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Supabase Beta May 2021
Supabase published its May 2021 beta updates covering authentication, regional infrastructure, and tooling. Developers can now implement Apple and Twitter authentication via Supabase Auth, addressing App Store requirements and expanding third-party login options. The platform introduced a Policy Editor with templates to configure Row Level Security for Storage, alongside CSV data retrieval capabilities. Infrastructure expanded with a new Tokyo region to support developers in Japan and China, while Supabase Grid was publicly released as part of an effort to open-source the dashboard. Additionally, community contributors advanced client libraries, completing postgrest-go and progressing development on Swift libraries including gotrue-swift, realtime-swift, and storage-swift.
Supabase Beta June 2021
Supabase announced a series of platform updates, documentation additions, and tooling enhancements for June 2021 ahead of its scheduled Launch Week. Developers can now provision a Postgres database directly through a new Vercel integration and enable authentication using Discord logins. Supabase Storage added support for upsert operations alongside public buckets that allow unauthenticated file access. The web dashboard introduced a restart button for debugging and a table policy editor with templates to configure Row Level Security. Additional backend improvements include timestamps for PostgREST error logs, an OpenAPI mode patch ignoring anonymous privileges for Prisma users, and an XSS fix in the Auth0 Next.js library.
Shipit! Presents: How We Write React Native Apps
A May 19, 2021 Shipit! event brought together Colin Gray, Haris Mahmood, Guil Varandas, and Michelle Fernandez, developers setting Shopify’s React Native standards, to discuss writing performant apps. The Q&A covers accessibility and design guidance through Restyle and Polaris documentation; their responsive layouts avoid fixed sizing where possible and use breakpoints for fonts and spacing. They describe React Native state as the usual source of truth while native modules manage internal state and send updates through EventEmitter, with SQLite in Point of Sale as an exception that still has one source of truth. Jest handles unit and integration tests, Buildkite manages CI builds, nightly builds are automatically distributed to testers, and failing feature-branch tests block merges to main.
Livebook is a secret weapon for documentation
The post presents Livebook as an interactive way to document the central logic of an Elixir or Phoenix application, helping new developers understand and explore code alongside existing documentation and tests. Using a TicTac example, it documents and executes the GameState module and struct, then describes and demonstrates a Horde-managed GenServer that runs the game state. Livebook connects to an existing project through a Mix standalone runtime, providing Elixir code completion, Markdown explanations, execution, and access to operations such as Ecto queries, context calls, and starting GenServers. The resulting notebooks are plain Markdown files, can be stored in source control, and support experimentation, reviewable diffs, and collaborative editing. The setup described requires Elixir 1.12: install Livebook with mix escript.install hex livebook, start livebook server, and connect through the locally generated token URL.
How Shopify Built An In-Context Analytics Experience
Shopify’s Analytics section gave merchants access to business data, but research and quantitative analysis found that they often moved between it and admin pages while making decisions. The team therefore designed an in-context experience for the Orders page, selecting metrics through workflow analysis, segmenting merchants by factors such as order volume and fulfillment process, and examining each metric’s usefulness, variability, and time horizon. Data scientists prototyped visualizations with real, segmented data and created a specification sheet so Web, Android, and iOS used consistent queries, data sources, fields, and aggregations; the project also tested batch models before streaming them. The launched experience surfaces order and fulfillment metrics for daily, seven-day, and thirty-day periods, with comparison indicators, and links to reports covering orders, returns, products, and fulfillment, shipping, and delivery times.
Using & Mixing Hugging Face Models with Gradio 2.0
The Hugging Face Model Hub hosts more than 10,000 user-submitted machine learning models spanning natural language processing, image classification, and audio processing. Machine learning developers increasingly need to demo models to interdisciplinary collaborators or non-programmers to uncover biases and failure points. The Gradio 2.0 library enables users to create graphical user interfaces and load almost any Hugging Face model using a single line of code. By default, these interfaces query Hugging Face's hosted Inference API without requiring an API key, though users can also run computations locally via the transformers library. Gradio interfaces can be customized through the Interface class and composed to load multiple models in parallel or chain them in series.
Elixir at Ramp 🧪
Ramp engineer Pablo describes why Elixir suited several critical services, while arguing that technology choice is only one part of engineering success. He contrasts BEAM, Erlang’s virtual machine, and OTP’s abstractions for reliable, concurrent systems with the adoption and developer-experience advantages of Elixir, including familiar syntax, Mix, iex, hot code reloading, doctests, macros, and sigils. Immutability and aliased module calls are presented as safeguards against hidden state changes and circular-import problems in growing codebases, though the post also notes that Erlang and Elixir lag behind bolted-on type checkers. Ramp uses Elixir for its real-time Authorization engine and Risk analysis platform; the Authorization service is described as exceptionally stable and flexible for rapid feature development. The company’s largest backend remains Python, and the conclusion prioritizes sleep, trust, ownership, and psychological safety over any specific stack.
Building a Hyper Self-Service, Distributed Tracing and Feedback System for Rule & Machine Learning (ML) Predictions
Grab's Trust, Identity, Safety, and Security team processes billions of daily rule and machine learning decisions for fraud detection, safety, and identity checks. Earlier logging approaches using plain text Kibana logs and the ActionTrace library lacked structured formats, dynamic entity customization, and fine-grained access controls. To resolve these limitations, the team built Archivist, a centralized tracing, statistics, and feedback system. Archivist ingests events through an SDK into Kafka streams, buffers and routes data into Elasticsearch indices and Amazon S3, and provides a role-based user portal. The platform handles 80 million daily logs across roughly 50 business scenarios, reducing scenario onboarding times from days to minutes.
Other Driven Developments
The article argues that development models turn implicit lessons into shared, conscious practices as teams, codebases, and responsibilities grow. It presents Grep Driven Development, emphasizing expressive and consistent names so behavior remains searchable, refactorable, abstractable, and predictable, while warning that premature abstraction and locally “improved” naming can hinder later change. It also makes the case for Copy/Paste Driven Development as a deliberate, judgment-based tool, noting that duplication can be cheaper than the wrong abstraction and that abstractions with flags and conditionals may create their own costs. The article names Ego Driven Development and Stickler Driven Development among its four approaches, then concludes with Human Driven Development: choosing, bending, or breaking rules to make work easier for present and future teammates.
Hooking Up Fly Metrics
Fly apps include built-in Prometheus instrumentation, exposing automatically collected metrics through a public Prometheus endpoint and allowing applications to export custom metrics. The walkthrough connects an organization’s Fly Prometheus URL to Grafana with a Fly token, then explains dashboard variables and PromQL queries using counters, gauges, histograms, labels, irate(), rate(), increase(), sum(), and regional grouping. Examples cover network traffic, TCP connections, HTTP response rates by region, and response-time heatmaps built from histogram buckets ranging from 5ms to 120s. Application instrumentation is shown for Go, Python Flask, and Node.js, with scraping configured in fly.toml through a metrics port and path. The post concludes that metrics are currently free, with some tier free forever, although a future pricing plan is expected for ambitious usage.
Fly's Prometheus Metrics
Fly describes the Prometheus-style metrics infrastructure behind its globally distributed micro-VM platform and explains why it favors metrics over traditional monitoring checks. The system collects counters and other measurements through HTTP /metrics endpoints, stores them in a clustered VictoriaMetrics (“Vicky”) deployment, and uses Telegraf, Vector, exporters, and Consul to connect sources and manage discovery. Within Firecracker instances, custom init, the Nomad driver, and a vsock carry node-exporter-type JSON statistics from guests for collection, while applications can expose their own exporters. Deployments can declare an exporter port and /metrics path in fly.toml, after which Fly routes those application metrics into Vicky alongside platform data. The resulting Prometheus API is available for Grafana queries, dashboards, and alerts, with labels such as app, region, host, and instance; the post argues that the same historical data supports anomaly detection, trending, and planning.
Three Ways We Share Context at Shopify Engineering
Shopify Engineering treats context—the why, what, and how behind development philosophies, approaches, and choices—as essential to company, team, and individual growth. It describes the Development Handbook, which evolved from a 2015 Google Doc into a searchable internal website in 2018, with hundreds of developer-contributed topics maintained through GitHub pull requests, reviews, and Markdown. Dev Talks began as a weekly open stage and became an on-demand recording program after the 2020 shift to Digital by Design, with most presentations lasting 15 to 20 minutes. The Technical Leadership Team’s Shift podcast provides monthly, in-depth discussions of technical decisions, distributed in audio and video with transcripts. The programs continue to evolve while facing content-maintenance and participation challenges.
Our Journey to Continuous Delivery at Grab (Part 2)
Conveyor, an in-house continuous delivery system at Grab, introduces hermetic deployments by tracking application code alongside static and dynamic configuration parameters. This hermeticity guarantees that production releases use combinations of versioned artifacts previously verified in staging, preventing rollback incompatibilities. Conveyor replaces single multi-environment pipelines with decoupled pipelines, while automating cluster locking, deployment slot scheduling, release note generation, and canary monitoring with automated rollbacks. These automations reduced production deployment failure rates from 1.5% to an average of 0.3% over a three-month period. Grab also doubled the volume of production changes between 2018 and 2020 while saving more than 5,000 man-days of engineering effort in 2020.
How I Define My Boundaries to Prevent Burnout
The post describes how an intense, single-tasking software leader developed a sustainable way to divide work, family, and recovery time. After 100-hour startup weeks caused scattered thinking, dulled analytical ability, wrist problems, and an inability to type, the author found that a focused 60-hour, 9-to-9 schedule delivered 80 to 90% productivity compared with 100 hours, then shifted to a 9-to-6 model in 2009. The approach separates concentrated work from non-work, schedules blocks for writing, planning, reviewing, and thinking, and uses email and Slack response patterns to communicate boundaries and expectations. It lets lower-priority tasks fall away instead of extending work indefinitely into personal hours. The author says this structure supports recovery, family presence, and prioritization, while emphasizing that each person should experiment with a sustainable model rather than treat nine-to-six as universal.
