10 Ready-to-Use LinkedIn Posts for AI Founders

We spent 6 weeks building a feature nobody asked for. Here's what happened: Week 1: Had a "brilliant idea" for our AI product. "Users will love AI-powered analytics dashboards!" Week 2-6: Built it. → Custom charts → Predictive insights → Real-time data visualization Week 7: Shipped it. Result: 3 out of 87 users even clicked on it. What we missed: We asked ourselves: "What would be cool to build?" We should have asked: "What problem are users actually trying to solve?" Turns out, they didn't need fancy dashboards. They needed faster CSV exports (ours took 2 hours, they needed 5 minutes). We built that in 2 days. 41 users activated it in the first week. The lesson: Fall in love with the problem, not your solution. Your users will tell you what they need— but only if you ask the right questions. What's a feature you built that nobody used?

Day 47 of building our AI product in public. Here's what happened this week: Monday: Rewrote our onboarding flow. → Old version: 8 steps, 15 minutes → New version: 3 steps, 2 minutes → Why: 60% of signups dropped off at step 4 Tuesday-Wednesday: Shipped API documentation. → Not sexy, but 12 developers requested it → Now they can build integrations themselves Thursday: Had a tough conversation with our first 10 users. → Asked: "What's the ONE thing holding you back from paying?" → Answer surprised me: pricing clarity (not price itself) Friday: Rewrote our pricing page. → Before: 3 tiers, confusing features → After: 2 tiers, clear value prop → Testing next week What I'm learning: Building a product is 10% coding, 90% listening. The developers who talk to users every week move faster than those who talk to users every quarter. Next week: → Ship faster CSV exports (most requested feature) → Talk to 10 more users → Iterate If you're building something, what did you ship this week?

We decided NOT to raise VC funding (at least not yet). Here's the reasoning: 6 months ago, we thought we needed $2M to build this product. Then we did the math: Option A: Raise $2M seed → Give up 20% equity → Hire 5 engineers → Burn $150K/month → 13-month runway → Pressure to grow fast Option B: Bootstrap to $10K MRR first → Keep 100% equity → Stay lean (2 founders + 1 contract engineer) → Burn $8K/month → Infinite runway (once we hit $10K MRR) → Grow at our own pace We chose Option B. Why: 1. Time to find product-market fit With $2M and 13 months, we'd be forced to scale before PMF. Bootstrapping gives us time to get it right. 2. Better terms later If we raise at $10K MRR, we raise at $5M valuation (not $2M). That's 10% dilution for $500K, not 20% for $2M. 3. Control We can say no to features investors want. We build what users need. The trade-off: We grow slower. That's fine. We'd rather be a $10M profitable company we own 100% of than a $100M company we own 10% of. This isn't the right choice for everyone. But it's right for us. Are you raising or bootstrapping? Why?

Unpopular opinion: You don't need a co-founder. Everyone says: "VCs don't invest in solo founders." "You need a co-founder for credibility." "Building alone is too hard." I disagree. Here's why: 1. Decision speed Solo = decide in 5 minutes With co-founder = 3-hour debate, compromise, resentment 2. Equity clarity Solo = you own 100% With co-founder = equity splits cause 40% of startup failures 3. Focus Solo = no politics, no conflict With co-founder = "should we pivot?" becomes a marriage counseling session But solo founders need to be self-aware: You NEED advisors (3-5 people you can call for advice). You NEED a support network (other founders who get it). You NEED discipline (nobody will push you but you). The real question isn't "do I need a co-founder?" It's "can I build the support system to succeed solo?" If yes → go solo. If no → find a co-founder. But don't force a co-founder just because "that's what you're supposed to do." Bad co-founder > no co-founder is a myth. Bad co-founder = startup death. Solo founder > bad co-founder. Are you solo or with a co-founder? What's working (or not)?

I analyzed 50 AI startups to understand what makes users pay. Here's what I found: Most AI products fail because they solve a "nice to have" problem. The ones that succeed solve "hair on fire" problems. Difference: Nice to have: "Our AI makes your emails 20% better" → User reaction: "Cool, but I'm fine with my current emails" Hair on fire: "Our AI cuts your customer support response time from 2 hours to 2 minutes" → User reaction: "I need this NOW. Our customers are angry." The pattern in all 50: Products that succeeded (15/50): → Saved time on a task users HATE doing (not just "could be faster") → Quantifiable impact (50% faster, $10K saved/month) → Users paid within first week of trying Products that struggled (35/50): → "Made things better" but not urgent → Vague value prop ("improve productivity") → Users signed up, never paid The lesson: Don't build something users "kinda want." Build something they're in pain WITHOUT. Ask yourself: "If my product disappeared tomorrow, would users scramble to find an alternative—or just shrug?" If they'd shrug, you don't have product-market fit yet. What problem are you solving? Is it "nice to have" or "hair on fire"?

