#Ensembling

Day 42/42: Making AI Reliable Day 42/42. If you made it this far, you’re no longer just a user. LLMs fail because of limits: hallucinations, reasoning gaps, bias, cutoffs. They become reliable through design: grounding, retrieval, tools, alignment, evaluation. No single trick fixes everything. Layering does. That’s the difference between demos and systems. If you missed earlier days, start at Day 1. This was the full mental model. I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for what’s next 🚀 #LLM #AIExplained #GenerativeAI #LearnAI #AIEngineering

🧠 OECD AI Lifecycle Part 3: Building & Selecting the Model The Model Building and Selection phase involves complex technical and ethical considerations regarding model architecture and the balance between performance and transparency.

8 AI model architectures, visually explained 🧠 (must know for AI engineers) Everyone talks about LLMs, but there's a whole family of specialized models doing incredible things. Here's a quick breakdown: 1️⃣ LLM (Large Language Models) Text goes in, gets tokenized into embeddings, processed through transformers, and text comes out. Examples: ChatGPT, Claude, Gemini, Llama 2️⃣ LCM (Large Concept Models) Works at concept level, not tokens. Input is segmented into sentences, passed through SONAR embeddings, then uses diffusion. Example: Meta's LCM 3️⃣ LAM (Large Action Models) Turns intent into action. Input flows through perception, intent recognition, task breakdown, then action planning with memory. Examples: Rabbit R1, Microsoft UFO, Claude Computer Use 4️⃣ MoE (Mixture of Experts) A router decides which specialized "experts" handle your query. Only relevant experts activate. Examples: Mixtral, GPT-4, DeepSeek 5️⃣ VLM (Vision-Language Models) Images pass through vision encoder, text through text encoder. Both fuse in multimodal processor. Examples: GPT-4V, Gemini Pro Vision, LLaVA 6️⃣ SLM (Small Language Models) LLMs optimized for edge devices. Compact tokenization, efficient transformers, quantization. Examples: Phi-3, Gemma, Mistral 7B, Llama 3.2 1B 7️⃣ MLM (Masked Language Models) Tokens get masked, converted to embeddings, processed bidirectionally to predict hidden words. Examples: BERT, RoBERTa, DeBERTa 8️⃣ SAM (Segment Anything Models) Prompts and images go through separate encoders, feed into mask decoder for pixel-perfect segmentation. Example: Meta's SAM 👉 Over to you: What else would you add? #ai #machinelearning #deeplearning

If you're serious about AI, you need structure. Not just courses. Not just threads. Not just saving posts. Structure. I realized I was consuming a lot of AI content… but I wasn’t organizing it like an engineer. So I created a GitHub repository called AI Engineering Roadmap. It’s a central place where I document: • LLM Engineering • Frontier models • Trade-offs • RAG systems • Production thinking The first topic I published is Frontier Models. And the biggest shift for me was this: Model selection is not about hype. It’s about constraints. Building this in public. Link in bio & comments! #AIEngineering #LLM #BuildInPublic #SystemDesign #MLOps

AI doesn’t think. Models calculate probability. Next: Training. #AIModels #NarrowAI #MachineLearning

Are prompt engineering skills going to become obsolete? Here's what I think... I've been thinking: understanding how LLMs work is way more critical than just crafting prompts. Prompt engineering feels a lot like coding in assembly back in the day, a phase we’re moving past. Drop a 🔥 if you're ready to learn the real game behind AI 🚀 Because in the next few years, optimizing prompts alone won't cut it. Most folks focusing only on prompt tuning might be left behind. Instead, if you dive into how these models actually work, you're building a skill that lasts. It's like moving from reading assembly to understanding the machine's language itself. What do you think? Will prompt engineering still have a home in AI? Let's chat below! 🔗 Link in bio for the full breakdown. Hashtags: #AIGrowth #FutureOfAI #PromptEngineering #MachineLearning #TechInsights

AI progress may be hitting an invisible wall known as the efficient compute frontier—the point where adding 10× more training power yields minimal improvement unless the core recipe changes. Simply scaling models isn’t enough anymore. Labs are now shifting focus from raw training size to smarter architectures, better data, and increased “thinking time” during inference. Instead of just building bigger systems, the race is about using compute more intelligently—extracting more capability from the same model through reasoning, optimization, and adaptive processing. #ArtificialIntelligence #AIRevolution #FutureOfAI #MachineLearning #AIInnovation

Most AI guesses the next word. This one doesn’t 🤖🚫 Eve Bodnia, Founder & CEO of Logical Intelligence, explains why energy-based models stop playing the guessing game and go straight to the solution. This is a very different way to think about AI. Link in bio. #AI #ArtificialIntelligence #EnergyBasedModels #FutureOfAI #MachineLearning

Same prompt. Different AI models. Different outcomes. This isn’t about hype — it’s about how models think, simulate, and execute logic differently. The gap between AI tools is getting wider. Choosing the right one is now a skill, not a preference. If you’re building with AI, this difference matters.

There’s an invisible wall in AI progress. It’s called the efficient compute frontier. It’s the point where throwing 10x more training compute improves the model only slightly unless you change the recipe fundamentally: the data, the architecture, or the learning dynamics. That’s why AI progress feels like it’s stalled despite trillions spent on chips. The race is shifting. Instead of training the model more, labs are throwing more compute into the model’s thinking time to get more performance out of the same model. Source: Welch Labs - YouTube Keeping you updated on the fast moving world of AI Follow @foundx.ai

Small vs. large AI model? 🤔 Most people assume bigger = smarter. Olivier Godement from @OpenAI says otherwise. Think of a small model like a brilliant intern — just as sharp, but with less context about the world. Here's the breakdown: 👉 Large models = more world knowledge, better for complex, open-ended tasks 👉 Small models = just as intelligent, faster + cheaper, best when the task is well-defined The assumption that bigger always means better? It's outdated. Share your thoughts below! 👇 #openai #artificialintelligence #learnai #techtalk #aicommunity

How do I know all of this?👇 I work in with ai every day It’s become quite obvious This tech has been developing for many years now We didn’t jump straight to agents talking to each other. We got here through years of training, iteration, and evolution of LLMs. First came models that could only respond. Then models that could use tools. Then agents with memory, permissions, and real action through things like MCP and OAuth. But LLMs still have a core flaw. They’re recursive. They’re mirrors. Bad inputs compound. Good outputs cost more and more. What comes next is physical AI. Vision. Mapping. Robotics. Models trained on the actual physics of the world, not just text scraped from the internet. That data feeds open-world models. Open-world models run real feedback loops. Goals. Consequences. Adaptation. That’s the path toward AGI. Not magic. Not hype. Slow, physical, data-driven intelligence. If this stuff interests you, follow along. I’m going to be talking about it a lot more. #agi #physicalai #aiautomation