Prashant Patil

DeepSeek is fantastic because it provides strong reasoning and coding support at a very accessible cost.I use it for coding help, debugging, technical explanations, research, writing, and breaking down complex problems. The deep thinking ability is especially useful when I need step-by-step reasoning for architecture decisions, algorithm ideas, or technical troubleshooting.What I like most is that DeepSeek can handle complex prompts without feeling too limited, and it gives high-quality responses for both development and research tasks. The free tier and open-source direction also make it very attractive for developers, students, and builders who want powerful AI without heavy subscription costs. DeepSeek is powerful, but the biggest improvement areas are reliability and speed.Sometimes the service can feel slow, busy, or timeout during heavy usage. The interface can also feel more like a tool than a polished assistant experience compared with some competitors.Improvements I’d like to see:Faster response times during peak usageFewer server busy errorsBetter stability for long conversationsStronger image understandingMore polished user experienceBetter handling of very long coding workflowsOverall, DeepSeek is already very useful, especially for reasoning and coding, but better reliability and performance would make it even stronger I also considered ChatGPT, Claude, Gemini, Perplexity, and other AI coding/research tools.I use different AI tools for different tasks, but DeepSeek stands out because of its strong reasoning, coding ability, and accessibility. It is especially useful when I want a low-cost or free AI model that can still handle complex technical work.Compared with some alternatives, DeepSeek feels like a strong option for developers who care about reasoning depth, coding help, and affordability. DeepSeek is strong for coding tasks. It can generate useful code, explain errors clearly, suggest fixes, and help reason through complex technical problems.It performs especially well when the prompt includes enough context, expected behavior, error messages, and existing code structure. Like any AI coding tool, the output still needs review and testing, especially for production code, security-sensitive logic, database changes, or larger architecture decisions.Overall, it is very useful for speeding up development, debugging, and technical problem-solving. DeepSeek is very strong for reasoning-heavy tasks, coding, debugging, research, and technical explanations. For everyday tasks like brainstorming, summarizing, rewriting, and coding help, it performs very well and is often a great alternative to ChatGPT.ChatGPT still feels more polished for general conversation, multimodal work, long project workflows, and overall user experience. But DeepSeek is impressive because it gives strong technical output at a very accessible cost, especially for developers and students. DeepSeek is fairly easy to integrate into developer workflows through API access and supported third-party tools. It can be used for coding assistance, debugging, documentation, research, and automation workflows.For IDE usage, the experience depends on the tool or extension being used. It works well when connected through compatible AI coding tools, but I would like to see even smoother official integrations for VS Code, Cursor-style workflows, and larger codebase context handling.

Google Cloud Platform has been my go-to cloud provider for deploying Python applications, APIs, data pipelines, and AI workloads. Cloud Run makes deployments simple, BigQuery is excellent for analytics, and Cloud Storage integrates seamlessly with the rest of the platform. The breadth of services means I rarely need third-party infrastructure, and everything scales well as projects grow. The platform is extremely capable, but pricing can become difficult to estimate across multiple services. IAM permissions are also quite complex for new users, and some products have documentation that could be more consistent. Better cost forecasting and simpler permission management would make the platform even easier to adopt. I evaluated AWS, Azure, and DigitalOcean. I chose Google Cloud because Cloud Run, BigQuery, and the overall developer experience fit my workflow better. For AI projects, data engineering, and containerized applications, GCP provides a strong balance between ease of use, scalability, and managed services. Costs are predictable when budgets, alerts, labels, and quotas are configured. Compute Engine and GKE are easier to forecast, while BigQuery needs closer monitoring because query volume and data scanned can change quickly. GCP migration from AWS or Azure is manageable if the architecture is containerized or API-driven. Moving apps to Cloud Run, GKE, Cloud Storage, and BigQuery is straightforward, but IAM, networking, and service mapping require careful planning. IAM and Cloud Armor are strong for access control and web protection. For compliance-heavy workloads, they should be combined with audit logs, VPC controls, encryption, organization policies, and proper role design.

