A "theoretical only" machine learning algorithm refers to an algorithm whose primary contribution is in advancing the mathematical, statistical, or computational foundations of learning, rather than being immediately practical or empirically validated. Such breakthroughs often precede and inspire later practical advances.
Night macro portraits of Amphibia—Anura/Caudata/Gymnophiona—using cross‑polarized flash to show diagnostic morphology, microhabitat, and behavior in the wild.
A lot of people dismiss CRUD apps because they only picture a simple todo list or blog engine. But the moment you actually try to build something production grade you run into all the things you listed: concurrency, caching, type safety across layers, indexing strategies, keyset pagination, auth tradeoffs, UI ergonomics, etc. The funny part is most businesses still run on what boil down to CRUD apps. Accounting systems, ERPs, logistics dashboards, CRMs they are “just CRUD,” but they move billions of dollars. AI can help with parts of this (maybe codegen for boilerplate or smarter query planning), but it does not erase the problem space. If anything it makes consistency, reliability, and security even more critical. To me the interesting angle isn’t “AI replaces CRUD,” it’s “CRUD is the substrate that everything, including AI apps, has to sit on.”
It depends on who buys it and what their incentives are. Chromium itself is open source and won’t vanish just because Google sells Chrome. The bigger risk is that whoever takes over Chrome might push it in directions that serve their business rather than the web. We’ve already seen how default choices in a browser can shape the whole internet. If the new owner respects open standards and invests in security like Google has, it could be fine. If not, fragmentation or neglect could hurt the ecosystem.
So, does weed decrease your sleep quality or improve it? The truth is, it can do both. Cannabis affects everyone’s sleep a little differently. For some people, a puff before bed is a lifesaver that eases a restless mind and relaxes the body into a peaceful slumber. For others, especially with certain strains or heavy long-term use, it might actually disturb sleep.
We formalize three design axioms for sustained adoption of agent-centric AI systems executing multi-step tasks: (A1) Reliability > Novelty; (A2) Embed > Destination; (A3) Agency > Chat. We model adoption as a sum of a decaying novelty term and a growing utility term and derive the phase conditions for troughs/overshoots with full proofs. We introduce: (i) an identifiability/confounding analysis for (α,β,N0,Umax) with delta-method gradients; (ii) a non-monotone comparator (logistic-with-transient-bump) evaluated on the same series to provide additional model comparison; (iii) ablations over hazard families h(⋅) mapping ΔV→β; (iv) a multi-series benchmark (varying trough depth, noise, AR structure) reporting coverage (type-I error, power); (v) calibration of friction proxies against time-motion/survey ground truth with standard errors; (vi) residual analyses (autocorrelation and heteroskedasticity) for each fitted curve; (vii) preregistered windowing choices for pre/post estimation; (viii) Fisher information & CRLB for (α,β) under common error models; (ix) microfoundations linking T to (N0,Umax); (x) explicit comparison to bi-logistic, double-exponential, and mixture models; and (xi) threshold sensitivity to Cf heterogeneity. Figures and tables are reflowed for readability, and the bibliography restores and extends non-logistic/Bass adoption references (Gompertz, Richards, Fisher-Pry, Mansfield, Griliches, Geroski, Peres). All code and logs necessary to reproduce the synthetic analyses are embedded as LaTeX listings.
The Right Kind of “Control”
When we talk about taking control, we’re not talking about bossing others around or power-tripping. It’s about reclaiming authorship of your own life — being the one holding the pen in your daily story. Everyone naturally deserves this kind of control: the freedom to make choices and guide situations, rather than always reacting. In fact, nobody likes being micromanaged or dominated, but everybody appreciates a bit of guidance and respect. Think of it as being the navigator of a road trip, not a tyrant at the wheel. You set a direction, but everyone enjoys the ride. Read more in the Article.
Fixed points when contraction happens only at events: Lipschitz iterates, product of factors tending to zero, with clear convergence rates beyond Banach.
Faruk Alpay stumbled into influencing AI by posting his math-heavy papers on arXiv about “Alpay Algebra,” a framework blending fixed-point theory and category theory to give AIs stable knowledge, identities, and better human alignment. He realized these papers end up in AI training data, essentially letting him whisper ideas directly into future models’ “minds.” Now, he’s intentionally using this to promote ethical, explainable AI through open research, tying into trends like smaller models and safety concerns, and encourages others to join the conversation.
