metarank
eth-phishing-detect
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metarank | eth-phishing-detect | |
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13 | 23 | |
1,981 | 958 | |
0.9% | 4.7% | |
9.1 | 10.0 | |
10 days ago | 3 days ago | |
Scala | JavaScript | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
metarank
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Ask HN: Is it ethical for open-source projects to have usage analytics tracking?
We’re building an open-source tool to do search/category/recommendation personalization https://github.com/metarank/metarank, eventually planning to create a business out of it. We have a small number of pilot projects with real feedback, but we rarely have a chance to see how new people interact with the service, as it’s self-hosted backend tool with no UI.
We have an idea to add anonymous analytics reporting to get a glimpse of real usage (and places where people are struggling to improve), but are concerned if it’s ethical or not to do such intrusive things.
Is it acceptable for an open-source project to have this type of tracking, considering our materialistic plans to transform it into a business?
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My Favorite Off-the-Shelf Data Science Repos, What Are Yours?
Here are my top off-the-shelf data science models for Marketing. Would be interested which other marketing data science tools you use?
Product Recommendation on Your Website with Metarank (https://github.com/metarank/metarank)
Metarank is a tool that helps you easily build an advanced recommendation engine for your products or content on your website. To get started you only need historical performance data of your products (e.g. number of clicks) and additional metadata like product rating, genre, ingredients or price. In a YAML file, you define the features and the model parameters (e.g. number of iterations, modeling technique). The API service integrates with Apache Flink and can be easily integrated into Kubernetes clusters.
User Journey Analysis on your Website with Retentioneering (https://github.com/retentioneering/retentioneering-tools)
Retentioneering helps you to understand the user journey on your website. Retentioneering is a Python library that allows you to easily connect your Google Analytics data (in Bigquery). You define user-id, event-type and time stamp. From this data input a comprehensive graph network is created with gains and losses as you know it from a customer journey. In addition, customer segments are created that have a similar customer journey. This reduces the complexity of a purely descriptive view of the data.
Marketing Mix Modeling with Robyn (https://github.com/facebookexperimental/Robyn)
Less third-party cookie means less attribution models. The answer to this is Marketing Mix Modeling. Marketing mix models are regression models that use statistical probability to calculate the effect size of marketing channels and other independent variables. The advantage is that business context can be modeled much more realistically. For example, Google Searches for the own brand can be integrated to determine the share of the own brand strength in the revenue. Likewise, offline advertising measures can be modeled with other metrics in this context (e.g. offline advertising with GRPs). Robyn takes into account adstock effects, ROAS calculation and multicollinarity in the marketing channels. In addition, with simple functionality, budgets can be optimized using the predictions and results from marketing tests can be integrated into the model for calibration.
- [P] Metarank - A low code Machine Learning tool that personalizes product listings, articles, recommendations, and search results in order to boost sales. A friendly Learn-to-Rank engine
- Show HN: 我们做了一个开源的个性化引擎 (Show HN: We made an open-source personalization engine)
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Show HN: We made an open-source personalization engine
As people with heavy e-commerce background, we feel that the main pain point of typical old-school offline personalization solutions is that 80% of customers in medium-sized online stores are coming only once:
* you have a very short window to adapt your store, as the visitor will never come back in the future.
* even if you have zero past knowledge about a new visitor, there is still something to compare with other similar visitors: are they from mobile? Is it ios or android? Are they US? Is it a holiday now? Did they come from google search or facebook ad?
* this knowledge is ephemeral and makes sense only within their current session. But a visitor can still do a couple of interactions like browsing different collections of items or clicking on search results, and it can also be taken into account.
But compared to Amazon and Google, it's you who define which features should be used for the ranking and how long they are stored (see the "ttl" option on all feature extractors in our docs for details).
For example, here is https://github.com/metarank/metarank/blob/master/src/test/re... the config of features used in the movie recommendations demo - in a most privacy-sensitive setup you can just drop all the "interacted_with" extractors and will get zero private data stored for each visitor.
- Metarank - A low code Machine Learning tool that personalizes product listings, articles, recommendations, and search results in order to boost sales. A friendly Learn-to-Rank engine
eth-phishing-detect
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MATRIX ABYSS SCAM (SCAMS ON UPWORK)
The project is called MATRIX ABYSS, but their website is https://abyssmatrix.world. When I looked up https://matrixabyss.world/ there is an error saying that this website has been flagged for phishing, by metamask eth-phishing-detect. Super sus.
- Collectively growing mainstream media and useless creator's blacklist.
- Blocked website legitimate website.
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Daily General Discussion - December 10, 2022
This was an issue with Metamask's fuzzy filtering, see here. The issue has been resolved, and it's been plenty of time for the change to propagate around. Not sure if you need to update your Metamask or clear your cache or something. Unfortunate that it's still causing trouble.
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Testing my new hot wallet on a live phishing site
MetaMask already has a blacklist for phishing websites: https://github.com/MetaMask/eth-phishing-detect
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Asking for feedback about my business website
Yea, no idea why: https://github.com/MetaMask/eth-phishing-detect/issues/7876
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Junoswap: Metamask Phishing Detection System??
they added sudoswap to their fuzzy list https://github.com/MetaMask/eth-phishing-detect/pull/7633 and that caused issues with other domains with "swap" in the name
- They admit it's a scam.
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Show HN: We made an open-source personalization engine
According to the code on https://github.com/MetaMask/eth-phishing-detect/blob/45ea5cf..., looks like that everything within Levenstein distance of 3 from whitelisted hosts (like "metamask.*") is blocked.
Metarank and Metamask have the distance of 3. I've made a ticket some time ago in their github repo (https://github.com/MetaMask/eth-phishing-detect/issues/6855), but it seems that it was lost in thousands of similar tickets.
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Daily General Discussion - January 30, 2022
Do you have an old version of Metamask? That site was accidentally picked up by a blacklist regex but was whitelisted again back in october - https://github.com/MetaMask/eth-phishing-detect/pull/5563
What are some alternatives?
recommenders - Best Practices on Recommendation Systems
ZeroNet - ZeroNet - Decentralized websites using Bitcoin crypto and BitTorrent network
Medusa - Building blocks for digital commerce
airdrop-addresses
retentioneering-tools - Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web analytics, transaction analytics, graph visualization, process mining, and behavioral segmentation in Python. Predictive analytics over clickstream, AB tests, machine learning, and Markov Chain simulations.
clarion
feathr - Feathr – A scalable, unified data and AI engineering platform for enterprise
RetroShare - RetroShare is a Free and Open Source cross-platform, Friend-2-Friend and secure decentralised communication platform.
Robyn - Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
betterscan-ce - Code Scanning/SAST/Static Analysis/Linting using many tools/Scanners + OpenAI GPT with One Report (Code, IaC) - Betterscan Community Edition (CE)
SynapseML - Simple and Distributed Machine Learning
teku - Open-source Ethereum consensus client written in Java