Software has never been capital light, says Nick Rogers. "There are no blast furnaces, no warehouses of unsold inventory waiting to rot, and no fleet of diesel vans depreciating in an industrial estate in Slough. You write the code once, upload it to the internet, and sell it an infinite number of times at a marginal cost that rounds cleanly to zero. The gross margins sit comfortably north of 80%, and a generation of investors whose predecessors analysed railways, paper mills, and oil refineries fell head-over-heels in love with an industry that seemed to produce compounding cash flows without ever demanding capital reinvestment." Yeah, that's what I heard my whole life, pretty much. "Firm A manufactures precision automotive brackets. It spends £50,000,000 designing, machining, and installing an automated hydraulic stamping press." "Firm B builds an enterprise logistics routing engine. It spends £50,000,000 paying one hundred senior engineers to design, build, and test a distributed database system." "Both machines take two years to build. Both cost £50,000,000 in cash. Both require £5,000,000 every year in specialised maintenance to prevent systemic failure. Firm A replaces hydraulic seals, repacks bearings, and recalibrates sensors. Firm B patches zero-day vulnerabilities, updates expiring cryptographic certificates, migrates schemas, and fixes integration regressions caused by upstream platform updates." "Steel rusts, bearings grind down, and concrete crumbles. Code, being nothing more than strings of ASCII characters frozen in silicon, is theoretically immortal. A piece of code written today will run identically in twenty years." "Anyone who has ever operated production software knows this is a fairy tale." Lol. Oh boy. I have a feeling I know what he's going to say next. "Physical machines suffer wear because they interact with friction, moisture, and heat. Software suffers wear because it sits inside a computational environment that refuses to stand still." "Deploy a production web application to a server, lock the codebase for three years, and walk away. When you return, the TLS certificates have expired, the dependencies have accumulated fifty critical security vulnerabilities, upstream payment APIs have deprecated your payload schema, and the application is dead." "When a B2B SaaS company boasts that it owns no physical servers, it is celebrating an accounting trick: it outsourced its capital expenditure to four publicly traded landlords. The four cloud hyperscalers (Amazon, Microsoft, Alphabet, and Meta) guided to roughly $700,000,000,000 in aggregate capital expenditure for 2026." "Consider an airline that leases every aircraft from a financing consortium. It owns zero aeroplanes. Its balance sheet carries no aluminium and no jet engines." I was expecting the whole time I was reading this to hear, "But with AI, ...". For example, "But with AI, AI can refactor dependencies so the database keeps responding, and the cost of human software engineers can be removed." Weird that that didn't happen. deadneurons.substack.com/p/s… #solidstatelife #accounting #gaap #venturecapital

AI software failure modes, according to Kate Meyer. "In 2026, most software companies have handed their engineers AI with the instructions to deliver more, faster." "New AI coding failure modes including codebase erosion (from the explosion of new code), increasing token cost (as more complex codebases are harder to maintain), and comprehension debt (where engineers don't understand how the code works). Most immediately, AI coding speeds up code generation and is creating a review bottleneck." "In the worst case engineers will generate code and merge it in sight unseen with a Google style LGTM. The codebase doesn't immediately fail, but erodes and bloats." LGTM stands for "Looks Good To Me". Or is it "Loki, Grafana, Tempo, Mimir"? Or is it "Let"s Get This Merged"? Or is it "Let"s Gamble, Try Merging"? "What persists isn't the best, it's whatever doesn't get filtered out. Companies are complex systems with many failure points beyond software. Past the point of avoiding AI failures the pressure to adapt falls off quickly and in fact overhauling to best practices has costs and pressures against it. So the new normal won't be across the board best practices, but more mundane. Using this we can review failures and make almost boring predictions about what will become the new normal for the software industry. Depending on the company and the work different failures will be hit first." "Bad AI code reaching production." "Ironically the CI/CD systems might get slower the exact moment we want them to be faster to match AI speed." "Too many agent PRs." ("PRs" means "pull requests", requests to merge new code into the main branch.) "Comprehension debt. Code remains the source of truth and so new ways of understanding it will appear." "Tooling or AI is used to explain how the code works." "The missing junior engineers." "Runaway token costs." "To reduce runaway token costs, using local, cheap, and expensive AI lab models together will become common." "Code bloat." "New pruning pipelines or skills/tools will be made entirely around shrinking the codebase." "Security." "The AI instigated security crisis will result in more standard modularization, containerization, and sandboxing to reduce the blast radius when things go wrong along with some form of automatic preemptive self-attacking by AI." "Where the Market Goes. Things that do not harmonize with AI coding will move out in favor of those that do harmonize with AI." For example, languages with slow compile times or high token costs will be abandoned in favor of other languages, and typed languages have an advantage which we see with accelerated adoption of TypeScript over Javascript. metamagic.substack.com/p/the… #solidstatelife #ai #genai #llms #codingai

"Decompiling a Nintendo 64 game in 84 days". "Snowboard Kids was compiled with IDO 5.3 rather than the GCC 2.7.2 compiler used by Snowboard Kids 2." "Most programmers will be familiar with GCC. It is a widely used open-source compiler still in active development today. IDO, on the other hand, was a proprietary compiler developed by SGI, whose own story is closely entwined with that of the Nintendo 64. Its source was not available, and its original development environment was tied to obsolete SGI hardware and software. To use and properly understand it today, the decompilation community has had to reverse-engineer and decompile parts of the compiler toolchain as well as statically recompile the IDO 5.3 and 7.1 suites to run on modern hardware. That closed history makes IDO harder to reason about than an open compiler such as GCC." "IDO splits optimisation and code generation across several different passes, transforming code quite aggressively along the way. Tiny changes to the C can then ripple through those passes and produce a completely different register allocation." "The community has made great strides in understanding IDO and its quirks, but this remains more of an art than a science. LLMs and I are not particularly good at reproducing its output. The usual workflow, for both me and the agents, was to figure out what a function did and then write C that approximated that purpose. From there, small tweaks assisted by the permuter could catch any remaining differences. But you can't permute yourself into a match in all cases, particularly when the underlying structure is wrong. IDO's behaviour made this workflow far less predictable." "IDO is weird, but it is often weird in recurring ways. Successfully matching one function could reveal a compiler quirk that applied to many others. Codex has become better at carrying lessons between tasks through features such as local memories. To make those lessons available beyond a single agent, I prompted agents to record observed IDO behaviour in a DECOMPILATION_LEARNINGS.md file. blog.chrislewis.au/decompili… #solidstatelife #ai #genai #llms #codingai #reverseengineering #decompilation

