Guys, Just try deepseek v4 flash. You'll love it. I see no reason to use Opus 5 (apart from frontend design work). GPT 5.6 Sol as executor and Deepseek v4 flash as executors (swarms of agents running loops). Deepseek v4 flash is a perfect "looper" model.

If you dont know, you can use your Codex subscription inside Claude Code. That means Fable 5 can call GPT 5.5 + 10 subagents to work together. This will save you atleast 60% of your Fable 5 token consumption. You just need to setup once. Here's how: Step 1: Open Claude Code and install the Codex plugin /plugin marketplace add openai/codex-plugin-cc /plugin install codex@openai-codex /reload-plugins Step 2: Tell Fable 5 to finish the setup Paste this prompt: "Set up Codex inside this Claude Code environment. Use the official OpenAI Codex plugin that was just installed. Run /codex:setup. If Codex CLI is missing, install it. If Codex is installed but not authenticated, ask me to authenticate with my ChatGPT account. After auth is complete, verify that Codex works from inside Claude Code. Then confirm that the codex:codex-rescue sub-agent is available. Do not change any project code during setup." Step 3: Authentication Fable 5 will trigger the Codex setup. You authenticate your ChatGPT/Codex account once. After that, Codex runs from inside Claude Code using your Codex subscription. Step 4: Tell Fable 5 how to delegate work Paste this prompt: "From now on, use this workflow: You are the orchestrator. > Use Fable 5 for planning, repo understanding, architecture decisions, task decomposition, and final review. > Use codex-rescue as the executor when a task needs heavy implementation, debugging, test fixing, refactoring, or multi-file code edits. When delegating to Codex, use /codex:rescue. Prefer GPT 5.5 (xtra high) as the go to Codex model. Keep Codex tasks focused and specific. After Codex finishes, inspect the result yourself before accepting it. Do not blindly trust Codex output." Pro Tips: 1. Turn this into a skill (i named it Fable-GPT) and call that skill at the start of the session. 2. Use skill + goal to get the heavy tasks done. Goals are best for long horizon tasks. 3. If you're on Codex 20x pro plan, you can use subagents. I use 5-7 agents at one time and never hit 5-hour limit. 4. Context rot is real so clear the conversation after 4 compactions. Use /handoff skill to preserve context.

Codex 5.5 use cases I found so far: > made my internet faster > made my local 6B SLM 3x faster > made my macbook pro faster like new > made a lightweight suite to write & test metal kernals > made a skill to communicate with claude code in realtime > made a pipeline to generate SFT dataset using Deepseek v4 > made a computer use workflow to fine tune models in Google Colab > made 4 routines to test workflows on autopilot 3 times/day

If you love fine-tuning open-source models (like me), then listen. > Start with 1B, 2B, 4B, and 8B models. (Don't start with a 27B model or bigger at first.) > Use WebGPU providers. I use Google Colab Pro for any model smaller than 9B. A single A100 80GB costs around $0.60/hr, which is cheap. Enough for small models. > Don’t buy GPUs unless you fine-tune 7 to 10 models. You'll understand the nitty-gritty in the process. > Use Codex 5.5 × DeepSeek v4 Pro to create datasets. Codex to plan, DeepSeek v4 Pro to generate rows. > Use Unsloth's instruct models as a base from Hugging Face. Yes, there are others too, but Unsloth also provides fast fine-tuning notebooks. > Use Unsloth's fine-tuning notebooks as a reference. Paste them into Codex, and Codex will write a custom notebook with the configs you need. > Spend 1 day learning about: - SFT (supervised fine-tuning) - RL training (GRPO, DPO, PPO, etc.) - LoRA / QLoRA training - Quantization and types - Local inference engines (llama.cpp) - KV cache and prompt cache > Just get started. Claude, Codex, and ChatGPT can design a step-by-step plan for how you can fine-tune your first AI model. Future tech is moving toward small 5B to 15B ELMs (Expert Language Models) rather than general 1T LLMs. So fine-tuning is an important skill that anyone can acquire today. Tune models, test them, use them. Then fine-tune for companies and make a career out of it. (Companies pay $50k+ to fine-tune models on their data so they can get personalized AI models.) Shoot your questions below. I'll be sharing in-depth raw findings about this topic in the coming days.