Most AI companies chase benchmarks nobody cares about. Macaron chases real intelligence that learns from actual experience. Research that ships: https://
macaron.im/mindlab
Product that works: https://
macaron.im Bigger models won't save you. Smarter training will.
MACHINE LEARNING
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Macaron focuses on smarter training over benchmark chasing
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RL Efficiency Gains in Production Apps
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Mini-apps went from 20 minutes to 2 minutes. That's not optimization. That's demolition. The RL efficiency isn't hiding in benchmarks. It's crushing it in production. Stop reading papers. Start using products that actually ship the research.
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Mind Lab’s Technical Innovations in AI Training and Memory Architectures
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Mind Lab's unfair advantages: 1T-scale LoRA RL. Train 10x cheaper. Everyone else is bleeding cash.
Memory Diffusion. Forgets the noise. Keeps what counts.
OpenAI, DeepMind, MIT, Tsinghua refugees. 200+ papers. 30k citations. These people actually built shit that matters. Not a -
Macaron achieves 1T parameter RL training at 10% cost
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Every AI lab is pissing away millions on compute. Macaron just ran RL on a 1 trillion parameter model at 10% cost.
Then open sourced the whole damn thing. Already integrated into NVIDIA Megatron and ByteDance Seed Verl while most companies are still writing Medium posts about -
CuspAI hiring: join generative models team for materials research
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🔥🔥Amazing opportunity 🔥🔥 Work with my awesome and wonderful colleagues @johannbrehmer @wellingmax and @lonepair on generative models for materials here at @cusp_ai! Fun and exciting research on real-world problems that actually matter! #MachineLearning #Materials Johann Brehmer (@johannbrehmer) Come work with us at @cusp_ai in the generative model team! Excited about flow / diffusion models and chemistry? Looking for impact? jobs.ashbyhq.com/cuspai/b810… Join a great team lead by @wellingmax & Aron Walsh, work in Amsterdam / Cambridge / London / Berlin. 1/2 — https://nitter.net/johannbrehmer/status/2000488276029813139#m
→ View original post on X — @wellingmax, 2025-12-15 09:08 UTC
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CuspAI hiring generative model researchers in multiple European cities
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Come work with us at @cusp_ai in the generative model team! Excited about flow / diffusion models and chemistry? Looking for impact? jobs.ashbyhq.com/cuspai/b810… Join a great team lead by @wellingmax & Aron Walsh, work in Amsterdam / Cambridge / London / Berlin. 1/2
→ View original post on X — @wellingmax, 2025-12-15 08:49 UTC
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2.6K Parameter RL System Matches 62B Specialized Model
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Oh My! I just built a new RL-native system with 2.6k parameters that matches a specialized 62B model from a 2024 paper, specifically on GSM8k. That's over 20,000,000x smaller than the baseline — but the approach is so different the traditional comparison doesn't make sense? 😉
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Technical Comparison of Claude and ChatGPT Memory Architectures
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someone just reverse-engineered claude's memory system and it's nothing like chatgpt's. turns out most people think AI memory works the same way everywhere. not really. chatgpt pre-computes summaries and injects them into every single prompt. convenient. predictable. always
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Try Genspark, ChatGPT, or Google Gemini
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Have you tried Genspark or ChatGPT? Google Gemini should work too, Amongst others
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RouteRAG: Adaptive Retrieval for Multi-Hop Reasoning Systems
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Adaptive retrieval is the way to go! And this RouteRAG paper shows why. Let's talk about it: RAG systems have a retrieval problem. The default approach to multi-hop reasoning today relies on fixed retrieval pipelines. It typically involves fetching text + maybe graph data, and