How I’m Coding in 2026 (The Super-App Strategy)
Riley Brown · 2026-04-17
💡 Quick Take
1. All major AI companies are building functionally identical products with similar interfaces.
2. The core interface features a left-hand panel for projects/chats and a central workspace.
3. This interface design prioritizes multitasking and quick switching between agent tasks.
4. Key competitive advantages for AI tools are their underlying models and integration capabilities.
5. Companies like Anthropic (Claude) and OpenAI (Codex) have a model advantage due to their proprietary, high-performing models.
6. Cursor is trying to train its own coding model but currently lacks a significant advantage.
7. Conductor, Manis, Perplexity, and Replet/Lovable are noted as not having their own model advantage.
8. Google's Anti-gravity is expected to compete with top-tier models.
9. Large companies like OpenAI, Anthropic, and Google can subsidize model usage to attract users to their platforms.
10. Meta's acquisition of Manis could lead to them developing their own strong AI model.
11. Replet and Lovable are pivoting from "vibe coding" to broader business applications.
12. An agent's usefulness hinges on its connectivity to all of a user's existing platforms and tools (Slack, email, calendar, etc.).
13. Google (Gmail, Docs, Drive) and Meta (Facebook, Instagram, WhatsApp) have a significant advantage in connectivity.
14. Manis is highlighted for its strong integration with Facebook Ads Manager and influencer outreach capabilities.
15. For coding tasks, Claude Desktop and Codex are recommended due to their superior models.
16. Users should focus on mastering one tool for a few months, as they are all converging on the same product.
17. Claude Desktop offers three modes: Chat (easy/low-risk), Co-work (medium-risk, folder-specific), and Code (hard/high-risk, full computer control).
18. Users seeking unrestricted agent operation may prefer the "Code" mode with bypassed permissions.
19. Scheduled tasks (e.g., daily reports) are a key feature for agent autonomy.
20. Agents should be "always on" for reliability, achievable through features like remote control or scheduled check-ins.
21. Claude's "Remote Control" feature (accessible via terminal) allows messaging the agent from external devices.
22. Mimicking "Heartbeat" features (like in OpenClaw) can be done by scheduling regular agent checks (e.g., email monitoring).
23. Customization through plugins, skills, and connectors is crucial for creating effective agents.
24. Creating detailed Standard Operating Procedures (SOPs) and documentation is essential for agent training.
25. Notion and Obsidian are recommended for organizing agent documentation and knowledge bases.
26. "Paragon templates" – high-quality examples of desired outputs – are vital for agent learning.
27. Agents should be able to access and interpret this internal documentation to understand user context and preferences.
28. Skills and connectors should reference this central documentation for consistent agent behavior.
29. Exporting skills and documentation makes them portable to different platforms.
30. Codex and Claude Code are currently the top coding models, with Codex having a slight edge.
31. Using Claude via its desktop app or terminal offers better value than through third-party platforms like Cursor.
32. Memory is a critical area of AI development, with companies working on both model-level and specialized database solutions.
33. Recording and organizing everything is the best current strategy for agent memory until breakthroughs occur.
34. Scheduled tasks (like nightly journaling) can simulate agent memory and self-reflection.
35. Claude's upcoming "Dreaming" feature aims to automate agent cleanup and documentation of daily activities.
36. To become proficient with AI agents, focus on documentation, understanding tool integrations, listing created documents, identifying time-wasters, and defining clear goals.
37. Agents need clear, documented goals and progress tracking mechanisms to be truly effective.
38. Storing goals and documentation in a platform-agnostic way (like Notion) is recommended for portability.
39. Manis is a strong contender, leveraging Anthropic models and offering excellent agent capabilities.
40. The key to success is to zoom out, document your work, and then delegate to your agents.
📊 Detailed Explanation
1. All major AI companies are building functionally identical products with similar interfaces. This is a huge observation! It means that the core functionality and user experience are converging across the board. Think of it like smartphones – they all do similar things now, but the nuances and ecosystems differ. This trend indicates that the industry has found a common ground for what users need and expect from AI applications, especially for productivity and coding.
2. The core interface features a left-hand panel for projects/chats and a central workspace. This is the visual manifestation of the converging product strategy. The left panel acts as a navigation hub, allowing users to easily switch between different projects, conversations, or agents. The central area is where the actual work happens – the AI's output, your input, code editing, etc. It's a tried-and-true UI pattern for managing multiple tasks.