2 years ago, I watched my dad spend 4 hours manually updating spreadsheets. He's a small business owner. 67 years old. Still works 60-hour weeks. I asked: "Why don't you use software for this?" He said: "I tried 3 different tools. Too complicated. Gave up. Went back to Excel." That conversation haunted me. My dad isn't "bad with tech." He runs a successful business. The problem: Enterprise software is built for enterprises. Nobody builds for small business owners like my dad— people who need automation but don't have time to learn complex tools. That's why I'm building [Your Product]. AI-powered [solution], designed for people who: → Don't have time for training videos → Don't have IT departments → Just need it to work, right now I'm building the tool my dad would actually use. We're not there yet. We're 3 months in, ~100 users, still iterating. But every time a user tells me "this just works," I think of my dad. He's my first user when we're ready. Why are YOU building what you're building?

We're stuck, and I'm not sure what to do. Here's the situation: We have 200 free users. They use the product 2-3 times/week. They tell us they love it. But when we ask them to pay ($49/month), they ghost. We've tried: → Lowering price to $29 → no change → Adding more features → no change → Offering annual discount → 2 conversions Current conversion rate: 1%. I know the advice: "Talk to users." "Find out why they won't pay." We did. Here's what they say: "I love it, but I'm not sure it's worth $49/month yet." When we ask "What would make it worth $49?", they say: "I don't know, just feels early." So we're stuck: Option 1: Keep building features until they feel it's "ready" → Risk: We build wrong things, waste time Option 2: Pivot to different ICP (maybe free users aren't our customers) → Risk: Start from scratch Option 3: Make it free with usage-based pricing → Risk: Never make money I'm leaning toward Option 2, but I'm not confident. If you've been here, what did you do? Not looking for "hustle harder" advice. Looking for "I was in this exact spot, here's what worked."

We rebuilt our AI inference pipeline and cut latency from 5 seconds to 800ms. Here's how: Original setup (the problem): → Single OpenAI API call → Streaming disabled (we wanted complete response) → Average latency: 4-6 seconds → Users complained: "Feels slow" The issue: We were waiting for the entire response before showing anything. Users perceived this as "broken" after 3 seconds. What we changed: 1. Enabled streaming → Show tokens as they generate → Perceived latency: <1 second (even though total time is same) 2. Parallel processing → Split prompt into independent chunks → Process simultaneously → Combine at end → Reduced total time by 40% 3. Caching layer → Common queries cached in Redis → 30% of queries hit cache → Instant response for those 4. Model optimization → Switched from GPT-4 to GPT-4-turbo for 80% of queries → Reserved GPT-4 for complex reasoning only → 60% cost reduction, same quality Result: → P95 latency: 800ms (down from 5s) → Cost per query: $0.008 (down from $0.025) → User satisfaction: "Feels instant now" The lesson: Perceived performance > actual performance. Streaming made the biggest UX difference, even though total time was similar. What's your biggest technical challenge right now?

We hit $1,000 MRR. I know that's not much. Most startups celebrate $10K or $100K. But for us, this is huge. Here's why: 3 months ago: → 0 users → 0 revenue → Just an idea and a landing page Today: → 87 users → 22 paying ($45/month average) → $1,000 MRR What got us here: 1. We talked to 50 potential customers BEFORE building → Validated the problem was real → Understood what they'd pay for 2. We charged from day 1 → No "free beta" excuse → If they won't pay $45, we don't have PMF 3. We shipped fast and ugly → MVP in 3 weeks (not 3 months) → Fixed bugs as users reported them 4. We responded to every piece of feedback → Users felt heard → They became advocates What's next: → Get to $10K MRR (need 200 paying users) → Reduce churn from 12% to <5% → Ship 3 most-requested features The goal isn't to build a huge company fast. It's to build a sustainable business that solves a real problem. $1K MRR is proof someone values what we built. Next milestone: $10K. I'll share that journey too. What's a milestone you're working toward right now?

I'm 4 months into building an AI startup. Some things are going well. Some things are a disaster. I'm answering questions for the next hour. Ask me anything: → What's working (and what's not) → Technical decisions → How we found our first users → Mistakes we made → Building in public → Founder life Nothing is off-limits. I'll be brutally honest. Drop your questions below.