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…

Skills 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.

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

Grégoire Locqueville | Solving a Wooden Puzzle Using Haskell (Part I)

Solving `Passport Application` with Haskell

Using logic programming to beat the game

Solving LinkedIn Queens with Haskell | Imiron

Solving LinkedIn Queens in the LogicT monad

The Curse of Knowing How, or; Fixing Everything | Blog

A reflection on control, burnout, and the strange weight of technical fluency.

Solving a "Layton Puzzle" with Prolog

I have a lot in the works for the this month's Logic for Programmers release. Among other things, I'm completely rewriting the chapter on Logic Programming...