i'm curious about how effective path tracking can be in comparison with computer vision based inverse kinematics of the body itself. do all forms of bad form have detectable imu signatures?

i wonder if it would make sense to consider it as a data problem, capture a bunch of high fidelity inverse kinematics data for various forms of bad form/dangerous lifting along with the imu data and then work from there. there could be some interesting and unexpected features that are easier to detect than straying from straight line paths with some tolerance.

so i'm guessing something like this would be caught by (open\|little)snitch. the raw c2 post coming from the python process would definitely be a red herring, but i wonder how obvious the git/github activity would be. it would seem kinda weird if it came from the python process itself, but if it were just git or gh in a subprocess, it would possibly look totally normal and even have a temporary allow rule in place...

maybe it's time for a nextgen opensnitch where the rules table is replaced by an active agent that watches connections and the process table?

i've found that multiple queries with the same prompt that requests a short answer is an excellent way to gain a confidence style measure that actually works.

welcome new systems programmers: unix is broken and you must write ugly non-pedagogical workarounds and do empirical testing. this is what reliable software and good software engineering actually is... surprise!@#%

hm. ml people love static evals and such, but have you considered approaches that typically appear in saas? (slow-rollouts, org/user constrained testing pools with staged rollouts, real-world feedback from actual usage data (where privacy policy permits)?

i once did a contract for a company that built a product around connectors for legacy lan e-mail products and an x.400 mta. it was a gigantic steaming pile of shit and made me appreciate the simple internet protocols so much more than i already did.

i find the best way to remember it is "it's not the epsilon you think it is."

epsilons are fine in the case that you actually want to put a static error bound on an equality comparison. numpy's relative errors are better for floats at arbitrary scales (https://numpy.org/doc/stable/reference/generated/numpy.isclo...).

edit: ahh i forgot all about ulps. that is what people often confuse ieee eps with. also, good background material in the necronomicon (https://en.wikipedia.org/wiki/Numerical_Recipes).

this perez model thing completely misses the communications revolutions of the telegraph, radio and television not to mention demonopolization of bell.

> Then came AI, revealing new dynamics. ChatGPT’s breakthrough didn’t come from a garage startup but from OpenAI,

i thought the transformer and large language models came from google research.

> There’s also social pushback—in the UK the campaigns against big ringroad schemes started in the late 1960s and early 1970s. And perhaps we’re seeing some of that about AI. The U.S. map of local pushback against data centres from Data Center Watch covers the whole of the country, in red states and blue. People seem to hate Google’s inserting of AI tools into its search results, and hate even more that it is all but impossible to turn it off.

the us had the highway revolts. in most cities where the revolts succeeded it is widely heralded today as a success.

the data center hate is interesting. i think many people are just learning what data centers are. but that said, they've come to represent something different in recent years. previously they were part of the infrastructure that made industry hum, now public messaging from tech leaders and academics is along the lines of "this is how your livelihood is going to be replaced" while the institutions that are supposed to provide any sort of backstop are being dismantled or slashed to pieces by crazypants trumpist politics. i think focusing the energy on the tangible like mundane buildings is interesting, but the hate makes a lot of sense.

addressing the core thesis, i'd argue that ai is not the next step in the 70s digital technological wave (especially considering the future of ai compute is probably hybrid digital-analog systems), but rather is something fundamentally new that also changes how technology interacts with society and how economics itself will function.

previous systems helped, these systems can do. that's a fundamental change and one that may not be compatible with our existing economic systems of social sorting and mobility. the big question in my mind is: if it succeeds, will we desperately try to hold onto the old system (which essentially would be a disaster that freezes everyone in place and creates a permanent underclass) or will we evolve to a new, yet to be defined, system? and if so, how will the transition look?

maybe a better approach to start with computers that already have ergonomic chassis (they exist) and then spend energy for modifying tools on what happens inside of them?

hmm. hoping that all the weird business requirements get confined to a specific distro with careful gating prior to upstreaming. it would be bad if they were allowed to pollute the ecosystem more generally (which one could argue is why windows is the way it is).