MuhammadShakil Patel

AI-Enabled Software Engineer

[MuhammadShakil Patel @patelshakil95]

-> Run tests against the actual implementation.

Visual: Requirements → Test scenarios → Assertions → Execution → Review.

Key takeaway: More tests do not automatically mean better tests. The assertions must detect real defects. #Day12 #SoftwareTesting #TestAutomation
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[MuhammadShakil Patel @patelshakil95]

#Day12 AI Can Help Write Tests—But Tests Need Judgment
-> Generate test cases from requirements.
-> Explore boundary conditions and invalid inputs.
-> Cover expected failures, not just happy paths.
-> Verify that assertions check meaningful behavior.
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[MuhammadShakil Patel @patelshakil95]

Visual: Generate → Review → Test → Secure → Approve.
Key takeaway: Code generation is not code verification. Never confuse a plausible implementation with a proven one.
#Day11 #AICoding #CodeReview #SoftwareQuality #SecureCoding #SoftwareEngineering
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[MuhammadShakil Patel @patelshakil95]

#Day11 AI-Generated Code Is a Draft
-> Review correctness against requirements.
-> Check security, permissions, and input validation.
-> Look for edge cases and error handling.
-> Run tests and inspect dependencies.
-> Refactor code that adds unnecessary complexity.
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[MuhammadShakil Patel @patelshakil95]

@AurevonLabs Exactly

[MuhammadShakil Patel @patelshakil95]

#Day8 AI Assistant vs. AI Agent
-> AI assistant: Responds to instructions and helps with a task.
-> AI agent: Can work through multiple steps toward a goal.
-> Agents may use tools, inspect results, and revise their approach.
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[MuhammadShakil Patel @patelshakil95]

Visual: Goal + Context + Constraints + Examples → Better-grounded output.
Key takeaway: Better context helps AI produce more relevant code; a longer prompt alone does not guarantee a better result.
#Day10 #ContextEngineering #PromptEngineering #AICoding #AIEngineering

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[MuhammadShakil Patel @patelshakil95]

#Day10 Context Is the Real Prompt
A useful AI coding request includes:
-> Goal: What outcome is needed?
-> Context: Which files, APIs, and business rules matter?
-> Constraints: What must not change?
-> Acceptance criteria: How will success be verified?

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[MuhammadShakil Patel @patelshakil95]

Key takeaway: An agent is not simply a chatbot with a different name; its ability to act and use tools changes the risk profile.

#Day9 #FutureOfWork #SoftwareEngineer #AIEngineering #TechCareers #ContinuousLearning
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[MuhammadShakil Patel @patelshakil95]

#Day9 AI Assistant vs. AI Agent
-> AI assistant: Responds to instructions and helps with a task.
-> AI agent: Can work through multiple steps toward a goal.
-> Agents may use tools, inspect results, and revise their approach.
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[MuhammadShakil Patel @patelshakil95]

-> Greater autonomy requires stronger permissions, checks, and oversight.

Visual: Assistant: Prompt → Response. Agent: Goal → Plan → Act → Observe → Iterate.

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[MuhammadShakil Patel @patelshakil95]

Key takeaway: An agent is not simply a chatbot with a different name; its ability to act and use tools changes the risk profile.
#Day8 #FutureOfWork #SoftwareEngineer #AIEngineering #TechCareers #ContinuousLearning
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[MuhammadShakil Patel @patelshakil95]

-> Greater autonomy requires stronger permissions, checks, and oversight.
Visual: Assistant: Prompt → Response. Agent: Goal → Plan → Act → Observe → Iterate.

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[MuhammadShakil Patel @patelshakil95]

-> Operations: Help interpret logs and investigate incidents.
Key takeaway: AI can assist across the SDLC, but engineers remain accountable for decisions and outcomes.
#Day7 #AISoftwareDevelopment #AIEngineering #SDLC #GenerativeAI #SoftwareEngineering
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[MuhammadShakil Patel @patelshakil95]

#Day7 AI-Assisted SDLC: AI Across the Lifecycle
-> Requirements: Summarize, clarify, and explore scenarios.
-> Design: Compare approaches and identify trade-offs.
-> Development: Generate and refactor code.
-> Testing: Suggest test cases and edge conditions.

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[MuhammadShakil Patel @patelshakil95]

@wordpressdotcom https://t.co/ek2PkaurwH

[MuhammadShakil Patel @patelshakil95]

Visual: Application → Container → Orchestrator → Cloud infrastructure.
Key takeaway: Cloud-native is not just hosting in the cloud; it is about building and operating software for that environment.
#Day6 #CloudNative #Docker #Kubernetes #CloudComputing #SoftwareArchitecture

[MuhammadShakil Patel @patelshakil95]

#Day6 Cloud-Native: Design for the Environment
-> Containers package applications and their dependencies.
-> Orchestration manages deployment and scaling.
-> Automation supports repeatable environments.
-> Observability helps teams understand running systems.

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[MuhammadShakil Patel @patelshakil95]

@coolcoder56 At this level 5 lac per month is just a penny amount. But anyway bro got some crazy experiences 🔥. IIT kanpur says it all.

[MuhammadShakil Patel @patelshakil95]

-> Feedback: Make failures visible and actionable.
Visual: Commit → Build → Test → Security checks → Deploy.
Key takeaway: Faster coding without reliable delivery automation can simply produce failures faster.
#Day5 #CICD #ContinuousIntegration #ContinuousDelivery #DevOps

[MuhammadShakil Patel @patelshakil95]

#Day5 CI/CD: Automate the Path to Production
-> Continuous Integration: Merge and validate code frequently.
-> Automated testing: Catch regressions early.
-> Continuous Delivery/Deployment: Automate release preparation or deployment.

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