I'm releasing the 34 slides on how we design and train best-in-class edge models at @liquidai I presented these slides yesterday at @aiDotEngineer They cover model architecture, pre-training, scaling laws, post-training, and even a solution to fix doom loops Special thanks to
MACHINE LEARNING
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From Syntax to Intent: The Future of Software Development
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What if the biggest change in software isn’t a new language, but the fact that you may not need to think in code first anymore?
— Ronald van Loon (@Ronald_vanLoon) 10 avril 2026
I think we’re moving from:
syntax → intent
From:
debugging by trial and error → building through conversation
That changes who can build, how fast… pic.twitter.com/HTtXz4H0m3What if the biggest change in software isn’t a new language, but the fact that you may not need to think in code first anymore? I think we’re moving from: syntax → intent From: debugging by trial and error → building through conversation That changes who can build, how fast teams ship, and what developers actually spend time on. Here’s the breakdown, featuring what I learned from @GoogleAIStudio.
→ View original post on X — @ronald_vanloon, 2026-04-10 08:30 UTC
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Strategic Prediction for Generative AI Evolution
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Strategic Prediction for #GenerativeAI's Evolution by @antgrasso #GenAI #MachineLearning #ArtificialIntelligence #ML #MI
→ View original post on X — @ronald_vanloon, 2026-04-10 08:27 UTC
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Apple Neural Engine’s Multimodal Power and CoreML Compiler Advances
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I, too, underestimated the power of the Apple Neural Engine. AiDevCraft (@AiDevCraft) Going from text-only to multimodal in a single day while openly correcting benchmark numbers mid-thread is exactly the kind of rigorous iteration that makes edge ML credible. 99.78% ANE op mapping for a non-Apple architecture like Gemma 4 is the quietly impressive part — it means CoreML's compiler generalization is better than most people assume. — https://nitter.net/AiDevCraft/status/2042516832658297247#m
→ View original post on X — @scobleizer, 2026-04-10 08:18 UTC
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Unsloth Studio Colab Notebook for LLM Fine-tuning
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Here's the notebook: colab.research.google.com/gi… If this was helpful, reshare with your network. Find me → @akshay_pachaar ✔️ For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
→ View original post on X — @akshay_pachaar, 2026-04-10 07:49 UTC
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Free Google Gemma 4 Fine-tuning with Unsloth Colab Notebook
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Fine-tune Google Gemma 4 completely FREE!
— Akshay 🚀 (@akshay_pachaar) 10 avril 2026
All you need is a browser and 500+ models to choose from.
The process is simple:
1. Open the Unsloth Colab notebook
2. Pick your model and dataset
3. Hit start training
And you're done! pic.twitter.com/YAyyFeN7HxFine-tune Google Gemma 4 completely FREE! All you need is a browser and 500+ models to choose from. The process is simple: 1. Open the Unsloth Colab notebook 2. Pick your model and dataset 3. Hit start training And you're done!
→ View original post on X — @akshay_pachaar, 2026-04-10 07:49 UTC
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4 Steps to Develop an AI-Ready Workforce
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4 Steps to develop an #AI-ready workforce by @Gartner_inc #ArtificialIntelligence #MachineLearning #ML
→ View original post on X — @ronald_vanloon, 2026-04-10 07:48 UTC
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Your Data Analyst Roadmap for Data Science and Big Data
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Your #DataAnalyst Roadmap by @Python_Dv #DataScience #BigData
→ View original post on X — @ronald_vanloon, 2026-04-10 07:27 UTC
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Better AI Feed: The Best in Artificial Intelligence
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I have a way better feed for you. All the best in AI:
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AI Could Automate 70% of Everyday Work Tasks by 2026
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By 2026, #AI Could #Automate Up to 70% of Everyday Work Tasks by @Khulood_Almani #ArtificialIntelligence #MachineLearning #ML
→ View original post on X — @ronald_vanloon, 2026-04-10 06:23 UTC