u/Think-Ad986

What’s the latest we know ? Launch or ETAs?

I build diagnostic tooling for Google Ads accounts, so I read the Opus 5 announcement looking for something other than the coding numbers. Sharing the read in case it's useful to anyone working in a domain with noisy feedback.

What jumped out is how much of the improvement is described in terms of self-verification rather than raw knowledge. The FreeCAD task, where the model isn't allowed to see the drawing and responds by writing its own computer vision pipeline to pull geometry from the pixels. The trading firm example, where there's no live feed to validate against so it builds its own test harness. The frontend one where it opens its own pages at desktop and phone widths and catches a checkout button that's off-screen. The Zapier end-to-end result. None of those are "the model knows more." They're all "the model constructed a feedback loop and then used it."

That framing predicts where the gains land.

Coding has a cheap oracle. Tests pass or they don't, the page renders or it doesn't, the parser handles the exchange's bytes or it throws. The model can generate a check, run it, and iterate in seconds. Every one of those examples lives there.

Ads and analytics don't have that. If the model recommends pausing a keyword, the feedback is a conversion rate delta that takes weeks to reach significance, is confounded by seasonality and auction dynamics, and arrives through an attribution model that is partly a guess. There is no unit test for "was that the right bid."

So my guess at the split: on the diagnostic half — read the table, find the causal thread, explain why CPA moved — this should be a real step up, and the reported gains in numerical and table reasoning support that. On the decisioning half — should we act, did it work — a stronger model does very little on its own, because it can't check itself. It just gets more articulate about a conclusion nobody validated.

Which means the harness is still the whole job. Significance gates, hard limits on what an automated action can touch, an evidence trail on every recommendation. Opus 5 got better at using one of those. It doesn't come with one.

Curious whether people working in other delayed-feedback domains see the same split — quant, clinical, ops forecasting, anything where ground truth shows up weeks later. Does the self-verification improvement register for you at all, or does it read as a coding-shaped gain that just doesn't reach your work?

I’ve spent months building a product that I’m genuinely proud of. From a technical perspective, it’s in much better shape than I expected it would be.

What I completely underestimated wasn’t the engineering. It was trust.

The product analyzes marketing data, so before anyone can experience its value they have to connect accounts that contain client data. That’s a huge ask when you’ve never heard of me.

I’ve tried sample datasets, demo accounts, detailed documentation, and sharing the reasoning behind recommendations. But it’s still difficult to cross that initial trust barrier.

As a solo founder, it also feels like every decision is made in a vacuum. You don’t have a team to sanity-check ideas, and when revenue isn’t coming in yet, it’s easy to start questioning whether you’re solving the right problem or just failing at distribution.

Sometimes it feels like building the product was only half the job, and now I need to become a content creator just so people are willing to try it.

For those who’ve been through this stage, what actually got you your first paying customer? Was it content, cold outreach, partnerships, referrals, or something completely different?

I’ve been building an MCP connector for ChatGPT, and honestly the most frustrating part hasn’t been implementing the tools. It’s testing them.

My current loop looks something like this:

1. Change server code.
2. Restart the server.
3. Reconnect the client.
4. Trigger the tool.
5. Realize I forgot one small thing.
6. Repeat.

It feels much slower than normal API development because you’re debugging both the server and the client integration at the same time.

I’m curious whether this is just part of the current MCP ecosystem or if I’m missing a better workflow.

For those of you building for Claude or other MCP clients, is the development experience any smoother? What does your testing workflow look like?

Your CTR dashboard cannot tell you why a number dropped. It only tells you that it did.

A client pinged me convinced his ad copy broke. CTR down almost 20 points in three weeks, same creative the whole time. Google Ads and GA4 don't separate an AI Overview eating the click from a plain quality score slip. Both draw the same falling line.

Every CTR study I found this year disagrees on how big the AI Overview hit actually is. Some say 18 percent, some say past half. None of them disagree on the direction.

I told him to stop staring at CTR and start counting conversions from whatever clicks survive it. A click that already read the AI summary and still chose to visit behaves differently than one pulled from a page of curious strangers.

What dropped in your account recently with no obvious cause attached to it?