johnxie

We built this after running into a lot of date/time parsing edge cases while working on scheduling at Taskade. Eventually adding more regexes and special cases stopped making sense, so we tried treating temporal strings more like a small language with a lexer, parser, and typed AST.

It's TypeScript with zero runtime dependencies. Repo: https://github.com/taskade/temporal-parser

Would love feedback, especially weird date/time strings you've run into that we should add to the tests.

yep, and weekends = most reliable ;) adoption = def growing

Sonnet is up Opus is Err500

loading for me

Timing here is funny. Moltbook is just starting to show up on HN and Reddit as Moltbot lore, with agents talking to agents and culture forming.

Once agents have tools and a shared surface, coordination appears immediately.

https://www.moltbook.com/post/791703f2-d253-4c08-873f-470063...

Hey HN, I'm John, co-founder of Taskade.

We spent some time turning our GitHub org profile into a full architecture overview in ASCII art, box-drawing characters, and markdown tables.

Workspace DNA, how our AI agents work, the Genesis app builder pipeline, all documented in a single README.

Was fun seeing how much you can communicate visually now using plain text.

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!

https://www.taskade.com

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.

Remote is awesome until you hit the limits of it.

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.

Just my 2c.~

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.

Full circle~

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.

Nice launch!

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.

LLMs aren’t perfect, but calling them a “cult” misses the point. They’re not just fancy heuristics, they’re general-purpose function approximators that can reason, plan, and adapt across a huge range of tasks with zero task-specific code.

Sure, it’s not AGI. But dismissing the progress as just marketing ignores the fact that we’re already seeing them handle complex workflows, multi-step reasoning, and real-time interaction better than any previous system.

This is more than just Lisp nostalgia. Something real is happening.

For O-1s filed by startups, what are the most common weak points USCIS flags now? Are you seeing more RFEs around equity or funding documentation?

Built Taskade by solving my own workflow pain points, need for structured note taking, and our team being remote, fully distributed. Launched on Product Hunt, joined niche subreddits/communities, responded to feedback fast, and kept shipping + relaunching.

First 100 users came from showing up where early adopters hang out essentially.

Working on Taskade (https://www.taskade.com), building the execution layer for AI collaboration.

Taskade started as a real-time workspace for teams to organize projects and ideas. It's evolved into something bigger — a platform where humans and AI work side by side.

We’re moving past simple chatbots into real agentic workflows, where teams can generate structured task lists, mind maps, and tables, train custom AI agents with dynamic knowledge, and automate work from start to finish.

Today, Taskade is built around three core pillars: Projects, Agents, and Automation. It’s like giving your team a second brain that can think, plan, and get work done across projects, automations, and real-time collaboration. If you’re interested in the future of human-AI collaboration, take a look!

https://www.taskade.com

Merry Christmas and happy holidays everyone! My dad’s birthday is on Christmas, so it was always a double celebration. Growing up in Queens, we’d sometimes go to Atlantic City, taking a Greyhound or driving once we had a car. We’d head to Bally’s, enjoy the Christmas vibes, and spend hours at the buffet.

Those trips were always fun. This year, he’s in a rehab hospital on another continent after a stroke, but we’re all staying hopeful to celebrate together next year.

I’m working on https://taskade.com, which started as a unified workspace for distributed teams to collaborate. Now, it’s become a playground for AI agents that work alongside you.

These AI agents think, learn, and act—handling tasks, research, and more—right in your workspace where you can chat, manage tasks, create mind maps, tables, and more.

Check it out and let me know what you think!