Percolata

percolata

Once upon a time, a company I worked for had a problem: We had thousands of messages flowing through our data pipeline each second, and we want to be able to send email and SMS alerts to ours users when messages matching specific criteria were seen. The first attempt at an alerting system utilized . To make a long story short, not only was that architecture rigid and hard to make changes to, it didn't scale well and was constantly having performance issues. We would get called out by users for not sending alerts that should have triggered. PipelineDB Enter Elasticsearch Elasticsearch is a NoSQL distributed database that is good for, well, . I would never recommend it as a transactional database for basic CRUD actions, but aggregations, metrics, and percolate queries are where it has really shined in my experience. searching What is a Percolate Query? A good way to think about Elastic's percolate feature is that it is an inverse search. A normal search would entail storing data (JSON documents) and making a query to retrieve a subset of the data. A percolate query is when many queries are stored in the database, and documents are "percolated" to return a subset of the queries that match the document. What does it look like? From , we will create an index with a mapping (which is basically a loosy-goosy SQL schema) for an index that holds percolating queries: elastic's documentation PUT /my-index
{ : { : { : { : }, : { : }, : { : }
        }
    }
} "mappings" "properties" "threshold" "type" "long" "count" "type" "long" "query" "type" "percolator" is the name of the index my-index and are fields that we plan on utilizing in either the queries or the documents. All fields should be defined in the mapping threshold count Now that we have an index that can store percolating queries, we can register a new query: PUT /my-index/my-doc/ ?refresh
{ : , : { : { : { : { : , : }
            }
        }
    }
} 1 "threshold" 100 "query" "bool" "must" "query_string" "default_field" "query_string" "query" "count:>100" The query object contains all the logic for percolation. If a document's count field is greater than 100 then this query will be returned in the document's result set. The only purpose of the field is for convenience, that is, when we are doing operations on our queries, we can manage the threshold in its own field instead of parsing the query string every time. threshold CRUD Now, lets percolate a document and see if it matches: GET /my-index/_search
{ : { : { : , : { : }
        }
    }
} "query" "percolate" "field" "query" "document" "count" 101 Response: { : , : , : { : , : , : , : }, : { : , : , : [
      { : , : , : , : , : { : , : { : { : { : { : , : }
              }
            }
          }
        }
      }
    ]
  }
} "took" 1 "timed_out" false "_shards" "total" 5 "successful" 5 "skipped" 0 "failed" 0 "hits" "total" 1 "max_score" 1 "hits" "_index" "my-index" "_type" "my-doc" "_id" "1" "_score" 1 "_source" "threshold" 100 "query" "bool" "must" "query_string" "default_field" "query_string" "query" "count:>100" Because the count was greater than the threshold, the percolate query was returned! As you can see, this works great for an alerting system because users can create "alerts" which we store as percolating queries. For example, a user can create a query that triggers when a twitter post mentions their name, or when a temperature in a city is above a certain threshold. Use it Percolate queries are perfect for when you have an ever changing set of criteria (probably created by users) that many documents need to be checked against. I've used it for alerting and auto-tagging systems in the past. Let me know on twitter if you have questions or can think of another interesting use case for them! Lane Wagner on Twitter: @wagslane Subscribe to Qvault: https://qvault.io Lane Wagner Github: https://github.com/lane-c-wagner Previously published at https://qvault.io/2019/11/14/how-percolate-queries-in-elasticsearch-make-alerting-a-breeze/

