What is Hackers' Pub?

‘Abolish ICE’ wins Chicago snowplow naming contest

Officials said the contest drew record participation this year, turning a routine naming event into a snapshot of public debate.

www.newsweek.com · Newsweek

Keebin’ With Kristina: The One With The Uni-body That Does The Splits

Personally, I love a monoblock or uni-body split. You’ll pry this Kinesis Advantage from under my cold, dead hands. But on the go, I really like the Glove 80, a true split that can be complet…

hackaday.com · Hackaday

minimitra

minimitra

codeberg.org · Codeberg.org

Osaka: Kansai Airport Proud to Have Never Lost Single Piece of Luggage;‘World’s Best Airport for Baggage Delivery’

<p>IZUMI-SANO, Osaka — Kansai Airport is proud to have never had a lost baggage incident in the 30 years since it opened in 1994, earning recognition as the airport with the world’s best baggage service.</p>

japannews.yomiuri.co.jp

I Taught My Dog to Vibe Code Games | Caleb Leak

My cavapoo Momo vibe coded playable Godot games. All I had to do was teach her to type, route her input to Claude Code, and build the right tools.

www.calebleak.com

DSLean: A Framework for Type-Correct Interoperability Between Lean 4 and External DSLs

Domain-specific languages (DSLs) mediate interactions between interactive proof assistants and external automation, but translating between the prover's internal representation and such DSLs is a tedious engineering chore. To simplify this task, we present DSLean, a framework for bidirectional translation between expressions in the Lean proof assistant and external syntax. DSLean requires only a specification of an external language and its Lean equivalents, abstracting away meta-level implementation details. We demonstrate DSLean's capabilities by implementing three new automation tactics, providing access to external solvers for interval arithmetic, ordinary differential equations, and ring ideal membership.

arxiv.org · arXiv.org

Unternehmer und Höffner-Gründer Krieger spendet 18.000 Euro an die AfD

In Chemnitz zerlegte sich 2025 die Stadtratsfraktion der rechten Partei inmitten von Korruptionsvorwürfen. Nun erscheint in einem Rechenschaftsbericht eine fünfstellige Zuwendung – und wirft neue Fragen auf.

www.rnd.de · RND.de

Tiny Tapeout Submission Form: for shuttles & workshops

swisschips.ethz.ch · SwissChips

AIのテストコードは信用するな🙅‍♂️

zenn.dev · Zenn

Samsung's Galaxy Upcycle promised to make old phones useful, then won an award for giving up

Your old phone could have been so much more.

www.xda-developers.com · XDA

fedicat/Sources/Fedicat/Platforms/MastoAPI/Snac.swift at main

fedicat - issues and non-UI code for the Fedicat fediverse client

codeberg.org · Codeberg.org

Large Language Model Reasoning Failures

Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, achieving impressive results across a wide range of tasks. Despite these advances, significant reasoning failures persist, occurring even in seemingly simple scenarios. To systematically understand and address these shortcomings, we present the first comprehensive survey dedicated to reasoning failures in LLMs. We introduce a novel categorization framework that distinguishes reasoning into embodied and non-embodied types, with the latter further subdivided into informal (intuitive) and formal (logical) reasoning. In parallel, we classify reasoning failures along a complementary axis into three types: fundamental failures intrinsic to LLM architectures that broadly affect downstream tasks; application-specific limitations that manifest in particular domains; and robustness issues characterized by inconsistent performance across minor variations. For each reasoning failure, we provide a clear definition, analyze existing studies, explore root causes, and present mitigation strategies. By unifying fragmented research efforts, our survey provides a structured perspective on systemic weaknesses in LLM reasoning, offering valuable insights and guiding future research towards building stronger, more reliable, and robust reasoning capabilities. We additionally release a comprehensive collection of research works on LLM reasoning failures, as a GitHub repository at https://github.com/Peiyang-Song/Awesome-LLM-Reasoning-Failures, to provide an easy entry point to this area.

arxiv.org · arXiv.org

M2F: Automated Formalization of Mathematical Literature at Scale

Automated formalization of mathematics enables mechanical verification but remains limited to isolated theorems and short snippets. Scaling to textbooks and research papers is largely unaddressed, as it requires managing cross-file dependencies, resolving imports, and ensuring that entire projects compile end-to-end. We present M2F (Math-to-Formal), the first agentic framework for end-to-end, project-scale autoformalization in Lean. The framework operates in two stages. The statement compilation stage splits the document into atomic blocks, orders them via inferred dependencies, and repairs declaration skeletons until the project compiles, allowing placeholders in proofs. The proof repair stage closes these holes under fixed signatures using goal-conditioned local edits. Throughout both stages, M2F keeps the verifier in the loop, committing edits only when toolchain feedback confirms improvement. In approximately three weeks, M2F converts long-form mathematical sources into a project-scale Lean library of 153,853 lines from 479 pages textbooks on real analysis and convex analysis, fully formalized as Lean declarations with accompanying proofs. This represents textbook-scale formalization at a pace that would typically require months or years of expert effort. On FATE-H, we achieve $96\%$ proof success (vs.\ $80\%$ for a strong baseline). Together, these results demonstrate that practical, large-scale automated formalization of mathematical literature is within reach. The full generated Lean code from our runs is available at https://github.com/optsuite/ReasBook.git.

arxiv.org · arXiv.org

Haskell meets Evariste

Since its birth as a new scientific body of knowledge in the late 1950s, computer programming has become a fundamental skill needed in many other disciplines. However, programming is not easy, it is prone to errors and code re-use is key for productivity. This calls for high-quality documentation in software libraries, which is quite often not the case. Taking a few Haskell functions available from the Hackage repository as case-studies, and comparing their descriptions with similar functions in other languages, this paper shows how clarity and good conceptual design can be achieved by following a so-called easy-hard-split formal strategy that is quite general and productive, even if used informally. This strategy is easy to use in functional programming and can be applied to both program analysis and synthesis.

arxiv.org · arXiv.org