Everything we know about AMOC collapse. "AMOC" stands for "Atlantic Meridional Overturning Circulation". "Atlantic" is pretty self explanatory. "Meridional" means we're talking about an ocean current that runs north and south. "Overturning" means the water changes depth -- specifically it goes down for its return trip. Which leads us to "Circulation" which means it's going in a loop. Thus "AMOC". Hey, scientists come up with weird terms for everything so it could be worse. The warm current on the surface warms the air, and the wind in this part of the world goes from west to east, so the wind carries the warmth to Europe. What makes the AMOC slow down is fresh water. Fresh water lowers the salt content of the current in the AMOC, and when it is less salty, when it reaches Greenland and Scandinavia, it doesn't sink as much, even though it is cold. Where does the fresh water come from? If you're guessing the Greenland ice sheet, you're half right. The other half is that as the planet warms, there's more rain over the ocean, and the rain is fresh water. As water sinks less, the entire AMOC loop slows down. If the AMOC collapsed completely, London's temperatures would go down by -18 F (-10 C), Bergen (Norway) would go down by -27 F (-15 C), and the largest drop (which according to the map looks like it would be Finland) would be -50 F (-28 C). The problem on the North American side is that, not only would it get warmer, because the current pulls ocean water away from the shore and there would be less of this "pull", the sea level would rise. One study from Copenhagen estimates the current will cross a tipping point and collapse between 2025 and 2095, with their middle estimate being 2057. That shocked people, but, she (Gwen Silveira, aka "Critical Latitude") argues, the 2057 date gives the impression of too much confidence. 2025 to 2095 is a wide range, and nobody actually knows if the AMOC actually has a tipping point that can make it collapse. In other models, the AMOC does not collapse, only weakens. The AMOC is measured three ways: 1. Directly using instruments that measure temperature, salt level, pressure, and current flow in the water. These have been places across the Atlantic, starting in 2004. 2. An assortment of readings from ships, buoys, and satellites show there is a cold blob off the coast of Greenland. Almost the whole ocean has gotten warmer over the last century except one patch between Greenland and Ireland that has gotten slightly colder. The theory is that the reason this one patch of ocean cools while everything else warms is the current that has been carrying warmth up to it is carrying less. That's the AMOC slowing. 3. When a deep current crosses the seafloor, it filters the sediment, aka mud. A fast current carries fine-grained particles away, leaving coarse-grained particles. By taking core samples of the seafloor, you can estimate the current backwards through time. The conclusion is that the AMOC has been declining recently and is at its lowest point in the last 1,000 years. youtube.com/watch?v=39t1IhGe… #climate #climatechange #amoc

"AI is destroying the human experience." I'm just posting this because, I had a conversation with a friend of mine a few days ago, and we were discussing the question, "Does AI make the world a better place?" His admittedly subjective feeling was "No." Obviously, AI makes the world a better place for employers and entrepreneurs who can get more done faster at lower cost. So if that's the metric, obviously AI makes the world a better place. Why then doesn't it feel like AI is making the world a better place? I usually mention that if you're a worker, the prospect of being automated out of a job doesn't make your life better. But this video isn't even about that, and it's closer to what my friend was feeling. It's about how "AI sludge" is going from the internet into the real world. He (the YouTuber) lives in Stuttgart, Germany, and Stuttgart is getting flooded with AI-generated advertising. Even the Stuttgart Volksfest (folk festival) is being promoted with AI-generated flyers. He gives other examples, which I'll not bother listing. The problem is his proposed solution won't work. His proposed solution is to boycott any product or service that is created using AI or promoted using AI. If a coffee shop's ads are made with AI, it doesn't matter how good the coffee is, never go there. The reason this won't work is because in blind tests, people can't tell the difference between AI generated text and images and human-generated text and images. And, to the extent it is sometimes possible now, it won't be possible forever as the AI systems get better and better. We are all going to have to adjust to living in a world filled with artifacts that we are unable to tell if are generated by humans or generated by AI. And, if my conversation with a friend is anything to go by, is going to leave all of us feeling like AI has made the world worse. youtube.com/watch?v=L2fnTlLP… Example of study seeing if people can distinguish human-generated text with AI-generated text. This is from a year ago so the models have improved since this study. arxiv.org/abs/2410.03723 #solidstatelife #ai #genai #llms #diffusionmodels #technologicalunemployment

