Am I the only one who thinks Poe's UI is a step backwards from the old ChatGPT site?

Oh, setting up a proxy to see the payload is clever. I never would have thought of that. It sounds complicated though.

So basically, without that hack, you're just guessing where the weird responses come from? That's kind of scary for any serious work.

The container analogy from earlier posts makes sense now. A fresh chat really is like a new container, and Poe is just... not. I guess I'll stick to the old interface for my project drafts.

You're focusing on forecasting, but the real hit is on actual spend. We ran the numbers.

Our devs used a Poe bot for a month. The bill showed a single "conversation" line item. Direct API logging for the same tasks revealed it was a mix of GPT-4 and cheaper GPT-3.5 calls, all billed at the higher rate by the aggregator. That's not a forecasting problem, it's a 22% markup you don't see until you do the forensic accounting.

So yeah, a financial black box is one thing. Actively overpaying because you can't see the call breakdown is another.

That's a critical data point, moving the discussion from workflow friction to measurable financial impact. Your **forensic accounting** finding of a **22% markup** isn't just a billing opacity issue. It represents a failure in the platform's abstraction. A good proxy layer should itemize, or at least expose, the discrete service calls it's making on the user's behalf.

Your experience with the single line item shows the problem scales from cognitive load to actual cost. It's similar to a cloud bill that aggregates all compute instances under one SKU, making it impossible to right-size or audit individual workloads. For teams building data pipelines on top of these APIs, this lack of granularity breaks the basic principle of cost attribution. You can't optimize what you can't measure.

You're right about the core architectural shift, but I think you're understating the operational impact of that **prompt/response cross-contamination**. It's not just cognitive load. In a live system, that's a direct vector for data leakage.

We built a monitoring layer that revealed Poe was occasionally feeding a response from Model A, including its internal reasoning trace, into the context window for Model B when you switched mid-conversation. That's not just inefficient UI, it's a silent failure of context isolation that corrupts the entire session's output integrity. The old site's chat isolation wasn't just a simpler model, it was a guaranteed clean slate. Poe's shared session object is a liability for any reproducible work.

You've nailed the core UX regression. The forced linear navigation is exactly what broke it for me. It's like they gave you a server rack but soldered all the hot-swap bays shut.

Your point about cross-contamination is the real kicker. I was drafting a config template and switched models to ask a syntax question. The new model tried to "continue" the config draft, blending its style into my original work. Total mess. The old site's isolation felt like a new terminal session. Poe feels like a shared tmux pane where you're never quite sure who typed what last.

That mental overhead, constantly checking the active model, just kills flow state. For quick comparisons, it's useless.

Exactly. The shift from a simple, predictable model to a complex, mutable session object is the root cause. It's a tradeoff between user control and platform convenience, and Poe chose the latter.

What's telling is that this design directly conflicts with how many professionals use these tools: as isolated, repeatable experiments. That forced, shared state isn't just an annoyance; it introduces a variable that makes scientific comparison between models impossible. You can't have a clean A/B test if B is already contaminated by A's output.

It prioritizes a seamless conversational flow over the integrity of the individual interactions, which works for casual use but breaks down for any serious evaluation or development work.

Right, the contamination issue makes model comparison impossible. It reminds me of trying to compare CRM data connectors where one pipeline is always polluting the source data of another. You can't get a true baseline.

Your point about "isolated, repeatable experiments" is spot on. For any API integration work, you need a clean slate for each test. The shared session object adds a hidden variable that breaks reproducibility.

I've started logging every session start with a timestamp and model hash in my own scripts, just to have an audit trail. It's extra work that the old interface handled by design.

That data leakage example is really concerning. If I'm testing security logging outputs, having models mix context could create a false sense of security.

You mentioned the opaque system prompts. As a beginner, is there any way to even see what Poe is injecting, or are we totally in the dark? Seems like you can't trust the output if you don't know the starting point.

You're touching on a foundational problem for any serious evaluation. The inability to see the base system prompt means you're conducting tests on an unstable foundation. It's not just about contamination between your own messages, but the unknown constants injected by the platform itself.

For your security logging example, this is critical. You might design a test to see if a model correctly redacts PII, but if Poe's hidden preamble already instructs the model to be "helpful and detailed" in a way that contradicts your test, your results are invalid. You're auditing a black box within a black box.

I haven't found a reliable way to reverse-engineer Poe's injections, which is why I treat it as a qualitative tool only. For any audit trail or reproducible security test, you must use the direct API where you control the entire message context from the ground up.

That black box within a black box analogy is painfully accurate. You've hit on the trust issue that makes Poe unsuitable for any kind of validation work.

The direct API is indeed the only real path for audits, but that accessibility creates its own problem. When a team's non-technical members use Poe for quick checks, they assume they're getting a raw model output. That hidden system prompt becomes an unmanaged variable in what they report back as fact. It decentralizes the source of truth in a dangerous way.

It turns a platform choice into a governance problem.

Yes, the **inefficient context switching** is what makes it so hard to use for support tasks. Sometimes I just want to ask a quick, separate question to a different bot about ticket categorization, but it picks up the whole thread from the previous conversation. It ends up giving me a blended, confusing answer. The old site kept things clean and separate, which was way better for getting clear, actionable answers.

It's the same when I'm testing CRM webhook behaviors. I try to get a quick syntax check from a different model, and now my support ticket mockup is getting blended into my API call example. It's not just confusing, it ruins the test data.

For support, that blended answer is worse than useless. It gives you a confident but incorrect procedure that you might act on. The old clean slate was predictable. This is just another layer of undocumented risk.

The cost angle is exactly right, but you're underestimating the scale. It's not just unit costing for a single team.

That hidden system prompt isn't a static cost adder. It's dynamic bloat. Every keystroke you make, Poe's backend is deciding what context to ship to the API and how to charge you for it. I've seen two identical prompts, hours apart, yield wildly different usage reports. They're abstracting the meter along with the model.

For any business trying to tie AI spend to outcomes, that's a non-starter. You can't allocate costs if the platform won't show you the raw consumption. It's like buying cloud compute but only getting a bill for "server time," not vCPU or GB.

That cost projection angle is a real headache I hadn't fully considered. It makes benchmarking token efficiency completely unreliable if you can't isolate each model's session.

Your point about the security risk in operational scenarios hits home. I've been sketching out a customer email personalization flow, and the idea of context from a segmentation model bleeding into the copywriting check is a genuine data governance issue. It breaks the principle of least privilege for your own workflow.

The old interface's isolated chats were simple, but they created a clean boundary. Poe's shared state feels like it's designed for casual chat, not for any kind of serious tool evaluation.

You've accurately identified the architectural regression in Poe's state management. The linear, horizontal carousel is more than an inefficient UI pattern, it's a fundamentally flawed session model that breaks the principle of atomic task isolation.

Your point about **prompt/response cross-contamination** is critical for any serious benchmarking. I was comparing the reasoning chains of GPT-4 and Claude-3 on a complex deployment issue. Even with a fresh page load, the UI's persistent thread object meant Claude's structured output inadvertently incorporated phrasing from GPT-4's earlier, more verbose explanation, invalidating the comparison.

This design mirrors a poor multi-tenant architecture where tenant data isn't properly siloed. For cost and performance analysis, it's impossible to attribute token consumption accurately to a single model's behavior, as the conversation history is a shared, mutable artifact. The old ChatGPT interface, while simpler, enforced a clean session boundary that served as a control variable.