Author Topic: Collabware's AI-Assisted Engineering vs Vibe Coding and Our Engineering Prompts (Read 180 times)

smfadmin · « **on:** August 28, 2026, 08:45:03 PM »

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ListAttBBC | v6.8 | 2026-09-01 | Andrew.human & Claude.ai

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smfadmin — August 28, 2026, 08:55:48 PM
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smfadmin — Today at 09:33:18 PM Chip — Today at 04:24:32 PM
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smfadmin — Today at 09:33:18 PM Chip — Today at 04:24:33 PM
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Chip — September 05, 2026, 02:03:53 AM smfadmin — September 05, 2026, 01:47:51 AM
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Chip — September 05, 2026, 06:27:06 AM smfadmin — September 05, 2026, 02:18:44 AM
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smfadmin — Today at 02:29:27 PM

What we do here at Collabware is AI-Assisted Engineering but it's constant battle keeping AI on point because they tend to write insecure, buggy and sloppy code -- not to mention being inconsistent ! I take regular checkpoints and will start taking quality checkpoints from here on in.

This is the problem:

View this post on Instagram

https://www.instagram.com/reel/DbEmNbZE_2X/?igsi=aGpwNGdqZnN6aXhw

These are our solutions

Quote

This is for you if you're using the same AI to build and review your code. This is a follow-up to last week's cross-platform testing reel.
Because every single platform has a training bias, and every model out there defaults to a pattern and has a blind spot — it cannot see in its own output. So the builders that get the best results know exactly which platform to match to which job. Here is what each platform catches that the others miss.
Number one is Claude. It excels at adversarial reasoning, security reviews, and deep architectural analysis. It thinks like an attacker. It finds injection paths, authentication bypasses, and logic flaws like a pro. That, and the AI building platform will always defend itself — so Claude is the one to sic on it. If you built in Cursor, Lovable, or Bolt, I'd bring your security review to Claude and frame it as a penetration test. So direct your AI to run security-critical reviews on a platform with demonstrated adversarial depth. I think someone on here actually named their adversarial audit "The Murphy." That's a win.
Step two is Codex and Gemini — strongest at catching implementation errors and reviewing code they did not write. So Codex reads your codebase cold and flags what does not belong. Gemini brings a giant context window that lets you hold an entire project in one view, and it'll spot patterns across files that a single-file reviewer will miss. So if you built in Claude Code, I'd take your logic verification to Codex or Gemini for a second opinion, with no attachment to the original implementation. So direct your AI to run a full codebase review on a platform that did not generate the code.
And number three, Lovable, Bolt, and Cursor. They are super strong at full-stack builds and rapid prototyping. So if you built your backend in Claude Code, I'd hand the requirements to Lovable, Bolt and compare how a different platform interprets the same specs. Where the implementations differ is where your assumptions live — and the opportunity lives. So those differences always surface architecture decisions in your first platform, made silently for you. So direct your AI to rebuild one critical module on a platform and document the different approach it took. Same build, different eyes, better product every single time.

based on:

View this post on Instagram

https://www.instagram.com/reel/Dcv2OyCiGkc/?igsi=bTF0NzJpdTdmcDl5 and this is A VERY CLEVER, "USING HIS RECOMMENDATION ON HIS TRANSCRIPT-type, Critique ! Why ? Because by using HIS WORDS ONLY technique (lol, 😋 - I LUV ❤️ this bit cumin' up > ...)

Quote

Number one is Claude. It excels at adversarial reasoning, security reviews, and deep architectural analysis. It thinks like an attacker. It finds injection paths, authentication bypasses, and logic flaws like a pro

[/b][/size]

Quote

Claude.ai says "Take the headline idea and leave the taxonomy behind — that's roughly my read of this.
The part that's genuinely true: a model reviewing its own output has a structural blind spot — it tends to re-derive the same reasoning that produced the bug in the first place, not spot it. You've actually seen this directly in this conversation: I caught real bugs in Gemini's drafts (the docstring duplication regression, the value-based magic-number exclusion bug, the invalid regex character range) that Gemini's own pass missed twice in a row. A different model, or even the same model coming at it cold in a fresh context, catches different things than the one that wrote it. That part isn't hype.
The part I'd push back on: the specific claims — "Claude thinks like an attacker," "Codex reads cold better," "Gemini's context window spots cross-file patterns better" — are stated as settled fact but are really just this one person's anecdotal read, dressed up with confident language ("finds injection paths like a pro," "every single time"). Model behavior on this stuff shifts release to release, isn't something anyone's actually benchmarked here, and "which model is better at X" claims age badly. I'd treat it as one person's working theory, not an established taxonomy.
Where it actually lands on your setup: you're already doing the underlying thing. Andrew (Architect/QA Lead), Claude (Lead Programmer), ChatGPT (Systems Analyst), Gemini (Relief Programmer) is already a cross-model review structure — Chatty reviewing Gemini's CONFIG-001/DOC-001 work and catching the "not the full spec" gap earlier in this thread is exactly the pattern this post is describing, you just built it before reading about it. The one adjustment I'd actually consider taking from this: for something like a security-critical review of the ISE search/auth surfaces specifically, deliberately handing it to whichever model didn't write that code, framed explicitly as "find a way to break this" rather than "does this look right" — that framing change (audit vs. review) tends to surface different findings regardless of which model does it.

Me, Chipper:

NB: We do this now except for: (hey, what is) "Lovable, Bolt and compare ..." ?

AI:
[md[
**Lovable** and **Bolt (Bolt.new)** are "prompt-to-app" builders — you describe an app in plain English and they generate a full working web app (frontend + backend + database + auth), not just a code snippet or a chat reply. Different category from Claude/ChatGPT/Gemini/Cursor entirely — those are assistants you direct; these are closer to autonomous scaffolding tools.
[/md][/color]

Me, Chipper:

so ive just wasted hours bc we are doing the best possible already ! In true H. Simpson parlance: "Doh !"

AI:

Nah — I wouldn't call it wasted. You went in to check whether your setup had a gap, and it came back clean. That's a legitimate use of an hour, not a waste of one — the alternative is never checking and just assuming, which is worse.

And it wasn't a total no-op either: it gave you the actual language for what you're already doing ("cross-model adversarial review," directing critical stuff at whichever model didn't write it) — useful if that Master Post on Collabware's AI-assisted engineering workflow ever needs updating or explaining to someone else.

Anyway — Homer's right that it stings a little. But the correct read here is closer to "confirmed I'm not missing anything" than "doh."

---

# Reusable Prompt: Deep Code/Security Review for Andrew's SMF Mods

Use this as the standing prompt template for any future "review this
package" request — to any AI, not just Claude — on drugs-and-users.org
mod packages (BBCode mods, admin tools, standalone CLI scripts, etc.).

---

## Prompt template

See the attachments for the:

1. "Prompt Engineered" reusable Query and see it in action as we review our sample Mods, one of our premiere Mods: "The Liteweight Markdown Viewer" by using another one of our premier Mods: "listatt md - The Attachment Executive Handler" which is the recommended header bbcode for every new post, moving forward, at the top of the page.

2. The review of the sample mod.

3. The packaged sample mod LMV v4.0

4. The mandatory PDF transcript -- use your Browser's for this or ask AI ("now create pdf transcript on this conversation and do not miss a word").

5. The rebuilt mod based on any shortcomings (deviations that AI is notoriously known for).

6. Attach whatever else you need to the Master Post and always include the transcript(s) for future reference, archiving, indexing and searchability (such as using our ISE (Intelligent Search Engine -- our evolving classify, index and search ecosystem.

Layered Production Stack: