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Insights, tutorials, and career tips from the engineering community
Learn what AI tokens are, how tokenization works, input vs output tokens, context windows, LLM token limits, API costs, and practical token optimization techniques.
Learn what Top-P Sampling is, how Nucleus Sampling works, token probability, Top-P vs Temperature, Top-K sampling, and practical LLM API examples for developers.
Learn what AI model temperature is, how it controls token sampling, low vs high temperature, Top-P comparison, practical examples, and how developers configure LLM APIs.
A practical promotion roadmap from senior .NET developer to tech lead: scope signals, stakeholder scripts, delegation habits, and real examples of what to do in your first 90 days.
Freelance .NET developer playbook for India in 2025: hourly vs fixed pricing, GST and TDS, contract clauses that prevent scope creep, and realistic rates by experience band.
Architecture lessons from a production LMS: bounded contexts, video delivery, progress tracking, multi-tenancy, and why the first version should not be 40 microservices.
Server-side payment integration for Indian and global gateways: order creation, webhooks, idempotency, and PCI scope reduction when using Cashfree and Stripe from ASP.NET Core.
Practical OWASP Top 10 mitigations for ASP.NET Core APIs: injection, broken auth, SSRF, security headers, and verification steps you can implement without a six-month security program.
Build, test, and deploy ASP.NET Core with GitHub Actions: a production-ready workflow with caching, integration tests, security scans, and slot deployment to Azure App Service.
A real AKS deployment story for .NET microservices: cluster design, Helm releases, ingress, Key Vault CSI, observability, and lessons from a production migration off App Service.
TypeScript for C# developers: map interfaces to records, structural typing vs nominal, async pitfalls, and real migration stories from ASP.NET teams adopting Angular or React frontends.
Angular interview questions for 2025 with real project war stories: change detection, RxJS, signals, routing guards, and LMS platform failures—what mid-level and senior loops actually ask .NET full-stack devs.
ML.NET vs generative AI: when classical machine learning still wins for fraud scoring, demand forecasting, and churn prediction on .NET—cheaper, auditable, and often more accurate than LLM guesses.
Local LLMs with Ollama for offline .NET development: run Llama and Mistral on your machine for docs, tests, and refactors without sending fintech or LMS code to the cloud—setup, limits, and honest trade-offs.
Prompt engineering for software engineers writing production code: structured templates, verification loops, and team patterns that survive code review—not hype for one-off ChatGPT demos.
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