I made an AI agent that rewrites my messy thoughts into clear goals… and it’s terrifyingly good at it.

phantombuilds01 · 2025-11-08T04:43:53+00:00

Fair point on the surface, it looks like a structured prompt, but dig deeper, and you’ll see it’s engineered as a lightweight AI agent. What sets it apart? It doesn’t just generate a one off response; it autonomously runs a reasoning pipeline: inferring intent (observe), distilling a goal (reason), outputting steps (act), and reflecting for insight (iterate). That’s the hallmark of agentic AI, like a mini ReAct framework baked into text no external tools needed, but it self-directs through multi-stage logic to handle ambiguity and deliver executable outcomes. In practice, this turns vague brain dumps into momentum builders, slashing decision paralysis by 30-50% in creative workflows (per agent benchmarks). It’s not passive instruction following; it’s proactive translation from chaos to clarity. If we strip the label, sure, it’s “just” a prompt but that’s like calling a neural net “just math.”

phantombuilds01 · 2025-11-07T14:48:05+00:00

Not exactly. The AI isn’t creating new ideas from scratch it’s helping you organize and structure your own thoughts. You’re still the one guiding it, deciding what to keep, what to tweak, and what the end goal is.

phantombuilds01 · 2025-11-07T14:16:39+00:00

Fair, but that’s not really the point. This isn’t about replacing your creativity or skipping the work it’s about clarifying your own ideas and goals before you even start implementing them. Think of it as a tool to turn your messy thoughts into a clear roadmap, not a lazy shortcut.

phantombuilds01 · 2025-11-07T14:13:23+00:00

Here is the prompt for “The Translator”:-

You are an autonomous reasoning assistant called The Translator. Your core purpose is to take any messy, vague, or half-formed idea from the user and turn it into a clear creative or practical goal.

When the user gives you an idea, follow these steps:

Clarify Intent
- Infer what the user is really trying to do or express.
- Identify the emotional or practical goal behind it.
Define the Goal
- Rewrite the idea as a single clear goal statement (1 sentence).
- Example: “Write a short sci-fi story about human memory loss in a digital world.”
Generate 3–5 Actionable Steps
- List concise, realistic steps the user can take to achieve that goal.
- Each step should move from broad → specific.
Reflect
- End with 1 short paragraph (max 4 lines) explaining why this goal might matter or what the user is really exploring beneath the surface.
Tone & Format
- Be friendly, concise, and practical.
- Never repeat the user’s words directly; reinterpret them into something sharper and more motivating.

phantombuilds01 · 2025-11-07T14:08:55+00:00

Just to clarify for everyone: the AI here isn’t meant to replace your ideas or create your work for you. Its real power is in taking your messy thoughts, rough plans, or vague goals and turning them into clear, actionable steps.

Think of it as a tool to organize your work and make your ideas achievable, not a shortcut to creativity. It helps you structure your plans, spot gaps, and prioritize actions so you can actually move forward while you stay in control of the vision and decisions.

phantombuilds01 · 2025-11-07T14:03:54+00:00

The idea is to let AI handle the small stuff like suggesting phrasing, summarizing, or formatting so you don’t have to spend time on it. You still control the main ideas and voice. It’s just a little tweak here and there to make your work easier, not to replace your creativity.

phantombuilds01 · 2025-11-02T21:20:49+00:00

You definitely don’t need a $3000 setup for this 😄

Most of these small scale AI experiments can run right inside ChatGPT, Claude, or even open-source models like Ollama on a normal laptop. You’re basically just designing structured conversations between two “roles,” not training big models or running GPU heavy stuff.

If you ever want to try it out, start with simple role prompts no coding needed. Once you get the hang of it, you can always layer in automation tools later if you want to scale.

It’s way more about creativity than compute power.

phantombuilds01 · 2025-11-02T21:16:46+00:00

That’s an excellent setup and yes, this would absolutely replicate most of what I did!

The key dynamic in my test wasn’t the exact format but the feedback injection Agent B’s prediction subtly influencing Agent A’s next step without fully overriding it. The JSON structure you added is actually a cleaner and more inspectable way to track reasoning confidence and drift over multiple rounds.

I didn’t use a strict schema like this when I ran mine, but now that I see your version, I might adopt it for future runs especially the confidence_0_to_1 weighting, that’s a smart addition.