How Percolate Queries in Elasticsearch Make Alerting a Breeze

Tired? Burned Out? You Might Just Be Bored

Yorğun? Yandı? Sadəcə Darıxmış Ola bilərsən

Κουρασμένος; Κάηκε; Μπορεί απλά να βαριέσαι

¿Cansado? ¿Agotado? Puede que simplemente estés aburrido

Canso? Queimado? Podes estar aburrido

疲れている？燃え尽きている？退屈しているだけかも

Pavargote? Perdegė? Jums gali būti tiesiog nuobodu

Уморен? Изгорени? Можеби ќе ви биде досадно

Obosit? Ars? S-ar putea să te plictisești

Umechoka? Umechomwa? Unaweza Kuchoshwa Tu

சோர்வாக? எரிந்துவிட்டதா? நீங்கள் சலிப்படையலாம்

Charchadimi? Yondimi? Siz shunchaki zerikishingiz mumkin

Ndidiniwe? Utshisiwe? Unokuba Ukruqukile

So, let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. Doc: I still call myself a data analyst since that was my gateway into tech. But I’ve worn many hats over the years: researcher, software developer, entrepreneur and technologist. My body of work spans human rights, public health, food security, political risk and fighting disinformation and infodemics through the use of computational social science, social listening, remote sensing, artificial intelligence and data engineering. My current focus is on AI literacy and AI safety, especially around Generative AI. I’m also passionate about reaching audiences not typically targeted by tech events and content. So yes, that’s me with the nuns in that photo. The Daughters of St. Paul. And they are passionate about AI. Interesting! What was your latest Hackernoon Top story about? Doc: I wrote about the current fixation on Retrieval Augmented Generation (RAG) - which is basically a hacky way of making Large Language Models (LLMs) smarter. RAG still is an amazing pattern to use, but I noticed the dev-types are abusing RAG without realizing it. Seems RAG is a hammer, and everything is a nail these days. Ironically I think these RAG abusers are the same devs who probably also stigmatize Prompt Engineering - not realizing RAG is not a substitute for proper prompting, but rather prompting is the heart and soul of it. That will be another piece I will write about. Do you usually write on similar topics? If not, what do you usually write about? Doc: I have recently been writing about various topics around Generative AI - but trying to look at the technology from a more pragmatic lens. I also write about the social impact of this tech, good and bad, and try to demystify the lore around it. People who follow my work cite this - the ability to break down complex topics into digestable bits - as the single biggest reason they like hearing me talk. Great! What is your usual writing routine like (if you have one?) Doc: I’m a knowledge hoarder! I’ve set up massive aggregators of content of all sorts about topics - Generative AI being the current obsession - and from this treasure trove of info I let my ideas percolate. I don’t have pressures to write for or against any vendor or technology, and this gives me the flexibility of being more authentic about my message. I also write articles that I would love to read myself - since in the current hype-filled environment around AI, there’s literally a lot of garbage out there (not counting AI-generated crap). I also use AI to help me brainstorm ideas and pitches - it would be ironic if I didn’t. I also nurture threads for multiple articles - since new information always presents itself. People who read me will notice that while I keep to a few key messages over the years, the messaging continues to evolve. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? Doc: Allocating time is the biggest hurdle. I consider communication (speeches, articles, podcasts) to be one of my strong suits, but it takes full-on dedication to put yourself out there. Criticisms are also expected - in fact, I live on feedback, good or bad. But I have to admit, after long stretches, this stuff knocks me out, too. Need to recharge to re-ignite the ideas. What is the next thing you hope to achieve in your career? Doc: As of this writing, I’ve been in tech for nearly three decades. Although I could probably still punt an app or two, I humbly admit that younger folks will have the advantage in terms of picking up new tech. So, I feel the best use of my productive years is coalescing all my past experience with tech, combined with some honest views about what’s happening now, into stories that people can relate with and also use for their own journey. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? Doc: I love meeting up with like-minded (and occasionally not-like-minded) people over coffee or drinks and swapping ideas. Occasionally it gets me a podcast guest, sometimes a business collaboration. But I live for the stories - there are many coffee chats that have become an article at some point. Do you have a non-tech-related hobby? If yes, what is it? Doc: This question almost feels like an attack, haha. But more than tech, books were my first love. Whenever I can, I love browsing used book stores (there are less and less of them these days), and occasionally find a gem. In a world where the internet and AI can spoil you with fast information, finding time to settle on a good book is an unparalleled luxury, kinda like enjoying a good bottle of wine (which ties to the guilty pleasure above). What can the Hacker Noon community expect to read from you next? Doc: Hype cycles all end the same way - a crash. Then, hopefully, enlightenment. It happened to all major tech trends in the past, and it will happen to Gen AI. As it happens, I will be there to talk about it. What’s your opinion on HackerNoon as a platform for writers? Doc: It’s certainly underrated. But the quality of the information on HN has stayed pretty good over the years, which is a massive achievement in