tegridydev/open-malsec
Preview
•
Updated
•
91
•
7
GO:OD:AM
tegridydev
AI & ML interests
Mechanistic Interpretability (MI) Research & sp00ky code stuff
Recent Activity
updated
a dataset
28 days ago
tegridydev/open-malsec
commented on
their
article
about 1 month ago
Open Source AI Agents | Github/Repo List | [2025]
commented on
their
article
about 1 month ago
Open Source AI Agents | Github/Repo List | [2025]
Organizations
None yet
tegridydev's activity
updated
a
dataset
28 days ago
commented on
Open Source AI Agents | Github/Repo List | [2025]
about 1 month ago
Added :D
commented on
Open Source AI Agents | Github/Repo List | [2025]
about 1 month ago
Added :D
reacted to
their
post with 🤗🔥
2 months ago
Post
2292
Open Source AI Agents | Github/Repo List | [2025]
https://huggingface.co/blog/tegridydev/open-source-ai-agents-directory
Check out the article & Follow, bookmark, save the tab as I will be updating it <3
(using it as my own notepad & decided i might keep it up to date if i post it here, instead of making the 15th_version of it and not saving it with a name i can remember on my desktop lol)
https://huggingface.co/blog/tegridydev/open-source-ai-agents-directory
Check out the article & Follow, bookmark, save the tab as I will be updating it <3
(using it as my own notepad & decided i might keep it up to date if i post it here, instead of making the 15th_version of it and not saving it with a name i can remember on my desktop lol)
commented on
Open Source AI Agents | Github/Repo List | [2025]
2 months ago
Added :D
reacted to
their
post with ❤️
2 months ago
Post
2292
Open Source AI Agents | Github/Repo List | [2025]
https://huggingface.co/blog/tegridydev/open-source-ai-agents-directory
Check out the article & Follow, bookmark, save the tab as I will be updating it <3
(using it as my own notepad & decided i might keep it up to date if i post it here, instead of making the 15th_version of it and not saving it with a name i can remember on my desktop lol)
https://huggingface.co/blog/tegridydev/open-source-ai-agents-directory
Check out the article & Follow, bookmark, save the tab as I will be updating it <3
(using it as my own notepad & decided i might keep it up to date if i post it here, instead of making the 15th_version of it and not saving it with a name i can remember on my desktop lol)
posted
an
update
2 months ago
Post
2292
Open Source AI Agents | Github/Repo List | [2025]
https://huggingface.co/blog/tegridydev/open-source-ai-agents-directory
Check out the article & Follow, bookmark, save the tab as I will be updating it <3
(using it as my own notepad & decided i might keep it up to date if i post it here, instead of making the 15th_version of it and not saving it with a name i can remember on my desktop lol)
https://huggingface.co/blog/tegridydev/open-source-ai-agents-directory
Check out the article & Follow, bookmark, save the tab as I will be updating it <3
(using it as my own notepad & decided i might keep it up to date if i post it here, instead of making the 15th_version of it and not saving it with a name i can remember on my desktop lol)
upvoted
an
article
2 months ago
Article
Open Source AI Agents | Github/Repo List | [2025]
By
•
•
25
published
an
article
2 months ago
Article
Open Source AI Agents | Github/Repo List | [2025]
By
•
•
25
reacted to
their
post with ❤️
2 months ago
Post
1923
WTF is Fine-Tuning? (intro4devs)
Fine-tuning your LLM is like min-maxing your ARPG hero so you can push high-level dungeons and get the most out of your build/gear... Makes sense, right? 😃
Here's a cheat sheet for devs (but open to anyone!)
---
TL;DR
- Full Fine-Tuning: Max performance, high resource needs, best reliability.
- PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
- Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
- RAFT: Best for fact-grounded models with dynamic retrieval.
- RLHF: Produces ethical, high-quality conversational AI, but expensive.
Choose wisely and match your approach to your task, budget, and deployment constraints.
I just posted the full extended article here
if you want to continue reading >>>
https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025
Fine-tuning your LLM is like min-maxing your ARPG hero so you can push high-level dungeons and get the most out of your build/gear... Makes sense, right? 😃
Here's a cheat sheet for devs (but open to anyone!)
---
TL;DR
- Full Fine-Tuning: Max performance, high resource needs, best reliability.
- PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
- Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
- RAFT: Best for fact-grounded models with dynamic retrieval.
- RLHF: Produces ethical, high-quality conversational AI, but expensive.
