minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group]
Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
Posts
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
Someone made a Lean Jupyter kernel! https://github.com/Verilean/xeus-lean/
I don't really like Jupyter notebooks but this is still super exciting because they figured out how to build Lean for WASM which could be really useful!
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
The MIT Museum will never give you up
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
Millions of dollars wasted and Firefox on-device AI can't even translate
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
My friend told me his laptop has a "AMD Ryzen™ AI Max Plus Pro 395" processor and I wish that was joke but it's not
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
minFac '⓫'.toNat|>λ_11↦(·+97)<$>[0/0,_11,-(⟨1,0,2,4⟩:ℍ[ℤ])^2|>.re.toNat,defaultMaxRecDepth%101,catalan 4,_11,(φ∘φ∘φ∘φ∘φ∘φ<|4‼‼)!,↑((4:Fin 24)-6),⌈deriv (sin ·^69) π⌉₊,_11,Nat.card<|Aₙ 2|>.Group] Mostly just learning random things, flailing around, and sleeping with 1/3 probability.
The MIT intro Python class redesigned their website: https://hz.mit.edu/6101/