Privacy builds here. Docs, tools, and support for builders. Ask us anything.
- Tweets 3,322
- Following 62
- Followers 5,078
- Likes 2,869
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.
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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.
piped.video/bzTljJk_dNs
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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
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Joe has been building a privacy-first health AI app in public and recording what he learns about private inference along the way. If you ship on top of LLM APIs and your users' data matters, here's where to start. 🧵
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Two more from inside the app. Privacy by Design walks through the Swift router and why an audit record gets written before a route commits. The safety layer video covers the checks that run before any AI call happens.
piped.video/watch?v=m3L_4mqZ…
piped.video/watch?v=Gx2i2PUZ…
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And the one that started the series: how Joe keeps health data private when building on the OpenAI or Anthropic APIs.
piped.video/watch?v=AuxKbKQ_…
Full playlist: piped.video/playlist?list=PL…
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In case you missed it last week, @near_ai can now be used on @OpenRouter and its really simple to get started.
If you're already on OpenRouter, private inference is a couple of clicks away as of this week due to @near_ai
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Or just use private inference like @near_ai
Introducing AgentCloak: Use any AI without sharing your real data.
Chinese AI services, ChatGPT, Claude, doesn't matter.
You probably try to hide details before asking: different names, fake numbers, no address.
But then the answer's useless because the AI is missing actual context.
AgentCloak runs in your browser.
It swaps your sensitive info for realistic fakes before sending anything, then swaps your real info back into the response.
You get what you need.
The AI gets nothing about you.
Works entirely in-browser.
Already trusted by some of the biggest companies in the world.
Now free to use.
agentcloak.ai/
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If you're already on OpenRouter, private inference is a couple of clicks away as of this week due to @near_ai
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What is a BAA, do I need one, and what does it have to do with private inference? Is it different than a TEE on @near_ai?
I covered all three in the new video. The answer to the second one is probably not, but it's good to understand the difference anyways.
piped.video/PQsvrw6hCv8
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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…
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Spend less than five minutes this morning pointing OpenCode at NEAR AI Cloud instead of the usual provider. Private inference should be your default.
Build with privacy. Don't let your work get used.
I’m currently testing NEAR AI with an open model running in a Private TEE.
Right now I’m using Qwen 3.8 27B inside OpenCode, connected directly to NEAR AI, to see how far I can reduce my dependency on centralized AI providers.
What interests me most is the model itself:
open AI + confidential inference + very competitive costs + crypto-native infrastructure.
If the experience can compete with tools like Codex, this could become one of the most concrete AI + blockchain use cases I’ve tested so far.
Privacy, compute, and model access in one stack.
$NEAR #NEAR #NEARAI #AI #Privacy #CryptoAI #OpenSourceAI #Web3 #ConfidentialAI
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