Perplexity's model is subscription-heavy ($20/mo Pro, enterprise seats $40+), now fully ditching ads to protect AI answer trust—no sponsored slop. Revenue (~$200M ARR) comes from users/companies who pay for accuracy, citations, and pro features like Deep Research. Aligns
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Automating Job Searches with AI Prompts
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L'IA peut maintenant automatiser TOUTE votre recherche d'emploi (gratuitement). Voici 15 prompts Claude 3.5 + DeepSeek qui décrochent 5+ entretiens en 48h. (Ajoutez en signet avant que tout le monde les utilise)
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Automating Job Search with AI Prompts
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L'IA peut maintenant automatiser TOUTE votre recherche d'emploi (gratuitement). Voici 15 prompts Claude 3.5 + DeepSeek qui décrochent 5+ entretiens en 48h. (Ajoutez en signet avant que tout le monde les utilise)
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Introducing the LFM2-24B-A2B Model on Hugging Face
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huggingface.co/LiquidAI/LFM2… [Translated from EN to English]
→ View original post on X — @maximelabonne, 2026-02-24 17:48 UTC
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Using Claude Code for household chore automation
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I just fired my kids from their chore duties. They were pretty bad at those. Claude Code does them much better now.
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World Labs Marble API Demo Showcases Innovation
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Oh I so love this demo using @theworldlabs ' Marble API!
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LFM2-24B-A2B: Liquid AI’s Fastest 24B MoE Model Released
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Meet LFM2-24B-A2B, @liquidai's largest model. 24B MoE, 2B active. Blazing fast even on CPU. Available now in LM Studio 👾💧 lmstudio.ai/models/lfm2-24b-…
→ View original post on X — @maximelabonne, 2026-02-24 15:44 UTC
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Liquid AI releases LFM2-24B-A2B model for on-device inference
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ollama run lfm2:24b-a2b .@liquidai's latest on-device model is here! It's the largest LFM2 model yet, and is designed to run fast on device, and fits on devices with 32GB of unified memory. Liquid AI (@liquidai) Today, we release our largest LFM2 model: LFM2-24B-A2B 🐘 > 24B total parameters > 2.3B active per token > Built on our hybrid, hardware-aware LFM2 architecture It combines LFM2’s fast, memory-efficient design with a Mixture of Experts setup, so only 2.3B parameters activate each run. The result: best-in-class efficiency, fast edge inference, and predictable log-linear scaling all in a 32GB, 2B-active MoE footprint. 🧵 — https://nitter.net/liquidai/status/2026301771539202269#m
→ View original post on X — @maximelabonne, 2026-02-24 14:36 UTC
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Downloadable 284‑page Introduction to Neural Networks
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Download 284-page PDF “Introduction to #NeuralNetworks” https://
dkriesel.com/en/science/neu
ral_networks
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#DataScience #AI #Algorithms #ML #MachineLearning #DeepLearning #Mathematics #Calculus #DataScientist -
Context Stacking for Better AI Results
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I finally understand why "act as an expert" prompts are destroying your results. After 200+ tests across Claude, ChatGPT, and Gemini I found what actually works. It's called "Context Stacking" and it doesn't ask the AI to pretend anything. Here's the technique ↓