Supabase Beta April 2021
Supabase focused on platform stability, security enhancements, and community support during its April 2021 beta update cycle. To address visual accessibility needs for developers with astigmatisms or those operating in brightly lit environments, the team delivered Light Mode within the dashboard settings. The release also introduced OpenAPI-powered Storage API documentation, community translations for the main repository spanning nineteen distinct languages, and an experimental synchronization server connecting Postgres with Stripe. Dashboard components are systematically migrating into an open-source UI library, establishing necessary foundations for local development tooling and self-hosted environments. Additionally, the team reported substantial contributor growth across its realtime server and UI repositories while continuing engineering on a Workflows engine aimed at a July release.
Building a Distributed Turn-Based Game System in Elixir
Mark Ericksen describes building a proof-of-concept distributed, clustered game system for turn-based multiplayer applications with Elixir and Phoenix LiveView. Rather than separate JavaScript and API layers or external state systems such as Kafka and Redis, the design uses server-rendered HTML over websockets, Phoenix.PubSub, and Horde’s distributed process registry. A player’s LiveView sends moves to a GameServer, which publishes updated game state so subscribed LiveViews re-render for every connected player; Tic-Tac-Toe supplies the model. Hosted on Fly.io, geographically close web servers connect over a private WireGuard network while a game’s single GameServer remains the source of truth, with libcluster handling cluster formation and healing. The resulting open-source proof of concept supports multiplayer games, four-letter game codes, on-demand creation, responsive updates, and globally clustered servers.
A Five-Step Guide for Conducting Exploratory Data Analysis
Exploratory data analysis (EDA) is presented as a practical way to describe a dataset, uncover patterns such as missing data, correlations, and outliers, and generate hypotheses that inform business questions and later data products. The guide recommends first clarifying the intended task with leadership and product, then checking dataset size, sample rows, and the unique row identifier; its synthetic merchant-behaviour example has 1,500 samples, 13 features, and one row per shop per day. It then advises examining missingness by feature, investigating why values are absent before deleting or imputing them, and classifying features as continuous, discrete, or categorical to guide analysis. Visualizing distributions and relationships helps reveal changes across samples and time, while box plots and percentile thresholds support systematic outlier review. The process is framed as a starting point whose findings can support regression, classification, or KPI-dashboard decisions.
Dynamic ProxySQL Query Rules
Shopify uses ProxySQL to manage application connections to MySQL databases, but applying query rules across thousands of ProxySQL instances is difficult, especially during incidents. ProxySQL rules can reroute, rewrite, or reject regex-matched queries, yet incorrect patterns may cause unintended rejection, rewriting, data corruption, or downtime. To make rule changes safer, Shopify added dry-run support to a ProxySQL fork, using the mysql_query_rules table’s active and log fields; dry-run rules log matched queries without executing their actions. A sidecar polls a global key-value store, synchronizes dynamic rules to each ProxySQL instance, caches rules, supports optional TTLs, and uses exponential backoff for broad logging patterns. The system favors consistency during synchronization failures, while static startup rules cover requirements for full availability.
Scaling-up BERT Inference on CPU (Part 1)
Scaling BERT-like model inference on CPUs presents practical deployment challenges as transformer architectures proliferate in production environments. Using a reproducible Hydra-based benchmarking framework, inference workloads were evaluated on an Amazon Web Services c5.metal instance featuring an Intel Xeon Platinum 8275 processor with AVX512 and VNNI support. Out-of-the-box evaluations showed PyTorch 1.8.1 delivering lower latency than Google TensorFlow 2.4.0, likely due to PyTorch relying on OpenMP and Intel MKL/oneDNN rather than Eigen. Implementing multiple inference streams by pinning parallel model instances to dedicated CPU cores yielded near-linear throughput scaling. Matching instance core allocations to specific problem sizes also demonstrated that smaller workloads can achieve optimal latency on fewer cores, enabling up to a sixfold reduction in cloud compute costs.
How We Improved Agent Chat Efficiency with Machine Learning
Agent typing time represented a large portion of Grab's chat support journey, and 85% of messages were still free typed because agents customized static templates to fit their personal style. To accelerate typing across multilingual markets without robotic templates, Grab built SmartChat, a machine learning feature that provides contextual sentence completion. The team opted for a lightweight seq2seq architecture using single-layered GRU encoder-decoders in TensorFlow instead of bulky attention models to keep model latency under 100ms. The user interface was implemented in React using a content-editable div with inline typeahead suggestions activated via keyboard shortcuts.
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