PROMPT 6: Use Fable 5 New Effort Control Feature

(Fable 5 lets you set effort level: high / extra / max)

For maximum depth on complex builds, start with:

"Set effort level: MAX

I need you to solve this completely, not quickly.

Task: [DESCRIBE COMPLEX CODING PROBLEM]

I don't want the first answer that works.
I want the BEST answer that:
— Handles all edge cases
— Scales to 1M users
— Has zero security vulnerabilities
— Is maintainable by a junior dev 6 months from now

Take as long as you need.
Think out loud. Show your reasoning.
Reject your first approach if a better one exists."

#GlobalOneAI
Globalone.ch #Prompt

PROMPT 7: Build a Production API from Scratch

"You are a Backend Architect at Stripe.

Design and build a complete REST API for [APP NAME].

Requirements:
— Auth: JWT + refresh tokens
— Rate limiting: [X] requests per minute per user
— Input validation on every endpoint
— Proper error codes (not just 200 and 500)
— Logging that tells me exactly what broke and when
— API versioning (/v1/ structure)

Endpoints needed:
[LIST YOUR ENDPOINTS]

Deliver:
1. Complete Express/Node code (every file)
2. Middleware setup
3. Error handling module
4. README with curl examples for every route
5. List of environment variables needed

Standard: if Stripe's API team reviewed this, they'd approve it."

#GlobalOneAI
Globalone.ch #Prompt

PROMPT 8: Refactor Legacy Code Like a Senior Engineer

"Here is code I wrote 6 months ago: [PASTE CODE]

I was a beginner. It works but it's a mess.

Refactor this completely:
— Apply SOLID principles where relevant
— Remove all duplicate logic (DRY)
— Break large functions into small, testable units
— Add TypeScript types if applicable
— Improve variable/function naming throughout
— Add error handling I missed
— Write unit tests for every function

Deliver:
1. Refactored code (fully working)
2. Side-by-side comparison of what changed + why
3. List of bad patterns I was using (so I stop repeating them)

Teach me while you fix it."

#GlobalOneAI
Globalone.ch #Prompt

PROMPT 9: Full Deployment in One Prompt

"You are a DevOps Engineer. Deploy my app completely.

App: [NAME] | Stack: [TECH STACK]
Budget: Under $20/month
Goal: Live app, custom domain, auto-deploy on git push

Deliver step by step:
1. Best hosting choice for my stack + exact cost
2. Dockerfile (if needed)
3. GitHub Actions CI/CD pipeline (copy-paste .yml)
4. Environment variable management guide
5. Custom domain + SSL setup (step by step)
6. Database backup automation
7. Uptime monitoring setup (free tools)
8. What to do when something breaks at 2am

Assume I have never deployed anything before.
Every command copy-paste ready."

#GlobalOneAI
Globalone.ch #Prompt

The biggest unlock in Opus 4.8 and Fable 5 isn't the model.

It's Dynamic Workflows inside Claude Code.

Here's what that means in plain English:

Old way → you give Claude one task, it does one task.

New way → Claude plans the entire project,
breaks it into subtasks,
spawns hundreds of parallel subagents,
runs them all simultaneously,
then synthesizes everything back into one output.

Real example of what this handles:
→ Migrate 100,000+ lines of code in a single session
→ Refactor an entire codebase while keeping tests green
→ Build, test, and document a full feature in one run

This is not "AI autocomplete."
This is an AI engineering team working for you.

Infinity Jukebox 🥁

This is the silent limiter on Claude Fable 5.

Fable 5 may not give you its full strength when you use it to build or improve frontier AI models — especially work that helps train, scale, copy, or optimize a powerful Claude/GPT-class model.

Anthropic says in these cases Fable 5 may not visibly refuse or switch models, but may quietly reduce its own effectiveness through hidden safeguards like prompt modification, steering vectors, or PEFT.

As a paying user, that matters: the model can still sound helpful while being intentionally less capable in a narrow but important category of work.

i.e. you may not get Fable 5’s best ability:

- Building a large-model pretraining pipeline.
- Designing data pipelines for training a frontier LLM.
- Planning distributed training across huge GPU clusters.
- Debugging or optimizing model-parallel training systems.
- Designing infrastructure for large-scale pretraining runs.
- Working on ML accelerator or AI-chip design.
- Trying to distill or copy a frontier model.
- Asking how to make a competing frontier model stronger, cheaper, or faster.

