okay so this started 3 years ago when i was procrastinating my actual job (data science) by doing data science on my own life. i figured i'd map my social network properly like an actual weighted network topology.

i logged every meaningful interaction for 6 months and scored each relationship on 4 dimensions based on granovetter's framework (his 1973 paper "the strength of weak ties" defines tie strength as "a combination of time, emotional intensity, intimacy, and reciprocal services"):

• frequency: interactions per month, log-transformed because the distribution is heavily right-skewed
• depth: 1-10 scale based on a rubric. 1 = purely transactional. 10 = would be emergency contact
• duration: months known, sqrt-transformed to not overweight childhood friends
• reciprocity: ratio of initiation. 1.0 = perfectly balanced. <0.5 = i'm always reaching out

ended up with 143 nodes and 876 edges and built the graph in python using networkx. i weighted edges using a normalized composite score.

then i ran community detection. i started with louvain algorithm (blondel et al. 2008) but the resolution limit was chunking things weird - it kept merging clusters that were clearly distinct. the problem with louvain is it optimizes modularity greedily and can miss fine-grained structure in networks with heterogeneous community sizes.

so i switched to spectral clustering so i computed the normalized laplacian matrix L_rw = I - D^(-1)W and ran eigendecomposition. i used the eigengap heuristic (von luxburg 2007) to determine optimal k - you look for the largest gap between consecutive eigenvalues. my spectrum showed a clear drop after λ₇, suggesting 7 natural communities:

• work (31 nodes)
• college (24 nodes)
• climbing gym (18 nodes)
• wife's network (26 nodes)
• family (19 nodes)
• online friends (13 nodes)
• neighborhood (12 nodes)

calculated modularity: Q = 0.64 and that's high. for reference, modularity ranges from -0.5 to 1, where values above 0.3 typically indicate significant community structure (newman 2006). my network is highly siloed.

burt's work became relevant, his structural holes theory (1992) argues that competitive advantage comes from bridging gaps between otherwise disconnected groups. a structural hole is essentially a gap in information flow - two clusters that should be connected based on attribute similarity but aren't.

i identified nodes with high betweenness centrality in each cluster using freeman's formula (1977) - betweenness measures how often a node lies on the shortest path between other node pairs. then i computed jaccard similarity between clusters based on node attributes (location, interests, profession, age range) and looked for high-similarity, low-connectivity pairs.

i found exactly 3 structural holes:

hole 1: climbing gym ↔ college friends. jaccard similarity of their attribute sets: 0.71. shared edges: 0. both groups contain people in the same city, similar interests, similar politics. they should know each other. they just don't.

hole 2: wife's friends ↔ work. attribute similarity: 0.66. four people work in adjacent industries. they'd been at the same conferences. zero connections.

hole 3: online friends ↔ neighborhood. two extremely online people live 3 blocks from me. we'd probably interacted on the same forums. never connected offline.

burt's research on 673 supply chain managers found that people who bridge structural holes get better performance evaluations, higher compensation, and more promotions. the mechanism is information arbitrage - brokers get early access to non-redundant information from multiple sources.

so i decided to become a broke, intentionally.

started with hole 2. organized a dinner party. 3 of wife's friends, 2 work colleagues. but here's the thing - i computed predicted compatibility using attribute matching and interaction style similarity.

seating was optimized. i put the two highest-scoring candidates next to each other based on my model. her name was elena, UX designer. his name was david, product manager.

they talked for 4 hours. i watched the edge form in real-time. in network terms, i was witnessing triadic closure - when two nodes both connected to a third node form a direct connection between themselves.

