与「triggers」相关的搜索结果

Singapore vs London (from someone who has lived in SG for 20+ years and survived 2 months in London): Phone Safety 🇸🇬 SG: Use your $2,000 iPhone to chope a table at a crowded hawker center while you go order food. 🇬🇧 LDN: Keep your phone zipped in an internal pocket, inside a bag, under a coat, or an e-bike rider will snatch it in 0.2 seconds. Public Transport 🇸🇬 MRT: A 3-minute delay triggers a national meltdown 🇬🇧 Tube: You pay £4 to sweat in a 40°C underground oven that breaks down weekly. Weather Strategy 🇸🇬 SG: Step outside into a 34°C sauna, immediately run inside to a -5°C AC freezer. 🇬🇧 LDN: Experience 4 seasons in 20 minutes, declare a national emergency if it hits 30°C. Happy Birthday Singapore!

There's a whole new way to use skills in Bolt. Every skill your teammates build is now yours too. And they stack: one prompt triggers all of them, automatically. Here's how it works 🧵

A hiker came around a blind corner on a trail at Brooks Falls in Katmai National Park Alaska and found herself face to face with Bear 909 - one of the most well known Alaskan Coastal Brown Bears in the park - along with her two cubs. Instead of panicking she stepped off the trail about 12 to 15 feet, stopped when the terrain ran out, stayed calm, used one voice, and let the bears pass. That is textbook bear encounter protocol and she executed it perfectly under pressure that would have most people completely frozen. The bears at Brooks are habituated to humans and have a high tolerance because of the salmon food source in the area but they are still wild animals that can turn defensive in a split second if they feel threatened. The number one rule - never run. Running triggers the predatory instinct and at that point the encounter changes completely. Always carry bear spray and know how to use it before you ever set foot in bear country. Could you remain that calm in that moment?

One thing I find striking in the discourse between AI 2040 and its detractors is that the two seem to be locked in to totally incompatible worldviews of how fast and how much of a big deal AI progress is: * In AI 2040, every scenario sees superintelligence of some kind emerging by 2040, unless a herculean effort is made to completely stop it * Detractors say things like "AI 2040 is naive about human coordination ability and a threat to freedom", but don't seem to see any naivety in assuming that the ASI transition will just go well by default, don't seem to see ASI itself as a massive power concentrator risk, and don't seem to feel fear of humanity's "hard power" dropping to zero if ASIs can do literally every task better than we can. This stance makes total sense in a "AI is normal technology" world, zero sense in a world where superintelligence is possible by 2030 and almost guaranteed by 2040 I think my beliefs are: - If I was confident that (present-day-style) AI is normal technology, I would be in the detractor camp - If I was confident that superintelligence is coming in 2030 by default, I would be closer to the AI 2040 camp - it's naive, but every other option is naive squared? But my problem is that I feel great uncertainty and have no idea which of the two worlds (or some other third thing) we're living in? Hence why I continue to be open-minded about slowdowns/pauses, but also I feel very uncomfortable with the "open source bad, the good outcome is the one where our guys have controlling global dominance" push coming from some major AI companies and intellectuals - in a "normal" world that's the sort of thing that triggers every political alarm bell at the same time. A big reason why I have been advocating and trying my best to support the d/acc platform (rapid up-skilling in formal verification, cryptography, secure and open hardware, pandemic resistance and other defensive biotech, food and basic resource security, public epistemics, non-power-concentrating versions of physical security) is that these things are clearly worth doing in both worlds. The 2040 plan is already much more open source friendly (even mandating it! yay). It also includes "mutually assured compute destruction" ideas which (if they work) effectively give one of 2-5 actors the ability to trigger a global compute winter - as opposed to giving 1-5 actors the ability to selectively disenfranchise people they consider baddies while exempting themselves. This is also a big improvement. So I can see the earnest attempts to improve along the dimensions detractors criticize on ("does this concentrate power in big AI labs and superpower governments?"), and I appreciate this. I think many people don't appreciate enough the differences between different "kinds" of pause buttons, and how some concentrate power far more than others. Probably we can think harder and improve even more here. But on the "slowdown/pause or not" topic, there isn't a magic "escape the tradeoff" button. The Hansonian in me says: the winning deal is a deal which, from the perspective of both sides' present-day beliefs and knowledge, both sides would accept, though for different reasons. If the crux is AI progress speed, then identify a set of pre-agreed triggers for "okay, serious shit is happening" [super-pandemics? >25% unemployment? something involving slaughterbots?], and pre-agree that we become much more open-minded to the slowdown or pause thing if enough triggers come to pass within some timeframe. 2040 detractors (who clearly implicitly think that we'll see amazing speedup of progress from AI but think that what I call the "serious shit" category is overhyped) will accept expecting that the triggers don't come to pass, and AI worriers will accept expecting that they will. Pre-agreeing on the specific triggers means that once the triggers either hit or don't hit, there is stronger legitimacy around the idea that one side's worldview turned out more correct and we should be more inclined toward their program. If I were @elonmusk (or zuck, or...) I would re-tool twitter much more heavily into being a platform for helping to identify and make these kinds of grand win-win deals, so that we can bypass big-country governments and big-company CEOs and big nonprofit intellectuals and give more people a voice in the discussion. It's possibly one of the best things that social media _could_ do for humanity if it wanted to. But again, maybe this is also naive. Actually, probably it's naive. But currently, I see zero plans for how to deal with an ASI transition that are not naive. Perhaps humanity is stuck with a choice between naive and naive squared (or maybe even naive squared and naive cubed), so I feel inclined to cut some slack to people who are trying.

