😸 Your AI terms Cheat Sheet for 2026, Pt 1

Welcome, humans.

So the r/RealOrAI subreddit sometimes posts the most LUDICROUSLY impossible to believe AI videos and plays dumb asking if its AI or not. Exhibit A:

Sure, could this rat actually be almost the full length of this cat? Maybe?? But also, when have you ever seen a rat just roll up on its mortal enemy like that? And then actually scare off the cat?

If a cat was just chilling in the gutter like this good boy, i’m pretty sure said gutter cat would be CHOWING down on big tummy tums the rat here.

That said, while it seems unbelievable to me, I am also one of the most terminally online people you can find outside of X, as I spend all day reading the internet so you don’t have to.

So what do you think? Does this even more gigantic rat look real to you? Or what about this cat having a mouse dropped on it and freaking out? Or this hamster getting massaged like dough? Or what about this kid skateboarding with one unstable foot? How about this man serving stew out of his radiator? What about this gilded ballerina? Or this pure snow-white moose? Or what about this guy getting his fast food hotdog lunch vacuumed out from under him in broad daylight? (FWIW, I'm pretty sure this is the only video that is ACTUALLY not AI... lol).

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Your AI Vocabulary Cheat Sheet for 2025

If you've felt lost in AI jargon this year, here's your catch-up: the essential terms and who's actually winning the model race.

Let's start with what matters:

LLM (Large Language Model): This powers ChatGPT, Claude, Gemini—any AI that reads and writes text. It's a neural network trained to predict the next word. The "large" part? Billions of parameters (tiny dials the AI adjusts during training).

Tokens: The building blocks of how LLMs process text. One token ≈ 3-4 characters. Why care? You pay per token, and models have a "context window" measured in tokens.

RLVR (Reinforcement Learning from Verifiable Rewards): The paradigm shift of 2025, per Andrej Karpathy. Instead of showing models examples, you give them math problems or code tests with verifiable answers. Get it right? Reward. This led to models like OpenAI's o1 and DeepSeek-R1 that genuinely "reason."

KV Cache: Why your AI conversations got faster and cheaper. When LLMs process text, they calculate Key and Value matrices for "attention." These don't change for processed text, so models save (cache) them. Result: 10x cheaper tokens and 85% faster responses.

Context Compression: When conversations get too long, you need compression. Factory.ai's research shows their structured approach scores 3.70 vs Anthropic's 3.44 vs OpenAI's 3.35. All achieve ~99% compression, but quality varies.

And the model landscape as of the end of 2025:

• OpenAI: GPT-5.2 launched Dec 11 with GPT Image 1.5 following Dec 16. CEO Sam Altman declared "code red" after losing market share to Google.
• Google: Gemini 3 Flash (Dec 17) now powers the Gemini app globally. Processing >1 trillion tokens daily.
• Anthropic: Claude Opus 4.5 and Sonnet 4.5 dominate coding benchmarks. Hit $2B annualized revenue in Q1 2025.
• xAI: Grok 4.1 topped LMArena in November, and there’s another big Grok model training as we speak…
• DeepSeek (The Wildcard): DeepSeek-R1 matched OpenAI's o1 after training for just $5.3M. Surpassed ChatGPT as #1 iOS app in January, causing an 18% NVIDIA stock drop.

Why these terms and players matter: There’s three key takeaways from 2025:

1. Reasoning models can now "think before speaking"
2. China proved frontier AI doesn't need billions in training costs
3. Efficiency optimizations (caching, compression) matter more than raw scale (then imagine scaling the most efficient model possible…).

Understanding these three elements should help you build better, cheaper AI products in 2026. As Karpathy put it: LLMs are “summoning ghosts”, not “evolving animals.” They're alien intelligences optimized for objectives completely different from human learning. They don't think like us. They won't reason like us. But they're getting scary good at looking like they do… and that's enough to transform how we work, create, and solve problems. So it’s best to learn how to work with them ASAP.

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Prompt Tip of the Day

Don’t “summarize” your notes. Turn them into a document someone could actually use.

Raw notes are usually a junk drawer: good stuff buried under duplicates, half-thoughts, and “???” moments. This prompt forces the model to act like an ops person: structure first, no fact changes, open questions called out so nothing quietly disappears.

Works for almost anything: meeting notes, kickoff docs, SOPs, PRDs, briefs, postmortems, research dumps.

“Turn this mess into structure” Prompt 

Organize the following into a structured [doc type: brief, PRD, meeting notes, SOP]. 
Keep all facts, remove repetition, and flag open questions at the end. 
Notes: [paste]

Treats to Try

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1. *Dell Pro Max with GB10 runs open models like NVIDIA Nemotron models entirely locally. With 128GB unified memory and 4TB storage, it handles workloads that usually require sending everything to OpenAI or Anthropic—except your data never leaves your network. Keep your data local.
2. Gambo AI turns your one-paragraph game idea into a playable game.
3. Exa lets you search the web and pull back clean page text/summaries so you can build answers with sources.
4. Ray3 turns a text prompt (or image) into a cinematic video you can iterate in Draft Mode, then export in HDR.
5. Lucy Edit lets you tell a video what to change (swap outfits, add objects, replace backgrounds) while keeping the motion and composition intact — free to try.
6. Notion Agents take a goal like “build a launch plan” and create the pages, tasks, and docs for you across your workspace.
7. tldraw gives you an infinite-canvas whiteboard SDK you can drop into your product (with real-time collaboration).

Around the Horn

1. Axios argued the real question isn’t “is this a bubble?” it’s whether AI demand ramps fast enough to justify today’s capex… because the market is already pricing tomorrow.
   1. Axios also argued OpenAI’s centrality could turn into “AI systemic risk,” where one company’s decisions/disruptions cascade like critical infrastructure.
2. 3D-RE-GEN claimed single-image indoor scene reconstruction can generate coherent, editable textured meshes (not just pretty renders) using camera recovery + spatial optimization.
3. Animate Any Character described controllable character animation in static 3D worlds—user supplies scene + character, then text-drives behaviors while the model maintains consistency over time.
4. 4D-RGPT argued video agents need depth-aware, region-level 4D perception and introduced R4D-Bench to test it.
5. SGI-Bench argued models still fail at end-to-end scientific workflows, then proposed a benchmark + test-time RL trick to push novelty and executability.
6. PhysBrain claimed a scalable path to embodied intelligence by translating human first-person videos into structured, grounded supervision at 3M-example scale.
7. LoRe argued reasoning progress needs predictable laws (monotonicity + compositionality) and showed finetuning can enforce those behaviors.

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