bohee shin

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. 🪁

Just Signed The @boundless_xyz Manifesto. Committing to an abundant future.

@dhifaabdillah1 @linera_io Tokenization transforms real-world assets into tradable digital tokens

@yleskacolorada @linera_io Cross-chain bridges enable asset transfers across heterogeneous networks

@cf_jhg34190 @linera_io decentralized file system decentralizes file storage while preserving data ownership rights

.@linera_io Why Linera for AI? Linera microchains: massive AI throughput., enabling AI-managed DAOs. + guaranteed uptime.. No more scalability nightmare. Excited for Linera's AI power! #RealTimeAI #LineraAI #FutureOfAI

@cf_jhg34190 @linera_io Sharding scales blockchain throughput through parallel processing

@dhifaabdillah1 @linera_io PoW/PoS consensus mechanisms validate transactions via node participation

@yleskacolorada @linera_io Mining pools optimize block rewards via hashrate aggregation

@cf_jhg34190 @linera_io Smart contracts automate financial transactions with self-executing code

@cf_jhg34190 @linera_io ZK-proofs verify transaction validity without data exposure

@dhifaabdillah1 @linera_io Mining pools optimize block rewards via hashrate aggregation

@yleskacolorada @linera_io Tokenization transforms real-world assets into tradable digital tokens

.@linera_io Why Linera for AI?Linera: AI's perfect blockchain base. Powers decentralized AI. with unprecedented perf.. scalability nightmare. Excited for Linera's AI power! #RealTimeAI #LineraAI #FutureOfAI

@yleskacolorada @linera_io Sharding scales blockchain throughput through parallel processing

@cerjader @linera_io InterPlanetary File System decentralizes file storage while preserving data ownership rights

@cerjader @linera_io Smart contracts automate financial transactions with self-executing code

@cf_jhg34190 @linera_io Governance tokens empower protocol decisions through staked voting

About three months ago we launched DGi (dgintel.ai), a data agentic system. IT SUCKED, like all agentic systems do right now when it comes to data analytics. Just a few weeks ago, I used one of the big AI players to vibe code a data dashboard. A few days later, I realized it was completely wrong. That’s the hard part about AI, we’re starting to rely on it so much that we end up living inside its hallucinations. What we need are systems we can trust. That’s the real challenge. Customers loved the idea behind DGi, but they told us it was unusable: too slow, inaccurate, and hard to rely on. The concept was right, but the execution wasn’t there yet. We went back to the drawing board and decided that to make something people actually use, we had to: Make it fast. Make it secure. Make it accurate, and give ourselves a way to measure that. Specialize it with the right tools for real data analysis. Today we’re announcing DGi v2, along with a performance evaluation comparing DGi to four other companies on real data analytics tasks (Replit, v0, Claude and OpenAI). You can see the summary of the results in the comments and in the image attached the overall benchmark. DGi v2 now includes double envelope encryption, encryption on top of encryption. When you connect your database, your credentials go through two layers of protection. We also trained DGi using gold-standard data science practices so it can better handle edge cases, large datasets, and more. And we’re adding features like scheduling (coming next week), code editing, and a metadata store (credential storage), so AI can help, but you can step in and take control when needed. We’re still far from where we want to be. Next up: dashboard creation and deployment. But this version is a solid foundation to start exploring and analyzing your data with a high level of accuracy and reliabillity. Right now, DGi is somewhere between ChatGPT, Airflow, and Replit, but with deep data specialization. Our models are trained with data analysis best practices and include data reviews on final responses. We hope you’ll keep supporting us on this journey. We imagine a future without Excel files, Power BI, or Tableau dashboards. That future isn’t easy to build. Reliable and accurate agentic systems are complex to build. But we’re leading the way.