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Software Repositories and Machine Learning Research in Cyber Security: Abstract & Introduction 

Propose new names for software engineer levels using a transparent and standardized classification system relevant to your company. Junior, Middle, and Senior are how a Software Engineer (SWE) career looks, right? But what does this mean? Different companies have different definitions, so borders are blurred. In this article, I’m going to share with you my considerations regarding levels in and try to rethink what the path might look like. A kind of disclaimer: this is only my vision and not the ultimate truth, so I’m happy to hear your feedback. software engineering What Is Wrong With Current Levels They are polysemantic. From what I can see on the market, from my experience, and those I tracked, different companies have different definitions of Junior/Middle/Senior engineers. Some of them have even more: Staff, Principal, and Distinguished engineers to have a better expression of seniority of highly experienced individual contributors. One of the key problems with “Senior SWE” is that people with absolutely different experiences might get this title. Technically, a Senior Mobile Engineer is not the same as a Senior Frontend Engineer or a Senior Backend Engineer. There are different specializations, and in general, that would not be correct to move from SME to SBE without any downgrade, but why? Logic dictates soft skills are the same, and life experience is the same as well (because it is still the same person). Only one thing changed - the ability to solve problems. You might be an extremely experienced Mobile Developer, but you have never solved issues within a web browser, problems with distributed systems, etc. So, let me take this particular criterion as a separator between levels. The first milestone is simple problem-solving. 1. Pathfinder: Random-Way Simple Problem Solver Originally, a pathfinder was someone who found or created a path through an unexplored or wild area. This term was often used to describe explorers or scouts who ventured into unknown territories, paving the way for others to follow. They were crucial in mapping new lands and navigating through difficult terrains. Sometimes, I hear, “You have to look after Junior dev, but the Middle one is working fully on one’s own." Is that true? Not. People on the Middle level usually do not care about the wider picture of the world, so they cannot make the best decision just by design. In any way, you need to look after every developer to help to stay within the project’s range of norms and not to allow to leak of over-engineered solutions. So, Random-way Simple Problem Solver (author: RwSPS short scares me as well), a.k.a Pathfinder, is able to solve atomic problems (not decomposable in a relevant way). Think about one task that someone else prepared for Pathfinder. Would you name it Junior? Middle? It doesn’t matter because you measure the results of these guys by their ability to solve business problems. Ok, Pathfinder will solve your problems. SOMEHOW, is it just enough? It depends. If you are creating a short-living project and all tasks are straightforward, a group of pathfinders will probably be enough. But for a long-living project, you need to solve problems, especially in a simple way. Otherwise, maintenance becomes a nightmare. 2. Specialist: Simple-Way Simple Problem Solver Specialist has deep, extensive knowledge and expertise in a specific field. Specialists are highly skilled in their area, often focusing on a narrow aspect of a discipline. Simple-way Simple Problem Solver (SwSPS), a.k.a Specialist, is the next level after Pathfinder. The key difference is that have enough experience to solve simple problems predictably in a simple way. That might be a proper framework/library usage, assembling solutions from existing components. For example: Specialists If tries to handle nulls by IFs, the will use nullable types to strict nullability by design. Pathfinder Specialist If might add logging at the start and the end of every method explicitly, will just use (those who believe that AOP is unacceptable should throw tomatoes in the comments) Pathfinder Specialists Aspect Oriented Programming If might refactor ten lines of code one by one. will use IDE’s multi-cursor to introduce changes in many places simultaneously. Pathfinder Specialists With experience and seeing more and more code that works, mastering tooling Specialists will provide more reliable solutions faster. This level is limited to atomic tasks only. Sounds like the next growth point! 3. Generalist: Random-Way Complex Problem Solver Generalist solves problems by synthesizing and applying knowledge from various domains. They are often effective in dealing with new or unforeseen challenges due to their adaptable and flexible approach. What is outside of a simple problem? Other simple problems! The source of all these simple ones is one or more complex issues that software engineers have to decompose before they start working. Let’s define . a complex problem In the context of this article, is a problem that might be decomposed for the sake of improved predictability of implementation time. Also, a complex problem might consist of other complex problems that should be decomposed eventually. a complex problem The key difference between a Random-way Complex Problem Solver (Generalist) and a Simple-way Simple Problem Solver ( ) is scale. A generalist is still able to solve simple problems in a simple way, but experience relevant to complex problems is not enough to follow the same approach for complex tasks. Here are a few examples: Specialist might design a new complex system starting with microservices and ignoring the fact that the customer-facing systems have <10 unique users in total yet. Generalists might start from on-premise db instead of just relying on managed services even if requirements do not specify that need and the key motivation is past experience. Generalists Generalists might bring redundant complex technologies from the previous company, ignoring the fact that the previous and the current ones are at different stages of business maturity. Getting experience in solving complex problems, starts finding ways to quickly get simpler solutions, and it means that the next level is coming. Generalist 4. Navigator: Simple-Way Complex Problem Solver Historically, navigators were crucial on ships and aircraft. In modern contexts, the term is used in roles requiring strategic planning and direction-setting, like in project management or leadership positions in companies. Counterintuitive that solving problems in a simple way is more complex, but the nature behind this fact is ignorance. At the beginning of your path, you have a high level of unawareness about already available solutions and ready-to-go components. Sometimes, they appear while you develop your own. Simple-way Complex Problem Solvers ( ) can deeply and seamlessly dive into an unknown environment, map their experience, find a simple solution, and basically have this expectation of something available instead of reinventing the wheel. A few examples: Navigator would never start by creating a marketplace if the business is about selling things but not SaaS. Navigator would research available opportunities before planning and designing. Navigator is fine with Google Sheets + Forms to launch the business. Navigator provides relevant solutions to the current business stage. Navigator Profits of the Alternative Level Classification Relative to the classical level set, this gradation: Is in terms of specific business requirements. more transparent Is in practical tasks. measurable Does correlate with experience. Does of a particular company. align expectations Conclusion The past “Senior” job title doesn’t say anything about your real ability to solve complex problems in the new company. People should align their skills with reality and not pretend to be seniors only because they already have this title. Movement through Pathfinder -> Specialist -> Generalist -> Navigator requires constant self-educating, so don’t waste your time. And please, don’t tell me that I showed myself as Pathfinder when describing such a simple topic in such a complex classification :) Also published . here

Redefining Software Engineer Levels: A Transparent Classification System for Your Company

Scratching the Singularity Surface: The Past, Present and Mysterious Future of LLMs

সিঙ্গুলারিটি সারফেস স্ক্র্যাচিং: এলএলএম-এর অতীত, বর্তমান এবং রহস্যময় ভবিষ্যত

Kratzen an der Oberfläche der Singularität: Die Vergangenheit, Gegenwart und mysteriöse Zukunft von LLMs

Rascando la superficie de la singularidad: el pasado, el presente y el futuro misterioso de los LLM

Gratter la surface de la singularité : le passé, le présent et l'avenir mystérieux des LLM

सिंगुलैरिटी सरफेस स्क्रैचिंग: द पास्ट, प्रेजेंट एंड मिस्टीरियस फ्यूचर ऑफ एलएलएम

特異点の表面をひっかく: LLM の過去、現在、そして神秘的な未来

특이점 표면 긁기: LLM의 과거, 현재, 신비한 미래

Arranhando a superfície da singularidade: o passado, o presente e o futuro misterioso dos LLMs

Tekillik Yüzeyini Kazımak: Yüksek Lisans'ın Dünü, Bugünü ve Gizemli Geleceği

Làm trầy xước bề mặt điểm kỳ dị: Quá khứ, hiện tại và tương lai bí ẩn của LLM

探索奇点表面：法学硕士的过去、现在和神秘的未来

You say “sweats”, someone else say “joggers”. He says “fifth wheel”, she says “camper”, your neighbor says “RV”. Some people call movie stars “wealthy”; others describe them as “rich”. The English language has evolved in interesting ways along with different groups of people’s lexicons so that we now have a wealth (richness?) of similar words and phrases — synonyms — to use as communication options. Plus, some of our words (for example, “mouse”) mean two entirely different things altogether. The ambiguity caused by this phenomenon can be a problem, such as when computer science gets involved. Through people’s use of natural language and unique words, massive amounts of information and collections of documents are created and shared every day: research studies, emails, school work, web pages, social media posts, blog posts, call transcripts, news stories, search queries. And in addition to creation of new documents, there’s plenty of editing of old ones. Thanks to (NLP), the language nuances various people are using and expecting to hear can be turned into information that software can more easily figure out and use or apply “intelligently.” natural language processing Website and app search, in particular, benefit from top-notch NLP technology. After all, if someone is searching for “tights” on an ecommerce website where they’re only referred to as “leggings”, and the search engine hasn’t been apprised that these terms are used interchangeably, or differently by different geographically based groups, the shopper using the wrong search term is likely to be out of luck. What is Latent Semantic Indexing? Fortunately, we have latent semantic indexing (LSI; also called latent semantic analysis or LSA for short), developed for creating vectors and performing information retrieval. This technical method of doing NLP utilizes a mathematical technique called singular value decomposition (SVD), which looks for relationships between concepts and words in unstructured data. LSA analyzes relationships between documents and the terms they contain by reducing the number of dimensions. It’s good at understanding document relatedness by analyzing which terms are often used together — for example, “royalty” and “queen” — as a prelude to building a model of semantic relatedness. LSI isn’t a new thing. Way before the Internet came on the scene — in the early days, the 1980s — this statistical model was developed to facilitate text matching. It’s a patented technology (Bell Communications Research). One of its major uses then was helping search engines understand relationships between concepts and words in order to provide people with the most relevant search results for their search queries. And although the patent expired in 2008, it’s still going strong in the world of data science. For enhancing search, it’s still being tapped for upgrading the search functionality on sites and apps. How the technology works is a bit complex, but suffice it to say that it’s related to creating vectors and performing information retrieval despite people entering “alternative” words to those housed in the site’s or app’s index. The benefit is its ability to help people find whatever piece of content they want faster, leading to both happier searchers and better metrics and revenues for organizations and businesses. What are LSI Keywords? You may have heard the term “LSI keywords” used in relation to Google search and search engine optimization (SEO) strategy. If so, you’re in good company, but we must temporarily digress. According to Google senior search analyst John Mueller, “LSI keywords” aren’t a viable thing (nor are things like “LSI keyword suggestions”, the act of “finding LSI keywords”, or plans to “use LSI keyword generators” or set up a Google keyword planner), at least in the context of Google-related keywords and SEO tools. Roger Montti notes that the myth of Google using LSI keywords may stem from use of such phrases as “‘Semantic Analysis,’ ‘Semantic Indexing’ and ‘Semantic Search’ having become SEO buzzwords, given life by Ask Jeeves’ semantic search technology and Google’s purchase of semantic analysis company Applied Semantics.” SEO guru Bill Slawski goes into depth in a . ’s Search Engine Journal blog post Good to know. But this Google situation aside, LSI is still a relevant concept in the world of search. Semantically related keywords are critical considerations. Semantic keywords are still something to think seriously about if you want an effective website or content marketing that works. is a viable undertaking that has ramifications in terms of adding search functionality to a company’s site or app. Semantic search Synonyms: the same idea with different versions To return accurate search results, search engine retrieval methods must be able to effectively “understand” and deal with synonyms, those pesky words and phrases that, through no fault of their own, have the same meaning as other words and phrases. Synonyms certainly aren’t a rarity. Even in just a single industry, like retail, they abound because of the diverse ways in which different groups of people, such as those with different dialects, thinking about items in different contexts — or natives of different countries — refer to the same item or idea. It happens more than you might think. A searcher on a clothing seller’s ecommerce store site might naturally think to enter a search query for a: shirt, blouse, top or handbag, bag, purse or pants slacks or jacket, parka, coat or hat cap or Some of these terms are victims of overuse; others are considered odd by conventional standards because only certain age groups use them, for instance. Regardless of the wide synonymy abyss, a search engine must intimately know them all. Polysemy: Different Things with the Same Name Table, mouse, sharp…these are all polysems — words with more than one meaning. And as is the case with synonyms, with polysemy, getting a search engine to confidently understand which version you’re referring to is key. How Latent Semantic Indexing Refines Search In terms of digital marketing, if a shopper can’t pull up a great results page for what they need, that’s a pressing problem. If you can’t suggest relevant when a shopper gets no helpful results for their main keyword, that’s a big red flag. For online business success, every synonym must be accounted for; every related term known, every possible semantically related word or phrase duly anticipated. related searches Does this linguistically challenging situation stump the average modern search engine? Not with LSI in the picture. By statistically analyzing words that appear in a text document, it can anticipate which of the synonymous word different meanings is being queried and respond with the most accurate search engine results page (SERP). And when it comes to polysemy, it can tell which version of the word the searcher wants. semantic search The Secret: Vectors At Algolia, this type of technology falls in the realm of . Using machine learning models that detect semantic relationships between objects in an index, it finds related objects that have similar characteristics. (Here’s some background on how cosine similarity determines closeness in word meaning). Vector embeddings (also known as “word embeddings” or just “vectors”) are applied, along with spelling correction, language processing, and category matching. semantic search vector search Artificial intelligence (AI) models powered by vector engines can instantly retrieve accurate information. They can figure out that words and phrases like “gardening” and “yard work” are related words. They know that someone searching for “Puma” is looking for athletic shoes as their relevant content, not a large wildcat. “They can quickly break through the limits of exact keyword matching and immediately deliver optimal, relevant results. Our search solution combines this AI-based vector-space functionality with traditional keyword search to offer true hybrid search, covering all the bases. By applying full-text keyword search and vector search to each query, searchers get super accurate results, and fast. If you’ve been wanting to ensure that your users or shoppers get the absolute right search results, feel understood in their information quests, and have an excellent user experience regardless of the specific keywords they initially enter in the search bar, you’re in the right place. We can help you to your site search functionality, create image-search capabilities, build a powerful recommendation system add natural language text search We can meet the needs of without additional overhead any size dataset Our weighs and compares matching items, ensuring that the best matches appear first. tie-breaking algorithm There’s an added bonus, too: upgrading your search functionality could lead to surprising improvements in your conversions; just ask our , who know this firsthand. clients Sign up for a free or when you’re ready to learn more. demo let us know Also published . here