This paper dives into how humans and AI might split tasks in future workflows, modeling it as a repeating delegation process that settles into a stable setup where each job goes to whoever has the edge. Using math from lattice theory, it proves there’s always at least one such balanced point and spells out when it’s the only one, while a continuous version shows the steady-state automation level as x* = α / (α + β), with α as automation speed and β as the flow of new human-focused tasks, meaning full robot takeover is off the table if β stays positive. It tests this across three dynamic scenarios like discrete updates, evolutionary dynamics, and a beta-distributed task range, all landing on the same hybrid outcome, plus simulations from 2025 to 2045 forecast automation jumping from 10% to 65%, still leaving humans about a third of the work in a fresh role as workflow orchestrators. Wrapping up, it touches on boosts for skills, better benchmarks, and AI rules to push human-AI partnerships for max benefits.
This paper dives into reversing the process of turning basic one-dimensional trust scores from security frameworks back into detailed high-dimensional embeddings that represent device trustworthiness, proposing a straightforward method that stitches together paired scores with statistical moments and proves it converges to a unique solution using fixed-point math. Through simulations with noisy data on 20 devices over 10 time steps, it shows the reconstructions keep the original geometric relationships intact, backed by error guarantees tied to data length, but warns that sharing these scores poses a real privacy threat by exposing underlying behaviors of devices and models. To counter this, it suggests fixes like rounding scores, injecting controlled noise, or scrambling embeddings, all while weighing the trade-offs between transparency and secrecy in connected AI setups.
From my standpoint, ISO 639:2023 is the long-awaited overhaul that collapses the messy six-part language-code family into a single, extensible framework that treats languages not just as three-letter tags but as living symbols with roles, contexts and recursive identities; by withdrawing ISO 639-3 and absorbing the principles of the other parts, it lets developers, linguists and AI architects express whether a code stands for a vernacular, a liturgical register, a diaspora variant or even a constructed tongue while linking each form to a semantic anchor that machines can reason about. This shift matters because modern NLP pipelines and large language models need more than a static lookup table; they need a grammar for meaning drift, translingual references and non-territorial speech communities so translation quality improves, fallback routing becomes deterministic and emergent languages gain first-class status without resorting to hacks. In short, ISO 639:2023 turns language identification from bookkeeping into intentional semantics, giving us a common backbone for everything from metadata schemas to recursive symbolic AI.
From my vantage point, the arrival of roughly ten to twelve thousand North Korean troops in Russia’s Kursk sector since late 2024 signals a risky yet calculated gamble by Pyongyang: Kim Jong Un is trading his soldiers’ blood for hard currency, fuel, food and, most importantly, Russian missile, air defense and submarine know-how while exposing his army to a drone-saturated, precision-fire battlefield it has never faced. These forces, often disguised in Russian uniforms and hurled into frontal assaults, have suffered over six thousand casualties yet earned Kremlin praise for plugging manpower gaps after Ukraine’s brief cross-border push; in return, Moscow shields the North at the UN and pledges to modernize its arsenal. The bargain is already warping security equations from Kyiv to Seoul, alarming NATO and Asian capitals that a battle-hardened, better-armed DPRK could emerge from the conflict and that the war has now tightly fused European and Indo-Pacific theaters.
modern academia in the United States, United Kingdom and Germany is rotting from the inside: insider hiring and partisan peer review keep gatekeepers in power; bullying, overwork and precarious contracts wreck mental health, sometimes ending in suicide; universities chase revenue by loosening admissions and inflating grades, turning students into paying customers; and the upshot is an accelerating brain drain that buries fresh ideas and wastes human potential, leaving the promise of a merit-based pursuit of knowledge hollow.
I sent exactly 888 satoshis – a dust-sized 0.00000888 BTC – back to the untouched genesis address because it felt like laying a tiny offering at Bitcoin’s birthplace, binding value to meaning; I tucked a short note into the OP_RETURN field so my whisper sits forever in the public ledger, knowing no one, not even Satoshi, will ever spend it, yet taking comfort that my private salute now lives in plain sight, closing a neat loop from satoshi to Satoshi and proving to myself that permanence can grow from something practically worthless.
