Transcription
So, if you're feeling the withdrawal effects of not using Fable 5 or even the FOMO of not having the chance to play with it before we lost it, then the last few days have definitely been rough. We had a taste, a glimpse of what a super intelligent PhD level scientist looks like living in your editor.
Now, I can't help you get the power of Fable back because that power lies in the raw model itself. But I've hacked a way that you can use your existing Clawude and Codeex models and just get them to behave a lot more like Fable in just a few steps. And you can even apply what I'm about to show you to your open-source models to get them to be that much more functional. So if you want the second best thing until we get our super intelligence back, then let's dive in.
All right. So the average person doesn't know that the majority of your conversations, whether they're codecs or cloud code, live in what are called JSON L files on your computer. And they're basically these behemoth files that are full of tool calls, metadata. And within this metadata is a series of gold that you can mine. And this file is one example of one session full of said metadata. You can see right here, these are all my MCP tool calls. But within here, you can also find things like the prompts that you've sent to a model as well as what the model responded with and how it actually planned out its tasks along with the tool calls it made.
So what you can do is have Claude code parse through all of your conversations to go from something like this to something like this where you have a full playbook that you can give pretty much any model whether it's open source or closed source and have it either injected at the beginning of a session using something like a hook or you refer to it in the middle of the session to get that model to behave that much more like Fable did.
Now, like you would have seen, the majority of that file was pretty much irrelevant to us. And while you could theoretically load hundreds of JSON L files within a conversation, you're going to unnecessarily bloat it with information that doesn't help you at all. So, what you can do is, and I'll show you in a second how you can do this in a terminal, is ask Claude Code to build a series of scripts to already preemptively parse through and only distill the information that matters and take set information and then analyze it for all the behavioral differences between different models. And this process would allow you to go from thousands of lines to only a fraction of that. And that fraction is what we can feed these language models. And the trick here is that all of these files actually tag which model sent which response. So we can filter down something like Opus 4.8 conversations against Fable 5 conversations to see the disparity of how they behaved, what tools they called, how they planned out their sessions, and try to imitate the delta.
Now, before I hop into the terminal, what if you've barely had a chance to even have conversations with Fable? You'd have no data to actually analyze to begin with. What you could do is go to this link in Hugging Face, and there are a series more like it where people have actually open sourced their sessions that they've had with Fable 5. So, you can go through them and do the exact same exercise even though it's not your specific information or your specific projects. And no need to screenshot the link. I'll make it available along with a few other things in the second link down below.
Now, popping into the terminal, I'm going to walk you through the handful of prompts that you can use to get to that synthesis file that you can use however you want. So, in this case, we can start off with asking how many JSON L files do we have from all of our sessions? And I asked us to see what is the blast radius. How many files do we have in the entire universe of our usage? Now, one key caveat here is only a small fraction of those would be Fable 5. So I'm just trying to get a sense for how many so I can then tell it to focus on that specific filter. So in our case we have close to 3,000 JSON L files across all of our projects.
Then once you understand that you can send a prompt just like this. So you could basically say the bloat in these files is the tool results the full file contents and command output that get echoed back into context. Key thing write me a small Python script. You can name it whatever you want. That takes a cloud code session file and strips all the lightweight transcripts. So, leave me with things like the timestamps, which model it was, what did I ask for, and what did the actual assistant respond with. Now, if you're trying this for the first time, have it just do one specific file so you can make sure that it's the exact format they're expecting. And once it parses through, it will give you this resulting file. You can take a peek at it and like I said, make sure it's what you're expecting. So in my case, I redacted some information, some personal information, but then it leaves you with the transcript, the back and forth between you and the agent. And you can also add different metadata like all the tool calls like you can see here. Here are exactly what tools it executed in what order. You can see this is the assistant and that specific model.
So you can easily then go to the next step which is parse the model-based conversations that matter. So then you can send a prompt like this where we basically tell it that every single conversation has this artifact that's called message model which we just saw right now. Pull every turn that came from Claude Fable 5 the exact model name out of my whole history across all of my projects into one combined corpus. So basically create a full playbook of every single conversation that I had using this model and all of the preamble or the context around that conversation. And on top of that you can ask it to do some synthesis. So I say give me the behavioral patterns as real measured numbers not just impressions. So something that is tangible versus just an intangible objective look at the quality of the conversation.
Now since we created a sample script before it's way faster to go from that point to this point where we have that completed file that has all the artifacts across all 10,000 records and then you can see all the volume of messages. It walks through and breaks down the numbers around tool use, the order of work. So one thing that you'd want to emulate using Opus or Codeex or even your open source models is how disciplined Fable seemed to be around using the right tools at the right time. And you can learn a lot from its rhythm. So you can even see its working rhythm here is being analyzed. This transitions between using different bash commands, chaining different steps, the way it read and edited files. Everything seemed to be a little bit more elegant and a little bit more refined and precise from something like Opus.
And once you have that, this is the key distinction. Depending on whatever model you used alongside Fable before Fable came out. Let's say it's Opus 4.8. It could be Opus 4.7 or even Haiku. You can say now run the exact same behavioral read against insert name of model here and put the two side by side. Show me the distance between the rhythm, the tool call cadence, the action sequences, and the ratios like reads before edits and tests after edits. So, we're trying to emulate the entire structure of how Fable executed things.
Once you get that full breakdown, it'll go and basically edit its script and it will come back with an overall summary like you can see here. So, you can see this is the side-by-side compare of Fable 5 and Opus 4.8 on your specific computer. Again, if you don't have enough history to have enough training data for this Fable analysis, you could use that open source example I showed you earlier. And when you go to the bottom here, if we scroll back up, you can see that for very similar problems, there were many more turns taken by Opus 4.8, a lot of times it doesn't think before it acts as much as Fable. So, a lot of these again, we can't change cuz they come from the model weights itself. But if you can implore or elicit opus to think that much longer or plan a little bit longer to be a lot more thoughtful, you still won't get Fable 5 performance, but you can get a much stronger opus execution.
Once you finish the analysis, you can ask it to distill all of its core findings of how Opus could act more like Fable in something like a playbook file. And what you could do is you can open a brand new session and refer to said file by tagging it. Or you could actually attach it to a hook. So you could tag the Cloud Code guide agent. And let's say we go into a brand new terminal session and we spin this up. Like I said, you could just bring the file in and drag and drop it. Or you could say I want to use the learnings from name of file let's call it MD and I want it always injected at session start. So in this case you could say CC the claude code guide agent right here and I can say attach a hook at the session start event to always inject this into context.
Now alternatively you could turn this into a skill. You could turn it into a series of lines in your cloud MD that are already auto injected in every session. There are different ways to go about integrating this that really depend on your day-to-day workflow. But the bottom line is you can improve the performance of all of your models by giving it something like this playbook. And to make it easier for you, I will give you my playbook as well if you want to skip this whole process and just take the synthesis of what I've observed through my cloud code sessions. And that's pretty much it.
So we can't clone the power of Fable 5, but you can do a few things to at least improve the models that you currently have in the meantime while we wait for all this to play out. Like I said before, you'll be able to find all the prompts I walked you through along with that link to that open-source data set along with that little guide I showed you right now in the second link down below. And if you always want to be on the front foot with things like cloud code and codecs and agentic workflows in general, then check out the first link down below for my early Aopish community. They already got a preview of this trick before I put it all together. So, if you always want to be ahead of the game, then make sure to check that out. And for the rest of you, if you found this helpful, I would super appreciate a like and comment on the video. helps the reach, helps the channel, and I'll see you in the next.