OpenAI has released 722 math papers "covering 372 result families" of previously unsolved problems in mathematics. So it looks like they have more than one paper on the same major math problem -- i.e. the problem got broken down into parts and there's individual papers on the parts. Note that the numbers go to 377 even though there are 372 -- this is because some numbers are missing (45 is missing for example). Crucially not all of these have computer-verifiable proofs in Lean, the proof assistant software. You have to watch for "Lean" links. I don't understand any of these. Passing this along for all you mathematicians out there. 1. Milne's rationality conjecture and algebraic specialization 2. The full BSD formula from low Selmer corank 3. The quasi-Riemann hypothesis 4. Hilbert's tenth problem over the set of rational numbers 5. Irrationality of Catalan's constant 6. Goldfeld's conjecture: densities and mean analytic rank 7. Ordinary two-point correlations and the corrected Elliott conjecture 8. The Deligne-Drinfeld conjecture 9. Function-field reconstruction from Milnor K-theory and Galois data 10. Unrestricted pro-modularity at the prime two 11. Prime-factor statistics of p-1 12. Independent largest prime factors of consecutive integers 13. Ostmann's inverse Goldbach conjecture 14. Restricted geometric Langlands, global Arthur enhancements, and generic Ramanujan 15. Torus-packet equidistribution in prime, quartic, and sextic degrees 16. Zilber-Pink in abelian varieties and the Siegel threefold 17. The irrationality exponent of pi is 2 18. The Margulis-Platonov conjecture over global fields 19. The local p-adic section conjecture and global consequences 20. Squarefree quartics and power-free polynomial values 21. A quadratic bound for Jacobsthal's function 22. The weak inhomogeneous Duffin-Schaeffer conjecture 23. Patterson's first moment for cubic Gauss sums 24. An asymptotic formula for the number of totients 25. Short Egyptian fractions 26. Positive lower density of large prime gaps 27. Potential integral density on curve character varieties 28. Uniformly bounded components of Gaussian-prime graphs 29. Primitive roots for every admissible integer base 30. Modularity of elliptic curves over imaginary quadratic fields 31. Uchida's conjecture for open homomorphisms of Galois groups 32. Hodge and Kuga-Satake results for all projective K3 surfaces 33. Iitaka subadditivity, variation, and logarithmic additivity 34. Log abundance for compact Kähler spaces under logarithmic Iitaka subadditivity 35. Log-canonical threefold abundance in numerical dimension one 36. Numerical semiampleness and generalized minimal models 37. The ordinary-double-point volume gap 38. Fujita's freeness conjecture 39. Nagata's conjecture and maximal Seshadri constants 40. Bloch's conjecture for complex surfaces 41. Hyperkähler SYZ and projective-space bases 42. Oka classification for minimal compact complex surfaces: Kodaira dimension zero and class VII 43. P = W for fixed-determinant SL_n moduli spaces 44. The equivariant cohomological Hikita conjecture 46. Shafarevich counterexamples in dimension two and with large fundamental group 47. Zariski cancellation and affine fibrations over the complex numbers 48. A characteristic-zero counterexample to Lipman-Zariski 49. A stable-coordinate counterexample in four variables 50. A counterexample to Griffiths' positivity conjecture 51. Kobayashi's canonical-ampleness conjecture 52. Tangent splittings and product decompositions 53. A counterexample to Pixton completeness in Chow 54. Irrational cubic fourfolds with Hodge-theoretic and categorical K3 associations 55. Gepner symmetry and large-volume stability on threefolds 56. Termination of projective and Kähler fourfold minimal model programs 57. Fundamental groups of special complex varieties and root orbifolds 58. Semialgebraic universal covers and bounded domains 59. Counterexamples to Zariski's multiplicity conjecture 60. The Global Spherical Shell conjecture 62. Projective contact classification and the LeBrun-Salamon conjecture 63. The generalized Mukai conjecture 64. Topological triviality of mu-constant surface singularities 65. Virasoro constraints for complete intersections and projective-bundle towers 66. Bounded klt complements for Fano contractions 67. The Campana-Peternell conjecture in dimension six 68. Anticanonical nonvanishing in every dimension 69. Global quantum geometric Langlands at irrational level 71. Koebe's circle-domain conjecture 72. Brennan's conjecture and the integral-means spectrum 73. The Falconer distance conjecture 74. Kakeya in three and four dimensions 75. The L log(L) Fourier-convergence conjecture 76. Real ultraflat Littlewood polynomials and unbounded binary merit factors 77. Fourier restriction for positively curved surfaces 78. The three-dimensional Bochner-Riesz conjecture 79. Local smoothing in three dimensions 80. The exact Sobolev endpoint for Schrödinger convergence 81. Riesz transforms and rectifiability in higher codimension 82. Annular variation and dyadic absolute bounds for the triangular Hilbert transform 83. Hilbert transforms along Lipschitz directions 84. The geometric case of the Erdős similarity conjecture 85. Endpoint Sobolev regularity of centered disk averages 86. An L^3 bound for the trilinear Hilbert transform 87. The Mahler conjectures, functional inequalities and polar-product symplectic width 88. Sharp projection-body inequalities and a counterexample to simplex maximization 89. Bounded-distortion L_1 embeddings of planar and bounded-treewidth graphs 90. Triangular-lattice optimality, long-range Riesz and Coulomb energies, and spherical logarithmic energy 91. Logarithmic and L_p Brunn-Minkowski inequalities and the B-conjecture 92. The optimal order of convex-body covering density 93. Dimension-free logarithmic Sobolev inequality for subgaussian log-concave measures 94. Subpolynomial dimension reduction in L_p 95. Hyperbolicity cones without semidefinite lifts 96. The Gaussian propeller conjecture in every dimension 97. The Euclidean Steinitz-Bergström bound 98. Compact counterexamples to bi-Lipschitz dimension reduction 99. The sharp exponential scale of edit-distance distortion 100. Cylinder coverings below the half-area bound 101. The sharp simplex conjecture for isotropic constants 102. The Unique Games Conjecture and optimal approximation thresholds 103. Exact derandomization of logarithmic space: L=RL=BPL 104. Quasipolynomial algorithms for mean-payoff, stochastic and parity games 105. Perfect completeness for 2-to-1 games 106. Hardness of coloring three-colorable graphs 107. Matrix multiplication with exponent at most 9/4 108. A cubic permanent-determinant lower bound 109. Integer multiplication below n log(n) 110. Optimal-order randomized k-server on arbitrary metrics 111. One-sample matroid prophet inequalities against an almighty adversary 112. Beyond the square-root exponent for depth-three circuits 113. Approximate counting and entropy of perfect matchings 114. Approximate counting of common integer polymatroid bases 115. Sampling and counting contingency tables with arbitrary margins 116. Uniform black-box noncommutative identity testing across characteristics 117. Uniform sparsest cut: hardness and semidefinite gaps 118. Bin packing and unbounded configuration-LP gaps 119. The Courtade-Kumar and Hellinger conjectures 120. Almost-linear-time exact matching and prescribed-degree factors in general graphs 121. Almost-linear approximation of edit distance 122. Quantitative trace-reconstruction bounds with a uniform decoder 124. Polynomial-time scheduling on three identical machines 125. The metric k-median approximation threshold and recovery 126. Exponential semidefinite complexity of perfect matching 127. Average sensitivity of polynomial threshold functions 128. A factor-two approximation for shortest common superstring 129. Exponential state costs for two-way automata 130. Exact Fourier transforms below n log(n) 131. Rapid mixing of graph switches for every degree sequence 132. A superquadratic separation of sensitivity and block sensitivity 133. The computational complexity of Weisfeiler-Leman refinement 134. Generalized star height at most three 135. Homogeneous depth-five lower bounds for iterated matrix multiplication 136. A quasilinear PCP theorem for PPAD 137. One-tape time simulation in two-fifths-power space 138. Subset Sum in O(2^0.49n) time 139. Subpolynomial query complexity for log-concave sampling 140. Memory-sample lower bounds for noiseless Gaussian regression 141. Existential-universal real sentences in the counting hierarchy 142. Deterministic polynomial factorization over prime fields 143. Hilbert's sixteenth problem: uniform bounds for limit cycles 144. Banach's simple Lebesgue-spectrum problem 145. Rokhlin's multiple-mixing problem 146. Positive metric entropy for the standard map 147. The near-boundary Birkhoff conjecture 148. The entropy-rate dimension formula for self-similar measures 149. Classwise permanence for weakly reversible mass-action systems 150. Weak mixing of triangular billiards with an irrational angle 151. A C^1 counterexample to the entropy conjecture 152. Zero entropy does not guarantee a smooth positive-volume model 153. Arithmetic classification and non-Pisot singularity for Bernoulli convolutions 154. Pointwise multiple ergodic averages for mixing transformations 155. A counterexample to periodic tiling in dimension three 156. Borsuk's conjecture fails in dimension nine 157. Graph coloring, clique minors, and Colin de Verdière invariants 158. The Euclidean plane cannot be colored with five colors 159. Erdős's reciprocal-sum conjecture and quasipolynomial Szemerédi bounds 160. Superexponential van der Waerden numbers 161. Counterexamples to Sidorenko's conjecture and the forcing conjecture 162. Counterexamples to Ryser's covering conjecture 164. Hindman's finite sums and products conjecture 165. The Harary-Hill and Zarankiewicz crossing-number formulas 166. The higher-dimensional Erdős distinct-distances conjecture 167. Planar distinct distances and unit-distance bounds 168. Combinatorial invariance of Kazhdan-Lusztig polynomials 169. Shareshian-Wachs elementary positivity 170. Sharp logarithmic exponents for off-diagonal Ramsey numbers 171. The hypercube Ramsey conjecture 172. Classification of finite Euclidean Ramsey configurations 173. Seymour's second-neighborhood conjecture 174. Deterministic construction of strong thin spanning trees 175. Talagrand's expectation thresholds, discrete convexity, and graph decompositions 176. The second Kahn-Kalai conjecture with an edge-count bound 177. Bounded-degree coboundary expanders 178. Deterministic nonbipartite Ramanujan graphs in every fixed degree 179. The circulant Hadamard and Barker-sequence conjectures 180. Barnette's Hamiltonian-cycle conjecture 181. The Erdős-Gallai cycle-decomposition conjecture 182. Power savings for intersective polynomial differences and prime arguments 183. Power savings for planar halving lines and k-sets 184. Correspondence coloring with a fixed forbidden subgraph 185. Counterexamples to infinite matroid intersection and packing/covering 186. Uniform influence and sharp thresholds for graph and hypergraph properties 187. Snaky in 21 Maker moves 188. The sharp terminal leave in random triangle removal 189. Cycle-clique Ramsey numbers 190. Polynomial removal fails for ordered binary matrices 191. A power improvement in the