i hope good things come from this, but i have doubts.
nomadicsynth PRO
nomadicsynth
AI & ML interests
architecture research, knowledge discovery
Recent Activity
upvoted a paper 5 days ago
AccessEval: Benchmarking Disability Bias in Large Language Models published a model 15 days ago
nomadicsynth/neon-360-0.1Organizations
reacted to Bc-AI's post with 👍 23 days ago
Post
2612
Smilyai News
Hello everyone! August has been a crazy month for us at Smilyai-Labs. We've been doing lots behind the scenes, so here's the latest 👇
1. MiniCoder
We are very close to releasing MiniCoder-1, our first-generation coding model designed for reasoning and coding. Our planned context window is 128K, but the earlier versions probably will not support that long! It's currently in the final stages of DPO so expect a release in early september.
Release: VERY SOON™🤣
2. Smilyai G1
So, the current plan is 20B parameter model total, with a MoE architecture, activating around 2B parameters per token. Its desgigned for maximum performance but keeping it runnable on consumer hardware. It's only a plan and i have no idea when me and the team can finish it. Expect a launch around the end of september to early october-ish. I have no guarantees so don't quote me on the launch date.
3. T1
Smilyai-T1 is another major model we are working on.
The goal for T1 is to take what we learnt from the countless architectural experiments and creating a powerful model designed for thinking. Think MiniCoder but reasons more and G1 but more capable. Its main goals are coding, math, reasoning and general capability.
4. Omni
We are also planning Omni, our first from scratch multimodal model. It will not launch this year as it will take a while. We are actively researching the best architecture for it and we will update progress as we go!
Thanks to our beta testers:
@guardamarcos
@ProCreations
@juiceb0xc0de
@Timmy6767
@Sbui503
@atom77777
@Fishtiks
@smartdigitalnetworks
@EmetTheGolum
@smilyai-large-team
@MUK-IS-GOAT
@Bc-AI
Thanks to my friends who work with me at lunchtimes (Smilyai-Labs team):
@MUK-IS-GOAT
@smilyai-large-team
August was wild. Let’s see what September brings. 🚀
— Bc-AI, on behalf of SmilyAI Labs
Hello everyone! August has been a crazy month for us at Smilyai-Labs. We've been doing lots behind the scenes, so here's the latest 👇
1. MiniCoder
We are very close to releasing MiniCoder-1, our first-generation coding model designed for reasoning and coding. Our planned context window is 128K, but the earlier versions probably will not support that long! It's currently in the final stages of DPO so expect a release in early september.
Release: VERY SOON™🤣
2. Smilyai G1
So, the current plan is 20B parameter model total, with a MoE architecture, activating around 2B parameters per token. Its desgigned for maximum performance but keeping it runnable on consumer hardware. It's only a plan and i have no idea when me and the team can finish it. Expect a launch around the end of september to early october-ish. I have no guarantees so don't quote me on the launch date.
3. T1
Smilyai-T1 is another major model we are working on.
The goal for T1 is to take what we learnt from the countless architectural experiments and creating a powerful model designed for thinking. Think MiniCoder but reasons more and G1 but more capable. Its main goals are coding, math, reasoning and general capability.
4. Omni
We are also planning Omni, our first from scratch multimodal model. It will not launch this year as it will take a while. We are actively researching the best architecture for it and we will update progress as we go!
Thanks to our beta testers:
@guardamarcos
@ProCreations
@juiceb0xc0de
@Timmy6767
@Sbui503
@atom77777
@Fishtiks
@smartdigitalnetworks
@EmetTheGolum
@smilyai-large-team
@MUK-IS-GOAT
@Bc-AI
Thanks to my friends who work with me at lunchtimes (Smilyai-Labs team):
@MUK-IS-GOAT
@smilyai-large-team
August was wild. Let’s see what September brings. 🚀
— Bc-AI, on behalf of SmilyAI Labs
reacted to ajibawa-2023's post with 🔥 3 months ago
Post
7065
Shell-Code-Large
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
reacted to javadtaghia's post with 👍 6 months ago
Post
221
We have published a complete paper about AtMem.Ai
Check it out here.
Please refer to the technical paper below.
https://github.com/aetna000/aetnamem/blob/e24d5aa74e62dbebeeb28a2e49b5c4d2006bbb4d/paper/aetnamem-control-plane.pdf
Check the benchmark
https://huggingface.co/spaces/Aetna000/MemoryStackBench
Check it out here.
