Connecting the dots from agents to workflow automation to infrastructure with Taskade Genesis.
LLMs made it easy to generate apps. The harder problem is running them as real businesses. Where they live, remember state, coordinate agents, trigger workflows, and keep operating day to day. We treat the workspace itself as that layer.
One prompt becomes a living system. CRM, ops hub, internal tool, business in a box. Memory, agents, and automations working together. Feels closer to early web hosting than modern SaaS. Not demos. Real systems.
Still early, but builders are shipping real internal apps and workflows, not demos. Excited for the future of AI from productivity to agents and workflows to Infra!
Cool to see open models catching up fast. For builders the real question is simple. Which model gives you the tightest loop and the least surprises in production. Sometimes that is open. Sometimes closed. The rest is noise.
We tried building with 3 founders across 3 timezones. On a good day it felt magical. On a bad day it felt like the kind of lag you remember from SC BW, CS 1.6, or classic WoW raids where one spike wipes the whole run just so everyone has to start over.
Async is great for shipping, but not when you are moving fast on hard problems where alignment is the whole game. The drag shows up slowly and you learn zero to one needs tight loops, high trust, and shared tempo. You cannot patch that with calls or docs.
Some teams crush remote. We did sometimes but not often enough and learned that the hard way. The work decides the model. For us it was about momentum and getting the fastest feedback loop possible. Ideas die in latency. Execution dies in drift.
At the end of the day it is not ideology. It is just whatever keeps the product moving as a startup, aiming high to become better, faster, cheaper than the status quo.
I kinda smile seeing this growing up in the real public_html days… Xanga, Geocities, Angelfire, copying HTML from those old Scholastic books to make my first little interactive Pokemon map to hosting WoW guild sites, DKP boards, CS 1.6 servers.
Feels like we’re back again with vibe-coding, app builders, v0, bolt, lovable, all of it. The AI infra even feels familiar. End users getting back the kind of control we had in the public_html days via cPanel shared-hosting, VPS era. And for backend, it’s Supabase or Neon / Postgres now instead of phpMyAdmin and MySQL.
The patterns are interesting, but they don’t imply “instructions.” Networks with thresholds self-organize long before they do anything meaningful. Good developmental signal, not evidence of built-in knowledge.
Feels like pricing is becoming a moving target again. Cursor’s experiments showed how fast teams will change plans the moment usage patterns shift, and LLM speed only accelerates that loop. Every new model drop forces you to rethink what’s “metered,” what’s “included,” and what users actually feel in the product.
The part that buckles first is always the billing logic. Not the API calls, but the lifecycle math behind experiments… and the experiments never stop now.
So anything that lets teams iterate without rewiring state machines every week is going to find an audience. Most people just want to ship, test, adjust, repeat, without their billing layer collapsing under the pace of AI.
Kudos on the launch! Most of the real work isn’t the chat box. It’s keeping context stable, memory reliable, and tool calls from drifting when things get complex. That’s where projects usually break, and also where the interesting problems are now. :)
I don’t think he meant scaling is done. It still helps, just not in the clean way it used to. You make the model bigger and the odd failures don’t really disappear. They drift, forget, lose the shape of what they’re doing. So “age of research” feels more like an admission that the next jump won’t come from size alone.