Teri sada hi Jay Ho Sankat Mochan 🚩
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Jay Ho Pawan Putra Hanuman🚩
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Bajrang Bala japun thari Mala🚩
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Teri sada hi Jay Ho Bajrangbali 🚩@grok
Across hundreds of high‑value professions, Qwen3.8‑Max is boosting productivity in real‑world workflows, demonstrating the true breadth of its capabilities.🚀
From corporate compliance counsel and UI/UX designers to structural engineers, it’s built to work where it matters most.
XPENG delivered 38,027 vehicles in July 2026, up 4% YoY—but our growth goes far beyond numbers.
On the heels of the XPENG L03 Global Launch, our new flagship enters 65 markets this year as we re-engineer smart, sustainable mobility. 🌍
$XPEV
Most RAG systems stop searching before they start thinking.
They grab a few chunks, stuff them into context, and hope the answer is somewhere inside.
Agentic RAG changes the loop:
- search broadly
- rewrite the query
- open the exact document
- find the relevant lines
- summarize before context overflows
- answer only when the evidence is enough
The real upgrade is not a larger context window.
It is giving the model control over what it needs to verify.
Build citations into retrieval from day one.
Otherwise your “agent” is just guessing from better chunks.
We're back with our latest edition of "Proof of AI Journals," by Proof of AI Lab.
In this Journal, our researcher Kevin Ros dives into how 2025 is the year of agentic AI, and Model Context Protocol (MCP) is quickly becoming the standard for connecting agents to tools like Slack, Uber, and Notion.
But there's a huge problem: authentication.
Each agent needs to authenticate with each tool individually.
If you're running 10 agents across 20 tools, that's 200 separate OAuth flows.
This leads to the M × N auth problem:
🔁 Redundant flows
🔓 Massive attack surface
🧱 No granular control over time, task, or scope
At Kite AI, we’re building a cryptographically secure transaction layer that solves this.
Agentic systems won’t scale until auth is reimagined, and we’re building that future. 🪁