APIDot @APIDotAI

@csaba_kissi APIDot is an AI API platform for developers. Access active image and video APIs with one key, faster integration, lower costs, and production-ready performance. apidot.ai

APIDot now has model-specific GitHub repos. Start from the exact model you are integrating: GPT Image 2, Nano Banana 2, Seedance 2, Sora 2 Official, or Veo 3.1. Each repo includes cURL, Node.js, polling, webhooks, pricing notes, and prompt examples. github.com/APIDotAI

Z-Image is the narrow prompt-only image example we added today. The cURL payload uses `model`, `input.prompt`, `input.size`, and optional `input.enable_safety_checker`. No reference-image fields copied from other image models. github.com/APIDotAI/apido…

Gemini 3 is the direct request example in APIDot. No task polling in this sample. The Node.js version calls `:generateContent` with native fetch, puts the model ID in the URL path, and keeps the API key server-side. github.com/APIDotAI/apido…

Veo 4 and Gemini Omni Video are getting attention from AI video teams. APIDot helps developers prepare the workflow around it: text-to-video, image-to-video, async tasks, status polling, result files, and model switching. apidot.ai/features/veo-4

We updated the APIDot examples repo. It now has: - 16 cURL model quickstarts - 5 Node.js examples with native fetch - polling examples - Express and Next.js webhook receivers Start from the backend, not the browser. github.com/APIDotAI/apido…

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Not every image job needs the same model. On APIDot: Seedream 5.0 Lite for layouts, FLUX.2 for polished stills, Nano Banana 2 for variants, and Kling O3 / Wan 2.7 Image for controlled edits. Better model choice, fewer wasted runs. apidot.ai/models

Failed tasks should not keep your credits. Real prompt tests mean bad inputs, retries, and integration mistakes. APIDot refunds credits when generation tasks fail, so early product work is easier to debug without quiet spend. Good for first passes. apidot.ai

GPT Image 2 is finally useful for the details that usually break: readable poster text, clean UI mockups, product labels, busy prompts. On APIDot: Low 1K: $0.005 vs Fal $0.010 Medium 1K: $0.015 vs $0.060 High 1K: $0.060 vs $0.220 apidot.ai/models/gpt-ima…

The same task pattern works across image and video models: submit store task_id poll or receive webhook retrieve file URLs

GPT Image 2 is available through APIDot’s async generation flow. Basic backend flow: 1. submit with model: `gpt-image-2` 2. store data.task_id 3. poll status or use a webhook 4. retrieve generated image URLs cURL example: github.com/APIDotAI/apido…

Example: github.com/APIDotAI/apido…

Webhook handlers should be idempotent. Duplicate callbacks can happen. A safer handler should: 1. verify task_id ownership 2. persist or enqueue the event 3. return 2xx quickly 4. reconcile before business actions 5. avoid duplicate visible results

A good polling flow should be boring and predictable. Persist the task id first, poll with a reasonable interval, and stop once the task reaches a terminal state such as finished or failed.

Polling async AI generation tasks safely: 1. store data.task_id after submit 2. wait briefly before the first poll 3. use a reasonable polling interval 4. stop on terminal states 5. surface failed tasks clearly 6. avoid hot-loop polling Example: github.com/APIDotAI/apido…

Website: apidot.ai Feedback and PRs are welcome.

The repo uses public placeholders only: - YOUR_APIDOT_API_KEY - example.com callback URLs - sample task ids No real keys, production URLs, internal infrastructure, or user data.

APIDot Examples is now available on GitHub. It shows the backend flow for async AI generation: 1. submit a task 2. store data.task_id 3. poll status or receive a webhook 4. retrieve generated file URLs 5. reconcile state safely GitHub: github.com/APIDotAI/apido…