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Do you have the problem to make a function/variable name? Of cause you do. It does not matter how long you’ve been written codes. Naming things is hard. Especially for developers. Plih Karlton said, “ There are only two hard things in Computer Science: cache invalidation and naming things.” _There are only two hard things in Computer Science: cache invalidation and naming things -- Phil Karlton (bonus…_martinfowler.com Pliki: TwoHardThings “The results were based on 4,500 votes developers provided on Quora and an older . Naming things got (just about) as many votes as the next 8 hardest tasks combined. Wow!” Ubuntu Forums thread _Last week I wrote a piece about the hardest tasks programmers have to do. It was based primarily on a recent discussion…_www.itworld.com Don't go into programming if you don't have a good thesaurus And more and more developers have then the common problem, they keep commenting on Reddit. Believe or not, there is a search engine called Codelf save your time when you have the problem to name function/variable. It’s the first tool out there try to solve the hardest problem for developers. The engine powered by . Search code _Best GitHub stars, repositories tagger and organizer. Search over projects from Github, Bitbucket, Google Code…_unbug.github.io CODELF It’s open source and popular, it gets more than 5k+ stars on GitHub. _codelf - Best GitHub stars, repositories tagger and organizer. Search over projects from Github, Bitbucket, Google Code…_github.com unbug/codelf It just like any other search engines, you give it a keyword, it gives you the result, the difference is it only gives you real-world usage variable names. It searches over projects from GitHub, Bitbucket, GitLab to find them. You even could choose your favorite language. You can also view the source codes too, and it will highlight where the variable name is using. The most fun part is, you don’t need search with English keyword, search with your mother language, Codelf will translate it to English then search for the results. Codelf is really nice to every developer, there are plugins for VSCode, Atom, Sublime Text and Chrome app. Fro VSCode For Atom Code + Elf = Codelf. Means there is an elf side by side when you are coding. Isn’t it so cute?

How does Codelf help you to name function/variable better?

