Company?
Software founders often hesitate to make their companies open source due to concerns regarding code scrutiny, security exploits, and intellectual property theft. Supabase operated as an open-source business from its inception, finding that community scrutiny actually accelerates code refactoring and surfaces security vulnerabilities early through public reporting channels. In addition, rapid execution and developer experience provide stronger competitive defenses against larger competitors than proprietary code obfuscation or software patents. Open repos also transformed the company's hiring strategy by replacing traditional take-home tests and LeetCode interviews with evaluation of real-world pull requests and asynchronous discussions. Finally, external contributors expand product capability by independently developing niche integrations and platform adapters that core startup teams lack the bandwidth to build.
Supabase Launch Week 4
Supabase announced the schedule and lineup for Launch Week 4, detailing daily product releases and events starting on March 28, 2022. The week initiated with Community Day to highlight community contributions and open-source tooling utilized across the platform. Technical additions across the week included the introduction of GraphQL data fetching for PostgreSQL databases, the rollout of Supabase Enterprise, and the launch of Edge Functions. Final announcements on April 1 revealed Supabrew alongside Supabase Realtime with multiplayer features, concluding with the start of a ten-day virtual hackathon running through April 9. For the hackathon, titled Bring the Func, Supabase partnered with the Future Forest Company to plant a tree for every submitted project on madewithsupabase.com.
How We Built the Add to Favorite Animation in Shop
The Shop app’s Add to Favorite interaction animates a product thumbnail into the Favorites tab bar heart after a buyer favorites a product. The implementation is based on a motion spec that defines animated properties, target values, durations, easing curves, and ordering, with the thumbnail sequence followed by the icon’s color and bouncing movement. In React Native, Reanimated uses one shared progress value for the whole sequence, and interpolation maps progress ranges to opacity, scale, and vertical translation, including clamping and an instant disappearance at step 3. A 50 ms pause is included between thumbnail scaling and its downward movement. The shared value also creates a match cut between the disappearing thumbnail and the icon; a setTimeout-based handoff was rejected because a busy JS thread could delay the transition, while withDelay would keep timing on the UI thread.
How telematics helps Grab to improve safety
Grab developed an in-house telematics engine to monitor driver-partner habits, detect crashes, and improve ride safety beyond passenger feedback. The mobile telematics SDK collects accelerometer, gyroscope, and GPS readings, running on-device processing for immediate accident detection while batching ride data for post-trip safety reports. Because triaxial sensor sampling rates vary independently across diverse devices, the SDK synchronizes data through interpolation to a uniform time grid followed by decimation to an output data rate. Telematics signals are transformed to the frequency domain using Fourier Transform for local compression before backend upload. Grab also combines telemetry with spatial data to identify dangerous road zones and plans to expand on-device event detection across all platform verticals.
A Data Scientist’s Guide To Measuring Product Success
The guide addresses the problem of turning product success after launch into concrete metrics that reflect user impact rather than model or pipeline performance. It recommends starting with end-user goals, defining a main product goal and subgoals, translating them into primary questions, and selecting metrics before building a measurement plan. In Shopify’s example, the contextual analytics experience surfaces inventory data, with adoption measured through shops interacting with its navigation bar and product goals progressing from data-aware to data-savvy merchants. Metrics should be simple, precise, feasible, sensitive, and directional, while tripwires or anti-success metrics identify unwanted outcomes alongside desired gains. The plan pairs metrics with comparison measures and benchmarks, supports segmentation by merchant characteristics, and separates short-term, mid-term, and long-term measurement periods so results can be interpreted after release.
Machine Learning Experts - Margaret Mitchell
Machine learning models often internalize harmful dataset skews and fail to evaluate real-world contexts accurately. During work on vision-to-language generation, visual recognition systems learned to misinterpret destructive disasters as positive scenes because training images predominantly featured sunsets and fireworks. Addressing these structural flaws requires moving beyond benchmark optimization toward critical data analysis, dataset genealogies, and standardized ethical AI protocols. ML organizations must establish shared vocabularies for power differentials and dismantle competitive cultural norms that marginalize underrepresented contributors. Furthermore, lowering technical barriers—such as enabling non-engineers to inspect data without SQL—allows diverse stakeholders to evaluate models and prevent widening societal power divides.
