harsh.c

🤔 We've built real-time martech stacks. Then bottlenecked them with human-time decisions. Every marketer knows this pain. You've got real-time personalization, identity resolution, clean rooms, orchestration engines, and a dozen other sophisticated tools. Your martech stack is a marvel of modern engineering. But how do you actually use it? Through educated guesses, periodic reviews, and 'strategic adjustments.' 🚢 It's like steering an ocean liner with janky controls in fog. The truth is that we're running complex, real-time marketing infrastructure with human decision-making that's anything but real-time. We make slow decisions with incomplete data, wait weeks/months to see results, then make more slow decisions. 🛑 We're the bottleneck in our own operation. The shift isn't just about adding AI - it's about fundamentally changing how we operate. Moving from: ⚡️ Human-in-critical-path → Human-in-loop ⚡️ Quarterly optimization → Continuous learning ⚡️ Segmentation guesswork → Scale experimentation ⚡️ Perfect control → Directional correctness Here's what this means practically: 🎯 Focus on the unsexy: Invest heavily in high-quality customer signals, context and the infra to support it 🎯 Define the experience we want for our customers declaratively - what we want instead of imperatively (step-by-step how to do it) 🎯 Let the model understand our customers deeply and execute the experience for them 🎯 Monitor and shape strategy vs micromanaging execution The question is whether we're ready to let go of the illusion of control and let the model navigate. 🤖 #martech #AI #marketing

@curious_vii ➕ classic—just because you can do it doesn’t mean you should. caveat, this only applies in the status quo.

@signulll I just assumed this is tech in general. what hubs are not this?

nyc rewards intensity but punishes stillness. it’s a place built for motion, not reflection. great for collecting inputs like ideas, people, serendipity but terrible for any sort of synthesis. after a while you’re just cycling stimulation with no real output. the city’s energy is addictive, but it drains more than it gives after a while. ill always have my place here but i suspect my full time days in nyc maybe over.

@mwseibel revenue + retention

It would be such a flex if startups replaced posting fundraising videos with posting revenue milestones.

💸💸 Price Ceilings Won't Solve Bill Shock Recently, a founder suggested that AI-native products should charge by usage but cap it at $X/month — to “avoid bill shock” and build trust by eliminating billing surprises. > That’s the “exactly wrong" approach for two reasons. 1. You attract your worst unit economics — Power users instantly see the arbitrage. They’ll drive heavy compute costs while paying your fixed cap. You’re subsidizing your most expensive customers. 2. You signal doubt in your own value prop — If your product actually delivers measurable impact — saves $10K, drives $20K in revenue — the customer will pay in proportion. If it doesn’t, they wouldn’t use it. > A price cap says you don’t believe your own ROI story. Bill shock is real, but the right fix isn’t artificial caps, there’s a much simpler, industry standard solution—tiered pricing based on usage. Tiers show confidence: “as you scale, the value scales.” They give customers predictability, discounting levers, and alerts, without destroying margins or signaling insecurity. AI has opened up great opportunities. It has never been the case where a third party builds out the compute infrastructure you need, and it improves itself every six months. You just need to leverage it and charge for it. Keep pricing simple, cost+ on top of the models is a good place to start. AI ROI is already under pressure. Don’t make it harder.

@mwseibel politics (managing people, flow of resources) is performance. the sooner you get started, the better.

In broken organizations, politics trumps performance.

This was Twitter just 2 weeks ago instagram.com/reel/DP4O_f7j1…

@ankrgyl 👍 I would find it near impossible to reason about this. Good to know people are pushing systems this hard.

@barusebi 5 worktrees writing rust

if you get enough GPUs generating code for you, CPU once more becomes the bottleneck

@designcoursecom i would say your organic loom style videos have a ton of value. and launch videos are overplayed. try both I guess

@barusebi I'm contemplating trying my hand at an AI-based commercial.

What do you all suggest goes in the middle? (If you suggest a mockup of the UI, you get punched in the head) I was thinking a cool rive animation of various lab values animating in through scrolling cards.

@hthieblot Talk to users

You can give a first-time founder just one piece of advice. what is it ?

Match messaging to actual adoption patterns. In the short term, solve for bounded workflows, expect variable quality, build for maker-checker usage patterns. Mid-long term, focus innovation budgets on roadmap to autonomous workflows.

The takeaway simply is: Position for where users are while building for where they’re going.

There’s a gap between how AI gets sold vs. how people actually use it.