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5 Steps to Bounce Back from a Negative Performance Review
An unfavorable performance review can feel like failing an exam, but it can also identify a clearer path to success when treated as feedback about role expectations rather than a judgment of personal worth. The five-step process is to avoid taking the review personally, adopt a growth mindset, clarify expectations with a lead, define measurable success, and establish a feedback loop. The author illustrates the approach with feedback about taking too long to ship code, which led to using smaller incremental improvements, and recommends tying goals to KPIs or other tangible metrics, supported by a lead or mentor. Regular check-ins, an up-to-date brag doc, and documented accomplishments can reduce surprises at the next review and help connect feedback to quarterly goals and observable progress.
How LiveView Took on a Life of Its Own
Phoenix LiveView began as an attempt to build dynamic server-rendered applications without writing JavaScript, using the server for interactions such as realtime reservation updates. Its initial design used stateful Phoenix Channels, a WebSocket connection, client event bindings, and full template re-renders sent back as HTML, a model that worked but produced redundant server work and large payloads. Adding Phoenix PubSub let LiveViews subscribe to reservation events and update all connected browsers through ordinary Elixir callbacks, without client changes. The implementation then evolved around diffing dynamic values, shared static template references, and cached live component templates, reducing transferred data and avoiding growing client bundles. The post concludes that this programming model sidesteps HTTP for dynamic features, with compact payloads and less client-side code.
Implementing "seen by" functionality with Postgres
Tracking unique post views within Postgres presents trade-offs between counter accuracy, row bloat, and concurrent write performance. To evaluate options under an architecture constraint preventing external dependencies, a benchmark suite generated synthetic users, skewed post distributions, and replayed view actions. The benchmark tested four implementations: a naive counter column, an hstore key-value approach, an association table, and HyperLogLog (HLL). Benchmark results demonstrated that simple-hstore achieved the lowest average latency among deduplicating approaches at 2.15 milliseconds, closely followed by HLL at 2.16 milliseconds. Despite hstore's raw performance in prototypes, HLL is recommended for production because it circumvents row bloat as view counts grow while avoiding expensive row counts.
Logbook - 2022-07-18
This Logbook entry records Fly.io product, platform, documentation, and interface changes on 2022-07-18. Highlights include $99 in usage credits for the Pro Plan, SOC2 compliance, persistent-volume expansion through fly volumes extend, SFTP access to apps over 6PN and WireGuard, and routing changes intended to improve latency. flyctl gained debug JSON formatting, expanded machine-app deployment and status commands, preserved mounted volumes during deployments, and added rolling deploys that wait for each VM to start. The entry also describes fixes for autoscaling, the Fly Redis image, GraphQL validation, Phoenix launches, and dashboard presentation, alongside updated Laravel and persistent-volume documentation and improved Heroku token-failure emails.
Spin Infrastructure Adventures: Containers, Systemd, and CGroups
Spin’s infrastructure team investigated instance instability after Kubernetes nodes experienced frequent failures, including roughly five nodes per day, and instances showed memory use that could exceed their assigned limits. Spin runs instances as Kubernetes pods, with systemd inside each container managing initialization and processes, and the team compared resource behavior across kubectl exec and Spin shell sessions. Stress tests showed that processes started through kubectl exec entered the kubepods cgroup hierarchy, while processes launched through Spin shell entered a hierarchy with an effectively unlimited memory limit, causing usage to appear on the node rather than the pod. Experiments comparing containerd, Docker, and Podman identified systemd cgroup delegation as the relevant difference: Podman isolated systemd and its child processes correctly, while the production setup used cgroupfs. The team identified switching COS to the systemd cgroup driver as the forthcoming fix, while noting that the bind-mounted cgroup filesystem enabled the escape.
Revamped Auth Helpers for Supabase (with SvelteKit support)
Supabase has released an updated version of its framework-specific Auth Helpers libraries, introducing official support for SvelteKit alongside existing React and Next.js tooling to simplify server-side rendering authentication. The updated libraries transition from the @supabase/supabase-auth-helpers namespace to @supabase/auth-helpers within a dedicated monorepo managed by Turborepo. Structuring the codebase with Turborepo and changesets enables publishing isolated packages per framework through GitHub Actions, ensuring each helper contains only its own relevant dependencies. Versioning has also been reset to sub-0.x to reflect early framework lifecycles like pre-1.0 SvelteKit and manage expectations for future breaking changes. Meanwhile, the team has documented migration steps from previous packages and announced that Remix Auth Helpers are currently in active development.
