Tootfinder

I appreciate the idea that you can respond to tickets really fast, but when it is obvious some sort of LLM is being used to respond and not answering the question asked, it is worse.
I'm looking at you https://clicks.tech

Typing with one leg up is not very comfortable.

I asked this Monchhichi what they thought of the recent news, but they refused to comment.

not sure if this counts but:
“Does anyone know how to set up an email filter for, say, ‘how do you type with boxing gloves on’? I asked The Cheat, but he lost me at the part where I had to do not-typing.”
#HashTagGames #FilmCharactersViralPost (strong bad isn’t exact…

not sure if this counts but:
“Does anyone know how to set up an email filter for, say, ‘how do you type with boxing gloves on’? I asked The Cheat, but he lost me at the part where I had to do not-typing.”
#HashTagGames #FilmCharactersViralPost (strong bad isn’t exact…

If-T: A Benchmark for Type Narrowing
Hanwen Guo (University of Utah, USA), Ben Greenman (University of Utah, USA)
https://arxiv.org/abs/2508.03830 https://…

If-T: A Benchmark for Type Narrowing
**Context:** The design of static type systems that can validate dynamically-typed programs (**gradually**) is an ongoing challenge. A key difficulty is that dynamic code rarely follows datatype-driven design. Programs instead use runtime tests to narrow down the proper usage of incoming data. Type systems for dynamic languages thus need a **type narrowing** mechanism that refines the type environment along individual control paths based on dominating tests, a form of flow-sensitive typing. In …

One my favorite Windows 10/11 keyboard shortcuts remains Win-H for "voice typing" or voice-to-text. Easy to access, accurate & built-in the OS.
Here's the list of special phrases to say for punctuation & voice commands:
https://

This https://arxiv.org/abs/2503.09831 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Strong normalization through idempotent intersection types: a new syntactical approach
It is well-known that intersection type assignment systems can be used to characterize strong normalization (SN). Typical proofs that typable lambda-terms are SN in these systems rely on semantical techniques. In this work, we study $Λ_\cap^e$, a variant of Coppo and Dezani's (Curry-style) intersection type system, and we propose a syntactical proof of strong normalization for it. We first design $Λ_\cap^i$, a Church-style version, in which terms closely correspond to typing derivations. Then…

Beyond QWERTY: A pressure-based text input approach for XR that enables a touch-typing like experience
Fabian R\"ucker, Torben Storch
https://arxiv.org/abs/2507.20741 https…

Beyond QWERTY: A pressure-based text input approach for XR that enables a touch-typing like experience
Text input in extended reality (XR) applications remains inefficient and tedious. Most solutions are derived from the traditional keyboard layout, yet fail to translate its positive characteristics to the spatial digital realm. This limits the productive use of immersive technologies. In this work, we analyze physical keyboard input to identify key characteristics that facilitate its comfort, touch typing and high typing speeds. Building on these findings, we propose a novel pressure- based tex…

I noticed yesterday that if you call Wells Fargo that their announcement now says "Your call may be recorded *or analyzed*"
Oh, and Wells also added that fake typing sound to their awful call-tree. What, do they think that we believe we are talking to a real person who is typing at a keyboard?

Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants #BCI

Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants
Brain–computer interfaces have enabled people with paralysis to control computer cursors, operate prosthetic limbs, and communicate through handwriting, speech, and typing. Most high-performance demonstrations have used silicon microelectrode "Utah" arrays to record brain activity at single neuron resolution. However, reports so far have typically been limited to one or two individuals, with no systematic assessment of the longevity, decoding accuracy, and day–to–day stability properties …

Always nice when you're typing something on Discord, Windows steals focus due to a file copy operation running (moving stuff from phone to PC) and does *something* as a result (cus it assumes you typing the message on Discord == you wanting to interact with said pop-up).

