Tootfinder

I finally did it: I now have a speaking page on my site. 🎤🎉
If you’re looking for someone to talk about design, the web, CSS, accessibility, or the independent web at your event, that’s where you’ll find what I do, what I’ve spoken about, and how to get in touch:
https://matthiasott.com/speaking

Speaking of the Web … · Matthias Ott
Matthias Ott is an independent user experience designer and web design engineer from Stuttgart, Germany.

Figma's stock closed down ~8% on March 18 after Google updated its Stitch AI coding tool for UI design; FIG is down ~80% since the company's IPO in August 2025 (John Wang/@j0hnwang)
https://x.com/j0hnwang/status/2034425352366473223

John Wang (@j0hnwang) on X
Figma tanked 8% today on this news Now down 80% from IPO in Aug 2025

I created a new repo/tool today to evaluate and collect the rapidly changing tooling configurations that everyone is trying to figure out (using statistical experimental design) I used Claude/Gastown to both make it and operate it and have some initial comparison data on opus/sonnet and Python/TS/Go etc. for a small test. I’d be happy for some github stars if people think it could be useful.

GitHub - adrianco/retort: Platform Evolution Engine. Distill the best from the combinatorial mess.
Platform Evolution Engine. Distill the best from the combinatorial mess. - adrianco/retort

Structural-Ambiguity-Aware Translation from Natural Language to Signal Temporal Logic
Kosei Fushimi, Kazunobu Serizawa, Junya Ikemoto, Kazumune Hashimoto
https://arxiv.org/abs/2603.28426 https://arxiv.org/pdf/2603.28426 https://arxiv.org/html/2603.28426
arXiv:2603.28426v1 Announce Type: new
Abstract: Signal Temporal Logic (STL) is widely used to specify timed and safety-critical tasks for cyber-physical systems, but writing STL formulas directly is difficult for non-expert users. Natural language (NL) provides a convenient interface, yet its inherent structural ambiguity makes one-to-one translation into STL unreliable. In this paper, we propose an \textit{ambiguity-preserving} method for translating NL task descriptions into STL candidate formulas. The key idea is to retain multiple plausible syntactic analyses instead of forcing a single interpretation at the parsing stage. To this end, we develop a three-stage pipeline based on Combinatory Categorial Grammar (CCG): ambiguity-preserving $n$-best parsing, STL-oriented template-based semantic composition, and canonicalization with score aggregation. The proposed method outputs a deduplicated set of STL candidates with plausibility scores, thereby explicitly representing multiple possible formal interpretations of an ambiguous instruction. In contrast to existing one-best NL-to-logic translation methods, the proposed approach is designed to preserve attachment and scope ambiguity. Case studies on representative task descriptions demonstrate that the method generates multiple STL candidates for genuinely ambiguous inputs while collapsing unambiguous or canonically equivalent derivations to a single STL formula.
toXiv_bot_toot

Screening Workers with Affirmative Action
Charles Po-Cheng Huang
https://arxiv.org/abs/2604.00615 https://arxiv.org/pdf/2604.00615 https://arxiv.org/html/2604.00615
arXiv:2604.00615v1 Announce Type: new
Abstract: This paper examines the optimal contracts in a two-dimensional screening model where one dimension(group identity) is verifiable by agents but not falsifiable. A principal offers contracts to agents who differ in cost types and group membership. Motivated by the United States Federal policy, Work Opportunity Tax Credit, the principal receives tax benefits for hiring agents from protected groups. Under the assumption that the protected agents tend to have higher cost types, the optimal contract induces full separation across both dimensions: agents reveal the cost type and the group identity through contract choice. Furthermore, the principal is willing to hire the trait agents with a higher cost threshold than the non-trait agents, and this threshold increases with the tax credit. Conversely, when the protected agents tend to have lower cost types, the optimal design without tax credits pools groups while separating by cost type. These results demonstrate that both affirmative action and non-discrimination can be optimal depending on the cost distribution ordering across groups.
toXiv_bot_toot