Pydantic AI brings type safety and FastAPI ergonomics to LLM agents. Combine it with a fine-tuned 4B model running on-device via llama.cpp and you get production-grade agents in mobile apps with zero API costs and validated outputs by construction.
We benchmarked the three best on-device tool-calling bases of 2026 — Qwen3-4B, Gemma 4 E4B, and Phi-4-Mini — across BFCL v4, real mobile latency, and post-fine-tune accuracy. Each wins a different scenario; here's how to pick.
Deploy custom AI models, no ML expertise required.
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Pydantic AI and LangGraph are the two production agent frameworks of 2026. Choose between them on type safety vs graph orchestration, then layer fine-tuning on top. Here's how to decide.
The OpenAI Agents SDK is intentionally model-agnostic. Swap the OpenAI client for an Ertas-trained model running on Ollama and you keep the developer experience while killing per-token costs. A drop-in tutorial.
TypeScript-first mobile builders don't have to use Python agent frameworks. Mastra and the Vercel AI SDK plus a fine-tuned 4B model running on-device through llama.cpp produce a complete agent stack with zero per-token costs.
Google's FunctionGemma (270M) and Gemma 4 E2B (2B) are the smallest credible function-calling models in 2026. They're not general-purpose — they're explicitly designed to be fine-tuned. That's the whole point.
Meta's Llama Stack is the canonical reference architecture for Llama-based agents. Combine it with a fine-tuned Llama 4 derivative and the Swift/Kotlin client SDKs and you get a complete agent stack running entirely on the user's phone.
A comprehensive snapshot of the open-weight AI model ecosystem as of April 2026 — Chinese-lab dominance, MoE architectural defaults, the unified thinking-mode pattern, and what it all means for production deployments.
Two distinct things from Nous Research now share the Hermes name — a model family released in 2025 and a self-improving agent framework released in 2026. Here's how to tell them apart and which to use when.
By April 2026, Chinese labs hold the top five open-weight models on aggregate intelligence benchmarks. The pattern isn't an accident — it reflects strategic, structural, and economic differences between US and Chinese AI development that took years to play out.
Models advertised with 1M or 10M token context windows don't actually retain useful retrieval accuracy across that full range. Here's what 'effective context' really means, why it matters for production deployments, and how to design around the gap.
MoE has become the default architecture for flagship open-weight models in 2026 — DeepSeek V4, Kimi K2.6, MiMo V2.5 Pro, GPT-OSS, Mistral Small 4 all use it. Here's why, how the design choices have evolved, and what it means for production deployments.
A comprehensive guide covering every approach to adding AI features to iOS and Android apps. Cloud APIs, on-device models, and hybrid architectures compared with real cost and performance data.
A practical guide to integrating OpenAI's API into iOS and Android apps, with honest cost projections at 1K to 100K users that most tutorials skip.
Three paths to AI in your iOS app. CoreML for Apple's ecosystem, cloud APIs for capability, and on-device LLMs via llama.cpp for cost and privacy. A practical comparison for Swift developers.
Three paths to AI in your Android app. Google ML Kit for common tasks, cloud APIs for full LLM capability, and on-device models via llama.cpp for cost and privacy. A practical comparison for Kotlin developers.
A side-by-side comparison of Anthropic's Claude and OpenAI's GPT models for mobile app integration. Pricing, rate limits, capabilities, and when neither is the right answer.
Google's Gemini API offers aggressive pricing and native Android integration. Here's what the pricing actually looks like at scale, where the free tier ends, and when on-device models make more sense.
How to add AI features to React Native apps. Cloud API integration with fetch, on-device inference with llama.cpp bindings, and a practical migration path from one to the other.
Three paths to AI in Flutter. Cloud APIs via the http package, TensorFlow Lite for classical ML tasks, and on-device LLMs via llama.cpp for text generation. A practical comparison for Dart developers.
Deploy custom AI models, no ML expertise required.
Free plan, no card. Paid plans from $25/mo USD.