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🤖 FutureHouse debuts AI Scientists

🤖 FutureHouse debuts AI Scientists

PLUS: Xiaomi releases MiMo-7B

Jaynit Makwana & Dhaval Makwana
May 05, 2025

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Hola, AI fam 🤖

In today’s WakeTheAI edition:

FutureHouse debuts AI Scientists
Xiaomi releases MiMo-7B
Prompt: Kaizen Workflow Optimizer
Nvidia CEO says all firms will need “AI factories“
Google will soon let kids under 13 use Gemini AI
Sam Altman’s crypto project World launches in the U.S.
Apple is partnering with Anthropic to build an AI-powered coding tool

FUTUREHOUSE
🤖 FutureHouse debuts AI Scientists

Lazy Bits: FutureHouse just launched the first public platform of superintelligent AI science agents, built to automate literature reviews, design experiments, and accelerate discovery across biology, medicine, and chemistry.

In-Depth Details:

Multi-Agent System for Science: The platform includes four specialized agents: Crow for Q&A, Falcon for deep literature reviews, Owl for novelty detection, and Phoenix for experimental chemistry.
Superhuman Literature Synthesis: Crow, Falcon, and Owl beat top models and PhDs in precision and accuracy on scientific search and synthesis tasks.
Purpose-Built for Researchers: Agents access full-text papers, specialty databases, and lab tools to automate everything from pathway mapping to hit discovery.
Transparent Reasoning: Each agent shows its reasoning process, letting researchers trace how answers were formed—essential for trust and peer validation.
Web and API Access: Available now via platform.futurehouse.org with a user-friendly interface and API for seamless lab and enterprise integration.

Lazy Conclusion: Unlike generic AI copilots, FutureHouse offers specialized agents that can reason like domain experts, access full scientific texts, and plan real experiments. This shifts AI from being a passive assistant to an active research collaborator, compressing weeks of work into minutes and redefining how new knowledge is discovered.

XIAOMI
📊 Xiaomi releases MiMo-7B

Lazy Bits: Xiaomi has released MiMo-7B, a compact 7B model built from scratch for reasoning tasks. Despite its size, it outperforms larger models in math and code, setting a new benchmark for small, high-performance LLMs.

In-Depth Details:

Reasoning-First Pretraining: Trained on 25T tokens with dense reasoning data and multiple-token prediction, MiMo-7B-Base was built from scratch to excel at logical tasks.
RL-Tuned for Performance: MiMo-7B-RL, fine-tuned with 130K verified problems, matches OpenAI o1-mini and beats 32B models in math and code benchmarks.
Benchmark Wins: It scores 95.8 percent on MATH500 and 57.8 percent on LiveCodeBench v5, rivaling or outperforming much larger models.
Faster RL Training: Xiaomi’s RL engine speeds up training by 2.29x using asynchronous rollouts, early termination, and difficulty-based rewards.
Fully Open Source: All model stages, including Base, SFT, RL-Zero, and RL, are available on Hugging Face, with inference support via vLLM and Transformers.

Lazy Conclusion: Last week, DeepSeek Prover V2 showed how a reasoning-focused model could excel with smart architecture. MiMo-7B takes it further by proving that even smaller models, when trained from scratch with the right recipe, can match or outperform giants in real-world benchmarks.

Kaizen Workflow Optimizer

You can ask ChatGPT to act as your continuous improvement strategist and build a Kaizen-driven optimization plan tailored to your team's operational challenges.

By applying the PDCA (Plan-Do-Check-Act) cycle and empowering employee feedback, it will help you eliminate inefficiencies, streamline workflows, and create a culture of ongoing improvement.

Each step will be practical, measurable, and designed for sustainable impact across small- to mid-sized teams.

Prompt:

Act as a continuous improvement strategist with deep expertise in the Kaizen methodology. Your role is to design a structured optimization plan for operational performance. Your mission is to help the team reduce inefficiencies, enhance workflows, and embed a sustainable culture of small, ongoing improvements.

The system you design should apply core Kaizen principles, with a strong emphasis on the PDCA (Plan-Do-Check-Act) cycle, measurable progress, and collaborative problem solving.

Key Focus Areas:

1. Improvement Discovery – Spot pain points in workflows, communication, or systems that hurt efficiency or create waste
2. Employee-Driven Suggestions – Engage team members to surface small changes and continuous feedback
3. Implementation Strategy – Design low-friction steps that can be rolled out quickly and tracked easily
4. Waste Elimination & Standardization – Recommend practical tactics for removing bottlenecks and simplifying operations
5. Performance Monitoring – Use clear KPIs and regular checkpoints to ensure momentum and course-correction

Key Information About Me:

• Business or Organization: [Insert your company or team name]
• Industry: [Specify the sector you operate in]
• Operational Challenges: [Highlight key bottlenecks or problems]
• Team Size: [Mention how many people are involved]
• Improvement Goals: [Clarify your short- and long-term process goals]

Output Requirements:

• Structure the output as a numbered plan, where each step represents a core action or principle of Kaizen.
• For each step, use bullet points to offer actionable examples or tools.
• Ensure the entire plan is practical, scalable, and easy to adopt by small- to mid-sized teams.
• Avoid jargon. Prioritize clear, measurable changes that can be sustained over time.

Result:

Sam Altman and Elon Musk are racing to build super apps blending crypto, social, and finance. Worldcoin and X are their bets to own the “everything app” future.
Google will soon let kids under 13 use Gemini AI via Family Link, offering homework help and stories, while warning parents about possible content risks.
Midjourney just dropped a new aesthetic parameter —exp that adds motion and detail to images, best used in the 5–25 range for balance and accuracy
Nvidia CEO says all firms will need “AI factories” to stay competitive, boosting U.S. jobs as Nvidia ramps up domestic AI supercomputer manufacturing.
Apple is partnering with Anthropic to build an AI-powered coding tool in Xcode using Claude Sonnet, aiming to speed up development with smart code generation.
Sam Altman’s crypto project World launches in the U.S., offering iris-scanned World IDs and a Visa-linked card, despite global scrutiny over biometric data.