A Simple Guide to Latent Semantic Indexing (analysis) and How it Bolsters Search

Were you wondering what else you haven't done regarding your site's on-page SEO? You have consumed lots of blog posts, infographics, videos, and articles about the topic. You have implemented and executed the information too and now that you have dedicated such an effort, you are wondering whether there's something you missed. Given the nature of SEO and the fact that there are so many little bits to consider, it's easy to miss an aspect or two. ‘Is it okay to say it's done now?’, You wonder. Can I rest assured that I've ticked all the boxes in the on-page SEO world? Well…, worry no more. Here's a complete on-page SEO checklist that enables you to confidently lay down the mouse, close the computer and stretch your body - rest sure that there's nothing else to do; apart from routine audits - and soon enough, you'll be reaping results from your on-page SEO efforts. This is a pretty straight-to-the-point, no fluff list, so you can quickly tick the boxes and reward yourself with a cup of hot chocolate. Let's get into it. The Complete On-Page SEO checklist - to tap maximally into organic traffic Put a practicable content marketing strategy in place You surely aren't just picking random topics and curating content around them right? Thank goodness. A solid content strategy is the backbone of any successful content marketing campaign and no tangible and sustainable results will be obtained from blindly picking keywords and topics around subject matters you 'think' will interest your audience and writing about them. None at all. That's such a black-hat technique that's bound to leave a sour taste in your mouth when you realize you aren't getting any sustainable results with it - in terms of long-term passive traffic from Google. How about short-term traffic? Well, if you are lucky enough to have chosen a non-competitive keyword and your content is really good, maybe for a short while. But long-term, continuous inflow of sustainable organic traffic? - absolutely not achievable without a solid content strategy. So, do you have a practicable content marketing strategy set up? One that you actually intend to follow? ‘ Set up an analytics tool To analyze the traffic-into your website, activities of that traffic on your website, and traffic-out of your website - on a granular level -, you should set up an analytics tool. Google Analytics is a great one - an SEO experts' cult-favorite. It is a web analytics tool that helps to track the performance of your website pages. On Google Analytics, you can track traffic sources, analyze people's behavior on your site(through metrics like bounce rate, dwell time, average session duration, top pages analytics e.t.c) and measure and track conversions - and also monitor Google ad performance, if your business uses paid advertisement. You can also integrate Google analytics seamlessly with other Google tools and services so this is an added advantage if you are a fan of Google services. Other alternatives to Google Analytics are keyword research software tools;- , and . Though they are primarily used to dig up lucrative keywords to optimize content for and analyze keywords one's competitors are ranking for, they also offer options to track web traffic and analyze web visitors' behaviors. Ahrefs Semrush, Moz If you are a SaaS brand, you will find them even more useful as the bulk of your content and inbound marketing strategies lies in SEO and keyword optimization for Google searches. Perform keyword research To ensure you aren't fighting a lost cause, perform keyword research to analyze keywords that are feasible for you to rank for. Depending on factors like your domain authority, industry and topic competitiveness, keyword relevancy, and search volume, it might be easier for you to rank for some keywords than others. If you are a start-up, it's advisable to choose BOFU(bottom-of-the-funnel) keywords that although have low search volume, have high conversions and offer you a higher chance of ranking. You should also focus more on long-tail keywords as they are easier to rank for. To know more about keyword competition and how to identify the best keywords to optimize for, Ahrefs Academy has great resources. Create content that meets the 3 pillars of quality content Which are… Relevance Visual appeal & Relatability Since content is big to on-page SEO, you want to be sure you are creating content that is relevant, visually appealing, and relatable. So, ask yourself these questions; Will my prospect find this content useful? Will it provide information for her to solve a burning problem? Is this content easy to read? Appropriately spaced? Are there multimedia inserted? Is it easy to scan through? Can my prospect relate to the subject matter/topic easily? Is this content contextual for her? Create content that favors your ranking Certain content types are better suited for specific conditions, industries, and topics than others. You should know when a long-form blog post is necessary to get a message across, different from when a listicle is the best way a point can be communicated. This is called matching the search intent - i.e providing the browser with the content type he really intends to consume when he makes the search. But how does one know the search intent? Simply search for the keyword in Google. Or search for your content title or an LSI fashion of the keyword if the keyword is a short tail one, too broad, or polysemic. Take for instance, when I searched for the term 'keyword research', I was presented with in-depth articles about the topic. If you are looking to rank for this keyword, you should produce a similar content piece because this search result shows that people who browse this topic - keyword research - want a well-detailed piece about it. So, run a quick search for the search intent behind the keyword you are optimizing for and make sure your content piece offers just that. Include your keyword in your URL The uniform resource locator(URL) is the address given to a given resource on the internet - in this case, the content page you are trying to optimize. The URL is a great place to insert your keyword because it enhances your ranking efforts by helping Google understand your content better. Even though it's not a primary ranking factor, it does contribute to some of the traffic influx. And with SEO, every little bit matters. Is your keyword inserted in your URL? Make your URL short and clear Still on URLs, it's optimal to insert your keyword but not random word combinations, numbers, and jargon. Keep it simple, and short and make it clearly describe what your content piece is about. If your URL necessitates the highest form of AI to understand it(literally speaking), it's too complex. A simple and clear URL enables Google bots to understand the content of your page quicker and better. A great example is… https://brokeblogger.com/blog/blogging tips-article From this URL, you can easily decipher the fact that the resource is an article about blogging tips from brokeblogger's blog. Make your title tag memorable & insert your keyword in it Write title tags that make browsers go 'seriously? I wanna read this'. I mean, compelling titles that hook browsers and inspire clicks. The type that builds curiosity and 'I Can't wait to see what the jist is' feelings in your prospects. Those kinds, yeah. While the title tag being compelling is not a direct ranking factor, it does drive up click-through rate and increase search traffic. And what's more? These landing prospects - haven been captured by your title hook - are more likely to click around your site for a longer while - as long as the content delivers - and engage with your brand further. What more could be better? So, analyze your title of choice again. Is it enough for the reader to stop scrolling and click on your article in milliseconds? Since title tags are important to give browsers a quick attention-catching idea of what your content is about - and whether it's for them or not(i.e worth clicking on or not) - it's important to clearly state - in the title - what your piece is about, who it is for and what your reader will benefit from consuming your content. Doing this makes your title tag descriptive - a quality Google desires in title tags. According to Google's guidelines, writing a descriptive and concise title tag is important. Title links are critical to giving users a quick insight into the content of a result and why it's relevant to their query. It's often the primary piece of information people use to decide which result to click on, so it's important to use high-quality title text on your web pages. Google Here are some tools to create better title headings… I won't 100% rely on these though. Writing better headings is more of a conscious amalgamation of content value, target audience persona, content audit data, and industry jargon than the whirl of an AI tool. Hubspot's Blog Ideas Generator The HOTH's Blog Topic Headline Title