Heilbronn triangle lower bound 192. Boolean functions violate the square-root degree bound by arbitrary factors 193. Serre's intersection-multiplicity conjecture 194. Lech's multiplicity conjecture 195. A counterexample to the small Cohen-Macaulay module conjecture 196. A counterexample to Kaplansky's zero-divisor conjecture 197. A torsion-free group algebra that is not directly finite 198. A counterexample to finitistic-dimension finiteness 199. Counterexamples to Auslander-Reiten, Tachikawa and related homological conjectures 200. Eisenbud-Green-Harris and lex-plus-powers 201. A counterexample to Kurosh's division-ring problem 202. The blockwise Alperin weight conjecture 203. Donovan's conjecture over fields and complete mixed-characteristic DVRs 204. Tensor saturation for even spin groups 205. Saxl's conjecture and universal tensor squares 206. Finite lattice representation and undecidability 207. The l-Bass conjecture for all discrete groups 208. Finite symmetric tensor categories and the Verlinde tower 209. Integral counterexamples to Gersten's conjecture 210. Foulkes' conjecture for sixth powers and quadratic stabilization 211. The geometric phase diagram, diffusion, and spectra of random planar maps 212. Planar first-passage geometry and the absence of bigeodesics 213. Critical percolation on every quasi-transitive graph 214. The Benjamini-Schramm nonuniqueness conjecture 215. Canonical O(3) continuum limit and exact O(4) mass asymptotics 216. Critical and near-critical XY scaling and BKT universality 217. The low-temperature Sherrington-Kirkpatrick fluctuation law 218. Conformal universality for weakly interacting and random-bond Ising models 219. GOE bulk universality for regular graphs with weak Anderson disorder 220. Directional zero-one laws beyond iid environments and iid ballisticity 221. The Mézard-Parisi formula for diluted spin glasses 222. Perceptron free energies and microscopic jamming exponents 223. Random-cluster interfaces: critical, disordered, thermal, and natural-time scaling 224. Critical and quenched near-critical universality for Poisson-Voronoi percolation 225. Gaussian free field limits throughout the balanced six-vertex regime 226. The double-dimer loop ensemble converges to CLE_4 227. Critical SK autocorrelation processes and dynamics across the temperature transition 228. Continuum phase transitions for radial pair potentials 229. Exact three- and four-state reconstruction thresholds and four-state tree capacity 230. Exact Hausdorff gauges for SLE 231. The free uniform spanning forest is a factor of IID 232. Gaussian fields and interfaces for triangular-lattice Lipschitz heights 233. The joint critical Ashkin-Teller current limit 234. All-temperature pressure of orthogonally invariant Ising spin glasses 235. Limiting random SAT thresholds, sharp variance and computability 236. The exact factor-of-IID threshold for free Ising spins on trees 237. The three-quarter exponent for honeycomb self-avoiding walk 238. Optimal logarithmic mixing of the Thorp shuffle 239. Sharp singularity rates for symmetric random sign matrices 240. Shelah's eventual categoricity and the prescribed-threshold obstruction 241. Rigidity of the Turing degrees 242. Single-fold Diophantine representations and undecidability under an at-most-one-solution promise 243. Separating choiceless counting from polynomial time and witnessed choice 244. The Partition Principle does not imply Choice 245. Weak normalization implies strong normalization in pure type systems 246. Cannon's conjecture 247. An infinite finitely presented residually finite 2-group and a finitely presented nil algebra 248. Thompson's group F is nonamenable 249. A finitely generated Eilenberg-Ganea counterexample 250. Boone-Higman embeddings with higher finiteness 251. Amenability, unitarizability, and strong Ulam stability 252. A torsion-free hyperbolic group that is neither residually finite nor linear over any field 253. An infinite finitely presented simple amenable group 254. Classifying spaces and geometric obstructions for Artin groups 255. Quasi-isometric recognition of virtually polycyclic groups 256. Nonsingular systems of equations over arbitrary groups 257. A hyperbolic group without a geometric CAT(0) action 258. Gersten's conjecture and virtual compact specialness of one-relator groups 259. A group without fixed price 260. Spacetime Penrose inequalities: enclosing area, charge, rotation, and anti-de Sitter extensions 261. Localization and delocalization in the Anderson model 262. Sharp finite-matrix Lieb-Thirring inequalities and all equality cases 263. The ionization and generalized ionization conjectures 264. Strong cosmic censorship near two-ended Kerr data 265. Area laws and tensor networks for two-dimensional gapped systems 266. Exactly three mutually unbiased bases in dimension six 267. Positive-temperature Bose-Einstein condensation and exact quantum depletion 268. The spin-one Haldane gap 269. Uniform Laughlin gap and stability under bounded scalar disorder 270. Threshold and positive-energy bound states of the BFSS matrix model 271. Bloch's law, its lattice correction, and the spherical magnetization law 272. Entanglement without distillable secret key 273. The entropy photon-number inequality 274. Parity is not in QAC^0 275. QMA-hardness of continuum Coulomb energy 276. Classical capacity of generalized amplitude damping 277. Threshold repetition for entangled games 278. Failure of Kohn-Sham ensemble representation 279. Exact quantum factoring over a fixed finite gate set 280. Unitary vertex operator algebras and conformal nets 281. QAOA attains the SK optimum in the thermodynamic-first limit 282. From scale symmetry to local conformal symmetry in four-dimensional QFT 283. Polynomial-time unitary synthesis from a Boolean oracle 284. The optimal quartic separation between randomized and quantum queries 285. Counterexamples to Baum-Connes and Kadison-Kaplansky 286. Rigidity and arithmetic of lattice von Neumann algebras 287. Isomorphism of the free group factors 288. Kadison's similarity conjecture 289. Strong Kadison-Kastler stability and its spatial boundaries 290. Relative bicentralizers and modular spectral recovery 291. Cuntz comparison, nuclear dimension, and equivariant Jiang-Su stability 292. Kirchberg's O_2 norm-ultrapower embedding problem 293. Invariant projections, hyperinvariant subspaces, and transitive algebras 294. Kaplansky's quasitrace conjecture and failure of tensor-product stable finiteness 295. The Kadison-Ringrose cohomology conjecture 296. The generator problem for finite factors 297. A ZFC counterexample to Naimark's problem 298. Two notions of free entropy differ even when both are finite 299. The Kirchberg-Rørdam character criterion and infinite tensor-power Jiang-Su stability 300. Approximation and quadratic strong-operator paving 301. Trace cones and Razak-Jacelon stabilization 302. Radius of comparison equals half the mean dimension 303. Weak pure infiniteness and Cuntz-algebra absorption 304. The Hilbert-Smith conjecture in every dimension 305. Four-dimensional disk embedding and Wall's conjecture 306. The purely cosmetic surgery conjecture 307. Failure of rational injectivity for maximal coarse assembly 308. Finite Smith-Toda complexes at every height 309. The Kervaire invariant problem at the prime three 310. Quillen's conjecture in rational homology 311. The Hovey-Strickland and Chai conjectures 312. The Grothendieck homotopy hypothesis 313. Finite generation for the K(n)-local sphere 314. Cyclic length and chromatic fixed-point loss 315. The four-dimensional Singer conjecture 316. Curtis's conjecture 317. Thomason model structures in all strict higher dimensions 318. Chromatic splitting: filtrations and counterexamples 319. Counterexamples to finite generation at chromatic height two 320. Nonhomeomorphic closed aspherical four-manifolds 321. A counterexample to Wall's finite D(2) problem 322. Tingley's sphere-isometry problem 323. Independence of the separable quotient problem 324. Lipschitz equivalent Banach spaces need not be linearly isomorphic 325. The complete Crouzeix conjecture 326. The cotype-cotype conjecture under the approximation property 327. Markov type characterizes superreflexivity 328. Nonexpansive fixed points in reflexive Banach spaces 329. A counterexample to metric-entropy duality 330. A uniformly discrete counterexample to bounded approximation in Lipschitz-free spaces 331. Reflexive midpoint convexity and diamond distortion 332. Metric Markov cotype of l_1 and Hilbert-space Lipschitz extension 333. Smooth isometric immersions of surfaces into the set of 4-dimensional real numbers 334. A smooth surface metric with no local isometric immersion in the set of 3-dimensional real numbers 335. Gromov's integral scalar-curvature bound for simplicial volume 336. Spectral scalar curvature, Urysohn width, and macroscopic dimension 337. Sharp Cartan-Hadamard isoperimetry and rigidity 338. Yau's uniformization conjecture 339. Katok's entropy rigidity conjecture 340. A counterexample to the nearby Lagrangian conjecture 341. Donaldson's hypersymplectic deformation conjecture 342. Donaldson's tamed-to-compatible conjecture 343. Symplectic ball packing in higher dimensions 344. The metric Blaschke conjecture 345. Infinitely many closed geodesics on Riemannian spheres and closed three-manifolds 346. Sharp singular-set bounds for stationary integral varifolds 347. Counterexamples to stable-Morse and strong Arnold fixed-point bounds 348. Nonnegative-curvature Einstein classification and an L^2 topological gap 349. The Solomon-Yau least-volume conjecture 350. Yau's nodal bounds: surfaces and higher dimensions 351. Scalar curvature and finite-time Ricci-flow singularities 352. A finite-time singularity of Calabi flow 353. Affine Bernstein rigidity through dimension nine and a smooth dimension-ten counterexample 354. The isoperimetric profile of the cubic three-torus 355. Unique tangent flows at the first surface singularity 356. Gigli's characterization of Alexandrov curvature 357. Bi-Lipschitz coordinates at every regular RCD point 358. A three-manifold without conjugate points or nonpositive curvature 359. Negative Kähler curvature without bounded holomorphic coordinates 360. Weak MTW curvature gives convexity and regular optimal transport 361. Failure of integer-degree harmonic dimension comparison 362. Global smoothness for relativistic Vlasov-Maxwell 363. Nonuniqueness with local conservation for the hard-sphere Boltzmann equation 364. Kinetic limits and fluctuations over the Boltzmann lifespan 365. Joint metric and connection recovery from one boundary patch 366. The planar Mumford-Shah regularity conjecture and local weak-L^4 gradient bounds 367. The critical dimension for the one-phase Bernoulli problem 368. The three-dimensional Ball-Evans approximation problem 369. The hot spots conjecture for simply connected planar domains 370. The Lane-Emden and Hénon-Lane-Emden conjectures 371. Stable blowup for the defocusing Schrödinger equation 372. Global uniqueness in smooth isotropic elasticity 373. Nonattainment of the three-marginal Coulomb Monge problem 374. Sharp one-third stability of Brenier maps 375. De Giorgi's conjecture in dimension eight 376. Universal computation in forced Navier-Stokes flows 377. Interior C^1,alpha regularity for infinity-harmonic functions github.com/openai/math/blob/… #solidstatelife #ai #genai #codingai #mathematics #proofs #lean

