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
Git Assistant is a collaborative coding tool between GitHub and ChatGPT, whose core purpose is to compare the code generated by ChatGPT with GitHub pull requests to assist in iterative coding. It primarily revolves around prompt iterations, GitHub comparisons, pull requests, code change reviews, and demo projects, making it suitable for development learning users who want to generate code with ChatGPT and observe differences through GitHub. Before use, confirm whether the account permissions, material or data source, export format, privacy boundary, billing method, and manual review requirements match the actual process. When it comes to public publishing, sales outreach, education and learning, health, game security, code, audio and video, portraits or commercial materials, also check for authorization, compliance and the risk of misjudgment of results, and retain manual review. Before formal adoption, it is recommended to test the output quality, cost, and review process with a small sample.
The value of Git Assistant is not to make the final judgment for humans, but to move tasks such as comparering ChatGPT-generated code to GitHub pull requests and assisting in iterative coding to the drafting and checking stages. It is more suitable for use in a clear work process, helping users organize materials, documents, projects, or business ideas into checkable intermediate results, and then make trade-offs, proofreading, and final publication.
It is suitable for learning prompt engineering, observing the differences in AI code changes, and organizing the generated results in the form of pull requests.
Suitable for development learners, prompt engineering learners, independent developers, and those who want to experiment with AI programming.
The limitation is that the official website is lightweight, and production code still needs to be tested, reviewed, and checked locally.
Prepare GitHub repositories, task descriptions, branching policies, and code review standards before use.
Verify that the input data comes from a legal, clear and authorizable source, and then check that the output can be understood, modified and traceable. Pictures, videos, music, voice, marketing content and social content should check copyright, character authorization, factual details and platform rules; Documentation, health, education, code, and project management results should be reviewed back to the source material for key conclusions; Automation tools also need to confirm how manual takeover occurs after failure.
If the task requires medical diagnosis, investment advice, formal legal opinions, exam answers, unaudited public releases, identity authentication, or automated execution by high-risk systems, the tool should not be left with ultimate responsibility. A safer use is to use it as a draft, clue, initial screening, assisted sorting, generating samples, or as a reference before manual decision-making.
What does Git Assistant mainly solve? **
It mainly solves the problem of using ChatGPT to generate code and then compare changes using GitHub.
Is Git Assistant suitable for direct use in formal processes? **
Suitable for learning and experimentation, the code must be tested and reviewed before merging.
What do I need to prepare before using Git Assistant? **
You need to prepare the repository, prompt, target change, and pull request checking process.
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.
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