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No "fair use" claims this time? /s

You are right. Let's just hand the keys to everything to the AI companies. They've proven to be trustworthy and ethical. I trust them so much, I'm just going to stop thinking and say yes.

/s

If AI wrote it, it's not your voice. If you can't communicate your thoughts then it reflects a lack of clarity on you as the author. If you lack the clarity, AI assisted anything is probably part of the problem.

If connecting with the audience and guiding them to understand your thoughts is irrelevant, then you are missing the point of writing and being an academic.

There is a reason AI slop is revolting to read. It doesn't care.

I assume anything in Google is public information. I trust Google to respect my privacy focused settings and opt out preferences.

So far they have. If they don't I'll leave, just as I abandoned social media after the Cambridge analytica scandal.

In terms of AI companies, I personally don't want an AI companion integrated into every aspect of my life so they will never get it from me.

These companies have demonstrated they have no ethics (despite their posturing). See absorbing every piece of copyright and intellectual property and calling it fair use.

Nothing nefarious. Just long time lurker tired of the AI echo chamber. My experience with these tools does not align with the hype. To the point I can't tell if most of it is marketing or genuine hype.

To my original question, can you share what specifically you are doing?

The metaphors of this article are painful.

Code has always been a means to build software based solutions. Code was only ever a stone if you did not know how to design and implement software.

The article alludes that LLMs have made changing code free? LLMs are not free. Tokens are not free.

Tinkering on small side projects or apps is great but not a reflection of how this works in larger production grade products.

I think we are.

It's hard to say because no one actually describes what and how they are doing to get AI to successfully refactor or operate in large code bases.

My experience is, slop on slop. Frontier models still can't handle even modest sized refactors without explicit hand holding. I still don't trust models with production scale refactors.

Apparently, everyone has the magic sauce and I'm blind. Or our definition of quality and production grade is very different.

Please enlighten me.

Sometimes it's just fun to do something absurd because you can. This is one of the reasons software is exciting.

What specifically has the tech done to make this process easier? Are you letting the AI impersonate you in legal and financial matters?

Seems completely dystopian to me.

I'm personally with you.

I'd never give any company this much access to my personal data and daily life. It is beyond creepy. Yes, they can probably join the data buts it's another thing to open the door to it.

Congrats on the launch! It takes courage to build something and share it publicly.

Some feedback:

1. Let me build a blog post and let me see what it would look like before asking me to sign up.

2. Landing page is very technical. Most bloggers won't understand it.

3. From the landing page, it is unclear how your platform differs from your competitors.

4. You mention accessibility but your gif moves at the speed of a Tik Tok video. I personally find it jarring (maybe I'm old). Users with motion sensitivity will feel worse.

5. Sample page has leaked css. https://bloffy.bloffy.blog/en

Recently joined a company that does just this. They are using the LLM to review text and produce a grade and a confidence interval.

I'm no expert in the field but it is clear that the model outputs are not reliable. To my frustration this was designed by "experts" so I originally trusted that this approach was sound.

How does this compare with descriptive confidence? For example, major v.s. minor? Intuition tells me it's a different flavor of the same illusion.

LLM code is junk food. Tastes great in the moment and makes you ship faster but its going to kill you slowly and surely.

It's the 10x programmer nonsense but at 1000x. My anecdotal experience is that LLMs collapse under the weight of their own slop in the same fashion that wreckless engineering always has as a code based grows. 1 shots quickly start becoming 10shots. We start having to decompose prompts into trivial levels of the work so claude and friends have a chance. If you are unlucky you realize you spent more time promoting the model than if you had done it yourself.

You slowly start to wake from the junk food sugar crash, and you realize how awful the code really is, despite all that dopamine that was hitting your brain telling you that it looked good enough. How could you have missed the slop? Then as you peel back the layer you realize the code is rotten all the way through and you are now completely dependent on the agent to fix it.

When the subsidized money and subscriptions have to face economic reality, we'll all be stuck unable to understand the mountains of slop we've built.

I'm not arguing that their product is not valuable or that it doesn't have customers.

I'm stating that their current business model, prices, and behavior they are trying to instill is not profitable. For example, token maxing every code change is great for Anthropic and terrible advise for Anthropic customers.

The moment they stop training, they become irrelevant. The moment they become irrelevant their valuation crashes down to normal valuation ranges for a company with their revenue and future growth

Profitable as in after expenses.

https://youtu.be/T-oXyXwD6sE?si=tauzXVyGmQ6MhI_5

And so the wildly unprofitable AI company explains how it token maxes it's code and review process.

Authors should really try and view this article from the perspective of someone who has to think about cost to use the models.