ALT HF Trending #1 JEV-27B-VL is in ScienceGuru Desktop 3.2.5. Visual decisions: routing, ranking, scoring, review. Guru-starred, with Guru Turbo 1.0/1.2. October first login: 7 free days + 100M tokens. All users. No coupon. No deployment—inside your research desktop. https://scienceguru.ai/ What would you have JEV decide?

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ALT What happens when an AI research system picks up where two years of human optimization left off? The benchmark is the NanoGPT Speedrun: train GPT-2 to 3.28 validation loss on FineWeb, the target set by @karpathy 's llm.c replication, which took 45 minutes to get there. The speedrun's code descends from llm.c's PyTorch trainer, itself descended from NanoGPT, hence the name. Over two years, 91 official records brought the time down to 67.56s. ScienceGuru just hit 24.90s on 8×H100 (five seeds, self-reported): 108× faster than where the benchmark started, 2.71× faster than the official record, and 3× faster than Recursive's June record of 75.4s. Earlier this month it also took #1 on Autoresearch@Home at 0.8895 BPB, ahead of @Recursive_SI 's 0.9109, and the validated lead on @MedARC_AI 's NanoPath v2. We built on open PRs by Deven Pietrzak (ANVIL2) and Herman Brunborg (Exact-match), credited in the repo. ScienceGuru's RSI recipe fused them, cut the schedule from 1,194 to 652 steps and r

ALT What happens when an AI research system picks up where two years of human optimization left off? The benchmark is the NanoGPT Speedrun: train GPT-2 to 3.28 validation loss on FineWeb, the target set by @karpathy 's llm.c replication, which took 45 minutes to get there. The speedrun's code descends from llm.c's PyTorch trainer, itself descended from NanoGPT, hence the name. Over two years, 91 official records brought the time down to 67.56s. ScienceGuru just hit 24.90s on 8×H100 (five seeds, self-reported): 108× faster than where the benchmark started, 2.71× faster than the official record, and 3× faster than Recursive's June record of 75.4s. Earlier this month it also took #1 on Autoresearch@Home at 0.8895 BPB, ahead of @Recursive_SI 's 0.9109, and the validated lead on @MedARC_AI 's NanoPath v2. We built on open PRs by Deven Pietrzak (ANVIL2) and Herman Brunborg (Exact-match), credited in the repo. ScienceGuru's RSI recipe fused them, cut the schedule from 1,194 to 652 steps and r

ALT What happens when an AI research system picks up where two years of human optimization left off? The benchmark is the NanoGPT Speedrun: train GPT-2 to 3.28 validation loss on FineWeb, the target set by @karpathy 's llm.c replication, which took 45 minutes to get there. The speedrun's code descends from llm.c's PyTorch trainer, itself descended from NanoGPT, hence the name. Over two years, 91 official records brought the time down to 67.56s. ScienceGuru just hit 24.90s on 8×H100 (five seeds, self-reported): 108× faster than where the benchmark started, 2.71× faster than the official record, and 3× faster than Recursive's June record of 75.4s. Earlier this month it also took #1 on Autoresearch@Home at 0.8895 BPB, ahead of @Recursive_SI 's 0.9109, and the validated lead on @MedARC_AI 's NanoPath v2. We built on open PRs by Deven Pietrzak (ANVIL2) and Herman Brunborg (Exact-match), credited in the repo. ScienceGuru's RSI recipe fused them, cut the schedule from 1,194 to 652 steps and r

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ALT 6/6 After migration, check your files, previous chats, long-term memory, and important projects. Need help? Use Support at scienceguru.ai. Include your OS, app version, folder path, and an error screenshot.

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