Generator Content Row's headline generator Tweak your Biz's title generator Coschedule's headline analyzer In addition, include your keyword in your title tag to further fuel your SEO efforts. You should also try to keep the keyword as close to the front as possible. That way, when Google crawls your title, it indexes the keyword first - in milliseconds - and gets a faster sense of what the content is about. Keep your title tag length within 60-65 characters You must have come across some truncated titles. This happens when the title length exceeds Google's title character limit which is between 60 and 65 characters. This is more reason to keep your keyword as close to the front of your title as possible. That way, if your keyword were to exceed the character limit, your keyword is still out there visible to the eyeballs of Google users. However, ideally, try to keep your title length within the ideal character limit so that your title can wholly serve its purpose of compelling browsers to click through to your site. Use keywords in your header tags Your subheadings deserve some keyword-love too -  insert your keywords into them too! In addition to helping to organize your writing, header tags can offer great ranking opportunities. An example is featured snippets which Google drags from sections of articles. Optimizing your header tags for search by using keyword-rich subheadings increases your chances of ranking for featured snippets. Include your keyword in the body of your article and also add its synonyms and LSI forms As you progress with discussing point after point in your writing - and writing sections after sections - you'll naturally be able to include your exact keyword multiple times. But where this is not feasible without outrightly keyword stuffing, borrow some words from the synonyms and LSI forms of your main keyword. You should do this, especially in your subheadings where it might not be natural to include the keyword word for word, letter for letter. You can freely make use of LSI versions and closely related synonyms of your keyword as Google uses RankBrain, an AI semantic tool that can understand these variants of your primary keyword. Switch up your keyword order Your keyword order - the sequence of words in a string of keywords - can determine how well you rank for close variants of your primary keyword, especially in a very competitive niche. So, play around with the keyword order. For example, if you are trying to optimize for 'latest CRM tools in 2021', you can also try using - in your content - keywords like… 2021 latest CRM tools CRM tools latest in 2021 latest tools for CRM in 2021 Use keyword once in the first 100-150 words Lots of marketers make the mistake of not being upfront with the keyword they are optimizing for in an article. Don't let this happen to you. Include your keyword within the first 100-150 words to enable Google to get a quicker sense of what your content speaks of while indexing it. Perform image SEO Image SEO - the act of optimizing images to rank on the image section of Google SERPs - is also part of on-page SEO. Add keywords to your image filenames, ALT tag description, and image titles. Use 5-8 external links External links build credibility and trust for your website. To support the opinions, points of view, and statistics in your content piece, link to… Top industry blogs Research sources - for the stats you use Expert quotes Thought leadership content Use internal links To distribute traffic across your website, link to 2-5 pages on your website. That's just an average though. More is absolutely merrier as long as it's done sensibly such that your content doesn't turn into a rainbow of hyperlinks. Don't forget to use descriptive anchor texts to ensure your reader gets a gist of the page you are sending her to. Write an arresting introduction & conclusion Also called the lede and the kicker in traditional journalism, the introduction and conclusion are key aspects of any great writing. Write an introduction that draws your reader into the story and end with a conclusion that takes her by surprise and makes her want to stay forever. Check for content or title duplication Do you think you might have another piece of content speaking about/optimizing for the primary keyword you have at hand? Or another article with a title very similar to the one for the current content? You should check…, and confirm. Keyword cannibalization - the situation whereby Google is confused about which of your content pieces to rank for a keyword or combination of keywords because there's quite a number of them, scattered all around your website - is not a pretty scenario to be in. You risk having Google decide to rank a content piece of lower priority instead of the best content piece for that subject matter because…., well, Google doesn't know they are different on your priority level. So, check through the content assets on your website. If there's another content piece or a number of them saying pretty much the same thing, you should consider combining them to produce a more detailed guide. Or you can otherwise choose the best and get rid of the remainder. Don't forget to give opportunities for distribution - add links to social media 61% of the world's population use social media and 93% of internet users are on social media sites, especially the more popular ones like Facebook, YouTube, and Twitter. So, your reader is most probably using them as well. Give her an opportunity to share your content with other prospects by adding social media links to your content. That way, you can drive more traffic to your blog while also gaining some social proof that your business produces valuable content. After all, no one shares poop content. Check for mobile-friendliness Is your website visibility top-notch on mobile? Can your website be navigated seamlessly on a mobile device? Top B2C industries like beauty, personal finance, travel, and food get most of their traffic from mobile. If you write for any of these niches or their significant others, you are killing your chances before having them if your site is not mobile optimized. But even if you are B2B, you shouldn't ignore mobile optimization just because mobile users account for just a minimal portion of your conversions. You might be getting most of your conversions and leads from tech-savvy, industry-jargon-proficient desktop users but you can't deny the fact that a good number of those individuals start their journey to your customer bucket from their mobile. In a fast advancing, technology-spearheaded world, where it's easier to swipe open your phone than it is to open your mouth - and I mean that literally - don't be surprised if Elon Musk joins your company's customer community page from an Android 10 device - sorry, make that an iPhone 13 Pro. Check for crawlability Google needs to crawl your content - and your entire website too - before it can index it for display in search results. If your content can't be crawled because of one issue or the other, you won't be shown on any search result page - not even the one at the deep bottom - because Google doesn't know that your content/website exists. Common issues that might prevent your website from being crawled include… Having a no-index tag on your site Improperly configured robots.txt file Broken redirects Lack of site map on website Use this AI tool from Ettvi to check your site's crawlability in minutes. Check for page load speed The average tolerable page load speed according to SEO experts is 1-3 seconds. If your speed loads slower than this, then…, well, lots of people will click off. A titanic 53% of people will leave your site if it doesn't load in 3 seconds. Our attention span as humans is unbelievably short… and our tolerability - especially of a search engine result that an alternative to is just a back button away - is even shorter. So, to avoid sending your dear dinner guests - prospects that you dedicatedly attracted with your title and meta descriptions - scrambling for the nearest fast food joint - your competitor's content most likely - since your promised delicacy took forever to arrive, you should always be keen on improving your page load speed. To take it directly from the horse's mouth, use Google's page speed insights tool to analyze your website's load speed on Google. The tool also offers suggestions to improve your page load speed after displaying your page load speed results. There is also an option to check for both mobile and desktop users. Final Thoughts On-Page SEO is a principal, non-negotiable part of any successful SEO campaign. If you are overwhelmed with the many tidbits surrounding On-Page SEO and want to ensure you are getting it right with every piece of the on-page SEO pie, the information you have just consumed ensures just that. In short, hit publish with confidence with this checklist…, checklisted. Now that you have the ultimate checklist for on-page SEO, here's to less stress(and less fretting) over SEO-optimizing your content pieces.

