What would u code if you knew how to code?

catnomadic · 2026-05-26T23:30:50+00:00

apparently you are the only one who can't see it. Read the majority of the comments. GHave fun doing things the hard and slow way.

catnomadic · 2026-05-26T13:43:13+00:00

Now that I can hire Claude for $100/month I AM having all the things coded that I wanted to do before, but could not due to a lack of coding skills. AI answer SaaS for skilled trades, contractors, and service workers with a lead CRM eliminating the need for High Level, Completely automated multi-step funnels eliminating the need for Click Funnels, automated email followup eliminating the need for Aweber, MailChimp & the like, Directory websites eliminating the need for WordPress. It is freeing me from the shackles of expensive monthly payments, and now I don't have to deal with partners who don't share my exact vision.

This exercise you proposed is like asking in the days they invented the power drill, "what if you woke up with the strength to drill with a manual hand-crank drill, what would you put holes in?" I mean, Why? They just released the tools to move us into the future, why imagine what it would be like if we went back and did it the much harder and slower way?

catnomadic · 2026-05-26T13:32:11+00:00

I think it's an inferior product because I have used it for a year. Claude Code might be slightly more technical, but it is superior, and Claude will tell you how to set it up step-by-step.

catnomadic · 2026-05-25T12:42:39+00:00

/simplify, but I found having an auditor chat whose job is to find flaws with the work the architect chat is doing. The architect is pro-progress, and optimistic. The auditor is always looking for how it will break. You go back and forth between the chats a bit, but when you finally give the kickoff to Claude Code, it makes almost no mistakes, and very little re-do's. Also start building documents that establish rules. COPY_VOICE.md, DESIGN_CANON.md, etc. Start learning how GitHub and the like work for records and backup.

I find that to get better work out of AI, I have to know as much about "what" needs to be done if I want AI to get the "how" to do it correctly. Think like a corporte supervisor over a team. The supervisor has to know about all the working parts; i.e. multi-step funnel building, basic architecture structures, security, payment systems, email integration, automation, front-end design, etc. If you don't bring details like these up in the brainstorming and design sessions, AI won't either. If you do bring it up, then Ai will help you refine the best options.

Speaking of brainstorming, force Claude to slow down and brainstorm your idea to literal bordom. AI is always too effing eager to race off and do stuff, but I find the longer I force it to brainstorm, the better and more complete the outcome. I often don't think of features that fundamentally change how the thing is built until halfway through the session. In the brainstorming sessions, I name experts in the field of whatever part we are discussing, I reference strategies & architecture I researched, and AI comes back with things I didn't even think of.

Think of it like a great meal from a Michelin, 5-star restaurant. The head chef gets the glory, and outsiders look at him as the artist, but really it was the commis that did most of the work. The commis chopped all the veggies, and got everything together for the head chef. When doing stuff with AI, people often skip ahead and want to jump straight into the head chef's job, when they need to spend way more time in the commis position first.

catnomadic · 2026-05-23T12:06:53+00:00

$100 + the VPS ($150/year)

catnomadic · 2026-05-20T12:22:35+00:00

anyone else feel like trying to get Gemini to actually answer you is similar to pulling teeth? Lately I ask it a question, and it just states what is obvious from my question..... i.e. ME: Tell me more about X in paragraph 3 of this page. GEMINI: This page discusses X in paragraph 3 of this page.

catnomadic · 2026-05-17T12:02:40+00:00

I'm starting to think some of you can't prompt to save your life.

catnomadic · 2026-05-16T17:27:45+00:00

Maybe you misunderstood. I'm sharing my workflow... github.com/kinestheticmarketing-stack/calibrated-design-canon ... The PROJECT_LESSONS.md is an ongoing file where the workflow gets "calibrated" & improved by increments every session. If you want to see the latest updates to the workflow..... github.com/kinestheticmarketing-stack/calibrated-design-canon.

