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Hospitals, law firms, and banks all hit the same wall with AI. Send your data to the best models in the cloud, and the provider can see it all. Keep it in-house, and you're stuck with weaker models. We broke down the five ways researchers are trying to get past that choice, and what each one costs you. Read the full post ⤵️

An MCP tool should do one narrow job with tight guard rails. Ours: the safety layer for Daml dev. Daml Reason won't approve code that hasn't compiled → intent in, pass/fail out. Golden corpus, drift-checked. Verified locally. Human-controlled. Read the full Daml Reason update 👇

We feel like this bears mentioning again: Forest drove a real miner to produce its first devnet blocks, and that’s simply massive. Breaking away from Go client dominance brings crucial client diversity, shielding the chain from shared single-language failures. Big ups to the Forest team! Read more ⤵️

A lot of companies connect their documents to an AI assistant using a method called retrieval-augmented generation, or RAG. The documents are split into small chunks. Each chunk is turned into an embedding, which is a list of numbers that represents its meaning. When someone asks a question, the question is turned into an embedding too. The database finds the stored chunks with the closest numbers and sends them to the AI to answer from. Teams assume the stored numbers are safe because they're not readable text. But, researchers at Cornell built a tool that turned the numbers back into the original text. On a set of real medical notes, it recovered 89% of full names. We reviewed every serious method for keeping this data private. Here is what exists today and what each one costs you. Read more ⤵️

The latest "This Month in Forest" is out! 🌲 ✅ Client diversity milestone: For the first time, a Forest node drove a real Filecoin miner (Curio / lotus-miner) all the way to producing blocks on a devnet, end to end. Next stop: calibnet. ✅ Enriched snapshots: Snapshots now bundle the extra indices a node would otherwise rebuild on import (like the epoch-to-tipset lookup table), so a freshly imported node gets fast height lookups out of the box. ✅ Dev gains: A broad Ethereum RPC performance + correctness pass, a cluster of drand/beacon fixes & more hardening against malformed input and misbehaving peers. More 👇🧵

Your cloud provider says your data is safe because it runs inside a "confidential VM" that even they can't access. Intel, AMD, and Nvidia sell the chips that are supposed to guarantee that. Last year, researchers at Georgia Tech and Purdue broke it with a device costing under $1,000, built from parts off e-commerce sites. They slid it between the memory stick and the motherboard and read the traffic going past. From that, they pulled out private signing keys and forged the attestation. Both AMD and Intel published bulletins stating that physical attacks are outside their threat models. AMD said it has no plans to ship a fix. Yes, it needs physical access to the machine. But with everyone directly or indirectly renting space in someone else's data centre. Physical access is the one thing they always have. So we read every serious defence in the field to see what actually holds up in LLM Privacy. Here's what exists today and what each approach costs you.

Read more ⤵️ blog.chainsafe.io/privacy-in…

Enterprise ledgers are shifting toward autonomous, machine-to-machine coordination. ChainSafe’s x402 Facilitator for @CantonNetwork replaces static API keys and subscriptions with high-velocity micro-billing built entirely for AI execution loops. Toolkit below 👇