On-Page SEO checklist to get your SaaS SEO Right from Day 1

Artificial Intelligence is currently one of the most sought-after technologies, and so is NLP, a subset of AI. Recent years have been marked by tremendous research and development in AI. NLP is an important technology, and we use it every day. This article will give us some perspective on this emerging technology. We will look at its history and some of its typical applications. How and where can we learn NLP to create various real-life projects. We also briefly look at Python, arguably the most favorable programming language for NLP and some of its essential NLP libraries. Along the way, some learning resources have also been recommended. What is NLP? First things first: what exactly is NLP? We all know that computers can only understand 0s and 1s, but we humans communicate in various languages, thousands of languages. , to be exact. This creates a deadlock in our interaction with the computers. The traditional solution to this deadlock was a methodical interaction with computers by clicking on various options to perform the desired task. But nowadays, we can achieve this by talking to our computers through Cortana or Siri in our own language, and they can serve our tasks and even talk back to us in the same language. The underlying technology that makes this happen is Natural Language Processing or the NLP. 7139 NLP is an emerging interdisciplinary field of linguistics, computer science and artificial intelligence that makes computers understand and generate human language. This is done by feeding a large amount of data or corpus in case of language data to the computer, which analyses them to understand and generate meanings. History of NLP Although NLP has become very popular in recent years, its history goes back to the 1950s. The genesis of all the AI technologies lies with the computer scientist Alan M. Turing and his seminal paper titled .” The central question of his article was ‘ ’. In the report, Mr Turing proposed a benchmark to test the intelligence of computers in comparison to humans. He called it the Imitation Game, which later became known as the . Among other criteria was the machine’s ability to ‘understand and speak’ natural languages. The of 1954 was another development early in machine translation. The experiment demonstrated a fully automatic translation from Russian to English. The field of NLP, along with AI, has undergone continuous development since then. The growth in the field of NLP can be divided into three categories based on the underlying method or approach for solving NLP problems described in the next section. “ Computing Machinery and Intelligence (1950) Can machines think? Turing Test Georgetown experiment NLP Methods Rule based NLP (1950s — 1990s) The earliest methodologies used to achieve natural language processing by computers was based on a pre-defined set of rules, also called Symbolic NLP. A collection of hand-coded rules was fed into the computer, and it yielded results based on that. The early research in NLP was focused primarily on machine translation. Rule-based machine translation (RBMT) required a thorough linguistic description of the source and the target languages. The basic approach involved two steps: 1) finding a structural equivalent of the source sentence and the output using a parser and an analyzer for the source language and a generator for the target language, and 2) using a bilingual dictionary for a word-to-word translation to finally produce the output sentence. Given that a human language is pervasive and ambiguous, infinitely many such rules are possible. Obviously, hand-coding such a large number of rules is not possible. Thus, these systems were very narrow and could only produce results in some given scenarios. For example, the much-celebrated Georgetown experiment could only translate some 60 plus sentences from Russian to English. Statistical NLP (1990s — 2010s) With the advent of more powerful computers with higher processing speed, it was now possible to process a large amount of data. Statistical NLP took advantage of this, and new algorithms based on machine learning came into being. These algorithms are based on statistical models which make a soft, probabilistic decision to yield output(s). Instead of hard-coding rules as in Rule-based NLP, these systems automatically learn such rules by analyzing large amounts of data fed through real-world parallel corpora. In the case of Statistical NLP, the system does not generate one final output. Instead, it outputs several possible answers with relative probability. The drawback, however, was that its algorithms were challenging to build. It required a complex pipeline of separate sub-tasks like tokenization, parts-of-speech tagging, word sense disambiguation, and many more to finally produce the output. Neural NLP (2010s — present) As deep learning became popular during the 2010s, the same was applied to the NLP. Deep neural network-style machine learning methods became widespread in NLP too. This approach, too, uses statistical models to predict the likelihood of an output. However, unlike Statistical NLP, it incorporates the entire sentence in a single integrated model wiping out the need to build that complex pipeline of intermediate subtasks as in statistical models. In this approach, the system uses an artificial neural network. The artificial neural network system, in theory, tries to mimic the neural network of the human brain. An artificial neural network consists of three layers: input, hidden, and output. The input layer receives the input and sends them to the hidden layer, where all the computations are performed on the data. The output is then transferred to the output layer. The connection between the neurons is called weight. The initial value of weights is set randomly, which changes during artificial neural network learning. These weights are crucial in determining the probability of an output. Common NLP Tasks Here is a non-exhaustive list of some of the most common tasks in natural language processing. Note that some of these tasks may not be an end in themselves but serve as subtasks in solving other tasks which have real-world applications. — separate a continuous text into words Tokenisation — determine the parts of speech of each word in a sentence Parts-of-speech tagging — filter out high-frequency words like to, at, the, for, etc Stopword removal — remove inflections (prefix and suffix) (e.g., driving → driv) Stemming — remove inflections and return base form of the word (e.g., driving → drive) Lemmatization — determine which word refers to which words in a sentence/text Coreference resolution — determine and visualize the parse tree of a sentence Parsing — select contextual meaning of a polysemic word Word sense disambiguation — determine the proper nouns in a sentence/text Named entity recognition — identify relationships among named entities Relationship extraction — determine the text printed in an image Optical character recognition (OCR) — convert speech into text Speech Recognition — separate a speech into words Speech Segmentation — convert text to speech Text-to-speech — produce a summary of a larger text Automatic summarisation — detect and correct grammatical errors in a text Grammatical error correction — automatically translate a text from one language to another Machine translation — convert text into machine-readable code Natural language understanding (NLU) — make machine produce natural language Natural language generation (NLG) Python for NLP Python is a preferred programming language for NLP. As of February 2022, it is the programming language. Python’s ubiquitous nature and its application to a wide array of fields make it so popular. most popular While programming languages like Java and R are also used for NLP, Python is a clear winner. Python is easy to learn and understand because of its transparent and straightforward syntax. Python offers perhaps the largest community of developers, which can be really helpful in case the code needs some debugging. In addition, Python seamlessly integrates with other programming languages and tools. Most importantly, Python is backed by an extensive collection of libraries that enables developers to quickly solve NLP tasks. Resources : Start with this five-part specialization program on Coursera. It will provide a complete overview of Python programming. Python for Everybody : Read this free to read online book by Al Sweigart for step-by-step instructions and guided projects. Automate Boring Stuff with Python the official Python tutorial and documentation : Python tutorial NLTK NLTK — the Natural Language Toolkit — is a suite of open-source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. is the most popular library for NLP. It has a massive active community with over 10.4k stars and 2.5k forks on its . It was developed at the University of Pennsylvania by Steven Bird and Edward Loper and was released in 2001. NLTK is freely available for Windows, Mac OS X, and Linux. It has built-in support for more than 100 corpora and trained models. NLTK comes with a free written by its creators, a comprehensive guide for writing Python programs, and working with NLTK. They also have an active on google groups. Natural Language Toolkit or NLTK GitHub repository ebook discussion forum Resources: : Watch this introductory video on freeCodeCamp’s YouTube channel for getting started Natural Language Processing (NLP) Tutorial with Python & NLTK : Read the official NLTK book written by its creators for a deeper understanding of NLP and NLTK Natural Language Processing with Python spaCy Industrial-strength Natural Language Processing (NLP) in Python. is relatively young but very hot right now. Its has more than 22.4k stars and 3.7k forks, much higher than NLTK. It was written in Python and Cython, making it fast and efficient at handling large corpus. It is an industry-ready library and was designed for production usage. spaCy GitHub repository Some features of spaCy are: It provides support for linguistically-motivated tokenization in more than 60 languages. It has 64 pre-trained pipelines in 19 different languages. It provides pretrained transformers like BERT. It provides functionalities for named entity recognition, part-of-speech-tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more. Resources: : freeCodeCamp’s video tutorial for getting started Natural Language Processing with spaCy & Python — Course for Beginners : The most essential concepts, explained in simple terms spaCy 101 CoreNLP A Java suite of core NLP tools. was initially written in Java and was developed at Stanford University. But it is equipped with wrappers for other languages like Python, R, JavaScript, etc. Thus, it is a library that can be used with most programming languages. It is a one-stop-shop destination for all the core NLP functionalities like linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. CoreNLP currently supports 8 languages: Arabic, Chinese, English, French, German, Hungarian, Italian and Spanish. It has 8.3k stars and 2.6k forks on its . CoreNLP GitHub repository Resources: : Official CoreNLP documentation and pipelines Documentation TextBlob Simplified Text Processing is a Python library for processing textual data. It provides a simple API for the most common NLP tasks like POS tagging, tokenization, n-grams, etc. It is beginner-friendly and the fastest among other libraries. and is made on top of and . It has 8k stars and 1.1k forks on its at the time of writing this article. TextBlob NLTK pattern GitHub repository Resources: : Official TextBlob documentation and quick start guide Documentation Gensim topic modelling, document indexing similarity retrieval Gensim is a Python library for and with large corpora. was created by Radim Řehůřek in 2009. It is implemented in Python and Cython, making it incredibly fast. Plus, all its algorithms are memory-independent, i.e. they can process inputs larger than RAM size. It is mainly used to identify semantic similarities between two documents through vector space and topic modeling. It supports algorithms like Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning. It works on vast data collections from specific categories and provides clear insight. Gensim’s has 12.9k stars and 4.2k forks. Gensim GitHub repository Resources: : Official documentation and tutorials. Documentation Conclusion Natural language processing is a sub-field of Artificial Intelligence under active research and development. We can see its practical applications all around us. From automatic captioning in YouTube videos to Chrome automatically translation webpages in foreign languages for us, assistive writing with Grammarly, our iPhone’s keyboard predicting keywords for un and so on. The possibilities are limitless. Natural language processing is indispensable for artificial intelligence and for our future technologies. References Bird, Steven, et al. . 1st ed, O’Reilly, 2009. Natural Language Processing with Python Budditha Hettige. . 2011. , . A COMPUTATIONAL GRAMMAR OF SINHALA FOR ENGLISH-SINHALA MACHINE TRANSLATION DOI.org (Datacite) https://doi.org/10.13140/RG.2.1.2330.6968 Heller, Michael. ‘Study Claims Siri and Google Assistant Are Equal’. , . Accessed 8 Feb. 2022. Phone Arena https://www.phonearena.com/news/Study-claims-Siri-and-Google-Assistant-are-equal_id115667 Singla, Karan. . 2015. , . Methods for Leveraging Lexical Information in SMT DOI.org (Datacite) https://doi.org/10.13140/RG.2.1.2138.7367 Turing, A. M. ‘I. — COMPUTING MACHINERY AND INTELLIGENCE’. , vol. LIX, no. 236, Oct. 1950, pp. 433–60. , . Mind DOI.org (Crossref) https://doi.org/10.1093/mind/LIX.236.433

Natural Language Processing with Python: A Detailed Overview

A completely inadequate way to evaluate coding abilities... What’s Wrong With Whiteboard Interviewing? It doesn’t test for coding ability, it tests for working under scrutiny. The goal of course is to weed out frauds, applicants who have been untruthful about their skills and experience. And there are plenty such. But it’s not a good way to do even that. Neither is it a way to judge the ability of honest applicants. For many entry level candidates the whiteboard interview is the first impression of the software industry and what it expects. The impressions it creates are false. They encourage bad work, bad priorities. The circumstances of the interview bear no resemblance to life in the workplace. Interviewers are usually untrained and out of their depth, repeating how they were interviewed (badly). Sometimes they enjoy the discomfiture of their candidate.The questions are predictable and often out of date. Then why is whiteboard interviewing the universal evaluation tool of choice? We’ve always done it this way. There is a lot more wrong with software development interviewing than using whiteboards; the entire procedure is deeply flawed. I’ll get to the whiteboard coding soon enough but first … These are my personal views — I’m a developer who has worked for Microsoft and other companies as a software engineer for over 30  years. I’ve had abundant interviewing experience on both sides of the table. These are my views. Incompetent Interviewers A developer is told that a candidate is coming in for interview; the developer has no training in interviewing and isn’t given any guidance. Likely the candidate will be run through the mill, more than one interview session with no coordination between interviewers, so the candidate may in fact be asked the same question more than once. Yes it really is that bad. The interviewer tries to relax the candidate by asking a few questions he thinks are innocent but which are actually illegal. Anything about the candidate’s age or family size is risky, age discrimination is a real thing A candidate with children may have to lose time at work to care for them A candidate with no children might be gay It is guaranteed that the interviewer, wholly untrained, has no idea that the innocent question had potential for a lawsuit. And some candidates know what questions are illegal. “That’s none of your business” is an impertinent response, deserved though it may be. do And the interviewer is behind on his own work already (that’s likely why they’re hiring) and his ill-concealed resentment at an hour taken from his day doesn’t help. The interviewer’s lack of skill creates its own tension; maybe he has an impenetrable accent and the candidate has to ask him to repeat, maybe several times. Now he feels insulted. Maybe he sniffs and fidgets and offers misleading suggestions when the interviewee pauses to think. And it’s under these conditions that the candidate is expected to concentrate and show his best work? This is unreasonable. Resentful Interviewers I hope this doesn’t come as a shock but some people are just jerks. In one interview years ago I had told the company before I drove in that I hadn’t studied COM yet; it was new at the time and while I knew about GUIDs and the three required methods I had never written a COM object. The interviewer, an overweight young man with a beard, immediately told me to (wait for it) write a COM object. I reminded him I hadn’t learned to do that yet. So he took the keyboard and put on a show of typing out a rudimentary COM class, acting smug and superior. What was the point of this? I had driven to this office, awaited my turn at bat, just so he could show off. He didn’t ask me anything else; having shown he knew how to do something I later learned in twenty minutes, the interview was over. He had me drive there, raising my hopes, just so he could do an insignificant boast. Some people are astonishingly petty, condescending, or even sadistic, enjoying the discomfiture they can put into someone at a disadvantage. In the desperate days after the Lehman collapse when there were tens of thousands of engineers out of work in the Seattle area I was interviewing for a job I absolutely didn’t want and which paid half what I had been earning; my interviewer was condescending. appallingly A hostile interviewer can turn your interview into an hour of hell. He may be so abusive that you just need to leave. And once you’re struggling to keep keep from teaching him some manners, you are not going to perform well on a whiteboard. In this case you should walk out, report your experience to the agency that arranged the interview. Just because you need a job doesn’t mean you have to put up with overtly abusive people. Antiquated Questions Then come the coding questions. There are a small number of questions that are used over and over, for example: reverse a linked list find the first repeated character in an array search a binary tree. Note what all these have in common; they are low-level algorithmic code, the kind that few of us ever have occasion to do anymore. This is where the ridiculousness becomes a tangible thing, with definable features. Legacy Interview Problems Lists, arrays, trees … hardly anyone works at this level. I worked with linked lists twenty years ago, I’ve worked with binary trees except in interview questions. All these primitive types have long ago been wrapped into classes that have thoroughly tested by thousands of users. never We don’t write much assembly language either. But they’re still in interview questions. This is akin to interviewing an architect of consumer electronic designs by having him repair a broken VHS tape. Yes it is possible that at some point a developer will have to write some very low-level code, but what is the point of centering the interview around evaluating abilities that the candidate will likely never use? Have me write a lambda expression or optimize a query and why I did it that way. Interviewers rarely ask for explanations. explain Stock Interview Questions There are a dozen or so such questions that are circulated in these interviews, and a lot of false positives come from