#Claude #Codex It’s common—Kosuke has never been used, but for websites where appearance matters, specifications and coding are separated. https://t.co/6ePkAFL6y4 【The Failure 90% Do】Why Does Letting AI Build an App Result in “This Isn’t What I Expected”? A Thorough Review of Development Workflows and Cloud Development Environment “Kosuke” to Prevent Missteps #chatgpt #claude Reck’s Forest of Knowledge #AISummary Thorough Review of the New Service “Kosuke” to Reduce AI Development Failures and Its Differences from Codex 🔳 Reasons for Unmet Expectations in AI Development When instructing AI to build a web app, ambiguous requirements often lead the AI to fill gaps with assumptions, or critical decisions may be lost midway through long conversations, resulting in outputs that deviate from intent. To mitigate this, it’s essential to break the process into stages: clarifying objectives, handing off to implementation, and verifying functionality. 🔳 Features and Mechanism of Kosuke Kosuke is a service that integrates a cloud development environment with structured workflows tailored for AI agents. It separates “Teacher,” responsible for requirement gathering and progress management, from “Development Sessions,” where actual coding occurs. This allows clarified discussions to be handed off cleanly to the implementation team. 🔳 Development Environment and AI Integration Each development session comes with its own independent branch, execution environment, and preview screen, enabling parallel workflows. Kosuke integrates with AI accounts like Codex and GitHub, allowing development to proceed without impacting existing codebases. 🔳 Demo: Weekly To-Do App A personal app was created that assigns one-hour tasks in priority order based on available time each week. It tracks goals, completion criteria, outcomes, and replanning when schedules change—but excludes login, sharing, external calendar sync, and notifications. 🔳 Requirement Clarification Before Coding Instead of writing code immediately, the AI was asked to clarify open questions and refine specifications. Goals, required features, excluded features, and completion criteria were defined; only after human validation did implementation proceed—reducing misalignment. 🔳 Handoff of Specifications to Implementation Team Finalized requirements are transferred from “Teacher” to a separate development session. Separating requirement gathering from implementation prevents irrelevant background noise and missed decisions—this structure was particularly praised by the author. 🔳 Review and Approval of UI Mockups Before full implementation, browser-based UI mockups were generated to confirm layout and interaction direction; revisions could be requested if needed. Note: mockups are for visual verification only and do not guarantee functional capabilities like data saving. 🔳 Code Review and Automated QA After UI approval, the process moves to implementation, AI-assisted code review, fixes, and automated QA. The actual app is tested via browser interaction, with recordings and check results preserved as evidence. During pre-validation, an issue was detected and fixed: old processes still executed after edit cancellation. 🔳 Final App Functionality Verification The implemented app successfully supported registering goals and completion criteria, adding tasks, setting priorities, allocating time, and replanning. Basic functions worked, but areas for improvement—such as color scheme and font size—were identified. This version prioritized minimal viable completion. 🔳 Integration with GitHub After implementation and verification, changes were reviewed via GitHub pull requests and merged into the main branch. The goal was formal code integration only; public deployment to external servers was not included. 🔳 Differences from Codex Codex alone can achieve a similar workflow by combining Plan Mode, Skills, and a local development environment—but you must manually construct the entire pipeline: requirement gathering, handoff to implementation, previewing, reviewing, QA, and GitHub integration. Kosuke’s strength lies in having all these steps pre-integrated from the start. 🔳 Pricing Plans and Additional Costs As of October 2026: Starter plan is $20/month with $25 in environment credits; Pro plan is $190/month with $250 in credits. Standard small environments cost $0.15/hour, consuming credits based on usage time. Separate credits are required for connected AI services like Codex. 🔳 Issues Encountered in Practice During pre-validation, issues arose: deployments showed as successful but previews returned 403 errors; some merges were blocked due to insufficient review approvals. These were resolved via configuration adjustments or manual GitHub merges—confirming that fully autonomous development is not yet possible. 🔳 Important Notes When Using the Service Even with advanced AI automation, human review and approval of UIs remain essential. Environment usage fees and AI service credits are separate; monthly fees do not grant unlimited usage. Additionally, this review was not a controlled comparative experiment—so it cannot be concluded that Kosuke outperforms Codex in speed or quality. 🔳 Request Methods Usable in Regular AI Chat When instructing AI, clearly define three items: “What you want to do,” “Conditions for completion,” and “What will not be done this time.” Then, before execution, ask the AI to clarify open questions; after agreement, organize and hand off to implementation: objectives, constraints, decisions made, remaining questions, and verification methods—to minimize unintended outcomes.🔳 Suitable for users who: Feel burdened by having to manually design AI development workflows each time, or prefer to streamline tasks from requirement analysis to validation within an integrated environment. Conversely, users who already manage their development environments and workflows freely using tools like Codex can achieve similar results using existing tools alone. 🔳 Verification conditions and referral rewards: This video was tested in a free environment provided with a Pro plan, featuring unlimited usage for three months. A referral reward is paid to the creator if a paid subscription is initiated through their referral link. Standard registration benefits and usage terms differ from this special offering. 🔳 The most important mindset for AI-assisted development: Rather than delegating tasks entirely to AI, it’s crucial to clearly define goals and success criteria, accurately pass on decisions, and iteratively refine outputs by verifying working results. The key to achieving desired outcomes lies not in relying solely on AI’s capabilities, but in establishing a development process where humans remain responsible for judgment and approval.