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
Not Diamond is an AI model routing and proxy cost control platform, which is mainly used to automatically select appropriate models for different queries, improve accuracy and reduce costs. It is suitable for AI application developers, agent system teams, platform engineers and model operators. It can recommend more suitable models upon request, help agent systems control model invocation costs, and can also be used to compare model performance and routing strategies. When using it, note that routing results need to be continuously verified in conjunction with the evaluation set. Critical tasks cannot rely solely on automatic recommendations. It is recommended to use one or two low-risk tasks to test input materials, output quality, manual modification amount and final adoption ratio before deciding whether to put them into a fixed process.
Not Diamond is suitable for targeted tasks such as automatically selecting the right model for different queries, improving accuracy and reducing costs. Its role is to turn the preliminary sorting, generation, identification or analysis work into a checkable draft, allowing users to see the direction faster, and then manually complete judgments and trade-offs.
These capabilities are suitable for automatically selecting the right model for different queries, improving accuracy and reducing costs. If the task is already related to customer delivery, commercial release, learning results or internal decision-making, it is recommended to let Not Diamond take charge of the auxiliary link first, and then let the person in charge confirm whether to enter the formal process.
It is safer to prepare three to five representative samples to test input requirements, generation speed, result stability and subsequent modification costs. This allows you to see the boundaries of Not Diamond in real tasks and avoid long-term adoption with just one demonstration.
Not Diamond is suitable for AI application developers, agent system teams, platform engineers and model operators. Such users usually already know what tasks they are going to complete and can also judge whether the output content, analysis results, or recommended solutions meet expectations. Individual users can start with a single task, while team use it requires additional permissions, review responsibilities and cost caps.
Routing results need to be continuously verified in conjunction with the evaluation set, and critical tasks cannot rely solely on automatic recommendations. If the input content involves customer data, real photos, voices, commercial materials, medical financial information, study assignments or legal documents, authorization, privacy and use boundaries must also be confirmed in advance.
Input conditions, output results, manual modification points and final adoption of each test can be recorded. Not Diamond is suitable for gradual incorporation into the process if it is stable multiple times in the main scene; it is more suitable as inspiration, draft or control material if the results often deviate from the goal.
It is mainly suitable for automatically selecting appropriate models for different queries, improving accuracy and reducing costs, and is especially suitable for tasks where the goals are clear, the materials are ready, and the results can be manually reviewed.
Not recommended. It can undertake the generation, organization, identification or analysis stages, but fact checking, compliance judgment, professional conclusions and final trade-offs still need to be completed by people.
It is recommended to prepare clear input materials, expected results and acceptance criteria. When the team uses it, it is also necessary to agree on who is responsible for review, what content cannot be input, and what standards the output meets before it can continue to be used.
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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