Tootfinder

This is cool: A user-defined string literal operator that turns string literals into their (compile-time) hash codes. Might be useful in some codebases to avoid having to maintain unique numerical values for a bunch of identifiers.
(But it might be that in large codebases, the time to run all these compile-time hash functions adds up to be unbearable.)
(I was specifically thinking of the "slot IDs" in

C++11 compile-time hash of literal strings.
C++11 compile-time hash of literal strings. GitHub Gist: instantly share code, notes, and snippets.

Snippets in Visual Studio Code
You can easily define your own snippets without any extension.
https://code.visualstudio.com/docs/editing/userdefinedsnippets#_create-your-own-snippets

I’d rate myself an #AI skeptic to agnostic. I hadn’t used the tools at all, because I had problems w/ creation of the models: resource usage, & IP theft, were top of mind. Hallucinations a big issue too.
But I tried it, and even though I can’t set aside my concerns I’ll say I was impressed.
I used Claude to produce a Zsh prompt to replace one I’ve used for years using Oh My Zsh.
What …

Zsh prompt from Claude
Zsh prompt from Claude. GitHub Gist: instantly share code, notes, and snippets.

Even for small projects, while it might work, I wouldn't want the context switching personally. Especially if the backend uses some weird templating language for generating the html snippets. I'd much rather use a fullstack framework like #SvelteKit where your endpoint is just a load function that returns some data (automatically serialized and made available to the page script), or create a proper API in another language that returns some JSON.

Has anyone used Windhawk?
https://windhawk.net/
It's a tweak/mod installer for Windows. Like Cydia or SIMBL or Application Enhancer. Released in 2022.
I'm impressed by the number of mods they have listed. Each mod has visible source code, a single file with metadata in comments, l…

Uncovering Intention through LLM-Driven Code Snippet Description Generation
Yusuf Sulistyo Nugroho, Farah Danisha Salam, Brittany Reid, Raula Gaikovina Kula, Kazumasa Shimari, Kenichi Matsumoto
https://arxiv.org/abs/2506.15453

Uncovering Intention through LLM-Driven Code Snippet Description Generation
Documenting code snippets is essential to pinpoint key areas where both developers and users should pay attention. Examples include usage examples and other Application Programming Interfaces (APIs), which are especially important for third-party libraries. With the rise of Large Language Models (LLMs), the key goal is to investigate the kinds of description developers commonly use and evaluate how well an LLM, in this case Llama, can support description generation. We use NPM Code Snippets, co…

Für Zettlr-Nutzer: Ich habe meine Zettlr-Ressourcen, inklusive meiner Snippets und Pandoc-Export-Konfigurationen (Defaults) auf GitLab veröffentlicht: https://gitlab.kit.edu/uhrhr/zettlr-ressourcen/

Using Code Snippets to Teach Programming Languages
Joshua Akingbade, Jianhua Yang, Mir Seyedebrahimi
https://arxiv.org/abs/2506.00404 https://

Using Code Snippets to Teach Programming Languages
Coding is a fundamental skill required in the engineering discipline, and much work exists exploring better ways of teaching coding in the higher education context. In particular, Code Snippets (CSs) are approved to be an effective way of introducing programming language units to students. CSs are portions of source code of varying size and content. They can be used in a myriad of ways, one of which is to teach the code they contain as well as its function. To further explore the use of CSs, a …

Been designing distributed counters for NATS. Pretty happy with this.
50k/second unoptimised and on a single counter - but we will support aggregation of regional to global etc.
Hard dist sys problems made trivial to use and operate 💪💪
https://gist.github.com/ripienaar/d95d

The tome's cover shifted whenever I looked at it—a cottage bordered in sunflowers, an underwater city of gleaming coral and abalone, a pair cuddled up in a rocker by a cozy fireplace. I stroked its spine and turned it over to read the jacket, but those words shifted, too, shimmering and abstract, so that I couldn't quite grasp them. But I caught snippets, such alluring snippets! I made myself a cup of tea and turned to the first page. #MastoPrompt #MicroPrompt #WSS366

