Aori Nevo, PhD

Learn what Playwright is, why teams use it, how it compares with other E2E tools, and how to scale tests with parallel execution.

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Your CI/CD bill is not exploding because of “expensive runners,” it is bleeding out through flaky tests, over-parallelized jobs, and slow feedback loops that quietly tax every deploy This piece shows you how to redesign pipelines so you cut compute waste, shrink queues, and speed up developer flow without adding yet another approval gate or YAML religion Full breakdown in the comments. #DevOps #CICD #PlatformEngineering

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Custom Apigee developer portals work. Until you have to keep evolving them. Auth changes. Docs drift. New APIs appear. Ownership never really goes away. Launchpad is a managed developer portal for teams that want stability without constant rebuilds.

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A simple 3‑step workflow to turn a prompt into a reliable team tool. ⚙️ Step 1 — Define the exact output. Don’t say “help me summarize.” Say: “Return a 5‑line customer reply in JSON with keys: tone, body, follow_up.” Make success binary. If it parses, it passes. Step 2 — Build the prompt skeleton. System role + one clear instruction + one example output. Include constraints: max tokens, required fields, and a failure token (e.g., "ERROR: MISSING_FIELD"). Short examples prevent ambiguity. Step 3 — Break it with MAIA. Feed messy inputs: missing fields, conflicting tones, truncated text. Ask MAIA to show failure cases and suggest guardrails. Iterate until the output either parses or returns the failure token cleanly. Why this matters: professionals ship when outputs are testable. A prompt that either produces valid JSON or a clear error is easy to automate, review, and debug. That’s faster than vague instructions or long feature demos. Try this in a 5–7 minute microblock: pick one recurring task, define the schema, write the skeleton prompt, then run three messy tests with MAIA. What’s the single prompt you could stress‑test with messy inputs this week? #AI #Learning #Microlearning #EdTech #Rebundle

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🚀 NgRx 21 — 10 Years of State Management, Now Signals-First NgRx 21 celebrates a decade of NgRx while taking a big step toward Angular’s Signals-based future with the introduction of @ngrx/signals/events. 🔍 What’s technically significant in NgRx 21? 🧩 @ngrx/signals/events A new primitive focused on event-driven architecture using Angular Signals: • Explicit event modeling instead of action overload • Clear separation of events → side effects → state updates • Reduced boilerplate compared to traditional Actions + Effects • Fine-grained reactivity with Signals (no unnecessary stream emissions) This makes complex flows easier to reason about while keeping NgRx’s core principles intact. ⸻ ⚡ Signals-First Reactive Model • Moves away from RxJS-heavy patterns where they aren’t required • Enables synchronous, predictable state transitions • Better performance via fine-grained dependency tracking • Ideal for modern Angular apps using signal(), computed(), and effect() ⸻ 🧠 Improved Architecture & DX • Cleaner APIs across NgRx packages • More composable building blocks • Easier onboarding for teams new to NgRx • Stronger alignment with Angular’s recommended direction ⸻ 🏗️ Why this matters for real-world apps For large-scale Angular applications: • Less cognitive overhead in complex workflows • More maintainable state and side-effect management • Better testability (events and state updates are easier to isolate) • Smooth migration path from classic NgRx to Signals-based patterns NgRx 21 proves that enterprise-grade state management doesn’t have to be verbose — and that Signals can coexist cleanly with proven NgRx patterns.

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Who needs self-service? In this article, Conduktor’s VP of Engineering, Matt Searle, talks about his own experiences attempting to balance developer autonomy alongside platform stability. His key insight? Govern Kafka, or risk havoc. Read his latest blog—and learn more about how Conduktor can help you implement self-service with guardrails. https://lnkd.in/ejWTRctu

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Stop blaming your CI/CD tools for being slow. Instead, change their job. Let your pipeline focus on component-level validation and move systemwide testing to developer canary testing. That’s how you break the bottleneck and unlock the true promise of microservices. By Arjun Iyer, thanks to Signadot

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Microservices architectures are complex—API changes or service disruptions often go undetected, breaking downstream systems. Tired of chasing down microservice failures after they hit production? BaseRock's Agentic QA brings order to your distributed systems—before things break. 🔍 Auto-discovers service interactions and generates integration tests for every microservice ⚙️ Runs autonomously in CI/CD to catch issues early—before they reach users 🔁 Self-healing tests adapt to service changes, minimizing manual upkeep Shift left with confidence. Let your tests evolve as fast as your architecture. https://hubs.li/Q03wpmmy0 #agenticqa #baserock #integrationtesting #codequality

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We recently sat down with David Biesack on the Naftiko Capabilities podcast to explore how JSON Schema serves as a critical tool for managing API complexity. The conversation offers a practical look at how a team can build obust workflows around schemas, YAML, and OpenAPI—and why these patterns matter even more in an era of AI agents and MCP servers. https://lnkd.in/eWTpBbva

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If you want to start using Playwright but aren’t sure where to begin, this guide breaks down the fundamentals in a way that aligns with how modern engineering teams actually build and test software today. Playwright’s speed, reliability, and flexibility make it a powerful choice for teams looking to move beyond brittle legacy tooling. Read more: https://lnkd.in/eCc5pfFp #playwright #testautomation #aiintesting

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Spotify's engineering team built a background coding agent with Claude Agent SDK that handles complex migrations across their massive codebase. Engineers describe what they need in plain text—via Slack, even—and the agent takes it from there, running tests and opening pull requests. They're now merging 650+ agent-generated PRs into production each month. Read more about how they built it, and what else Claude is enabling at Spotify: https://lnkd.in/gVhivXu6

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Phoenix and Ecto aren't just tools, they're guides. Doug VonMoser explores how the framework's architecture and abstractions naturally lead toward robust, maintainable, secure application design. Best practices aren't something you chase; they're baked into the framework.

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"Shoppers are strategically shifting toward more gift card purchases to maintain holiday gifting traditions amid economic pressures, according to new research from Blackhawk Network (BHN), a leading global provider of branded payment solutions. BHN's annual holiday forecast, which surveyed over 2,100 U.S. consumers, reveals that while 13% are purchasing gifts for fewer people due to financial constraints, overall holiday gift spending continues to rise and gift cards will represent 39% of total holiday budgets—a 12% increase over last year." https://lnkd.in/gQxrpy35

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Imagine knowing exactly where your software delivery process can move faster. Observability in your CI/CD pipelines unlocks a new level of insight for engineering teams. With SigNoz, you gain the ability to: - Spot PR bottlenecks: Instantly see which pull requests need attention, helping your team keep momentum. - Track merge and deploy times: Monitor trends to ensure your releases stay on schedule. - Measure deployment latency: Understand how long it takes for code to reach production and find opportunities to accelerate delivery. These insights help to streamline workflows, reduce context switching, and maintain a steady delivery pace. Giving your team the visibility and confidence to build and ship faster.

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Now our developers can worry less about software migrations — just as the technology behind them is getting more interesting. Read about how we’re using Anthropic’s Claude Code to push the boundaries of large-scale automation inside Spotify. 🤖🚀

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