3. This interface design prioritizes multitasking and quick switching between agent tasks. Why is this important? Because AI agents are designed to handle tasks that might take humans a long time. While one agent is crunching numbers or writing code, you don't want to be idle. This interface lets you seamlessly jump to another chat or task, making your workflow incredibly efficient. It's all about maximizing your productivity by working on multiple things in parallel.
4. Key competitive advantages for AI tools are their underlying models and integration capabilities. This is the fundamental differentiator. Think of it like this: the model is the engine, and the integrations are the chassis and wheels. A powerful engine (model) can do amazing things, but if it can't connect to anything or interact with the real world (integrations), it's limited. Companies that excel in both will likely dominate.
5. Companies like Anthropic (Claude) and OpenAI (Codex) have a model advantage due to their proprietary, high-performing models. This means they've developed AI models that are simply better at specific tasks, especially coding. These models are the result of massive R&D and data. Having a superior model means faster, more accurate, and more capable AI assistance, which is a huge draw for users.
6. Cursor is trying to train its own coding model but currently lacks a significant advantage. This shows the difficulty of competing at the highest level. While Cursor is making an effort, they haven't yet matched the performance of the leading proprietary models. This is why they're trying to leverage other models, but it means they're not leading the pack in terms of raw AI capability.
7. Conductor, Manis, Perplexity, and Replet/Lovable are noted as not having their own model advantage. These tools are likely built on top of other companies' models (like Anthropic's or OpenAI's). Their strength lies in their user experience, specific features, or integrations, rather than their foundational AI technology.
8. Google's Anti-gravity is expected to compete with top-tier models. Given Google's vast resources and AI expertise, it's a safe bet that their offerings will be competitive. They have the data, the talent, and the infrastructure to develop powerful models.
9. Large companies like OpenAI, Anthropic, and Google can subsidize model usage to attract users to their platforms. This is a strategic move to lock users into their ecosystem. By offering generous token usage for a lower price (e.g., $200 for $4000 worth of usage), they're making it incredibly attractive to use their platform, even if it means slightly higher costs for the platform provider. The goal is user acquisition and retention.
10. Meta's acquisition of Manis could lead to them developing their own strong AI model. This is a smart play by Meta. By acquiring a popular tool and its talent, they're positioning themselves to potentially develop their own proprietary models, leveraging their massive user data and infrastructure. It's a way to catch up in the model race.
11. Replet and Lovable are pivoting from "vibe coding" to broader business applications. This reflects a market shift. While "vibe coding" (quick, experimental coding) was popular, the real value is seen in broader business applications. They're adapting to where the demand is shifting, moving towards agents that can manage entire business workflows.
12. An agent's usefulness hinges on its connectivity to all of a user's existing platforms and tools (Slack, email, calendar, etc.). This is HUGE. An agent that can't access your data or interact with your tools is like a super-smart person locked in a room. The real power comes when it can seamlessly integrate with your daily workflow, pulling information and taking actions across all your platforms.
13. Google (Gmail, Docs, Drive) and Meta (Facebook, Instagram, WhatsApp) have a significant advantage in connectivity. These companies already own vast ecosystems of user data and communication platforms. This gives them a massive head start in building agents that are deeply integrated into people's lives. It's hard to compete with that level of existing connectivity.
14. Manis is highlighted for its strong integration with Facebook Ads Manager and influencer outreach capabilities. This is a specific example of powerful integration. If you're in marketing or social media, Manis's ability to directly control ad campaigns and automate influencer outreach is incredibly valuable. It's a niche but powerful use case.
15. For coding tasks, Claude Desktop and Codex are recommended due to their superior models. If you're a developer or do a significant amount of coding, these are your go-to tools. The underlying models are simply better at understanding and generating code, making your development process much smoother and faster.
16. Users should focus on mastering one tool for a few months, as they are all converging on the same product. Since all these tools are becoming so similar, the best approach is to pick one and really dive deep. Become an expert in its features and workflows. This will give you a solid foundation that can be easily transferred to other similar tools later on.
17. Claude Desktop offers three modes: Chat (easy/low-risk), Co-work (medium-risk, folder-specific), and Code (hard/high-risk, full computer control). This tiered approach to risk and capability is smart. Chat is for simple queries, Co-work is for more focused tasks within a defined area, and Code is for when you need the agent to have maximum access and power to perform complex operations across your system.
18. Users seeking unrestricted agent operation may prefer the "Code" mode with bypassed permissions. This is for power users who want their agents to be as proactive and autonomous as possible. By bypassing some permission checks, you allow the agent to act more freely, speeding up workflows, but it does come with increased risk if not managed carefully.
