autodistill VS google-search-results-nodejs

Roboflow | Open Source Software Engineer, Web Designer / Developer, and more. | Full-time (Remote, SF, NYC) | https://roboflow.com/careers?ref=whoishiring0224

Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.

Over 250k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. We now host the largest collection of open source computer vision datasets and pre-trained models[2]. We are pushing forward the CV ecosystem with open source projects like Autodistill[3] and Supervision[4]. And we've built one of the most comprehensive resources for software engineers to learn to use computer vision with our popular blog[5] and YouTube channel[6].

We have several openings available but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. Our engineering culture is built on a foundation of autonomy & we don't consider an engineer fully ramped until they can "choose their own loss function". At Roboflow, engineers aren't just responsible for building things but also for helping us figure out what we should build next. We're builders & problem solvers; not just coders. (For this reason we also especially love hiring past and future founders.)

We're currently hiring full-stack engineers for our ML and web platform teams, a web developer to bridge our product and marketing teams, several technical roles on the sales & field engineering teams, and our first applied machine learning researcher to help push forward the state of the art in computer vision.

[1]: https://roboflow.com/?ref=whoishiring0224

[2]: https://roboflow.com/universe?ref=whoishiring0224

[3]: https://github.com/autodistill/autodistill

[4]: https://github.com/roboflow/supervision

[5]: https://blog.roboflow.com/?ref=whoishiring0224

[6]: https://www.youtube.com/@Roboflow

The places in which a vision model is deployed are different than that of a language model.

A vision model may be deployed on cameras without an internet connection, with data retrieved later; a vision model may be used on camera streams in a factory; sports broadcasts on which you need low latency. In many cases, real-time -- or close to real-time -- performance is needed.

Fine-tuned models can deliver the requisite performance for vision tasks with relatively low computational power compared to the LLM equivalent. The weights are small relative to LLM weights.

LLMs are often deployed via API. This is practical for some vision applications (i.e. bulk processing), but for many use cases not being able to run on the edge is a dealbreaker.

Foundation models certainly have a place.

CLIP, for example, works fast, and may be used for a task like classification on videos. Where I see opportunity right now is in using foundation models to train fine-tuned models. The foundation model acts as an automatic labeling tool, then you can use that model to get your dataset. (Disclosure: I co-maintain a Python package that lets you do this, Autodistill -- https://github.com/autodistill/autodistill).

SAM (segmentation), CLIP (embeddings, classification), Grounding DINO (zero-shot object detection) in particular have a myriad of use cases, one of which is automated labeling.

I'm looking forward to seeing foundation models improve for all the opportunities that will bring!

Autodistill

Thanks for the suggestion! Definitely agree, we’ve seen that work extremely well for Supervision[1] and Autodistill, some of our other open source projects.

There’s still a lot of polish like this we need to do; we’ve spent most of our effort cleaning up the code and documentation to prep for open sourcing the repo.

Next step is improving the usability of the pip pathway (that interface was just added; the http server was all we had for internal use). Then we’re going to focus on improving the content and expanding the models it supports.

[1] https://github.com/roboflow/supervision

[2] https://github.com/autodistill/autodistill

Roboflow | Multiple Roles | Full-time (Remote, SF, NYC) | https://roboflow.com/careers?ref=whoishiring0823

Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.

Over 250k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. We now host the largest collection of open source computer vision datasets and pre-trained models[2]. We are pushing forward the CV ecosystem with open source projects like Autodistill[3] and Supervision[4]. And we've built one of the most comprehensive resources for software engineers to learn to use computer vision with our popular blog[5] and YouTube channel[6].

We have several openings available, but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. Our engineering culture is built on a foundation of autonomy & we don't consider an engineer fully ramped until they can "choose their own loss function". At Roboflow, engineers aren't just responsible for building things but also for helping figure out what we should build next. We're builders & problem solvers; not just coders. (For this reason we also especially love hiring past and future founders.)

We're currently hiring full-stack engineers for our ML and web platform teams, a web developer to bridge our product and marketing teams, several technical roles on the sales & field engineering teams, and our first applied machine learning researcher to help push forward the state of the art in computer vision.

[1]: https://roboflow.com/?ref=whoishiring0823

[2]: https://roboflow.com/universe?ref=whoishiring0823

[3]: https://github.com/autodistill/autodistill

[4]: https://github.com/roboflow/supervision

[5]: https://blog.roboflow.com/?ref=whoishiring0823

[6]: https://www.youtube.com/@Roboflow

> Their SKILL tool involves a set of algorithms that make the process go much faster, they said, because the agents learn at the same time in parallel. Their research showed if 102 agents each learn one task and then share, the amount of time needed is reduced by a factor of 101.5 after accounting for the necessary communications and knowledge consolidation among agents.

