Seems like a pretty pointless post that still centers around output tokens.

In agentic coding, cached input tokens is 90% of the API "cost". It doesn't require GPU compute, and DeepSeek has shown that it can be done 50~100x cheaper with MLA/CSA/HCA, and a whole bunch of disks. This should collapse the margin.

I also think we’ll approach a point where increasing intelligence is not really going to suddenly improve most work tasks. I bet that’s already happened actually.

We’re oohing and aahing about models, when the ones a few versions ago did a good enough job for most of the dumb coding, etc we do

There is mention of GLM 5.2's poor web search capabilities, but I see that as a harness responsibility.

I've set up my own SearXNG instance on my VPS and integrated it into Pi alongside the webfetch tool, and GLM 5.2 has so far been great at finding things. I asked it to give me the current news from an Austrian online newspaper that's difficult to parse because of its aggressive ad overlays. Both ChatGPT and Claude failed in their native chat apps. GLM 5.2 in Pi was clever enough to search for the RSS feed and gave me a detailed overview.

The lack of vision is a real shame, though. I've implemented workarounds in Pi that are okay, but they're not as good and the whole experience feels awkward.

I don’t see it. GLM 5.2 seems noticeably worse than Opus and especially GPT 5.5, the poor vision capabilities are also a massive strike against it since these are a huge improvement in the frontier models that can make all the difference when working on anything visual. Running it locally is its biggest advantage but for a lot of use cases that isn’t needed and is a burden to set up and maintain.

Companies like Amazon taking out loans to fund more AI infrastructure, coupled with AI companies that are massively overvalued and burning cash, coupled with companies saying there’s no ROI from AI investments, coupled with the margin from selling AI models trending to zero paints a nasty picture that can’t continue much longer.

I wonder if anyone has actually measured the difference in verification time between these models. A senior dev in a high cost of living area costs the company something like $200 an hour. If a cheaper model produces code that takes an extra 20 minutes to debug or verify because it missed a subtle edge case, you have already lost any savings from the lower API bill. It feels like the real moat for labs like Anthropic is the level of trust a reviewer can have in the output without reading every single line. Curious how much people trust GLM over something like Opus? Is there much of a marginal difference here?

History is littered with the corpses of companies that had exceptional but expensive products that were replaced with cheap, good enough products.

The author mentions lack of good Web search. I've been using slightly modified crawl4ai and searXNG together with firebase for the rare sites that insist on throwing wrenches in the works of my LLMs.

I also have my fork of metamcp that replaces firebase MCP spec with my own that tells the model to use crawl4ai and SearXNG instead.

I've been using this wia Librechat with every commercial and open weight model I tested.

The search is way better than OpenAI and what ClaudeCode uses, but Gemini is way faster. That will change soon as I'm planning to put these instances in a DC with gigabit pipe.

Firebase is not cheap, but it retrieves everything, bypasses captchas and so on.... As long as one uses it for 1% of Web queries the cost is manageable.

An entertaining thread on just what a dismal rat race the life of a clanker shepherd has become.

"There is no doubt that using Z.ai's official API and subscription is almost certainly a non-starter, with their terms being at best weak and the deep connection to Mainland China."

This is the key statement in the article. I think people don't realize that these "open" weight models exist because giving away your product at a loss is a time honored marketing strategy. There's nothing guaranteeing that the next iterations will be open (remember "Open"AI?).

The Chinese labs are profit seeking companies. If they can't recoup their investment through API use, they won't be able to train more models. But if the argument is 'who cares, training models will be so cheap anyone will be able to do it ',then check the comment elsewhere on this comment section about free alternatives for consumer and enterprise software.

Oh... And the variation 'what we have today is already good enough for everyone' argument is just another incarnation of '640Kb should be enough for everyone'.