Je sais que pendant les vacances, vous êtes plein d'étudiants en IA à chercher des projets portfolio à montrer en entretien. J'en ai un qui se fait en 3 jours pour un pirate. C'est du topic modeling, c'est ultra visible et je vous garantis que c'est un sujet que 100 % des boîtes
MACHINE LEARNING
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Research Challenges Scaling Myths for AI Agent Tool Calling
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This paper quietly destroys one of the most repeated myths in AI: that bigger models are always better for agents. The researchers took a 350M-parameter model and fine-tune it specifically for agentic tool calling. Not chatting. Not creativity. Just one job: pick the right tool,
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Using World Models to automate companies, thanks @spisallyouneed
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Thanks @spisallyouneed for such an interesting look at how you are using World Models to change how companies can automate themselves.
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Self-driving car improves weekly, first human-free drive this year
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It improves every week or so. It is a DRAMATICALLY DIFFERENT experience today than it was 12 months ago. And this year was the first time it drove around without a human in the car.
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LLaDA2.0: Converting Auto-Regressive Models to Discrete Diffusion
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LLaDA2.0, a new method that converts existing auto-regressive models into discrete diffusion models using a novel 3-phase training scheme. This approach preserves the model's learned knowledge while unlocking parallel decoding. The resulting models, LLaDA2.0-mini (16B) and
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Causal prediction alone enables strong visual learning
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A new mind-blowing finding:
causal prediction alone is sufficient for strong visual learning, no need for any fancy reconstruction, masking, or contrastive loss In this paper NEPA, instead of reconstructing pixels, they train a vision model to autoregressively predict the next -
Share AI insights and LLM tutorials with your network
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If you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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Machine Learning Success: Mastering the Consistent Workflow Loop
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ML isn’t magic — it’s a workflow. 1. understand data 2. choose right algorithm 3. train 4. test 5. optimize 6. deploy + monitor + retrain The winners are the teams who run this loop consistently. #MachineLearning #AI #DataScience #MLops
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Collider Bias Critique in AI Research Methodology
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Critique of the paper based on collider bias https://
zenodo.org/records/180021
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Building AI Applications: Complete Architecture Pipeline with LangChain
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Building AI Applications from Scratch Made by the LangChain Community Learn complete AI app architecture through a 5-step pipeline using LangChain's Document Loaders, Vector Stores, Retrievers & Agents—solving context limits and hallucinations. Watch: