Content Writer | Web3 Advisor | NFT | 🎮| BD | ambassador→ @PhalaNetwork, partner with @hyrotrader_com | KOL | linktr.ee/Soulmannn
Joined March 2022
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5 Sep 2023
Gamers find a way.💪🎮
We face challenges head-on because gaming is our passion, and nothing can stop us from exploring the vast worlds of our favorite titles. Adapting to the gaming universe is just part of the journey.
It must be win-win in the digital world 🌎
#Web3Gaming
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soulman 🎮 retweeted
GLM-5.2 is live on Phala for enterprise-level security.
@Zai_org’s open-source SOTA model is now positioned for confidential AI: TEE-backed inference, privacy-first deployment, and an OpenAI-compatible path for production workloads.
phala.com/models/z-ai/glm-5%…
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Svelte is one of the best tools out there for building fast, reactive frontends without a ton of overhead. But once your app starts doing more than just rendering UI, things like handling API credentials, running server side logic, or processing real user data, you need a place to run that part securely. That’s exactly what @PhalaNetwork Cloud is built for.
You can now deploy your Svelte app inside a TEE backed CVM, which means the sensitive parts of your application run in a verifiably secure environment instead of just sitting on a regular server hoping nothing goes wrong. The frontend experience stays exactly the same for users, but underneath it, your logic and data have real protection baked in.
This is useful especially for anyone building apps that institutions or security conscious users will actually trust, since you’re not asking them to take your word for it that things are safe. The template is ready to go, check it here: cloud.phala.com/templates/sv…, the code is open, and getting started takes just a few minutes.
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If you’re a builder or institution team working on something that needs both a clean frontend and real backend security, this is worth trying out for yourself.
The code is open on GitHub under Phala and the upstream framework too. Check it in this post below 👇🏻
Jun 15
Template code: github.com/Phala-Network/pha…
Upstream: github.com/sveltejs/svelte
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soulman 🎮 retweeted
Jun 15
Svelte compiles reactive UI into small app frontends.
Deploy it on Phala Cloud when server-side app logic, API credentials, and user data flows need a TEE-backed CVM.
Deploy: cloud.phala.com/templates/sv…
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OpenMed takes the messy clinical notes doctors and clinics write every day and turns them into structured data that healthcare AI can actually work with. The interesting part is where that processing happens. The notes, the NLP pipeline reading them, and the credentials needed to run the app all stay inside a @PhalaNetwork TEE CVM, basically a locked compute environment where nobody outside, not even whoever runs the underlying server, can see the data going in or the results coming out. For a developer or a hospital system thinking about adopting AI, that’s the difference between trusting a privacy policy and actually having no way to leak the data in the first place.
This is also a good snapshot of why Phala keeps coming up in serious conversations about AI infrastructure. They’re not building hype around confidential computing, they’re shipping templates people can deploy in minutes for problems that actually need this kind of protection, healthcare being one of the clearest examples.
If you want to see it yourself, the deploy template is at cloud.phala.com/templates/op… the code behind it is on Phala’s GitHub, and OpenMed’s original work is credited from maziyarpanahi’s repo.
Jun 13
OpenMed turns clinical text into structured healthcare AI insights.
Keep clinical notes, NLP pipelines, and app credentials inside a Phala TEE CVM.
Deploy: cloud.phala.com/templates/op…
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soulman 🎮 retweeted
22h
LMCache speeds up LLM serving by reusing KV cache across requests.
Run it on Phala Cloud when cache state, prompts, and serving metadata need a confidential TEE CVM.
Deploy: cloud.phala.com/templates/lm…
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Jun 15
PaddleOCR is now available as a deployable template on @PhalaNetwork Cloud, and it’s worth paying attention to if you’re building anything that needs to extract structured data from documents without exposing the contents to anyone, including the infrastructure running it.
What PaddleOCR does at its core is take PDFs and images and turn them into structured, machine readable data. That’s useful on its own. But the reason this deployment matters is where it runs, which is inside a TEE CVM on Phala Cloud. That means the OCR process, the pipeline logic, and the extracted results all stay private inside a confidential compute environment. No one outside the enclave sees what’s being processed, not even the node operators.
Think about the use cases this opens up. Financial documents, legal contracts, medical records, internal reports, anything where you need to run OCR but can’t afford to have raw document contents sitting in a standard cloud environment. With this setup, the extraction happens inside the enclave and the output comes out clean, structured, and ready to feed into an AI pipeline, all without the source data being exposed at any point.
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The template is live at cloud.phala.com/templates/pa… and the code is open on GitHub under Phala. if you want to dig into how it’s wired together. The upstream PaddleOCR project from PaddlePaddle is what’s powering the actual recognition engine.
Check the GitHub under Phala and the Upstream in the post below 👇🏻
Jun 12
Template code: github.com/Phala-Network/pha…
Upstream: github.com/PaddlePaddle/Padd…
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Jun 15
Flue gives TypeScript agents a proper structure to work with, handling skills, tools, sessions, and sandboxes out of the box so you’re not building that scaffolding yourself. What makes the @PhalaNetwork deployment interesting is where that all runs: repo context, prompts, and tool calls stay inside a TEE confidential VM, which means the compute environment itself is verified and isolated at the hardware level. Nobody outside that environment, including Phala, can read what’s happening inside.
