Awesome List Updates on Apr 20, 2025
6 awesome lists updated today.
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1. Awesome for Beginners
JavaScript
- iD (â3.6k) (label: new contributor opportunity)
The easy-to-use OpenStreetMap editor in JavaScript.
2. Awesome Css Frameworks
Specialized
- Orbit - CSS framework designed for building any kind of radial UI.
Docs, Repo (â633) | #SCSS
3. Awesome Firebase Genkit
Articles / Dart - Official
- Extending Your AI Application with Genkit MCP - Learn how to integrate Google Maps using Genkit MCP client to query MCP servers for enhanced location-based AI features.
4. Awesome Osint
People Investigations / GitHub
- Apollo.io - Free B2B Phone Number & Email Finder. 1200 credits per user/year for free plan.
- ContactOut - Unlock the world's most accurate contact data. Find emails & phone for 300M professionals.
Email Search / Email Check / GitHub
- Secureito - Secureito detects data breaches involving your email and suggests protection measures.
5. Awesome Go
Standard CLI
- go-readline-ny (â29) - A customizable line-editing library with Emacs keybindings, Unicode support, completion, and syntax highlighting. Used in NYAGOS shell.
6. Awesome Agi Cocosci
Abduction / Scientific Discovery
- When to trust the data: Further investigations of system error in a scientific reasoning task - Memory & Cognition, 1996. [All Versions]. When evaluating experimental evidence, how do people deal with the possibility that some of the feedback is erroneous? The potential for error means that evidence evaluation must include decisions about when to âtrust the data.â This paper presents two studies that focus on subjectsâ responses to erroneous feedback in a hypothesis testing situationâa variant of Wasonâs (1960) 2â4â6 rule discovery task in which some feedback was subject tosystem error: âhitsâ were reported as âmissesâ and vice versa. Results show that, in contrast to previous research, people are equally adept at identifying false negatives and false positives; further, successful subjects were less likely to use a positive test strategy (Klayman & Ha, 1987) than were unsuccessful subjects. Finally, although others have found that generating possible hypotheses prior to experimentation increases success and task efficiency, such a manipulation did little to mitigate the effects of system error.
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