Choose wisely and match your approach to your task, budget, and deployment constraints.
I just posted the full extended article here
if you want to continue reading >>>
https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025
posted
an
update
2 months ago
Post
1923
WTF is Fine-Tuning? (intro4devs)
Fine-tuning your LLM is like min-maxing your ARPG hero so you can push high-level dungeons and get the most out of your build/gear... Makes sense, right? 😃
Here's a cheat sheet for devs (but open to anyone!)
---
TL;DR
- Full Fine-Tuning: Max performance, high resource needs, best reliability.
- PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
- Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
- RAFT: Best for fact-grounded models with dynamic retrieval.
- RLHF: Produces ethical, high-quality conversational AI, but expensive.
Choose wisely and match your approach to your task, budget, and deployment constraints.
I just posted the full extended article here
if you want to continue reading >>>
https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025
Fine-tuning your LLM is like min-maxing your ARPG hero so you can push high-level dungeons and get the most out of your build/gear... Makes sense, right? 😃
Here's a cheat sheet for devs (but open to anyone!)
---
TL;DR
- Full Fine-Tuning: Max performance, high resource needs, best reliability.
- PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
- Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
- RAFT: Best for fact-grounded models with dynamic retrieval.
- RLHF: Produces ethical, high-quality conversational AI, but expensive.
Choose wisely and match your approach to your task, budget, and deployment constraints.
I just posted the full extended article here
if you want to continue reading >>>
https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025
upvoted
an
article
2 months ago
Article
WTF is Fine-Tuning? (intro4devs) | [2025]
By
•
•
6
published
an
article
2 months ago
Article
WTF is Fine-Tuning? (intro4devs) | [2025]
By
•
•
6
upvoted
an
article
3 months ago
Article
LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs
By
•
•
6
published
an
article
3 months ago
Article
LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs
By
•
•
6
reacted to
their
post with ❤️
3 months ago
Post
1442
Open-MalSec v0.1 – Open-Source Cybersecurity Dataset
Evening! 🫡
📂 Just uploaded an early-stage open-source cybersecurity dataset focused on phishing, scams, and malware-related text samples.
This is the base version (v0.1)—a few structured sample files. Full dataset builds will come over the next few weeks.
🔗 Dataset link:
tegridydev/open-malsec
🔍 What’s in v0.1?
A few structured scam examples (text-based)
Covers DeFi, crypto, phishing, and social engineering
Initial labelling format for scam classification
⚠️ This is not a full dataset yet (samples are currently available). Just establishing the structure + getting feedback.
📂 Current Schema & Labelling Approach
"instruction" → Task prompt (e.g., "Evaluate this message for scams")
"input" → Source & message details (e.g., Telegram post, Tweet)
"output" → Scam classification & risk indicators
🗂️ Current v0.1 Sample Categories
Crypto Scams → Meme token pump & dumps, fake DeFi projects
Phishing → Suspicious finance/social media messages
Social Engineering → Manipulative messages exploiting trust
🔜 Next Steps
- Expanding datasets with more phishing & malware examples
- Refining schema & annotation quality
- Open to feedback, contributions, and suggestions
If this is something you might find useful, bookmark/follow/like the dataset repo <3
💬 Thoughts, feedback, and ideas are always welcome! Drop a comment or DMs are open 🤙
Evening! 🫡
📂 Just uploaded an early-stage open-source cybersecurity dataset focused on phishing, scams, and malware-related text samples.
This is the base version (v0.1)—a few structured sample files. Full dataset builds will come over the next few weeks.
🔗 Dataset link:
tegridydev/open-malsec
🔍 What’s in v0.1?
A few structured scam examples (text-based)
Covers DeFi, crypto, phishing, and social engineering
Initial labelling format for scam classification
⚠️ This is not a full dataset yet (samples are currently available). Just establishing the structure + getting feedback.
📂 Current Schema & Labelling Approach
"instruction" → Task prompt (e.g., "Evaluate this message for scams")
"input" → Source & message details (e.g., Telegram post, Tweet)
"output" → Scam classification & risk indicators
🗂️ Current v0.1 Sample Categories
Crypto Scams → Meme token pump & dumps, fake DeFi projects
Phishing → Suspicious finance/social media messages
Social Engineering → Manipulative messages exploiting trust
🔜 Next Steps
- Expanding datasets with more phishing & malware examples
- Refining schema & annotation quality
- Open to feedback, contributions, and suggestions
If this is something you might find useful, bookmark/follow/like the dataset repo <3
💬 Thoughts, feedback, and ideas are always welcome! Drop a comment or DMs are open 🤙
posted
an
update
3 months ago
Post
1442
Open-MalSec v0.1 – Open-Source Cybersecurity Dataset
Evening! 🫡
📂 Just uploaded an early-stage open-source cybersecurity dataset focused on phishing, scams, and malware-related text samples.
