🤖🎉 Mercury 2: because who doesn't want their
#AI reasoning model to be powered by the wonders of "diffusion"? Clearly, the secret sauce to faster thinking is pretending
#latency is a new
#problem we just discovered! 🚀🔧 Who knew that production AI systems ever needed more than a single prompt-response before they became, oh, I don't know, useful? 😂
https://www.inceptionlabs.ai/blog/introducing-mercury-2 #Innovation #Diffusion #Solving #HackerNews #HackerNews #ngated
Introducing Mercury 2 – Inception
Today, we're introducing Mercury 2 — the world's fastest reasoning language model, built to make production AI feel instant.
I claim that I have experience with that
#mind #technology. And most specifically
#order #of #operations. I'm really good at that when I'm
#solving #problems and can ignore my life that falls apart around me while I do this. That part. She's not obsessed. No we're way past that. 👽🚀🛸👩🏼🚀☄️😹💃🏻
The
#sound #bites are there and the
#results are
#not. I am a solution architect, a system solution architect at that. I can do details really well, and sometimes I even like it, but when
#solving a
#problem I understand the right direction.
#Torn apart all of the old
#data #analysis notions before.
Developers are Solving The Wrong Problem - Caseysoftware
Everyone is either offended or excited about "vibe coding." It's all the rage and going to solve all your problems, or it's the next great evil spewing crap code all over your systems. Those of us who love well structured clean code which is modular and concise seem to be a dying breed. For someone
Watch: Yoo Yeon Seok Gets Chaotically Possessed By Ghost Clients While Solving Their Unfinished Cases In “Phantom Lawyer” - KpopNewsHub – Latest K-Pop News, Idols & Korean Entertainment
The upcoming SBS’s Friday–Saturday drama “Phantom Lawyer” has unveiled a new special teaser!
🚀💻 Quantum computers won't magically solve all your problems overnight, folks—sorry to burst that
#sci-fi bubble! 🤯 Meanwhile, Scott Aaronson's "imminent" claims are as flip-floppy as a politician's promises. 🙃
https://scottaaronson.blog/?p=9425 #quantumcomputing #reality #check #ScottAaronson #technews #problem #solving #HackerNews #ngated
More on whether useful quantum computing is “imminent”
These days, the most common question I get goes something like this: A decade ago, you told people that scalable quantum computing wasn’t imminent. Now, though, you claim it plausibly is immi…
🎉 Oh, look! Another "groundbreaking" open standard that promises to revolutionize the world but can't manage to move beyond spamming "Contact Sales" like a parrot on repeat. 🦜 Just what we needed—an AI agent that excels at relentless self-promotion rather than
#solving real problems. 🙄
https://claude.com/blog/organization-skills-and-directory #openstandard #AIinnovation #technews #selfpromotion #problem #HackerNews #ngatedSkills for organizations, partners, the ecosystem | Claude
In October, we introduced skills—a way to teach Claude repeatable workflows tailored to how you work. Today we're making skills easier to deploy, discover, and build: organization-wide management for admins; a directory of partner-built skills from Notion, Canva, Figma, Atlassian, and others; and an open standard so skills work across AI platforms.
Solving a Million-Step LLM Task with Zero Errors
LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans, organizations, and societies has remained out of reach. The models have a persistent error rate that prevents scale-up: for instance, recent experiments in the Towers of Hanoi benchmark domain showed that the process inevitably becomes derailed after at most a few hundred steps. Thus, although LLM research is often still benchmarked on tasks with relatively few dependent logical steps, there is increasing attention on the ability (or inability) of LLMs to perform long range tasks. This paper describes MAKER, the first system that successfully solves a task with over one million LLM steps with zero errors, and, in principle, scales far beyond this level. The approach relies on an extreme decomposition of a task into subtasks, each of which can be tackled by focused microagents. The high level of modularity resulting from the decomposition allows error correction to be applied at each step through an efficient multi-agent voting scheme. This combination of extreme decomposition and error correction makes scaling possible. Thus, the results suggest that instead of relying on continual improvement of current LLMs, massively decomposed agentic processes (MDAPs) may provide a way to efficiently solve problems at the level of organizations and societies.
GitHub - brianberns/Pips: A New York Times "Pips" game solver
A New York Times "Pips" game solver. Contribute to brianberns/Pips development by creating an account on GitHub.
👨💻 Oh joy, another
#GitHub repository promising to revolutionize the world of physics-based
#simulations with the excitement of... *contact solving*. 🚀 The average coder will dive right into "sttechppfcontactsolver" with the same enthusiasm they have for reading stereo instructions. But hey, at least GitHub wants you to believe
#AI will write better code for you. 🤖
https://github.com/st-tech/ppf-contact-solver #Physics #Contact #Solving #Coding #HackerNews #ngated
GitHub - st-tech/ppf-contact-solver: A contact solver for physics-based simulations involving 👚 shells, 🪵 solids and 🪢 rods.
A contact solver for physics-based simulations involving 👚 shells, 🪵 solids and 🪢 rods. - st-tech/ppf-contact-solver
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