serjester

It seems strange to write a blog post about being proven wrong, without a single example of being wrong. Especially with all the current Claude.md drama.

Everyone loves being wrong in the abstract - this reads more like a self-congratulation than a genuine reflection.

Exciting but it’s priced at 2.5X Sol - we haven’t seen pricing this high since GPT 4.5. We will see if the real world use cases outweigh the sticker shock.

Cursor is a victim of its own success. Clearly the future is agents but the average user just wants them for a smarter tab completion.

I also cancelled my subscription since I’m not particularly sold that I need a middle man between me and OpenAI / Anthropic.

There's a potentially amazing use case here around parsing PDFs to markdown. It seems like a task with insane volume requirements, low budget, and the kind of thing that doesn't benefit much from autoregression. Would be very curious if your team has explored this.

The vast majority of people don't need smarter models and aren't willing to pay for a subscription. There's an argument to be made that ads on free users will subsidize the power users that demand frontier intelligence - done well this could increase OpenAI's revenue by an order of magnitude.

This is going to be tough to compete against - Anthropic would need to go stratospheric with their (low margin) enterprise revenue.

This is a hand wavy article that dismisses away VLMs without acknowledging the real world performance everyone is seeing. I think it’d be far more useful if you published an eval.

Congrats on the launch - you're value-add is quite confusing as someone that's at the applied AI layer. This comes off as more of a research project than a business. You're going to need an incredibly compelling sales pitch for me to send my data to an unknown vendor to fix a problem that might be obviated by the next model release (or just stronger evals with prompt engineering). Best of luck.

Seems like their critique boils down to two areas - pandas limitations and fewer built ins to lean on.

Personally I've found polars has solved most of the "ugly" problems that I had with pandas. It's way faster, has an ergonomic API, seamless pandas interop and amazing support for custom extensions. We have to keep in mind Pandas is almost 20 years old now.

I will agree that Shiny is an amazing package, but I would argue it's less important now that LLMs will write most of your code.

If cloudflare goes down, you can blame them. If your hand rolled solution fails when cloudflare exists, you’re going to have a tough pitch to leadership why you’re in charge of the technical roadmap. Choose your battles, and this is not a hill worth dying on.

It's disappointing there's no flash / lite version - this is where Google has excelled up to this point.

As someone that also worked at a large automakers, I think you’re making large, unfounded assumptions.

Warranty data flows up from the technicians - good luck getting any auto technician to properly tag data. Their job is to fix a specific customer’s problem, not identify systematic issues.

There’s a million things that make the data inherently messy. For example, a technician might replace 5 parts before they finally identify the root cause.

Therefore, you need some sort of department to sit between millions of raw claims and engineering. I would be curious what kind of alternative you have in mind?

Seems like a very natural fit for fine tuning - would have loved to see more on the LLM side.