The hardest part of AI is not about writing the perfect prompt.
The hardest part is knowing who you’re actually talking to. AI won’t fix bad targeting.
It just helps you scale it faster. Automated “AI-generated outreach” fails for the same reason this text did: → No context
Lower delivery costs sound like pure progress. 10,000+ unmanned vehicles and ultra-cheap last-mile pricing within 30km is impressive. But cost reduction is only one side of the equation. What happens when logistics becomes this cheap? Volume increases. Delivery frequency rises. Urban traffic patterns change. And the system becomes more dependent on continuous, automated flows. There are also practical questions: How do these vehicles perform in dense, unpredictable environments? What happens at scale when edge cases become daily cases? Where does responsibility sit when something goes wrong? Efficiency is real. But so are the second-order effects. The transformation of logistics isn’t just about cheaper delivery. It’s about how systems behave when friction approaches zero. #Logistics #Automation #AI #Innovation #SupplyChain Source 🙏 @sutoroveli_news
This guy literally gave the clearest breakdown of Claude Managed Agents you can find.
In 12 minutes he covers: → the real definition (Platform as a Service for AI) → who actually needs it and who doesn't (4 personas) → a raw look at the live console (sessions, analytics,… pic.twitter.com/FGqZREpioI
This guy literally gave the clearest breakdown of Claude Managed Agents you can find. In 12 minutes he covers: → the real definition (Platform as a Service for AI) → who actually needs it and who doesn't (4 personas) → a raw look at the live console (sessions, analytics, costs) → the crazy math ($2.58 to fulfill a $1k service) He even dropped a free Google Doc that deploys your first agent when you give it to Claude Code 🔥 Full video in 🧵↓
MiniMax has open sourced M2.7, their open-source model designed "for agent-based workflows, complex reasoning, and real-world engineering tasks." It introduces self-evolution capabilities, where the model improves itself through iterative experimentation, achieving 30% performance gains and a 66.6% ML competition medal rate. Honestly, this is more impactful than expected. On the performance side, M2.7 delivers strong software engineering results (56.22% SWE-Pro), near top-tier benchmarks, and excels in multi-agent collaboration, tool use, and productivity tasks like document editing. With high ELO scores, fast incident recovery (<3 min), and 97% skill compliance, it positions itself as one of the most capable open-source AI systems right now.
Innovators, this is your moment! The AB PM-JAY #AutoAdjudicationHackathon is here to redefine healthcare claims processing with AI. Your AI solution can power India’s largest health scheme with faster approvals, reduction in errors and improvements in patient outcomes across