Stanislaw Lem, The Star Diaries, where Tichy at the same time apologizes and brags about how he retroactively tried to create/fix the world and how he failed.
You need to study Stanislaw Lem if you want a glimpse into the future... in Cyberiad he invented the Electronic Bard whose description is uncannily close to chatgpt. Including the poets losing jobs and protesting.
And more... Adams is kind of more approachable version of Lem.
My first computer was Spectrum. With broken cassette recorder. There was no other way to play games but writing them down from a recipe book. And picking up programming along the way. Discovering the new world, the world of the electron and the switch and then the beauty of the baud.
Our team is developing machine learning algorithmic solutions that improve outcomes for our advertisers. It is part of Outbrain’s Recommendations Group - about 40 machine learners, data scientists and machine-learning engineers who are responsible for everything that Outbrain recommends in its feeds and widgets. The team uses an interplay of Python, Java and Rust, in addition to Spark, Bigquery, and Tensorflow to form our ML and AutoML pipelines.
Team’s responsibilities are:
- Leverage Outbrain's rich data sources, large-scale computing resources, and proprietary algorithms to build a state of the art models to improve outcomes for our publishers and users
- Implementation and integration of algorithmic feature to the high scale production system
- A/B testing and monitoring of new features
- Data analysis over huge datasets to validate the various hypothesis
- Collaborate with the operations team to further improve our tooling and results
If Mastercard or Visa did an app that would work across all of their cards, that would be ok. But how can a separate app from each bank be considered better than SMS? It's just an annoying lock-in. And the quality of apps from many banks is sub-par.
Outbrain | Backend Engineer in Machine Learning | Full-time | Partially on Site | Israel
Outbrain's AutoML team is looking for an Experienced ML Python Engineer. The AutoML team is part of Outbrain’s Recommendations Group - about 40 machine learners, data scientists, and backend engineers who are responsible for everything that Outbrain recommends in its feeds and widgets. The team uses an interplay of Python, Java, and Rust, in addition to Spark, BigQuery, and Tensorflow to form our ML pipelines.
Outbrain's Machine Learning teams are doing some of the most high-volume machine learning in the industry, doing 100M predictions per second that directly impact business results.
Outbrain | Israel | Backend Engineer in Machine Learning Department | FULL-TIME
Help build internal AutoML tools we use for improving our models for large volume predictions - 100M predictions per second. Basically we're trying to automate our data scientists as much as possible.
You would work with data scientists to devise new ways to search the model spaces, run massively parallel training of models and improve our tooling. Work is in Python, Java and Rust.
Zemanta/Outbrain | Senior Data Scientist | Ljubljana, Slovenia| ONSITE, VISA, Fulltime
Outbrain powers content recommendations to increase user engagement and page views on sites like CNN, FoxNews, MSN, and Time Inc. Outbrain also enables publishers and top brands to distribute their content across our wide publisher network. We recommend 250 billion articles and videos each month to more than a half a billion people worldwide. Zemanta is Outbrain's subsidary handling real time bidding.
Data Scientist - Marketplace Optimization:
Zemanta/Outbrain is seeking an experienced Data Scientist with strong backend engineer skills to optimize its bidding into a dynamic and large-scale online marketplace. The position requires modeling price points that will optimize Zemanta's gain from bidding on media, in a dynamic environment of hundreds of millions of auctions per day, involving other strategic players. In addition to modeling the optimal buying dynamics, the successful candidate is expected to be able to implement the optimized media-buy policy in a production-grade environment.
A ready-made platform is offered by Visionect - their dev kits for system integrators: https://www.visionect.com/development_kits . Meant for professional use and thus the price.
It works by using the display purely as a framebuffer, everything is rendered on server and sent over wifi/3g. It gets you amazing autonomy times and is easily powered by solar in outdoor installations.
As usual, when moats are deep enough, prices raise. I think this is basic economics that governs all such businesses. Github feels they have entrenched themselves enough that they can increase prices by segmenting the market differently, which is probably true.
Naturally this opens up space for competition which will figure out how to take a bite here and there. But that will take quite some years.
Zemanta is a native ads DSP platform making sure the right content find the right audience. Our market is mainly US. We use Go-lang and Python and modern infrastructural solutions.
If you are interested in building: Low latency and high throughput distributed systems applied for real time bidding on native ad inventory. Analytics and data processing pipeline for providing insights and -monitoring of campaigns run through our platform. Machine learning algorithms applied for optimizing campaigns for performance. Monitoring and testing infrastructure for making sure our services are of high quality, are up and stay performant.
App positions are in Ljubljana, Slovenia. It's a lovely country and with high quality of life and a great place for engineers due to a growing start-up and IT sector. We will help with relocation and a visa.
Zemanta is a native ads DSP platform making sure the right content find the right audience. Our market is mainly US. We use Go-lang and Python and modern infrastructural solutions.
If you are interested in building: Low latency and high throughput distributed systems applied for real time bidding on native ad inventory. Analytics and data processing pipeline for providing insights and -monitoring of campaigns run through our platform. Machine learning algorithms applied for optimizing campaigns for performance. Monitoring and testing infrastructure for making sure our services are of high quality, are up and stay performant.
App positions are in Ljubljana, Slovenia. It's a lovely country and with high quality of life and a great place for engineers due to a growing start-up and IT sector. We will help with relocation and a visa.