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Big Iron Chris

@bigironchris • 11,172 subscribers

co-founder @levantolabs. Launched the first decision model. MENSA denier, no highschool diploma. Investor. Prev: Director of Marketing @Consensys @Lineabuild

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CRAZY 🚨 I built a scanner that shows you world's sentiment on social media in real-time Right now, it is pointed at Netanyahu's speech at the United Nations Imagine reading all posts on X and Instagram at the speed of light Meet How does it work? 1. It picks up posts from a set of keywords, topics, and accounts from X and Instagram 2. then uses the Sage decision model to filter if it's relevant to the UN speech, whether it's supportive or critical for Benjamin Netanyahu - בנימין נתניהו, and the post's narrative topic (ie., is the post about the Speech, Israel, or Iran?) 3. The image and text are classified separately, providing different weights to the sentiment and narrative rankings. This all produces short, mid, and long-term sentiment measurements. 1. Ben reacts every 5 seconds. 2. Narrative weights are updated every hour. 3. The 24h dial shows conglomerate sentiment measurement. I built this yesterday to test out Sage's new reasoning and image capabilities. It's the only AI model we used to build it. This is the power of decision models. Sage reasons when things are complicated and can interpret images, making this app possible. Disclaimer: This tool is politically neutral. It's difficult to touch anything related to geopolitics and be fairly neutral. We did our best on filter selection - if you think the method could have been better, please drop a comment below. I will use World Signal for future trends. If you'd like me to point it at a narrative next week, send me a comment below.

CRAZY 🚨 I built a scanner that shows you world's sentiment on social media in real-time Right now, it is pointed at Netanyahu's speech at the United Nations Imagine reading all posts on X and Instagram at the speed of light Meet How does it work? 1. It picks up posts from a set of keywords, topics, and accounts from X and Instagram 2. then uses the Sage decision model to filter if it's relevant to the UN speech, whether it's supportive or critical for Benjamin Netanyahu - בנימין נתניהו, and the post's narrative topic (ie., is the post about the Speech, Israel, or Iran?) 3. The image and text are classified separately, providing different weights to the sentiment and narrative rankings. This all produces short, mid, and long-term sentiment measurements. 1. Ben reacts every 5 seconds. 2. Narrative weights are updated every hour. 3. The 24h dial shows conglomerate sentiment measurement. I built this yesterday to test out Sage's new reasoning and image capabilities. It's the only AI model we used to build it. This is the power of decision models. Sage reasons when things are complicated and can interpret images, making this app possible. Disclaimer: This tool is politically neutral. It's difficult to touch anything related to geopolitics and be fairly neutral. We did our best on filter selection - if you think the method could have been better, please drop a comment below. I will use World Signal for future trends. If you'd like me to point it at a narrative next week, send me a comment below.

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This is the first decision model to level up a World of Warcraft character. It died at level 2 😆 Then it learned to heal itself mid-fight. 128+ kills later the dwarf hit level 5, at 2¢ a kill. The model is Sage, the decision model we build at Levanto Labs. At each step, our harness, SageCraft, hands it a screenshot and a short list of moves. Sage picks one. The harness checks the pick and turns it into keyboard and mouse input. LLM agents have played games before. They write their own actions. Sage only chooses. Picking turned out to be the easy part. Sage keeps no memory between calls, so the harness has to carry the goal, what just happened, and what to try when it's stuck. > 32 sessions, 5h 29m of play, 4,573 decisions > 103 troggs, 19 wolves, 6 boars > stuck in one tree long enough that I stopped the session by hand It wasn't hands-off. A coding agent fixed harness bugs between sessions and helped revive the dwarf while I slept. The last 18 minutes, from late level 4 to 5, ran with no manual input, code changes or restarts. The video is me narrating it unscripted, watching most of it back for the first time. This is my first harness build, and as far as I can tell, the first time a decision model is leveling up a live WoW character. It's open source. The harness and notes on the build are in the first reply below 👇 What do you think could be done better?

