QPU 1 MCP
💬
1
Execute quantum computing code and experiment with quantum states
Sk md saad amin
Reality123b
AI & ML interests
None yet
Recent Activity
new activity
about 11 hours ago
DataMuncher-Labs/README:Quantum Computing
updated
a Space
about 11 hours ago
lap-quantum/QPU-1-MCP
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scthornton's
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3 days ago
SecureCode: security-aware code models (3B–20B), trained for review + remediation
I’ve been frustrated by how often code assistants recommend patterns that pass tests but fail security review (e.g., string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, etc.). So I built **SecureCode**: a collection of **8 code models (3B → 20B)** trained to behave more like a security reviewer.
What you should expect from SecureCode:
- identify likely vuln patterns and explain *why* they’re risky
- outline plausible abuse paths (defensive framing)
- propose a secure rewrite (drop-in where possible)
- include defense-in-depth guidance + regression tests/checks
Links:
- **Models:** https://huggingface.co/collections/scthornton/securecode
- **Dataset:** https://huggingface.co/datasets/scthornton/securecode-v2
- **Paper:** https://arxiv.org/html/2512.18542v1 https://huggingface.co/papers/2512.18542
**How to test it (copy/paste prompt):**
```
> You are a senior application security engineer. Review the code below.
> Output: (1) findings with severity, (2) likely exploit scenarios (high level), (3) secure rewrite,
> (4) defense-in-depth recommendations, (5) regression tests/checks.
> Code: `...`
```
**I’m looking for real-world feedback**
- Your “this slipped through review once” snippets (sanitized is fine)
- False positives / false negatives you observe
- Contributions of new CVE-grounded examples
If you drop a snippet, please include language/framework + what the *correct* remediation looks like in your environment. If you have any contributions or suggestions for the dataset, I'd be happy to hear them. I have some new features and enhancements planned for v3 that are already underway, but for now, I'm focused on testing as many use cases as possible. Appreciate you all!
Organizations
Quantum Computing
15
#2 opened 12 days ago
by
Reality123b
updated
a
Space
about 11 hours ago
reacted to
scthornton's
post with 👍
3 days ago
Post
2095
SecureCode: security-aware code models (3B–20B), trained for review + remediation
I’ve been frustrated by how often code assistants recommend patterns that pass tests but fail security review (e.g., string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, etc.). So I built **SecureCode**: a collection of **8 code models (3B → 20B)** trained to behave more like a security reviewer.
What you should expect from SecureCode:
- identify likely vuln patterns and explain *why* they’re risky
- outline plausible abuse paths (defensive framing)
- propose a secure rewrite (drop-in where possible)
- include defense-in-depth guidance + regression tests/checks
Links:
- **Models:** https://huggingface.co/collections/scthornton/securecode
- **Dataset:** scthornton/securecode-v2
- **Paper:** https://arxiv.org/html/2512.18542v1 SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models (2512.18542)
**How to test it (copy/paste prompt):**
**I’m looking for real-world feedback**
- Your “this slipped through review once” snippets (sanitized is fine)
- False positives / false negatives you observe
- Contributions of new CVE-grounded examples
If you drop a snippet, please include language/framework + what the *correct* remediation looks like in your environment. If you have any contributions or suggestions for the dataset, I'd be happy to hear them. I have some new features and enhancements planned for v3 that are already underway, but for now, I'm focused on testing as many use cases as possible. Appreciate you all!
I’ve been frustrated by how often code assistants recommend patterns that pass tests but fail security review (e.g., string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, etc.). So I built **SecureCode**: a collection of **8 code models (3B → 20B)** trained to behave more like a security reviewer.
What you should expect from SecureCode:
- identify likely vuln patterns and explain *why* they’re risky
- outline plausible abuse paths (defensive framing)
- propose a secure rewrite (drop-in where possible)
- include defense-in-depth guidance + regression tests/checks
Links:
- **Models:** https://huggingface.co/collections/scthornton/securecode
- **Dataset:** scthornton/securecode-v2
- **Paper:** https://arxiv.org/html/2512.18542v1 SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models (2512.18542)
**How to test it (copy/paste prompt):**
> You are a senior application security engineer. Review the code below.
> Output: (1) findings with severity, (2) likely exploit scenarios (high level), (3) secure rewrite,
> (4) defense-in-depth recommendations, (5) regression tests/checks.
> Code: `...`**I’m looking for real-world feedback**
- Your “this slipped through review once” snippets (sanitized is fine)
- False positives / false negatives you observe
- Contributions of new CVE-grounded examples
If you drop a snippet, please include language/framework + what the *correct* remediation looks like in your environment. If you have any contributions or suggestions for the dataset, I'd be happy to hear them. I have some new features and enhancements planned for v3 that are already underway, but for now, I'm focused on testing as many use cases as possible. Appreciate you all!
reacted to
branikita's
post with 🔥
3 days ago
Post
2709
Robonine (Educational Robotics) completed a structural optimization of our 6-DOF robotic manipulator after a structural optimization study. By increasing structural rigidity through topology optimization and design refinement, we reduced end-effector deflection by over 60% (from ~1.05 mm to ~0.41 mm) and improved motion stability. The final configuration delivers higher precision and reliability for industrial applications.
