Video understanding with local LLMs means running a multimodal model on your own hardware to analyse, describe, or answer questions about video content — without sending frames to a cloud API. The capability became practical for local deployment in 2025 as models like LLaVA-NeXT-Video, InternVL2-Video, and Qwen2-VL made video understanding available in open-weight form. Local … Read more
Analysing charts with a vision LLM means asking a multimodal model to read, interpret, and reason about visual data — bar charts, line graphs, scatter plots, pie charts, heatmaps — without converting them to numbers first. The use case is practical and common: extracting values from a chart image in a PDF report, comparing trends … Read more
Multimodal RAG extends retrieval-augmented generation beyond text to include images, charts, figures, and tables embedded in documents. A standard text RAG pipeline loses everything visual — diagrams in technical manuals, charts in financial reports, figures in research papers — because it can only index and retrieve text. Multimodal RAG preserves these visual elements and makes … Read more
Whisper set the standard for open-source speech recognition when OpenAI released it in 2022. It remains the most widely deployed local speech-to-text model in 2026, but it is no longer uncontested — several alternatives have emerged that outperform it in specific scenarios: faster inference (Faster-Whisper, Parakeet), better real-time streaming (Silero, streaming Whisper variants), improved accuracy … Read more
Audio language models are multimodal AI systems that process audio natively — they can transcribe speech, understand spoken language, respond to audio queries, identify speakers, detect music, and even generate speech — without converting audio to text first as an intermediate step. The category expanded rapidly in 2024–2026 as native audio understanding moved from a … Read more
InternVL2 and LLaVA are two of the most widely benchmarked open-source vision-language models, but they represent different design philosophies and perform differently across task categories. LLaVA (Large Language and Vision Assistant) established the blueprint for efficient multimodal models through its simple visual instruction tuning approach. InternVL2 is a more recent series from Shanghai AI Lab … Read more
The swarm agent pattern organises multiple agents as peers that hand tasks between themselves based on which agent is best suited to handle the current step. Unlike the supervisor pattern, there is no central coordinator — each agent makes its own routing decisions, passing the task to the most appropriate next agent when the current … Read more
Debugging AI agent loops is one of the most practically important skills for anyone building agent systems, and it is harder than debugging conventional software. The non-determinism of language model outputs means the same input can produce different behaviour on different runs. Failures can occur anywhere in a multi-step loop and propagate silently through subsequent … Read more
The supervisor agent pattern is one of the most widely deployed multi-agent architectures. It organises agents into a two-tier hierarchy: a supervisor (also called a manager, orchestrator, or coordinator) that plans and delegates, and worker agents that execute specific tasks within a defined scope. The supervisor receives the high-level goal, decides which workers to engage … Read more
Smolagents is Hugging Face’s minimal agent framework, released in early 2025. The name captures the design philosophy: small agents, small codebase, small API surface. Where frameworks like Agno and LangGraph add features and abstractions, Smolagents deliberately removes them — the entire core is a few hundred lines of Python that anyone can read and understand … Read more
