Codex is stuck and not responding? Check by approval, terminal, and log check
If Codex is stuck and unresponsive, do not send the same message repeatedly. Confirm in order whethe
Kombai is an AI coding agent for front-end development, emphasizing understanding user interfaces, accessing browser context, and generating high-quality UI code like front-end developers. It is suitable for front-end engineers, design system teams, product development teams, and scenarios where interfaces are implemented from design drafts to accelerate component development, page implementation, and interface code generation. The platform emphasizes the need for manual annotations or automated layout. Code quality, responsive performance, maintainability, design system consistency, and security dependencies should be checked before use. Before use, it is recommended to conduct a small-scale test with real materials, focusing on observing the output quality, review cost, payment boundaries, data permissions, and whether the team can establish a stable manual review process.
Kombai focuses on front-end development, combining AI coding capabilities with an understanding of browser and interface structure to generate usable UI code from designs or requirements.
Suitable for front-end engineers, design system teams, startup product teams, and developers who need to implement interfaces quickly. Pure back-end tasks or complex business architectures still require regular engineering.
AI-generated code needs to check accessibility, responsiveness, state management, dependency security, and project style. You can't just look at visual similarity and merge.
When evaluating Kombai, you can choose a real component or page to compare the generated code to meet existing technology stacks, design systems, and code review criteria.
Before official use, it is recommended to do a small-scale test with a set of real materials, recording inputs, outputs, manual modifications, and final adoption results. This allows you to see its actual performance in terms of quality, cost, speed and review cost, and also facilitates the team to form a consistent usage standard in the future.
In a team or public release scenario, acceptance criteria should also be agreed upon in advance, such as which results can go directly to the next step, which must be reviewed by the person in charge, which assets cannot be uploaded, and how long the generated records need to be retained. This inspection process helps teams put AI tools into traceable processes, reducing rework due to inconsistent result provenance, authorization, or quality judgments.
If the tool handles customer data, personal information, commercial materials, financial data, medical-legal content, or personas, privacy, copyright, portrait licensing, and platform rules need to be included in the pre-use checklist. When publishing to the public, it is recommended to keep manual modification records and final confirmers to avoid mistaking experimental outputs for reviewed content.
Kombai primarily serves the front-end or full-stack?
Its core positioning is front-end UI development.
Can I go live directly with generated code? **
It is not recommended to go live directly, and requires engineers to review, test, and adapt to the project specifications.
Is it suitable for design transcoding? **
This is especially useful for these scenarios, especially when you need to understand the interface structure and don't want to manually annotate.
Google Antigravity is an AI programming environment for the "agent-first" era, helping developers collaborate with multiple agents to complete the entire process from planning to coding, debugging and delivery. Google Antigravity embeds agents in IDEs, terminals, browsers, and other development tools, supporting task decomposition, automated execution, and traceable artifact records for easy review and reproducibility. With powerful reasoning and tool calling capabilities, Google Antigravity significantly improves code generation, test orchestration, script execution, and cross-project collaboration, making it suitable for individuals and teams to quickly build modern applications and services.
Kiro is an AI-powered integrated development environment (IDE) powered by AWS that creates a full-process experience from prototype to production for developers. It uses a spec-driven development model that automatically converts natural language prompts into detailed requirements, system designs, and specific tasks, and performs code generation, documentation maintenance, unit testing, and performance optimization through intelligent agents. Built-in agent hooks support event-driven automation (such as saving file triggers) and Steering files to give users custom control over AI behavior. Kiro natively integrates Model Context Protocol (MCP) to connect to multiple tools and services (e.g., databases, documents, APIs), and is compatible with VS Code plugins and settings, supporting multimodal inputs such as image indication UI or architectural logic. Currently in preview, the core features are open for free, and tiered subscriptions are available for professional users.
ZOER is an AI full-stack web app builder aimed at entrepreneurs, product managers, and no-code developers. Its value is not that it decides everything for the user at once, but that it provides actionable assistance around the idea of building front-end, back-end, and database applications: users can describe requirements, build full-stack applications, preview and deploy code, and then complete the follow-up process based on their own business judgment. When choosing such a tool, you need to pay attention to code quality, data security, and online testing, especially when it comes to accounts, customer profiles, contracts, courses, audio, video, or code output. Its visibility capabilities include AI web app generator, frontend, backend, and DB, making it more suitable for rapid application prototyping.
ZETIC.ai is an end-side AI deployment and NPU-optimized platform aimed at AI engineers, mobile development teams, and edge device teams. Its value is not that it does everything at once, but provides actionable assistance around deploying models to end-side devices and optimizing inference performance: users can convert models, test hardware, optimize NPUs, monitor performance, and then complete subsequent processing based on their own business judgments. When choosing such tools, you need to pay attention to device compatibility, model accuracy, and deployment validation, especially when it comes to accounts, customer profiles, contracts, courses, audio, video, or code output, all of which should be reviewed manually. Its visible capabilities include on-device AI, NPU optimization, and benchmark on devices, making it better suited for end-side AI engineering.
ZeroTrusted.ai is an AI zero-trust security and LLM firewall platform aimed at security teams, AI application teams, and enterprise IT managers. Its value is not to make all the work for users at once, but to provide actionable assistance around securing data, identity, and AI prompt interactions: users can configure LLM firewalls, anonymous prompts, monitor health status, and handle security incidents, and then complete follow-up processing based on their own business judgment. When choosing such tools, you need to be mindful of privacy data, policy misjudgments, and corporate compliance, especially when it comes to accounts, customer profiles, contracts, courses, audio, video, or code output. Its visibility capabilities include LLM firewall, data protection, prompt anonymization, and SOAR, making it more suitable for enterprise AI security governance.
ZeroThreat is an AI web application and API security testing platform aimed at security teams, development teams, and DevSecOps personnel. Its value lies in not making all the decisions for users at once, but rather providing actionable assistance around scanning web applications and APIs for vulnerabilities and assisting in automated penetration testing: users can configure targets, run scans, view vulnerabilities, generate remediation recommendations, and follow up with their business judgment. When choosing such a tool, you need to pay attention to the scope of authorization testing, false positives, false positives, and fix verification, especially when it comes to accounts, customer information, contracts, courses, audio, video, or code output. Its visibility capabilities include AI-powered scanning, automated pentesting, and web/API security, making it more suitable for authorized security testing.
If Codex is stuck and unresponsive, do not send the same message repeatedly. Confirm in order whethe
codex exec In CI, it can analyze code without making changes; first check the sandbox: non-interacti
If Codex Skills is installed but does not trigger, first check if it is located in the scan director
Codex config.toml changes that don't take effect usually don't mean the TOML file is corrupted, but
After exiting the Codex CLI, you don't need to redescribe the entire task; just run codex resume sel
On August 3, 2026, the Qwen team released the Qwen 3.8-Max on the official Qwen blog, positioning it