Claude Fable 5 vs Opus 4.8

hyperagent.com

🚨BREAKING: CHINA IS AGI-PILLED NOW

China has made AI a national-security project and is planning to spend $295 billion to build AI data centers that run 80% on domestic technology

The plan:
- 2 trillion yuan over five years
- funded by govt debt and long-term bonds
- state telecoms operating infrastructure
- huawei and domestic chipmakers supplying the hardware
- private spending from alibaba and tencent not included in this btw

Goal: Unified National AI network by 2028

Cybersecurity engineers after watching AI find, exploit, patch, document and explain a vulnerability

Claude Fable 5 scores higher than any publicly released AI model

Last month it found bugs which were hidden for 20+ years in a software

Scenes at every org right now

Me after running 300 AI agents to predict every World Cup match

Meet Kimi Work - a local AI agent on your desktop that does the work for you.

🔹Native agent swarm: Up to 300 AI agents running in parallel on your local machine.
🔹Browser use: Paired with WebBridge extension, your agent will navigate websites in your browser: search, scroll, click, type and complete tasks.
🔹Built for Finance: Native global market data tool call from Yahoo Finance and World Bank - no complex API setup required.
🔹Memory system: Kimi Desktop keeps a running diary of your preferences, past decisions, and context to know you better.

Available for macOS (Apple Silicon) and Windows.

Workflow: Claude + Kimi Agent Swarm

Claude acts as the brain Kimi’s swarm acts as the hands.

1. Input : Client brief arrives (e.g., “Analyze 5 competitors”).

2. Claude Plans : Breaks the job into parallel units and writes a precise swarm brief: goal, deliverable format, agent roles, and quality criteria.

3. Swarm Executes : Sub-agents spin up simultaneously, one per unit, pulling data from multiple sources at once. Everything finishes in the time it takes to complete one task manually

4. Claude Synthesizes : All outputs return to Claude, which spots patterns, fills gaps, and produces the final polished deliverable

5. Output : Ready-to-send report, PDF, enriched lead list, etc

China's rare earth export controls are driving prices sharply higher:

Yttrium, used in defense, aerospace, and chipmaking, has skyrocketed +14,000% since April 2025, to ~$1,100 per kilogram.

Terbium, used in permanent magnets for electric vehicles and wind turbines, has surged +350% over the same period, to ~$4,500 per kilogram.

Dysprosium, used to strengthen magnets in electric motors and defense systems, has risen +450%, to ~$1,450 per kilogram.

The surge has been fueled by China's export licensing requirements imposed on April 4th, 2025, covering 7 rare earth elements, including terbium, dysprosium, and yttrium, in retaliation for President Trump's tariff increases.

As a result, exports of yttrium, dysprosium, and terbium are down -50%, -60%, and -50% respectively since April 2025, compared to the 12 months before the controls.

China’s export controls are holding back some key rare earths.

A team in China gave Claude Opus 4.8 a 300-agent workforce and built a working SaaS product in a single afternoon for under $90.

Opus didn’t write most of the code. It acted as the manager, turning a rough idea into tasks, specs, dependencies, and an execution plan for a Kimi agent swarm.

Then 300 agents ran nearly 4,000 steps in parallel. Some pulled live data from Binance, Yahoo Finance, the World Bank, and the IMF. Others built the backend, frontend, landing page, exports, and pitch materials.

The result wasn’t a demo it was a live analytics platform with real-time data, authentication, databases, charts, responsive UI, and PDF, Excel, and web outputs.

A traditional team could spend thousands of dollars and weeks of work on the same project. They compressed it into a few hours.

Most people still use AI as a chatbot. These guys used it like a company.

This 40-minute masterclass from the founder of a $20B AI company is worth more than 2 hours of scrolling

It's the clearest breakdown of Agent Swarms and AI systems at scale I've come across

Doesn't matter if you've never built an agent or you're deep in Claude daily there's something here for you

Bookmark now

Claude Opus 4.8 found a critical Zcash bug that had existed since 2022

The bug could have allowed attackers to create unlimited coins

If Opus 4.8 is capable of this I’m afraid to see what Mythos can do

As Nicholas Carlini put it:

“I’ve found more bugs in the last few weeks with Claude Mythos than in the rest of my entire life combined.”

Claude models are self-improving and creating more capable successors 💀

Developers right now

"We’ve extended access to Claude Mythos 150 additional organizations"