This is the base version (v0.1)—a few structured sample files. Full dataset builds will come over the next few weeks.
🔗 Dataset link:
tegridydev/open-malsec
🔍 What’s in v0.1?
A few structured scam examples (text-based)
Covers DeFi, crypto, phishing, and social engineering
Initial labelling format for scam classification
⚠️ This is not a full dataset yet (samples are currently available). Just establishing the structure + getting feedback.
📂 Current Schema & Labelling Approach
"instruction" → Task prompt (e.g., "Evaluate this message for scams")
"input" → Source & message details (e.g., Telegram post, Tweet)
"output" → Scam classification & risk indicators
🗂️ Current v0.1 Sample Categories
Crypto Scams → Meme token pump & dumps, fake DeFi projects
Phishing → Suspicious finance/social media messages
Social Engineering → Manipulative messages exploiting trust
🔜 Next Steps
- Expanding datasets with more phishing & malware examples
- Refining schema & annotation quality
- Open to feedback, contributions, and suggestions
If this is something you might find useful, bookmark/follow/like the dataset repo <3
💬 Thoughts, feedback, and ideas are always welcome! Drop a comment or DMs are open 🤙
Evening! 🫡
📂 Just uploaded an early-stage open-source cybersecurity dataset focused on phishing, scams, and malware-related text samples.
This is the base version (v0.1)—a few structured sample files. Full dataset builds will come over the next few weeks.
🔗 Dataset link:
tegridydev/open-malsec
🔍 What’s in v0.1?
A few structured scam examples (text-based)
Covers DeFi, crypto, phishing, and social engineering
Initial labelling format for scam classification
⚠️ This is not a full dataset yet (samples are currently available). Just establishing the structure + getting feedback.
📂 Current Schema & Labelling Approach
"instruction" → Task prompt (e.g., "Evaluate this message for scams")
"input" → Source & message details (e.g., Telegram post, Tweet)
"output" → Scam classification & risk indicators
🗂️ Current v0.1 Sample Categories
Crypto Scams → Meme token pump & dumps, fake DeFi projects
Phishing → Suspicious finance/social media messages
Social Engineering → Manipulative messages exploiting trust
🔜 Next Steps
- Expanding datasets with more phishing & malware examples
- Refining schema & annotation quality
- Open to feedback, contributions, and suggestions
If this is something you might find useful, bookmark/follow/like the dataset repo <3
💬 Thoughts, feedback, and ideas are always welcome! Drop a comment or DMs are open 🤙
reacted to
their
post with 👀
3 months ago
Post
1407
So, what is #MechanisticInterpretability 🤔
Mechanistic Interpretability (MI) is the discipline of opening the black box of large language models (and other neural networks) to understand the underlying circuits, features and/or mechanisms that give rise to specific behaviours
Instead of treating a model as a monolithic function, we can:
1. Trace how input tokens propagate through attention heads & MLP layers
2. Identify localized “circuit motifs”
3. Develop methods to systematically break down or “edit” these circuits to confirm we understand the causal structure.
Mechanistic Interpretability aims to yield human-understandable explanations of how advanced models represent and manipulate concepts which hopefully leads to
1. Trust & Reliability
2. Safety & Alignment
3. Better Debugging / Development Insights
https://bsky.app/profile/mechanistics.bsky.social/post/3lgvvv72uls2x
Mechanistic Interpretability (MI) is the discipline of opening the black box of large language models (and other neural networks) to understand the underlying circuits, features and/or mechanisms that give rise to specific behaviours
Instead of treating a model as a monolithic function, we can:
1. Trace how input tokens propagate through attention heads & MLP layers
2. Identify localized “circuit motifs”
3. Develop methods to systematically break down or “edit” these circuits to confirm we understand the causal structure.
Mechanistic Interpretability aims to yield human-understandable explanations of how advanced models represent and manipulate concepts which hopefully leads to
1. Trust & Reliability
2. Safety & Alignment
3. Better Debugging / Development Insights
https://bsky.app/profile/mechanistics.bsky.social/post/3lgvvv72uls2x
- 1 reply
liked
a
model
3 months ago
