RegExr VS devdocs

When thinking about how I might compare an arrangement to the contiguous group of damaged springs, I used regexr.com to experiment with very specific regexs that used the numbers.

There are plenty of online regex tools to test and experiment with regex patterns. Some popular ones include RegExr, RegEx101, and RegexPlanet.

Using regexr.com it at least appears to work as expected.

If you are going to use RE's, use something like https://regexr.com/ to double check that they're doing what you want. I was suspicious of your 'cols = re.findall(r'\d+ .....', i)' line, and indeed it does miss some columns. You should rethink your column detection, and either not use REs or learn how to use capture groups and \w. There would then be no reason to use yet another RE in your column iterator to extract the numbers which you've already detected.

First time posting here, let me know if I need to edit post to conform to any rules. My issue is that I'm trying to match regex pattern to separate out the number of cubes drawn and its color but my Matcher object seems to not be returning any matches so it's throwing a no match found exception when I try to call digitMatcher.group(). I have tested my regex pattern on sites like regexr and it seems to pass there but it's not working for some reason here. I use the same type of regex on day one and it work there so I'm not sure where my regex pattern is failing here. I'm talking about specifically in my isGameValid() method where I create a matcher base on a pattern I made above. Through debugging I know that I separated the string color pairing correctly and that my Matcher object has the correct regex pattern, it's just not matching for some reason. Any help would be appreciated. Code below:

Regexr has been an invaluable tool as a beginner.

RegExr simplifies working with regular expressions. This online tool provides a visual interface for building and testing regex patterns in real-time, making regex less intimidating.

Ho did you arrive at the regex? I usually use a website to , such as https://regex101.com/, https://regexr.com/, https://regex-generator.olafneumann.org/ in combination of each other, as some explain better than the other.

I pulled this into regexr.com and it yielded the same results except it removed :41:

Hi HN,

I am Jiayuan, and I'm here to introduce a tool we've been building over the past few months: Devv (https://devv.ai). In simple terms, it is an AI-powered search engine specifically designed for developers.

Now, you might ask, with so many AI search engines already available—Perplexity, You.com, Phind, and several open-source projects—why do we need another one?

We all know that Generative Search Engines are built on RAG (Retrieval-Augmented Generation)[1] combined with Large Language Models (LLMs). Most of the products mentioned above use indexes from general search engines (like Google/Bing APIs), but we've taken a different approach.

We've created a vertical search index focused on the development domain, which includes:

- Documents: These are essentially the single source of truth for programming languages or libraries; I believe many of you are users of Dash (https://kapeli.com/dash) or devdocs (https://devdocs.io/).

- Code: While not natural language, code contains rich contextual information. If you have a question related to the Django framework, nothing is more convincing than code snippets from Django's repository.

- Web Search: We still use data from search engines because these results contain additional contextual information.

Our reasons for doing this include:

- The quality of the index is crucial to the RAG system; its effectiveness determines the output quality of the entire system.

- We focus more on the Index (RAG) rather than LLMs because LLMs evolve rapidly; even models performing well today may be superseded by better ones in a few months, and fine-tuning an LLM now has relatively low costs.

- All players are currently exploring what kind of LLM product works best; we hope to contribute some different insights ourselves (and plan to open source parts of our underlying infrastructure in return for contributions back into open source communities).

Some brief product features:

- Three modes: - Fast mode: Offers quick answers within seconds. - Agent mode: For complex queries where Devv Agent infers your question before selecting appropriate solutions. - GitHub mode(currently in beta): Links directly with your own GitHub repositories allowing inquiries about specific codebases.

- Clean & intuitive UI/UX design.

- Currently only available as web version but Chrome extension & VSCode plugin planned soon!

Technical details regarding how we build our Index:

- Documents section involves crawling most documentation sources using scripts inspired by devdocs project’s crawler logic then slicing them up according function/symbol dimensions before embedding into vector databases;

- Codes require special treatment beyond just embeddings alone hence why custom parsers were developed per language type extracting logical structures within repos such as architectural layouts calling relationships between functions definitions etc., semantically processed via LMM;

- Web searches combine both selfmade indices targeting developer niches alongside traditional API based methods. We crawled relevant sites including blogs forums tech news outlets etc..

For the Agent Mode, we have actually developed a multi-agent framework. It first categorizes the user's query and then selects different agents based on these categories to address the issues. These various agents employ different models and solution steps.

Future Plans:

- Build a more comprehensive index that includes internal context (The Devv for Teams version will support indexing team repositories, documents, issue trackers for Q&A)

- Fully localized: All of the above technologies can be executed locally, ensuring privacy and security through complete localization.

Devv is still in its very early stages and can be used without logging in. We welcome everyone to experience it and provide feedback on any issues; we will continue to iterate on it.

[1]: https://arxiv.org/abs/2005.11401

> I've started to preface all python searches with 'site:python.org'

You might find DevDocs to be useful: https://devdocs.io/

DevDocs - Aggregates documentation from various sources into a single, easy-to-navigate interface, covering frontend and backend technologies. DevDocs

DevDocs, an offline API documentation browser, supports multilingual, offering developers a quick and efficient way to access tech docs. From front-end to back-end and mobile development, it integrates official documentation, providing a sleek, user-friendly interface.

Environmental lighting conditions rule the day! I have astigmatism and I prefer bright backgrounds; #000 text on #fff backgrounds works great for me, but that's because I work in a room lit by a 250W 30,000 lumen corn-cob LED bulb[0] that makes my small office as bright on the inside as the shaded ground from a tree on an overcast day (which is quite bright compared to usual indoor lighting). In a room that bright, high contrast text works great and is highly readable, with "dark mode" often looking washed out and muddy. Even small reductions in contrast (such as what https://devdocs.io does with text of #333 in light mode) can make me notice and wish for greater contrast.

[0] - https://www.benkuhn.net/lux/

Here's how to add a new scraper: https://github.com/freeCodeCamp/devdocs/blob/main/.github/CO...

Or open an issue and wait for somebody else to implement the scraper.

Imagine a single, intuitive platform where you can access comprehensive documentation for a vast array of programming languages, frameworks, libraries, and tools. That's the magic of DevDocs. This exceptional resource eliminates the frustration of juggling multiple tabs and websites in your quest for information. DevDocs brings everything together into one easy-to-use interface.

Mosh for a stable connection, Offline documentation such as msdn, wikipedia (via kiwi etc), zeal for local access to https://devdocs.io/; Self host tabby for ai autocompletion. For many shell programs check what mulinux was using back then, and what are the modern replacements such as elinks instead of links. Mutt for mail, for irc doesn't matter much, use a desktop one but setup a bouncher on a vps, I used to have one on a raspberry pi 1, you can use rss reader for reddit (not sure if still works) and blogs