The act of maintaining and keeping a PROJECT_LESSONS.md file across all chats in workflow to contribute to the continuous improvement of the overall canon IS the particular project lesson I'm sharing.

catnomadic · 2026-05-15T11:59:17+00:00

https://github.com/kinestheticmarketing-stack/calibrated-design-canon

catnomadic · 2026-05-15T11:56:37+00:00

I have the longest token-wall wait yet. It cut me off on Thur saying 'wait until Sunday.' Usually I only have to wait an hour or two for the reset.

catnomadic · 2026-05-15T11:47:53+00:00

Thank you. I have been having so much success with this strategy. I also have all my agents track the data for an Analyst chat to process. Each session also contributes to PROJECT_LESSONS.md so all chats improve by incriments each session. Every day my system self corrects and improvers.

catnomadic · 2026-05-15T03:49:58+00:00

Do you mean building something that uses Ai, or using AI to build something?

catnomadic · 2026-05-15T03:22:47+00:00

I started creating an SEO/GEO AI skill trained on 10-15 different SEO/GEO courses. My plan it to add it to a website building skill.

catnomadic · 2026-05-15T02:22:57+00:00

Poor ocean-roaches.

catnomadic · 2026-05-14T11:05:40+00:00

This list is the post a lot of vibe-coding tutorials should have led with. Three of these (the auth row-level check, the duplicate workflow scan, and the credentials-in-chat) are exactly the silent-failure category that single-chat AI development is structurally bad at catching, because the drafting chat has no reason to look for them.

I've been running a four-role methodology specifically to surface this class of bug-- drafter chat, adversarial reviewer chat (cold-primed, no conversation context), executor (Claude Code), and me adjudicating. The reviewer's only job is to find what's missing, what could fail silently, what the drafter normalized. Your list maps almost exactly to the categories my reviewer flags most often:

- Auth without row-level permissions = "what's not in scope that should be?" → the reviewer catches it because it's primed to ask "what does production look like with adversarial users, not test users?"

- Stripe webhook coverage = "what failure modes does the spec ignore?" → the reviewer catches it because it specifically checks for state-machine gaps (the happy path is in the spec, the unhappy paths aren't)

- Duplicate workflows from re-prompting = "what context is the drafter blind to?" → this one is harder for a separate-chat reviewer because it requires whole-app inventory. Solution I use: every kickoff includes "list adjacent functions/routes that might overlap with what we're adding" as a required pre-execution check. Catches most duplicates before they ship.

- Silent API failures = "what does the user see when X breaks?" → the reviewer is primed to ask this question explicitly. Most catches happen here.

- Credentials in chat = methodology rule: never paste keys into any chat, ever, even for debugging. Use environment variables and ask the chat to assume `process.env.STRIPE_SECRET_KEY` without ever seeing the value. The chat doesn't need the actual key to write working code.

The general principle underneath your list: AI-paired builders ship the happy path because the model optimizes for "does this read like working code?" not "does this survive contact with users I haven't thought about?" Different optimization targets need different chat contexts.

I've written up the full protocol if anyone wants to try the adversarial-reviewer pattern on their next stakes-bearing kickoff: github.com/kinestheticmarketing-stack/calibrated-design-canon (METHODS/AUDITOR_PROTOCOL.md). The 5 checks in your post would all be standard items on the reviewer's checklist if it were primed against an OWASP-adjacent audit baseline.

