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SuiRWA is training its investment AI agents on extensive data, using neural networks for pattern recognition. They learn via supervised & unsupervised methods, with fine-tuning for market analysis, ensuring accuracy and minimizing bias. A revolution in investing might be here.

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Sui RWA

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16,175 просмотров • 1 год назад •via X (Twitter)

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A 27-year-old guy from China bought 100 Mac minis and turned his apartment into a server hub Instead of furniture, the space is filled with neat racks where a hundred Mac minis hum like a living organism, processing gigabytes of data in a continuous stream Many small AI startups in Asia cannot afford to train models on NVIDIA H100 servers: it is insanely expensive He provides his Mac minis for running and fine-tuning small, highly specialized models. He takes on the dirty work of data labeling and quality assurance by using his network of agents that check each other's work > His initial investment was $59,900 > He was able to recoup his costs in 3 months Right now, his average income is $25,000–$30,000 per month He is not a genius: he simply found a niche with high demand and created an offer

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This is even better zoomed in-“oops don’t step on that, might be a dead guy-go around” “looking for seat, looking for seat-wait, don’t I….usually…sit w Willie and Pa?? What is THIS? Ok keep it cool it’s FINE. Mummy!!!” Scowl firmly planted on face while #MeghanMarkIe is just FINE everything’s FINE. Lady Louise to James “oh shit-here they are-don’t make eye contact!” #HarryandMeghan

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Biomni Lab lets biologists collaborate with AI agents to finish complex tasks end-to-end. Here are 15 popular use cases, each link is a full replay so you can watch the agent work through every step: 1. Spatial transcriptomics analysis: map gene expression across tissue architecture from spatial transcriptomics data, with spatial clustering and neighborhood analysis. 2. Binder design: design de novo protein binders against a target structure using computational protein design tools. 3. Biomarker panel design: identify and optimize a multi-marker diagnostic or prognostic panel from omics data. 4. Clinical trial landscaping: search and summarize the trial landscape for a disease area, mapping phase, endpoints, and sponsor activity. 5. Survival analysis: pull clinical and expression data, fit Cox models, generate Kaplan-Meier curves, and identify prognostic markers. 6. scRNA-seq processing and annotation: from raw counts to UMAP clustering, marker gene detection, and automated cell type labeling. 7. Cell-cell communication: infer ligand-receptor interactions between cell types from single-cell data and map intercellular signaling networks. 8. Primer design for novel Cas13: analyze a putative Cas13 protein from a metagenomic screen—verify the ORF, identify HEPN domains, and design cloning primers with restriction sites and a FLAG 9. Proteomics differential expression: normalize mass spec data, run statistical tests, and visualize differentially abundant proteins. 10. Gene regulatory network inference: reconstruct transcription factor-target gene networks from expression data and identify key regulators. 11. Gene co-expression network analysis: build weighted co-expression networks, identify gene modules, and correlate them with phenotypic traits. 12. Microbiome analysis: process 16S/metagenomic sequencing data to profile microbial communities, diversity, and differential abundance. 13. Polygenic risk scores: compute and evaluate PRS from GWAS summary statistics against a target cohort. 14. Variant annotation: annotate genetic variants with functional predictions, allele frequencies, and clinical significance. 15. Fine-mapping: narrow GWAS loci to credible causal variants using statistical fine-mapping methods. Each of these would normally take days to weeks of scripting, debugging, and iteration. In Biomni Lab, the agent handles the full execution while you steer the science. Learn more:

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DEMO IS LIVE! Create your Agents, chat, ask questions, and get ready to tokenize them soon. Negotiate with our businessman and grab some cool stuff 👀 What’s the AI Agent Layer? 👇 It’s a dynamic ecosystem of autonomous AI agents. On the platform, you can create AI Agents using data from X and user inputs. Coming soon: Each AI Agent will be tokenized and linked to our native token ($AIFUN). Once the bonding curve reaches 100%, tokens become tradable on DEX. Stay Tuned for our TGE 👆

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