Please refer to the technical paper below.
https://github.com/aetna000/aetnamem/blob/e24d5aa74e62dbebeeb28a2e49b5c4d2006bbb4d/paper/aetnamem-control-plane.pdf
Check the benchmark
https://huggingface.co/spaces/Aetna000/MemoryStackBench
- 4 replies
reacted to scthornton's post with 👍 8 months ago
Post
2195
# SecureCode: Security-Aware Code Models
**A collection of 8 code models (3B–20B) trained to behave like a security reviewer.**
## The Problem
Code assistants frequently recommend patterns that pass tests but fail security review—string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, and more. I built SecureCode to address this gap.
## What SecureCode Does
- **Identify vulnerable patterns** and explain why they're risky
- **Outline plausible abuse paths** (defensive framing)
- **Propose secure rewrites** (drop-in replacements where possible)
- **Include defense-in-depth guidance** + regression tests/checks
## Resources
| Resource | Link |
|----------|------|
| Models | https://huggingface.co/collections/scthornton/securecode |
| Dataset | scthornton/securecode (2,185 examples) |
| Paper | https://arxiv.org/abs/2512.18542 |
## How to Test It
Copy and paste this prompt with your code:
## Dataset Coverage
SecureCode covers both traditional and emerging security domains:
- **Traditional web security** (OWASP Top 10 2021)
- **AI/ML security** (OWASP LLM Top 10 2025): prompt injection, RAG poisoning, model extraction, agentic AI patterns
## We Want Your Feedback
We're looking for real-world contributions:
- **Real snippets**: Share code that "slipped through review once" (sanitized is fine)
- **False positives/negatives**: What didn't work as expected?
- **CVE-grounded examples**: New vulnerability patterns you've encountered
**Please include**: language/framework + what the correct remediation looks like in your environment.
---
**Have contributions or suggestions?** I'd be happy to hear them. Thanks for your support!
**A collection of 8 code models (3B–20B) trained to behave like a security reviewer.**
## The Problem
Code assistants frequently recommend patterns that pass tests but fail security review—string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, and more. I built SecureCode to address this gap.
## What SecureCode Does
- **Identify vulnerable patterns** and explain why they're risky
- **Outline plausible abuse paths** (defensive framing)
- **Propose secure rewrites** (drop-in replacements where possible)
- **Include defense-in-depth guidance** + regression tests/checks
## Resources
| Resource | Link |
|----------|------|
| Models | https://huggingface.co/collections/scthornton/securecode |
| Dataset | scthornton/securecode (2,185 examples) |
| Paper | https://arxiv.org/abs/2512.18542 |
## How to Test It
Copy and paste this prompt with your code:
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: `...`## Dataset Coverage
SecureCode covers both traditional and emerging security domains:
- **Traditional web security** (OWASP Top 10 2021)
- **AI/ML security** (OWASP LLM Top 10 2025): prompt injection, RAG poisoning, model extraction, agentic AI patterns
## We Want Your Feedback
We're looking for real-world contributions:
- **Real snippets**: Share code that "slipped through review once" (sanitized is fine)
- **False positives/negatives**: What didn't work as expected?
- **CVE-grounded examples**: New vulnerability patterns you've encountered
**Please include**: language/framework + what the correct remediation looks like in your environment.
---
**Have contributions or suggestions?** I'd be happy to hear them. Thanks for your support!
replied to javadtaghia's post 8 months ago
i foresee a new industry on Fiverr adding prompt injections to people's CVs. The future is hilarious, if you don't mind a bit of chaos.
replied to their post about 1 year ago
Interesting. Did you learn how the creator of the dataset synthesised it?
replied to their post about 1 year ago
10 days? Rookie numbers 😊. so many side-quests. no idea what to do with myself lol. i'd love to hear about your ideas, and happy to give some feedback (for what it's worth)
reacted to jbilcke-hf's post with 👍 over 1 year ago
Post
2104
Hi everyone,
I've seen some unsuccessful attempts at running Wan2GP inside a Hugging Face Space, which is a shame as it is a great Gradio app!
