a1j9o94

You are right that someone could go through that effort bat that only really makes sense if they give you some.idea how to use it.

Reading the exchange it seems like you got caught up on the illustrative examples the other person used. If a web server isn't a good example of a simple problem for F* the landing page should have an example of something that is. It shouldn't take going through that many steps to understand the so what.

Disclaimer I work at Zapier, but we're doing a ton of this. I have an agent that runs every morning and creates prep documents for my calls. Then a separate one that runs at the end of every week to give me feedback

This is effectively how I treat my AI agents. A lot of the reason this doesn't work well for people today is due to context/memory/harness management that makes it too complex for someone to set up if they don't want a full time second job or just like to tinker.

If you productize that it will be an experience a lot of people like.

And on the UI piece, I think most people will just interact through text and voice interfaces. Wherever they already spend time like sms, what's app, etc.

You would only use the base model during training. This is a distillation technique

I fall into this trap a lot. The platonic ideal argument is a fun mental exercise but doesn't get anything done

This is an interesting space. Right now we've gotten to a point where agents can do most tasks, but they will get lazy/skip steps if you're not precise in the requirements. We need ways to validate that expands beyond software tests. This is a good direction but a few thoughts: 1. From what I can tell the agent who does the task is running the validation. Keeping the validation agent as a separate context avoids the validator knowing what the software is supposed to do vs what it does 2. There's a lot of prior art around org structures to validate things that we've built out over the last ~100 years that we can apply in this space. E.g. look at the way that blind trials are run

Honestly just didn't think about it. Added it.

Hey HN,

  I spent the last few weeks exploring whether AI systems could benefit from generating video predictions before making decisions—like how humans mentally simulate "what happens if I pour this coffee?" before acting.

  The idea: Show an AI an image, ask "what happens if I push this?", have it generate a video prediction, then compare that prediction to reality. If the prediction looks wrong, maybe the AI could catch its own mistakes.

  The result: Current models can't do this. But I learned some interesting things along the way.

  What I tested:
  - 7 different architectures for predicting future video frames from VLM latent space
  - Whether perceptual similarity (LPIPS) between predicted and actual video correlates with correctness
  - Self-correction loops where the model gets feedback on its predictions

  Key findings:

  1. VLMs can't predict the future – Every architecture I tried performed worse than just copying the current frame as the "prediction." The model understands what's in an image but can't predict what will change.
  2. Visual similarity ≠ semantic correctness – This one surprised me. Wrong predictions often looked MORE similar to reality than correct ones (LPIPS correlation: 0.106). You can't use "does it look right?" to catch mistakes.
  3. Some things worked – Hybrid encoders (DINOv2 + VLM) preserve spatial information that VLMs lose. VLMs understand generated video well (93% semantic retention). Small adapters (10M params) work better than large ones (100M).

  I'm releasing this as a benchmark proposal. Video generation is improving fast—capabilities that don't exist today might emerge in future models. Seems worth tracking.

  Links:
  - Demo video: https://youtu.be/YJxDt_zCrUI
  - Code + paper: https://github.com/a1j9o94/foresight
  - Live demo: https://foresight-demo-kappa.vercel.app

  Built with Qwen2.5-VL, LTX-Video, Modal (GPUs), and the Something-Something v2 dataset.

  Happy to answer questions about the experiments or methodology.

Pretty much every major LLM client has web search built in. They aren't just using what's in their weights to generate the answers.

When it gives you a link, it literally takes you to the part of the page that it got its answer from. That's how we can quickly validate.

I would argue that's just your coworker giving you a bad answer. If you prompt a chatbot with the right business context, look at what it spits out, and layer in your judgement before you hit send, then it's fine if the AI typed it out.

If they answer your question with irrelevant context, then that's the problem, not that it was AI

Honestly if you have a working relationship/communication norms where that's expected, I agree just send the 5 bullets.

In most of my work contexts, people want more formal documents with clean headings titles, detailed risks even if it's the same risks we've put on every project.

I know I'm an outlier on HN, but I really don't care if AI was used to write something I'm reading. I just care whether or not the ideas are good and clear. And if we're talking about work output 99% of what people were putting out before AI wasn't particularly good. And in my genuine experience AI's output is better than things people I worked with would spend hours and days on.

I feel like more time is wasted trying to catch your coworkers using AI vs just engaging with the plan. If it's a bad plan say that and make sure your coworker is held accountable for presenting a bad plan. But it shouldn't matter if he gave 5 bullets to Chat gpt that expanded it to a full page with a detailed plan.