Faster Pull Request Merges
Ramp’s growing Python pytest suite took over 12 minutes, while pull requests could wait up to one to two hours between merge queueing and production. Previously, Kodiak kept pull requests in a merge queue, requiring each PR to be up-to-date with master and rerun the full suite after every merge. Removing that requirement exposed a failure mode: parallel branches could introduce Alembic migrations with multiple heads, making the database migration history invalid. Ramp instead added migrations/migration-hash.txt, a hash of migration filenames that creates merge conflicts between migration-changing PRs while leaving other PRs free to merge; the deploy pipeline still runs tests on master before deployment. This reduced average merge time from more than an hour to 12 minutes.
Shopify’s Bug Bounty Program Raises Maximum Payout in 2022
Shopify’s Application Security team reports on its 2021 bug bounty results and announces program changes for 2022. Effective immediately, the maximum bounty for a CVSS 10.0 issue doubles from $50,000 to $100,000, with higher High and Critical brackets, while Shopify Plus, Shop, and Shop Pay services move into the Core asset category; mobile-client-specific issues remain Non-Core. The program received more than 3,000 reports, paid over $1 million in bounties and bonuses, and saw approximately 23% of reports judged valid, up from 19% in 2020. After its first full year using CVSS-based scoring, Shopify reports more consistent decisions and a reduction in average time to bounty from 249 hours to 137, while adding a CVSS guide, more resources, and further open-source support.
Announcing the 🤗 AI Research Residency Program
Hugging Face launched a nine-month Research Residency Program designed to train participants into impactful machine learning researchers. Residents collaborate directly with researchers from the Science Team to formulate research questions, develop novel machine learning techniques, and openly publish their findings. The initiative specifically encourages proposals from marginalized groups—including women, LGBTQ+ individuals, people of color, and working-class communities—to address disparities exacerbated by artificial intelligence progress. Applicants must demonstrate mathematical and programming abilities through coursework, open-source projects, and end-to-end proposals targeting positive societal impact. The full-time role is fully remote, provides benefits including medical coverage depending on location, and excludes concurrent student enrollment or employment.
Using Terraform to Manage Infrastructure
Shopify’s Conversations team used Twilio TaskRouter to route voice and chat tasks to agents according to routing rules and skills. As routing needs grew, configuration through Twilio’s GUI became difficult to understand and lacked clear change history, tests, and rollback support. Terraform provided an infrastructure-as-code workflow, backed by a reliable API, a Go client library, and a provider implementing CRUD operations; the team built a provider because Twilio did not have one at the project’s start. Resource definitions included schemas, dependencies, and an importer, while Terraform’s plan and apply commands previewed and executed changes. Integrated with Atlantis and GitHub pull requests, the workflow added plans, reviews, revision history, and easier rollbacks, while enabling business and support teams to submit infrastructure changes themselves.
Fine-Tune a Semantic Segmentation Model with a Custom Dataset
Fine-tuning a semantic segmentation model requires domain-appropriate training data and an efficient pipeline. Existing autonomous driving datasets feature roadway imagery captured by cars, creating a distribution mismatch for sidewalk-based delivery robots. To resolve this discrepancy, a dedicated dataset of sidewalk imagery is loaded from the Hugging Face Hub, split into training and test sets, and augmented on-the-fly using SegformerImageProcessor and torchvision. The smallest SegFormer architecture, B0, is fine-tuned using Hugging Face's Trainer API with mean Intersection over Union evaluation metrics. The final pipeline pushes the fine-tuned model to the Hub and executes inference by upsampling output logits to original image dimensions.
Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia
Production deployments of BERT and Transformer architectures often face cost and latency challenges because these models are significantly larger and more computationally intensive than traditional algorithms. To optimize text classification workloads, developers can compile vanilla PyTorch models for AWS Inferentia using the AWS Neuron SDK and its tracing utilities. Because the Neuron SDK requires static tensor dimensions, the model is traced with fixed input lengths and packaged alongside a custom inference script configuring one Neuron Core per worker. Deploying the resulting artifacts to an Amazon SageMaker ml.inf1.xlarge endpoint yields an average latency of 5 to 6 milliseconds for a sequence length of 128 across 10,000 synchronous evaluation requests.
Image search with 🤗 datasets
Hugging Face datasets expanded its capabilities by introducing an Image feature type, enabling image processing and integration with vector indexing tools. The library was applied to a sample of historical book embellishments extracted via OCR from the British Library. Images were loaded using the ImageFolder loader, enriched with filename metadata, and pushed to the Hugging Face Hub. A FAISS index and CLIP embeddings were used to retrieve images matching natural language queries, such as categories, specific objects, and boolean operators. While the retrieval system demonstrated reasonable semantic search results across various prompts, full public deployment was avoided due to CLIP model card restrictions and potential bias in the historical dataset.
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