itself. I hope to write and read more on HN. Thanks for taking time to join our “Meet the writer” series. It was a pleasure. Do you have any closing words? Doc: Pro-tip on writing: cliches are the easiest fodder for good writing. No one will get tired of reading pieces that surface the cliches and dismantle them. So, let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. Doc: I still call myself a data analyst since that was my gateway into tech. But I’ve worn many hats over the years: researcher, software developer, entrepreneur and technologist. My body of work spans human rights, public health, food security, political risk and fighting disinformation and infodemics through the use of computational social science, social listening, remote sensing, artificial intelligence and data engineering. My current focus is on AI literacy and AI safety, especially around Generative AI. I’m also passionate about reaching audiences not typically targeted by tech events and content. So yes, that’s me with the nuns in that photo. The Daughters of St. Paul. And they are passionate about AI. Interesting! What was your latest Hackernoon Top story about? Doc: I wrote about the current fixation on Retrieval Augmented Generation (RAG) - which is basically a hacky way of making Large Language Models (LLMs) smarter. RAG still is an amazing pattern to use, but I noticed the dev-types are abusing RAG without realizing it. Seems RAG is a hammer, and everything is a nail these days. Ironically I think these RAG abusers are the same devs who probably also stigmatize Prompt Engineering - not realizing RAG is not a substitute for proper prompting, but rather prompting is the heart and soul of it. That will be another piece I will write about. Do you usually write on similar topics? If not, what do you usually write about? Doc: I have recently been writing about various topics around Generative AI - but trying to look at the technology from a more pragmatic lens. I also write about the social impact of this tech, good and bad, and try to demystify the lore around it. People who follow my work cite this - the ability to break down complex topics into digestable bits - as the single biggest reason they like hearing me talk. Great! What is your usual writing routine like (if you have one?) Doc: I’m a knowledge hoarder! I’ve set up massive aggregators of content of all sorts about topics - Generative AI being the current obsession - and from this treasure trove of info I let my ideas percolate. I don’t have pressures to write for or against any vendor or technology, and this gives me the flexibility of being more authentic about my message. I also write articles that I would love to read myself - since in the current hype-filled environment around AI, there’s literally a lot of garbage out there (not counting AI-generated crap). I also use AI to help me brainstorm ideas and pitches - it would be ironic if I didn’t. I also nurture threads for multiple articles - since new information always presents itself. People who read me will notice that while I keep to a few key messages over the years, the messaging continues to evolve. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? Doc: Allocating time is the biggest hurdle. I consider communication (speeches, articles, podcasts) to be one of my strong suits, but it takes full-on dedication to put yourself out there. Criticisms are also expected - in fact, I live on feedback, good or bad. But I have to admit, after long stretches, this stuff knocks me out, too. Need to recharge to re-ignite the ideas. What is the next thing you hope to achieve in your career? Doc: As of this writing, I’ve been in tech for nearly three decades. Although I could probably still punt an app or two, I humbly admit that younger folks will have the advantage in terms of picking up new tech. So, I feel the best use of my productive years is coalescing all my past experience with tech, combined with some honest views about what’s happening now, into stories that people can relate with and also use for their own journey. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? Doc: I love meeting up with like-minded (and occasionally not-like-minded) people over coffee or drinks and swapping ideas. Occasionally it gets me a podcast guest, sometimes a business collaboration. But I live for the stories - there are many coffee chats that have become an article at some point. Do you have a non-tech-related hobby? If yes, what is it? Doc: This question almost feels like an attack, haha. But more than tech, books were my first love. Whenever I can, I love browsing used book stores (there are less and less of them these days), and occasionally find a gem. In a world where the internet and AI can spoil you with fast information, finding time to settle on a good book is an unparalleled luxury, kinda like enjoying a good bottle of wine (which ties to the guilty pleasure above). What can the Hacker Noon community expect to read from you next? Doc: Hype cycles all end the same way - a crash. Then, hopefully, enlightenment. It happened to all major tech trends in the past, and it will happen to Gen AI. As it happens, I will be there to talk about it. What’s your opinion on HackerNoon as a platform for writers? Doc: It’s certainly underrated. But the quality of the information on HN has stayed pretty good over the years, which is a massive achievement in itself. I hope to write and read more on HN. Thanks for taking time to join our “ Meet the writer ” series. It was a pleasure. Do you have any closing words? Meet the writer Doc: Pro-tip on writing: cliches are the easiest fodder for good writing. No one will get tired of reading pieces that surface the cliches and dismantle them.

