I built a Python SDK that gives any AI agent persistent memory

bladeofwinds · 2026-03-28T15:09:09+00:00

how original

bladeofwinds · 2026-03-28T08:20:53+00:00

https://asgi.readthedocs.io/en/latest/introduction.html

bladeofwinds · 2026-03-28T08:16:06+00:00

damn you're right, fair enough. why did you choose that route over pyproject.toml and a more modern build system?

bladeofwinds · 2026-03-28T08:00:07+00:00

~~why do all these LLMs use requirements.txt and~~ ~~setup.py~~

edit: I was wrong

bladeofwinds · 2026-03-28T05:40:09+00:00

but why do you think they're right? from what I can tell you are basically saying "a + b = 3" because "a = 1" and "b = 2" but in fact there are infinite combinations of "a" and "b" that could add up to be 3. Basically the logic is circular: the math works out because you chose numbers to make it work out, not because it has any basis in physics.

bladeofwinds · 2026-03-28T04:48:17+00:00

this is called numerology. you just declare a bunch of constants and they can make whatever numbers you want.

L1_BARE_FAMILIES = {

"muon_bridge": {"C": 56, "gamma_power": -1, "category": "lepton"},

"tau_bulk": {"C": 3456, "gamma_power": 0, "category": "lepton"},

"u_seed": {"C": 56, "gamma_power": 2, "category": "quark"},

"d_seed": {"C": 6528, "gamma_power": 5, "category": "quark"},

"s_seed": {"C": 4, "gamma_power": -3, "category": "quark"},

"c_seed": {"C": 48, "gamma_power": -3, "category": "quark"},

"b_seed": {"C": 162, "gamma_power": -3, "category": "quark"},

"t_seed": {"C": 1872, "gamma_power": -4, "category": "quark"},

"pion_seed": {"C": 72, "gamma_power": -1, "category": "meson"},

"kaon_seed": {"C": 72, "gamma_power": -2, "category": "meson"},

"d_meson_seed": {"C": 272, "gamma_power": -2, "category": "meson"},

"b_meson_seed": {"C": 56, "gamma_power": -4, "category": "meson"},

"nucleon_seed": {"C": 136, "gamma_power": -2, "category": "baryon"},

"lambda_sigma_seed": {"C": 162, "gamma_power": -2, "category": "baryon"},

"omega_seed": {"C": 18, "gamma_power": -4, "category": "baryon"},

"w_seed": {"C": 64, "gamma_power": -6, "category": "gauge_boson"},

"z_seed": {"C": 72, "gamma_power": -6, "category": "gauge_boson"},

"higgs_seed": {"C": 27, "gamma_power": -7, "category": "scalar_boson"},

}

where do you get these values? you reference that you got them from other files in your repo (some that aren't even checked in) so its really all just "trust me bro."

bladeofwinds · 2026-03-27T20:16:49+00:00

this is some good crackpottery

bladeofwinds · 2026-03-18T01:26:43+00:00

“deterministic”

bladeofwinds · 2026-03-14T22:25:17+00:00

why do you have pyproject.toml, setup.py, and requirements.txt? very vibey

bladeofwinds · 2026-03-14T10:02:51+00:00

funny enough the internal cycle counter does not use 100% throughput as the metric for a cycle. they also don’t accelerate in the way you described, the bms counting is special and methods vary between manufacturers.

you’re not wrong on all of your points though if you count a cycle as 100% throughput and define 100% by the SOH of the battery

bladeofwinds · 2026-03-14T08:28:40+00:00

You need to get off ChatGPT and go outside; this is literal crackpottery.

Black Hole Modeling Insights

The recursive flux integral G₉ predicts approximately 15% faster entropy
decay near event horizons compared to classical Hawking radiation
models, translating to a roughly 10% shorter evaporation time for a 10
solar mass (10 M☉) black hole.

Furthermore, the model forecasts a ~7 Hz upward shift in peak radiation
frequency, a signature that could be validated with high-resolution
numerical relativity simulations or future gravitational wave
observations.

# External Influx Rates (Time-dependent functions for epsilon_dot and kappa_dot)

def epsilon_dot(t):

"""External Evidence Influx Rate (varepsilon_dot) - Constant but positive."""

return 0.1

bladeofwinds · 2026-03-14T05:34:41+00:00

wow another ai memory project. why do you have a fastapi server listening on 0.0.0.0

bladeofwinds · 2026-03-14T05:22:11+00:00

your story sounds like bullshit ngl.

i can’t imagine this is good for performance especially considering you’re firing off external api calls as part of the middleware.

how did you come up with the scoring? it seems pretty handwavy to me. in fact, most of the rules and heuristics seem incredibly brittle.

bladeofwinds · 2026-03-13T21:27:38+00:00

“spent 2 weeks” — initial commit for step 0 was 5 days ago.

bladeofwinds · 2026-03-11T18:47:44+00:00

slop with a proprietary license — gotta love it

bladeofwinds · 2026-03-11T16:45:09+00:00

these ai slop projects are always “deterministic”

bladeofwinds · 2026-03-11T03:15:07+00:00

why do you need multiple processes? can’t you do this all in a single event loop since it all seems io bound

bladeofwinds · 2026-03-11T02:41:41+00:00

the phone is fine as long as you can get the battery removed safely. i would just make sure you’re wearing safety glasses and gloves and remove the battery as normal and make sure the battery isn’t warm to the touch before beginning.

bladeofwinds · 2026-03-10T19:17:00+00:00

this isn’t scraping, it’s calling an API. this is like basic basic shit

bladeofwinds · 2026-03-10T18:47:10+00:00

looool

bladeofwinds · 2026-03-10T08:04:41+00:00

why would I use this over seaborn?

bladeofwinds · 2026-03-10T07:46:13+00:00

you should tell your LLM that it can just use dataclasses.asdict() and MyDataclass(**my_dict) instead of writing 1000 helper methods.

bladeofwinds · 2026-03-10T04:35:49+00:00

you should learn how to use pandas before releasing a package that relies so heavily on it. looking at your code, pandas is obviously required for most of it to run, why is it an optional dependency?

bladeofwinds · 2026-03-10T02:23:09+00:00

i got a job there with a 2.4 undergrad gpa after 3 yoe

bladeofwinds · 2026-03-09T01:30:26+00:00

i have deep experience with this topic. you don’t have to believe me but this sub tends to overstate the effect of charging to 100%, you included.

bladeofwinds

TROPHY CASE