19. Scheduled tasks (e.g., daily reports) are a key feature for agent autonomy. This is how you make agents truly proactive. Instead of waiting for you to ask, they can perform recurring tasks on a schedule. Imagine an agent that automatically generates a daily sales report or checks for new leads every morning. That's the power of scheduled tasks.
20. Agents should be "always on" for reliability, achievable through features like remote control or scheduled check-ins. Reliability is paramount. If your agent is only available when you're actively using it, its utility is limited. Features that keep the agent accessible and operational, even when you're not directly interacting with it, are crucial for a seamless experience.
21. Claude's "Remote Control" feature (accessible via terminal) allows messaging the agent from external devices. This is a game-changer for accessibility. Being able to message your agent from your phone, even if it's running on your desktop, means you can delegate tasks or get information from anywhere. It extends the agent's reach beyond your physical workstation.
22. Mimicking "Heartbeat" features (like in OpenClaw) can be done by scheduling regular agent checks (e.g., email monitoring). This highlights how to replicate advanced functionalities. If a tool doesn't have a specific feature, you can often build it yourself using scheduled tasks and integrations. For example, setting up an agent to periodically check your email and flag important messages is a form of "heartbeat" monitoring.
23. Customization through plugins, skills, and connectors is crucial for creating effective agents. This is where you tailor the agent to your specific needs. Plugins, skills, and connectors are the building blocks that allow the agent to interact with different services and perform specialized tasks. The more you can customize, the more useful the agent becomes.
24. Creating detailed Standard Operating Procedures (SOPs) and documentation is essential for agent training. Think of this as teaching the agent your job. You need to break down your tasks into clear, step-by-step instructions. The more detailed and accurate your documentation, the better the agent will understand how to perform tasks exactly the way you want.
25. Notion and Obsidian are recommended for organizing agent documentation and knowledge bases. These tools are excellent for creating structured, searchable repositories of information. They allow you to build a comprehensive "brain" for your agent, making it easy for the AI to access and retrieve the knowledge it needs.
26. "Paragon templates" – high-quality examples of desired outputs – are vital for agent learning. This is a powerful training technique. Instead of just telling the agent what to do, you show it examples of what "good" looks like. By providing well-crafted examples (like a perfectly written intro), you guide the agent towards producing high-quality results.
27. Agents should be able to access and interpret this internal documentation to understand user context and preferences. The documentation isn't just for you; it's for the agent. It acts as the agent's reference manual, allowing it to understand your role, your goals, and your preferred way of working. This context is what makes an agent truly personalized and effective.
28. Skills and connectors should reference this central documentation for consistent agent behavior. This ensures that all the agent's actions are aligned with your established processes and preferences. When a skill is invoked, it should be able to pull relevant information from your documentation, guaranteeing consistency and quality.
29. Exporting skills and documentation makes them portable to different platforms. This is crucial for future-proofing. If you invest time in creating custom skills and documentation, you want to be able to move them to a new platform if a better one emerges. Keeping them in a flexible format (like Notion files) ensures this portability.
30. Codex and Claude Code are currently the top coding models, with Codex having a slight edge. This is a direct recommendation for developers. If coding is a significant part of your work, focusing on these tools will yield the best results. The slight edge for Codex suggests it might be marginally better at complex coding tasks.
31. Using Claude via its desktop app or terminal offers better value than through third-party platforms like Cursor. This is about cost-effectiveness. When you use a model through its native application or terminal interface, you often get more "bang for your buck" in terms of token usage compared to using it via a third-party integration, which may add its own markup.
32. Memory is a critical area of AI development, with companies working on both model-level and specialized database solutions. This is one of the biggest challenges in AI right now. For agents to be truly useful, they need to remember everything about you and your past interactions. This involves complex engineering at both the core model level and in how data is stored and accessed.
33. Recording and organizing everything is the best current strategy for agent memory until breakthroughs occur. Until AI memory becomes truly seamless, your best bet is to be meticulously organized. Keep detailed notes, document everything, and ensure your information is easily accessible. This provides the raw material for future AI memory advancements.
34. Scheduled tasks (like nightly journaling) can simulate agent memory and self-reflection. This is a practical way to build a semblance of memory. By having your agent periodically journal or summarize its activities, you create a log of its "experiences," which can be referenced later. It's like giving the agent a diary.
35. Claude's upcoming "Dreaming" feature aims to automate agent cleanup and documentation of daily activities. This is an exciting development. It suggests that agents will become more self-sufficient, capable of organizing their own work and creating summaries without explicit prompting. This will significantly reduce the manual effort required to maintain agent knowledge.