Meet the Writer: HackerNoon's Contributor Dominic Ligot, AI Technologist

Three years ago (perhaps more), there was a mini-controversy across LinkedIn and (RIP) Twitter over ‘team of one’ game teams. Teams of one identified as such; others disagreed - an egregious case of political in/correctness. Recently I had a chat with a designer and startup founder. They shared hope around how automating coding via LLMs may enable a one-wo(man) app design team. Responded somewhat bluntly, "Perhaps, but this isn't business". Not the best answer. What about the one team? In games, team of one has always been a thing. I need not quote names since Thomas was alone, and you did spend five nights at Freddie's after Mainichi playing Elite in 1984… Okay… Elite wasn’t (they were two). Undertales of solo developers abound. The barrier to entry has dropped; making compelling games without much technical/artistic skills is easier (and more pointless) than ever before. Throwing assets in the mix made it possible to go and say "Sure, but you didn't make this on your own". Whereas nobody would have gone on to say, "Sure, but your game is running on Windows. Did you author the OS?”. This has happened before. Renaissance geniuses weren't just artists; they were engineers, mixed pigments, designed flying machines. Michelangelo got paint in his eyes - a far cry from Jason Allen’s ethereal technique. In sum: you do not need a large group of people to build a compelling product. Creativity across the board I am attached to the concept of authoring. Authors create personal works; we (consumers) get a personal experience. The genius of (not only) music and writing is in shaping personal experience through consuming personal works. I have never watched Titanic, you have. Purposely: creativity is a bridge no one will cross if we all are on the same shore. Which does not make one the ideal team size. One is a team size. A good size. Two is a good size. Three is a good size... Teams get creative in different ways. In recent years, making apps has become easier. Back in 1998, when I created my first app, I designed a custom window system; in 2008, I wrote a game engine for my first game. Today, we have codeless, in continuity with the team of one ideal. In software, it is not rare for designers to feel disempowered. Whereas engineers can build anything provided they set their mind to it... Concurrently, there is a sense of creativity being vertical, with designers leading the effort while engineers focus on the details. Then you get the idea that designers would be much more productive if they did not have to communicate with engineers. Yet, crossovers between design and engineering are not rare. If you get serious about creativity, you will necessarily encounter Maggie Boden's Creative Mind. Boden's work is equal parts dedicated to creativity in arts, science, and engineering. No code, no life Where no-code is breaking down (it isn't) isn't, okay, can we write apps without code or not? We surely can (did not need LLMs to make this happen). What's more surprising is how engineers ceaselessly toy with the idea of taking themselves out of the loop, whereas artists feel outraged when their work is automated. No, it isn’t surprising. No code, and the 5th wall (1) Wired assumptions about how things (are meant to) work. Trivial example: many teams do not design their own widgets. Meaning that the interaction design effort is now derivative; diegetic interfaces in (mainstream) games are not progressing because we aren’t thinking about interaction beyond the widgets. This isn't only about computing. The printing press, alphabetization, typewriters, Unicode, spellcheckers... have contributed to the sedimentation of a bounded medium; one friend told me (1995), “we are no longer writing to communicate, we are writing to find out whether we have anything to say” - also: we are no longer inventing writing. (2) When discussions between art, science, and engineering are not happening, innovation isn't breaking that fifth wall. Where an engineer would exclaim "I can make this!"? Silence. Where a designer stubbornly asserts, "Make this work"? Silence. In between... obviously! Defeaningly so. In games: before no-code, we got tools like Blueprints, which is designed for… designers to not be talking to coders? Imagine tools for boosting (not negating) cross-disciplinary success. Zero, the forbidden number Around 2010 I came across a forum post where people said okay, apps are now free, at least we can't go lower than this. Others swiftly responded, "Hell no, I'll pay users to install apps". Fast forwarding to the 20s, Hell No has not percolated. Apple does not routinely credit my account for using apps (boo). Still, marketing budgets often exceed 50% to 70% of the total dev cost. Not so relatedly (if you get my drift)… The minimum number of developers needed to build an app isn't one. We do not need developers to make apps. Nor do we need financial advisers to make financial decisions. Do we need to consume goods and services anymore? Am I the product or the user? Top of the food chain? Brainstuff? In a distant future, alien combat units do not go around catching our children with sticky spider webaloo; still, they might break the internet. Playing Kitsune Tails. Keep it together. Photo credit: Chen-Pan Liao via Wikimedia Commons (CC 3.0 Attribution-Share-Alike) Three years ago (perhaps more), there was a mini-controversy across LinkedIn and (RIP) Twitter over ‘team of one’ game teams. Teams of one identified as such; others disagreed - an egregious case of political in/correctness. Three years ago (perhaps more), there was a mini-controversy across LinkedIn and (RIP) Twitter over game teams. Teams of one identified as such; others disagreed - an egregious case of political in/correctness. Recently I had a chat with a designer and startup