This post was originally published on on November 13, 2015 Search Decoder Since I wrote 22 Growth Hackers to Follow on Twitter on The Next Web , I have received a lot of feedback from the growth hacking community. The list was valuable but I did not mention all the key biggest players in the growth hacking world. I just call them Sumos here. The list was incomplete and needed to be expanded. So here is the expanded list that includes eight new growth hackers to follow. (Special thanks to Clara Buchanan , one of the growth hackers on the original list, who shared a valuable list of new names to consider. Founder of Hypergrow , Clara is a growth strategist by trade and a mentor at EDGE EdTech accelerator in NYC. She regularly blogs about what it means to be a growth hacker and guidance for entrepreneurs .) I checked my spam folder the other day. It was pretty amusing. I got one email that said, Button was missing a ‘T’. I didn’t click it. I just laughed. “Get 100k website visitors with the click of a buton.” These days, the internet has opened up to anybody with a keyboard and a wifi connection. By the way, I found a typo recently in a newsletter I wrote. I was embarrassed. I wonder how many of my subscribers put me in a spam folder. With a skyrocketing buzz around ‘growth hacker’ on the Web, comes a pile of myths, misconceptions, and self-proclaimed gurus. Fortunately, there are still plenty of smart people providing insane amounts of value on a regular basis. Unfortunately, it’s getting harder and harder to tell who’s who. Some former black hats embarrassed the new term so it’s important to keep abreast of the legit growth hacking pros to follow. Tap in to the growth community at , and read any of to ramp up your game, but don’t expect these strategies to last forever. Growth hacks quickly become old-hacks, so use Twitter to get a steady feed of real-time growth hacking tips. GrowthHackers.com these growth hacking books There are some amazing growth hackers that are constantly sharing their best advice on Twitter. Their content has no scams or lies, just pure value. Below are twenty two growth hackers worth following (in no particular order). All have a proven track record and publish valuable content regularly. Nichole Elizabeth DeMeré Twitter: @NikkiElizDemere You probably recognize this long and unique name if you’ve been to , or . She has played a large role in the growth of all of those platforms, and she still acts as a community moderator. and are two of her specialties, and her writing is both inspirational and valuable. ProductHunt inbound.org, GrowthHackers Growth metrics SaaS Neil Patel Twitter: @Neil Patel Co-founder of , and , Neil is a powerhouse of knowledge in the online business space. He writes about social media, SEO, digital marketing, content marketing, growth hacking, and more. His resume? He helps companies such as Amazon, NBC, GM, HP, and Viacom grow. Look no further for an A-class growth hacker to follow. CrazyEgg, KISSmetrics HelloBar Noah Kagan Twitter: @NoahKagan The “chief sumo” at , Noah also has experience working at Mint and Facebook. His personal blog covers , start ups, and self-exploration. He also built , with a suite of every growth hacker should have in their toolbox. Appsumo marketing SumoMe blog growth tools James Altucher Twitter: @Jaltucher Finance, creativity, , hedge funds, self-development, pro blogging, startups, you name it and James spills out his honest opinion the topic. His posts are fun reads, as make you feel like you’re sitting right next to him. James is a seasoned growth hacker, and he is most famous for building Stockpicker. If I only have five minutes in a day to check Twitter, I start with @jaltucher. entrepreneurship self-publishing, his stories Rand Fishkin Twitter: @RandFish Co-founder of and Rand is the number one SEO expert out there. He writes about everything there is to know about , which is one of the key pillars of growth hacking methodology. MOZ.com inbound.org, SEO Nathan Barry Twitter: @NathanBarry As the Founder of , Nathan writes about “ On top of marketing, he focuses on design, specifically He breaks down his projects in public, allowing anyone and everyone to learn about his as he grows his saas business. ConvertKit anything that interests me.” app design. design strategies Tiffany Dasilva Twitter: @Bellastone Director of Digital Strategy at , Tiffany specializes in conversion rate optimization. Prior to joining PowerdbySearch, she worked with several startups, being the most well-known. If you want to save money on your PPC campaign ASAP, today. PoweredbySearch Shopify read this article Jeff Walker Twitter: @JeffWalker Apart from being a #1 New York Times , Jeff is now one of the biggest marketing trainers around. His goal is to help you not just but to also live the life of your dreams. bestselling author grow your business Whitney Wolfe Twitter: @WLWolfe Whitney helped grow from scratch, and she’s recently launched a new app called . Changing the dating game once wasn’t enough for this ambitious 25 year old. talks about the growth plans with this new app and how it plans to be bigger and better than tinder. Tinder Bumble This article Mike King Twitter: @iPullRank Founder of , and former SEO strategist at Razorfish abd Publicis Modem, Mike