But is compute efficiency the thing that matters?

Build in Lovable, from Claude. New native integrations with our MCP server = chat to @claudeai, export from Claude Design, and ship with Claude Code, directly to Lovable. To get started, go to Claude → Click on the + sign → Connectors → Browse Connectors → Lovable.

A new kind of license-plate-reading camera will also scrape the smart devices you take with you, and wrap all that data in a nice little bow for law enforcement and the government.

I'm trying to connect Openclaw to PaperclipAI with the agent invite workflow. Where do I find or enable logs from When I send: curl -X POST https://paperclipproxy.aiqa.net:443/api/invites/

Taxonomy of the Occlupanida Taxonomy Class Occlupanida (Occlu=to close, pan= bread) are placed under the Kingdom Microsynthera, of the Phylum Plasticae. Occlupanids share phylum Plasticae with “45” record holders, plastic juice caps, and other often ignored small plastic objects. Methodology In constructing a taxonomy, many factors such as genetics, derived traits, growth and development, sexual dimorphism, reproduction, and a fossil record are taken into account. For a synthetic taxonomy, all of these critical keystones of information are entirely absent. The synthetic taxonomist is forced to construct a lineage much like Carl Linnaeus winged it in the 1700’s: group things that look similar, draw lots of complex-looking charts, and hope your future descendents work out occlupanid genetics somehow. Since most occlupanids sport a confounding jumble of homologous structures, occlupanologists focused on the oral groove to establish taxa. Much like insect wings, occlupanids are grouped according to the dentition (or lack thereof) in their oral groove, which often dictates both their ecological niche and biogeographic location. A proposed phylogeny starts with the reasonable assumption that the most basal occlupanids resembled something like the Archignatha, with subsequent orders placed according to their dentition arrangement. In time as more and greater niches arose in the civilized world, greater speciation has occurred, resulting in new orders arising and spreading out as older ones wane. HORG collection holdings

Why WER/CER misjudge Indian-language ASR when scripts mix and spellings vary. Covers LLM-WER, LLM-CER, Intent, Entity, and COMET-plus open evaluation tooling.

I work in robotics, and have for almost a decade now. I keep noticing that a huge chunk of search-under-uncertainty problems, in robotics and outside it, converge on the same two-step architecture: generate variation indiscriminately first, then apply a scoring/selection pressure that keeps what works and discards what doesn't. No model of the problem is required upfront. Most the "intelligence" lives in the selection step, not the generation step. The clearest version of this in our own field is sampling-based motion planning. RRT and its relatives don't try to compute a path analytically. They expand randomly in many directions through the configuration space and then retain/extend the branches that make progress toward the goal, pruning the rest. Genetic algorithms and evolution strategies (CMA-ES, for instance) run an identical loop in parameter space instead of configuration space: generate a population of variants, score them against a fitness (cost) function, keep the survivors, repeat. Simulated annealing is a single-particle version of the same thing, generate a random perturbation, accept or reject it based on a score. Once I started paying attention to this pattern, I noticed it shows up well outside robotics too, in places that have nothing to do with computer science: Slime mold expanding in all directions through a maze of food sources, with the inefficient tendrils pruned back, and famously reconstructing something close to the Tokyo rail network when food sources are placed at the positions of major stations. Evolution itself: random mutation generates variation with zero regard for whether it's useful, and survival does the selecting after the fact. Neural development: neurons and synaptic connections proliferate in directions that aren't pre-planned, and dopamine-linked reinforcement selectively stabilizes the ones that turn out to matter. Once I started looking for more instances, I found two more that fit the same structure almost exactly: The immune system: B-cells mutate antibody variants somewhat randomly (somatic hypermutation) and the ones that bind the pathogen get clonally selected and expanded. Thought/creativity: you can't generate a genuinely novel idea by deduction from evidence that it's correct. The evidence only exists after the idea does. Novelty has to come first; judgment comes second. That last one turns out to have a surprisingly direct precedent. Henri Poincaré, describing how he worked out the theta-fuchsian functions, wrote that ideas rose in crowds and collided in his mind until pairs interlocked into stable combinations, almost like watching his own unconscious work made partially visible to consciousness, and that what got selected from that flood of combinations was governed by something close to an aesthetic sense of mathematical elegance. That's a generate-then-select loop running inside a human mind, described in 1908. The principle has been formalized more than once since then, from different directions: Richard Dawkins' Universal Darwinism: the claim that variation/selection/retention isn't a biology-specific mechanism but a substrate-independent algorithm that biology happens to be one instance of. Donald Campbell's blind variation and selective retention (BVSR), later developed extensively by Dean Keith Simonton, which applies the same two-step structure directly to creative cognition. Karl Popper's conjectures and refutations model of how knowledge grows: blind generation of new theories, followed by selective retention of the ones that survive criticism. Popper explicitly treated this as the same process as biological evolution, just running on ideas instead of organisms. Gerald Edelman's Neural Darwinism (Theory of Neuronal Group Selection), the formal version of the neuron/dopamine point above: synaptic overproduction followed by activity-dependent selective stabilization. Worth flagging here: I'm not claiming the expansion step is ever truly random. In every example above, the variation is guided. Slime mold follows chemoattractant gradients, not isotropic noise. Mutation isn't uniform across a genome, there are hotspots and repair biases. Axon growth follows chemical guidance cues, not random angles. Informed RRT* deliberately biases sampling toward the goal region instead of sampling uniformly. Even Poincaré's account isn't pure randomness, he describes an aesthetic sense that seems to steer which combinations even get generated, not just which ones survive afterward. Liane Gabora has made this exact critique of BVSR, that calling the variation "blind" overstates how random it actually is. But that's the part I find more interesting, not less. The expansion step across all of these systems is intelligently informed, biased toward promising regions by something the system already "knows," and yet it still needs the separate selection/scoring step on top of that guidance to actually converge. Neither half does the job alone: the guidance is too crude or too local to solve the problem outright (that's why expansion is still happening at all instead of direct computation), and the selection pressure has no foresight of its own, it only works because it's filtering output that the guided expansion already biased toward viable territory. Has anyone else found that this specific combination, intelligently biased expansion paired with a separate selection/scoring step, actually performs best in practice? I have personally found, that at least for my applications in autonomous vehicles and motion planning, this combination works the best. Curious whether other people doing sampling-based planning, evolutionary algorithms, or other search methods have found the same thing I have: that this architecture outperforms the alternatives, rather than just being one option among several that works comparably well. I will personally only ever use this type of algorithm after realizing this is how nature does it. Lastly, I am not proposing that analytical algorithms are better or worse than machine learning algorithms. I believe that HOW this is solved is irrespective of the fundamental search algorithm of the universe I have observed. I believe that it is the expand, then score mechanism that is important here, and does not exclude any method of expansion or scoring, as I have observed this in the above stated forms across nature. Thanks for the read if you're here, I've been thinking about this all year and needed to post it somewhere. submitted by /u/Pepperoni-Flame [link] [Kommentare]

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Yesterday we wrote about the Trump administration forcing Anthropic to shut down Fable 5 and Mythos 5. The short version: dumb. Today, Axios got White House officials on the record, and it turns ou…