phonies who have memorized the answers. Since the interviewer has no training and has little time to prepare he will just trot out a few of these stock questions. Questions for which the interviewee has researched online and memorized the answers. This yields a false positive; he passed the coding test. It is likely the last time he will ever work at this level. Until his next interview. Make no mistake; it’s important to be able to do this kind of work. But where in the early days of C, from whence the whiteboard interview originated, we all had to be able to do this. If it comes up again then yes, we should be able to think closer to the binary level. But why make uncommon work the centerpiece of almost all interviews? Isn’t it much better to explore the candidate’s wisdom? ! Optimize! Optimize Real developers with any experience (other than slavishly following team standards) know that solidity and stability are more important than speed. Nobody remembers if a page load takes a few extra milliseconds; nobody forgets crashes and broken functionality. Optimization is more a design- and query-level than a code-level operation. Overemphasis of Optimization I’ve never had a whiteboard interview where I wasn’t asked to “make it run a little faster.” This is the strongest imperative in whiteboard programming; take shortcuts, shave off a microsecond here, break the rules there. It makes little sense to evaluate a candidate by seeing evidence he can do things we mostly stopped doing a quarter century ago. Optimized code is by definition rule-breaking code. Optimized code is more fragile. Optimization should be reserved for the small percentage of code where performance is actually critical. And any developer worth his salt know that the place to improve performance is at the level of design. I can improve a page load a hundred times more with a query optimization than with a low-level code hack. Bad First Impressions Since the interview is an entry-level applicant’s first exposure to software as profession,  this imperative to optimize is taken as an important lesson. Thereafter all his energies go into shaving off microseconds, applying code-level optimizations absolutely everywhere. Our industry is overridden by performance-obsessives who never notice how much of their carefully optimized code runs at user speeds. This is not only destabilizing, not only wasted time, but pulls attention away from the kind of optimization that actually matters. Do a low-level trick to populate a list … but put a network call into the loop that fetches its data. Wrong Tool for the Job A whiteboard isn’t a text editor. It forces the interviewee to write on a wholly unsuitable medium holding a solvent-scented marker that gives him a headache. Inserting code does not push down a closing brace and it requires him to plan spacing in a way that isn’t a consideration in work, not ever. We want to see how you work under pressure Unrealistic Pressures Saving the best for last. In a whiteboard interview the candidate is under constant scrutiny. In my first such interviews programming was a hobby to which I had no sense of personal attachment and I breezed through the process, easily thinking on my feet and ignoring the other person in the room who sought to throw me off guard with his misleading questions and his fidgeting. Later my work was more and more part of my identity, my sense of authenticity and I began to freeze up from being watched. The justification for this is “we want to see how you work under pressure.” Well, “pressure” is a polysemous noun, and the pressure that we feel trying to meet a critical deadline has no more to do with the pressure of working under scrutiny than it has to do with the pressure in a hyperbaric chamber used to reverse the bends in a diver who has surfaced too quickly. As justification, this is dishonest. As I became more experienced, better at solving problems, wiser in my work, my ability to concentrate under these conditions left me completely. I couldn’t think at all, I would blow the interview, and on my way back to my car even awash with bitterness I would solve the same problem three separate ways in my head in a few seconds. Once in a while it worked during the interview, but most times I simply froze up and couldn’t concentrate at all, and I know many others who have had the same experience. This is a false negative. (Photo by on ) You X Ventures Unsplash Alternatives to Whiteboards The best interviews I have ever had, the kind where I have always been the winning candidate, are the ones where we talk about problem solving, about software design, about striking balance between stability and performance. In one we designed a memory manager and after half an hour had ideas about garbage collection and free-space coalescing that would have worked very well in implementation. . Hired In another we talked about when and why to do code-level optimization rather than actually writing any optimized code. . Hired In yet another we talked about ways to do site queries with serial fetches of more data than needed but discarding the unused, and re-fetching aggregated data rather than doing single calls with complex JOINs. . Hired All of these were discussions between two experienced and insightful people, one the -er and one the -ee. And neither a whiteboard nor marker pen in sight. If someone has been in positions of increasing responsibility for decades it’s pretty damned unlikely that he has been unable to write code that whole time. Deceptions of skill level have a shelf life of weeks at most, days more likely. Not years. Not decades. Wisdom Beats Cleverness I have conducted dozens of interviews, never once sending the candidate to the board. Many times I rejected an applicant whose answers showed he hadn’t really learned anything; however The Team did their work was how he would do his. Conformity was his highest virtue. He had no real understanding of what he was doing. Other times I gave the up-thumb to applicants who hadn’t learned that much yet but showed promise because they were already thinking about their work and trying to find balances. Jettison the whiteboard interview.

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