EB66

> I think most of it is bot driven spam by the various labs. It's hard not to notice 3 month old accounts with very strong opinions about various frontier labs and little else.

I've assumed the same as well.

I also assume that many of the companies developing these models engage in benchmaxxing.

At my company we've developed our own internal benchmarks for evaluating LLM models as they become available. The benchmarks are tailored to our particular use cases but the utility and knowledge our benchmarks assess is still fairly universally applicable. I see wide differences between what our internal benchmarks report and what the major benchmarks do.

There was a whole lot of fanfare about how amazing GLM 5.2 was when it was released, but it was pure rubbish on our internal benchmark -- far behind OpenAI, Anthropic, Gemini, DeepSeek, etc. I don't know how to reconcile the fact that GLM 5.2 performed very well on some of the major public benchmarks, but consistently performs so poorly on ours. OpenAI models tend to dominate our internal benchmarks.

That's interesting, we've had the same results as you at my company. gpt-5-mini was the clear pareto frontier leader for our in-house LLM benchmarks -- benchmarks that we built and tailored to our specific use cases. Then gpt-5.6-luna came along with the price cuts and immediately supplanted gpt-5-mini.

I always thought it was a little odd that gpt-5-mini was the leader for so long when more popular benchmarks placed gpt-5-mini further down the roster, but it seems you had the same result too.

> I would say GT is hardest to get in, but most inexpensive. While the later two are much easier.

Regarding the acceptance rates, it's actually the opposite. GT OMSCS has a very high acceptance rate (~80%) and UIUC and UT Austin online masters are much more selective (~30%).

The fact that Gemini returns the highest rate of fake positives aligns with my experience using the Gemini models. I use ChatGPT, Claude and Gemini regularly and Gemini is clearly the most sycophantic of the three. If I ask those three models to evaluate something or estimate odds of success, Gemini always comes back with the rosiest outlook.

I had been searching for a good benchmark that provided some empirical evidence of this sycophancy, but I hadn't found much. Measuring false positives when you ask the model to complete a detection related task may be a good way of doing that.

Sharding can be made mostly transparent, but it's not purely a DB-level concern in practice. Once data is split across nodes, join patterns, cross-shard transactions, global uniqueness, certain keys hit with a lot of traffic, etc matter a lot. Even if partitioning handles routing, the application's query patterns and its consistency/latency requirements can still force application-level changes.

I also had a close family member who committed suicide shortly after going on Prozac -- this also happened nearly 30 years ago. His young son later went on Prozac himself (several months after his fathers suicide) and immediately started demonstrating bizarre disinhibited anti-social behavior (e.g., damaging property, stealing from friends, etc). He was immediately yanked off Prozac when he started articulating his own thoughts of suicide. The bizarre anti-social behavior improved after discontinuing Prozac.

For some people, Prozac is a very dangerous drug. It is fully deserving of its FDA black label warning (which it didn't have 30 years ago).

> I lived there for a very long time

It sounds like your last visit was during the COVID-19 pandemic. Homeless encampment conditions in downtown Seattle and throughout the city have much improved since then. Today, visible homelessness is effectively the same as it was back in ~2005.

I don't know why you're being downvoted. I've lived in the area for nearly 20 years and I agree that his description is far exaggerated. It was true in the depths of the COVID-19 pandemic (when any eviction or forcible displacement of people was prohibited), but not since then. Today, the visibility of homeless encampments in Seattle is roughly the same as it was 20 years ago.