36. To become proficient with AI agents, focus on documentation, understanding tool integrations, listing created documents, identifying time-wasters, and defining clear goals. This is a concise roadmap for anyone looking to leverage AI agents effectively. It covers the essential pillars: knowing your work (documentation), knowing your tools (integrations), knowing your output (documents), knowing your inefficiencies (time-wasters), and knowing your direction (goals).
37. Agents need clear, documented goals and progress tracking mechanisms to be truly effective. An agent without clear goals is like a ship without a destination. It can do a lot, but it won't necessarily be doing the *right* things. Defining measurable goals and tracking progress allows the agent to focus its efforts and deliver meaningful results.
38. Storing goals and documentation in a platform-agnostic way (like Notion) is recommended for portability. This reinforces the idea of flexibility. By keeping your critical information in a tool that integrates with multiple AI platforms, you ensure that your knowledge base isn't tied to a single vendor. This gives you freedom and control.
39. Manis is a strong contender, leveraging Anthropic models and offering excellent agent capabilities. This is a specific endorsement of Manis as a powerful agent tool. Its ability to use top-tier models like Anthropic's, combined with its own unique features, makes it a highly capable option.
40. The key to success is to zoom out, document your work, and then delegate to your agents. This is the ultimate takeaway. Don't get lost in the details of individual tools. Understand your overall workflow, meticulously document every aspect of it, and then use your AI agents to execute those documented processes. This strategic approach will unlock the true potential of AI.
🎯 Expert Opinion
Wow, this video really nails the current state and near-future trajectory of AI agent development. The observation that all major players are converging on a similar interface and product paradigm is spot on. This isn't surprising; it's a natural evolution driven by user needs for efficiency and multitasking. The left-panel-for-navigation, central-panel-for-action layout is a well-established pattern for complex applications, and AI agents, with their potential for parallel task execution, absolutely benefit from it. It's the digital equivalent of a well-organized workbench.
The distinction between model advantage and integration advantage is critical. Companies like OpenAI and Anthropic are indeed leading the charge on the "engine" side with their proprietary models. This gives them a significant edge in raw capability, especially for specialized tasks like coding. However, the real "stickiness" and long-term value will come from deep integrations. This is where Google and Meta, with their vast existing ecosystems, have an almost insurmountable advantage. Imagine an agent that can fluidly orchestrate tasks across Gmail, Google Drive, Calendar, *and* seamlessly post to Instagram or manage Facebook Ads. That's a level of workflow automation that's hard to beat.
The pivot of platforms like Replet and Lovable from pure coding environments to broader business applications is a smart strategic move. "Vibe coding" is fun, but the real economic value lies in agents that can manage business processes, automate workflows, and drive tangible business outcomes. This shift reflects the market's maturation – users are moving beyond novelty and looking for practical, productivity-enhancing tools.
The emphasis on **connectivity** as a core agent requirement is perhaps the most important insight for anyone building or using these tools. An agent that can't access your data or interact with your existing software stack is severely handicapped. This is why the "always on" and "remote control" features are so vital. We're essentially talking about creating a digital extension of ourselves that can operate independently across our entire digital life. The ability to message an agent from your phone while you're out and about, asking it to summarize a PDF you just downloaded, is the kind of seamless integration that will redefine productivity.
The discussion around **memory** is also incredibly prescient. This is arguably the biggest bottleneck for true AI agent autonomy. Until agents can reliably remember context, past interactions, and user preferences without constant re-prompting, their utility will remain somewhat limited. The current strategies – meticulous documentation, scheduled journaling, and upcoming features like "Dreaming" – are all workarounds. The true breakthrough will be when agents have innate, robust memory capabilities, akin to human long-term memory. This will likely be the next major frontier in AI development, potentially unlocking entirely new applications and levels of human-AI collaboration.
My professional take is that the future belongs to platforms that can effectively bridge the gap between powerful underlying models and deep, seamless integrations with a user's existing digital life. Companies that can offer both a top-tier model *and* an unparalleled integration ecosystem will dominate. For users, the advice to focus on **documentation and clear goals** is paramount. You are essentially building the "operating system" for your AI agent. The more structured and comprehensive your documentation, the smarter and more effective your agent will be. Think of it as creating your own internal Wikipedia for your AI. This is not just about using AI; it's about *training* your AI to be an indispensable part of your workflow. The ability to export and move these skills and documentation will be key for users to avoid vendor lock-in and adapt to the rapidly evolving AI landscape.
Kanal: Riley Brown