Meet the Startup That Used AI and OpenClaw to Automate Its Own Developers - WSJ
Meet the Startup That Used AI and OpenClaw to Automate Its Own Developers WSJ
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YC Bench: a Live Benchmark for Forecasting Startup Outperformance in Y Combinator Batches
arXiv:2604.02378v1 Announce Type: new Abstract: Forecasting startup success is notoriously difficult, partly because meaningful outcomes, such as exits, large funding rounds, and sustained revenue growth, are rare and can take years to materialize. As a result, signals are sparse and evaluation cycles are slow. Y Combinator batches offer a unique mitigation: each batch comprises around 200 startups, funded simultaneously, with evaluation at Demo Day only three months later. We introduce YC Bench, a live benchmark for forecasting early outperformance within YC batches. Using the YC W26 batch as a case study (196 startups), we measure outperformance with a Pre-Demo Day Score, a KPI combining publicly available traction signals and web visibility. This short-term metric enables rapid evaluati
The slow death of the accelerationist.
The year is 2024. Summer has just begun. National discourse, for now, is solely focused on the upcoming presidential election, with many a journalist or political commentator critiquing the current, rather fiery state of political affairs. Tech and its associated public commentary has centered upon artificial intelligence as its new darling, hailing OpenAI as a savior for what was once deemed an idea stuck in science fiction, and looking to burgeoning startups such as Cursor and Windsurf as early examples of how agents could automate software engineering tasks. Logging onto Twitter, one would catch glimpses of Beff Jezos, an aptly named satirical account, relentlessly posting optimistic odes about how our own silicon creations will soon enable us to solve all of our problems, enabling us t
Outcome Routing in Autonomous Vehicles: Fleet Intelligence Without Location Data
The Data Paradox at the Heart of AV Fleet Intelligence Every autonomous vehicle on the road is a data generation machine. A single vehicle running for eight hours produces somewhere between 4 and 20 terabytes of raw sensor data — LiDAR point clouds, camera frames, radar returns, inertial measurements, and the continuous stream of routing decisions that glue it all together. Now multiply that across a fleet of a thousand vehicles and you have a compelling picture of collective intelligence: a swarm of machines that, in theory, could share everything they know about road conditions, near-miss events, edge cases, and environmental hazards. In practice, almost none of that sharing happens — at least not in real time, and not in any form that preserves privacy. The reason is straightforward. Th
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The Paradox of Prioritization in Public Sector Algorithms
arXiv:2604.02641v1 Announce Type: new Abstract: Public sector agencies perform the critical task of implementing the redistributive role of the State by acting as the leading provider of critical public services that many rely on. In recent years, public agencies have been increasingly adopting algorithmic prioritization tools to determine which individuals should be allocated scarce public resources. Prior work on these tools has largely focused on assessing and improving their fairness, accuracy, and validity. However, what remains understudied is how the structural design of prioritization itself shapes both the effectiveness of these tools and the experiences of those subject to them under realistic public sector conditions. In this study, we demonstrate the fallibility of adopting a p
Making Written Theorems Explorable by Grounding Them in Formal Representations
arXiv:2604.02598v1 Announce Type: new Abstract: LLM-generated explanations can make technical content more accessible, but there is a ceiling on what they can support interactively. Because LLM outputs are static text, they cannot be executed or stepped through. We argue that grounding explanations in a formalized representation enables interactive affordances beyond what static text supports. We instantiate this idea for mathematical proof comprehension with explorable theorems, a system that uses LLMs to translate a theorem and its written proof into Lean, a programming language for machine-checked proofs, and links the written proof with the Lean code. Readers can work through the proof at a step-level granularity, test custom examples or counterexamples, and trace the logical dependenc
Rewriting Structured Cospans
arXiv:2001.09029v3 Announce Type: replace-cross Abstract: We develop a theory of rewriting for structured cospans in order to extend compositional methods for modeling open networks. First, we introduce a category whose objects are structured cospans, and establish conditions under which it is adhesive or a topos. These results guarantee that double pushout rewriting can be applied in this setting. We then define structured cospan grammars and construct their associated languages via a 2-categorical framework, capturing both network composition and rewrite dynamics. As an application, we show that for graphs, hypergraphs, Petri nets, and their typed variants, any grammar induces the same language as its corresponding discrete grammar. This equivalence enables an inductive characterization
HistMSO: A Logic for Reasoning about Consistency Models with MONA
arXiv:2604.03085v1 Announce Type: cross Abstract: Reasoning about consistency models for replicated data systems is a challenging task that requires a deep understanding of both the consistency models themselves and a large part of human inputs in mechanized verification approaches. In this work, we introduce an approach to reasoning about consistency models for replicated data systems. We introduce HistMSO, a monadic second-order logic (MSO) for histories and abstract executions, the formal models of executions of replicated data systems introduced by Burckhardt. We show that HistMSO can express 39 out of 42 consistency models from Viotti and Vukolic hierarchy. Moreover, we develop a method for reducing HistMSO satisfiability and model-checking to the same problems for MSO over words. Whi
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