📰 How to Secure #1 on Product Hunt: DO’s and DON'Ts / Experience from PitchBob – AI Pitch Deck Generator & Founders Co-Pilot

I was feeling stuck for awhile, with too many options and not enough focus to pursue any of them. I felt like I'd lost my momentum and focus. Now I'm moving again.

The table is cleared.

Entrepreneurs, startup founders, and product managers often debate whether to launch on Product Hunt, whether aiming for first place is worthwhile, and discuss the value and ROI/value for money of the team’s time and money investment.

I want to share our experience of launching on Product Hunt, detailing our motives, goals, results, and the actions that led us to the product of the day, the second product of the week, and the AI product of the week. Let's go.

Manish Lachwani is the founder of HeadSpin, a SaaS tool for mobile app-testing. Lachwani was just sentenced to 1.5 years in prison for lying to investors about the company’s financials. At the peak of the fraud, HeadSpin raised at a $1.1B valuation and attracted top tier investors (Google Ventures, Iconiq, Dell Technologies Ventures, Battery Ventures, and Tiger Global) 🤯

One of my most popular blog posts is How to Trick Investors. While I am not sure how to feel about my audience being so interested in that post, essentially everything in that post was about the “grey areas” of how companies might attempt to intentionally (or unintentionally) juice financial metrics. The point of the post was to make fun of people doing these things and discourage others from trying them. Most investors are smart and would see right through them.

But what Lachwani did at HeadSpin wasn’t just financial metric creativity to make financials look a bit better…it was outright fraud and deceit.

Canva’s mission is to empower everyone in the world to design anything and publish anywhere. An essential part of our effort toward this goal is the Canva Creators program. Since we launched the program 3 years ago, usage of our creator content has doubled every 18 months. Now we pay creators based on billions of content usages each month. This usage data not only includes templates but also images, videos, and so on. Building and maintaining a service to track this data for payment is challenging in the following ways: • Accuracy. The usage count should never be wrong, and we want to minimize issues such as data loss and overcounting because the income and trust of content creators are at stake. • Scalability. We need to store and process usage data with this large volume and exponential growth over time. • Operability. As usage data volume grows, the operational complexity of regular maintenance, incident handling, and recovery also increases. This blog post introduces the various architectures we’ve experimented with and the lessons we learned along the way.

“Researchers consider math to be a frontier of generative AI development,” the Reuters journalists wrote. Large language models are “good at writing and language translation,” but “conquering the ability to do math — where there is only one right answer — implies AI would have greater reasoning capabilities resembling human intelligence.”

This was a bit of a head-scratcher. Computers have been able to perform arithmetic at superhuman levels for decades. The Q* project was reportedly focused on word problems, which have historically been harder than arithmetic for computers to solve. Still, it’s not obvious that solving them would unlock human-level intelligence.

The Reuters article left readers with a vague impression that Q* could be a huge breakthrough in AI — one that might even “threaten humanity.” But it didn’t provide readers with the context to understand what Q* actually was — or to evaluate whether feverish speculation about it was justified.

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