Article: https://robonine.com/increasing-the-structural-rigidity-of-the-manipulator/
Article: https://robonine.com/increasing-the-structural-rigidity-of-the-manipulator/
- 5 replies
published
a
Space
4 days ago
posted
an
update
4 days ago
Post
192
Today, is a good day for me.
I have completed development of my QPU-1, the most powerful Quantum Processing Unit you can access through MCP (as far as I know and have tried).
Try it out for yourself using my mcp enabled space: lap-quantum/QPU-1-MCP
(And PS. This is my first MCP server, so give me suggestions if you want :D)
I have completed development of my QPU-1, the most powerful Quantum Processing Unit you can access through MCP (as far as I know and have tried).
Try it out for yourself using my mcp enabled space: lap-quantum/QPU-1-MCP
(And PS. This is my first MCP server, so give me suggestions if you want :D)
- 1 reply
liked
a
Space
4 days ago
published
a
Space
4 days ago
liked
a
model
5 days ago
really? didn't know it was a 500M$ mystery.
liked
a
Space
6 days ago
replied to
TravisMuhlestein's
post
14 days ago
and it fails mostly at instruction following
liked
a
dataset
15 days ago
Formatting
7
#1 opened 21 days ago
by
Roman190928
...
1
#1 opened 18 days ago
by
Reality123b
replied to
Ujjwal-Tyagi's
post
19 days ago
Yes, the guardrails in OpenAI’s models are complete [bad] it literally doesn't respond to a harmless programming query and says that Clifford+T gate is "not universal quantum computing", yes it happened to me once and I was so annoyed.
reacted to
Ujjwal-Tyagi's
post with 👍
19 days ago
Post
2683
For more better details and analysis, you can read the article here: https://huggingface.co/blog/Ujjwal-Tyagi/steering-not-censoring, We are sleepwalking into a crisis. I am deeply concerned about AI model safety right now because, as the community rushes to roll out increasingly powerful open-source models, we are completely neglecting the most critical aspect: safety. It seems that nobody is seriously thinking about the potential consequences of unregulated model outputs or the necessity of robust guardrails. We are essentially planting the seeds for our own destruction if we prioritize raw performance over security.
This negligence is terrifyingly evident when you look at the current landscape. Take Qwen Image 2512, for example; while it delivers undeniably strong performance, it has incredibly weak guardrails that make it dangerous to deploy. In stark contrast, Z Image might not get as much hype for its power, but it has much better safety guardrails than Qwen Image 2512.
It is imperative that the open-source community and developers recognize that capability without responsibility is a liability. We must actively work on protecting these models from bad actors who seek to exploit them for malicious purposes, such as generating disinformation, creating non-consensual imagery, or automating cyberattacks. It is no longer enough to simply release a powerful model; we must build layers of defense that make it resistant to jailbreaking and adversarial attacks. Developers need to prioritize alignment and robust filtering techniques just as much as they prioritize benchmark scores. We cannot hand such potent tools to the world without ensuring they have the safety mechanisms to prevent them from being turned against us.
This negligence is terrifyingly evident when you look at the current landscape. Take Qwen Image 2512, for example; while it delivers undeniably strong performance, it has incredibly weak guardrails that make it dangerous to deploy. In stark contrast, Z Image might not get as much hype for its power, but it has much better safety guardrails than Qwen Image 2512.
It is imperative that the open-source community and developers recognize that capability without responsibility is a liability. We must actively work on protecting these models from bad actors who seek to exploit them for malicious purposes, such as generating disinformation, creating non-consensual imagery, or automating cyberattacks. It is no longer enough to simply release a powerful model; we must build layers of defense that make it resistant to jailbreaking and adversarial attacks. Developers need to prioritize alignment and robust filtering techniques just as much as they prioritize benchmark scores. We cannot hand such potent tools to the world without ensuring they have the safety mechanisms to prevent them from being turned against us.
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- 17 replies
liked
a
dataset
20 days ago
reacted to
nroggendorff's
post with 😔
20 days ago
Post
2921
I am now being charged for paused and unstarted spaces out of the blue.
I think this is it, folks. o7
The unstarted spaces I can get behind. I would've appreciated a warning email first, but whatever. However, every time I restart the active usage goes up, despite all of my spaces being moved to CPU (free), and being paused.
I think this is it, folks. o7
The unstarted spaces I can get behind. I would've appreciated a warning email first, but whatever. However, every time I restart the active usage goes up, despite all of my spaces being moved to CPU (free), and being paused.
- 13 replies
posted
an
update
20 days ago
Post
1094
We (DataMuncher-Labs) just made a new dataset for inducing mathematical and analytical reasoning in LLMs.
DataMuncher-Labs/UltraMath-Reasoning-Small
DataMuncher-Labs/UltraMath-Reasoning-Small
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- 1 reply