This is the first decision model to level up a World of Warcraft character. It died at level 2 😆 Then it learned to heal itself mid-fight. 128+ kills later the dwarf hit level 5, at 2¢ a kill. The model is Sage, the decision model we build at Levanto Labs. At each step, our harness, SageCraft, hands it a screenshot and a short list of moves. Sage picks one. The harness checks the pick and turns it into keyboard and mouse input. LLM agents have played games before. They write their own actions. Sage only chooses. Picking turned out to be the easy part. Sage keeps no memory between calls, so the harness has to carry the goal, what just happened, and what to try when it's stuck. > 32 sessions, 5h 29m of play, 4,573 decisions > 103 troggs, 19 wolves, 6 boars > stuck in one tree long enough that I stopped the session by hand It wasn't hands-off. A coding agent fixed harness bugs between sessions and helped revive the dwarf while I slept. The last 18 minutes, from late level 4 to 5, ran with no manual input, code changes or restarts. The video is me narrating it unscripted, watching most of it back for the first time. This is my first harness build, and as far as I can tell, the first time a decision model is leveling up a live WoW character. It's open source. The harness and notes on the build are in the first reply below 👇 What do you think could be done better?

56,972 просмотров • 10 дней назад

CRAZY 🚨 I built a scanner that shows you world's sentiment on social media in real-time Right now, it is pointed at Netanyahu's speech at the United Nations Imagine reading all posts on X and Instagram at the speed of light Meet How does it work? 1. It picks up posts from a set of keywords, topics, and accounts from X and Instagram 2. then uses the Sage decision model to filter if it's relevant to the UN speech, whether it's supportive or critical for Benjamin Netanyahu - בנימין נתניהו, and the post's narrative topic (ie., is the post about the Speech, Israel, or Iran?) 3. The image and text are classified separately, providing different weights to the sentiment and narrative rankings. This all produces short, mid, and long-term sentiment measurements. 1. Ben reacts every 5 seconds. 2. Narrative weights are updated every hour. 3. The 24h dial shows conglomerate sentiment measurement. I built this yesterday to test out Sage's new reasoning and image capabilities. It's the only AI model we used to build it. This is the power of decision models. Sage reasons when things are complicated and can interpret images, making this app possible. Disclaimer: This tool is politically neutral. It's difficult to touch anything related to geopolitics and be fairly neutral. We did our best on filter selection - if you think the method could have been better, please drop a comment below. I will use World Signal for future trends. If you'd like me to point it at a narrative next week, send me a comment below.

CRAZY 🚨 I built a scanner that shows you world's sentiment on social media in real-time Right now, it is pointed at Netanyahu's speech at the United Nations Imagine reading all posts on X and Instagram at the speed of light Meet How does it work? 1. It picks up posts from a set of keywords, topics, and accounts from X and Instagram 2. then uses the Sage decision model to filter if it's relevant to the UN speech, whether it's supportive or critical for Benjamin Netanyahu - בנימין נתניהו, and the post's narrative topic (ie., is the post about the Speech, Israel, or Iran?) 3. The image and text are classified separately, providing different weights to the sentiment and narrative rankings. This all produces short, mid, and long-term sentiment measurements. 1. Ben reacts every 5 seconds. 2. Narrative weights are updated every hour. 3. The 24h dial shows conglomerate sentiment measurement. I built this yesterday to test out Sage's new reasoning and image capabilities. It's the only AI model we used to build it. This is the power of decision models. Sage reasons when things are complicated and can interpret images, making this app possible. Disclaimer: This tool is politically neutral. It's difficult to touch anything related to geopolitics and be fairly neutral. We did our best on filter selection - if you think the method could have been better, please drop a comment below. I will use World Signal for future trends. If you'd like me to point it at a narrative next week, send me a comment below.

32,291 просмотров • 25 дней назад

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