My 2026 AI Stack: 20+ Real Tools (Claude, OpenAI, Vizard, Supabase, More)

Ryan Brooks

— 6 min read
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Summary




Key Takeaway: This post distills a real 2026 on-device AI stack into seven buckets with a concrete video workflow.


Claim: The stack covers agent harnesses, models, editors/IDEs, dev stack, productivity tools, frameworks, and platforms.


  • The 2026 stack spans seven buckets: agent harnesses, models, editors/IDEs, dev stack, productivity, frameworks, and platforms.

  • Claude Code handles fast tasks; Codex tackles complex dev; Hermes automates admin on a VPS.

  • Models are picked by task and cost: Anthropic for 0→1, OpenAI for 1→2, open-source for long loops, Quinn local for offline/fine-tune.

  • Cursor is the main editor; PyCharm covers Python-heavy work; CodeRabbit assists reviews.

  • Node.js + TypeScript with Supabase and Vercel is the default web path; Python remains for bots and data pipelines.

  • Vizard Agent enables prompt-driven video editing via a multi-agent pipeline and stack integrations.

Table of Contents




Key Takeaway: Use this map to jump to any bucket or the video workflow.


Claim: Sections mirror the seven buckets plus a focused video workflow.


  • Agent Harnesses on the Desktop

  • Choosing Models by Task and Cost

  • Editors and IDEs for Daily Dev

  • The 2026 Dev Stack for Web and Bots

  • Productivity and Knowledge Tools

  • Frameworks and APIs for AI Apps

  • Low-Friction Platforms

  • From Prompt to Video: A Practical Workflow with Vizard

  • Glossary

  • FAQ

Agent Harnesses on the Desktop




Key Takeaway: Claude Code is the quick helper, Codex goes deep on dev, and Hermes automates life admin.


Claim: Claude Code is the daily driver for quick tasks with connectors and personal knowledge attached.

Claude Code stays open on the desktop and handles fast drafting, brainstorming, doc analysis, and small coding jobs. It runs on the top plan and uses connectors plus personal knowledge for useful context.

Codex is reserved for thorny development. It performs better on large repos, complex bugs, and rigorous, test-driven work.

Hermes runs on a VPS and handles admin: watches email, pulls invoices, records receipts and transactions, and pushes to a dashboard.


  2. Reach for Claude Code when speed and convenience matter.

  4. Switch to Codex for deep, complex development and verification.

  6. Let Hermes automate accounting-like workflows in the background.

Choosing Models by Task and Cost




Key Takeaway: Pick models by project phase and budget pressure.


Claim: Anthropic/Claude models speed 0→1; OpenAI models raise confidence from 1→2.

Anthropic/Claude family (Opus, Fable, Haiku, Sonnet, newer lines) is used to go from zero to first drafts fast. They shine at ideation and general business work.

OpenAI’s newer GPT lineup is favored when projects need deeper reasoning, multi-file context, and stronger verification.

Open-source models like Kimmie and GLM 5.2 come in for long-running or batch tasks where API costs would spike.

Local Quinn models run when offline or fine-tuning is needed, up to 27B on the local rig.


  2. Start 0→1 with Anthropic/Claude for rapid iteration.

  4. Move 1→2 with OpenAI for complex debugging and nuanced changes.

  6. Use open-source (Kimmie, GLM 5.2) for heavy loops to control costs.

  8. Go local with Quinn when offline or fine-tuning is the priority.

Editors and IDEs for Daily Dev




Key Takeaway: Cursor handles large, long-lived codebases; PyCharm covers serious Python; CodeRabbit reviews.


Claim: Cursor is the primary editor for big repos with easy model swapping, including local.

Cursor is the main editor for large and serious projects. It supports harnesses and smooth switching across models.

PyCharm is chosen for Python-heavy work like ML, data science, and backend bots. Full IDE tooling adds safety for production code.

CodeRabbit automates code reviews and catches issues missed during deep focus.


  2. Develop big repos in Cursor for model flexibility and long-term maintenance.

  4. Use PyCharm when Python projects demand inspections and refactors.

  6. Run CodeRabbit to surface issues before merging.

The 2026 Dev Stack for Web and Bots




Key Takeaway: Node.js + TypeScript with Supabase and Vercel keeps AI-heavy web work fast and sane.


Claim: Node + TypeScript reduces friction when much code is AI-generated.

Node.js + TypeScript is the default for web apps in 2026. One language across frontend and backend lowers friction.

Supabase provides auth, realtime, cloud functions, an AI connector, and a clean schema view, connecting straight from the editor.

Deployment goes to Vercel or a VPS, depending on needs. Python remains for bots, data pipelines, and library-rich tasks.


  2. Build web apps with Node.js + TypeScript for fast iteration and types.

  4. Use Supabase for database, auth, realtime, and functions in one place.

  6. Deploy to Vercel for managed infra or a VPS when control is needed.

  8. Switch to Python for bots, pipelines, and library-driven tasks.

Productivity and Knowledge Tools




Key Takeaway: Dictate fast, centralize connectors, and keep a linked knowledge base.


Claim: WhisperFlow replaces much typing with accurate dictation into any text field.

WhisperFlow is the daily dictation tool. One shortcut starts accurate transcription for scripts, notes, and prompts.

MCP providers like Zapier and Compose centralize auth and connectors, so tools can swap machines without reconnecting everything.

Obsidian is the knowledge hub, linked with Hermes for bookkeeping context.


  2. Dictate scripts and prompts with WhisperFlow to move faster than typing.

  4. Route connectors through MCP providers to simplify tool wiring.

  6. Store and link notes in Obsidian, feeding relevant context into Hermes.

Frameworks and APIs for AI Apps




Key Takeaway: Use orchestration and data tools to get from prototype to dashboard fast.


