CLOUDWALK
NIMBUS
> Choose your level:
Goal
Build a chatbot that explains what CloudWalk is, its products (like InfinitePay), mission, and brand values.
Tasks
Input/Output
- Input: User questions via chat interface.
- Output: Natural language answers (optionally Markdown or links).
Requirements
- Retrieval-augmented generation (RAG) from public sources
- 3 sample conversations in README
Bonus Features
Hints
Goal
Given a snippet of text (from a book, paper, article, or transcript), find the original video where it was spoken.
Tasks
Input/Output
- Input: Text snippet (plain text).
- Output: { video_id": "yt123", "timestamp_start": "00:14:05", "timestamp_end": "00:14:38" }
Requirements
Bonus Features
Hints
- Use YouTube transcripts, embeddings + search APIs (Serper, Tavily, etc)
Submission
- Choose the challenge that interests you the most and, once completed, send the full project to nimbus@cloudwalk.io.
- Did you find these challenges boring? Send us your new challenge proposal to the same email.
Goal
Build a fully autonomous company that generates and promotes a new game daily.
Tasks
- Game Generator Agent: Publishes a new JavaScript game every day at one website
- Marketing Agent: Posts daily ads (Twitter, LinkedIn, Reddit)
- Billing Agent: Charges $1 for access
Requirements
- Agents orchestrated with CrewAI, AutoGen, LangChain, etc
- Persistent logs or DB of actions
- Unit test simulating 3 days of operation
Bonus Features
- A/B testing of ad performance
- Global leaderboard
- Infrastructure as code (Terraform/Pulumi)
Hints
Goal
Autonomous newsletter company with curation, billing, and marketing agents.
Tasks
- Curation Agent: Picks trending topics and writes newsletters with LLM
- Billing Agent: Charges $1/month
- Marketing Agent: Promotes daily via posts or emails
Requirements
Bonus Features
- User segmentation
- Topic voting via embedded forms
Hints
Goal
Autonomous music generation company with AI music creation, billing, and marketing.
Tasks
- Music Agent: Generates AI music daily (using Suno, Diff-Singer, Riffusion)
- Billing Agent: Handles monthly payment ($1)
- Marketing Agent: Posts to social media with samples
Requirements
Bonus Features
- Spotify-like auto playlist builder
- Mood-based music generation
Hints
Goal
Autonomous ebook generation company with LLM writing, design, and distribution.
Tasks
- Ebook Agent: Uses LLM + DALL·E to write and design ebooks
- Billing Agent: Monetizes downloads (R$1/month)
- Marketing Agent: Generates ads and previews
Requirements
Bonus Features
- Topic selection via poll
- Auto-distribution via Telegram bot
Hints
Goal
Create a brand-new, never-before-seen final episode of the classic animated series Dungeons & Dragons (Caverna do Dragão). For decades, fans have wondered about the fate of the group of friends trapped in the Realm: did they finally return home, or did they choose to stay? This challenge is to use artificial intelligence tools to imagine and bring to life this long-awaited “lost episode” and give closure to the legendary story.
Tasks
- Story & Script Agent: Write an original script for the final episode, providing a conclusion to the group’s journey.
- Video Generation Agent: Produce the episode using AI video generation tools (e.g., Sora, Runway, Pika, D-ID, ElevenLabs).
- Narration & Sound Agent: Add voices, soundtrack, and sound effects to immerse viewers in the story.
Requirements
- The video must be at least 2 minutes long.
- Use at least one AI video generation tool.
- Submit a document including:
- A step-by-step breakdown of the process (prompts, tools used, creative decisions).
- A link to the final video published online.
Bonus Features
Hints
Submission
- Choose the challenge that interests you the most and, once completed, send the full project to nimbus@cloudwalk.io.
- Did you find these challenges boring? Send us your new challenge proposal to the same email.
Goal
Build a team of agents that autonomously participate in Kaggle competitions and aim for top 20%.
Tasks
- Collect data
- Train models
- Submit solutions via API
- Monitor leaderboard
Requirements
Bonus Features
Hints
Goal
Estimate the number of swimming pools in São Paulo using ML + satellite images.
