chandan_maruthi

*Distributable Expertise: Why We’re Entering the Age of Shareable “Micro‑Robots”*

I’ve been thinking about a new pattern in our community: experts aren’t just writing blog posts or tutorials anymore—they’re packaging their entire workflow into runnable automation templates. I’m calling it *Distributable Expertise*: bundling domain know‑how and code into “micro‑robots” that anyone can import and run in minutes.

### What’s changing?

* *Implementation barrier gone.* Instead of reading a ten‑page guide and coding for hours, you download a ready‑made workflow (n8n, Zapier, Make, etc.) and have it in action almost instantly. * *Instant scaling.* One person writes an optimal lead‑gen or data‑cleanup bot; hundreds of teams can clone it, tweak it, and benefit immediately. * *Democratized specialist skills.* No data scientist? No problem—grab an AI‑analysis micro‑robot and get insights from your CSV in seconds.

### Real examples:

* n8n’s community library now hosts thousands of workflows—from scraping news sites into Slack to auto‑deploying servers based on load. * Zapier templates let you integrate CRM, email, and spreadsheets as a single chain. * RPA “bot stores” offer off‑the‑shelf bots for invoice processing or HR onboarding.

### Why it matters:

1. *Productivity boost:* We stop reinventing the wheel; best practices spread by copy‑paste, not lengthy docs.

2. *Learning in action:* You run the expert’s code, then dissect and learn from it.

3. *Future of work:* As these micro‑robots proliferate, our role shifts from implementer to curator and integrator of expertise.

*Bold take:* In five years, asking “Who can solve this?” will sound archaic. We’ll ask “Which automation template handles this?” and have it live in minutes.

What micro‑robot have you run lately? Where do you see this trend going next? Let’s discuss.

Most AI agent ideas are lame; they’re essentially a glorified Zapier (I don’t hate Zapier; it has its place). I have been struggling to understand what a good AI agent should look like.

I have been using Cursor for a few weeks, and here is why it’s a good AI Agent.

* It is a complete experience: Cursor did not integrate into VS Code as a plugin; they just forked the damn thing—owning the coding experience. Cursor does not need permission from VS Code; it sees everything, and hence it’s better.

* It’s actually doing work: It’s not providing information to a human so they can do it better. It actually does the work a human wants to be done. Coding agents write code; support agents should resolve issues.

* Narrow the use case: Things that don’t work well in coding agents are edges where your code speaks with other systems. This takes lots of tries and may eventually need human effort.

* It can get complex jobs done with some back and forth: It’s right on the first try about 20% of the time, and correct eventually 90% of the time with some back-and-forth.

* Lets humans focus on the “what” (not the “how”): When working with an AI agent, the engineer can focus on what needs to be done, while the agent handles how it needs to be done.

* Does work better than the bottom half: Code written by AI is often better than that written by the bottom 40% of human engineers.

* I do not see AI replacing engineers, but I see human engineers becoming even more productive using AI agents. This applies to every field impacted by AI.

It’s clear that in a few years, coding without an agent will be like not using an IDE.

HN, where have AI agents slayed your toughest workflows—and where have they flat-out failed? Share your war stories and wins!

Btw this is my startup https://www.twig.so and no, we don't do this yet. We are crawling towards it.

We did something interesting recently, but not sure how to proceed. Ideas are welcome.

• Context: We (an AI for Support startup) recently onboarded a support team that had no formal KB/HelpCenter. Their “knowledge” was scattered across tickets and Slack threads.

• What we did: – Ran an AI pipeline to convert ~6 months of support tickets into 1700 FaQ/KB-style entries. — The typical entry was under 200 words, and the average of 150 words. – These ended up as Q&A/FAQ snippets rather than polished, full-length KB articles. — Such information was not available elsewhere, but they cannot be called KB articles, as they don’t look and feel like KB articles; they are more like FAQs. — The customer asked if we could auto-tag these articles; that's the next step. We have not done it yet.

• Insights: – Volume ≠ structure: Writing 1,700 full articles manually is impossible. AI gave us breadth, but these entries aren’t organized like a typical KB (no headings, related topics, etc.). – Search-ability wins: Even though they’re rough around the edges, the Q&A format indexes really well—agents find answers fast via search, which was the primary goal. – User experience gap: Navigating dozens of standalone Q&A entries in a HelpCenter is clunky. There’s no clear hierarchy or grouping by topic. So this is not good UX for navigation. • Outcome: The customer is happy—agents can now “search and serve” answers instead of digging through old tickets. That immediate boost in efficiency seems valuable.

• Next steps (brainstorm): – Should we layer a lightweight organization (e.g., tags or categories) on top of these Q&A entries? – Can we automatically cluster related Q&As into mini-articles or topic pages? – Is there merit in a hybrid approach: AI-generate bulk Q&As, then enlist human editors to refine high-impact sections?

I’m convinced there is something here. not sure what it is. Thoughts?

Just realized. Our society values unique takes on subjects. They do not penalize bad takes; both good and bad takes receive the same value.

To me, this seems to make sense. If everything is an experiment, then every unique take adds to the corpus of knowledge. Whether a take is good or bad does not affect the value added to the total knowledge.

What do you think?

Last week I spent three days wrestling with a data‑pipeline script. The API returned pages of JSON that—despite using cursors—felt impossible to checkpoint reliably. Every new edge case sent my code spiraling into complexity.

Then I did something old‑school: I grabbed a yellow notepad, sketched the entire flow, and within minutes I had an elegant design I was actually proud of. It was:

Simple to implement

Reliable in edge cases

Easy to debug

Why does sketching on paper work so well? Seeing the whole problem laid out on a single page seems to unlock our spatial reasoning in a way screens can’t.

I wonder where else I can be using this method to unlock a brain level, I often dont seem to utilize.

Hi we are getting ready to launch our first self onboarding flow at Twig.so. I love the idea of customers understanding what we do and getting started self serve without a "Book a demo". My big question today is should I 1.) Place a stripe CC paywall with a 30 day trial? 2.) Allow users to try out the product with you using a credit card and then show warnings closer to 30days.

I have not done this before. So looking for advice on what works and what doesnt and any learnings you may have.

My goal is to accelerate the speed at which good customers(Customers that have a need for our product) try out the product and start paying for a service they deem valuable.

Please share your thoughts in detail. I looking for deep insights and learnings from your experience.