paul lodder

@AndrewDeJackson @uptimerobot thanks, i just migrated to betterstack, but will keep pulsetic in mind if betterstack disappoints as well

@paul__lodder @uptimerobot Compare them with Pulsetic - maybe it will fit your needs better.

is it me or is @uptimerobot surprisingly flaky for a status monitoring service?

@SimonNom1 @claudeai termux + vps you ssh into works quite alright

Is there no way to do a Coding setup with Claude Code on Android? There seems to be no legit way to actually use it on Android. Claude App doesn't have CC, CC Web link simply doesn't work. @claudeai

a pattern i use with claude code/codex a lot: have it write a one-off script to diagnose, reproduce, and fix an error/bug and only then apply it into the actual codebase it can iterate in isolation which reduces the cognitive load for the agent

(me coping with my ever-growing dependency on llms to write code for me)

i wonder: when cars were invented, were people worried they'd forget how to ride a horse?

i wonder how many codex features are not implemented out of fear of copycatting claude code too much like not being able to use C-n/C-p to move up/down a list... why do i have to use my arrow keys???

what a time to build

depending on hyperscale cloud provider (aws, azure gcp) to host services is unnecessarily complex for the majority of use cases just rent a server and host it yourself

@rickasaurus good point and i think it's context dependent with adapters for instance, removing the right degrees of freedom and defining the protocol that each adapter needs to adhere to feels crucial to scale that to any reasonable number

@paul__lodder Show me the math, whats your estimate for cost savings vs effort.

It’s dawning on me that the era of building generic systems is over. Code is cheap now, build hyper specific systems quickly. Reuse matters much less.

this captures so well what it's like to stumble upon *the right* abstraction

a $5 Hetzner box + systemctl restart is 90% of what most startups need

cloud is a psy-op making you believe bare metal is rocket science

@DSPyOSS but why would the data be too underspecified for this but not for prompt optimization? cause the problem representation space is so much bigger?

@paul__lodder sure but what signal is the optimizer going to use? the data? the data is noisy and underspecified (and small) there's no replacement for programming well, that is, giving the most precise name to your fields

working with @DSPyOSS shifts the focus from prompt engineering to problem representation LLMs are semantic machines. even changing a field name can materially affect results because you're changing the semantic relationship the model sees

@DSPyOSS yeah so it's a bit of a stretch it feels akin to differentiable neural network architectures in the is_spam example, the ultimate output is is_spam, but there are many paths to get there

@DSPyOSS exactly. i guess you'd almost want a meta-optimizer that searches across different problem representations and post-processing configurations to find what optimizes for your end goal

@paul__lodder yes, hence "programming---not prompting---language models". it's not so much "even changing a field name can matter". it's *designing field names* is the most pivotal part of the work; it's the bulk of the task definition. that's why even short signatures like `a, b -> c` work.

@DSPyOSS for instance, this pattern has made a big impact for me: instead of is_spam: bool classify as promotional/phishing/personal/automated then return category in [promotional, phishing] gives the model richer semantic structure even though you only need the binary result

@DSPyOSS this tripped me up initially. dspy resembles classical ml approaches, so i assumed labeled data was enough. but problem representation still matters enormously