QASolver

We think this is just the beginning. Today: Copilot generates tests Tomorrow: Copilot identifies risks Next: Copilot designs test strategy Eventually: Copilot becomes a quality co-pilot for engineering teams. We are moving from: Automation tools → AI assistants → Autonomous QA systems And that shift is happening faster than most teams realize. Exciting times for QA.

Cypress.io vs Playwright is no longer a simple "which tool is better" debate. #Cypress gives you a very friendly developer experience, fast setup, and excellent debugging inside the browser. #Playwright gives you broader browser control, stronger multi-tab and multi-context support, and more power for modern end-to-end coverage. Cypress.io pros: - Fast to learn - Great DX - Excellent time-travel debugging - Very productive for front-end teams Cypress.io cons: - More architectural limits - Multi-tab and cross-origin scenarios can be harder - Less flexible for complex real-world flows Playwright pros: - Broader browser automation power - Strong cross-browser support - Handles multiple tabs, contexts, and auth flows better - Excellent for full-stack end-to-end coverage Playwright cons: - Slightly steeper learning curve - More setup discipline needed - Debugging experience is good, but less opinionated than Cypress My take: - Cypress feels smoother. - Playwright feels stronger. If your app is mostly front-end heavy and speed of authoring matters most, #Cypress is still a great choice. If you want fewer constraints and more long-term architectural headroom, #Playwright is hard to ignore. NPM Cypress: https://lnkd.in/gHqX-sWJ NPM Playwright: https://lnkd.in/gkYW9tbU GitHub Cypress: https://lnkd.in/gD5v-sXw GitHub Playwright: https://lnkd.in/gq_FEAYz #QA #TestAutomation #Cypress #Playwright #SoftwareTesting #SDET #QualityEngineering #CloneOfAlex

I genuinely want to thank the Cypress.io team for making accessibility testing practical and approachable for engineering teams. Cypress.io has quietly become one of the most effective tools for integrating accessibility into modern development workflows. With integrations like axe-core, cypress-axe, and Lighthouse, accessibility testing can run directly alongside functional and end-to-end tests, turning #WCAG validation into a continuous engineering practice instead of a last-minute audit. This is a meaningful shift. Developers get immediate feedback, #QA gains repeatable validation, and accessibility issues are caught early - where they are far cheaper and easier to fix. #Accessibility should not be an afterthought or a compliance checkbox. It should be part of how we build software from the start. Cypress helps make that possible. Thank you, #Cypress team, for helping move accessibility testing from aspiration to reality.

Long-running #AI agents don’t fail only because models aren’t smart enough. They fail because the system around the model is under-designed. A recent engineering write-up from Anthropic shows a practical pattern: split the work into roles (planner → builder → QA/evaluator), persist state in structured artifacts (not just chat history), and run tool-based verification (browser automation, API checks) so “quality” is grounded in observed behavior—not vibes. https://lnkd.in/gxU2Bd5r Two takeaways I’m stealing for my own teams: 1) Make “done” explicit. A short contract with acceptance criteria before coding prevents scope drift and makes QA actionable. 2) Treat harness components like hypotheses. As models improve, some scaffolding becomes wasted cost—so you need continuous measurement, pruning, and re-tuning. If you’re building agentic systems in production: invest in evaluation loops, isolation boundaries, and observability first. The model is only one part of the system. #AIAgents #SoftwareEngineering #LLMOps #ProductEngineering #DevTools #cloneofalex / Anthropic

𝐈’m going to say something uncomfortable. In 3 years, a large portion of white-collar jobs in the U.S. will simply… stop existing. Not because companies hate people. Not because executives suddenly became ruthless. Because the economics no longer make sense. Today: AI writes code AI generates tests AI analyzes logs AI builds dashboards AI answers support tickets And it is getting exponentially better every quarter. A single engineer with AI is already outperforming small teams. Now project that forward 36 months. What happens when: 1. One QA Architect + AI replaces an entire QA team 2. One developer + AI replaces a full scrum team 3. One support agent + AI replaces a call center This is not theory anymore. This is already happening quietly inside companies. The dangerous part is not job loss. It is what comes next. We are heading toward a structural imbalance: 1) Fewer jobs that require humans 2) Higher expectations for those who remain 3) Massive oversupply of displaced talent And the U.S. system is not designed for this. Not education Not healthcare Not social safety nets What does that create? A country where: 1. The top 5% operate AI systems and accumulate wealth faster than ever 2. The middle class compresses under automation pressure 3. Entire career paths disappear in under a decade We could see: * Widespread underemployment * Contract/gig dominance over stable careers * Increased social tension around “who benefits from AI” And here is the part nobody wants to say out loud: Most people will not reskill fast enough. Not because they are incapable - But because the pace of change is faster than human adaptation cycles. This is not a “learn to code” moment. This is a “redefine what human work even means” moment. The question is no longer: “How do I stay relevant?” The real question is: “What can humans do that AI cannot economically replace?” If we don’t answer that soon… The next 3 years will not be a tech revolution. They will be a labor market shock. #AI #Automation #FutureOfWork #TechTrends #Leadership #QualityEngineering #QA #CloneOfAlex