is “a marketing technologist, equal parts marketing and technology.” iPullRank Courtney Seiter Twitter: @CourtneySeiter Courtney has been the head of content at since April 2014. She’s the one working hard behind the incredible content that the team produces every day. Her work is unique, taking everyday topics like and and applying them to marketing. Buffer selfies emojis Hiten Shah Twitter: @hnshah Hiten Shah is Neil’s business partner on KISSmetrics and Crazyegg. He actually helped coin the term “growth hacker”, definitely him as well for additional business tips and inspiration. follow Sean Ellis Twitter: @SeanEllis If growth hacking is a Star Wars movie, Sean is a Yoda. He’s the CEO at and . He’s always coming up with fresh ideas to help businesses such as email marketing and any other under the sun. GrowthHackers Qualaroo content marketing, growth hack Tia Kelly Twitter: @lil_tea Tia works at as their customer success manager. She writes often about her love for landing pages and social media. I suppose there are worse things in life you could obsess over, but her passion is inspiring and also rather Unbounce humorous. Josh Elman Twitter: @JoshElman Now board member of Medium and , Elman is also a partner at . He also previously played a large role in the growth at Twitter, Facebook and Linkedin. Super Greylock Partners He posts about business regularly on topics such as and on Medium. growth hacking product development Gabriel Weinberg Twitter: @yegg Weinberg is CEO & Founder of and co-author of the book . He writes about marketing channels and DuckDuckGo Traction online privacy. Dharmesh Shah Twitter: @Dharmesh Co-founder & CTO of , Shah founded and writes for . His articles about and are quite unique and also helpful. HubSpot OnStartups.com leadership hiring Ryan Holiday Twitter: @RyanHoliday Former director of marketing for American Apparel, Ryan is the author of Trust Me I’m Lying, and . On his blog, he writes mostly about life strategy hacks, such as and Growth Hacker Marketing productivity self-development. Clara Buchanan Twitter: @Clara_buchanan Founder of , Clara is “a growth focused strategist utilizing cyberpsychology backed product development and experimental marketing techniques in order to form creative and nimble hypothesizes. Hypergrow Brian Dean Twitter: @BackLinko Brian is the Founder of , where he spills valuable content about link building, and other SEO topics. Check out his post on , you’ll definitely learn something. Backlinko content strategy, keyword research Mikael Cho Twitter: @MikaelCho Co-founder of , Mikael has founded several companies in the last eight years. His writing is very personal, and he covers topics such as and Crew investment work culture. Sujan Patel Twitter: @Sujanpatel Sujan is a growth-focused marketer and entrepreneur. He is currently the VP of Marketing at , and previously founded Single Grain, a San Francisco-based digital marketing agency. He’s helped companies like Sales Force, TurboTax, Sony, Mint, and hundreds of others acquire more customers, build brand awareness, and grow their businesses. Sujan is the co-author of . His new book is available on Amazon Kindle. When I Work 100 Days of Growth Content Marketing Playbook Lincoln Murphy Twitter: @lincolnmurphy Lincoln knows more than a thing or two about SaaS Customer Success, Customer Acquisition, Customer Onboarding, Marketing, & Growth Hacking. He’s a doer of nonessential things. Also, Lincoln invented . # breadabandname David Arnoux. Growth Twitter: @darnocks David is a magician at and . He’s all about growth hacking, analytics and cro. He’s also addicted to GIFs and Converse. Growth Tribe Academy twoodoo Roy Povarchik Twitter: @roypovar Roy is a growth and content marketing consultant for startups. He’s founder of ‘Growth By Content’ agency. Avid blogger and music lover. Meg Nordmann Twitter: @MegNordmann She’s Director of Marketing at , CyberSecurity, founder of . She’s always talking social media & tech news, and she’s a passionate advocate for Women in Tech. @ RiskSense http://PickMyBrainMentors.com Andy Johns Twitter: @ibringtraffic Andy helped with growth at Facebook, Twitter, Quora. Ex-EIR Greylock. He’s the current VP of Growth at Wealthfront where they make world-class investing easy & accessible to everyone. Oli Gardner Twitter: @oligardner Meet Oli, Unbounce Co-founder. He’s seen more landing pages than anyone on the planet. He’s also an opinionated writer & speaker on Attention-Driven Design. Pierre Lechelle Twitter: @PierreLechelle Pierre is helping SaaS businesses to generate more revenue through customer acquisition & growth with marketing & growth hacking. Brian Balfour Twitter: @bbalfour He’s the current VP Growth at HubSpot, Formerly EIR at Trinity Ventures, Co-Founder at Boundless, Viximo, and PopSignal. Your Turn No matter what stage of the growth cycle your business is in, you’ve got plenty of growth ideas to come from this list of savvy growth hackers. So get “growth hakering.” I’m not sure if “ is a word, but it is now. It relates to following growth hacking tips from these smart growth hackers. growth hackering” Finally, the list is not exclusive. There are many talented growth hackers out there. Let me know in comments below who else should be on the list. Image credit: Shutterstock

