Long time lurker here. My family had a pen-and-paper game we'd play on long drives to visit my great-grandmother. After she passed, I spent the holidays recreating it:
<a href="https://a26z.fun" rel="nofollow">https://a26z.fun</a><p>How it works:<p>Find 15 words from a category (like "Stone Fruits," "US States," or "Dog Breeds") as fast as you can. Once you meet the 15 word minimum, you can play for as long as you want.<p>Each letter shows how many target words start with it (A¹ = one word starts with A, N² = two words start with N)<p>That small ² in the bottom-right? Multi-word answers allowed. For "US States" with N², both "NEW YORK" and "NORTH DAKOTA" count<p>Unlimited guesses, 2 hints, and a shuffle button to reorder by frequency.<p>Example:
Category: US States | Letters: A¹ M¹ N² S²
Answers: ALABAMA, MONTANA, NEW MEXICO, SOUTH DAKOTA<p>If you're into Connections or Strands, this scratches a similar itch but with a deduction twist.
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During the holidays, 50k people were dropped from one AI training project without the story reaching the front page. That tells you how invisible this expert workforce still is.<p>Right before the new year, the AI training community absorbed one of its biggest shockwaves: 50k contributors waking up to sudden removal and a one-line “quality requirements changed” message, with no real path to recover. For many, it meant losing time, momentum, and income.<p>This isn’t a post against AI training, just more of a defense for experts’ contributions. RLHF and data annotation help make models reliable, effective, and safe in the real world, and scaling it will demand deep expertise across industries, languages, and edge cases.<p>If we’re serious about scaling it, we need to start elevating the expert workforce that shapes AI across domains. We can’t treat them as disposable or erase them overnight.