"Can AI agents decide what experiment to run next?" "LabBench v1 spans seven experimental domains: nascent transcription, gene regulation, 3D genome organization, assay and library development, target and disease-model validation, hit-to-lead chemistry, and drug-response profiling." "We construct the benchmark from the historical artifacts of real-world wet-lab work, spanning drug discovery and academic genomics research. Each task is a snapshot in time: the agent receives the experimental records that existed at a decision point (files include slide exports, figures, tables, scripts and sequencing outputs), while the lab's own interpretation and subsequent decision are withheld. The agent must produce a report that commits to a decision: in six tasks, a ranked set of next experiments; in the rest, which condition, model, comparison or claim the evidence supports." "Ground truth is the decision the lab's scientists actually made. Each task is graded against 20-22 binary criteria, each tied to the visible evidence and the hidden record that supports it, so an agent is scored both on whether it reaches the experts' decision and on the reasoning that leads there." My first thought when I read "Ground truth is the decision the lab's scientists actually made" is that you've inadvertently prevented the model from coming up with a better decision than what the lab scientists actually made. But let's continue. "Our core finding is that agents are often extensively informed about the specific topic, but routinely fail to connect the evidence to the correct next step. Across five frontier agents, 45% of criteria were passed by none, including 9 of the 23 core decisions, and in the six tasks that ask for ranked next experiments, no agent passed any of the 13 criteria on which experiment should come first. With a single sentence added to the prompt that directs the agent toward the relevant evidence without stating the answer, GPT-6 Astra reached all five never-passed core decisions we tested, reasoning about and designing experiments comparable to the work of human experts. The knowledge is latent, but agents do not recall it unprompted." In the paper, see Figure 1 (page 3), which illustrates the criteria by using dot size to represent the number of criteria for each cross-section of "nascent transcription", "gene regulation", "3d genome", "assay development", "target validation", "hit-to-lead chemistry", and "drug response" on the x-axis vs "method reconstruction", "measurement semantics", "evidential status", "comparison & baseline", "confounds & data quality", "scope & generalization", "quantitative reading", "core decision", "evidence integration", and "experiment design" on the y-axis. And see Table 1 (page 4) which lists biological themes with examples: Nascent transcription: "Reconstruct how control genomic regions were chosen in a nascent-RNA analysis" Gene regulation: "Compare the chromatin context of early- and late-bound transcription-factor sites" 3D genome: "Assess a 3D chromosome model against imaging distance measurements" Assay & library development: "Choose library preparation conditions from fragment-size traces" Target & disease-model validation: "Choose a matched cell comparison before testing a DNA-repair vulnerability" Hit-to-lead chemistry: "Reassess a compound series and its lead after new data" Drug-response profiling: "Separate plate artifacts from drug effects in a transcriptomic screen" And see Table 2 which lists the reasoning skills ("method reconstruction", "measurement semantics", "evidential status", "comparison & baseline", "confounds & data quality", "scope & generalization", "quantitative reading", "core decision", "evidence integration", and "experiment design") and describes what a passing answer looks like. All in all it looks like a decent effort to be rigorous. The resulting ranking was: GPT-6 Astra: 40.8% Claude Opus 5.5: 40.5% Grok 4.7: 29.6% Muse Spak 1.3: 28.2% Gemini 3.8 Flash: 18.4% Seeing GPT-6 Astra and Gemini 3.8 Flash made me think a lot of this ranking is down to timing: the evaluation was done after GPT-6 Astra was made available but before Gemini 4 was made available. The researchers go on to say: "Where agents differ: GPT-6 Astra leads on core decisions (52%) and measurement semantics (56%); Claude Opus 5.5 on evidence integration (53%), confounds and scope (52%) (Figure 5A). Gemini 3.8 Flash trails most on evidence integration and evidential status (11-12%). Assay development, target validation and hit-to-lead chemistry (25-28%) and reviews and designs (23-24%) are hardest." "Our results demonstrate a clear reasoning bottleneck for frontier agents on real-world biological tasks. Their biological knowledge is frequently extensive, enough that agents can already work alongside expert biologists when experimental design stays with humans, but their ability to design experiments independently is weak. They often present several options, or otherwise fail to commit to an experiment and to discriminate the best one from the alternatives. In real biological experimentation, wall-clock time is irreducible: cells grow, differentiate and respond on their own schedules, and each experiment consumes weeks and material. As a result, autonomous and correct experimental design is arguably the single most important capability for making AI agents superhuman at these tasks. LabBench measures this directly and penalizes failures along this axis. Improved results on LabBench should indicate an improved ability for AI agents to carry out supervised, autonomous wet-lab experimentation, and eventually to run full programs themselves. To get there, models must develop a more cohesive world model of what experiments cost and of the specific evidence that would count against a hypothesis." gamowlabs.com/labbench-bench… #solidstatelife #ai #genai #llms #biology #wetlab

