The AI Intern Is Specialising
We're moving past generalist chatbots and into the era of trainable, expert AI.
The magic trick of AI is over. Now we're teaching it how to do our actual jobs.
Your AI Just Got a Ph.D. in 'Your Stuff'
Claude Skills Hub lets you stop prompting and start training.
Anthropic's new 'Skills' for Claude aren't just another feature. They're a way to teach the AI specific, repeatable workflows, embedding your team's unique knowledge directly into the model. Instead of explaining your brand voice for the tenth time, you build it a 'skill' once and it just knows.
This marks a fundamental shift from prompting to training. We're moving beyond treating AI like a clever search engine and starting to manage it like a specialist employee. The real work isn't just asking questions anymore; it's architecting systems of knowledge that let the AI execute complex, multi-step tasks autonomously and consistently. It's less about the prompt, and more about the playbook.
For any team drowning in repetitive tasks—from marketing content to code reviews—this is the unlock. While others are focused on general intelligence, Claude is building a framework for specialised labour. This is how AI stops being a novelty and becomes a deeply integrated, high-value part of the org chart.
The New AI Plumbing
While front-end AIs learn new jobs, the back-end is being rebuilt to make deploying them ridiculously fast.
Metorial: The ultimate AI agent switchboard.
It offers over 600 integrations out of the box, turning months of integration work into a few hours. This is the unglamorous but essential infrastructure needed to connect AI agents to real business software.
ChatGPT Atlas: OpenAI's bid to own the interface.
By embedding ChatGPT into a browser, OpenAI wants to stop being a destination and start being the context layer for your entire internet experience. It's a direct shot at Google, arguing the browser itself is the ultimate AI agent.
Ship Faster, Argue Later
The pressure to build faster is leading to tools that shortcut entire layers of development.
DevReadyKit: A professional front-end in a box.
This free UI kit for React and Tailwind lets solo founders ship polished products without a dedicated front-end team. It commoditises design to accelerate speed-to-market.
Fumadocs 16: Documentation that isn't a chore.
This framework for React and Next.js makes building beautiful docs part of the development workflow, not an afterthought. Good docs are a growth hack, and this tool makes it easier to get right.
Quick hits
ProblemHunt: Find the pain, then build the product.
This platform connects founders with people who have real problems they will pay to solve, tackling the number one startup killer: a lack of market need.
Ito: Your voice is now a power tool.
This open-source voice assistant understands intent, not just words, turning spoken commands into smart text in any app.
To-Do List Hell: Finally, a task manager with a personality.
A gamified to-do list with vim-inspired key bindings that makes tackling tasks feel less like a chore and more like a game.
My takeaway
The era of the generalist AI chatbot is officially over.
We spent the first wave marvelling at the magic trick of an AI that could talk. Now, the real work begins: building the specialised brains, plumbing, and interfaces to make it perform actual jobs. This next phase is less about flashy demos and more about deep, unglamorous integration.
This means the most valuable skill is shifting from prompt engineering to systems architecture. We're transitioning from being AI users to being AI managers, responsible for training and directing our new digital colleagues. The big question is no longer 'what can AI do?', but 'what specific, high-value task can I teach it?'.
What's one repetitive workflow you wish you could outsource to a specialised AI right now?
Drop me a reply. Till next time, this is Louis, and you are reading Louis.log().