indeed as the author mentioned, LLMs can greatly help in areas where there is a long tail of not so important, easy to solve problems, that humans just don't have the time or priority to focus on. But combine this long tail of problems that can be solved: accumulated this might still be very beneficial as a sum of things.

another best practices every single solution using LLMs/agents should implement is "never trust the llm".

How I understand it, without really reading into it:

- Thomson Reuters did not "create" a frontier model, they gave some money, maybe a bit of their data to Imperial College London, and took Alibaba's Qwen 3.6 35B A3B Model for a basic fine tune.

- "They" (some undergrads at Imperial College London) used an existing ablation framework to undo some of the topic alignment of the original model.

- "They" fine tuned on some domain knowledge trying to preserve general knowledge - here maybe, just maybe some data came from Thomson Reuters.

As a result: one of 100's of Qwen 3.6 35B A3B sparse model fine tunes, just for publicity, to write overstating headlines like "X created their own frontier model"

Sadly they seem to not be releasing a sparse 35b A3b or anything inbetween "too large to host for mortals" and "fits into a consumer rtx". Probably not to eat away their profits on their API serving. 120b - 300b is a dead space right now, very few good releases in that size range. (I know there are, but the big labs aren't releasing stuff here)

This means we don't need a centralized maintainer for smaller tools. the facebook ad blocking cat and mouse game is something I cna very well imagine to just be something my computer would solve in real time, updating the uBlock filter/userscript to adapt to any fb changes in near real-time.

I then moved abroad to Bangkok, working an office job. Although BKK is great for consumerism and convenience, especially with cheap labor available for almost anything, you can get quite lazy. The bad traffic, non-pedestrian friendly (non existent) city planning and little nature left also makes it a bit cumbersome to find nature nearby. This made me appreciate nature, hiking and nice scenery. (Of course Thailand has lots of beautiful nature and scenery, but not so much of an active outdoor scene)

Coming back to Switzerland after 6 years, I became the biggest tourist, going hiking every weekend, spending time at our tourist destinations, but also all the second tier ("unseen") places only locals know. I tried so much stuff that in the past I thought is tourist stuff, and most of it is simply great.

I also became much more understanding, open and helpful to expats, foreigners and tourists in my country.

if you can't afford to do that, look at a lot of them, eg. on artificialanalysis.com they merge multiple benchmarks across weighted categories and build an Intelligence Score, Coding Score and Agentic score.

where it gets interesting is when you have a custom system that your LLM surely never saw (custom ERP) that has 50 sometime cryptic tables, unclear look up tables and unexplained flags.

something no text2sql solution solved for us.

we built a second mcp that lets the agent look up business logic (generated from source code) and then does better queries. that i think is something i never read in a blog post about a text2sql solution.

In many of these cases, the issue isnt failed logical reasoning. Its ambiguity, underspecified context, or missing constraints that allow multiple valid interpretations. Models often fail not because they can’t reason, but because the prompt leaves semantic gaps that humans silently fill with shared assumptions.

A lot of viral "frontier model fails THIS simple question" examples are essentially carefully constructed token sequences designed to bias the statistical prior toward an intuitively wrong answer. Small wording changes can flip results entirely.

If you systematically expand the prompt space around such questions—adding or removing minor contextual cues you'll typically find symmetrical variants where the same models both succeed and fail. That suggests sensitivity to framing and distributional priors (adding unnecessary info, removing clear info, add ambiguity, ...), not necessarily absence of reasoning capability.

https://news.ycombinator.com/item?id=25148942

https://news.ycombinator.com/item?id=6718067

https://news.ycombinator.com/item?id=16254569

https://news.ycombinator.com/item?id=30523955

https://news.ycombinator.com/item?id=33745146

https://news.ycombinator.com/item?id=35209424

https://news.ycombinator.com/item?id=16272392

https://news.ycombinator.com/item?id=8975735

It's quite easy to build accurate geo-related applications in Switzerland due to the excellent work of the government office "Swiss Topo" that maps every tree, every house, every road in the whole country. Trees in cities have metadata such as: year planted, type etc. :)

Johnny Harris, the map aficionado mentioned Swiss maps and Swiss Topo's dedication multiple times in his videos.