What is Hackers' Pub?

@reiver ⊼ (Charles) :batman: (@reiver@mastodon.social)

1/ I can tell you from experience of hosting many people's Fediverse server instances for a number of years now that — Many Fediverse software (at least currently) do a poor job of cleaning-up their various caches Caching other people's profiles, images, posts, etc And, this often ends up filling up the storage drive And as a result, this is a very common thing that causes down-time for people. Probably the most common #DeSo #FediAdmin #FediCache #FediDev #FediDevs #Fediverse #SpaceHostBTS

mastodon.social · Mastodon

Leanabell-Prover: Posttraining Scaling in Formal Reasoning

Recent advances in automated theorem proving (ATP) through LLMs have highlighted the potential of formal reasoning with Lean 4 codes. However, ATP has not yet be revolutionized by the recent posttraining scaling as demonstrated by Open AI O1/O3 and Deepseek R1. In this work, we investigate the entire posttraining of ATP, aiming to align it with breakthroughs in reasoning models in natural languages. To begin, we continual train current ATP models with a hybrid dataset, which consists of numerous statement-proof pairs, and additional data aimed at incorporating cognitive behaviors that emulate human reasoning and hypothesis refinement. Next, we explore reinforcement learning with the use of outcome reward returned by Lean 4 compiler. Through our designed continual training and reinforcement learning processes, we have successfully improved existing formal provers, including both DeepSeek-Prover-v1.5 and Goedel-Prover, achieving state-of-the-art performance in the field of whole-proof generation. For example, we achieve a 59.8% pass rate (pass@32) on MiniF2F. This is an on-going project and we will progressively update our findings, release our data and training details.

arxiv.org · arXiv.org

Canonical for Automated Theorem Proving in Lean

Canonical is a solver for type inhabitation in dependent type theory, that is, the problem of producing a term of a given type. We present a Lean tactic which invokes Canonical to generate proof terms and synthesize programs. The tactic supports higher-order and dependently-typed goals, structural recursion over indexed inductive types, and definitional equality. Canonical finds proofs for 84% of Natural Number Game problems in 51 seconds total.

arxiv.org · arXiv.org

R2 Data Catalog: Managed Apache Iceberg tables with zero egress fees

R2 Data Catalog is now in public beta: a managed Apache Iceberg data catalog built directly into your R2 bucket.

blog.cloudflare.com · The Cloudflare Blog

From Blind Solvers to Logical Thinkers: Benchmarking LLMs' Logical Integrity on Faulty Mathematical Problems

Consider the math problem: "Lily received 3 cookies from her best friend yesterday and ate 5 for breakfast. Today, her friend gave her 3 more cookies. How many cookies does Lily have now?" Many large language models (LLMs) in previous research approach this problem by calculating the answer "1" using the equation "3 - 5 + 3." However, from a human perspective, we recognize the inherent flaw in this problem: Lily cannot eat 5 cookies if she initially only had 3. This discrepancy prompts a key question: Are current LLMs merely Blind Solver that apply mathematical operations without deeper reasoning, or can they function as Logical Thinker capable of identifying logical inconsistencies? To explore this question, we propose a benchmark dataset, FaultyMath, which includes faulty math problems of rich diversity: i) multiple mathematical categories, e.g., algebra, geometry, number theory, etc., ii) varying levels of difficulty, and iii) different origins of faultiness -- ranging from violations of common sense and ambiguous statements to mathematical contradictions and more. We evaluate a broad spectrum of LLMs, including open-source, closed-source, and math-specialized models, using FaultyMath across three dimensions: (i) How accurately can the models detect faulty math problems without being explicitly prompted to do so? (ii) When provided with hints -- either correct or misleading -- about the validity of the problems, to what extent do LLMs adapt to become reliable Logical Thinker? (iii) How trustworthy are the explanations generated by LLMs when they recognize a math problem as flawed? Through extensive experimentation and detailed analysis, our results demonstrate that existing LLMs largely function as Blind Solver and fall short of the reasoning capabilities required to perform as Logical Thinker.

arxiv.org · arXiv.org

Sequential consistency without borders: How D1 implements global read replication

D1, Cloudflare’s managed SQL database, announces read replication beta. In this post, we do a technical deep dive of the read replication implementation and how your queries can remain consistent across all regions.

blog.cloudflare.com · The Cloudflare Blog

Making Super Slurper 5x faster with Workers, Durable Objects, and Queues

We re-architected Super Slurper from the ground up using our Developer Platform — leveraging Cloudflare Workers, Durable Objects, and Queues — and improved transfer speeds by up to 5x.

blog.cloudflare.com · The Cloudflare Blog

Just landed: streaming ingestion on Cloudflare with Arroyo and Pipelines

We’ve just shipped our new streaming ingestion service, Pipelines — and we’ve acquired Arroyo, enabling us to bring new SQL-based, stateful transformations to Pipelines and R2.

blog.cloudflare.com · The Cloudflare Blog

Tariffs will change climbing

100% tariffs = 100% more expensive gearWe're going to do everything we can to keep gear affordable and available.👉 Learn and SHOP at https://www.hownot2.com...

www.youtube.com · YouTube