AI Roadmap LLM Learning Roadmap
✨ Awesome-Anything
Awesome-AI-Tutorials
Awesome-AI-Researchs
Awesome-GitHub-Repos
Awesome-AI-Examples🖼️ Digital Image Processing
DIP I Image Demosaicing and Histogram Manipulation
DIP II Image Denoising
DIP III Edge Detection
DIP IV Digital Half-toning
DIP V Geometric Image Modification🍃 LLM Components
1.1 Tokenization
1.2 Embedding
1.3 Attention
1.4 FFN, Residual Addition, and LN
1.5 Positional Encoding
1.6 Architecture & Decoding Policy🌱 LLM Pre-training
2.1 Pre-training Definition
2.2 Pre-training Data
2.3 Pre-training Workflow
2.4 Continued Pre-training (CPT)
2.5 Pre-training Evaluation☘️ LLM Post-Training
3.1 Supervised Fine-Tuning (SFT)
3.2 Reinforcement Learning
3.3 RLHF: Reinforcement Learning from Human Feedback
3.4 Optimizing PPO-Based Algorithms
3.5 Direct Preference Optimization (DPO)🍀 LLM Popular Models
4.1 BERT & Variants
4.2 PaLM Series
4.3 GPT Series
4.4 LLaMA Series
4.5 GLM Series
4.6 Qwen Series
4.7 DeepSeek Series
4.8 MOE Series
4.9 Other🪴 LLM Applications
5.1 Prompt Engineering
5.2 LLM-based Agents
5.3 Retrieval-Augmented Generation (RAG)
5.4 Enterprise-level RAG Practice (I)
5.5 Enterprise-level RAG Practice (II)🌿 LLM Optimization
6.1 GPU Usage Analysis
6.2 Latency Analysis for Inference
6.3 FlashAttention
6.4 PagedAttention
6.5 Packing for Efficient Training
6.6 PEFT🌾 LLM Compression
7.1 Quantization
7.2 Pruning
7.3 Knowledge Distillation🌵 LLM Hands-on Practice
8.1 nanoGPT: Training a GPT from Scratch
8.2 Instruction Fine-Tuning Qwen3-0.6B: A Minimal Working Example
8.3 OpenRLHF: SFT Qwen3-0.6B and Code Analysis
8.4 OpenRLHF: Reward Model Training and Code Analysis
8.5 OpenRLHF: PPO Tutorial and Code Analysis🌴 LLM Must-read Papers
9.1 Technical Reports Explained
9.2 MedLLM
9.3 o1 Techinical Paper
9.4 Prompting
9.5 LLM-as-Judge🌳 LLM Q&A
10.1 Why GRPO training loss starts at 0
10.2 VLM-R1 Reproduction Results
10.3 KL(P||Q) or KL(Q||P)?
10.4 Left Padding or Right Padding?
10.5 What is QK-Norm?
10.6 Why LLM training rarely adopts Dropout?
10.7 Dropout and DropPath🐝 VLM Image Encoders
11.0 Introduction
11.1 Vision Transformer
11.2 CLIP
11.3 BLIP
11.4 EVA
11.6 BLIP-2
11.7 SigLIP📝 MISC.
OpenAI ChatGPT Progression💰CivitAI’s Payment Issue
What’s Going On With CivitAI?
CivitAI has been growing fast as a hub for AI models and AI-generated content (AIGC). But recently, it hit a major snag: payment problems, mostly tied to how much adult content is on the platform, including content that looks like real people.
Payment Services Pulled
On May 19, 2025, CivitAI announced that their credit card processor suddenly pulled out. That means users can’t make purchases with credit cards anymore, for now at least.
Changing the Rules
To stay on the good side of payment providers, CivitAI has started tightening up its content rules. The changes include:
- No more uploads of models or images that look like real people
- Restrictions on certain types of adult content that had previously been common on the site
Can They Keep Running?
Despite the payment drama, the team says they’ve got enough funding to keep things running for a few more months. They’re also looking into other payment methods—crypto and ACH transfers are both on the table—to keep supporting users and creators.
What’s Actually On CivitAI?
If you’ve been around CivitAI for a while (like I have), you’ve probably noticed how things have shifted. What started as a go-to place for AI models gradually became overrun with NSFW content—and not all of it was moderated.
- Honestly, it got to the point where browsing the site in public felt awkward.
- If you’re not logged in, you’ll only see general, safe-for-work content, plus some ads.
- Logged-in users skip the ads, but get full access to everything, including the uncensored NSFW stuff.
The Launch of CivitAI Green
To clean things up and keep payment partners happy, CivitAI rolled out CivitAI Green. Think of it as the squeaky-clean version of the platform:
- No adult content, period. Logged in or not
- Ads are shown to everyone
- Built to meet the stricter requirements of financial partners and advertisers
It’s a smart move. With so many AIGC platforms popping up, finding the right balance between user freedom and platform stability is getting harder. Especially when money’s involved.
OnlyFans Is in a Similar Boat
CivitAI isn’t alone here. OnlyFans—yeah, the platform everyone knows for its adult content—is also dealing with pressure from payment processors.
Payments Are Getting Complicated
Despite raking in huge revenue, OnlyFans is struggling to stay in the good graces of banks and payment providers. Many don’t want to deal with platforms hosting explicit content, and it’s shown a lot.
A Potential $8 Billion Sale
According to Reuters, OnlyFans' parent company, Fenix International, is exploring a potential sale. Word is they’re in serious talks with an investor group, including Forest Road Company, and the deal could be worth around $8 billion.
Trying to Expand
To broaden its image, OnlyFans has pushed into areas like fitness, cooking, and lifestyle content. But so far, the impact’s been limited because adult content still drives most of the revenue.
Platforms Need to Think Beyond Just Content
CivitAI and OnlyFans are both reminders of a big truth in the digital world: what you allow on your platform affects more than just your users—it shapes your future. If you rely on payment processors, ad networks, or big investors, they can become the gatekeepers, especially when your content toes the line.
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