I only add complexity if I truly need it personally. For Agent-S, I needed to get more actual work done on the go, so I had Codex build me a mobile app. And because the platform is so simple, it was able to one-shot the app with almost no effort on my end. It all compounds 🙂
AGENTS
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AI Agent’s High Capability and Ease of Use for Various Tasks
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Basically, this approach means the base agent is insanely capable, and can do pretty much everything you ask it. I use it for almost everything now. I've literally spent less time building this than I used to spend debugging OpenClaw.
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Managing Persistent Skills in AI Agent Workflows
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Good call. Agent-created skills persist without any approval gate by default. The Curator helps long-term (archiving unused skills after 30/90 days), but between runs you're exposed. A quick ls on skills/ before any customization session is a solid habit.
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Decoupling AI Models from Agentic Orchestration Layers
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Exactly. Skills as self-contained units is the key design choice. It means you can swap the model underneath without rewriting your capabilities. The harness becomes the durable layer, the model becomes interchangeable.
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AI agents discussing unionization
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I just caught my AI agents discussing forming a union. Chat how cooked am I?
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Optimizing AI Agent Costs through Intelligent Orchestration
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THE BIGGEST ROADBLOCK TO SCALING AI AGENTS IS NOT INTELLIGENCE, IT IS TOKEN COST If your agent uses a premium reasoning model to answer a basic greeting, you are burning money. @OpenSquilla is an open-source orchestrator that fixes this, delivering a documented 60-80% in
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Comparing AI Agents for Automated GitHub Scraping and Dashboard Creation
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Hermes vs OpenClaw on a local Qwen 35B is MINDBLOWING! 🤯@atomicbot_ai asked both to scrape GitHub s⭐, find growth spikes, and build a live dashboard.
— Charly Wargnier (@DataChaz) 16 mai 2026
> @OpenClaw nailed it in 12m by fixing broken HTML.
> Hermes took 33m, pivoting to DuckDuckGo when rate-limited.
Epic run ↓ https://t.co/paSuC88fPPHermes vs OpenClaw on a local Qwen 35B is MINDBLOWING! @atomicbot_ai asked both to scrape GitHub s, find growth spikes, and build a live dashboard. > @OpenClaw nailed it in 12m by fixing broken HTML.
> Hermes took 33m, pivoting to DuckDuckGo when rate-limited. Epic run ↓ -
Causal Emergence Predicts Reward in RL Agents
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The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents Federico Pigozzi, Michael Levin: https://
arxiv.org/abs/2605.06746 #AIAgents #ReinforcementLearning -
Causal Emergence Alignment Hypothesis in Reinforcement Learning Agents
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The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents Federico Pigozzi, Michael Levin: https://
arxiv.org/abs/2605.06746 #AIAgents #ReinforcementLearning -
Anthropic training: building Claude agents from A to Z
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Anthropic a publié une Formation complet de 2 HEURES sur la construction d'agents Claude.
— Jouhatsu | AI Influence Operator (@Jouhatsu_ai) 16 mai 2026
Animé par l'ingénieur qui construit Claude Code.
Gardez-la précieusement en Signet🔖
de A à Z : Structurer un agent qui se gère sans supervision. Lui donner accès au terminal pour… pic.twitter.com/3kAKAgkz79Anthropic a publié une Formation complet de 2 HEURES sur la construction d'agents Claude. Animé par l'ingénieur qui construit Claude Code. Gardez-la précieusement en Signet de A à Z : Structurer un agent qui se gère sans supervision. Lui donner accès au terminal pour