So here is a fork that you can use, with some instructions on how to do this:
jbilcke-hf/Wan2GP_you_must_clone_this_space_to_use_it#1
Note : some things like persistent models/storage/custom LoRAs might not be fully working out of the box. If you need those, you might have to dig into the Wan2GP codebase, see how to tweak the storage folder. Happy hacking!
I've seen some unsuccessful attempts at running Wan2GP inside a Hugging Face Space, which is a shame as it is a great Gradio app!
So here is a fork that you can use, with some instructions on how to do this:
jbilcke-hf/Wan2GP_you_must_clone_this_space_to_use_it#1
Note : some things like persistent models/storage/custom LoRAs might not be fully working out of the box. If you need those, you might have to dig into the Wan2GP codebase, see how to tweak the storage folder. Happy hacking!
reacted to ArturoNereu's post with 👍 over 1 year ago
Post
1776
I just finished AI Engineering by Chip Huyen. Probably the best resource I’ve seen that covers the full AI stack. People wondering how to shift their careers toward AI might find this very useful.
I recently shared this list of resources I’ve been using to learn AI:
🔗 https://github.com/ArturoNereu/AI-Study-Group
I recently shared this list of resources I’ve been using to learn AI:
🔗 https://github.com/ArturoNereu/AI-Study-Group
reacted to codelion's post with 🚀 over 1 year ago
Post
3660
🧠 We just implemented Andrej Karpathy's "third paradigm" for LLM learning!
System Prompt Learning (SPL) enables LLMs to automatically learn problem-solving strategies from experience, rather than relying on static prompts.
🚀 How it works:
Your LLM builds a database of effective strategies, selects the best ones for each problem, and refines them over time based on success rates.
📊 Results across math benchmarks:
Arena Hard: 29% → 37.6% (+8.6%)
AIME24: 23.33% → 30% (+6.67%)
OptILLMBench: 61% → 65% (+4%)
The best part? All strategies are human-readable and the system gets progressively better at problem types you use frequently.
✨ Key benefits:
🔄 Cumulative learning over time
📖 Transparent, inspectable strategies
🔌 Works with any OpenAI-compatible API
⚡ Simple integration: just add "spl-" prefix to your model
Built as an open-source plugin in optillm. After 500 queries, our system developed 129 strategies and refined 97 of them!
This feels like a genuine step toward AI that learns from experience while staying completely interpretable.
🔗 GitHub: https://github.com/codelion/optillm/tree/main/optillm/plugins/spl
📖 Full article: https://huggingface.co/blog/codelion/system-prompt-learning
🐦 Original Karpathy tweet: https://x.com/karpathy/status/1921368644069765486
Have you experimented with advanced system prompting? What strategies would you want your LLM to learn?
System Prompt Learning (SPL) enables LLMs to automatically learn problem-solving strategies from experience, rather than relying on static prompts.
🚀 How it works:
Your LLM builds a database of effective strategies, selects the best ones for each problem, and refines them over time based on success rates.
📊 Results across math benchmarks:
Arena Hard: 29% → 37.6% (+8.6%)
AIME24: 23.33% → 30% (+6.67%)
OptILLMBench: 61% → 65% (+4%)
The best part? All strategies are human-readable and the system gets progressively better at problem types you use frequently.
✨ Key benefits:
🔄 Cumulative learning over time
📖 Transparent, inspectable strategies
🔌 Works with any OpenAI-compatible API
⚡ Simple integration: just add "spl-" prefix to your model
Built as an open-source plugin in optillm. After 500 queries, our system developed 129 strategies and refined 97 of them!
This feels like a genuine step toward AI that learns from experience while staying completely interpretable.
🔗 GitHub: https://github.com/codelion/optillm/tree/main/optillm/plugins/spl
📖 Full article: https://huggingface.co/blog/codelion/system-prompt-learning
🐦 Original Karpathy tweet: https://x.com/karpathy/status/1921368644069765486
Have you experimented with advanced system prompting? What strategies would you want your LLM to learn?
posted an update over 1 year ago
Post
2882
Anyone using AI and ML to help neurodivergent people? I'd love to hear what you're doing.
- 4 replies
replied to ProCreations's post over 1 year ago
Every time you use a HF space you randomly start dancing for 5 minutes
This one fr i'm dancing all day anyway idk how people survive in cubicles
reacted to codelion's post with 🚀 over 1 year ago
Post
2899
🧬 Hey everyone! Just released **OpenEvolve** - an open-source implementation of Google DeepMind's AlphaEvolve system.