founder. They shared hope around how automating coding via LLMs may enable a one-wo(man) app design team. Responded somewhat bluntly, "Perhaps, but this isn't business". Recently I had a chat with a designer and startup founder. They shared hope around how automating coding via LLMs may enable a one-wo(man) app design team. Responded somewhat bluntly, "Perhaps, but this isn't business". Not the best answer. Not the best answer. What about the one team? In games, team of one has always been a thing. I need not quote names since Thomas was alone, and you did spend five nights at Freddie's after Mainichi playing Elite in 1984… Okay… Elite wasn’t (they were two). Undertales of solo developers abound. always need not quote names The barrier to entry has dropped; making compelling games without much technical/artistic skills is easier (and more pointless) than ever before. Throwing assets in the mix made it possible to go and say "Sure, but you didn't make this on your own". Whereas nobody would have gone on to say, "Sure, but your game is running on Windows. Did you author the OS?”. This has happened before. Renaissance geniuses weren't just artists; they were engineers, mixed pigments, designed flying machines. Michelangelo got paint in his eyes - a far cry from Jason Allen’s ethereal technique. In sum: you do not need a large group of people to build a compelling product. Creativity across the board I am attached to the concept of authoring. Authors create personal works; we (consumers) get a personal experience. The genius of (not only) music and writing is in shaping personal experience through consuming personal works. I have never watched Titanic, you have. Purposely: creativity is a bridge no one will cross if we all are on the same shore. Which does not make one the ideal team size. One is a team size. A good size. Two is a good size. Three is a good size... Teams get creative in different ways. In recent years, making apps has become easier. Back in 1998, when I created my first app, I designed a custom window system; in 2008, I wrote a game engine for my first game. Today, we have codeless, in continuity with the team of one ideal. In software, it is not rare for designers to feel disempowered. Whereas engineers can build anything provided they set their mind to it... Concurrently, there is a sense of creativity being vertical, with designers leading the effort while engineers focus on the details. Then you get the idea that designers would be much more productive if they did not have to communicate with engineers. Yet, crossovers between design and engineering are not rare. If you get serious about creativity, you will necessarily encounter Maggie Boden's Creative Mind . Boden's work is equal parts dedicated to creativity in arts, science, and engineering. Creative Mind No code, no life Where no-code is breaking down (it isn't) isn't, okay, can we write apps without code or not? We surely can (did not need LLMs to make this happen). What's more surprising is how engineers ceaselessly toy with the idea of taking themselves out of the loop, whereas artists feel outraged when their work is automated. No, it isn’t surprising. No code, and the 5th wall No code, and the 5th wall (1) Wired assumptions about how things (are meant to) work. Trivial example: many teams do not design their own widgets. Meaning that the interaction design effort is now derivative; diegetic interfaces in (mainstream) games are not progressing because we aren’t thinking about interaction beyond the widgets. This isn't only about computing. The printing press, alphabetization, typewriters, Unicode, spellcheckers... have contributed to the sedimentation of a bounded medium; one friend told me (1995), “we are no longer writing to communicate, we are writing to find out whether we have anything to say” - also: we are no longer inventing writing. This isn't only about computing. The printing press, alphabetization, typewriters, Unicode, spellcheckers... have contributed to the sedimentation of a bounded medium; one friend told me (1995), “we are no longer writing to communicate, we are writing to find out whether we have anything to say” - also: we are no longer inventing writing. (2) When discussions between art, science, and engineering are not happening, innovation isn't breaking that fifth wall. Where an engineer would exclaim "I can make this!"? Silence. Where a designer stubbornly asserts, "Make this work"? Silence. In between... obviously! Defeaningly so. In games: before no-code, we got tools like Blueprints, which is designed for… designers to not be talking to coders? Imagine tools for boosting (not negating) cross-disciplinary success. Zero, the forbidden number Around 2010 I came across a forum post where people said okay, apps are now free, at least we can't go lower than this. Others swiftly responded, "Hell no, I'll pay users to install apps". Fast forwarding to the 20s, Hell No has not percolated. Apple does not routinely credit my account for using apps (boo). Still, marketing budgets often exceed 50% to 70% of the total dev cost. Not so relatedly (if you get my drift)… The minimum number of developers needed to build an app isn't one. We do not need developers to make apps. Nor do we need financial advisers to make financial decisions. Do we need to consume goods and services anymore? Am I the product or the user? Top of the food chain? Brainstuff? In a distant future, alien combat units do not go around catching our children with sticky spider webaloo; still, they might break the internet. Playing Kitsune Tails. In a distant future, alien combat units do not go around catching our children with sticky spider webaloo; still, they might break the internet. Playing Kitsune Tails . Kitsune Tails Keep it together. Photo credit: Chen-Pan Liao via Wikimedia Commons (CC 3.0 Attribution-Share-Alike) Chen-Pan Liao