MeetTwins is an app that enables you to send an AI in your place to your Google Meet calls. Once you start using it, all your colleagues will use it to send AIs in their places to the Google Meet calls. Once there are no humans, and only AIs in the Google Meet calls, the next step is to stop having the AIs telling you and your colleagues what decisions were made and what work you have to do, and have the AIs do the work themselves. theregister.com/ai-and-ml/20… #solidstatelife #ai #genai #llms #technologicalunemployment

"DHH has gone completely off the rails...". In order to get the double entendre, you have to know that David Heinemeier Hansson (DHH) is the creator of the Ruby On Rails framework for the Ruby programming language. Basically what happened here is David Heinemeier Hansson (henceforth DHH) went to a conference for Ruby On Rails developers (RailsWorld) and told a thousand Rails developers to stop using Ruby and Rails. He said stop writing code by hand -- if you still write code by hand you're a *loser* -- and instead use AI to write code -- but don't write Ruby On Rails code, write code in native languages for native apps on the front end (iPhone, Android) and Rust on the backend. He hates Rust and thinks it's ugly but since he only interacts with it through AI, it's great and produces small, high-performance executables that save money on server resources. So you see, DHH has gone completely off the Rails. I think he's right that writing code by hand is dead (mostly), but he was kinda rude in how he went about saying it. youtube.com/watch?v=OuNKBjuV… #solidstatelife #ai #genai #llms #codingai #rubyonrails #rust

