Learning course · Updated June 2026
How to actually wire AI into a video product — computer vision, speech, multimodal models, generative video, real-time inference, and agents — plus the AI-assisted tools video engineers use to build faster. Written by Fora Soft engineers, model by model, from the first hook point to production.
Two tracks woven through every phase: AI features you ship inside the product, and the AI tooling you build with. Every lesson names the model that wins in 2026, what it costs to run, and where it breaks. 250+ projects shipped since 2005.
Outcomes
Ten chapters that take you from “where does AI even attach to a video product” to a production-grade build. By the end, you can choose the right model, wire it into a real video pipeline, hit a latency and cost budget, and operate it without it falling over in production.
Pick a path
The same 57 articles, ordered for what you actually need to do this quarter.
From “where does AI fit in a video product” to one feature running in your pipeline. Orientation, the CV and speech primitives that ship most, and the cost model.
Put models inside a live call and keep them under budget. WebRTC + AI, blur, noise suppression, captions, translation, moderation, LiveKit agents, and the serving/eval/cost layer underneath.
The 2026 frontier as an engineering problem. Video VLMs, generative-video integration with cost and C2PA gates, the agent-framework decision, and AgentOps.
Syllabus
Every chapter is self-contained. Read in order, or jump straight to the phase you need — from computer-vision primitives to agentic AI.
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Talk to the engineers who build it. Fora Soft helps teams choose models, wire AI into video pipelines, hit real-time latency budgets, control inference cost, and ship compliant generative and agentic features for conferencing, OTT, telemedicine, and surveillance.
Featured
Hand-picked deep dives across vision, speech, generative, real-time, and agents — the highest-impact reads first, before you commit to a learning path.
Reference
150 terms with crisp definitions, aliases, and links to the deep dives where each is unpacked. From YOLO and SAM 2 to VLMs, LangGraph, and diffusion — the full A–Z is one click away.
Agentic AI
AI built to act toward goals over multiple steps, deciding and using tools, as opposed to generative AI that produces content on request.
VLM (Vision-Language Model)
A model that reasons over images or video frames and text together — the basis of video understanding, captioning, and grounded Q&A.
LangGraph
A graph-based framework for building stateful, multi-step LLM agents; the production default many video-AI teams reach for first.
RAG (Retrieval-Augmented Generation)
Grounding a model’s output in retrieved context; over a video archive it powers search and Q&A across hours of footage.
Diffusion
The denoising process behind modern generative video models (Sora, Runway, open weights like CogVideoX).
YOLO
“You Only Look Once,” a family of fast single-pass object detectors widely deployed for real-time video.
Written and maintained by
FAQ
AI for video engineering is the practice of wiring AI into a real video product — and the AI tools you build with along the way. The course runs two tracks: Track A covers AI inside the product (object detection on a stream, speech-to-text in a call, generative b-roll, agents that review footage), and Track B covers the AI-assisted tools video engineers actually use to ship faster. Both are taught model by model, wired into a real pipeline, never derived from scratch.
Engineers who already know either ML or video and need to ship AI features in a video product. Both halves are welcome: ML engineers with no video background, and video engineers with no ML background. Product leads scoping an AI feature will get the architecture and cost framing they need without the math.
No. The course doesn't re-derive backprop, transformers, or generic LLMs — excellent resources already do that, and we link to them. We focus on what's missing on the open web: how to wire a model into a video pipeline, what it costs, and where it breaks in production.
The ones that ship in 2026 — YOLO v8–v12, SAM 2, Grounding DINO, PaddleOCR, Whisper and WhisperX, Pyannote, ElevenLabs, LLaVA and Qwen-VL, Sora, Runway, Kling, CogVideoX, LangGraph, CrewAI, vLLM, Triton, and LiveKit Agents — always wired into a real video product with quality and cost gates, not catalogued for academic completeness.
Yes. Lessons ship with runnable code, MIT-licensed, on public GitHub, with a maintainer keeping the repos working for 24 months. Each lesson is signed by an author and carries a visible last-updated date; the fastest-moving phases (multimodal and generative) are refreshed quarterly.
Every lesson passes two tests: it's about AI in video engineering specifically, and the information is genuinely hard to find on the open web. Generic ML theory, generic Cursor tutorials, and vendor docs are linked and cut. What's left is Fora Soft engineering recipes, 2026 frontier-model assessments that don't exist elsewhere, and production failure-mode walkthroughs.
Fora Soft has built video, real-time, and AI products since 2005 — WebRTC, LiveKit, computer vision, generative pipelines, and AI agents at scale. Tell us what you’re building and we’ll send a real engineer your way.
Specialist software house for video, real-time and AI products. Founded 2005. 50 in-house engineers.
