... Three numbers from it get quoted. Users who saw an AI summary clicked a traditional result in 8% of visits; users who didn't, 15%. Users ended their browsing session 26% of the time with an AI summary present versus 16% without. And the one that should matter most to anyone buying GEO: users clicked a link inside the AI summary itself in 1% of visits. One per cent. That is the click value of the thing the entire optimisation ... ... more often. Just 8% of one- or two-word searches produced one. For searches of ten words or more, 53%. For searches starting with a question word - who, what, when, why - 60%. Sit with that next to the 8%-versus

... because: Probability is non-deterministic by definition. Models continuously drift as weights update and context accumulates. Instead of pretending we can turn probabilistic models into predictable binaries, production AI needs to focus on Constrained Reliability through Auditability. You need: Causal Lineage: Querying why an agent took a specific branch (walking execution trees, not just reading raw logs). State Snapshots: Isolating exact context windows to debug drift. Human-in-the-Loop Interceptions: Triggering governance before rogue actions hit production. Comment from ... ... uncertainty to engineering around it. LLMs are probabilistic systems, so reliability comes from making their behavior observable, traceable, and governable rather than forcing deterministic patterns on top. The missing layer is often the runtime substrate: capturing agent state, preserving causal

... often, though, is a simple a json prettier - especially now that so many of the top-ranking ones are cluttered with unnecessary features and ads. Comment from Joan Garcamo(@joan_garcamo): your second question made me think about how causally teams share snippets during debugging. A database sample or API payload can contain sensitive information even after obvious fields are removed. Maybe utility tools should include automatic redaction before processing pasted content. Comment from Ludovica Eleazer(@ludovica_eleazer): @addie_miranda ...

... recommend the right repos for your specific workflow per request, and those recommendations get more precise over time. Thank you for bringing this up. Comment from Vlad Polonskiy(@sandopolo): @gaspard_dupuich  100% agree, Gaspard. That's the causality of endless possibilities in action: what you're describing is the mental load that comes with more options to evaluate. A couple of things keep it from tipping into overload. Repos are sorted into categories, so it reads as navigation rather ...

... advin_jadis users are raving over our MCP server at how seamless the connectivity feels across tools. The freemium is generous enough to start, give it a try :) Comment from Jason Scott(@jason_scott8): I think power users and causal users may view this very differently. Enthusiasts who constantly jump between ChatGPT, Claude, and other agents will immediately see the benefit. Newer users might not realize they have a context problem until they've lost the same information ...

... works when the observability backend is down. A local SDK policy can reject the next action when spend, iteration count, or a repeated-tool fingerprint crosses a threshold, while queued traces continue feeding analysis. I d also carry one causal ID across every handoff so a single user request can be reconstructed across agents. Disclosure: I m Ege, co-founder/CEO of Atlantic in SF; we ve been wrestling with the same control-versus

... According to @RevenueCat 's State of Subscription Apps 2026 report, " hard paywalls convert 5x better than freemium, but with significantly wider variance." Day 35 download-to-paid, freemium vs. hard paywall Does access method impact download-to-paid conversion within 35 days? Quoting the report: Median D35 conversion sits at 10.7% for hard paywalls. Freemium is 2.1%. That gap alone should make you pause. But the more revealing part is the distribution. Hard paywall top decile apps approach 40% conversion ... ... moved to a much more flexible model. Some features are free, others unlock later, and we're still experimenting to find the right balance. I don t think we ll go back to a hard paywall, even though many competitors

... both have their upsides and downside, it depends on what you as an individual/couple have the capacity to juggle. Thank you Comment from Joao Seabra(@joao_seabra): Not a bubble, it's a real pattern. But I think the causality is more complicated than it looks. I'm a founder with two kids under 5. My wife runs an international NGO and travels extensively. We're deep in it on both sides simultaneously, and I won't pretend ...

... plays a significant role in how products appear in Google search results. What surprised us was that AI assistants like ChatGPT were rarely citing Product Hunt in product recommendations. AI assistants such as ChatGPT and Gemini rely on reviews, alternative lists, and structured product information gathered across the web. Product Hunt is strong across all three. In theory, this should make Product Hunt a natural source for AI-driven product recommendations and comparisons. In practice, it was not happening ... ... many tools which systematically run prompts against several AI models daily, and measure visibility of a product within prompts. We're using a new tool called @Gauge . We chose because a) their citation tracking is more useful than the alternatives

... haven't. Comment from Nika(@busmark_w_nika): @andreitudor14  It was just noticing this phenomenon, because when I had a look at their website, they usually had the number above 500. But maybe that's not the causality. Comment from Aima Atigari(@aima): @byalexai  This needs to be framed! The only thing I don't understand is what being featured means. Comment from Bhavin Sheth(@allinonetools_net): @busmark_w_nika  Yeah I meant that first impression moment ...

... Adam Cohen is the cofounder of @Weave , an engineering analytics company that helps teams go AI-first. Before Weave, he was head of operations and sales at education software firm Top Hat and VP of operations and revenue at @Causal , which was acquired by Lucanet in 2024. In sales, you always know exactly where you stand. In engineering, you don t. The sales leaderboard shows the top performers, the basement-dwellers, and everyone in between. At first, this is terrifying ... ... success and marketing, there was nothing. No leaderboard, no analytics, no tools like Gong. Without clear and meaningful metrics, career and company development became guesswork. I was talking about this with (my now-cofounder) Andrew, a founding engineer at Causal

... ragsontherocks sometimes it also happens to me :( Comment from Sean Hwang(@seanphwang): I resonate with that a lot! Not so long ago, I found myself re-reading written emails or texts dozens of times before sending them (even the causal ones) when in reality, it probably wasn't really that serious. Perfectionism wastes a lot of time. My fundamental mindset shift happened after experiencing what it's like to be a loser as a high school athlete. Instead of blaming ...