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📹 Watch the Complete Video Tutorial
📺 Title: The Best AI Coding Tools for Developers in 2025 (That I Actually Use)
⏱️ Duration: 912
👤 Channel: Tech With Tim
🎯 Topic: Best Coding Tools
💡 This comprehensive article is based on the tutorial above. Watch the video for visual demonstrations and detailed explanations.
If you’re a developer drowning in repetitive tasks, tangled codebases, or endless meetings, you’re not alone—but you can fight back. In a recent deep-dive video, a seasoned developer revealed the exact AI tools he uses daily to supercharge his coding, communication, automation, and even interior design projects. Forget flashy demos or one-off experiments—this is the real-world stack that powers his workflow across front-end, back-end, terminal, meetings, and beyond.
Based on over 30+ AI tool reviews and years of hands-on experience, he’s distilled his go-to toolkit into a battle-tested lineup of editors, assistants, agents, and automations. This comprehensive guide extracts every tool, technique, insight, and real-world example from his full transcript—no detail skipped. Whether you’re building full-stack apps, managing students, or just trying to dictate faster, you’ll discover tools you’ve never heard of and learn exactly how to use them like a pro.
Why This AI Tool Stack Is Different
Unlike generic “top 10 AI tools” lists, this guide reflects a developer’s actual daily workflow. The creator emphasizes that he only includes tools that are consistently open on his computer—tools that earn their place through real utility, not hype. He’s tested dozens of AI editors, agents, and assistants, but only a select few survive the cut. His criteria? Speed, accuracy, contextual awareness, and seamless integration into development cycles.
Importantly, he maintains manual control over his code. Even with AI generating logic, he reviews, refactors, and moves files himself—highlighting that these tools augment, not replace, developer expertise.
Cursor: The Primary AI-Powered IDE for Hybrid Projects
Cursor is the developer’s main integrated development environment (IDE), especially for hybrid projects that combine front-end, back-end, and multiple components. It functions as an AI-native code editor where you can issue natural language commands like “change the title of my website” and have the AI write, modify, and preview code directly within the editor.
He notes that Cursor has dramatically improved over time—initially lagging behind competitors but now outperforming them by rapidly adopting and enhancing key features. While he once preferred Windsurf (see below), Cursor’s acceleration has made it his default.
How Cursor Works in Practice
- Users can select their preferred AI model (e.g., GPT-5).
- The AI generates code changes that appear inline for review.
- Developers retain full control—they can accept, reject, or modify suggestions.
- Ideal for complex, multi-file projects requiring deep contextual understanding.
Windsurf: The Former Favorite AI Editor
Windsurf was the developer’s go-to AI editor when it first launched, thanks to features that Cursor initially lacked—such as superior performance and smarter context handling. However, as Cursor evolved and “pretty much copied all of them,” Windsurf fell out of daily use.
He acknowledges that this could change: “Maybe Windsurf will come out with an update and then I’ll start using it again.” This highlights the fast-paced nature of AI tooling—today’s leader can be tomorrow’s footnote.
ChatGPT: The All-Purpose Daily Companion (Used Most Frequently)
Despite the rise of specialized coding tools, ChatGPT remains the #1 most-used AI tool in his workflow—not for coding, but for everything else. He uses it across phone and desktop, often keeping it open for hours on a second monitor.
Real-World Uses of ChatGPT
- Writing and responding to emails
- Reviewing legal contracts
- Managing students in his Dev Launch program
- Resume reviews
- Video scripting
- Accounting tasks
- Answering random real-world questions (e.g., identifying unusual wall outlets by photo)
The Power of Long-Term Context
Because he’s used ChatGPT since its early days, it has accumulated deep personal context: his car, travel habits, project plans, and more. While this raises privacy concerns (“probably not a good thing security-wise”), it enables hyper-relevant responses that newer tools can’t match—even if they’re technically superior at specific tasks.
“When I go to ChatGPT and ask something, it has such relevant context that other AI tools simply cannot have because I haven’t been using them for 2, 3, 4+ years.”
Whisper Flow: The Dictation Tool That Triples Productivity
Whisper Flow is described as a “total game-changer” for coding and general productivity. It’s a real-time dictation tool that runs in the background across Windows, Mac, and iOS (via keyboard extension) and works in any application—unlike OS-level voice input.