Collaborative Editable Model
Kaiwen Tang, Aitong Wu, Yao Lu, Guangda Sun
https://arxiv.org/abs/2506.14146 https://arxiv.org/pdf/2506.…

Collaborative Editable Model
Vertical-domain large language models (LLMs) play a crucial role in specialized scenarios such as finance, healthcare, and law; however, their training often relies on large-scale annotated data and substantial computational resources, impeding rapid development and continuous iteration. To address these challenges, we introduce the Collaborative Editable Model (CoEM), which constructs a candidate knowledge pool from user-contributed domain snippets, leverages interactive user-model dialogues c…

https://blog.atuin.sh/atuin-scripts-shareable-syncable-shell-snippets/ @… scripts makes it really easy to turn a sequence of a few commands into a script.
Since I've started using it I created a few scripts I probably wouldn't have if I had to create them the old fashioned way

LLMs in Coding and their Impact on the Commercial Software Engineering Landscape
Vladislav Belozerov, Peter J Barclay, Askhan Sami
https://arxiv.org/abs/2506.16653

LLMs in Coding and their Impact on the Commercial Software Engineering Landscape
Large-language-model coding tools are now mainstream in software engineering. But as these same tools move human effort up the development stack, they present fresh dangers: 10% of real prompts leak private data, 42% of generated snippets hide security flaws, and the models can even ``agree'' with wrong ideas, a trait called sycophancy. We argue that firms must tag and review every AI-generated line of code, keep prompts and outputs inside private or on-premises deployments, obey emerging safet…

#Modulith VS. #Microservices by @…

sl = StringList()
sl @= "line1"
sl @= "line2"
print(sl) # prints line1\nline2\n
https://gist.github.com/jevinskie/30717932f0c6bdf6f4f7094fee7e92c2

Python StringList for @= line concatenation
Python StringList for @= line concatenation. GitHub Gist: instantly share code, notes, and snippets.

#Clojure map-interval
Like `map` -- apply function `f` to each element in `col` in turn and return the values returned in sequence, but in a single thread and pausing between each invocation of `f`.

Useful for example in circumstances where you're querying a rate-limited service.

Map, but with a delay
Map, but with a delay. GitHub Gist: instantly share code, notes, and snippets.

The tome's cover shifted whenever I looked at it—a cottage bordered in sunflowers, an underwater city of gleaming coral and abalone, a pair cuddled up in a rocker by a cozy fireplace. I stroked its spine and turned it over to read the jacket, but those words shifted, too, shimmering and abstract, so that I couldn't quite grasp them. But I caught snippets, such alluring snippets! I made myself a cup of tea and turned to the first page. #MastoPrompt #MicroPrompt #WSS366

This https://arxiv.org/abs/2505.23908 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Transforming Podcast Preview Generation: From Expert Models to LLM-Based Systems
Discovering and evaluating long-form talk content such as videos and podcasts poses a significant challenge for users, as it requires a considerable time investment. Previews offer a practical solution by providing concise snippets that showcase key moments of the content, enabling users to make more informed and confident choices. We propose an LLM-based approach for generating podcast episode previews and deploy the solution at scale, serving hundreds of thousands of podcast previews in a rea…

MGS3: A Multi-Granularity Self-Supervised Code Search Framework
Rui Li, Junfeng Kang, Qi Liu, Liyang He, Zheng Zhang, Yunhao Sha, Linbo Zhu, Zhenya Huang
https://arxiv.org/abs/2505.24274

MGS3: A Multi-Granularity Self-Supervised Code Search Framework
In the pursuit of enhancing software reusability and developer productivity, code search has emerged as a key area, aimed at retrieving code snippets relevant to functionalities based on natural language queries. Despite significant progress in self-supervised code pre-training utilizing the vast amount of code data in repositories, existing methods have primarily focused on leveraging contrastive learning to align natural language with function-level code snippets. These studies have overlooke…