This is a really interesting idea. It's like the reverse of knowledge distillation (which I've been thinking about a lot[1]) where you have one giant model that knows a lot about a lot & you use that model to train smaller, faster models that know a lot about a little.

Instead, you if you could train a lot of models that know a lot about a little (which is a lot less computationally intensive because the problem space is so confined) and combine them into a generalized model, that'd be hugely beneficial.

Unfortunately, after a bit of digging into the paper & Github repo[2], this doesn't seem to be what's happening at all.

> The code will learn 102 small and separte heads(either a linear head or a linear head with a task bias) for each tasks respectively in order. This step can be parallized on multiple GPUS with one task per GPU. The heads will be saved in the weight folder. After that, the code will learn a task mapper(Either using GMMC or Mahalanobis) to distinguish image task-wisely. Then, all images will be evaluated in the same time without a task label.

So the knowledge isn't being combined (and the agents aren't learning from each other) into a generalized model. They're just training a bunch of independent models for specific tasks & adding a model-selection step that maps an image to the most relevant "expert". My guess is you could do the same thing using CLIP vectors as the routing method to supervised models trained on specific datasets (we found that datasets largely live in distinct regions of CLIP-space[3]).

[1] https://github.com/autodistill/autodistill

[2] https://github.com/gyhandy/Shared-Knowledge-Lifelong-Learnin...

[3] https://www.rf100.org

The Google Search URL parameters are important to understand whether you are maximizing the conversion rate in your ad groups and optimizing your cost per click(CPC) rates in Google Analytics for your ad campaigns, improving your SEO(Search Engine Optimization) metrics for your e-commerce business, or collecting data for your social media project. Using custom parameters for your search will affect the Search Engine Result Page (SERP) data significantly in your favor. SerpApi unlocks the power of SERP data to you with minimal effort, the fastest response times, and in the most complete form.

A SERP API key set in the SERP_API_KEY variable.

SerpApi | https://serpapi.com | Junior-to-Senior Fullstack Engineer | Illustrator and Graphic Designer | Based in Austin, TX but remote-first structure | Full-time | ONSITE or FULLY REMOTE | $150K - 180K a year 1099 for US or local avg + 20% for outside the US

SerpApi is the leading API to scrape and parse search engine results. We deeply support Google, Google Maps, Google Images, Bing, Baidu, and a lot more.

Our current stack is Ruby, Rails, MongoDB, and React.JS. We are looking for more Junior and Senior FullStack Engineers.

We have an awesome work environment: We are a remote first company (before Covid!). We do continuous integration, continuous deployments, code reviews, code pairings, profit sharing, and most of communication is async via GitHub.

We value super strongly transparency, do open books, have a public roadmap, and contribute to the EFF.

Apply at: https://serpapi.com/careers

SerpApi | https://serpapi.com | Junior-to-Senior Fullstack Engineer | Illustrator and Graphic Designer | Based in Austin, TX but remote-first structure | Full-time | ONSITE or FULLY REMOTE | $150K - 180K a year 1099 for US or local avg + 20% for outside the US

SerpApi is the leading API to scrape and parse search engine results. We deeply support Google, Google Maps, Google Images, Bing, Baidu, and a lot more.

Our current stack is Ruby, Rails, MongoDB, and React.JS.

This code needs two API keys: one for the OpenAI API (GPT-4 is used by the CrewAI "Agents" by default) and one for the SerpAPI (you can create an account for free).

Sign up for free at SerpApi.

SERPApi: SERPApi is a powerful tool that provides developers with an easy and efficient way to extract search engine results page (SERP) data using API.

Assuming privacy is not a concern for coding questions, you can use the following web search APIs to augment your LLM's knowledge - Google's web search API: https://serpapi.com/ - You.com's web-search API: https://api.you.com/ - Metaphor's web-search API: https://platform.metaphor.systems/ - StackExchange question search API: https://api.stackexchange.com/docs/advanced-search

I run a large Rails application (https://serpapi.com), and the issues that would be solved with the type system would be close to nil.

SerpApi is an API for scraping Google and other search engines with fast, easy, and complete solutions. Our team is tackling a challenge within our operational database where we house a Locations collection featuring predefined data structures. We aim to offer these locations to clients reliably and allow them to utilize these in their searches with SerpApi's Google Search API, demonstrating the interconnected functionality of various types of databases. You may register to claim free credits to try out our products