Key Features of Whisper Flow
- Activates with a quick keyboard shortcut
- Dictates at ~190 words per minute (vs. his 60–70 WPM typing speed)
- Saves full history of dictations to prevent lost prompts
- Integrates contextually with AI editors like Cursor and Windsurf—tagging variables and files during dictation
- Supports custom vocabulary, built-in notes, and team sharing
Why Voice Dictation Boosts AI Prompt Quality
The developer notes a crucial psychological benefit: “When I speak, I always give more detail because I’m just not as lazy as if I have to type it out.” This leads to richer, more contextual prompts—resulting in better AI outputs.
He uses Flow everywhere: Cursor, Windsurf, Lovable, ChatGPT, WhatsApp, Discord—anywhere he needs to write longer messages or detailed instructions.
Deep Agent by Abacus AI: For Complex, Long-Running Agentic Tasks
When tasks require multi-step reasoning, internet research, and extended execution time, the developer turns to Deep Agent by Abacus AI. Unlike chat-based assistants, Deep Agent operates as an autonomous agent capable of completing complex, open-ended projects.
Real Projects Built with Deep Agent
| Project | Description | Outcome |
|---|---|---|
| Apartment Interior Design | Provided floor plan and design preferences | Generated full interior design plan |
| Dev Launch Roadmap Tracker | Requested a custom student progress platform | Built a functional web application for generating student roadmaps |
| Construction Impact Analysis | Asked about nearby buildings: height, timeline, view obstruction, noise | Researched public records and delivered accurate predictions on construction impact |
These tasks took hours or even days to complete—but Deep Agent delivered high accuracy where ChatGPT and similar tools would fail due to scope or context limits.
Warp: The AI-Powered Terminal for Backend and DevOps
Warp reimagines the terminal as an AI-native command-line interface. Marketed as an “agentic AI editor,” it blends traditional shell commands with AI-generated suggestions and automation—ideal for backend, deployment, and infrastructure tasks.
How the Developer Uses Warp
- Built a full Discord bot including deployment, configuration, and containerization
- Uses it for Kubernetes, Docker, and other DevOps workflows
- Switches between normal terminal mode and “agent mode” for AI assistance
Unique Capabilities of Warp
- Parallel agent execution: Run multiple AI tasks simultaneously in different tabs
- AI suggests commands based on context (e.g., “What files are here?”)
- Open and edit files directly within the terminal interface
While he still uses Cursor and Windsurf for application logic, Warp is his go-to for terminal-based automation and infrastructure work.
Lovable: Rapid Frontend and Landing Page Generator
Lovable is used exclusively for simple frontends and landing pages—never for full-stack applications. The developer emphasizes its strength in rapid prototyping and design, not complex logic or data-heavy apps.
Real-World Example: Dev Launch Vault
He used Lovable to build the entire landing page for vault.devlaunch.us—a new affordable course offering. The process included:
- Generating the main landing page via natural language prompts
- Creating supporting pages: privacy policy, disclaimer, company info
- Exporting the code from GitHub
- Importing into Cursor for final adjustments and serious development
His workflow: Use Lovable for initial design and static content, then migrate to a full IDE like Cursor once complexity increases.
TL;DV: The Meeting Assistant You Can’t Live Without
TL;DV is an AI meeting recorder that automatically joins Zoom, Google Meet, and other calls as a bot. But it goes far beyond recording—it delivers structured insights that fuel follow-up actions.
Core Features of TL;DV
- Auto-generates AI summaries and full transcripts
- Supports custom templates based on meeting type (e.g., student onboarding vs. strategy session)
- Integrates with Zapier for automations
How the Developer Uses TL;DV Daily
- Records every meeting automatically
- Copies transcripts into ChatGPT for deeper analysis (e.g., extracting action items, student needs)
- Uses Zapier to send meeting summaries to Discord for team visibility
- Receives private DMs with next-step instructions for student follow-ups
He uses it multiple times per day and calls it “genuinely cannot live without”—a rare endorsement in a crowded market of AI note-takers.
Blitzy: The Deep-Context Software Development Agent
Blitzy stands out for its emphasis on deep project understanding before execution. Unlike tools that respond in minutes, Blitzy takes 1–3 days to analyze codebases and generate comprehensive technical documentation before performing any development task.
How Blitzy Works
- Upload an existing codebase (e.g., a previous startup’s entire repository)
- Blitzy generates a 200+ page technical document including architecture diagrams, flowcharts, and system maps
- User writes a detailed prompt (as if briefing a junior developer)
- Blitzy executes the task at ~5 minutes per file
Real Results with Blitzy
- Completed tasks taking 8–9 hours or even 4 days
- Generated over 200,000 lines of code (with capacity for 3M+)
- Delivered highly contextual refactors and feature additions
While still new to his stack, he finds the auto-generated technical docs “extremely interesting” and valuable for onboarding or legacy system understanding.
Zapier: The AI Automation Backbone
Though not an AI tool itself, Zapier is essential for connecting AI tools into automated workflows. The developer has over 100 Zaps (many confidential), but shared one key example:
TL;DV + Discord Automation
- Trigger: TL;DV meeting ends
- Filter: Only for “Dev Launch Strategy” calls
- Action 1: Post summary + recording link in team Discord channel
- Action 2: Send private DM with student-specific next steps
This turns passive meeting data into active workflows—ensuring nothing falls through the cracks.
AI Development Frameworks: Building Custom Agents
Beyond off-the-shelf tools, the developer builds his own AI agents using a robust stack of open-source frameworks. His core environment is Python-based, with the following key libraries:
| Framework/Library | Primary Use Case |
|---|---|
| LangChain | General-purpose AI agent development |
| LangGraph | Building stateful, multi-actor AI workflows |
| LiveKit | Voice-enabled AI agents |
| VPY | Voice agent development (alternative to LiveKit) |
| Deepgram | Speech-to-text and voice processing |
| Injest | AI orchestration and pipeline management |
| Chroma DB | Vector database for AI memory and retrieval |
These frameworks power custom tools that integrate with his daily stack—extending capabilities beyond what commercial products offer.
Tool Comparison: When to Use Which AI Editor
Choosing the right AI editor depends on project scope and workflow stage. Here’s his decision framework:
| Tool | Best For | Avoid When |
|---|---|---|
| Cursor | Hybrid full-stack projects, daily coding, complex logic | Simple landing pages or pure design work |
| Windsurf | Potentially faster performance (monitor for updates) | Currently outperformed by Cursor in most scenarios |
| Lovable | Static landing pages, basic frontends, rapid prototyping | Full-stack apps, data-heavy backends, complex state |
| Warp | Terminal tasks, DevOps, containerization, backend automation | Frontend development or visual design |
Productivity Multipliers: Voice, Context, and Automation
Three meta-principles underpin his entire stack:
- Voice Dictation (Whisper Flow): Speaking > typing for prompt richness and speed
- Long-Term Context (ChatGPT): Persistent memory enables personalized, relevant responses
- Workflow Automation (Zapier + TL;DV): Turn passive data into active tasks
These aren’t just tools—they’re force multipliers that compound over time.
Real-World Impact: From Apartment Design to Student Roadmaps
The developer’s examples prove AI’s versatility beyond coding:
- Used Deep Agent to analyze construction impact on his new apartment—saving potential future headaches
- Built a custom student roadmap tracker to scale his educational business
- Generated entire course landing pages in hours, not days
These aren’t hypotheticals—they’re live, revenue-impacting projects powered by the right tool for the job.
Security and Privacy Considerations
He openly acknowledges a trade-off: feeding ChatGPT personal details (“what car I drive, where I travel”) boosts utility but compromises security. His advice? Be intentional:
- Use specialized tools (like Deep Agent or Blitzy) for sensitive codebases—they don’t retain long-term memory
- Limit personal context in general-purpose assistants unless the benefit outweighs the risk
- Never share confidential data in tools without clear data policies
Future-Proofing Your AI Stack
The developer stresses that tool preferences evolve. Windsurf may regain dominance. New agents may surpass Deep Agent. The key is to:
- Regularly audit your daily tools
- Test new entrants against your actual workflow—not hype
- Maintain control: AI suggests, you decide
Getting Started: Your Action Plan
Ready to upgrade your toolkit? Follow this sequence:
- Install Whisper Flow—start dictating today to boost prompt quality
- Set up TL;DV for your next meeting—automate note-taking
- Try Cursor for your next coding session—compare with your current IDE
- Build one Zap connecting an AI tool to your team chat
- Experiment with Lovable for a simple landing page
Conclusion: Build Your Own Battle-Tested AI Stack
This isn’t a theoretical guide—it’s a real developer’s daily toolkit, forged through 30+ tool reviews and countless shipped projects. From AI IDEs (Cursor) to meeting bots (TL;DV), voice dictation (Whisper Flow) to agentic builders (Deep Agent, Blitzy), each tool solves a specific, recurring problem.
The ultimate lesson? Productivity isn’t about using more AI—it’s about using the right AI, in the right context, at the right time. Start small, integrate one tool, and watch your output multiply. As the developer proves: with the right stack, you can design apartments, teach students, build bots, and ship code—all before lunch.