This is worth paying attention to if you’re building agents that handle sensitive logic, proprietary prompts, or anything you’d rather not expose to a cloud provider’s infrastructure.
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You can spin it up directly from cloud.phala.com/templates/fl…, and the template code lives at the Phala Network GitHub if you want to dig into how it’s wired. The upstream Flue project is maintained at withastro/flue for anyone tracking the base framework.
Find them in the post below 👇🏻
Jun 11
Template code: github.com/Phala-Network/pha…
Upstream: github.com/withastro/flue
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Jun 14
Here’s something worth paying attention to if you’re building LLM powered applications and thinking about context window costs or data privacy.
There’s a tool called Headroom that compresses tool outputs, logs, files, and RAG chunks before they hit the LLM. That means less token usage, lower API costs, and leaner pipelines without you having to manually trim every payload going into your model. The interesting part is where it runs inside a @PhalaNetwork TEE Confidential VM. your API keys, compression rules, logs, and payloads stay inside that encrypted environment and don’t leave it. Nothing is exposed to the host machine or any outside party during processing.
The Headroom deployment is a small but concrete illustration of the kind of tooling that becomes possible when you have confidential compute as a base layer. you can deploy it today, inspect the code, and build on top of it. That’s the kind of ecosystem maturity that serious builders and institutions should be paying attention to.
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For builders handling sensitive data, agent pipelines, or anything where you need to guarantee what’s happening inside your compute, that distinction matters a lot.
You can deploy it directly from a template at cloud.phala.com/templates/he…, and the code is open on GitHub under Phala with the upstream from chopratejas/headroom if you want to dig into how it works or fork it for your own setup. Check it out in the below post 👇🏻
Jun 10
Template code: github.com/Phala-Network/pha…
Upstream: github.com/chopratejas/headr…
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soulman 🎮 retweeted
Jun 13
OpenMed turns clinical text into structured healthcare AI insights.
Keep clinical notes, NLP pipelines, and app credentials inside a Phala TEE CVM.
Deploy: cloud.phala.com/templates/op…
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soulman 🎮 retweeted
Jun 13
@DigitalXLtd sat down with @qlabsofficial and 01 Quantum this week to work through what post-quantum security actually means for digital asset custody as quantum computing continues to advance.
This is the kind of conversation institutions should be having right now. Custody risk in a post-quantum world is not a problem that shows up overnight and gets fixed in a week. It is a multi year question that requires positioning well before the threat becomes urgent, and the institutions that start early are the ones that will not be scrambling when Q-Day arrives.
If you are a protocol, custodian, exchange, or treasury team thinking about what quantum risk means for your assets, this is the conversation worth having before it becomes urgent.
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soulman 🎮 retweeted
Jun 11
GitHub Copilot SDK lets you embed Copilot Agent directly into your own apps and services. @PhalaNetwork took that and did something worth paying attention to. They built a deployment template that runs the Copilot Agent inside a TEE CVM, which means your repo context, your prompts, and the agent’s execution state never leave a protected environment. Nobody, not the infrastructure provider, not Phala, can see what’s happening inside that compute environment.
That’s the core thing Phala keeps demonstrating across different integrations, whether it’s AI inference, agent frameworks, or now Copilot, they keep showing up with actual deployable infrastructure that solves a real problem rather than just talking about confidential compute in theory.
Check the second thread below so you can deploy it today
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Jun 11
GitHub Copilot SDK lets you embed Copilot Agent directly into your own apps and services. @PhalaNetwork took that and did something worth paying attention to. They built a deployment template that runs the Copilot Agent inside a TEE CVM, which means your repo context, your prompts, and the agent’s execution state never leave a protected environment. Nobody, not the infrastructure provider, not Phala, can see what’s happening inside that compute environment.
That’s the core thing Phala keeps demonstrating across different integrations, whether it’s AI inference, agent frameworks, or now Copilot, they keep showing up with actual deployable infrastructure that solves a real problem rather than just talking about confidential compute in theory.
Check the second thread below so you can deploy it today
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Jun 11
If you’re building with agents and you care about what happens to sensitive context while the agent is running, this is the kind of stack worth exploring. The template is live, the upstream SDK is open, and the path from zero to a private Copilot-powered deployment is shorter than it’s been before.
Check it out here 👇🏻
Jun 9
Template code: github.com/Phala-Network/pha…
Upstream: github.com/github/copilot-sd…
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Jun 11
@qlabsofficial just opened early access to qVAULT and this is worth paying attention to.
Every wallet on Hyperliquid, like every other major blockchain, is secured by ECDSA. A powerful enough quantum computer will break that. And under a harvest now decrypt later model, attackers can record your exposed public keys today and decrypt them once the hardware catches up. That risk is not coming, it is already accumulating.
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It is currently the only live product that lets Hyperliquid users hold HYPE in post-quantum self custody. The advisory board includes a co-author of a NIST selected post quantum algorithm and a former NSA and DARPA technologist. Independent audits are published. This is not theoretical.
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Early access is opening gradually for $HYPE and $qONE holders.
Apply at qonetoken.io and follow @qlabsofficial.
Read the full article from where this thread is adopted from below 👇🏻
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