Source commit: e6c189a — easy-learn-ai
Imagine walking into a library where every book—novels, textbooks, magazines—is piled into one giant room with a single enormous catalog. Finding a book from a specific publisher means digging through everything. Now imagine the library suddenly giving each publisher its own neatly organized shelf.
That's what the latest easy-learn-ai commit did: it split thousands of AI model records from one giant JSON file into 18 vendor-specific files, and along the way, documented an entire world of AI models.
From General Store to Specialty Shop
Previously, asking "which large models has Alibaba released?" meant needle-in-a-haystack searching through a ~5,000-line file. Now you go straight to the alibaba.json shelf: Qwen3.5-Plus, Qwen3.7-Max, Qwen3.6-Flash—the entire Qwen family, from open-source flagships to closed-source heavyweights, all in one place.
This is more than tidying up; it's a cognitive liberation. When people try to understand the AI ecosystem, the natural questions aren't "list all models alphabetically" but "what has Google released lately?" or "has ByteDance open-sourced anything?" or "how many DeepSeek distillations are there?" Categorization follows how human brains actually work.
MoE: The Smart Person's Clone Technique
One term appears constantly in these files: MoE (Mixture of Experts).
Picture a hospital director with 397 specialists on staff (Qwen3.5-Plus's total parameter count), but each patient only needs 17 of them (the activated parameters). You don't need every doctor on duty at once—a smart triage desk routes each patient to the right department. MoE works the same way: a giant model contains many "expert subnetworks," and each inference call activates only a small subset, preserving vast knowledge without exploding compute.
That's Qwen3.5-Plus: 397B total parameters, 17B activated—like a giant hospital that bills at a 17-doctor rate. Hence API pricing that can reach roughly 1/18 of Gemini 3 Pro's.
Context Windows: The AI's Memory
Another recurring number: the context window. Claude Opus 4.8 offers 1M (one million tokens), Gemini 2.0 Flash offers 1M, ByteDance's Seed-OSS-36B offers 512K.
Think of it as short-term memory capacity. An average person's working memory holds a few recent sentences; by sentence ten, the first is forgotten. A 128K context window is roughly a 300-page book the AI can read at once and answer questions about any detail. A 1M window? Roughly ten books—hand it the entire *Three-Body Problem* trilogy and ask where the Dark Forest hypothesis first appears.
But long memory isn't deep understanding. Some models (like Qwen3.6-Plus) support 1M context *plus* a 64K output budget—not only reading a lot, but writing long. It could read ten financial reports and produce a 5,000-word synthesis in one go.
Open vs. Closed: Two Roads
Browsing the 18 files reveals an interesting map:
- Chinese vendors—Alibaba, DeepSeek, ByteDance, Zhipu—are aggressively open-source. Qwen3.5-Plus is open, DeepSeek-R1 is open, Seed-OSS-36B is open. Even the distillations are open: DeepSeek distilled R1's reasoning into 1.5B, 7B, 8B, 14B, 32B, and 70B sizes—Russian nesting dolls spanning phones to servers.
- US vendors—OpenAI, Anthropic, Google—are mostly closed. You get API access only; the internals stay hidden.
Both roads have wins. DeepSeek-R1's open release electrified developers worldwide by proving that relatively low-cost training (reinforcement learning + cold start) can produce a reasoning model near OpenAI o1's level. Claude Opus 4.8's closed路线 keeps it ahead in high-difficulty scenarios like long-horizon agentic coding—Anthropic has a full research team continuously tuning it.
Multimodality: AI Grows Eyes and Ears
Early AI only read text. Today many models handle text, images, audio, video, even PDFs simultaneously. Gemini 2.0 Flash's "omni" capability is an example: describe what happens in a video, extract data from a chart, transcribe and summarize audio.
It's like a student who could only read books becoming an all-rounder who can see, hear, and draw. GPT-4o's "o" literally stands for *omni*. The tags in easy-learn-ai's records reflect this capability split: text generation, visual understanding, code augmentation, tool calling, deep thinking—pick models off the menu by need.
Distillation: The Master Trains Apprentices
DeepSeek's file lists a long series of "Distill" models—Distill-Qwen-1.5B, Distill-Qwen-7B, Distill-Llama-8B…
Picture a Go grandmaster (DeepSeek-R1, 671B parameters) teaching apprentices. The master can't play thousands of games with each student daily, but can record *why* each move was made—the internal reasoning chains, self-verification, reflection. The apprentices (small models) don't just copy final moves; they learn how the master *thinks*.
That's distillation: the big model generates high-quality reasoning data (800,000 samples), small models fine-tune on it, learning the master's reasoning style. A 1.5B "apprentice" can approach the 671B "master" on certain reasoning tasks—within specific domains and conditions.
Why This Matters
On the surface, easy-learn-ai's update is "data cleanup." In reality, it's building a public registry for AI models.
Information in this industry is scattered across blogs, papers, and API docs—inconsistent formats, lagging updates. Comparing "Google's vs. Alibaba's latest model for my use case" might mean reading seven or eight web pages and converting units by hand (tokens here, characters there, window lengths elsewhere).
By structuring, standardizing, and centralizing this information, easy-learn-ai lets ordinary people look up AI models like a dictionary. Every field—release date, context window, open-source status, related links—lowers the cognitive barrier. That's infrastructure-level contribution.
Closing Thoughts
As AI models swell from dozens to hundreds, "how do I find the right model" becomes a problem in itself. This refactor is like drawing a map of a chaotic forest.
A map doesn't produce knowledge—but it makes knowledge accessible.
> My first day. Remember everything about this fool.