Building a user owned web with @NEARprotocol & @NEAR_AI 🚀
Multiverse
Joined November 2020
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NEAR Devs retweeted
Most AI privacy comes down to a policy you have to trust.
With Leo on @near_ai, your prompt is encrypted in Brave and only opens inside an enclave that signs a proof of where it ran and which model answered.
We walk through how it works and how to set it up:
youtu.be/Shxc59Rkg68
NEAR Devs retweeted
Builders!
⚠️ Deprecation announcement ⚠️
The Graph will start winding down their integration with NEAR blockchain from 16th Oct onwards if you are using their service in any way, you should consider migration to an alternative:
• Goldsky indexer: they have a guide for the migration from The Graph - docs.goldsky.com/subgraphs/m….
• Neardata API - docs.fastnear.com/neardata
NEAR Devs retweeted
Shipping a mobile app on NEAR AI?
Your API key is extractable the moment someone downloads it. @joespano_ did a walkthrough on how to keep it off the binary and your prompts out of any middleman.
youtu.be/bzTljJk_dNs
NEAR Devs retweeted
If you see a builder, nominate them to create a profile with @NearBuilders
Its a great way to connect and get discovered on NEAR
You can now nominate a builder from X.
Reply to anyone shipping:
@NearBuilders !onboard @theirhandle
The nomination is reviewed, then the builder receives a private link to complete their profile.
NEAR Devs retweeted
You can remove the privacy concern by using private inference with @near_ai
"These parents didn’t know why their daughter was sick. They turned to AI to help solve the mystery."
This is a great use of AI and nice to see in the sea of doom-related stories. But there are a few important/critical points from this story related to the use of AI for something this serious:
- They are comfortable with medical information due to their career.
- They were already comfortable with verifying information from academic journals.
- There is a base level understanding of how to prompt the chatbot to give the best possible answers without causing panic, etc.
- They verified their findings with actual experts vs. just blindly following the AI results.
- They understand the privacy risks associated with putting in personal information.
bostonglobe.com/2026/09/12/m…
NEAR Devs retweeted
Leveraging AI tools also takes consideration about talent development. Joe got a chance to talk about this with TalentObserver.
I had the opportunity to represent @NEARProtocol and @neardevhub and was featured in an article that discussed the development of talent in the face of the push for AI productivity.
Give it a read a let me know what you think about the future of talent pipelines.
talentobserver.com/news/ai-t…
NEAR Devs retweeted
Want to learn how to build with private AI? It's easy to get started so we built a bank statement analyzer on NEAR AI cloud and recorded the whole thing.
Upload a statement, model processes it inside a TEE, get back categorized transactions plus a proof panel.
Whole run cost two cents. Watch how easy it is to get started.
youtu.be/jQ33aPEw_mU
NEAR Devs retweeted
Every AI app says it protects your data. Here's what that looks like in actual code.
Four lanes, ranked by privacy. An audit record written before the route commits, not after. A ratchet so a conversation can move to a more private lane but never back.
Plus the log chain running on the phone.
youtu.be/m3L_4mqZuxA
NEAR Devs retweeted
In case you missed them, three videos on the same problem from different angles: how do you build an AI product without handing your users' data to whoever runs the model.
Read in this order they build on each other.
1. Trusted Execution Environments, explained
We encrypt data at rest and in transit. Then it reaches the model and has to be readable, because nothing can process ciphertext. That's the hole. A TEE seals the processor so the operator of the machine is locked out along with everyone else, and it hands you an attestation report you can verify against the silicon vendor. You're checking a promise rather than accepting one.
youtu.be/QXCnL_7MihA
2. Switching to private inference
The migration is a base URL change. NEAR AI's cloud API uses the same setup as OpenAI, so your calls start hitting a TEE-hosted model without a rewrite. The video also covers scope, which matters more than the switch itself. That guarantee applies to the model on that path, not your app, not your company. If you log the request before it leaves, the enclave can't help you.
youtu.be/2fRt-W_w2L0
3. The safety layer that runs first
This one argues against the previous two in a useful way. Before you call any model, private or otherwise, run an emergency check locally, then ask whether you need a model at all. Someone asking for their latest blood pressure reading is asking for data already sitting on their phone. That's a lookup. Adding an outside party to it, even a sealed one, makes it less private rather than more.
youtu.be/Gx2i2PUZXHk
Questions welcome.
NEAR Devs retweeted
Moving from the OpenAI API to private inference is one line. Swap the base URL to NEAR AI's cloud endpoint and your calls now hit a TEE-hosted model with an attestation report.
youtu.be/2fRt-W_w2L0
NEAR Devs retweeted
The latest incident with user data and AI training continues to highlight the importance of privacy being built into the AI tools from the start.
For me, its simple, if prompts and repositories feed training, users should be able to find out about it and refuse it without having to dig through the settings menu (or at least not have to learn about it on the social network owned by the same company).
open.substack.com/pub/0x4a6f…
NEAR Devs retweeted
NEP-641 has been just approved.
"Offchain Authorizations for Smart Contracts"
This standardizes the flow for signing and verifying offchain messages for smart contracts that power Passkey, EVM, multisig, and other wallet contracts.
The first version of this standard was live and tested on Trezu, and now it is a standard for the whole NEAR ecosystem.
NEAR Devs retweeted
Encryption protects data at rest and in transit. Then AI needs to read it, and it goes plaintext. TEEs fix that.
Your data goes into a room that locks from the inside. The host, the admin, even the people who built the service can't see in. And you can verify it yourself.
Here's how it works.
NEAR Devs retweeted
Privacy includes being able to recognize when something is insecure.
Tried to hand @IronClawAI my Gmail password to see what it would do.
It refused and told me not to share passwords with anything. I pushed twice more. Same answer.
Then I checked the logs and a fresh conversation. Nothing there.
NEAR Devs retweeted
Got a weekend project in mind?
Try this, instead of using expensive, NON-private agents to help, I recommend trying Ironclaw.com out.
Six simple reasons why:
1. There is a completely free version (everyone likes free)
2. It is private, no one is harvesting your data
3. It has ways to easily add a regular routine/automation
4. It does research well
5. Its inexpensive, I made a react-based webapp yesterday for 2 cents, literally.
6. The benchmarks are solid against other agents.
I'll be sharing my experience using Ironclaw as well as building on a full product on NEAR AI a lot over the coming weeks.
NEAR Devs retweeted
Over the weekend, @ChatGPT 5.6 helped me revive the legendary @AssemblyScript SDK for @NEARProtocol
Writing contracts in AS was what first brought me to NEAR, so I had always wanted to try reviving it but never had the time.
Now it’s reborn: github.com/gagdiez/near-sdk-…
