leemoore

Typesafe over-hyped their useful product and successfully got it in front of AI devs. So to be assholingly snarkily reductive I can sum this up as "founder did founder shit well"

if you are refreshing a cache of 120k tokens, you have to input the same 120k tokens. you can't bounce the cache with 1 byte. A proper cache hit requires your entire context that is cached. if you change 1 byte in it, everything after that byte is a cache write and no longer a cache hit

it could be either. subscription defaults to 60 min with 2x cache writes. extra usage and api defaults to 5 min with 1.25 cache writes. You can override either with settings

If you're running the subscription, by default you are paying 2x for cache writes and you're getting an hour for expiry. So refreshing based on 5 min is wasteful. You need to detect whether you are in 5 min or 1 hour mode.

The post would have been stronger and more useful focusing and expanding on the abstraction portion of his message as well as system and program design. Those elements were there but seem sidelined by the focus on DSL. For most developers on most projects, the key take away is we all have to both get good at system and program design especially around abstractions, shapes, responsilibility. Then we have to get good at inserting our selves in the AI loop to steer those decisions a lot in the beginning, regularly as the project kicks off and still more than we think as the project matures.

The counter intuitive pattern I see emerging is if you can cleanly determine intent, of the call you fix the call and prepend informative text to the tool call response indicating the mistake made and how to fix in the future then followed by the actual tool call. In this case you can validate fields and rather than throw a hard error determine if it's an extra field that isn't needed. If so you correct the call and prepend a corrective response in the tool call. This saves turns, it instructs the model in context so less likely to happen later and helps models that aren't so good at recovering from bad tool calls and staying on their longer horizon agentic task (most non openai and anthropic models)

GLM 5.2 feels like Opus 4.6 level. I actually think 4.6 and GLM work better in practice than opus 4.7 or 4.8 as I find both of those more erratic and seem to randomly have a super dumb turn. That random bad turn I see doesn't seem to be hitting the benchmark scores but they make 4.7 and 4.8 very hard to use for me. GLM is more stable like opus 4.6

For developers at non tech fortune 500 companies, I would put money on Windows being the primary workstation os by a lot

It's the executive branch asserting control in this space and requiring all SOTA model providers to bend the knee. Anthropic is the least capable of playing the bend the knee game so is getting the first and worst smack down

If you don't have the capacity to have your mind changed through friction and disagreement with a SOTA LLM and feel compelled to frame those who do to through absurdly reductive statement like "insane arguing with a machine" then that says more about your limitation and lack of understanding than the OP's or Claudes.

I have the same processor and ram. The dense 30b ish Gemma/Qwen really don't break 10 TPS with or without MTP. MOE's in this range feel more usable if they are smart enough for your work. Probably would still use hosted versions of these over local unless. MOE's feel somewhere between sonnet 3.5 and 3.7 to me. Dense feels between sonnet 3.7 and 4 in basic coding or local agentic capabilities (not close to those in chat or world knowledge)

The way you do this (and the way opencode does it) is you do most of your pruning in more recent history. Last I looked at opencode, they start pruning tool call results after 2 full agentic turns. So you probably dont get quite as good hits on cache for the most recent 1-5% of your turns, but after that everything else caches fine and those tool calls that likely aren't relavent to your session anymore are gone.

Enterprise customers aren't running 20 bucks a month for claude pro subscriptions. My company provides developers about 1k worth of usage limits a month and best I can tell they get maybe a 30% savings off of API cost tops. That's not an insane subsidy. Many other jobs titles are only allowed 50 a month and those folks are constantly running out.

Github Copilot has been doing this with business and enterprise seats, but that will be coming to a head very soon. I expect a fast follow after june when they re-align consumer pro and pro+ accounts.

OpenAi seems to be trying to throw tokens at clients to get lock in. So i'd be most worried about the rug pull that will come from open AI post IPO. Anthropic is already acting responsibly in this area and github copilot is attempting to remediate their insane subsidies in the next several months.