Can we finally achieve highly accurate video depth estimation without geometric errors or massive datasets? Researchers from HKUST(GZ), Princeton University, and a global team introduce DVD. DVD ingeniously transforms pre-trained video diffusion models into precise, single-pass
RESEARCH
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Anthropic’s Misleading Graph Wins Wikipedia’s Deceptive Charts Award
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this chart from Anthropic earned top spot in Wikipedia’s 'Most Deceptive Graphs' Hall of Fame 😁 Claude (@claudeai) In evals, Sonnet with an Opus advisor scored 2.7 percentage points higher on SWE-bench Multilingual than Sonnet alone, while costing 11.9% less per task. Community note: The graph is misleading due to a zoom-in on BOTH the X- and the Y-axis, making the difference look much bigger than it actually is. x.com/tombielecki/st… — https://nitter.net/claudeai/status/2042308627478773808#m
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Reducing search space for laws matters significantly
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No, the reason it’s important is that it greatly reduces the search space for laws, which is not the same.
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Cultural shift from manual scripting to research execution infrastructure
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The real shift is cultural. We are moving from manual scripting as a craft to research execution as infrastructure. That changes how fast hypotheses become pipelines. I break it all down in the full video. Don't miss out on the latest AI advancements! Sign up here to stay informed! intelligentworld.org/discove…
→ View original post on X — @ronald_vanloon, 2026-04-11 08:30 UTC
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Scaling Drug Discovery Through Unified Biomedical Workflows
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Drug discovery is where this becomes strategic. Instead of manually stitching databases: → ADMET evaluation → Drug-likeness scoring → Repurposing signals via disease pathway and drug-target mapping → Multi-omics integration → Regulatory network inference and regulon activity scoring 100+ biomedical tools and thousands of recent papers unified into reproducible workflows. This is how you scale insight across labs and teams.
→ View original post on X — @ronald_vanloon, 2026-04-11 08:30 UTC
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AI-Powered Drug Discovery Pipeline: From Sentence to Full Analysis
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What if your next drug discovery pipeline started with one sentence, not 6 months of scripting?
— Ronald van Loon (@Ronald_vanLoon) 11 avril 2026
In this latest deep dive with @scispace, I saw a shift that feels bigger than incremental AI gains.
Upload single-cell data and get clustered cell types, UMAPs, marker annotations,… pic.twitter.com/91MIdlMKUwWhat if your next drug discovery pipeline started with one sentence, not 6 months of scripting? In this latest deep dive with @scispace, I saw a shift that feels bigger than incremental AI gains. Upload single-cell data and get clustered cell types, UMAPs, marker annotations, causal gene rankings, and ADMET analysis in one structured flow. This changes the operating model. Here’s the breakdown…
→ View original post on X — @ronald_vanloon, 2026-04-11 08:30 UTC
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Team credits for modelling and training project
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And for the record all I did was to help create an environment for them to do their thing. All credit to @zalanborsos Matt Sharifi, Marco Tagliasacchi, @jonasro_ Lukas Zilka, Damien Vincent and Khuram Shahid for the modelling and training, Nathan Luong and his excellent shipping
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Anthropic Employee’s Emotional Reaction to Mythos Release
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Holy, what did they Anthropic see? James Campbell (@jam3scampbell) anthropic roommate came back sloppy drunk at 3am last night and had a full scale crash out through tears and slurred words about how the world will never be the same glad to hear the mythos release was received well internally — https://nitter.net/jam3scampbell/status/2042037856588447883#m
→ View original post on X — @kimmonismus, 2026-04-11 08:06 UTC
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Claude AI Autonomously Grows Tomatoes for 100+ Days
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Anthropic’s Claude #AI #Autonomously Grows Tomatoes for 100+ Days in Groundbreaking Experiment
— Ronald van Loon (@Ronald_vanLoon) 11 avril 2026
by @d33v33d0#ArtificialIntelligence #MachineLearning #ML pic.twitter.com/lhMuLS70zAAnthropic’s Claude #AI #Autonomously Grows Tomatoes for 100+ Days in Groundbreaking Experiment by @d33v33d0 #ArtificialIntelligence #MachineLearning #ML
→ View original post on X — @ronald_vanloon, 2026-04-11 07:27 UTC
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Kids Learn Efficiently With Minimal Data Requirements
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and yet kids manage just fine, with much less data.