"Employment of young workers (ages 22-25) in AI-exposed occupations now stands 19% below where it would be had it kept pace with that of their less-exposed peers; experienced workers show no comparable gap." This came out back in August but I didn't see it until today. It looks like it may be that the primary effect of AI on the labor market is to stop hiring rather than to cause mass layoffs. "This divergence has widened steadily since we first documented it in August 2025. It operates primarily through reduced hiring of young workers rather than increased separations." digitaleconomy.stanford.edu/… #solidstatelife #ai #technologicalunemployment

Paranoia Sans is a self-censoring, conspiratorial typeface that uses ligatures (an open type font feature that replaces a combination of strings with other characters) to redact terms that are popular in conspiracy myths. github.com/flegfleg/Paranoia… #solidstatelife #typefaces #conspiracytheories

Grok: Create an image of what the world would look like if I were in charge based on my tweets @waynerad

Jev vs Laya. (And some other models.) "Decision models under pressure." "Seven systems do the same job: take a piece of text, a question, and a list of candidate answers, and return a probability over those candidates. I measured them against each other as that job gets harder in the three ways it gets harder in production. The candidate list grows, the option order changes, and the wrong answers stop being obvious." (The other models are gliclass-large-v3.0, a 439M-parameter text classification model, deberta-v3-base-zeroshot-v2.0, a 369M-parameter text classification model, deberta-v3-large-zeroshot-v2.0, a 435M-parameter text classification model, bge-large-en-v1.5, a 335M-parameter dense embedding model for semantic search, retrieval-augmented generation (RAG), and text clustering, and gte-large, a 670-M parameter text embedding model.) "TypeSafe's Jev held up best as the list grew and best when the wrong answers got plausible. Shuffle the option order, though, and it changes its answer on one decision in seven. Hold the order fixed and it still changes its answer on one in twenty-three, because it does not repeat itself. Two of the open models never change theirs at all, because they cannot." "Jev's makers call it a new class of model. Laya's author has said publicly that he published the same idea a year earlier. On the first claim the numbers are not kind to the marketing, and on the second they are not kind to Laya: the idea does look older than Jev, and Jev is still the better implementation of it by a wide margin." "The other five models are open ones I added so those two numbers would mean something. Most comparisons of these systems report one accuracy number at one candidate-set size, which hides most of what matters, because the ranking changes depending on how many options you offer." "Everything ran on one frozen dataset, with the comparisons and the pass/fail rules written down before the first call. n is 200 items per domain, which is enough to separate the large effects below and not enough for the small ones." "Jev starts highest and stays highest. At 128 candidates it answers 60% correctly, where Laya manages 39% and the best open model 41%. Chance at that list length is 0.8%, so everything here is doing real work; the question is how much of it survives a longer list." "Its decline per doubling of the list is the shallowest of the seven, shallower even than the embedding scorers whose design is supposed to help them scale." "Notice how little a single-number benchmark would tell you. At two candidates Laya sits second of seven and trails Jev by two points. At 128 it has fallen to fourth and trails by twenty-two." They go on to describe giving the model tightly related choices rather than distantly related choices. So "create alarm competes against delete alarm and snooze alarm rather than against musical work." "At 64 candidates Jev drops from 96.8% to 86.3%, losing about a tenth of what it had. Laya drops from 90.5% to 56.0% and gliclass from 87.0% to 51.2%, each losing something closer to four tenths. That is the widest spread in the whole experiment and the one I'd care most about in production, where candidate sets are full of near misses." When shuffling the options, Laya changes the most 49.4%, then gliclass 28.2%, then Jev 14.6%, then gte-large 2.0%, deberta-base 0.2%, bge-large 0.0%, deberta-large 0.0%. Logically, shuffling the options should have no effect, but it does. github.com/gazelle93/decisio… #solidstatelife #ai #genai #rlcd #jev

"Today, we release Paper Office, a suite of Python packages that allow agents to manipulate Word, PowerPoint, and Excel files with added safety, correctness and breadth, building on legacy open source packages: python-docx, python-pptx and OpenPyxl." "We" here refers to Paper Instruments, whoever that is. "Across five models and 61 tasks, Paper packages plus guidance passed 92.5% of trials, versus 80.7% for upstream packages without skills and 69.5% with Anthropic's comparable Office skills. Agents also wrote code to edit Office file internals directly in just 1.6% of Paper runs, compared with 78.7% without skills and 50.5% with Anthropic skills. We note that basic software, in addition to prompt, skills, and tools, remains an important lever for harness optimization." They go on to say: "Agents still have low penetration in the daily work of consultants, lawyers, bankers, and operators. We believe the bottleneck to professional adoption is fidelity to real workflows. Agents dont manipulate existing documents with the same techniques that humans do, and the resulting decks, sheets, and documents sit in an uncanny valley that aren't fit for client consumption." "DOCX, PPTX, and XLSX files use Office Open XML (OOXML): each is a ZIP archive containing XML files, images, and other resources linked together, rather than a single text file. Editing them means keeping those parts and their relationships consistent, so even a small visible change can require updates in several places." paperinstruments.com/blog/in… Full documentation is here: docs.paperinstruments.com/do… #solidstatelife #ai #genai #llms #technologicalunemployment