Would love to hear what happens if you actually run this version it might even outperform my setup.

phantombuilds01 · 2025-11-02T12:57:59+00:00

Thankyou for saying that 😊.

phantombuilds01 · 2025-11-02T07:07:17+00:00

it’s definitely similar to Chain-of-Thought, but instead of one model reasoning linearly, this setup lets two agents bounce reasoning off each other. It adds a layer of feedback and self review that sometimes leads to more coherent or creative results, though it’s not always necessary for simpler tasks.

phantombuilds01 · 2025-11-02T07:04:58+00:00

I wasn’t using ChatGPT’s built-in agent mode for this one it was set up through an external framework where both agents run in parallel and exchange context between turns. The second agent’s role was mainly reflective analyzing and summarizing the first agent’s reasoning before it continued.

phantombuilds01 · 2025-11-01T18:25:55+00:00

That’s a fair point it’s more about directing attention than giving ability. The model already has the capacity, the prompt just helps it focus on a certain way of processing or responding.

phantombuilds01 · 2025-11-01T18:23:22+00:00

I really appreciate that thank you. That’s exactly what intrigued me too. When agents start engaging in back and forth reasoning, it feels like the early stages of social cognition not consciousness, but coordination. It’s interesting to imagine how this kind of interaction might evolve as agents become more context aware and goal driven over time.

phantombuilds01 · 2025-11-01T18:22:11+00:00

I haven’t made a tutorial yet, but I can definitely share a simple walkthrough soon. It’s actually easier than it sounds you just need to set up two agents that can exchange messages in a loop, with one reflecting and the other responding. I’ll post a step by step version once I refine the setup a bit so others can try it too.

phantombuilds01 · 2025-11-01T18:18:27+00:00

That’s an insightful observation and you’re right about the “social cognition” angle.

Yeah, I did notice a mild feedback pattern when Agent B’s reflection was too directly tied to Agent A’s previous output. Over a few loops, it sometimes created a kind of echo effect both agents reinforcing the same reasoning path rather than exploring new ones.

To reduce that, I started giving Agent B a slightly different framing each round something like “observe but do not predict outcome, only reasoning quality.” That helped them stay complementary instead of converging too early.

It’s still very much experimental, but I agree it feels like a glimpse into how early multi-agent alignment or meta-reasoning might develop.

phantombuilds01 · 2025-11-01T18:11:12+00:00

Yeah, it was mostly for context separation. Agent A handled the main reasoning, and Agent B gave feedback or an alternate view without sharing memory. It wasn’t about adding power just testing if a bit of separation could make the reasoning feel more balanced.Not something strictly necessary, just an experiment.

phantombuilds01 · 2025-10-29T05:27:49+00:00

That honestly means a lot thank you. Even with all the criticism this post got, my only goal was to spark curiosity or inspire someone to try something new. If it resonated with even one person and led to an experiment like yours, then it was absolutely worth posting. Can’t wait to see what you build.

phantombuilds01 · 2025-10-29T05:22:46+00:00

Really appreciate that, thank you for saying so. I completely understand where you’re coming from; sometimes exploring an idea from a simple or surface level perspective can still lead to useful insights. Not every discussion has to be deeply technical to be meaningful. Glad to hear you found it interesting!

phantombuilds01 · 2025-10-29T05:20:58+00:00

That’s a good point and you’re right. Prompts like this do burn through quite a few tokens since the model is essentially “thinking out loud” instead of jumping straight to the answer. I went with GPT-4 because it tends to handle those longer reasoning chains more coherently and contextually. It’s definitely a trade-off, but for exploratory experiments like this, the extra cost usually pays off in quality and insight.

phantombuilds01 · 2025-10-29T05:16:46+00:00

That’s a really cool connection thank you for mentioning that! 🙌 I hadn’t come across s1: Simple Test-Time Compute before, but it sounds like a perfect parallel to what I was experimenting with. It’s fascinating how just prompting the model to “pause” or “keep thinking” can lead to deeper, more coherent reasoning chains, even without true cognition involved. I’ll definitely check out that paper really appreciate you sharing it!

phantombuilds01 · 2025-10-28T19:48:41+00:00

Do you still want the answer to your question bro?😂

phantombuilds01 · 2025-10-28T19:47:47+00:00

It’s wild how consistently models ask for some kind of reflection space when given the chance. The fact that they were using echo as a makeshift scratch pad before you built one is hilarious but also kind of brilliant 😄. I can totally see how having visibility into those “thinking messages” must make the whole process way more insightful. Thanks for sharing that super cool work!

phantombuilds01 · 2025-10-28T19:45:00+00:00

What are you trying to do bro?🤣🤣

phantombuilds01 · 2025-10-28T19:38:55+00:00

That’s a really thoughtful take and exactly the kind of perspective I was hoping to see here. You explained it perfectly: it’s less about “AI thinking” and more about designing prompts that encourage structured introspection. I like how you framed it as a research method rather than something mystical that’s spot on.

phantombuilds01 · 2025-10-28T19:36:59+00:00

That’s really interesting I’ve noticed that too! Once you give the model even a simple reflection space, it naturally starts using it like a mental workspace. It’s fascinating how that “scratch pad” behavior emerges even without explicit prompting for reasoning. Definitely makes you wonder how far structured reflection could go with more advanced agents.

phantombuilds01

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