A newsletter post by Jon Stokes introducing the concepts behind AgentGPT, BabyAGI, LangChain and LLM-powered agents. It covers core agent concepts, how LLMs make plans, chain of thought reasoning, action planning, and example prompt-response pairs.
Potpie
Potpie is an AI-native SDLC automation platform. It maps how systems work into a context graph and runs AI agents on it inside the user's own VPC, according to the page. The documentation page describes creating and updating documentation from connected product knowledge.
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- maps how your systems actually work into a living context graph Source: potpie.ai
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0A newsletter post by Jon Stokes introducing the concepts behind AgentGPT, BabyAGI, LangChain and LLM-powered agents. It covers core agent concepts, how LLMs make plans, chain of thought reasoning, action planning, and example prompt-response pairs.
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0MindStudio is a visual platform for designing, building and deploying AI agents without coding, with optional code extensibility. It offers over 100 templates and connects to 200+ AI models. A product agent named Remy, in alpha, builds applications from a shared spec.
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0BrainSoup supports AI agents, automated workflows, orchestration, or delegated tasks.
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0Twig offers Sera, an AI receptionist that answers, qualifies and books calls, texts and web chats. Plans start at $49 per month for the Starter tier, and every plan includes a 15-day free trial with voice. Twig also publishes developer documentation covering its API, MCP server, SDKs, data connectors, RAG strategies and agent configuration.
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0Westworld is a multi-agent simulation library for simulating and optimizing systems with multiple interacting agents. Inspired by Unity and Unity ML Agents and adapted in Python, it provides grid environments, agents, obstacles, collectibles and triggers, spawning, simple pathfinding and wandering behaviors, maze generation and simulation visualization and export. The project is described as extremely experimental and in alpha release.
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0A freeCodeCamp handbook for developers on organizing AI agents for real business cases. It covers prerequisites, large language models, definitions of AI agents, and a decision algorithm for creating optimal agents, including checking budget, starting with one agent, and measuring performance. It uses a Google Research, Google DeepMind, and MIT paper as its primary source and code examples in a Google Colab notebook.
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