The most interesting result in Anthropic's latest paper isn't the 8x increase in code output.
It's this:
Claude Mythos Preview suggested a better research direction than humans 64% of the time.
We're moving beyond AI that writes code.
We're approaching AI that helps decide
@ingliguori
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Anthropic’s Claude Mythos preview outperforms humans in research direction 64% of the time
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Comparing AI models for autonomous workflows
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AI AGENTS Which model would you trust for autonomous workflows today? Claude Opus 4.8 GPT-5.5 Qwen 3.7 Max GLM 5.1 Planning, tools, MCP, reasoning, multi-step execution… Which one are you actually using and why? #AIAgents #AgenticAI #LLM #GenAI
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Choosing the Best Coding Model for Deployment
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CODING MODELS
If you had to deploy one coding model tomorrow, which would you choose? Claude Opus 4.8 Qwen 3.7 Max DeepSeek V4 GPT-5.5
Benchmarks are useful.
Production experience is better.
Which model are you actually using and why? #AI #Coding #LLM #GenAI -
NVIDIA Nemotron 3 Ultra solves AI agent fatigue and cost issues
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AI agents don't just get expensive.
They get tired.
As workflows become longer, agents suffer from goal drift, context overload, and rising token costs.
NVIDIA's Nemotron 3 Ultra aims to fix that: Hybrid Mamba + Transformer 1M-token context 5x throughput 30% lower -
8 LLMs for Agentic AI: Reasoning, Perception, Planning, Action
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8 types of LLMs used in AI agents GPT • MoE • LRM • VLM • SLM • LAM • HRM • LCM Different models for reasoning, perception, planning, and action — not just chat. Agentic AI = model orchestration. #AI #LLMs #AgenticAI #GenAI #MachineLearning
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AI is a process: 8 steps for real value
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AI isn’t magic. It’s a process. 8 steps:
define problem
collect/prepare data
choose model
train
evaluate
fine-tune
deploy
ensure ethics & safety Real value comes from running this loop well. #AI #MachineLearning #DataScience #ResponsibleAI -
9 Steps to Build AI Agents from Scratch
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How to build AI agents from scratch (9 steps): 1. Purpose & scope 2. I/O schemas 3. System instructions 4. Reasoning + tools 5. Multi-agent orchestration 6. Memory & context 7. Multimodal 8. Structured outputs 9. UI / API Ship agents that do work, not just talk.
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Cheat sheet matching AI tools to specific tasks
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Cheat sheet: ChatGPT = create/build (writing, coding, workflows)
Grok = live trends + punchy takes
Gemini = Google Workspace-native collaboration
Claude = deep reading + long-doc reasoning
Perplexity = research w/ citations Right tool for the right job.
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AI Agents: Orchestrating Multiple LLMs for Enhanced Capabilities
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AI Agents ≠ single LLM They’re systems of models • General LLMs → reasoning
• Domain LLMs → expertise
• RAG → real-time data
• Tools → actions & automation
• Open-source → control & privacy Real power = orchestration of multiple LLMs Via Giuliano Liguori -
AI memory shifts from retrieval to compilation with LLM Wiki
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RAG is already becoming the “old way” The future of AI memory is not retrieval.
It’s compilation. Here’s the shift in one sentence: From searching information To structuring knowledge The new model? LLM Wiki Instead of: Chunking documents Running similarity