
log8
Keep the scores. Settle the rivalry.
What is log8?
log8 is a score tracker for the games you play with friends, from badminton and FIFA to carrom and chess. You log a result in seconds, the other players approve it, and head-to-head records, partnerships and cups build themselves from every result that counts. The app covers 70+ games across nine sections, from team sports and combat to races, records, cards and esports, and each game scores the way it is actually played, such as sets for tennis, a finish and a round for a boxing bout, or times and distances for a race.
It is built for friend groups, office leagues, college students, clubs, gaming nights and families. Group boards, a chat next to the scores, invites with a waitlist, and round-robin, knockout or best-of cups help a regular group keep its games organized. You can also add a friend as a guest so a game counts straight away, and their games move to their account when they join.
What sets log8 apart is its approval rule. Nobody marks their own homework: a result is provisional until the people who played it agree, and a result nobody answers within 48 hours closes without counting. Records are shared only with the people you played, the app has no advertising identifier or analytics SDK, and location is optional. log8 is in closed testing on Android and iPhone and is coming soon to both app stores.
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2722 dispatches from hundreds of founders, pulled from the week's best podcasts.
Creative generation for ads is probably the least valuable place to apply AI. What you really care about is the concept. What I see people using AI well for is creative prospecting — pulling competitors' ads and using an LLM agent to synthesize what's working. That would have been a full-time job three years ago.
AI's real marketing value is creative prospecting at scale, not variant generation
Generating 200 variants of the same 10 concepts adds near-zero value — the 190th variant is not meaningfully different from the first. The leverage is in concepting: finding angles that wouldn't emerge from a human brainstorm. AI-powered competitive synthesis (scraping ad libraries, agent interpretation, hypothesis generation) automates what used to require a full-time analyst.
I think that we'll see a lot of winners win harder because people will just get started and not want to fight the AI that has just sort of been trained to like 'oh use this solution.'
AI defaults mean winners win harder — get embedded as the default-recommended tool
LLMs default to whatever has the most training data, so Bolt and Lovable standardized on Supabase, and models reliably steer users to React, Expo, and a handful of incumbents. For distribution, getting embedded as the AI-recommended default compounds aggressively — fighting the model's defaults costs energy most users won't spend. New tools need to either crack the default or be unmissably superior.
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