Kalman-and-Bayesian-Filters-in-Python VS devdocs

A fantastic interactive introduction to Kalman filters can be found on the following repo:

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

It explains them from first principles and provides the intuitive rationale for them but doesn't shy away from the math when it feels the student should be ready for it.

No thread on Kalman Filters is complete without a link to this excellent learning resource, a book written as a set of Jupyter notebooks:

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

That book mentions alpha-beta filters as sort of a younger sibling to full-blown Kalman filters. I recently had need of something like this at work, and started doing a bunch of reading. Eventually I realized that alpha-beta filters (and the whole Kalman family) is very focused on predicting the near future, whereas what I really needed was just a way to smooth historical data.

So I started reading in that direction, came across "double exponential smoothing" which seemed perfect for my use-case, and as I went into it I realized... it's just the alpha-beta filter again, but now with different names for all the variables :(

I can't help feeling like this entire neighborhood of math rests on a few common fundamental theories, but because different disciplines arrived at the same systems via different approaches, they end up sounding a little different and the commonality is obscured. Something about power series, Euler's number, gradient descent, filters, feedback systems, general system theory... it feels to me like there's a relatively small kernel of intuitive understanding at the heart of all that stuff, which could end up making glorious sense of a lot of mathematics if I could only grasp it.

Somebody help me out, here!

This provides an excellent intro that jumps right into code. https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

If you know a bit of Python and you find it sometimes tough to grind through a textbook, take a look here:

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

Interactive examples programmed in Jupyter notebooks.

Thank you, very good resource! Timely too, as I am revising this topic.

My work is mostly in python. I found this interactive book using Jupyter that explains Kalman filters from first principles.

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

Try this book

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python And on robotics in general

- Kalman and Bayesian Filters in Python

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