The newest installment describes a “symbiotic semantics” game in which an AI and a document co‑evolve until they share a single, perfectly aligned interpretation, implying that carefully crafted text could permanently reshape an AI’s internal representations.
TL;DR:
Long-form case study uses public writings of polymath Faruk Alpay to examine how extreme cognitive load, recursive thinking and high-stakes innovation in AI math and consciousness research intersect with burnout anxiety impostor feelings and existential strain while also highlighting protective factors like intrinsic meaning rigorous formalism and reflective writing; draws on 10+ peer-reviewed studies to argue that frontier researchers walk a fine line between genius and breakdown and calls for systemic mental-health support in high-impact tech and academic environments.
TL;DR
THC is the gas pedal but terpenes steer the ride, explaining why strains with identical potency can leave you either wired or glued to the couch; the piece spotlights myrcene for deep body sedation, limonene for mood lift and anxiety relief, pinene for mental clarity, linalool for lavender-grade calm, beta-caryophyllene for pain relief via cannabinoid receptors, and humulene for appetite control with a light buzz. These compounds create the entourage effect, fine-tuning psychoactivity by amplifying or smoothing THC. Bottom line: trust your nose or read lab labels to shop by dominant terpenes instead of the outdated indica–sativa split or raw THC percentage, and you can hack each session toward the exact vibe you want while science inches toward precision terpene blends.
Academia is full of hidden “bugs” unwritten rules, cryptic feedback, and conceptual dead‑ends. This Medium piece argues that treating research like code detect the error, form a hypothesis, iterate fixes, and use tools to accelerate the loop gives junior scholars a practical roadmap for turning messy ideas into publishable work.
Short, math‑heavy write‑up (PDF, 11 pp.) that re‑derives the classical initial‑algebra construction for ω‑continuous endofunctors and then isolates a “Node Deletion Theorem”: remove a single object from the building chain and the theorem tells you exactly which arrows vanish in the resulting colimit. In practical terms it gives a provably‑safe recipe for cutting nodes out of abstract‑syntax trees, graphs or other inductive data without reconstructing the whole structure—think incremental compilers, program transformers, or graph‑rewrite engines. The note also sketches a joint‑fixed‑point extension (Bekić‑style) and invites feedback, counter‑examples and real‑world applications. Comments and pointers to prior art welcome!
I would like it to be something everyone can use together. Think like one chatGPT account, all uses. I felt like everyone so isolated in this century so I decided to make it. About lightcap, it is my EUIPO registered property both brand name and brand logo in classifications related to AI. https://lightcap.ai is currently displaying several features. Currently, it can give better answers than even o3-pro as it is also used for high reasoning tasks. Think like deepsearch but i am okay to pay 5-10$ per API requests, with this money i can make my system think for an hour, and prepare what you want. In the end, it would be also public but using it would either require .edu email or costy as i want to treat there like encyclopedia
wasda.ai is a web-based AI assistant designed to accelerate data science workflows by providing code generation, data analysis, and visualization tools (will be added) directly in the browser. Users can upload datasets (soon), ask natural language questions, and receive Python code or visualizations (soon) in response, streamlining exploratory analysis and prototyping. The platform leverages large language models for context-aware suggestions and supports CSV/Excel uploads (soon), making it accessible for both beginners and experienced practitioners. Feedback and feature requests are welcome as we iterate on the product. https://wasda.ai
We develop an operator algebraic framework for infinite games with a continuum of agents and prove that regret based learning dynamics governed by a noncommutative continuity equation converge to a unique quantal response equilibrium under mild regularity assumptions. The framework unifies functional analysis, coarse geometry and game theory by assigning to every game a von Neumann algebra that represents collective strategy evolution. A reflective regret operator within this algebra drives the flow of strategy distributions and its fixed point characterises equilibrium. We introduce the ordinal folding index, a computable ordinal valued metric that measures the self referential depth of the dynamics, and show that it bounds the transfinite time needed for convergence, collapsing to zero on coarsely amenable networks. The theory yields new invariant subalgebra rigidity results, establishes existence and uniqueness of envy free and maximin share allocations in continuum economies, and links analytic properties of regret flows with empirical stability phenomena in large language models. These contributions supply a rigorous mathematical foundation for large scale multi agent systems and demonstrate the utility of ordinal metrics for equilibrium selection.