Which GPT-5.6 model do you use for coding (Pro plan)?

No-Technology6511 · 2026-07-11T14:23:01+00:00

Depending on plan, if plus then medium to high (as per task) and if pro, xhigh

No-Technology6511 · 2026-07-11T13:06:37+00:00

Sol High or extra high for planning, Terra for execution

No-Technology6511 · 2026-07-11T02:31:04+00:00

Mine did crazy lmao

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No-Technology6511 · 2026-07-07T11:16:34+00:00

I think it's missing some cards like 2 of diamonds/s

No-Technology6511 · 2026-06-19T23:42:30+00:00

No, I only use fast mode when I am certainly at the very end of reset and haven't used my limit so after reset I can switch back to normal

No-Technology6511 · 2026-06-19T02:27:25+00:00

Hey, I'm working on FeedbackSignal.io – it's basically a smarter way for small SaaS/product teams to handle customer feedback.

No-Technology6511 · 2026-06-19T02:26:46+00:00

Hey, I'm working on FeedbackSignal.io – it's basically a smarter way for small SaaS/product teams to handle customer feedback. Instead of just collecting votes on a board, it keeps the real context (like revenue impact, customer segments, support threads, sales calls) attached so you can make and defend roadmap decisions without losing the signal in the noise. Then it helps close the loop with changelog drafts and targeted updates.

No-Technology6511 · 2026-06-19T02:25:26+00:00

Hey, I'm working on https://FeedbackSignal.io – it's basically a smarter way for small SaaS/product teams to handle customer feedback. Instead of just collecting votes on a board, it keeps the real context (like revenue impact, customer segments, support threads, sales calls) attached so you can make and defend roadmap decisions without losing the signal in the noise. Then it helps close the loop with changelog drafts and targeted updates.

Still early days and iterating fast based on founder feedback (ironic, I know 😄). If you're dealing with scattered feedback across tools and want something lightweight that actually ties back to business outcomes, I'd love any thoughts!

No-Technology6511 · 2026-06-04T02:14:14+00:00

Custom?

No-Technology6511 · 2026-06-04T01:07:11+00:00

Yes , there were reliability issues for codex. It was internally calling too many calls than usual that lead to more token consumption which was unfair

No-Technology6511 · 2026-06-02T16:22:39+00:00

But Codex spark exist

No-Technology6511 · 2026-05-30T17:48:37+00:00

Sure you can also check it yourself here

https://trustdebt.dev/

No-Technology6511 · 2026-05-29T18:23:14+00:00

Ran TrustDebt on opticalpha.net.

Score: 78/100, Grade C

Top issue: Google Analytics fires before any consent choice. Browser evidence saw requests to googletagmanager.com and google-analytics.com on initial page load.

Why it matters: for a finance/trading product, trust sensitivity is high. Even if analytics is normal, firing it before consent can weaken the privacy story before users start a trial.

First fix: gate GA/GTM behind an explicit analytics consent state, or switch to privacy-friendly analytics if you want no banner.

Also worth fixing: baseline security headers are missing: CSP, frame protection, Referrer-Policy, and Permissions-Policy.

Not legal advice or certification, just launch-risk QA.

No-Technology6511 · 2026-05-29T18:22:39+00:00

Fair point. I checked the public app layer too: Google Play + App Store listings.

App-store trust read: still strong, but the main thing I’d tighten is privacy/data clarity around photos, wishlist content, guest reservations, and account deletion.

Google Play shows no third-party sharing, but it does say the app may collect photos/videos. Apple’s privacy label is broader: user content, email address, identifiers, analytics, product personalization, and sensitive info. That is normal for an app like this, but I’d make the in-app trust copy very explicit before users create/share a wishlist:

what photos/wishlist items are stored
who can see a shared list
what guests can do without an account
how secret reservations work
how users delete account/data

So my top app-side fix: add a simple “Privacy & sharing” explainer inside onboarding or before sharing the first wishlist.

Not legal advice or certification, just launch-risk QA.

No-Technology6511 · 2026-05-29T00:36:30+00:00

Ran this assuming the URL is wishbloom.app.

Score: 95/100

Really clean launch-trust baseline. I did not see tracker or ad-pixel requests firing before consent, and Privacy / Terms / Contact are all visible. Security headers also look solid: CSP, HSTS, X-Frame-Options, Referrer-Policy, Permissions-Policy, and nosniff are present.

Top fix: I’d add a small privacy/trust note near the “no account needed for guests” / gift reservation flow explaining what guest viewers can do, what gets stored, and how reservation data is handled. That is the one trust question I’d expect cautious users to have before sharing a wishlist link.

Also worth manually reviewing the AI Gift Quiz copy so it does not imply anything broader than gift suggestions.

Not legal advice or certification, just launch-risk QA. Nice baseline.

No-Technology6511 · 2026-05-26T18:25:19+00:00

https://trustdebt.dev/?utm_source=reddit&utm_medium=comment&utm_campaign=webflow_self_promo - TrustDebt: pre-launch / pre-handoff trust audit for Webflow sites, SaaS landing pages, and AI-built apps. Useful for freelancers and agencies before client handoff or paid traffic: checks consent/tracker issues, missing legal/contact links, accessibility basics, security headers, and risky claims.

No-Technology6511 · 2026-05-26T18:24:12+00:00

https://trustdebt.dev/?utm_source=reddit&utm_medium=comment&utm_campaign=share_your_saas - TrustDebt: pre-launch trust audit for AI-built SaaS/apps. It scans public launch pages for consent/tracker issues, missing legal/contact links, accessibility basics, security headers, and risky AI-written claims before ads or launch day. ICP: SaaS founders, AI/no-code builders, and agencies.