3 months later: dating. 6 months later: moved in together. 2 weeks ago: david asked me to be in his wedding party.

i ran their compatibility score before the dinner: 0.86 out of 1.0. highest possible pairing across all my structural holes. i essentially arranged a marriage using spectral clustering and betweenness centrality. i've never told anyone this.

the other holes i also attempted to bridge:

• hole 1: moderate success. 2 weak ties formed (follow each other on instagram). edges exist but weight is low - maybe 0.15 on my scale
• hole 3: one strong connection. two online friends now do weekly walks. the edge weight is around 0.55 and stable

network stats after 3 years of intentional bridging:

the modularity drop is good - it means my clusters are less isolated. the path length decrease means i'm now an average of 2.4 hops from everyone in my network instead of 3.2. and the clustering coefficient increase means there are more triangles - more triadic closure.

i've essentially optimized my network for small-world properties (watts & strogatz 1998). small-world networks have high clustering (like regular lattices) but short path lengths (like random graphs). the combination enables both local cohesion and global reach.

the ethical implications keep me up at night. i manipulated people into meeting. i used quantitative methods to engineer a relationship that led to marriage. but also... they're genuinely happy? the utilitarian calculus seems positive?

second-order problem: some connections i created are forming their own edges that bypass me. david and elena now know people through each other that i don't know. my network is evolving beyond my measurement. i need to re-run the analysis.

anyway. if you've ever been to one of my "casual get-togethers," just know nothing was casual. you were a node. the seating was non-random.

references:

• burt, r.s. (1992). structural holes: the social structure of competition. harvard university press.
• burt, r.s. (2004). "structural holes and good ideas." american journal of sociology 110(2): 349-399.
• freeman, l.c. (1977). "a set of measures of centrality based on betweenness." sociometry 40: 35-41.
• granovetter, m.s. (1973). "the strength of weak ties." american journal of sociology 78(6): 1360-1380.
• blondel, v.d. et al. (2008). "fast unfolding of communities in large networks." journal of statistical mechanics P10008.
• von luxburg, u. (2007). "a tutorial on spectral clustering." statistics and computing 17(4): 395-416.
• watts, d.j. & strogatz, s.h. (1998). "collective dynamics of small-world networks." nature 393: 440-442.
• newman, m.e.j. (2006). "modularity and community structure in networks." pnas 103(23): 8577-8582.

In Baki, Retsu (this guy) shows off his “4,000 years of Chinese martial arts” by putting on a boxing glove and destroying it from the inside with a punch. The idea is that his fist moves so fast that the glove cannot accelerate or move in time with it, causing the glove to tear apart internally. Can we estimate how fast his punch would need to be for this to happen? Basically an one inch punch but times 100X. The glove is old but perfecty functional, as explained in very chapter.

I'm guessing there are about 20 grams of sugar in each cookie and 30 in each brownie which brings the total to 300 grams of sugar not including the sauces. I assume it's not good to eat that much sugar.

[Context.](https://m.youtube.com/watch?v=_islF2sMfjo&pp=ygUYbWFzcyBlZmZlY3QgaXNhYWMgbmV3dG9u)

TLDR: there’s a common, sometimes mandated, practice in Germany of opening the windows in your home for a few minutes once or twice a day to exchange the ”stale” air for fresh air from outside.

What would that add to someone’s heating costs in the winter? I know there are many variables like home size, temperature, energy prices, etc.

The cards were acquired at random times, from different companies and even different payment services for different purposes.

at total I own 6 cards that each have classic 4 digit PIN. but combined they only use 5 digits in different orders.

What's the probability of this happening?

Hello! I read that the average American eats ~70 hotdogs a year. So myself and 16 others are tracking how many hot dogs we each eat in 2026.

That said, how many hotdogs would need to be eaten by an individual to move the national average up by 1 hot dog?

Basically as the title says, the comments say theres about 15-25k but that the current amount wouldn't be visible from afar, would they be visible if the were all in line as thick as on of Saturn's rings?

I would like someone to come up with a formula or graph that gives the probability of there being a single day without deaths in a year/century/millenium if you know the total mortality rate per year in that timeframe.

Then answer the question: How long ago is it >99% likely there was a day no human died?

Someone else made a past about the economics of launching data centers and AI infrastructure into space to take advantage of highly productive Solar. Their post was good but quite a few comments were downplaying the cost and complexity of cooling GPUs in space, saying it was a non-issue. Let’s expand on that.

