We wire everything up with Vision Agents as the voice agent framework, Stream for WebRTC audio and video, OpenAI Realtime for speech in and speech out, Anam so the agent shows up as a face on the video, and Supermemory so answers come from search over your uploaded documents instead of guesswork. The code stays small and most of the behavior lives in one registered function that asks the memory store for relevant... - Source: dev.to / 3 months ago

In this tutorial, we build exactly that kind of AI teammate. Instead of piecing together complex infrastructure (audio pipelines, transcription, NLP, TTS, avatars), we'll use Vision Agents to tie everything together, using Stream's WebRTC APIs, and give the AI both a voice and a human-like video presence using ElevenLabs voice and Anam avatars. The system we’ll create can listen, query Postgres, and respond during... - Source: dev.to / 4 months ago

Hey HN, we're Ben and Caoimhe, cofounders of Anam. We built a service for interactive avatars and just shipped our latest model, cara-3. Try it at [anam.ai](http://anam.ai/), no sign-up required, or build with it at [lab.anam.ai](http://lab.anam.ai/) or [anam.ai/cookbook](http://anam.ai/cookbook). Some context on why we're working on this: faces carry emotional signal that text and voice don't. Almost half the... - Source: Hacker News / 6 months ago

Import httpx Import os PIXELAPI_KEY = os.environ["PIXELAPI_KEY"] Def color_grade(image_url: str, style: str) -> str: response = httpx.post( "https://pixelapi.dev/api/color-grade", headers={"Authorization": f"Bearer {PIXELAPI_KEY}"}, json={ "image_url": image_url, "style": style, }, timeout=30, ) response.raise_for_status() return... - Source: dev.to / 3 months ago

I shipped PixelAPI's /v1/pattern endpoint yesterday — 8 styles, 512px or 1024px output, recolor + upscale ops, fully seamless tileable. At $0.008/pattern, it's 2-5× cheaper than PatternedAI's GUI sessions. - Source: dev.to / 4 months ago

Import fs from "fs"; Import path from "path"; Import fetch from "node-fetch"; Import FormData from "form-data"; Async function addShadow(imagePath) { const form = new FormData(); form.append("image", fs.createReadStream(imagePath)); const response = await fetch("https://pixelapi.dev/api/shadow-generator", { method: "POST", headers: { Authorization: `Bearer ${process.env.PIXELAPI_KEY}`, ... - Source: dev.to / 4 months ago

Free credits at pixelapi.dev — no card needed. Run your hardest test images through it. - Source: dev.to / 4 months ago