As a solo dev, I can't afford to ship something and then realize it's unprofitable. But estimating token usage before launch is hard.

Do you just... guess? Build a prototype first? Or is there an actual way to forecast what users will actually cost you?

I was looking through the newly released benchmark comparison, and one thing stood out immediately.
Opus 5 outperforms Fable 5 on many of the benchmarks that matter to me:
* Agentic terminal coding: 43.3% vs 33.7%
* Knowledge work: 1861 vs 1747
* ARC-AGI-3: 30.2% vs
* Computer use: 70.6% vs 66.1%
* Business workflows: 26.0% vs 17.4%

Yet from what I ve seen, the API pricing is also lower than Fable s.
As someone building AI products, that s a pretty interesting combination. Usually when a model takes the lead on benchmarks, it s also the most expensive option.
I m curious about real-world experience though.
Has anyone here already switched from Fable to Opus 5?
* How does it perform on long coding sessions?
* Is the benchmark advantage noticeable in production?
* Any hidden downsides (latency, tool use, context handling, reliability)?
Would love to hear from people who ve actually deployed both.

Hey Product Hunt community!

We ve all been through this pain: you feed a 50-page PDF, financial report, or contract into an LLM, ask for a quick summary, and it confidently hands you data that is completely made up.

If a creative writing bot hallucinates, it s fine. But when it happens during document processing like in your RAG pipelines or data extraction it s an absolute disaster.

Honestly, my team and I have been losing sleep over this exact problem. It s why we are building Ethos by DocuShell (it's an open-source verifier for document citations, source evidence, and RAG). While building it, we realized just how easily LLMs get totally confused by simple tables or negative sentences (like missing a "not" or "except").

I've been pretty impressed at the amount of products people (including myself) have been able to create which got me curious... do vibe coders or AI-primary builders have a place in a company or team?
My thinking is the more technically adept would work on the core-focus while vibecoders can assist with other tasks that shouldn't be the main devs focus...like a potential feature add, minor changes, or even exploring different ways of modifying the existing product.
I'm curious what you all think, would you hire a vibe coder?

I keep seeing the same pattern across early-stage teams:

the MVP works until it really doesn t.

For many founders, the hardest part isn t getting something online
it s everything that comes after:
infra that cracks under real users
code that no dev wants to touch
rewriting the whole stack
AI-built projects no one can maintain
the moment you realize your prototype isn t a product

A few months back I set up custom instructions for Claude stop opening with agreement, tag its confidence on claims, push back when I'm wrong instead of validating me. Then I mostly forgot about it. Kept getting the normal helpful-assistant experience.

Last weekend, mid-project, it started actually doing what I'd asked. Pulled up my own past chats and launch checklist to see what I'd actually committed to. Called out that I'd blown past my own launch deadline without noticing. Refused to let me reprioritize a payment-provider decision over just talking to five actual users.
Told me a "quick bug fix" I described was actually five missing subsystems.

Was it mean? Maybe, some of it. But all of it was annoying in the specific way that's actually useful the way a good cofounder or an honest advisor is annoying.

Felt strange, Do I need this? Should I tone down the instructions?
I don't know if everyone can replicate this exactly.
Curious who else has tried shaping how their AI pushes back, not just what it outputs.

Like a lot of you, my API bills have been crazy lately from feeding entire directories and massive logs into the context window for hours.

I noticed a structural loophole in how models are priced: text tokens are billed by character density, but vision tokens are billed by flat image dimensions.

I started taking my massive text payloads and converting them into heavily compressed, high-density PNGs before uploading them to Claude and Gemini. It forces the model to use its OCR, and it actually drops the context cost by about 60% while retaining perfect accuracy.

Has anyone else experimented with multimodal hacks like this to save on API credits, or are you mostly relying on RAG and prompt trimming to keep costs down?

The real founder coding vibe:

• You ship a small fix break 3 things

• You deploy confidently production crashes

• Local works perfectly Vercel says nah

• You spend 1 hour coding 4 hours debugging

• Slack notifications hit instant anxiety

Founders know the pain:
You re coding, marketing, fixing bugs, writing docs, and answering customer emails all at the same time.

What s your funniest or most painful founder-coding moment recently?
Let s vibe in the chaos

Hey everyone!

I love the concept of "vibecoding", getting into the flow, using AI to build fast, and focusing purely on the product. But for a lot of developers (especially in emerging markets like Indonesia), that vibe instantly dies when it's time to deploy.

Suddenly, you are dealing with complex server setups, fighting DevOps configurations, or getting blocked because you don't have an international credit card to spin up a basic server.

Deployment shouldn't ruin the vibe. It should be just as seamless as writing the code.

AI coding is pretty mindblowing but sometimes it's a headache built on a mountain of bugs. Usually that comes down to issues with how you prompt. So, with that in mind, I'm starting a crowdsourcing discussion so we can all improve our prompts and in turn our apps.

Share the exact prompts that turn blank AI requests into real world code. Show us how you go from generate a REST API to a deployable service in just a few steps.

What to post: