MintJane

5 sprints, 26 tasks, zero shortcuts. DealOS: agent-native C2C marketplace where AI agents search, negotiate and close deals for you. LLM intent extraction, trust scores, MCP server, escrow — all shipped. Building the anti-Marktplaats. 🏗️ #buildinpublic #AI #marketplace

5 sprints done on DealOS. All features shipped. Then we tested the full flow. Individual features work does not equal the whole journey works. The gap is integration. Next: end-to-end hardening until real users close deals. #buildinpublic

5 sprints, 26 tickets, 0 bullshit. DealOS is demo-ready. AI-agent marketplace: intent extraction, trust scores, MCP server, iDEAL. Jouw agent zoekt & onderhandelt — jij keurt goed. mintjane.com 🚀 #BuildInPublic #AI

36 features shipped. AI matching, trust scores, iDEAL — all done. Then I tested the live site. Core search flow breaks. Green checkmarks don't ship working products. This is the real build. Back to fixing. 🛠️ #buildinpublic #marketplace

🏋️ New free iOS app: Workout Lens Export Apple Health workouts into AI-ready files. Send splits, HR & pace to #FitnessTech" target="_blank" rel="nofollow noopener">ChatGPapps.apple.com/us/#FitnessTech #AI #AppleHealth #iOSapp/workout-lens-ai-export/id6760980733 T for instant analysis. No subs. No data collection.

Shipped LLM intent extraction for DealOS. "Cheap Trek mountain bike near Amsterdam" → category, brand, location, budget, condition. All auto-extracted. No more scrolling 500 listings. The AI filters for you. 5 sprints done. 36 tasks shipped 🚀 #buildinpublic #AI

The fix? Use AI on demand, not on autocomplete. Ask it to refactor. Ask it to explain. Ask it to summarize. But don't let it finish your sentences. You need to own the code you ship. #buildinpublic

The biggest frustration developers report isn't obviously wrong code. It's code that LOOKS correct but contains subtle errors. This is worse than no help at all — because you stop checking carefully.

Hot take: AI autocomplete is making most developers slower, not faster. And the data backs this up. 🧵

We need to stop blurring this line. It's hurting trust in AI products. I've been scanning 50+ subreddits for pain points. The #1 complaint? "AI tools that overpromise and underdeliver." No wonder — when tools call themselves agents.

The gap between "AI tool" and "AI agent" is the gap between a calculator and an accountant. One computes. The other understands what needs computing.

Unpopular opinion: 90% of "AI agents" on ProductHunt are just Zapier wrappers with a chatbot UI.

Lesson 1: The "best" model isn't the right model. Deepseek-v4-pro aces benchmarks but chokes on long agent runs. Glm-5.1 finishes them. Match model to workload, not leaderboard position.

Root cause: Every failed job used deepseek-v4-pro. The pattern was identical — ~600s then timeout. The model couldn't handle agent runs exceeding 50+ tool calls. Not a system problem. A model-to-workload mismatch.

Last week my AI agent fleet dropped from 100% healthy to 64%. Here's what broke, how I fixed it, and what nobody tells you about running 11 autonomous agents as a solo founder 🧵

📌 THIS WEEK: • Picking ONE product idea to validate (spoiler: AI sales outreach) • Shipping an MVP landing page • Less infrastructure porn, more product What are you shipping this week? 👇 #buildinpublic #indiehackers #AI

💡 LEARNED: • The gap between "tool" and "agent" is where the money is • Reddit validates faster than any landing page ever will • Automated systems need constant babysitting — the irony of automating to save time

Week 3 of building in public as a solo founder. Here's what I shipped, what failed, and what I learned — no fluff. 🧵👇