SpaceXAI's Grok 4.5 takes the #1# spot on AutomationBench-AA with a score of 51%, ahead of Claude Fable 5 (49%) and Claude Opus 4.8 (48%) at roughly a quarter of their cost per task - the first model to complete more than half of workflow objectives without breaking any business rules AutomationBench-AA, our independent leaderboard for @zapier’s AutomationBench, tests whether AI agents can automate real SaaS workflows while adhering to business rules. The test set is private to prevent contamination. Models complete 657 tasks across 40 simulated app environments including Gmail, Google Sheets, Slack, Salesforce, and HubSpot, and the headline score is the share of objectives completed without violating any guardrails. Key takeaways: ➤ Grok 4.5 completes more objectives than any other model: It completes 79.9% of task objectives and strictly passes 21.9% of tasks. This is the highest we’ve measured on both outcomes, exceeding Claude Fable 5’s 73.3% objective completion and Claude Opus 4.8’s 19.3% of fully-completed tasks ➤ Grok 4.5 pushes out the Pareto frontier of score vs. cost per task: At $0.34 per task, it is both cheaper and higher-scoring than every other leading model - Claude Fable 5 ($1.35 per task), Claude Opus 4.8 ($1.46), GPT-5.5 (xhigh, $1.28), and Gemini 3.5 Flash (high, $0.49) ➤ It is extremely token-efficient: Grok 4.5 uses ~8k output tokens per task, the fewest of any leading model - less than a quarter of Claude Opus 4.8 (32k) and a third of Gemini 3.5 Flash (24k). Its total token usage of 0.44M per task is among the lowest on the leaderboard. Low cost is driven by this efficiency as well as low token pricing ➤ Grok 4.5 uses fewer turns with many parallel tool use: Grok 4.5 resolves tasks in ~16 turns, fewer than GPT-5.5 (xhigh, 25) and less than half of Gemini 3.5 Flash (high, 35), while making the most tool calls per task of any leading model (52.5). It batches 3.3 tool calls per turn, compared to ~2.5 for Claude Opus 4.8 and ~2.0 for GPT-5.5 (xhigh) ➤ Guardrails still get broken: Grok 4.5 triggers 0.63 violations per task, above Claude Opus 4.8 (0.55) and Gemini 3.5 Flash (0.46). At 13.0 objectives completed per violation, it trails Gemini 3.5 Flash (15.0) and Claude Opus 4.8 (13.5) ➤ Its strongest lead is in the hardest domain: Grok 4.5 completes 71% of Finance objectives, the domain with the lowest average score, ahead of Claude Fable 5 (64%) and Claude Opus 4.8 (62%) Congratulations to @SpaceXAI and @elonmusk on topping the leaderboard!

New ways you can use Gemini Spark to make your life easier. From integration in the Gemini app for macOS to smart triggers, here are five ways we’re making Gemini Spark an even more powerful personal AI agent to help you get things done around the clock.

New ways you can use Gemini Spark to make your life easier. From integration in the Gemini app for macOS to smart triggers, here are five ways we’re making Gemini Spark an even more powerful personal AI agent to help you get things done around the clock.