I know the difference between a period and a comma but my typing finger doesn't,

Phi-Ground Tech Report: Advancing Perception in GUI Grounding
Miaosen Zhang, Ziqiang Xu, Jialiang Zhu, Qi Dai, Kai Qiu, Yifan Yang, Chong Luo, Tianyi Chen, Justin Wagle, Tim Franklin, Baining Guo
https://arxiv.org/abs/2507.23779

Phi-Ground Tech Report: Advancing Perception in GUI Grounding
With the development of multimodal reasoning models, Computer Use Agents (CUAs), akin to Jarvis from \textit{"Iron Man"}, are becoming a reality. GUI grounding is a core component for CUAs to execute actual actions, similar to mechanical control in robotics, and it directly leads to the success or failure of the system. It determines actions such as clicking and typing, as well as related parameters like the coordinates for clicks. Current end-to-end grounding models still achieve less than 65\…

Towards Machine-actionable FAIR Digital Objects with a Typing Model that Enables Operations
Maximilian Inckmann, Nicolas Blumenr\"ohr, Rossella Aversa
https://arxiv.org/abs/2505.16550

Towards Machine-actionable FAIR Digital Objects with a Typing Model that Enables Operations
FAIR Digital Objects support research data management aligned with the FAIR principles. To be machine-actionable, they must support operations that interact with their contents. This can be achieved by associating operations with FAIR-DO data types. However, current typing models and Data Type Registries lack support for type-associated operations. In this work, we introduce a typing model that describes type-associated and technology-agnostic FAIR Digital Object Operations in a machine-actiona…

Surprisingly fun. Was stumped by the last enemy but got through by the skin of my teeth. https://llmarena.com

LLM Arena: a wolf versus a rabbit
This is a game in which two fighters compete in an arena controlled by LLM to determine who is the best. You control one fighter and can help them win by typing anything you think can help. The goal is to win using as few characters as possible.

I appreciated Hillel Wayne’s latest article on “typing is not the bottleneck”.
https://buttondown.com/hillelwayne/archive/maybe-writing-speed-actually-is-a-bottleneck-for/
Typing is not the bottleneck, but typing faster…

Die Nerds werden es kennen, ich selbst habe aber erst vor Kurzem entdeckt, dass man man trocken oder nass programmieren kann – wobei „trockenes“ Coden die bevorzugte Variante ist.
DRY: Don’t repeat yourself
WET: We enjoy typing
Vor allem Letzteres fand ich ziemlich putzig.

Save time typing :3 by mapping the enter button to :3\n

This https://arxiv.org/abs/2503.19704 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Can Invisible Psychological Traits Organize Visible Network Structure? A Complex Network Analysis of Myers-Briggs Type Indicator-Based Interaction Patterns in Anonymous Social Networks
Exploration of the impact of personality traits on social interactions within anonymous online communities poses a challenge at the interface of networked social sciences and psychology. We analyze whether Myers-Briggs Type Indicator (MBTI) personality types impact the dynamics of interactions on an anonymous chat system with over 288,000 messages from 6,076 users. Using a data set including 940 users voluntarily providing MBTI typing and gender, we create a weighted undirected network and appl…

LowKeyEMG: Electromyographic typing with a reduced keyset
Johannes Y. Lee, Derek Xiao, Shreyas Kaasyap, Nima R. Hadidi, John L. Zhou, Jacob Cunningham, Rakshith R. Gore, Deniz O. Eren, Jonathan C. Kao
https://arxiv.org/abs/2507.19736

LowKeyEMG: Electromyographic typing with a reduced keyset
We introduce LowKeyEMG, a real-time human-computer interface that enables efficient text entry using only 7 gesture classes decoded from surface electromyography (sEMG). Prior work has attempted full-alphabet decoding from sEMG, but decoding large character sets remains unreliable, especially for individuals with motor impairments. Instead, LowKeyEMG reduces the English alphabet to 4 gesture keys, with 3 more for space and system interaction, to reliably translate simple one-handed gestures int…

This AI phone agent is playing an audible sound effect of a clackety mechanical keyboard as if someone is listening and typing.
I understand what they are trying to do, but it has the complete opposite effect on me. Whatever goon middle manager opted for this? I want them to suffer.
And the amount of typing that it pretends is ludicrously longer than the information provided. It's like an amplifier for my irritation.

I couldn’t find any toots on Mastodon about the ice hockey semi between USA & Sweden.
On the other hand, when typing Swe in the search box on X it suggested Sweden hockey

Oh wow. It looks like I last used HaikuOS for real in 2023 judging by screenshots, but holy moly, it's come a long way since. Typing this from my LibreWolf with extensions running and everything. There are minor issues with some of these Wayland/GTK ports, but they are absolutely usable. And lots of software has shown up in HaikuDepot since last time. I just wish sound was working on my hardware so this could be a fully viable desktop for me.