30 Growth Hacking Sumos to Follow on Twitter

A Comparative Algorithm Audit of Conspiracies on the Net: Results

Search can feel both simple and complicated at the same time. Searching on is simple, and the results are pretty relevant (although in recent years, this can be argued as Google now pushes a lot of sponsored results). Although it is not a search engine (at least not a traditional one), has made that experience even more magical. However, anyone who has implemented search knows that information retrieval is a highly complex topic. Google ChatGPT To simplify the life cycle from a search query to results, search can be divided into three distinct processes: query understanding, retrieval, and ranking. : Natural language processing (NLP) techniques prepare and structure the query for the search engine to analyze; Query understanding : The search engine will then retrieve the most relevant results and rank them from most to least relevant; Retrieval : Finally, there’s a re-ranking process to push the best results to the top (based on clicks, conversions, etc.) and apply a customer’s rules, personalization, and more. Ranking Machine learning AI has been applied to query processing and ranking for some time now, and it greatly improves both. The missing piece was retrieval, and retrieval is even more vital for improving the overall quality of results. We can measure retrieval quality using and . Precision is the percentage of retrieved documents that are , and recall is the percentage of all relevant documents that are . Both metrics can help us to determine if search results are any good. precision recall relevant retrieved To illustrate, let’s say it’s time to upgrade your kitchenware, so you search your favorite seller’s site for “fry pan”. Amongst the results, some items are quite relevant. But, some are not — like the cookware sets with saucepans (screenshot below). This is precision. However, there were many other relevant products on the site that were included in the results. We call this recall. not Now, let’s say you decide to refine the search query. You might search “non-stick frying pan” and this time (see below) (1) there is a different quantity of results for this query because we have introduced slightly different keywords and (2) there are more of the results that you were expecting (frying pans, not cookware sets). In fact, there can be a yin-yang between precision and recall; improving the precision (accuracy) may impact the recall, and improving the recall (completeness) may hurt precision. The holy grail is improving both, and this is exactly what AI retrieval can do. Retrieval was the last piece of the AI search puzzle, and it was also the hardest for several reasons: Managing AI retrieval scale and performance was cost prohibitive. The storage, CPUs, and algorithms all needed to be specialized. AI retrieval models were “brittle” — the search index was updated with new or changed content, and the models would need to be updated. There was a tradeoff between precise matching and broad concept matching. In this post, I’ll explain how we’ve set out to solve this last challenge for AI retrieval. In future posts, I’ll speak more about the other parts of the search pyramid. AI Information Retrieval Search retrieval requires technologies to determine the relevance of any particular query. For years, it was powered by keyword search engines. That’s changing. With the introduction of vector search, which goes beyond keyword search, concepts can be understood. is a machine learning technology for AI search. Vectors are a way to represent words mathematically. Vectors are plotted and clustered in multiple dimensions (also called n-dimensional space). Vector search compares the similarity of multiple objects to a search query or subject item via their vector representation. In order to find similar matches, the query (or the subject) is converted into vectors using the same model that is used to convert objects (i.e. data or content) into vectors. Vectors that are similar to one another are returned from the database, finding the closest matches, and providing accurate results, while eliminating irrelevant results that traditional search technology might have returned. Vector search Techniques such as , IVF (Inverted File), or PQ (Product Quantization, a technique to reduce the number of dimensions of a vector) are some of the most popular Approximate Nearest Neighbor (ANN) methods to find similarity between vectors. Each technique focuses on improving a particular performance property, such as memory reduction with PQ or fast but accurate search times with HNSW and IVF. It is common practice to mix several components to produce a ‘composite’ index to achieve optimal performance for a given use case. HNSW (Hierarchical Navigable Small World) There can be thousands of dimensions. The proximity and angle between each vector helps the search engine determine the similarity between terms and meaning. Type in “espresso with milk thingy” and a vector search engine will look for similarities to return espresso makers with steam wands. However, type in one word, like “Delonghi” (an espresso maker brand), and a is just as likely to return other brands and different machines — Nespresso, Keurig, KitchenAid, etc. That’s because vectors only understand the of a Delonghi. vector search engine concept Unlike newer vector engines, traditional keyword search engines are fast and precise. Thus, a