I use 12 ChatGPT Prompts That Handle Customer Complaints Better Than Humans
They boost satisfaction and lower costs
• Use empathetic responses
• Clarify issues effectively
• Reduce resolution time Click below to read more: https://
godofprompt.ai/blog/12-chatgp
t-prompts-that-handle-customer-complaints-better-than-humans
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GENERATIVE AI
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Using ChatGPT Prompts to Optimize Customer Service Interactions
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Anthropic’s Claude Managed Agents threatens dozens of AI startups
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Yeah. Anthropic just casually kill3d dozens, hundreds, thousands of startups.
— Chubby♨️ (@kimmonismus) 8 avril 2026
Again. https://t.co/bI9gcY0SPFYeah. Anthropic just casually kill3d dozens, hundreds, thousands of startups. Again. Claude (@claudeai) Introducing Claude Managed Agents: everything you need to build and deploy agents at scale. It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days. Now in public beta on the Claude Platform. — https://nitter.net/claudeai/status/2041927687460024721#m
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AI models performing real-world professional tasks
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Also note that ex-chatbot these things can do Real Work (TM) which doesn't look like systems engineering and you can check that Real Work (TM) in the typical ways that one checks Real Work (TM). They can compile a balance sheet from source documents.
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Anthropic’s Report on Mythos: Key Takeaways and Implications
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What Should We Take From Anthropic’s (possibly) Terrifying New Report on Mythos? – @garymarcus’s latest @CACMmag cacm.acm.org/blogcacm/what-s…
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Expert adoption and risk management of LLMs in professional domains
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I think better calibrated experts use LLMs in e.g. adjacencies to their professional domain fairly pervasively these days, accepting some risk of a hallucination based on e.g. cost of checking evidentiary trail, how important that output is and where it is exposed, etc.
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Neurosymbolic AI Vindication: Beyond Pure Language Models
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it’s surely NOT a pure LLM (eg heavily uses symbolic tools and is hardcoded to do so), and surely includes neurosymbolic components, so it is to some degree vindication for my central claims.
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Mythos ASI Claims: Competitive Advantage and Regulatory Reality
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this is wrong for many reasons
– mythos is not ASI, not even ANthropic is claiming that
– others seem to be close behind, as I posted earlier today; it’s likely a 3 month “win” or something like that, not an enduring advantage.
– regulations and treaties are routine for many -
The challenge of verifying LLM output accuracy
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Even accounting for hallucinations, there is substantial overlap between what LLMs are *manifestly* capable of doing and what a given user is *not conveniently capable of checking*, including when a relatively sophisticated user would say "I mean just check source URLs."
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7 Pillars of Agentic AI by Python Developer
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7 Pillars of #AgenticAI
— Ronald van Loon (@Ronald_vanLoon) 8 avril 2026
by @Python_Dv
#LLM #GenerativeAI #ArtificialIntelligence #MI #MachineLearning pic.twitter.com/dGCIxouNTT7 Pillars of #AgenticAI by @Python_Dv #LLM #GenerativeAI #ArtificialIntelligence #MI #MachineLearning
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Local AI Models Essential Backup When Cloud Services Fail
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At 4 p.m. today @NousResearch's CTO will be on an audio space here today to talk about Hermes.
— Robert Scoble (@Scobleizer) 8 avril 2026
Why is that important? We all need to move to local models, at least as a backup for when the big models in the cloud are down. https://t.co/rTVqP36vULAt 4 p.m. today @NousResearch's CTO will be on an audio space here today to talk about Hermes. Why is that important? We all need to move to local models, at least as a backup for when the big models in the cloud are down. BridgeMind (@bridgemindai) Claude Code is down again. This is a $200/month product. Developers are building businesses on this infrastructure. Every outage is lost productivity and lost revenue. Anthropic, you just launched Claude Mythos for AWS, Apple, and Microsoft. Maybe make sure your paying customers can use it. — https://nitter.net/bridgemindai/status/2041935526962315294#m