MiniMax Agent: Clawdbot + Cowork + Skills in one app
MiniMax Agent might be the most complete AI desktop agent right now.
It’s like Clawdbot + Claude Cowork + Agent Skills combined, but more polished.
For more context:
Clawdbot is an open-source, self-hosted AI assistant that lives in your messaging apps (WhatsApp, Telegram, Slack, Discord). It has persistent memory, can control your browser, execute terminal commands, and proactively reach out to you.
Claude Cowork is Anthropic’s new desktop agent (launched Jan 2026). You point it at a folder, describe what you need, and it executes autonomously.
Agent Skills are Anthropic’s specialized instruction sets that Claude loads on-demand.
MiniMax Agent brings all of this together in a single desktop app.
We tested it by feeding an AI paper and asking it to create a presentation. We expected something basic. But the output was actually usable with good structure, relevant visuals, and clean export.
Watch the demo above.
But presentations are just the start.
Here’s what else you can do with it:
Deep research via search, browser use, and MCPs
Build full-stack apps with auth, DB, and Stripe
Connect to GitHub, Figma, Slack out of the box
Custom MCPs for your specific workflows
Multimodal (supports videos, audio, and images)
One feature we really liked: the ability to roll back and re-run from any step using checkpoint restore if you make a wrong turn.
You can download the MiniMax Agent here →
PaperBanana by Google for automatic academic illustration generation
PaperBanana is an agentic framework by Google that generates publication-ready academic illustrations from methodology descriptions.
Here’s how it works:
Five specialized agents collaborate in sequence:
Retriever: finds relevant reference diagrams from a curated set of NeurIPS papers. matches by visual structure, not topic.
Planner: translates your methodology text into a detailed visual description using in-context learning.
Stylist: applies aesthetic guidelines (color palettes, typography, layout), auto-summarized from hundreds of top-tier papers.
Visualizer + Critic loop: generates the image, critiques it against the source text, and refines. Repeat for 3 rounds.
One surprising finding: randomly selected examples work nearly as well as semantically matched ones. what matters is showing the model what good diagrams look like, not finding the topically perfect reference.
In blind evaluations, humans preferred PaperBanana outputs nearly 3 out of 4 times.
It also extends to statistical plots using code-based generation for numerical precision.
Thanks for reading!
P.S. For those wanting to develop “Industry ML” expertise:
At the end of the day, all businesses care about impact. That’s it!
Can you reduce costs?
Drive revenue?
Can you scale ML models?
Predict trends before they happen?
We have discussed several other topics (with implementations) that align with such topics.
Here are some of them:
Learn everything about MCPs in this crash course with 9 parts →
Learn how to build Agentic systems in a crash course with 14 parts.
Learn how to build real-world RAG apps and evaluate and scale them in this crash course.
Learn sophisticated graph architectures and how to train them on graph data.
So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.
Learn how to run large models on small devices using Quantization techniques.
Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using Conformal Predictions.
Learn how to identify causal relationships and answer business questions using causal inference in this crash course.
Learn how to scale and implement ML model training in this practical guide.
Learn techniques to reliably test new models in production.
Learn how to build privacy-first ML systems using Federated Learning.
Learn 6 techniques with implementation to compress ML models.
All these resources will help you cultivate key skills that businesses and companies care about the most.
