We're Building Password Managers for AIs Now
This isn't science fiction, it's just Tuesday.
The second wave of AI is all about plumbing. We've seen the magic trick, now we're figuring out how to give the magician a key to the building without it burning down.
Your AI Assistant Just Asked for the Wi-Fi Password
Multifactor is building a password manager for a world where your software has its own to-do list.
Multifactor lets you share access to accounts without ever revealing the password. Think of it like sharing a Google Doc, but for logins. The reason this caught my attention isn't just secure sharing for humans; it's designed from the ground up to give AI agents secure, controlled access to the tools they need to do their jobs.
This is the unglamorous, but critical, next step for AI. We're giving agents the power to act on our behalf, but we haven't built the infrastructure to manage their permissions. Multifactor is a sign that we're moving past the "wow" phase of generative AI and into the "how" phase of practical implementation. The real story isn't about another password manager, it's about building the identity layer for a non-human workforce.
This isn't a theoretical problem; it's happening now. Teams are already giving AI agents access to internal systems, and doing it securely is a huge challenge. This matters for any team trying to automate workflows, because persistent access for an AI means persistent risk if not handled correctly.
The Automation Factory
AI is getting a real job, moving from creative partner to assembly line worker.
1stCollab: The AI campaign manager that never sleeps.
This automates the entire influencer marketing workflow, from finding creators to handling payments and taxes. It’s not about finding influencers faster; it’s about making the human-led process obsolete for micro-campaigns.
Build0: Conjure internal apps from a single prompt.
Build0 turns a text prompt into a full-stack internal application, bypassing the engineering queue entirely. This isn't about no-code, it's about shifting development power to the people who actually have the problems.
Refinder AI: Your chat window is now a command line.
Refinder turns natural language requests in Slack into executed tasks across your other apps. It's the first step towards chat becoming a true operating system for work, not just a place to talk about it.
The New Rules of Learning
The best tools don't just teach you things, they change how you work.
Data Science Goes Beyond Pandas: Your Python skills need a refresh for today's massive datasets.
A Reddit deep-dive shows the pros are moving to tools like Polars and Dask to handle data that won't fit in memory. The real skill is no longer just analysis, but engineering scalable, reproducible data pipelines.
Parrot: Learning Spanish by doomscrolling.
Parrot uses a TikTok-style feed to teach Spanish, leaning into our addiction to short-form video. This proves the most effective learning tool is the one you can't put down.
AskBlake: An AI that just builds the boring stuff.
Instead of trying to build a whole app, AskBlake generates the boilerplate React components you build a dozen times a month. Specialised, focused AI tools that solve a single, annoying problem are infinitely more useful than generalists.
Quick hits
Odyssey-2: Real-time interactive AI video.
This turns passive video into a live experience you can direct with text prompts, blurring the line between watching and creating.
AI Professors by Aden: Your new corporate trainer is an avatar.
It creates AI-led training rooms from your documents, aiming to make corporate learning less of a box-ticking exercise.
AccioJob: A tech bootcamp that gets paid when you do.
Their pay-after-placement model for data science training is a bold bet on their own curriculum and a smart way to de-risk career changes.
My takeaway
We’ve moved from asking "what can AI create?" to "how do we manage what AI does?".
This shift is creating an entirely new layer of infrastructure, from AI-specific security tools to automated workflow managers. It's the less glamorous, but far more critical, phase of building the plumbing for our new AI workforce. This is where the real, defensible value will be created over the next few years.
The hard work of integration has begun, and it looks a lot like building specialised tools and robust security policies. This isn't about the next magical demo; it's about who builds the best plumbing. The uncomfortable truth is that the infrastructure might become more valuable than the models themselves.
What happens when managing the AI workforce is a bigger job than managing the human one?
Drop me a reply. Till next time, this is Louis, and you are reading Louis.log().