The future of AI agents may not belong to the biggest models. It may belong to the smartest combination of models. A lot of companies are currently using large language models for everything:
summarization,
classification,
tool routing,
structured outputs,
simple repetitive
LLMS
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Future of AI agents lies in smart model combinations, not size
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Perplexity open-sources Bumblebee (blog + GitHub)
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Blog post + GitHub repo: http://
perplexity.ai/hub/blog/perpl
exity-is-open-sourcing-bumblebee
… http://
github.com/perplexityai/b
umblebee
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MiniMax releases M2 series technical report for agentic tasks
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MiniMax just released a technical report for the MiniMax-M2 Series! The MiniMax-M2 series, a family of Mixture-of-Experts models designed from the ground up for agentic tasks. The flagship M2 packs 229.9B total parameters but activates only 9.8B per token—thanks to an
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Top 6 Design Patterns for Agentic AI
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Top 6 Design Patterns for #AgenticAI
by @ingliguori #LLM #GenAI #ArtificialIntelligence #MachineLearning -
Building SaaS Apps with Multiple LLMs in One Prompt
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🚨 One Prompt To A Fully Functional SaaS App And Complete Software
— Abacus.AI (@abacusai) 27 mai 2026
Opus 4.7 -> front-end
GPT 5.5 -> long running complex backend
Gemini 3.5 – > AI chatbot embedded in the app
Sonnet 4.6 -> scheduled task mask management
Kimi 2.6 -> simple cron jobs
One prompt will build AND… pic.twitter.com/nzMkP7ripuOne Prompt To A Fully Functional SaaS App And Complete Software Opus 4.7 -> front-end
GPT 5.5 -> long running complex backend
Gemini 3.5 – > AI chatbot embedded in the app Sonnet 4.6 -> scheduled task mask management
Kimi 2.6 -> simple cron jobs One prompt will build AND -
Super Study Guide for Transformers and Large Language Models
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Super Study Guide: Transformers & Large Language Models: http://
amzn.to/3SW6YYm by @afshinea & @shervinea Beautifully presented, excellent content, timely, thorough, educational #LLMs #MachineLearning #AI #GenAI #DataScience #DataScientist #GenerativeAI -
Huawei’s EDCO: Dynamic Curriculum Orchestration for Real-Time LLM Adaptation
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What if fine-tuning a large language model could adapt its training in real-time, not just follow a static plan? Researchers from Huawei introduce EDCO, a dynamic curriculum orchestration framework. Instead of pre-ordering training samples by difficulty, EDCO prioritizes data
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Looped Transformers: Frozen Checkpoint Inference Optimization
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Another cool research on Looped Transformers They ask the question: "Can we loop a frozen, off-the-shelf checkpoint directly at inference time without any modifications?" So naive repetition pushes hidden states outside the distribution later layers expect, so performance
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Language Models Sleep: Context Replay for Deep Reasoning
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"Language Models Need Sleep" Instead of thinking longer at answer time, this paper makes LLMs sleep before forgetting. They replay old context, write it into fast weights, clear the KV cache, and answer later at normal speed. More sleep improves deep reasoning over long and