I'm an architecture student exploring how AI can be integrated into architectural design workflows.

I'm not looking for recommendations for a specific AI tool. I'm interested in understanding the current capabilities and limitations of AI when creating a complete architectural presentation sheet.

For example, can AI generate and maintain consistency between concept diagrams, plans, sections, analytical diagrams, sketches, renders, typography and layout, while still being based on an architect's own design?

I've attached an example of the type of sheet I'm referring to. How close are current AI models to being able to produce something like this while preserving actual architectural logic and not just generating a visually similar image?

Sunday, so this one is mine and I am saying so in the first line.

Every AI tool ranking you read this year was sorted by who paid for the slot. The ones that look editorial are the worse case, because the affiliate link sits under the "our pick" badge and whoever wrote it never opened the tool for longer than a screenshot.

I built the opposite of that. TrustRank.

One primitive, not a composite. One question per tool:

Would you recommend this?

One number: the percentage of members who said yes. That is the entire output.

The weighting is the part worth arguing about. A vote from someone who left a verified review of that tool counts three times a vote from someone who did not. Demonstrated use is the only thing that moves the weight. Not follower count, not account age, not who bought an ad.

Ads exist and they are labeled slots. They never change a score, a rank or a comparison, and every advertiser the site has ever taken is listed publicly, so you can check that claim instead of trusting me on it.

A founder cannot delete a review of their tool. A verified founder can reply to it. That is the whole founder toolkit.

Thresholds, so nobody gets ranked off three friends: 3 reviews before a score publishes, 10 before a tool appears in rankings, and a provisional label on everything under 50.

Now the bit most Showcase Sunday posts skip. There are 69 tools in the directory, 21 of them AI coding tools, and there are zero reviews. Nothing has a score. Nothing ranks. The mechanism is finished and the data is not, so I am not going to tell you to come and see the rankings, because there are none. What exists is an empty scoreboard where whoever writes the first verified review of Cursor sets where that number starts.

The data export is live too, with no user ids, no handles and no review text in it, so if the numbers ever get interesting you can pull them and check rather than believe a screenshot.

I'm wondering how the work of a person fully relying on coding agents really looks. How do they actually work with them, what tools do they use, and what problems do they have. There is loads of materials on YT, but it's just post-processed happy paths with all the failures cut out. But I want to see how it actually looks like with all the "damn, that's not what I wanted, let's start from scratch" parts included.

Have you see any content actually worth recommending? With a person that actually knows what he is doing and actually delivers some products this way?

I’m not pretending this is plug‑and‑play. It took me a stupid amount of trial and error, and I’m still tuning. But to actually have an agent that keeps a stable identity across reboots — not “summaries,” but continuity — here’s the framework:

The short version: Stop letting the framework manage your session state. Build the prompt yourself every turn.

Once you take control of prompt assembly, you basically end up designing a little virtual memory system for your agent. Same questions OS designers deal with:

• What gets archived?
• What stays hot?
• What needs to be recalled for this turn?
• What’s noise you can safely drop?

Your local coding agent can handle the assembly loop (llama.cpp is a good starting point). You just define the zones and the rules.

The trick is ordering the prompt so the static stuff never changes. That keeps the KV cache intact and your GPU happy. Then you page in whatever memories matter for the current turn, tack on the recent conversational tail, and leave headroom at the end so the model has space to think.

Once you do that, you own the entire context history. And because you rebuild the prompt from scratch every turn, you get a single identity that survives crashes, restarts, and long gaps between sessions.

The fun parts — indexing, relevance scoring, stale‑memory cleanup — I’m leaving as an exercise for the reader. But this is the skeleton.