jamel's profile on Product Hunt

About

Holder of a Professional Master’s degree in Digital Management and a Bachelor’s degree in SME Management from the Faculty of Economics and Management of Tunis, I have experience in digital marketing and e-commerce.

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Tastemaker

Tastemaker 5

Gone streaking

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Forums

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16h ago

Does AI change how you price small client fixes?

I do WordPress and WooCommerce fixes for clients. A bug that used to take an hour to find can now be located in ten minutes with AI help, but the careful part (testing on staging, checking nothing else broke) still takes the same time.

So what do I charge for? The minutes it took, or the risk and the result the client gets?

p/murror

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4d ago

OpenAI's new model costs $2 in, $10 out per million tokens. The cheap part was never the tokens.

Cheaper near-frontier models make it tempting to send more context on every call. For anything personal, that is the wrong place to spend the savings.

OpenAI announced GPT-6.1 Sol at DevDay on 2 October. Its API page lists $2.00 per million input tokens, $0.10 for cached input and $10.00 for output, with a 1,050,000-token context window. Prompts over 272K input tokens are billed at double the input rate and 1.5x output for the whole request. OpenAI says the model gives near-Astra intelligence at a fifth of Astra's price. That comparison is OpenAI's own claim about its own product, and I haven't seen an independent benchmark of it, so treat it as marketing until someone you trust has run it on your workload.

The same event added computer use to the Agents API, so an agent can operate software through its interface, available through the API and in Codex and ChatGPT Work on Pro 500 and Enterprise plans.

Here is what I think a small team should take from it. When the per-token price drops, the first instinct is to spend the savings on more context: send the whole history, the whole document, the whole account. The model can take a million tokens, so why not use them.

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5d ago

Are AI-generated features causing more problems with MVP maintenance than ever before?

AI has allowed me to quickly create an MVP by automating the process of coding and adding features has been a breeze.

However, the trouble appeared only much later.

Each newly added feature would introduce a new dependency or edge case, or even a duplicate of the existing logic. The project looked slick but changing anything meant breaking something else.

This experience taught me that fast development is not only about the speed at which we can introduce features but also about the simplicity of further changes to the code base.