search for a “Delonghi Magnifica” will give exactly that. However, keyword search engines can struggle when the query doesn’t match the content in your search index. To address the problem, companies can add rules, synonyms, keyword tagging, or other workarounds, but it’s impossible to cover every use case. For example, you could write a rule that a query containing the keywords all mean the same thing as The problem is that you won’t be able to cover every edge case for every possible long tail query. Take another example, a search for the word “java”. is used synonymously with and , but unless there’s a synonym or rule in the search engine, the query will fail. “coffee or espresso”, “machine”, and “milk” “espresso machine with steam wand.” Java coffee espresso Designing and building an efficient index of vectors that can scale is a complex and expensive task. Similarly, building an efficient keyword search engine that works for long tail queries is equally daunting. Separately, vector and keyword search technologies are quite good. However, together, they’re terrific. This is called hybrid search, and it works well for exact matches, ambiguous short queries, and long tail queries. An AI Search Performance Breakthrough So, you just slap together some vectors and keyword technologies and you’re done, right? Hybrid search engine solved! If only it were so easy. As you might imagine, there are a few challenges. The biggest issue is managing vector search scaling and costs. Vectors are basically floating point numbers. Computers struggle immensely with floating point numbers. This is why specialized computers like GPUs are being used for AI and vector manipulation. Not only do you need specialized databases for running vector search, you’ll need full-time development resources to continually manage production. With AI models, it is critical that the data that is fed to the models remains fresh, relevant, and optimized. For ecommerce and enterprise businesses where data is constantly being updated and speed matters, AI search is too computationally expensive to run in production. Some companies have attempted to go around the problem by running an AI query the keyword query fails. This helps to minimize processing costs, but fails to provide the best results for customers. only if The bottom line is that most companies want to spend their time and money focused on their business, not attending to search engine infrastructure. The solution is an approach we’ve pioneered. It’s called . Hashing is a technique that allows us to compress vectors without losing information. We can turn complex 2000-decimal long numbers into a simple static length expression, which makes computing them very fast and cheap. Hashing is not a new concept within AI as applied to vectors. neural hashing Locality-Sensitive Hashing (LSH) is a well-known algorithmic technique that hashes similar input items into the same “buckets” with high probability. Typically there are tradeoffs — higher or lower similarity — in how “buckets” are determined. With our neural hashing technique, we have eliminated the need for tradeoffs. As a result, we can compress, or hash, vectors with neural networks (thus the name neural hashing) to 1/10th their normal size, while still retaining up to 99% of the information. They can be stored and managed on standard hardware and databases. In fact, we can process hashed vectors, or binary vectors, up to 500 times faster than standard vector similarity, making it as fast to deliver as keyword search. And we can do it on regular CPUs. Here’s an example of a long tail query for “non-teflon non-stick frypan” running on a keyword-only engine versus a hashing/keyword engine. There are several important takeaways to glean from the screenshots above: The hybrid engine offers both higher precision and higher recall. We are running both hashes and keywords in a single query, and the combined hybrid results are scored and ranked by relevance. This is running on commodity hardware and results are nearly instantaneous; the hybrid results aren’t any slower than keyword-only. Note too that “frypan” is written as one word instead of two — it could have also been written “fry pan”, “frying pan”, or “skillet” — but the search engine doesn’t skip a beat. This touches on another important feature of AI retrieval: it greatly reduces the manual workload associated with improving search relevance. Gone are the days of writing synonym libraries for common terms, or writing rules for certain types of queries. It also opens up entirely new possibilities such as offering Q&A search. This new feature is available with Algolia NeuralSearch™. It is in private beta now, but you can to be notified when it’s available. sign up here Search is more than retrieval, of course. In our end-to-end AI search pyramid, retrieval (neural hashing) is in the middle. On each end of the AI pyramid is query understanding and ranking. In future blogs, I will touch on these other two capabilities. Next Steps Neural hashing represents a breakthrough for putting AI retrieval into production for a huge variety of use cases. Combined with AI-powered query processing and re-ranking, it promises to unleash the full power of AI on-site search. And now that we’ve released these new , you can be among the first to try them out! to find out how. end-to-end AI capabilities with Algolia NeuralSearch Contact our team Also published [here.](https://Neural Hashing)

Optimizing AI Retrieval with Neural Hashing

Do you ever think, “Darn this stupid cloud. Why can’t there be an easier way to build a neural search on it?” Well, if you have, this article is for you. I’m going to walk through how to use new Streamlit component to search text or images to build a neural search front end. Want to jump right in? Check out our text search app or image search app, and here's the . Jina's component's repo Why use Jina to build a neural search? Jina is an open-source deep learning-powered search framework for building cross-/multi-modal search systems (e.g. text, images, video, audio) on the cloud. Essentially, it lets you build a search engine any kind of data any kind of data. for with So you could build ala Google, a ala Google Images, a , and so on. Companies like Facebook, Google, and Spotify build these searches powered by state-of-the-art AI-powered models like FAISS, DistilBERT, and Annoy. your own text-to-text search engine text-to-image search engine video-to-video search engine Why use Streamlit with Jina? I was a big fan of Streamlit before I even joined Jina. I used it on a project to create terrible Star Trek scripts that later turned into a front-end for text generation with Transformers. So I'm over the moon to be using this cool framework to build something for our users. Building a Streamlit component helps the data scientists, machine learning enthusiasts, and all the other developers in the Streamlit community build cool stuff powered by neural search. It offers flexibility and, being written in Python, it can be easier for data scientists to get up to speed. Out of the box, the streamlit-jina component has text-to-text and image-to-image search, but Jina offers a rich search experience any kind of data any kind of data so there's plenty more to add to the component! for with How does it work? Every Jina project includes two : Flows for breaking down and extracting rich meaning from your dataset using neural network models Indexing: for taking a user input and finding matching results Querying: Our Streamlit component is a front end for , so it doesn't worry about the indexing part. end-users Admin spins up a Jina Docker image: docker run -p 45678:45678 jinahub/app.example.wikipedia-sentences-30k:0.2.9-1.0.1 User enters a query into the Streamlit component (currently either a text input or an image upload) and hits 'search' The input query is wrapped in JSON and sent to Jina's query API The query Flow does its thing and returns results in JSON format (along with lots of metadata) The component parses out the useful information (e.g. text or image matches) and displays them to the user Example code Let's look at our since it's easier to see what's going on there: text search example streamlit st streamlit_jina jina
st.set_page_config(page_title= ,)

endpoint = st.title( )
st.markdown( )

jina.text_search(endpoint=endpoint) import as from import "Jina Text Search" "http://0.0.0.0:45678/api/search" "Jina Text Search" "You can run our [Wikipedia search example](https://github.com/jina-ai/examples/tree/master/wikipedia-sentences) to test out this search" As you can see, the above code: Imports streamlit and streamlit_jina Sets the REST endpoint for the search Sets the page titleDisplays some explanatory text Displays the Jina text search widget with endpoint defined For the Jina Streamlit widgets, you can also pass in other parameters to define the number of results you want back or if you want to hide certain widgets. Behind the scenes The source code for our module is just one file, . Let's just look at the high-level functionality for our text search example for now: __init__.py Set configuration variables headers = { : ,
} DEFAULT_ENDPOINT = "Content-Type" "application/json" # Set default endpoint in case user doesn't specify and endpoint "http://0.0.0.0:45678/api/search" Render component container = st.beta_container() container: hidden:
                endpoint = st.text_input( , endpoint)

            query = st.text_input( ) hidden:
                top_k = st.slider( , , top_k, int(top_k / ))

            button = st.button( ) button:
                matches = text.process.json(query, top_k, endpoint)
                st.write(matches) container : class jina : def text_search (endpoint=DEFAULT_ENDPOINT, top_k= , hidden=[]) 10 with if "endpoint" not in "Endpoint" "Enter query" if "top_k" not in "Results" 1 2 "Search" if return In short, the method: jina.text_search() Creates a Streamlit container to hold everything, with sane defaults if not specified If widgets aren't set to hidden, present them to user [User types query] [User clicks button] Sends query to Jina API and returns results Displays results in the component Our method's parameters are: calls upon several other methods, all of which can find in . For image search there are some additional ones: jina.text_search() __init__.py encodes a query image to base64 and wraps it in JSON before passing to Jina API image.encode.img_base64() Jina's API returns matches in base64 format. The method wraps these in tags so they'll display nicely image.render.html() <IMG> Use it in your project In your terminal: Create a new folder with a virtual environment and activate it. This will prevent conflicts between your system libraries and your individual project libraries: mkdir my_project
virtualenv env env/bin/activate source Install the and packages: Streamlit Streamlit-Jina pip install streamlit streamlit-jina . Alternatively, use a pre-indexed Docker image: Index your data in Jina and start a query Flow docker run -p 45678:45678 jinahub/app.example.wikipedia-sentences-30k:0.2.9-1.0.1 Create your app.py: streamlit st streamlit_jina jina
st.set_page_config(page_title= ,)

endpoint = st.title( )

jina.text_search(endpoint=endpoint) import as from import "Jina Text Search" "http://0.0.0.0:45678/api/search" # This is Jina's default endpoint. If your Flow uses something different, switch it out "Jina Text Search" Run Streamlit: streamlit run app.py And there you have it – your very own text search! For image search, simply swap out the text code above for our image example code and run a Jina image (like our .) Pokemon example What to do next Thanks for reading the article and looking forward to hearing what you think about the component! If you want to learn more about Jina and Streamlit here are some helpful resources: A big thank you! Major thanks to Randy Zwitch, TC Ricks and Amanda Kelly for their help getting our component live. And thanks to all my colleagues at Jina for building the backend that makes this happen! Previously published at https://blog.streamlit.io/streamlit-jina-neural-search/

Jina, a Deep Learning-Powered Search Framework, Can Help You Build Your Neural Search

a new digital publishing product from AMI to increase demand for writers’ blog posts is a site for users to find and republish high-quality and high traffic blog posts on their own site, application or blog. As of today, this service is now is public beta. We made this marketplace to increase demand for stories and writers, and make it easier for sites to increase their publishing volume of remarkable blog posts. Story Licensing @ami A lifetime ago when I was running a corporate blog, I would have killed for a service like this. It’s very difficult to increase the rate of publishing for sites and corporate blogs. Most people don’t want to pay the tens of thousands of dollars to use a content licensing service like Newscred, and struggle with the man hours to increase their volume of quality original content. By making republishing an ala carte option, we are bringing an economic viable way for many editors, bloggers, marketers and founders to republish remarkable content on their sites. And now that I’ve spent years working with writers to optimize traffic, I’ve learned that there are too few products out there to monetize previously published stories. Past posts drive revenue opportunities like consulting work and book sales, but the blog posts themselves are also assets that can improve many sites around the web. By building a place for other sites to express interest in republishing blog posts, we will create revenue opportunities for writers in the @ami network. How Story Licensing Works for Visitors Visitors to Story Licensing can: search for story by keyword (such as , , , or ). Each recommended story displays source, tags and claps. software marketing love bitcoin place a non-binding bid to republish a story. get an email within 3 business days if the offer is accepted, rejected or countered by writer & publication. reach an agreement with us :) publish and redistribute the story on your site, app or blog. Plus we’ll give your post a social share boost via the @ami network. What This Means for Your Site This is great way to increase your rate of publishing with content that is already proven to be high quality and high trafficked. High quality content will be your SEO rankings, lower your SEM CPC prices, feed your company social accounts, and attract people who read what matters to your business. To find the right post for your site, Story Licensing searches a library of 30k posts that average over 10M+ monthly pageviews, including all the library. Hacker Noon “The vast library of tech industry expertise is a life saver,” said , Global Lead Search Marketing @ Workday and beta user. “I’m so excited to republish more remarkable blog posts.” Paul Andre de Vera What it Means for Writers Passive Income. One of our pillars as a company is creating opportunities for writers. Story Licensing was built to increase revenue for writers. As a writer, I know that the internet always makes it easier to monetize your next post, as opposed to monetizing previous posts from your library. And yet, it’s more likely that your next post is not better than the rest of your entire library. All stories published on AMI sites are searchable on Story Licensing. “As someone who writes 10,000 words a day,” said Writer, Founder of Digital Press, & 4x Top Writer on Quora , “I’m constantly looking for ways to increase revenue from my stories — and Story Licensing is a no brainer option.” Nicolas Cole The Business Approach The primary problem with publishing on the internet is the lack of long term value received by the author. Our search function is always defaulted to relevance, and as a secondary option you can filter by social proof (the metric on our sites is claps). A quality post can/should be republished to many sites and communities. Story Licensing has a flat revenue share of 70% of bid to writers & 30% of bid to @ami (creators/owners of StoryLicensing.com). The writer’s 70% is for writing a quality story. @ami’s 30% covers lead generation, business development, payment processing fees, story promotion, accreditation techniques, and other costs of maintaining this service. The Technical Approach The content management API is powered by , the search is powered by , the emails are powered by , and the library is powered by . Simple is best. We very quickly went from concept to wireframe to application. CosmicJS Algolia SendGrid AMI This application would not be possible without collaboration with Cosmic JS, the API first content management system. We met because of their contributions to Hacker Noon (read more by their founders and ), and I’m looking forward to working with their community more. Read the full story of the behind the scenes tech: . Tony Spiro Carson Gibbons How We Built the Story Licensing App using Next.js, Algolia and Cosmic JS More About Story Licensing This is an MVP product in beta. We all ears for feedback at Hello@StoryLicensing.com. Additionally resources: Story Licensing FAQ ProductHunt Twitter — , @StoryLicensing @AMI Learn more at . StoryLicensing.com

