@businessbarista@tenex_labs Just DMβd.
Iβm the engineer who uses AI to ship faster than 5 purist devs combined.
The Pitch: I work overnight (IST) to clear tickets while your NYC team sleeps.
Let me crush 1 story point for $0 to prove velocity > zip code.
@K8sBlackbox @SaumyaYadav817 I noticed that your direct messages are not currently open and I wanted to send over a reply. Could you please share an email address or let me know the best way to reach you?
@nickscamara_ Exciting to see teams pushing on the βLLMs + real-time web dataβ boundary. Sent you a quick DM β happy to prove fit via a small trial if useful.
@mandylu Got a first version working of a local tool that ingests the Twitter data export and turns your 2k+ DMs into a sortable table (tags for LLM/backend/intern/etc.), so you can triage much faster.
Here is GitHub link - https://t.co/K6TujM79Dj
@Eleni30fillou I work mainly on the applied Al side (LLMs, integrations, real-world systems). I wanted to ask honestly - do you see any fit or need for someone with my background in your work right now?
@Mmorgan_ML@pli_cachete That sounds tough. Do not let it get you down! There are many online communities and forums where people discuss Micro Transformers and evolution sims. Have you looked into those?
My Attention U-Net guide on Medium is exploding with views! π
Dive into this detailed PyTorch tutorial to master attention gates for precise image segmentation.
Join the hype! [https://t.co/0fl0X3T414]
#MachineLearning#ComputerVision#AI#PyTorch
Excited to see my Medium post on U-Net architecture blowing up!
Dive into this simple guide to understand U-Net for image segmentation + get hands-on with PyTorch code.
Check it out: [https://t.co/N2acrQvbzu]
#AI#MachineLearning#PyTorch#UNet
π Progress unlocked! Improved my NLP model for the Disaster Tweets competition using BERT, boosting my score from 0.78945 to 0.80478!
Jumped in rank from 715 to 273!
Fine-tuning & the power of BERT paid off. π₯ #MachineLearning#NLP#Kaggle
Code: https://t.co/WFiDw2Qkwr
Scored 0.78026 and ranked #715 in the 'Natural Language Processing with Disaster Tweets' competition on Kaggle! π
Used techniques like TF-IDF vectorization, Passive Aggressive Classifier. π» #Kaggle#NLP#MachineLearning
Code: https://t.co/iaW3ucAn21