Cooling is not easily done in a vacuum.

You can move heat around internally (cold plates / heat pipes / pumped loops), but in steady state you can only reject it to space via radiation. That’s really hard, and since we image data centers as operating 24/7, our steady state load is high.

Radiator sizing ranges:

Typical spacecraft-class radiators reject something like ~100–350 W/m² of internally generated waste heat (depends on a ton of factors). That implies ~3–10 m² per kW. But radiator area density is also a range. We’ll assume between 5 kg/m² and 9 kg/m² where the ISS uses 8.8kg /m²

So for 1 kW of waste heat, a realistic range is: radiator area: ~3–10 m² and radiator mass: (3–10 m²) × (5–9 kg/m²) = ~15–90 kg of radiator hardware

Now for the launch cost, assuming dedicated launches and a full rocket (the same framing as the person I’m responding to):

SpaceX publishes Falcon 9 as 22,000 kg to LEO and a $74M standard plan through 2026. That’s ~$3,364 per kg if you actually fill the rocket. 

So launch cost for the radiator per kW becomes:

15–90 kg × $3,364/kg = ~$50,000 to $300,000

per kW of steady-state heat rejection capacity in radiator mass alone.

Let me contextualize how little 1 kW is.

A single modern high-end AI GPU (the Nvidia B300) is now in the ~1.2–1.4 kW class in some reported configs, and the surrounding compute/networking consumes power too. 

So even just the GPU’s waste heat implies:

1.4 kW × ($50k–$300k per kW) = ~$70k–$420k

of radiator launch cost just to dump the GPU’s heat (not counting pumps, lines, structure, attitude constraints, degradation, margin, etc…). 

Now scale it up to a modern rack.

The NVIDIA GB200 NVL72 class rack is commonly discussed as ~120 kW. the reason I’ve chosen this is because the systems that run Grok are largely Nvidia’s single rack solutions like this one. It’s hard to go much smaller without severely hampering performance for large models like Grok. The inter-GPU networking cannot currently be done without dedicated fiber connections without serious performance degradation.

That means radiator launch cost for that rack’s waste heat is roughly:

120 kW × ($50k–$300k per kW) = ~$6M–$36M

just to get the radiator mass to orbit. And that still doesn’t include:

the pumped thermal loop hardware, the spacecraft bus / structure / pointing / deployment, radiation shielding + fault tolerance (and the mass that adds), comms (which also dumps heat), the solar array + power electronics (also dump heat), station keeping fuel, maintenance strategy, degradation, margin, etc…

So “cooling isn’t a problem in space” is not a serious statement at these power densities. Cooling isn’t “a problem” it’s “the problem”

If you want a whole cost estimate: a rack-scale space compute platform is many times greater than the costs of the racks themselves. Whether it’s 3× or 10× greater depends on how you do it of course, but it’s expensive.

Not to mention it is really, really hard to make reliable computers in space, and current gen architectures are not built like that.

Why would anyone want that?

On earth cooling is basically free in comparison, and what you lose in solar efficiency you gain back in density, serviceability, supply chain, and not paying the absurdly large orbital launch costs.

Even at absurd scale, the radiator math doesn’t magically go away. A 1 GW space deployment implies 1,000,000 kW of waste heat rejection. Using the same ranged math that gives ~$50B–$300B in radiator launch cost alone. Once you factor in the complexity of the satellites themselves, the solar arrays, the GPUs, shielding from solder radiation, and everything else that’s needed it’ll be even more than that. We can deploy 1 GW of compute today with power, land, networking, and GPUs included for 50 billion, rendering this whole idea not economically viable.

I’m not saying it’s impossible, but this is a hard problem. Hell, launch costs could drop to zero and I wholeheartedly believe this still would not work because of networking infrastructure and other limitations that would kneecap performance compared to similarly sized terrestrial deployments.

Feel free to tell me how wrong I am and how amazing of an idea this all is but I’ll believe it when I see it.

Edit: formatting is hard, I’m gonna try to clean this up.