How we automated FAQ responses at Grab
Internal engineering on-call engineers at Grab spent substantial working hours handling repetitive questions in Slack channels, such as how-to inquiries and access permission requests. To resolve this without building an in-house tool, the team conducted an anonymized vendor comparison and selected OneBar through an employee voting process and a phased proof-of-concept. Initial rollouts were restricted by contract to 20 channels, leading the team to prioritize deployment based on Slack message volume and member counts. Populating the knowledge base required roughly a quarter of consistent updates alongside tech talks, while a targeted crowdsourcing campaign among new onboarders expanded the glossary and grew usage to approximately 3,000 users.
SOC2: The Screenshots Will Continue Until Security Improves
Fly.io’s SOC2 certification prompts an explanation of what the audit measures, when companies should pursue it, and what security value customers should assign to it. The post characterizes SOC2 as an accounting-style audit of documented company security processes, centered on an auditor-customized Document Request List (DRL) and screenshots, rather than adversarial product testing. It distinguishes Type I’s point-in-time snapshot from Type II’s multi-month confirmation and recommends doing the engineering work early, while delaying certification until customer demand justifies it. Fly.io prepared for more than a year and describes controls including Google SSO, certificate-based SSH access through Teleport, VPN gating, GitHub SSO, and session audit logs. The resulting certification improved processes but remains a weak positive security signal: SOC2 can miss much of a company’s technical security model, so penetration tests and clear security reporting still matter.
Shopify’s Technical Interview Process: What to Expect and How to Prepare
Shopify’s guide outlines the interview process for individual-contributor technical roles, while noting that stages can vary by role and team and that leadership hiring may add team-specific interviews. Candidates may have a one-hour recruiter “Life Story” conversation, a 40-minute CoderPad coding exercise, a 75–90-minute remote pair-programming session, and a 60-minute Technical Deep Dive focused on previous projects. The coding exercise assesses algorithmic thinking and problem solving, while pairing emphasizes communication, implementation, edge cases, scalability, limitations, and testability. The deep dive asks candidates to discuss impact, architecture, technical decisions, trade-offs, failures, successes, lessons learned, teamwork, and mentorship; the guide also recommends clarifying questions, running code early, using permitted references, and choosing projects they know well.
Typeahead Search at Nextdoor
Nextdoor built a proximity-based autocomplete service to power typeahead search and mention features across its hyperlocal platform for hundreds of millions of entities, including users and businesses. The system shards geographic data using Uber's open-source H3 geohashing library and stores prefix indexes in memory using Redis sorted sets. By adopting a Command Query Responsibility Segregation architecture, ingestion writes are processed on Redis primary nodes and replicated to read-only search nodes with under 10 milliseconds of replication lag. Dedicated APIs handle indexing, typeahead lookups, and ranking before returning hydrated results. Operating since August 2021, the service processes hundreds of millions of monthly typeahead queries while maintaining a P95 search latency below 30 milliseconds.
Supabase Beta June 2022
Supabase released its Beta June 2022 updates covering developer tooling, authentication, and infrastructure changes. The team decoupled Supabase Auth Helpers into dedicated packages for Next.js and React, while introducing project pause and restore features to enable unlimited free projects. Developers can now initiate project creation directly through the Supabase CLI and toggle realtime updates from the table editor side panel. Authentication updates include exposing client IP addresses in audit logs, introducing an auth.jwt function, bubbling PostgreSQL errors from GoTrue, and deprecating older authentication functions. Additionally, infrastructure migrations are underway to support multiplayer features, accompanied by community-led tutorials, sponsorships, and platform growth reaching fifty thousand GitHub stars.
Shopify and Open Source: A Mutually Beneficial Relationship
Shopify describes its relationship with Ruby, Rails, and the open-source community as mutually beneficial, linking the company’s success to its Rails foundation and its engineering culture to the Rails Doctrine. It frames a 100-year company ambition around making Ruby and Rails viable for large teams, mature codebases, millions of lines of code, and billions of web requests through production performance, architecture guidance, upgrades, tooling, and selective typing. To support that goal, Shopify contributes projects and initiatives spanning developer tooling, gradual typing, VS Code support, automated upgrades, YJIT, Ruby performance work, TruffleRuby collaboration, $500,000 in research funding, Bundler and RubyGems, and RubyGems.org security. The post argues that company participation improves engineering skills, application longevity, and influence over foundational software, while describing contributors who range from occasional participants to full-time maintainers.
The Story Behind Shopify’s Isospin Tooling
Shopify’s Isospin is the systemd-based core of Spin, its in-house cloud development platform, designed to run multiple applications inside a single Linux VM. The earlier implementation relied on shell scripts, but growing application counts made it difficult to split applications into components, express dependencies, schedule jobs, and isolate processes. Isospin uses systemd unit graphs, targets, template units, and generators to create service configurations dynamically, including per-application copies of dependencies such as MySQL and Elasticsearch. Additional tooling assigns semi-stable ports by hashing service names and uses systemd readiness notifications, including a notify-port wrapper that checks HTTP availability; the resulting system reuses systemd’s dependency and health mechanisms, with more complex cross-application relationships under investigation.