Say it with me: LLM's offer next likely token prediction.
If you want to predict likely tokens (E.g., predictive keyboard typing), great!
If you want ANYTHING OTHER THAN NEXT LIKELY TOKEN PREDICTION you don't want an LLM.
No, not even if you add RAG.
No, not even if it's "agentic".
If you want certainty, if you want rule adherence, if you want comprehension of any kind, you don't want an LLM. No matter what OpenAI tells you.

Last week I watched a new apprentice take 15 minutes to type a simple email – using the eagle-search system, eyes glued to the keyboard.
Over 12 years of #school education behind them, able to analyze Shakespeare and solve math problems, but typing as if they're seeing a computer for the first time!
HOW is it possible that in 2025 we are still producing graduates who can…

Just made a typo typing an email and called FB Faecebook
I'm never going back

I can't be the only one accidentally hitting return when typing something on the phone and having random linebreaks in posts or replies

It's 2025. If your library still doesn't have types in it (parameter, return, and property), I assume it's abandoned and I should not use it.
There are no exceptions to this statement. Not typing your PHP code in 2025 is irresponsible. No, docblocks are not good enough.
#PHP

All this brings me back to some text I was writing yesterday for my students, on which I’d appreciate any thoughtful feedback:
❝You can let the computer do the typing for you, but never let it do the thinking for you.
This is doubly true in the current era of AI hype. If the AI optimists are correct (the credible ones, anyway), software development will consist of humans critically evaluating, shaping, and correcting the output of LLMs. If the AI skeptics are correct, then the future will bring mountains of AI slop to decode, disentangle, fix, and/or rewrite. Either way, it is •understanding• and •critically evaluating• code — not merely •generating• it — that will be the truly essential ability. Always has been; will be even more so. •That• is what you are learning here.❞
11/

from my link log —
Bidirectional typing.
https://dl.acm.org/doi/10.1145/3450952
saved 2025-05-25 https://dotat.at/:/9X6QQ.h…

I was going to post to say '#FF2025 reviewers, check your inboxes - your reviews have been assigned', but the first review was in before I could start typing! An excellent start! #AI4LAM

Fast-VAT: Accelerating Cluster Tendency Visualization using Cython and Numba
MSR Avinash (Presidency University, Bangalore), Ismael Lachheb (EPITA School of Engineering,Computer Science, Paris, France)
https://arxiv.org/abs/2507.15904

Fast-VAT: Accelerating Cluster Tendency Visualization using Cython and Numba
Visual Assessment of Cluster Tendency (VAT) is a widely used unsupervised technique to assess the presence of cluster structure in unlabeled datasets. However, its standard implementation suffers from significant performance limitations due to its O(n^2) time complexity and inefficient memory usage. In this work, we present Fast-VAT, a high-performance reimplementation of the VAT algorithm in Python, augmented with Numba's Just-In-Time (JIT) compilation and Cython's static typing and low-level …

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Edit: fixed some typos.
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and thus more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of management caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

Customer Service Representative's Perception of the AI Assistant in an Organization's Call Center
Kai Qin, Kexin Du, Yimeng Chen, Yueyan Liu, Jie Cai, Zhiqiang Nie, Nan Gao, Guohui Wei, Shengzhu Wang, Chun Yu
https://arxiv.org/abs/2507.00513

Customer Service Representative's Perception of the AI Assistant in an Organization's Call Center
The integration of various AI tools creates a complex socio-technical environment where employee-customer interactions form the core of work practices. This study investigates how customer service representatives (CSRs) at the power grid service customer service call center perceive AI assistance in their interactions with customers. Through a field visit and semi-structured interviews with 13 CSRs, we found that AI can alleviate some traditional burdens during the call (e.g., typing and memori…

A note about #chatgpt tasks ☝️ 🕐
Although you (might) no longer see the model with task support in the model selector, you can still create new tasks with chatgpt by just choosing #o3 and typing "create a new task that...."
And you can still manage your tasks via the browser: …

I think this is related to the issue that some people have with static typing: it’s a series of micro-interruptions that serve to distract from the goal.
While I never found this to be a problem in somewhat-typed languages such as Java, and I no longer have this problem most of the time in Haskell, I still experience it in Rust, where the borrow checker might require me to totally rethink my approach.
I can absolutely see why some people prefer not to have a type system.

