Welcome, humans.

So, GPT-5.6 is rumored to launch this Thursday, which means we are legally required to say: please do not tattoo these supposed leaks on your forearm just yet.  

That said, the “rumors” are unusually specific. The chatter points to a possible June 25 launch with a 2M-token context window, cheaper pricing, better agentic coding, stronger image-to-code replication, cleaner frontend generation, and Playwright-style browser testing inside ChatGPT. 

TBH, vision model capabilities are the missing piece of AI development right now. IMO, solve machine vision, you solve engineering. Then it’ll just be about how good the human prompter’s idea is. We think Claude still has some room to grow here, so if GPT 5.6 is better at vision, that could be a huge deal. 

Anyway, Corey broke down everything we think we know about GPT-5.6, because the interesting part is where OpenAI seems to be aiming: models that can use tools better, check their own work, fix mistakes, and ship something closer to finished because they can see it better. Tiny request from the humans: please let the models play their own games to test them. TY TY :D 

One more to kick things off: A viral r/aivideo Seedance trailer imagined an 80s Avengers movie and turned AI video into an 80s dream-casting debate.

Y’know, in a couple years, if some of these actors license their likenesses and get paid fairly to do so, you could actually make an alt marvel cinematic universe with these ppl in it… just saying…

Here’s what happened in AI today:

…and a whole lot more that you can read about here.

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😼 Sakana Fugu Turns a Team of AI Models Into One API

Most AI apps still make you pick a model like you’re choosing a SaaS plan: GPT 5-whatever for this, Claude Poetic whatever for that, Gemini 3 point whatever (or Nano Banana for funsies) when the first two start acting weird.

Sakana AI wants to hide that kinda choice behind one button. Its new Sakana Fugu works like a project manager for other models: one request goes in; Fugu decides which AI agents should plan, execute, verify, or synthesize the answer.

Here's what happened:

How to try it:

Why this matters: The default AI strategy has been “pick the smartest model.” Fugu makes a different bet: the best answer may come from coordination, where one system chooses the right models, asks them the right questions, and checks their work.

That fits Sakana’s broader June product push. Marlin (featured in treats to try as well) applies similar long-horizon thinking to business research, running for hours to produce strategy reports and slides. Fugu turns that philosophy into infrastructure other products can call.

Our take: This is the agent market growing up. Companies usually hand-build messy chains of model calls. Fugu packages the chain as the product. The meme is that everyone wants to build an orchestrator now, and that teams of models working together is the next hot product. Perplexity has been doing this for a while, and Claude and OpenAI do versions of this with their sub-agent teams. 

The concern is visibility. If Fugu chooses which models touch your data, route your work, and judge the final answer, customers will want logs, controls, and boring enterprise paperwork documenting the trails and traces (who saw my data, on what API, when?). So while the future of AI may be a bunch of models in a trench coat… procurement still wants to know who’s inside the coat.

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🎓 AI Skill of the Day: Stop Worrying About Perfect Prompts

Hot take: you don’t actually have to worry that much about perfect prompt formatting.

Try voice-dump prompting instead: hold down the dictation tool (most AI tools have them), ramble for a few minutes, and give the model every fragment, caveat, example, constraint, and “vibe” in your head. As Guinness Chen put it, LLMs are literally built to reconstruct intent from language.

Then ask it to reconstruct your latent intent, meaning the goal hiding underneath the messy language.

Copy this before or after your brain dump:

Here’s where you can find the dictate tool on ChatGPT and Claude: 

Total AI beginner? Start here (goes with this video).

Have a specific skill you want to learn? Request it here. 

🍪 Treats to Try

📰 Around the Horn

NEW FROM THE NEURON: Diffusion LLMs got their first serious transparency test, and Google DeepMind’s DiffusionGemma suggests researchers may be able to inspect how these faster models revise answers mid-generation.

Click here for absolutely EVERYTHING that happened in AI this week.

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🔧 Tuesday Tool Tip:

So, yesterday we talked all about GLM 5.2. Today, we’re gonna share how to use it. The easy door is OpenRouter. Think of OpenRouter like a universal remote for AI models: instead of setting up GLM 5.2 yourself, you pick it from OpenRouter's model list and send your prompt there. It is the best first test if you just want to compare GLM 5.2 against Claude, GPT, or Gemini on a few real tasks. If you want a more private, secure instance of GLM 5.2, you can use something like Baseten or Fireworks.

The serious version of this is using Unsloth's GLM-5.2-GGUF. That version is built for running the model on your own machine or private server (although this one is a beast so don’t think you can run it on something with less than 200GB of VRAM). Use this path when you care about privacy, lower repeat costs, or customizing the setup. The easiest local version is Unsloth Studio: install it, open the browser app it gives you, search for unsloth/GLM-5.2-GGUF, and start chatting without building the whole stack yourself.

If you have a technical teammate, here is the handoff: on OpenRouter, use the model name z-ai/glm-5.2 and the OpenRouter API address. For local testing, use Unsloth's GGUF version through Ollama or llama.cpp.

Quick API test through OpenRouter:
model = "z-ai/glm-5.2"
base_url = "https://openrouter.ai/api/v1"

Simple local test through Ollama:
ollama run hf.co/unsloth/GLM-5.2-GGUF:UD-Q4_K_M

Local server route through llama.cpp:
llama-server -hf unsloth/GLM-5.2-GGUF:UD-Q4_K_M

Our rule: try OpenRouter first, because it is the fastest way to learn whether GLM 5.2 is actually good enough for your work. See if you even like it first. Move to Unsloth/local only after you know the model is useful and you want more control over cost, privacy, or setup. If you want something that actually can run on a smaller graphics card, try Gemma 4 12B. 

A Cat’s Commentary

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