Flutter Tutorial: building a Flutter chat app
Developers can construct cross-platform real-time chat applications for iOS, Android, and web by pairing Flutter with Supabase. The architecture relies on Supabase to manage authentication and provide direct SDK access to a Postgres database without requiring custom backend server code. The database schema defines profiles with regex validation constraints and a messages table configured for live subscriptions through Postgres publication settings. Flutter clients capture user input, insert new records into the messages table, handle database exceptions, and format chat bubbles with dynamic timestamps. While this initial setup implements user registration and message broadcasting in a shared room, securing chat rooms requires subsequent row-level security configuration.
Graph Networks - 10X investigation with Graph Visualisations
Fraud detection traditionally required investigators to manually combine large datasets from disparate anti-fraud systems using statistical methods, which proved slow and inefficient. Grab built an interactive Graph Visualisation platform to transform raw records into connected visual maps without requiring manual queries or switching tools. The platform manages over three billion nodes and edges, allowing investigators to selectively expand data points and replay chronological events using temporal filters. Visual relationship mapping helps teams verify account appeals, uncover device-sharing rings, and spot anti-money laundering behavior through transaction density patterns.
The Serverless Server
The post explains what “serverless” means by reconstructing the machinery beneath a Lambda-like platform. It starts with bare-metal x86 servers, a Type 1 hypervisor, and services for placement, VM management, request forwarding, and function execution; dedicated customer workers, shared warm pools, rate limits, and container-style isolation address multitenancy, cold starts, and bursty demand. The runtime design uses object storage for function code, chroot, privilege dropping, namespaces, seccomp, cgroups, and freezing to constrain idle instances and workloads. Its limits on initialization, invocation duration, and idle lifecycles support migration away from unhealthy instances but disadvantage applications with expensive startup, dynamic local caches, or uncertain response times.
How to Build Trust as a New Manager in a Fully Remote Team
The post explains why building trust as a new manager in a fully remote organization differs from joining a team with established relationships: passive cues and casual interactions are harder to observe, so remote work requires intentionality. The proposed 90-day plan focuses on the environment during the first 30 days, the team during the next 60, and the manager thereafter. Early actions include pairing sessions, one-on-ones, peer-group sessions, understanding the wider organization, setting up remote tools, and building cross-company relationships. Team leadership emphasizes spending time with members, using asynchronous communication for productivity and synchronous meetings for connection, and creating psychological safety through open communication and transparent discussion of failures. The final phase addresses FOMO, workday design, and experimentation through feedback and reflection.
Policy Gradient with PyTorch
Policy gradient algorithms optimize reinforcement learning policies directly without learning intermediate action-value functions. Instead of assigning discrete Q-values to actions, these methods parameterize a stochastic policy and apply gradient ascent on an objective score function. This setup eliminates manual exploration tuning, resolves perceptual aliasing in identical states, and naturally accommodates continuous or high-dimensional action spaces. The Reinforce Monte Carlo policy gradient algorithm operationalizes this approach by collecting full episodic trajectories and adjusting parameters along the gradient of the log-action probabilities scaled by return. Practitioners implement this algorithm using PyTorch to evaluate agent robustness across environments like CartPole-v1, PixelCopter, and Pong.
Visualizing Supabase Data using Metabase
Organizations seeking to analyze database records can bypass code-heavy Python visualization libraries by connecting a Supabase backend directly to Metabase. Deploying the open-source Metabase Docker container exposes an initial setup interface on default port 3000, allowing administrators to input PostgreSQL connection credentials retrieved from Supabase database settings. Once connected, Metabase automatically surfaces table insights and automated x-rays across the public schema, exposing metrics such as inventory count ranges, price distributions, and column-specific statistics without requiring manual queries. For specialized analytics, users can execute custom SQL joins across relational tables like Product and Vendor to render tailored visual dashboard formats, including configurable bar charts mapped to designated axis fields.
Navigating Recurring Payments in India: A Backend Perspective
India’s October 2021 Reserve Bank of India (RBI) directive required recurring payments to use payer-authorized e-mandates, Additional Factor of Authentication (AFA), and pre-debit notifications at least 24 hours before charges. Shopify’s billing platform therefore had to model e-mandates, support recurring and one-time payments across cards and Unified Payments Interface (UPI), and integrate a local payment provider. The payment service reused its source object for the e-mandate token, recorded the temporary authorization as a charge for auditability, and refunded it asynchronously. To avoid two consecutive card authentication flows, the team used recurring-payment tokenization for both payment paths, accepting an atypical notification experience as a short-term trade-off; UPI required asynchronous webhook-driven handling and a hard mandate limit. After the beta launch, billing-payment success rates in India increased from pre-project levels, and the team said it would soon make UPI generally available to Indian merchants.
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