tegridydev/infosec-tool-output
Viewer
•
Updated
•
551
•
40
•
1
GO:OD:AM
tegridydev
AI & ML interests
Mechanistic Interpretability (MI) Research & sp00ky code stuff
Recent Activity
updated
a dataset
3 days ago
tegridydev/infosec-tool-output
liked
a dataset
3 days ago
tegridydev/infosec-tool-output
published
a dataset
3 days ago
tegridydev/infosec-tool-output
Organizations
None yet
updated
a
dataset
3 days ago
liked
a
dataset
3 days ago
published
a
dataset
3 days ago
updated
a
dataset
10 months ago
commented on
Open Source AI Agents | Github/Repo List | [2025]
10 months ago
Added :D
commented on
Open Source AI Agents | Github/Repo List | [2025]
10 months ago
Added :D
reacted to
their
post with 🤗🔥
11 months ago
Post
2389
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]
11 months ago
Added :D
reacted to
their
post with ❤️
11 months ago
Post
2389
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
11 months ago
Post
2389
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
11 months ago
Article
Open Source AI Agents | Github/Repo List | [2025]
•
28
published
an
article
11 months ago
Article
Open Source AI Agents | Github/Repo List | [2025]
•
28
reacted to
their
post with ❤️
11 months ago
Post
1931
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
11 months ago
Post
1931
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
11 months ago
Article
WTF is Fine-Tuning? (intro4devs) | [2025]
•
7
published
an
article
11 months ago
Article
WTF is Fine-Tuning? (intro4devs) | [2025]
•
7
upvoted
an
article
12 months ago
Article
LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs
•
16
published
an
article
12 months ago
Article
LLM Dataset Formats 101: A No‐BS Guide for Hugging Face Devs
•
16
reacted to
their
post with ❤️
12 months ago
Post
1475
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 🤙