It's an evolutionary coding agent that uses LLMs to discover and optimize algorithms. I successfully replicated DeepMind's results on circle packing (99.97% match!) and evolved a random search into a simulated annealing algorithm.
✨ Key features:
- Evolves entire codebases (not just single functions)
- Works with any OpenAI-compatible API
- LLM ensemble approach for better results
- Multi-objective optimization
👉 Check it out:
GitHub: https://github.com/codelion/openevolve
Blog post: https://huggingface.co/blog/codelion/openevolve
Would love to hear your thoughts or answer any questions about it!
It's an evolutionary coding agent that uses LLMs to discover and optimize algorithms. I successfully replicated DeepMind's results on circle packing (99.97% match!) and evolved a random search into a simulated annealing algorithm.
✨ Key features:
- Evolves entire codebases (not just single functions)
- Works with any OpenAI-compatible API
- LLM ensemble approach for better results
- Multi-objective optimization
👉 Check it out:
GitHub: https://github.com/codelion/openevolve
Blog post: https://huggingface.co/blog/codelion/openevolve
Would love to hear your thoughts or answer any questions about it!
reacted to ProCreations's post with 👀 over 1 year ago
Post
1911
I made a space!
Check out
https://huggingface.co/spaces/ProCreations/realtime-ai-visualization
This cool space visualizes a real neural net in real time. It trains a real 199 parameter model on XOR. With baby mode for non-devs and advanced mode for developers or enthusiasts, (hopefully) everyone will understand!
Check out
https://huggingface.co/spaces/ProCreations/realtime-ai-visualization
This cool space visualizes a real neural net in real time. It trains a real 199 parameter model on XOR. With baby mode for non-devs and advanced mode for developers or enthusiasts, (hopefully) everyone will understand!
posted an update over 1 year ago
Post
559
How do you talk about AI’s promise without sounding like you’re selling out to the next tech gold rush?
- 1 reply
reacted to AdinaY's post with 👀 over 1 year ago
Post
2600
Matrix Game 🎮 an interactive foundation model for controllable game world generation, released by Skywork AI.
Skywork/Matrix-Game
✨ 17B with MIT licensed
✨ Diffusion-based image-to-world video generation via keyboard & mouse input
✨ GameWorld Score benchmark for Minecraft world models
✨ Massive Matrix Game Dataset with fine-grained action labels
Skywork/Matrix-Game
✨ 17B with MIT licensed
✨ Diffusion-based image-to-world video generation via keyboard & mouse input
✨ GameWorld Score benchmark for Minecraft world models
✨ Massive Matrix Game Dataset with fine-grained action labels
reacted to ArturoNereu's post with 🔥 over 1 year ago
Post
4515
I’ve been learning AI for several years (coming from the games industry), and along the way, I curated a list of the tools, courses, books, papers, and models that actually helped me understand things.
I turned this into a GitHub repo:
https://github.com/ArturoNereu/AI-Study-Group
If you’re just getting started, I recommend:
📘 Deep Learning – A Visual Approach: https://www.glassner.com/portfolio/deep-learning-a-visual-approach
🎥 Dive into LLMs with Andrej Karpathy: https://youtu.be/7xTGNNLPyMI?si=aUTq_qUzyUx36BsT
🧠 The 🤗 Agents course](https://huggingface.co/learn/agents-course/
The repo has grown with help from the community (Reddit, Discord, etc.) and I’ll keep updating it.
If you have any favorite resources, I’d love to include them.
I turned this into a GitHub repo:
https://github.com/ArturoNereu/AI-Study-Group
If you’re just getting started, I recommend:
📘 Deep Learning – A Visual Approach: https://www.glassner.com/portfolio/deep-learning-a-visual-approach
🎥 Dive into LLMs with Andrej Karpathy: https://youtu.be/7xTGNNLPyMI?si=aUTq_qUzyUx36BsT
🧠 The 🤗 Agents course](https://huggingface.co/learn/agents-course/
The repo has grown with help from the community (Reddit, Discord, etc.) and I’ll keep updating it.
If you have any favorite resources, I’d love to include them.
replied to ProCreations's post over 1 year ago
Is the GPU offer still available? 😂
replied to ProCreations's post over 1 year ago
@ProCreations relatable