#multimodal
8 articles
Computer Use and GUI Agents: Screenshot Loops, Grounding, and Why It Is Hard
How GUI agents see screens, decide actions, and click their way through UIs — the grounding problem, safety architecture, and why computer use agents fail in production.
Cascaded pipeline vs native speech-to-speech: when to use each
Chain STT, LLM, and TTS for control — or use one speech-native model for latency. The real tradeoffs, with numbers, and when each architecture wins.
The Voice Pipeline: STT to LLM to TTS End-to-End
Build the STT to LLM to TTS voice pipeline end-to-end — streaming each stage, budgeting latency, and hitting sub-second time-to-first-audio.
Multimodal RAG: retrieving images, PDFs, and text together
Build a RAG pipeline that handles mixed corpora of images, charts, and PDFs — covering embedding strategy, retrieval architecture, and production failure modes.
Modalities Explained: From Text-Only to Omni Models
What modalities are, how frontier models encode images, audio, and video into transformer attention, and how to pick the right model for your pipeline in 2026.
Document AI and OCR in 2026: When to Use VLMs vs Classic Engines
VLMs now beat classical OCR on noisy documents, but the right choice depends on layout complexity, volume, and cost. A decision framework with real benchmarks.
Calling Vision APIs in Production: GPT-4o, Claude, and Gemini Compared
Calling vision APIs in production: image tokens and cost math, resolution tradeoffs, OCR accuracy, and how GPT-4o, Claude, and Gemini actually compare.
How Vision-Language Models Work: Architecture from CLIP to Native Multimodal
From CLIP contrastive pairs to natively multimodal transformers — how vision-language models actually see images, and what that means in production.