Cursor has significantly improved my development workflow. It feels like working with an experienced AI pair programmer that understands the project context, helps with multi-file changes, explains code, generates implementations, and assists with debugging. The seamless VS Code experience and strong code editing capabilities make it one of the best AI coding tools available today. Occasionally the AI loses context during very large refactoring tasks, and long-running agent sessions can require manual guidance. Better memory across sessions, improved handling of massive codebases, lower resource usage, and more transparent reasoning during complex edits would make the experience even better. I also evaluated Claude Code, GitHub Copilot, Windsurf, Codex, and VS Code with AI extensions. I chose Cursor because it offers an excellent balance of project awareness, intelligent code editing, multi-file refactoring, debugging assistance, and an interface that fits naturally into my existing development workflow. For most development tasks, yes. Cursor's agents handle code generation, refactoring, debugging, and multi-file changes well. Complex, long-running tasks may still require occasional guidance or review, but the overall reliability is excellent for day-to-day software development. Cursor is generally fast and responsive. AI features naturally use more resources than a standard editor, especially on large projects, but the productivity gains outweigh the additional memory usage in my experience. For typical development workflows, Cursor provides the controls I need. When working with sensitive or proprietary code, it's still important to review the available privacy settings and choose the deployment option that aligns with your organization's security and compliance requirements.

ChatGPT has become one of the most valuable tools in my daily workflow. I use it for brainstorming ideas, coding assistance, debugging, research, content creation, documentation, and problem-solving. It helps me move faster, explore different approaches, and reduce time spent on repetitive tasks. As a developer and founder, it feels like having an always-available assistant for both technical and business work. Occasionally responses can be inaccurate or overly confident, especially for niche or rapidly changing topics. Better long-term memory across projects, more consistent source attribution, and improved handling of very large codebases or documents would make the experience even better. I also use Claude, Gemini, Perplexity, DeepSeek, and other AI tools. I chose ChatGPT because of its balance between reasoning, coding assistance, content generation, research capabilities, and overall reliability. It integrates well into my workflow and consistently delivers high-quality results across a wide range of tasks. Yes. ChatGPT adapts well to different writing styles, technical levels, and workflows. The personalization and memory features help make responses more relevant over time, especially for ongoing projects and recurring tasks. Yes. Integrations and automation capabilities have the potential to save significant time by connecting AI directly to business tools, workflows, and data sources. This can reduce manual work and make ChatGPT even more useful for everyday tasks. Absolutely. I regularly switch between desktop and mobile, and having conversations sync across devices makes it much easier to continue work, research, and discussions without losing context.

GitHub has become an essential part of modern software development. It provides reliable version control, seamless collaboration through pull requests, issue tracking, and powerful automation with GitHub Actions.What I appreciate most is its massive developer and open-source community. Whether working on personal projects, client work, or large team collaborations, GitHub makes it easy to manage code, track changes, review contributions, and deploy applications efficiently.For developers, GitHub is more than a code repository—it's the standard platform for building, sharing, and maintaining software. The interface can feel complex for beginners, especially around permissions, actions, branches, and repository settings. GitHub has the best developer ecosystem, strong open-source community, reliable repository hosting, pull request workflow, issue tracking, and GitHub Actions for CI/CD. It is also easier for clients and teams to collaborate because most developers already use it. Yes. GitHub's pull request workflow is one of its strongest features. Code reviews, comments, approvals, branch comparisons, and merge controls are fast, reliable, and easy to use. It makes collaboration across teams much smoother and more organized. Codespaces is useful for quickly spinning up development environments and reducing local setup time. While I still prefer local development for larger projects, Codespaces works well for testing, collaboration, and onboarding new contributors. Yes. For most individual developers, open-source projects, and small teams, GitHub's free tier provides everything needed, including private repositories, pull requests, issue tracking, and GitHub Actions. I've used the free plan for many projects without limitations.