Your AI Is Smarter in a Team
And Claude is the critic we all need.
It turns out we might be using AI all wrong. The key isn't a single, all-knowing oracle, but a team of specialized agents that check each other's work.
Single-Shot AI Prompts Are a Dead End
A deep dive into 500 complex prompts reveals why multi-agent systems are the future of getting things done with AI.
A rigorous test of GPT, Claude, and Gemini on 500 complex prompts reveals a simple truth: asking an AI a single question is the least effective way to get a good answer. The real breakthrough comes from multi-agent systems, where different AI instances collaborate on sub-tasks, dramatically outperforming the single-shot approach.
The real story isn't about which model 'won,' but that certain models have specific talents. Claude, for instance, consistently identified logical flaws and errors in reasoning that other models missed, making it a terrible oracle but an incredible editor. This suggests we should stop treating AIs like a single brain and start using them like a team of specialists.
This fundamentally changes how we should build with AI. The goal isn't finding the one 'best' model, but architecting systems where AIs can check each other's work. For the rest of us, it’s a reminder to break down complex problems into smaller, sequential steps to get a more reliable result.
The Automated Co-pilot
The next wave of AI isn't about chatbots, it's about invisible agents automating the boring stuff.
Code Mode: Give your AI a secure playground to code its own solutions.
This is a huge leap for agentic AI, turning clumsy tool-calling into efficient, self-directed execution. It makes agents faster, cheaper, and fundamentally more capable.
Dim Notes: The note-taking app that auto-tags your brain dumps.
This solves the biggest problem with note-taking: the overhead of organising everything. By automating tagging, it removes the friction that stops most of us from staying organised.
AI-Powered Creation and Connection
These tools are collapsing the distance between an idea and its execution, whether it's a design or a sales lead.
ProspectEcho: Turn Facebook's Ad Library into your personal lead list.
This is clever opportunism, targeting companies already spending money on ads. It’s a direct line to businesses with active budgets, cutting through the noise of cold outreach.
DesignLumo: Generate editable, ad-ready designs from a text prompt.
This moves beyond static image generation by providing editable assets. It bridges the gap between AI magic and the practical reality of design workflows.
SuperIntern Translation: Real-time meeting translation, without the awkward bot.
This makes multilingual collaboration seamless by embedding translation directly into the workflow. It's not a feature; it's about removing a fundamental friction from global communication.
Quick hits
Ember: An AI diet coach that understands you ate 'a bowl of pasta' so you don't have to weigh it.
Finally, a calorie counter that speaks human, not just grams.
Imagen 3: An AI image generator from Google that finally gets text right.
Google's latest image model focuses on studio-quality precision for when 'good enough' isn't.
Felo LiveDoc: Turns your static documents into a collaborative workspace with a team of AI agents.
This is a peek at the future of work, where documents are living projects managed by human-AI teams.
My takeaway
The next breakthrough in AI won't be a better model, but a better way of making models work together.
We've been treating LLMs like solo geniuses, throwing single prompts at them and hoping for the best. The real power comes from orchestration, creating systems where different AIs can specialise, collaborate, and critique each other. This is the shift from just using AI to architecting intelligence.
This means the most valuable skill is no longer just prompt engineering, but system design. It forces us to think less like a user and more like a manager of a team of digital minds. The only question is what we build once it's normal to assemble a bespoke team of AIs to solve any problem.
What's the first problem you'd solve with a custom-built team of AI agents?
Drop me a reply. Till next time, this is Louis, and you are reading Louis.log().