MindAgents model errors

100%

MindAgents model errors

✓ Published0🌍 Public

Last edited Feb 14, 2025

Created on Feb 14, 2025

This visualization captures a log from a terminal session running the MindAgents game, showing three large language models—LLAMA_3.3_70B, GEMINI_1_5_FLASH, and GPT35_TURBO—attempting to play a cooperative card game. The ASCII-art interface displays the game state with hearts for lives, stars for points, and per-player card numbers, while concurrently revealing an error log. The example demonstrates how a Python CLI using `asyncio` orchestrates model interactions, with a 404 `model_not_found` error halting play when the specified Llama model is inaccessible. The rendering relies on box-drawing characters and emoji from the terminal output, sourced from a gist by BBischof.

AI-generated description

Similar vizzes

Loading thumbnail…

Some notes on AI / ML tools that seem interesting/useful (largely aiming to focus on open source tools)

This interactive data-visualization gallery maps the landscape of AI/ML tools, categorizing a curated gist of open-source projects and resources. It shows a network of interconnected nodes representing tools like Ollama, vLLM, and LangChain, grouped by categories such as SDKs, agents, and model hosting. Built with React and WebGL, the visualization uses a graph layout to display relationships and hierarchies, with the plain-text markdown content from the source gist serving as the underlying dataset. The interactive scene allows users to explore the tool ecosystem directly within the browser.

JJastor11

51% match

Loading thumbnail…

aaronson-oracle

An interactive visualization demonstrates a predictive text model learning from a user's binary choices. As the user presses the left or right arrow key, the system builds n-gram sequences from the history of prior inputs to predict the next likely key. A large SVG tree diagram visualizes the conditional probabilities of each possible four-bit sequence, showing which sequences are active and frequently occurring. A sticky score panel displays the model's rolling accuracy over the last 100 predictions, while the log shows a running history of actual versus guessed inputs. The implementation uses D3.js selection, event handling, and SVG rendering.

11wheel

48% match

Loading thumbnail…

Gist ff27bd2aedab264f5a259d4befd7f54e

This example shows a configuration panel for a BERT model, displaying the file paths and checkpoint settings used to initialize the language model. It presents the Python dictionary `BERT_PRAMS` with its keys and values, including the vocabulary file, BERT config, and model checkpoint. The visualization renders these parameters as a structured, readable layout, likely using a text-based or table-like rendering approach. The code relies on standard Python data structures and does not employ any external plotting or graphics libraries, focusing instead on the raw configuration data.

HHirosaji

48% match

Loading thumbnail…

Gist 01500f8ab6e9b7d98dd0aeea50347b9f

This example shows how a classroom activity integrates generative AI tools to design community micro-campaigns. It demonstrates a structured session where learners use ChatGPT, Adobe Firefly, and Adobe Express for ideation, visual production, and operational strategy. The visualization relies on text-based prompts and workflow steps rather than code libraries. It displays the pedagogical flow, including phases for guided activity, critical reflection, and optional mini-challenges, without using data-driven rendering or computational graphics.

IIoTeacher

48% match

Loading thumbnail…

Gist 34f3e588a35b33357c08853c2370036b

This visualization catalogs AI tools and resources, organizing hundreds of links to libraries and APIs for foundation models, model development, vector databases, and multimodal systems. The layout groups entries by category—such as OpenAI function calling, Microsoft’s BitNet framework, and Hugging Face collections—using a hierarchical list format. It references tools like LM Studio for local deployment, Pyannote for speaker diarization, and Edge-TTS for text-to-speech, with GitHub repositories and documentation pages as primary sources. The rendering relies on standard Markdown headers and hyperlinks to create a browsable, text-based reference.

JJastor11

48% match

Loading thumbnail…

Manus tools and prompts

This visualization maps the structure of Manus, an AI agent, by displaying its system prompt, tool definitions, and configuration files as interconnected nodes in a network graph. Built with React, it uses a force-directed layout to position elements and color-coding to distinguish between files, message tools, and programming utilities. The interface allows viewers to explore how the agent’s core instructions relate to its available functions, with tooltips revealing the underlying JSON schema for each node.

JJastor11

46% match

Loading thumbnail…

🧠 Arquitectura RAG: Capas de un Sistema de IA

This visualization dissects the layered architecture of a Retrieval-Augmented Generation (RAG) system, presented as a vertical flowchart. It shows the sequential pipeline from data ingestion through chunking, embedding generation, vector search, reranking, and final LLM generation. The diagram highlights key technical components, including FAISS and Pinecone for vector databases, along with RAGAS for evaluation metrics. The layout visually separates each processing stage with distinct blocks, illustrating the flow of data through semantic search and generative AI layers. It provides a clear technical map of how external knowledge bases integrate with language models.

IIoTeacher

46% match

Loading thumbnail…

prompt text for AI train guy

This visualization is a network graph of a scripted conversation between two characters, "Col" and "Jeff," rendered as a force-directed layout. The data comes from a text file containing the dialogue, which is parsed to extract characters and their speaking turns. It shows how conversational topics and tone shift across different locations and situations. The graph uses D3.js’s `d3.forceSimulation` to position nodes, with links representing dialogue edges. Node colors encode the comment topic, while node size reflects message frequency. The rendering relies on SVG paths and `d3.forceLink` for edge bundling.

CColinEberhardt

46% match