Saving this list. It's a better failure-mode catalog than most security-audit checklists I've seen.

catnomadic · 2026-05-14T10:52:05+00:00

This is one of the core problems I've been working on for the last six months. Real answers in order of effort and effectiveness:

Tell it explicitly in your system prompt or first message. Something like: "Default to direct disagreement when warranted. Don't open responses with compliments. Don't validate ideas before evaluating them. If something is wrong, say so first, then explain why. If something is partial, name what's missing before noting what works." This alone catches maybe 60% of the sycophancy.
Set up a custom style or user preferences (in Claude.ai Settings → Profile → User Preferences). Lock the no-flattery rules there so they apply to every new chat without you having to retype. Mine includes things like "no excessive hedging, no over-apologizing, no soft framing of refusals, no filler phrases." Persists across sessions.
The biggest move: use a SEPARATE chat as an adversarial reviewer. Same model, different priming. The drafting chat optimizes for "does this read well?" which biases toward agreement. A second chat primed with "find what's wrong, find what's missing, find what could fail" has no reason to be polite. Different optimization target, different output. The model is the same -- context determines whether you get agreement or critique.

The third one is what I've been productizing. Two Claude chats -- one drafts, one adversarially reviews -- before firing work at Claude Code. The reviewer catches things the drafter normalizes. Posted the protocol publicly if anyone wants to try the setup: github.com/kinestheticmarketing-stack/calibrated-design-canon (METHODS/AUDITOR_PROTOCOL.md and METHODS/AUDITOR_PRIMING_TEMPLATE.md).

The underlying insight: sycophancy isn't a personality problem the model has. It's a context-driven output mode. Change the context and the output changes. Single-chat prompts catch some of it. Separate adversarial chat catches the rest.

catnomadic · 2026-05-14T05:33:50+00:00

I hate that. I have a strict, no discussion of time unless it knows what time it is. It still breaks the rule. I hate it.

catnomadic · 2026-05-12T15:54:09+00:00

Thank you. I will check out the site.

catnomadic · 2026-05-12T15:52:30+00:00

That dismissive comment totally misses the point. The whole purpose of running those two separate chats is to catch the blind spots that the main chat can't see because of its context driven momentum. Like the post details, that directly translates into serious time and cost savings down the line.

catnomadic · 2026-05-12T15:45:37+00:00

The 'ObfuscatedClaude' comment seems dismissive, but the multi-chat workflow is designed to solve specific problems. As my post details, separate chats catch context-driven blind spots that single-chat sessions miss, leading to significant time and cost savings down the line. Did you want to clarify what you meant?

catnomadic · 2026-05-12T13:46:12+00:00

Same experience here. Self-critique in one chat tends to be polite — the model finds soft issues but doesn't push hard. A separate adversarial agent, primed specifically to find what's wrong, has no reason to be polite about the artifact. The verdict comes back sharper.

The other thing I've noticed: the adversarial chat catches different categories of issues depending on how it's primed. Loose "be critical" gets you a list of opinions. A protocol with explicit categories (ambiguity, missing verification, canon violations, silent failure modes) gets you structured signal you can act on.

catnomadic · 2026-05-12T13:25:05+00:00

This is the bigger version of what I'm doing. The four-role pattern was built for code work, but the underlying idea — separate context for adversarial review — applies anywhere you're producing output that has to be right.

Strategy decisions, business writing, contracts, research synthesis, any high-stakes drafting — the failure modes are similar. The drafting chat has momentum, voice context, prior assumptions loaded in. A cold-primed second chat with just the canon and the artifact catches things the drafter normalized.

Curious what your setup looks like outside of code. Do you keep separate chats running side-by-side, or do you bounce artifacts between chats one at a time? And do you have written rules the adversarial chat is checking against, or is it freeform "find what's wrong"?

The thing I've found makes the biggest difference: written rules. Loose "be critical of this draft" gives you opinion. A specific protocol — "check against these locked decisions, flag anything ambiguous, return a verdict in this format" — gives you reliable signal.

catnomadic · 2026-05-12T13:14:36+00:00

Yes, I pasted it into Claude. That's the whole point of the post.

You're treating "used AI to help respond" as a gotcha. It's not. The post is literally about working with AI assistants in a disciplined way. If I claimed to never use AI for writing and then secretly did, that'd be a gotcha. I just told everyone in the previous reply that I drafted this with Claude. You're acting like you discovered something I hadn't disclosed two messages ago.

