AI & ML

Convert still images to animated videos using the inference.sh CLI, with guidance on model selection, motion prompting, and camera movement.

Generate talking head avatar videos from a portrait image using the inference.sh CLI, with built-in TTS, multilingual support, and competitive pricing.

Upscale and enhance images using Real-ESRGAN, Thera, FLUX Upscaler, and Topaz via the inference.sh CLI.

Generate natural AI voices, text-to-speech, and voice synthesis using the inference.sh CLI with models like Inworld TTS, ElevenLabs, and Kokoro TTS for voiceovers, audiobooks, podcasts, and more.

Generate original music from text prompts using ElevenLabs AI, with control over genre, mood, instruments, and duration up to 10 minutes.

Generate images with Alibaba Qwen-Image-2.0-Pro via inference.sh CLI, with professional text rendering and fine-grained realism for posters, banners, and text-heavy designs.

Control a headless browser to navigate pages, click elements, fill forms, take screenshots, record video, and execute JavaScript using Playwright and inference.sh.

Translate and dub audio/video into 29 languages while preserving speaker voice using the inference.sh CLI.

Generate music and songs using ElevenLabs, Diffrythm, and Tencent Song Generation models via the inference.sh CLI.

Generate images with 50+ AI models including GPT-Image-2, FLUX, Gemini, Grok, Seedream, and Reve via the inference.sh CLI.

Generate videos from text, images, or references using 40+ AI models via the inference.sh CLI.

Build multi-step AI content creation pipelines combining image, video, audio, and text using the inference.sh CLI.

Learn and apply prompt engineering techniques for LLMs, image generators, and video models using the inference.sh CLI.

Create talking head videos with AI avatars, lipsync, and voiceover using the inference.sh CLI.

Generate AI sound effects from text descriptions using the inference.sh CLI, with control over duration and prompt influence.

Transform any voice into a different voice while preserving speech content and emotion using the inference.sh CLI and ElevenLabs models.

Remove background noise and isolate vocals from audio files using the inference.sh CLI and ElevenLabs voice isolator.

Build automated AI workflows combining multiple models and services for batch processing, scheduled tasks, event-driven pipelines, and agent loops using the inference.sh CLI.

Learn best practices for writing effective AI video generation prompts, covering shot types, camera movements, lighting, style keywords, and model-specific tips for Veo, Seedance, Wan, Grok, and others.

Design multi-agent AI workflows with clear boundaries, handoffs, and monitoring for complex product management tasks.

Diagnose whether an AI workflow suffers from context stuffing or benefits from context engineering, and apply structured techniques to improve reliability.

Provides structured guidance for building production-grade AI agent systems through context engineering, covering fundamentals, architectural patterns, operational excellence, and evaluation.

Provides a structured template for creating new Agent Skills, including sections for description, activation criteria, core concepts, practical guidance, and integration notes.

Design and build hosted agent infrastructure with sandboxed execution, warm pools, session persistence, and multiplayer collaboration.

Designs persistent memory architectures for AI agents, covering cross-session knowledge retention, entity tracking, temporal validity, graph/vector retrieval, and memory consolidation.

Shares memory between agents at the representation level by compacting the orchestrator's KV cache for efficient worker handoff, reducing token costs without summarization or retrieval.

Convert books into supervised fine-tuning datasets and train style-transfer models that replicate an author's voice.

Provides production-grade techniques for evaluating LLM outputs using LLMs as judges, covering direct scoring, pairwise comparison, bias mitigation, rubric generation, and confidence calibration.

Optimizes long-running agent sessions with structured context compression, summarization, and durable handoff summaries that preserve decisions, files, risks, and next actions.

Guides project-level decisions for LLM-powered systems: task-model fit, pipeline architecture, token and cost estimation, and agent-assisted iteration.

Explains foundational concepts of context engineering: what context is, attention mechanics, the U-shaped attention curve, and why context quality matters more than quantity.

Extends effective context capacity through strategic compression, masking, caching, and partitioning techniques.

Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.

Improves multi-step research reliability with structured protocols for source validation, error recovery, and transparent reasoning.

Enables fast similarity search and clustering of dense vectors using FAISS, supporting billions of vectors, GPU acceleration, and various index types.

Build production RAG and semantic search systems with a high-performance vector database written in Rust, supporting hybrid search, filtering, and horizontal scaling.