cpard

Today, we are discussing if AI cheated by picking the easy problem to solve which means that we at least still comprehend what’s going on.

I wish mathematics and the rest of the human intellect wouldn’t turn into content marketing that is generated primarily to trigger strong human emotions.

I feel that this is going to hurt both AI and the disciplines that can benefit the most from it

You can make writes faster and part of that is by not dealing with schema resolution but you do push the work somewhere else there too.

I guess the same principles apply on many different levels, from when you write to the file system up to how you deal with conflicted data types during data ingestion.

I have experienced similar behavior between opus and haiku when benchmarking Dara engineering tasks. The “cheaper” model takes many more turns to figure out the task and this is without taking into account other important factors.

Another interesting behavior that I observed is that Haiku tended to cheat more maybe because it was having a harder time to find the root cause of the problem.

Benchmarking and evaluation of agentic systems is very interesting and if there’s one thing that someone should keep from the Databricks post is how important is for everyone to build and run their own.

A deterministic layer like a compiler or generator of code with some kind of IR that the LLM generates and feeds it with.

I feel we will be seeing this more and more in the near future.

Priests were interpreting the oracles (at least at a place like Delphi) according to the context of the people asking the questions aka participating in politics of that ancient times.

Subjectivity was a feature and I’m not sure that fits to mathematics though.

I wonder if mathematics as a science field moves more into engineering or if a different branch will emerge that is closer to that because to the point of the article, science is about understanding not just results.

Instead they were focusing more on optimizations of the existing algorithm that has been implemented. Maybe it's an artifact of the problem I was throwing to them (I was asking to optimize the implementation of select_k in Arrow, which is currently using a max-heap streaming algorithm).

I've started documenting my journey with this here: https://www.kostasp.net/posts/16-ai-experiments-apache-arrow in case you want to take a look. Any advice would be highly appreciated, I'm looking for more inspiration on how to torture myself with that stuff.

My feeling is that the research is converging to what the paper claims, that the combination of two is the right way to do it and it's a matter of how you combine the two as part of the harness you built that makes the difference.

At the AID-Wild / ACM CAIS 2026 workshop that happened recently, there are plenty of examples in the accepted papers on that.

A great example is AI-PROPELLER: Warehouse-Scale Interprocedural Code Layout Optimization with AlphaEvolve. It uses AlphaEvolve and Vizier to evolve compiler code-layout heuristics. (https://arxiv.org/abs/2606.00131)

What questions I'd like to answer with the diffing is more like: will the grain go from one-row-per-user to one-row-per-user-per-day, will a key stop being unique, will a join start fanning out and quietly double a measure, will something additive become non-additive.

This diff is over structure but this structure is latent in the transformation that produces it and to make things harder, if we are talking about some declarative language being used (e.g. SQL) the code doesn't even describe how things are getting done, but what the output would be.

What I've ended up doing is recovering the structure from the code by analyzing it and then using * cheap * profiling than a full row compare.

As an example, my equivalent impact sub-command output would be something like this: "this change makes account_id non-unique three models downstream"

Or maybe what works in math doesn’t work with chemistry?

Now someone can argue that lines of code are not a good proxy of engineering productivity, but I wouldn’t be surprised if the audience they target with this content is not the HN commenters of this thread.

What is a bi-temporal record? I don’t think I’ve heard the term before and I’d love to learn more.

This is the equivalent of Claude Design but for security.

Different harness, different packaging and obviously different distribution because the persona is different.

It’s funny because from all the posts I’ve read from companies reporting on Mythos, everyone is building their own harness for it.

Cisco even published a specification for one.

But Anthropic is the one who has figured out how to package and distribute this. Great GTM!

I wonder though, is such a dialect better for agents? Have you tried to measure if an agent performs better expressing queries in such a language instead of SQL?

What you describe has been pitched by many different products for different parts of the data platform. Fivetran for example claims to do that for the extraction and loading part, good old Informatica was offering the ETL in a graphical interface etc.

The problem that many teams ended up having is the explosion of the tooling needed by data teams.

But there’s no free lunch, building and maintains data infrastructure that is reliable requires work. Many companies don’t realise that when they start their analytical journey and aggressive marketing doesn’t help. That’s the point I was trying to make.

maybe you can say it’s more of an ELT pattern but anyone who’s interested into using this for realistic analytics they will have to transform the data at some point.

If an org is early enough to think that they can use a solution like this and just get in duckdb and start spitting out reports, they will be up for a really bad experience.

Please educate people to do the right thing and realize the scope of the work they are facing, it might feel that it hurts your growth in the short term but it will benefit you greatly in the mid-long term as a vendor.

Distribution was always the hardest part in building a business, sure today it’s easier to vibe code something in a day or two but there is a reason that for a long time now the standard advice to technical founders has been to not build before you have conviction on the product market fit.

And product market fit means that you have also figured out how to get to the market, aka distribution.