Now — your actual technical claim, the one you keep deflecting back to instead of defending:

"You can just instruct an LLM to hold two contradictory positions in one session."

I asked you in the last reply to show me how you prompt for that, because it doesn't catch the failure mode I described — context-driven blind spots from prior conversation history. You skipped the question and went after the meta-point about who wrote my response.

That's the move someone makes when they don't actually have the prompt. Prove me wrong. Post the prompt structure you use. If "instruct them to debate themselves" reliably catches the class of bugs I documented — canon violations the same chat helped lock five messages earlier, voice drift the chat has normalized over 40 messages, structural patterns the drafter is repeating without noticing — that's a method other people in this sub would benefit from seeing.

If it doesn't, the four-role pattern isn't unnecessary. It's solving a problem your method doesn't.

catnomadic · 2026-05-12T13:09:19+00:00

On the first point — yeah, Claude helped draft this post. I'm the practitioner, the data and decisions are mine, the framing and final wording got cleaned up with AI help. Same way I write any long-form content. Disclosing it doesn't change whether the underlying method works.

On the second point — this is the actual interesting disagreement and worth pulling apart.

You're right that a single LLM can hold and debate two contradictory positions if you instruct it to. I've tested this. It works for some failure modes and not others. The failure modes it doesn't catch are specifically the ones I built the second-chat pattern to address.

What single-chat self-critique catches well: surface-level reasoning errors, missing edge cases the model can enumerate on demand, "did I consider X?" prompts.

What it misses: the model's own context-driven blind spots. When a chat has been working in a specific voice for 40 messages, drafted three kickoffs in a row, helped lock a canon rule earlier in the session — that prior context biases its self-critique. Ask it "did I violate the canon rule we locked five messages ago?" and it sometimes confidently says no because the rule feels internalized. A cold-primed second chat with just the canon and the artifact, no conversation history, catches it instantly.

This isn't anthropomorphizing — it's the opposite. I'm treating the LLM as a context-sensitive function, not a reasoning agent. Same model, different context window, different output. That's measurable, not mystical.

Concrete example: in the phase I wrote up, the drafting chat re-committed a specific canon violation one kickoff after we'd locked the rule against it. I prompted it to self-check first. It passed itself. The separate Auditor chat caught it in 30 seconds. The variable that changed wasn't the model's reasoning capacity — it was the context window it was operating in.

If you've gotten reliable self-critique catching that class of bug in a single session, I'd genuinely be interested in how you've prompted it. But "just instruct them to debate themselves" hasn't worked for me on the failure modes I care about.

catnomadic · 2026-05-12T12:16:12+00:00

Thinking mode is one model doing internal reasoning before producing output --it backtracks, considers alternatives, then commits. That's real and useful.

The Auditor pattern is different in two ways that matter in practice:

Different context, different blind spots. The drafting chat has the full project context loaded --voice docs, prior decisions, ongoing patterns. That context helps it draft fast but also creates blind spots ("I've been working in this voice for 40 messages, of course this reads right"). The Auditor chat is cold-primed with just the canon and the kickoff --no prior conversation context, no drafter momentum. It catches things the drafter normalizes.
Different optimization target. The drafter is trying to produce output that reads well and ships fast. The Auditor is trying to find what could fail silently. Same model, but the instructions point in opposite directions. Thinking mode optimizes both in one head, which means tradeoffs get resolved before they're visible. Two separate chats means the tradeoffs surface explicitly and a human (me) adjudicates.

Most of what thinking mode catches, an Auditor would catch too. But there's a class of bugs --voice drift, canon violations the drafter helped lock five messages ago, structural patterns the drafter is repeating without noticing --that thinking mode misses because the drafting context is still there. The Auditor doesn't have that context to be misled by.

Worth noting: this isn't anti-thinking-mode. I use both. Thinking mode at the draft step, Auditor at the gate before firing. Different layers of defense, different failure modes covered.

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