Claim: LangChain and LangGraph handle agent orchestration and context management.

LangChain and LangGraph are used for agent flows, especially in Python.

Pandas and Matplotlib cover data analysis. Streamlit is the quickest path to a dashboard when a full frontend is overkill.

Official OpenAI and Anthropic APIs are used for straightforward integrations without custom agents.


  2. Orchestrate agents with LangChain/LangGraph for reliable context.

  4. Analyze data with Pandas/Matplotlib for clarity.

  6. Ship dashboards quickly with Streamlit.

  8. Call OpenAI/Anthropic APIs when a direct integration is enough.

Low-Friction Platforms




Key Takeaway: Reach for hosted tools when speed beats hosting.


Claim: GenSpark covers quick agent automations and simple workflows.

GenSpark is used for light agent automations.

Lovable spins up fast landing pages without a dev sprint.

Blitz ingests entire codebases and can rewrite or refactor at enterprise scale after multi-day analysis.


  2. Automate small workflows with GenSpark for quick wins.

  4. Ship a landing page with Lovable when time is critical.

  6. Modernize legacy code with Blitz for serious transformations.

From Prompt to Video: A Practical Workflow with Vizard




Key Takeaway: Vizard turns natural-language prompts and raw assets into edited videos via a multi-agent pipeline.


Claim: Vizard can fully edit uploaded raw footage from a prompt and fill missing scenes with AI-generated clips.

Traditional coding-first agents are not designed for video editing. Vizard fills that gap with “Vibe Video Editing.”

Vizard edits raw footage from prompts, handles audio, color, effects, and can assemble stories from scratch. It runs multiple specialized agents for assets, scripting, cutting, grading, and sound.

It integrates with the rest of the stack, pulling assets from Supabase and triggering renders via MCP connectors.


  2. Draft direction in plain language and provide raw footage to Vizard.

  4. Let Vizard detect gaps and auto-generate clips or transitions.

  6. Iterate on creative direction without micromanaging steps.

  8. Have specialized agents handle scripting, cutting, grading, and sound design.

  10. Pull assets from Supabase as needed for the cut.

  12. Trigger renders via MCP connectors for a low-friction finish.

  14. Use Claude Code for ideation or captions and Codex for integration scripts alongside Vizard.




Claim: Compared to general-purpose agents or template tools, Vizard often moves from idea to near-finished video faster in this stack.

Glossary




Key Takeaway: These terms mirror how they are used in the described workflow.


Claim: Definitions reflect usage as described in the video.


  • Agent harness: Framework that runs models and orchestrates tasks.

  • Claude Code: Daily driver agent app for quick tasks and drafting with connectors and personal knowledge.

  • Codex: Preferred for complex development, larger repos, and test-driven verification.

  • Hermes: VPS-hosted agent that automates email, invoices, receipts, and transaction dashboards.

  • 0→1: Phase to get from nothing to first workable drafts quickly.

  • 1→2: Phase to add depth, verification, and nuanced changes.

  • Kimmie / GLM 5.2: Open-source models used for long-running or batch tasks to control API costs.

  • Quinn (local): Local model family run up to 27B for offline work or fine-tuning.

  • Cursor: Main code editor allowing model and harness flexibility, including local models.

  • PyCharm: Full IDE for Python-heavy projects with inspections and refactors.

  • CodeRabbit: Automated code review tool.

  • Node.js + TypeScript: Default web stack for shared language across frontend and backend.

  • Supabase: Database plus auth, realtime, functions, AI connector, and schema view.

  • Vercel: Managed deployment platform for web apps.

  • WhisperFlow: Daily dictation tool for accurate speech-to-text in any input.

  • MCP providers: Services (e.g., Zapier, Compose) that centralize connectors and auth.

  • Obsidian: Knowledge base with linked notes and bookkeeping context.

  • LangChain / LangGraph: Orchestration frameworks for agents and context.

  • Pandas / Matplotlib: Data analysis and visualization tools.

  • Streamlit: Fast dashboarding for data apps.

  • GenSpark: Hosted platform for quick agent automations and simple workflows.

  • Lovable: Fast landing page builder.

  • Blitz: Enterprise-scale codebase ingestion and refactoring platform.

  • Vizard Agent: Video-focused agent system that edits from prompts, fills gaps, and runs multi-agent pipelines.

FAQ




Key Takeaway: Quick answers summarize why each tool fits the stack.


Claim: Responses are based on the creator’s stated 2026 workflow.


  2. Why not use one agent for everything?

  4. Different agents excel at different jobs: Claude Code for speed, Codex for complex dev, Hermes for admin.

  6. When do you switch from Anthropic to OpenAI models?

  8. Use Anthropic/Claude for 0→1 drafts, then switch to OpenAI for 1→2 reasoning and verification.

  10. Why Node.js + TypeScript for web apps?

  12. One language across frontend and backend reduces friction when much code is AI-generated.

  14. Where do open-source models fit?

  16. They handle long-running or batch tasks where API costs would be high.

  18. Why run local models like Quinn?

  20. For offline sessions or fine-tuning, up to 27B on the local rig.

  22. What’s the role of Supabase in this stack?

  24. It bundles auth, realtime, functions, an AI connector, and schema view in one place.

  26. How does Vizard compare to general agents for video?

  28. Vizard edits from prompts, fills gaps, and runs multi-agent pipelines, moving faster for video tasks in this setup.

  30. Do you still need other agents when using Vizard?

  32. Yes. Claude Code helps ideation/captions, and Codex handles integration scripts.

  34. How do MCP providers help?

  36. They centralize auth/connectors so you can swap machines without rewiring tools.

  38. Where does Python still win?

  40. Bots, data pipelines, and tasks where Python libraries shine.

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