"jevmetrics is an experimental OpenTelemetry Collector metrics processor. It calls TypeSafe's Jev model to infer the likely operational value of metric instruments from their metadata, then applies deterministic policy to the returned probabilities." More technology built on Jev within a week of its release. OpenTelemetry is an open source observability framework, which is a fancy way of saying logging of programs' execution, internal states, communication messages, and corresponding log processing and program execution tracing. "Use it to assess unfamiliar instrumentation, review candidates for reduced retention, and selectively filter metrics before they reach a primary backend. Inference runs asynchronously, and cached assessments let subsequent batches use the same decision without another API call." "A new service or library can introduce metrics nobody has classified yet. Names, descriptions, units, instrumentation scope, and attribute keys provide evidence about what those metrics mean -- even before there is a history of dashboard or query usage." "jevmetrics uses that evidence to ask Jev whether a metric is likely useful enough to retain. Explicit protection rules and thresholds determine what the Collector actually does with the answer." github.com/ishantanu/jevmetr… #solidstatelife #ai #genai #llms #rlcd #jev #opentelemetry

grug.chat. "tiny vocabulary. big thought." "TypeSafe says Jev isn't an LLM. Let's talk to it anyway. Grug is a tiny cave-chat experiment that makes Jev hold a conversation by repeatedly choosing its next word from a compact vocabulary." I tried the live demo. I asked, "What's the meaning and purpose of life?" Grug said: "life is live to enjoy happy play the enjoy live is." I asked, "So I should not worry, be happy?" Grug said: "yes just happy sure yes just happy sure right so good you right so good you should be..." Alrighty then. I asked, "Should I try to prove the Riemann Hypothesis?" Grug said: "hard difficult no not hard difficult no not bad big difficulty is too very big bad too difficulty..." github.com/mkotlikov/jev-gru… #solidstatelife #ai #genai #jev

"GPT-6 Astra surpasses the human baseline in action efficiency on ARC-AGI-3. It used fewer actions than the median tested human on 96% of levels." They go on to say "GPT-6 Astra scores 62.7% for $26K on ARC-AGI-3 Semi-Private with our Standard harness, and 99.9% for $19K with a Provider Adapter harness", and "A key behavior observed in GPT-6 Astra was its ability to turn unfamiliar environments into compact symbolic world models. It represented game mechanics as logical rules and developed its own domain-specific language shorthand to track state and plan actions." If you're unfamiliar with the ARC-AGI tests, they're pattern-matching tests that are intended to be easy for humans but hard for AI despite AI being able to solve math problems, etc, that are hard for humans. It's intended to give a more realistic idea how close AI models are to surpassing human intelligence at more or less everything, not just specific tasks that are hard for humans. This threshold is called "artificial general intelligence" (AGI) which is not exactly an intuitive term (but math and science and the AI field are full of terms that are not intuitive -- can you come up with a better one?). ARC-AGI-3 is the 3rd version of the test, because AI keeps getting better and they keep having to make the test harder. "These environments only contain core knowledge priors and are difficulty-calibrated through controlled testing with human participants. Humans can solve 100% of the environments." "The goal of the ARC-AGI series is to measure the 'residual gap' between current artificial intelligence and AGI. We define AGI as a system's ability to acquire any skill a human can, as efficiently as a human can." If you're wondering what the explanation is for the dollar amounts in the description above, they say: "For a cost comparison, during our controlled testing, human participants were paid $115 per 90-minute session, plus $5 per game completed. Participants attempted approximately nine games per session, roughly $12.78 per attempted game before bonuses." "Most of this fee pays for the participant's time and willingness to take the test, rather than the energy their brain uses (a closer proxy to compare with AI). If we look at only the brain's energy, and price it as electricity, the estimate drops to about 0.6 cents per session, or 0.067 cents per game attempted." "Beyond the scores, Astra's replays show how it turns unfamiliar game mechanics into useful working models. Three findings stood out: the compact algebraic notation it develops, its action efficiency compared with humans, and the custom tools it builds." If you're wondering what the bit about "action efficiency" is all about, they say: "For each level, we defined the 'human baseline' using the median action count among players who completed it. This gives us a reference for comparing human and AI performance. An AI that needs more actions is less action-efficient, while one that needs fewer actions is more action-efficient." "Before we launched ARC-AGI-3, we hypothesized that action efficiency would remain a dividing line between humans and AI. We anticipated that even when an AI solved an environment, it might require substantially more exploration (actions) than a person. That remains true of brute-force approaches, but frontier AI shows a more binary-like pattern. Once frontier AI 'understands' the mechanics, it generally executes within the range of human efficiency." "Astra's results show that it needed fewer interactions than the human baseline to execute a solution." There's also some stuff about "harnesses". They say: "The Standard harness enables a model to carry forward notes it chooses to keep with it throughout the environment", and "The Provider Adapter harness preserves opaque reasoning state between requests and uses compaction for longer conversations, allowing the model to reuse prior work." arcprize.org/blog/astra #solidstatelife #ai #genai #llms #agi

jev-lint (no capitalization) is a static analyzer-ish program that analyzes source code -- only JavaScript and TypeScript -- against a document in English of conventions the code has to follow, and generates error messages that are intended for your code-generating AI agent to read. As the name implies, it uses Jev. github.com/zdenham/jev-lint #solidstatelife #ai #genai #llms #codingai #rlcd #jev #staticanalysis

Poker as a test of Jev. How does Jev compare with a deterministic poker solver (TexasSolver)? I'm not knowledgable enough in poker but I'm quoting the beginning of this piece so you all can decide whether you want to click through and read the whole thing. "1. The setup: I solved one flop with TexasSolver: heads-up, 100bb deep, LJ opens and BTN calls, flop Q-spade 9-diamond 4-spade. Eight minutes, 7.6GB, 0.59% exploitability. That gives the correct strategy for every hand either player can hold, on every turn card, at every decision." ("bb" stands for "big blinds".) "Jev gets a state object describing the table the way a player sees it, and one question: which action should hero take, from the options the solver's tree offers. Pot odds, stack-to-pot ratio, hero's hand rank and outs are computed in Python first, so Jev never does arithmetic." "2. An easy spot: good. Hero holds J-spade T-spade on Q-spade 9-diamond 4-spade 2-heart facing a bet of 8 into 10.5. Fifteen outs, needs 30% equity to call, has 33%." "Option: Solver vs Jev" "Call 8: 96% vs 94%" "Raise to 24: 4% vs 2%" "All-in 95.5: 0% vs 2%" "Fold: 0% vs 2%" "Jev calls at 94%, the solver calls at 96%. 215ms. Across 30 random spots it matched the solver's top action 63% of the time." He goes on to describe situations that Jev gets wrong. Then he goes on to add bits of information (such as revealing one of the opponent's cards) to Jev's input bit by bit to see how it improves Jev's probability. backnotprop.com/blog/jev-pok… #solidstatelife #ai #genai #rlcd #jev