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Hermes Agent Fundamentals In 29 Minutes
Tina Huang
Tina Huang walks through a comprehensive 29-minute setup guide for Hermes Agent, covering hardware options (from a Mac Studio with 64GB RAM to budget alternatives like old laptops or a VPS), installation via the desktop app or terminal, and key features including tools, skills, cron jobs, and integrations with platforms like Discord and Telegram. She explains Hermes' two-tier default memory system—core markdown files (memory.md, user.md, soul.md) plus a searchable SQLite archive of past sessions—and recommends enhancing it with an external plugin called Haunchose, which passively collects behavioral patterns across interactions. The video also demonstrates how users can create custom skills (e.g., a business idea evaluator) directly from conversations, and teases more advanced multi-agent frameworks as a next step.
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Stop Building AI Agents-Start Building AI Teammates
Krish Naik
Videoen præsenterer Hyper Agent, en sponsoreret platform, der giver brugerne mulighed for at køre en flåde af specialiserede AI-agenter, som arbejder sammen om at løse komplekse opgaver. Hver agent kører i sit eget isolerede cloud-miljø med adgang til en rigtig browser, shell og filsystem, og kan integreres med hundredvis af virksomhedsapplikationer som Slack, Google Drive og Salesforce. Krish Naik demonstrerer, hvordan man opretter en agent via "Create with chat"-funktionen, og viser et konkret eksempel, hvor en agent bygges til at hjælpe studerende med at holde sig opdateret på de nyeste forskningsartikler om LLM'er og RAG. Outputtet fra agenterne er ikke blot tekst, men virkelige produkter som websider, PDF'er, dashboards og kode.
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Is Local AI Coding Actually Good?
Tech With Tim
Tech With Tim gives an honest assessment of local AI coding models, testing several models (including Qwen3 Coder 30B and Qwen3.6 27B) on a high-end machine with an RTX 4090 (24GB VRAM). The mixture-of-experts Qwen3 Coder achieved around 80–90 tokens per second while the dense Qwen3.6 ran at roughly 30–32 tokens per second, both producing functional but imperfect code results. A major practical bottleneck is compatibility: local models don't integrate cleanly with preferred coding agent harnesses like Cursor, and features like interactive browser testing often fail. His verdict is that local models are "good enough" if you already own very high-end hardware, but most users are better served by cheaper hardware combined with subsidized cloud AI subscriptions, since cloud models are faster, more capable, and still relatively affordable.
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