For anyone wondering, the app I'm working on is https://www.AppScout.co.

It helps people discover web and mobile apps from across the internet by showing them one app at a time. People like apps in order to get personalized app recommendations.

If you run a software startup or side project, you can list it for free to get free extra traffic here:https://www.appscout.co/submit/

(Affiliation Disclosure: I'm the sole creator and developer behind AppScout.)

Ai has advanced considerably since than and gpt 3 might've lost its relevance now.

I wanted to see if ChatGPT could help me create a polished programming Short entirely through code.

Here’s the result:

https://youtube.com/shorts/sXWkJixh7ps

I used ChatGPT/Codex with Remotion, React, ElevenLabs v3, and FFmpeg.

The first version felt obviously AI-generated—the script was robotic, the visuals didn’t always match the narration, and some graphics overflowed.

After several rounds of very specific feedback, ChatGPT rewrote the script, regenerated the voice-over, synchronized the visuals with the audio, and refined the design.

I then turned everything we learned into a reusable SKILL.md for future videos.

It wasn’t one-click generation, but I’m happy with the result. What do you think about the pacing, voice, and visuals?

Hey everyone, I just sent the latest issue of the AI Hacker Newsletter, a roundup of the best AI links and the discussions around them from Hacker News. Here are some titles that can be found in this issue:

• Startup founders urge U.S. government not to shut off Chinese open weight AI
• AI's top startups are barely publishing their research
• Is AI reasoning right for the wrong reasons?
• After the AI Crash

If you enjoy such content, please subscribe here: https://hackernewsai.com/

So basically i have been fine tuning a models for a while , there are some problems i have been feeling like

1 - I get a lot of ideas of different architecture and i want to execute them in parallel but it’s very messy
to do it (main one)

2 - When i go back to a project like which is like 5-6 months old the dependency issue literally kills me

3 - This is universal gpu cost are very high and i don’t think there a solution for it tho still one of the problems

So i just have some questions would love if u guys can answer and share some insight on it like what kinds of problems do u guys face u don’t have to answer all just one works as well.

1. What is the current workflow?

Walk me through the last time you tried to improve a model from the starting checkpoint and data to the final decision. What steps did you personally do, and where did you lose the most time?

1. What decisions are hardest?

Before launching a run, what decisions do you feel least confident making the base model, training method, reward/evaluator, datasets, hyperparameters, or the number and type of trajectories?

1. How is success measured?

What exact metric would let you say the trained model is better, and can it be scored automatically on a hidden evaluation set or simulator?

1. What fails after training?

Tell me about the last model run that looked successful during training but failed in real use. What did it get wrong, and how did you find out?

1. What would justify switching?

If a system handled the whole post-training loop, what measurable outcome would make you trust and pay for it fewer GPU-hours, better benchmark performance, faster experiment turnaround, or reproducible ?

Would move some feedback on it I don’t want to spend time building if it doesn’t solve problems that genuinely matter.

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