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break time muna hehe 🧠: Trisha Martin #BINI #BINI_1stEP #BINI_SalaminSalamin

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stacey 🪐

614,773 subscribers

520,405 просмотров • 2 лет назад •via X (Twitter)

ОбразованиеИскусствоМузыка#BINI#BINI_1stEP#BINI_SalaminSalamin
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Комментарии: 9

Фото профиля Ica Villanueva
Ica Villanueva2 лет назад

Pakagaling!!!!! Twinning with @bini_jhoanna ?

Фото профиля stacey
stacey2 лет назад

@bini_jhoanna ikr ugh ginaya niya ako sa jorts😡

Фото профиля taurus | fan
taurus | fan2 лет назад

staku teka lang marupok ako sa mga jennie stan na katulad mo 😣

Фото профиля TEAM BINI_BB
TEAM BINI_BB2 лет назад

I LOVE YOU BINI STACEY GRABE KA NA KALMA KA PLEASE 🙏

Фото профиля Neneng H
Neneng H2 лет назад

It's giving ChaeLisa 16 shots vibes 😳😳😳

Фото профиля ‎ً
‎ً2 лет назад

SUMOSOBRA KA NA PERO SIGE PA

Фото профиля 𝐠𝐥𝐞𝐢
𝐠𝐥𝐞𝐢2 лет назад

"Kalma" pero parang gusto mo namang atakihin kami sa puso staks 🫠🔥

Фото профиля yawns ⟡
yawns ⟡2 лет назад

MYGWAD🔥🔥🔥

Фото профиля sella santos ni stacey
sella santos ni stacey2 лет назад

:( you know i love so much that you really find joy in the things that you do.

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I am beyond grateful. This tour was THE highlight of all highlights of my life. I had no expectations coming in, I just wanted to help take good photos, for the team and the blooms as well. However, I didn't expect that my heart would be so full of love and friendship after my own 'tour' of 8 states, and 11 cities in 23 days. I never felt exhaustingly tired, until now. Naramdaman ko na rin ang sakit ng katawan 😅 In retrospect, this was maybe because at every stop, from NY to SEA, everyone was all positivie vibes, walang epal. Everybody had a good time before and after each concerts. I never met anybody who said otherwise. TEAM BLOOM USA , I will never stop thanking the people leading this group for the trust and opportunity. If not for your hard work and perseverance, this will not come to fruition. And to the rest of the USblooms I've met along the way, thank you for making every stop memorable. To Team Bini, thank you for accepting me into your group. Working with you all was a breeze. I really appreciated the camaraderie and for giving me the chance to be my own creative self. I've seen now why BINI is so good because of the team that supports them behind the scenes, cares about each one of our walo. They held each other accountable and keep them grounded as well. To BINI, our walo, we are so proud of you all. Thank you for persevering despite all the doubt, for taking the challenge head on despite the uncertainty. You've conquered each stage with your aptitude, grit and determination. Congratulations for a job well done! It was an honor and privilege of mine to photograph and be a part of your journey. Honestly, I really felt the pressure the first time I had to photograph you, sino ba naman ako to take pictures of my idols. But everything became zero pressure as you made it easy for me. Thank you for being you, your sweet, malambing and fun selves. I started as a fan of your music, and of your personalities. Now and after I was given the ultimate chance as a bloom, to work with you, I end the tour with an even more deeper appreciation of your body of work and what you all do. You're setting the bar really up high as artists and as performers. Do not believe anyone who would say otherwise. To BINI, to the whole bini team, and together with the blooms worldwide, I will be borrowing the motto of the state of NY, a full circle moment for me, as it is where I started my own 'tour'. The motto states in Latin and I will leave you all with it: Excelsior! Or in other words: onwards and upwards, to greater glory! 2, 3 thank you po! Paalam muna sandali, 'til we meet again. 🥹🫶🏻 #BINI #BINI_PH #BINIverseWorldTour2025

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🤖🔬 Can AI actually do science end-to-end? 🧠📈 And how would we know when it matches, or surpasses, humans? ⚡🧪 AI is rapidly automating scientific discovery, but benchmarking full-cycle discovery, from 💡 ideation → 🧑‍💻 execution → 📊 conclusions, remains unsolved: 🧐🧐🧐 ❌🛠️ Open-ended discovery → manual validation (costly, unscalable) ❌📏 Metric-driven benchmarks (e.g., MLE-Bench) → convenient but narrow (is higher accuracy really enough?) ❌🤖⚖️ LLM-as-judge → useful, but fundamentally risky if used alone 🔥🚀 Introducing FIRE-Bench🔥: Fullcycle Insight Rediscovery Evaluation 👉🌐 📚✨ A benchmark that turns fresh, human-verified insights from recent 🏆 NeurIPS / ICLR / ICML papers into masked, end-to-end discovery challenges 🧩 🌍🔐 Constrained open-ended discovery–backed by ground truth. 📌 Key takeaways: 1⃣ 📖🧱 Reference-based evaluation still matters: constrained LLM judging helps, but human-grounded references remain essential until agents can consistently match human conclusions 2⃣ 🏆🧠 Expert-validated ground truth: all tasks come from recent NeurIPS / ICLR / ICML papers, with contamination carefully controlled 3⃣ 🔁🎭 Rediscovery, not reproduction: original 🧪 methods, 📊 experiments, 💻 implementations, and 📈 analyses are fully masked to create real discovery challenges 🔑 Key empirical findings: 💡 The "Science Gap" is Real: Even the best setup (Claude Code + Sonnet-4) caps out at an F1 score of 46.7. On hard tasks, agents struggle to break 30 💡 Success is a "Lottery": Performance has incredibly high variance. Reliability is a major unsolved issue. 💡 Coding is no longer the bottleneck; high-level reasoning and analysis are: ~74% of errors stem from flawed planning, not coding ⚙️ How it works: 🔹 Research-Problem Trees: We parse papers into trees (from broad roots to concrete leaves). This allows us to select intermediate nodes that perfectly balance open-ended exploration with verifiable ground truth. 🔹 Claim-Level Evaluation: We match AI conclusions against human conclusions using granular claim decomposition (F1 score). 🔹 Creativity Check: We score false positives to see if agents are finding novel truths (Spoiler🚨: they aren’t creative yet). 🔹 New Diagnostic Taxonomy: failures traced across four stages: 🧠 Planning → 🛠️ Implementation → ▶️ Execution → 🧾 Conclusion 🔹 Additional Analyses: cost efficiency, contamination checks, and more. 👀 The Future: 🚀 Live-FIRE-Bench: a live, continuously updated FIRE-Bench to track real-time progress on the latest research (Newest LLMs should be benchmarked with the newest research) 🚀 Stronger scaffolding (search + planning + coding) 🧠🧰 and converting FIRE-Bench into interactive environments for training research agents 🚀 Toward real creativity: We want better systems that can produce genuinely novel conclusions toward creativity 🎨⏳ 🚀 Better systems 🧠✨ and better benchmarks 📏 must co-evolve 🔄 over time 📜🎥 Paper, video, demo, and research trees: 👉🌐 #AI 🤖 #MachineLearning 📚 #AI4Science 🔬 #LLMs 🧠 #Research 🧪 #AgenticAI 🚀 #FireBench 🔥
0:42

🤖🔬 Can AI actually do science end-to-end? 🧠📈 And how would we know when it matches, or surpasses, humans? ⚡🧪 AI is rapidly automating scientific discovery, but benchmarking full-cycle discovery, from 💡 ideation → 🧑‍💻 execution → 📊 conclusions, remains unsolved: 🧐🧐🧐 ❌🛠️ Open-ended discovery → manual validation (costly, unscalable) ❌📏 Metric-driven benchmarks (e.g., MLE-Bench) → convenient but narrow (is higher accuracy really enough?) ❌🤖⚖️ LLM-as-judge → useful, but fundamentally risky if used alone 🔥🚀 Introducing FIRE-Bench🔥: Fullcycle Insight Rediscovery Evaluation 👉🌐 📚✨ A benchmark that turns fresh, human-verified insights from recent 🏆 NeurIPS / ICLR / ICML papers into masked, end-to-end discovery challenges 🧩 🌍🔐 Constrained open-ended discovery–backed by ground truth. 📌 Key takeaways: 1⃣ 📖🧱 Reference-based evaluation still matters: constrained LLM judging helps, but human-grounded references remain essential until agents can consistently match human conclusions 2⃣ 🏆🧠 Expert-validated ground truth: all tasks come from recent NeurIPS / ICLR / ICML papers, with contamination carefully controlled 3⃣ 🔁🎭 Rediscovery, not reproduction: original 🧪 methods, 📊 experiments, 💻 implementations, and 📈 analyses are fully masked to create real discovery challenges 🔑 Key empirical findings: 💡 The "Science Gap" is Real: Even the best setup (Claude Code + Sonnet-4) caps out at an F1 score of 46.7. On hard tasks, agents struggle to break 30 💡 Success is a "Lottery": Performance has incredibly high variance. Reliability is a major unsolved issue. 💡 Coding is no longer the bottleneck; high-level reasoning and analysis are: ~74% of errors stem from flawed planning, not coding ⚙️ How it works: 🔹 Research-Problem Trees: We parse papers into trees (from broad roots to concrete leaves). This allows us to select intermediate nodes that perfectly balance open-ended exploration with verifiable ground truth. 🔹 Claim-Level Evaluation: We match AI conclusions against human conclusions using granular claim decomposition (F1 score). 🔹 Creativity Check: We score false positives to see if agents are finding novel truths (Spoiler🚨: they aren’t creative yet). 🔹 New Diagnostic Taxonomy: failures traced across four stages: 🧠 Planning → 🛠️ Implementation → ▶️ Execution → 🧾 Conclusion 🔹 Additional Analyses: cost efficiency, contamination checks, and more. 👀 The Future: 🚀 Live-FIRE-Bench: a live, continuously updated FIRE-Bench to track real-time progress on the latest research (Newest LLMs should be benchmarked with the newest research) 🚀 Stronger scaffolding (search + planning + coding) 🧠🧰 and converting FIRE-Bench into interactive environments for training research agents 🚀 Toward real creativity: We want better systems that can produce genuinely novel conclusions toward creativity 🎨⏳ 🚀 Better systems 🧠✨ and better benchmarks 📏 must co-evolve 🔄 over time 📜🎥 Paper, video, demo, and research trees: 👉🌐 #AI 🤖 #MachineLearning 📚 #AI4Science 🔬 #LLMs 🧠 #Research 🧪 #AgenticAI 🚀 #FireBench 🔥

Zhen Wang

18,292 просмотров • 7 месяцев назад

Meet Apodex 1.1: Scaling Agentic Intelligence for Complex Work Open Source Harness: Open Weights: We’re excited to introduce Apodex 1.1, our new model family built to scale agentic intelligence for professional work. 🧠 Frontier-level intelligence for complex work Apodex 1.1 brings frontier-level agentic performance across complex professional work, scientific research, financial analysis, and deep search. 🤝 Asynchronous Agent Team Apodex 1.1 can break down complex tasks, coordinate multiple agents in parallel, continuously integrate their findings, and let you step in to guide or redirect the work at any time. 🔬 Open-source research workbench We’re open-sourcing FrontierAgent—a locally deployable research workbench for the Apodex 1.1 family, including asynchronous Agent Team. Available now: 🔹 Apodex 1.1 — our most capable frontier model, available through the Apodex online workbench 🔹 Apodex 1.1 mini — open-weight model for running complex work locally 🔹 FrontierAgent — open-source, locally deployable research workbench 🌐 Live workbench: 🦾 API platform: 📃 Paper:
1:22

Meet Apodex 1.1: Scaling Agentic Intelligence for Complex Work Open Source Harness: Open Weights: We’re excited to introduce Apodex 1.1, our new model family built to scale agentic intelligence for professional work. 🧠 Frontier-level intelligence for complex work Apodex 1.1 brings frontier-level agentic performance across complex professional work, scientific research, financial analysis, and deep search. 🤝 Asynchronous Agent Team Apodex 1.1 can break down complex tasks, coordinate multiple agents in parallel, continuously integrate their findings, and let you step in to guide or redirect the work at any time. 🔬 Open-source research workbench We’re open-sourcing FrontierAgent—a locally deployable research workbench for the Apodex 1.1 family, including asynchronous Agent Team. Available now: 🔹 Apodex 1.1 — our most capable frontier model, available through the Apodex online workbench 🔹 Apodex 1.1 mini — open-weight model for running complex work locally 🔹 FrontierAgent — open-source, locally deployable research workbench 🌐 Live workbench: 🦾 API platform: 📃 Paper:

Apodex

926,129 просмотров • 22 дней назад

🚧 Genius at Work. 🚧 Young children don’t always get the credit they deserve for their complex thinking. Take this clever little one, for example. When it’s time for Dad to change his shirt for work, Mom intervenes, getting Baby off his back (literally) to allow him the space to navigate. But our determined hero is having none of it. Hoping to return to climbing on Dad, she attempts to maneuver Mom’s hand away, but finds limited success. Then she has a flash of inspiration. 🧠 If she can get her right leg into place, she can trap Mom’s hand against the mattress and make a break for it - which, after a failed first attempt, is just what she manages to do. Such complex planning from this little one at well under a year of age. Talk about goal driven! This little problem solver - and her loving parents - were shared to IG by jayla_mariee_official.
1:04

🚧 Genius at Work. 🚧 Young children don’t always get the credit they deserve for their complex thinking. Take this clever little one, for example. When it’s time for Dad to change his shirt for work, Mom intervenes, getting Baby off his back (literally) to allow him the space to navigate. But our determined hero is having none of it. Hoping to return to climbing on Dad, she attempts to maneuver Mom’s hand away, but finds limited success. Then she has a flash of inspiration. 🧠 If she can get her right leg into place, she can trap Mom’s hand against the mattress and make a break for it - which, after a failed first attempt, is just what she manages to do. Such complex planning from this little one at well under a year of age. Talk about goal driven! This little problem solver - and her loving parents - were shared to IG by jayla_mariee_official.

Dan Wuori

117,107 просмотров • 1 год назад