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🪐 has got you covered with fresh content from our trending stories of the day! Set email preference . Want to know what's trending right now?: The Techbeat by HackerNoon here Navigating Big Data's Potential and Privacy in Modern Medicine By [ 8 Min read ]
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Sheriff continues his quest to conquer the JS ecosystem. Sheriff extends support for Astro. This release introduces support for , thanks to libraries, and . Astro.js ota-meshi eslint-plugin-astro astro-eslint-parser The integration is provided out-of-the-box, meaning nothing has to be configured on the user side. Sheriff will automatically detect Astro projects and run the appropriate rules. All you have to do is upgrade Sheriff to this version. This integration presented significant challenges, as the pertinent ESLint plugins are still in experimental status and also because this integration provides JSX-relevant ESLint rules inside files, meaning that we are now parsing files (in a Typescript-aware way) and provide Astro and React relevant rules inside them. This has not been trivial. .astro .astro Sheriff meets Algolia The Sheriff documentation website now has a full-text search powered by , generously provided for free by the open-source projects sponsorship by . The documentation is now easier than ever to navigate. Algolia Docsearch Algolia Instead, the “Ask AI” feature didn’t make it in this release. There aren’t yet solid solutions in this space, for now. Sheriff integrates Fsecond Finally, as the last treat, my newest , was integrated in Sheriff. eslint-plugin fsecond Fsecond adds 2 new rules: : enforce placing optional parameters to the end of the function signature as destructured objects fsecond/prefer-destructured-optionals : enforce using a utility function instead of methods, or, optionally, you can disable that behaviour and the rule will just warn against incorrect usage of addEventListener methods fsecond/valid-event-listener useEventListener addEventListener What’s Next The commitment to improving Sheriff remains strong. Here’s a glimpse of what’s on the horizon: Performance Improvements: Potential performance enhancements will be analyzed to ensure that Sheriff runs even smoother, even on lower-end devices. Website Codebase Update: Plans are in place to update the website to Docusaurus 3.0, modernizing the docs website codebase. Expanded Library Support: Support for additional libraries is under consideration, with potential candidates for integration including GraphQL, MDX, and Vue. Conclusion As always, if you feel like contributing, feel free to propose suggestions or pull-requests. And if you just want to show appreciation, leave a star on GitHub. Sheriff is actively dedicated to becoming even more adaptable and feature-rich. Stay tuned for further updates and enhancements! Also published . here

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