Tasks
- Sample rooftops from Google Maps or INPE
- Train a detector (>0.65 mAP) for pools
- Use statistics to extrapolate total count
Requirements
Bonus Features
- Folium map with pool density
- District-wise comparison
Hints
Goal
Use AI to decode the Voynich Manuscript, one of the most mysterious books in the world, written in an unknown script and language.
Tasks
- Build a pipeline that ingests transcribed Voynich text (EVA or Takahashi transcription)
- Use LLMs, embeddings, or custom models to find patterns, possible meanings, or linguistic structures
- Try to match parts of the text with known languages, glyph frequencies, or hypothesized semantics
Requirements
- Use AI reasoning to explore unknown language or construct hypotheses
- Provide clear logs of your process
- Explain why you believe your approach may uncover meaning
Bonus Features
- Visual overlay of decoded terms on manuscript images
- Model fine-tuned on similar ciphered texts
- Timeline of symbol usage evolution across manuscript pages
Hints
Submission
- Choose the challenge that interests you the most and, once completed, send the full project to nimbus@cloudwalk.io.
- Did you find these challenges boring? Send us your new challenge proposal to the same email.
Goal
Enable the Unitree G1 humanoid to exhibit generalist, high-level behaviors by integrating it with NVIDIA Isaac GR00T, a vision-language-action foundation model for humanoids. The robot should understand multimodal inputs and generate coherent actions in simulation.
Tasks
- Integrate the G1 URDF with NVIDIA Isaac GR00T.
- Build a demo where natural-language or image-based inputs trigger corresponding robot actions.
- Implement and document a fine-tuning pipeline for adapting GR00T to the G1 embodiment.
- Show an end-to-end execution from multimodal command to simulated action.
Requirements
- Working Isaac Sim/Isaac Lab setup with the G1 URDF.
- At least one multimodal input interpreted and executed.
- Clear documentation of the fine-tuning approach.
Bonus Features
- Multiple distinct commands successfully executed.
- Evidence that fine-tuning pipeline is extensible (ablation tests, dataset curation plan).
- Training checkpoints or partial fine-tuning included.
Hints
Goal
Build a custom simulated environment for humanoid locomotion or navigation using a game engine (Unity/Unreal), then train an RL agent (PPO, SAC, etc.) to achieve complex motor behaviors such as running, obstacle traversal, or expressive motions.
Tasks
- Configure Unity ML-Agents (or equivalent) with the G1 humanoid.
- Define a custom locomotion/navigation task with clear goal conditions.
- Train an RL agent and evaluate learned behavior in simulation.
- Show consistent autonomous task completion.
Requirements
- Functional G1 humanoid environment in Unity or another engine.
- RL training pipeline using standard algorithms.
- At least one behavior demonstrated successfully and consistently.
Bonus Features
- Learning curves showing policy improvement.
- Robustness testing with domain randomization or dynamic variations.
- Support for multiple tasks or behaviors in the same environment.
Hints
Goal
Develop a hybrid Imitation Learning + Reinforcement Learning pipeline that enables the Unitree G1 humanoid robot to imitate diverse human motions (walking, jumping, dancing…) and creatively blend them. RL should refine these motions to ensure balance, robustness, and smooth transitions in simulation.
Tasks
- Use mocap datasets (e.g., Unitree Human Motion Dataset) or teleoperation to collect demonstrations.
- Train an imitation model (e.g., Behavior Cloning, GAIL) to replicate motions.
- Extend training with RL (PPO, SAC, etc.) to improve stability and enable transitions.
- Demonstrate G1 performing at least three distinct behaviors and transitions.
Requirements
- End-to-end pipeline from demonstration → policy → simulation.
- Robot reproduces multiple distinct motion sequences.
- RL fine-tuning demonstrably improves performance.
Submission: Provide source code, datasets used or references, checkpoints, README, and technical docs. Include videos, logs, and plots showing multi-skill behaviors and transitions.
Bonus Features
- Comparison of pure imitation vs IL+RL performance.
- Robust transitions under randomization (terrain, sensor noise).
- Training/validation curves showing creativity or novelty.
Hints
Submission
- Provide code/configs, checkpoints, README, technical documentation, and experiment evidence (videos, plots, logs). Ensure the repository is reproducible end-to-end.
- Choose the challenge that interests you most and, once completed, send the full project to nimbus@cloudwalk.io.