A Quantum-Control Lambda-Calculus with Multiple Measurement Bases
Alejandro D\'iaz-Caro, Nicolas A. Monzon
https://arxiv.org/abs/2506.16244 https://

A Quantum-Control Lambda-Calculus with Multiple Measurement Bases
We introduce Lambda-SX, a typed quantum lambda-calculus that supports multiple measurement bases. By tracking duplicability relative to arbitrary bases within the type system, Lambda-SX enables more flexible control and compositional reasoning about measurements. We formalise its syntax, typing rules, subtyping, and operational semantics, and establish its key meta-theoretical properties. This proof-of-concept shows that support for multiple bases can be coherently integrated into the type disc…

SplashNet: Split-and-Share Encoders for Accurate and Efficient Typing with Surface Electromyography
Nima Hadidi, Jason Chan, Ebrahim Feghhi, Jonathan Kao
https://arxiv.org/abs/2506.12356

SplashNet: Split-and-Share Encoders for Accurate and Efficient Typing with Surface Electromyography
Surface electromyography (sEMG) at the wrists could enable natural, keyboard-free text entry, yet the state-of-the-art emg2qwerty baseline still misrecognizes $51.8\%$ of characters in the zero-shot setting on unseen users and $7.0\%$ after user-specific fine-tuning. We trace many of these errors to mismatched cross-user signal statistics, fragile reliance on high-order feature dependencies, and the absence of architectural inductive biases aligned with the bilateral nature of typing. To addres…

@… Absolutely. One of the reasons I’m in favour of some kind of gradual typing (not TypeScript) is because I don’t think it makes sense for me to think hard about memory allocation before I’ve thought through whether a feature is even useful.
I really wish Rust had a transparent “just reference-count everything in this module/function/whatever” switch.…

AdapTT: Functoriality for Dependent Type Casts
Arthur Adjedj, Meven Lennon-Bertrand, Thibaut Benjamin, Kenji Maillard
https://arxiv.org/abs/2507.13774 http…

AdapTT: Functoriality for Dependent Type Casts
The ability to cast values between related types is a leitmotiv of many flavors of dependent type theory, such as observational type theories, subtyping, or cast calculi for gradual typing. These casts all exhibit a common structural behavior that boils down to the pervasive functoriality of type formers. We propose and extensively study a type theory, called AdapTT, which makes systematic and precise this idea of functorial type formers, with respect to an abstract notion of adapters relating …

OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion
Xian Guo, Ben Chen, Siyuan Wang, Ying Yang, Chenyi Lei, Yuqing Ding, Han Li
https://arxiv.org/abs/2506.06913

OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion
Query suggestion plays a crucial role in enhancing user experience in e-commerce search systems by providing relevant query recommendations that align with users' initial input. This module helps users navigate towards personalized preference needs and reduces typing effort, thereby improving search experience. Traditional query suggestion modules usually adopt multi-stage cascading architectures, for making a well trade-off between system response time and business conversion. But they often s…

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Exploring Keyboard Positioning and Ten-Finger Typing in Mixed Reality
Cecilia Schmitz, Joshua Reynolds, Scott Kuhl, Keith Vertanen

Day 7
✅ 24 test suites, 153 tests passing.
Solid coverage across service and controller layers in my modular monorepo. Strict typing (TypeScript), full DTO validation, and realistic mocks across complex relations (TypeORM).
Next: fine-tuning error handling & exploring e2e strategies.
https://write.tyolabs.com/?p=25

Replaced article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- Denotational Semantics of Gradual Typing using Synthetic Guarded Domain Theory (Extended Version)
Eric Giovannini, Tingting Ding, Max S. New

Dependent Multiplicities in Dependent Linear Type Theory
Maximilian Dor\'e
https://arxiv.org/abs/2507.08759 https://arxiv.org/pdf…

Dependent Multiplicities in Dependent Linear Type Theory
We present a novel dependent linear type theory in which the multiplicity of some variable - i.e., the number of times the variable can be used in a program - can depend on other variables. This allows us to give precise resource annotations to many higher-order functions that cannot be adequately typed in any other system. Inspired by the Dialectica translation, our typing discipline is obtained by embedding linear logic into dependent type theory and specifying how the embedded logic interact…