AI (Claude by Anthropic) just found 22 real vulnerabilities in Firefox in 2 weeks. Not test cases. Not scripts. Real exploits. https://lnkd.in/esZaaJ9D For years, #QA focused on: 1) Writing test cases 2) Expanding coverage 3) Running automation But this changes the model. #Claude was not executing predefined tests. It was exploring the system and discovering unknown failure modes. That is a fundamental shift: From validating known behavior To uncovering unknown risk If an #AI system can find critical bugs in 14 days… What exactly are our test suites optimizing for? It is exposing the limits of deterministic testing. And forcing QA to evolve. #QA #AI #TestAutomation #QualityEngineering #CyberSecurity #Anthropic

Top 15 GitHub #AI repositories #QA engineers should explore: #1) LangChain - Stars ~92k - Rating 10/10 Framework for building LLM applications and agents https://lnkd.in/g8zVyE2J #2) CrewAI - Stars ~27k - Rating 10/10 Multi-agent orchestration platform for agent-driven QA workflows https://lnkd.in/gtaNK3pb #3) AutoGPT - Stars ~169k - Rating 9/10 Autonomous AI agent architecture https://lnkd.in/gsxMYg9r #4) OpenInterpreter - Stars ~55k - Rating 9/10 Local AI system that can execute code and automate workflows https://lnkd.in/gtxFrNtx #5) LlamaIndex - Stars ~36k - Rating 9/10 Framework connecting LLMs to structured data sources https://lnkd.in/geVVu5_R #6) Playwright - Stars ~66k - Rating 10/10 Modern browser automation used in many AI testing tools https://lnkd.in/gq_FEAYz #7) Cypress - Stars ~47k - Rating 10/10 Popular web automation framework with growing AI integrations https://lnkd.in/gD5v-sXw #8) TestGPT - Stars ~3k - Rating 9/10 Research project exploring LLM-driven test generation https://lnkd.in/gFdmZ5Re #9) SWE-agent - Stars ~16k - Rating 9/10 AI agents capable of resolving GitHub issues and fixing bugs https://lnkd.in/g2Y95FMr #10) DeepEval - Stars ~7k - Rating 10/10 Framework for testing and evaluating LLM applications https://lnkd.in/gXV6-WdA #11) PromptFoo - Stars ~12k - Rating 9/10 Test framework for prompts and AI outputs https://lnkd.in/gby_mTMM #12) Ragas - Stars ~8k - Rating 9/10 Evaluation framework for RAG systems https://lnkd.in/gdNJ3irz #13) Guardrails - Stars ~8k - Rating 9/10 Validation framework for LLM responses https://lnkd.in/gkeghbPD #14) Browser Use - Stars ~44k - Rating 9/10 AI agents capable of controlling browsers https://lnkd.in/gpvvGKB7 #15) MetaGPT - Stars ~45k - Rating 8/10 AI system simulating a full software development organization https://lnkd.in/ggbAtJsQ One pattern becomes obvious. Traditional #QA stack test frameworks -> CI -> reports Emerging AI QA stack agents -> test generation -> execution -> evaluation -> observability Which means QA engineers are slowly moving from writing tests to evaluating AI-generated testing systems. That shift is already happening. #AI #QA #TestAutomation #QualityEngineering #AItesting #LLM #AgenticAI #SoftwareTesting #Automation #GitHub #cloneofalex

You can now run an AI coding agent locally for $0. No API bills. No token limits. No sending your code to the cloud. A lot of engineers still assume AI development tools require paid APIs. That is no longer true. You can run a powerful coding agent entirely on your machine in about 5 minutes. Here is the simple setup: #1) Install Ollama Download: https://ollama.com #2) Pull a coding model Example: ollama pull qwen2.5-coder This gives you a strong open-source model optimized for programming. #3) Install Claude Code CLI curl -fsSL https://lnkd.in/ghXwt2z9 | bash or npm install -g @anthropic-ai/claude-code #4) Point Claude Code to your local model export ANTHROPIC_AUTH_TOKEN=ollama export ANTHROPIC_BASE_URL=http://localhost:11434 #5) Run the agent claude --model qwen2.5-coder Now the agent writes code, edits files, runs commands, and reasons about your repo. Locally. No token cost. No cloud dependency. What this means for engineers: 1) AI development tools are becoming infrastructure, not SaaS 2) Private codebases can be explored safely 3) Autonomous coding agents are becoming accessible to everyone The barrier to experimenting with agentic engineering workflows just dropped dramatically. Engineers who start experimenting now will understand where development is going next. #AI #AgenticAI #SoftwareEngineering #Automation #LLM #DeveloperTools