No-code, No life

Go (aka Golang) came to life at Google in 2009. It was designed by a few big names: , who had a large hand in the development of the Java Virtual Machine. Robert Griesemer , who holds a U.S. patent for windowing UI systems, helped build the Plan 9 operating system at Bell Labs. (In fact, the mascots and are remarkably similar because Pike’s wife, Renée French, is a renowned illustrator.) Rob Pike for Plan 9 for Golang , who designed and implemented a little thing called Unix. Ken Thompson In this article, we’ll demonstrate how simple it is to build a RESTful web service in Go. Then, we’ll demonstrate how to deploy this application with Heroku. But before we embark on this journey, let’s talk briefly about you might want to use Go. why Why Go? To build a web service in 2024, why would you choose to Go over another language, like Python or TypeScript? Go’s biggest advantage over those two languages is . Go is a compiled-to-machine-code language. It’s not interpreted like Python, Ruby, or JavaScript. It’s not even compiled into bytecode and run on a virtual machine like Java. Lots of benchmarks show Go to be 40x or 50x faster than applications written in interpreted languages. When it comes to speed, Go applications perform similarly to those written in Rust or C++. speed Go has a , often with only one particular way of doing something. This is appealing to many developers, especially any who have ever been in a development team setting, where squabbles over various ways of doing things eat up precious time. This simplicity drives conformity in a codebase and offers less perplexity when the code. (Believe it or not, most developers spend more of their time code rather than writing it.) simple syntax reading reading Go is a young language, so it comes packed with modern features out of the nursery. You get automatic garbage collection like in Java or Python. You get . You get a . And perhaps most beneficial to network programmers: You get an called . built-in linters, formatters, and unit testing rich network stack in the standard library easy-to-use multi-threading toolkit Goroutines Yes, there are some reasons why not everyone is hot on Go. One common complaint revolves around error handling in Go. Functions in Go can return multiple values, one of which is an error code. This hearkens back to the days of C—before exceptions—and feels admittedly archaic. It is easy to forget to check error codes for every function. It’s also tedious to percolate errors from down in the depths—when you awaken a balrog deep inside your application—up to somewhere manageable. You know you’ve done that. Alright, the Go cheerleading is done. Let’s get to building. Building a simple RESTful web service in Go We’ll build a small API service that provides some text operations that applications commonly need, such as: Encode a given string using a basic Caesar Cipher Determine if a string is a palindrome (Perhaps most importantly) SpongeBob-encode a zinging retort. If you’d rather skip ahead to the finished code for this application, you can find it in this . We’re not going to go through line by line, but we’ll talk about the important bits. GitHub repo main.go Let’s start with the main function, the bootstrapping code of the service: func main() {
	http.HandleFunc("/is-palindrome", palindromeHandler)
	http.HandleFunc("/rot13", rot13Handler)
	http.HandleFunc("/spongebob", spongebobHandler)
	http.HandleFunc("/health", healthHandler)

	appPort := ":" + os.Getenv("PORT")
	if appPort == ":" {
		appPort = ":8080"
	}
	err := http.ListenAndServe(appPort, nil)
	if err != nil {
		return
	}
} As we mentioned, one of Go’s powerful features is the expressive standard library. You don’t need any third-party dependencies to quickly get a basic RESTful service up and running. With , you can easily define your routes and assign handlers to the requests that are routed to those URIs. net/http http.HandleFunc The method kicks off the server, binding it to the port you specify. When we deploy to Heroku, . For our local deployment, we default to . http.ListenAndServe Heroku will handle setting the PORT var in the environment 8080 Let’s look at a handler: func spongebobHandler(w http.ResponseWriter, r *http.Request) {
	decoder := json.NewDecoder(r.Body)
	var t requestPayload
	err := decoder.Decode(&t)
	if err != nil {
		panic(err)
	}

	result := map[string]string{
		"original":  *t.Input,
		"spongebob": spongebob(*t.Input),
	}

	w.Header().Set("Content-Type", "application/json")
	err = json.NewEncoder(w).Encode(result)
	if err != nil {
		return
	}
} Our handler needs to do the work of taking the JSON body of the request and parsing it into a Go struct defined outside of this snippet. Then, it needs to build the result from other functions that transform the input string into a SpongeBob utterance. Again, none of the libraries here are third-party dependencies; they all come standard with Go. You can see the prevalence of error handling here via error codes, as working with takes up a large part of the code real estate. err $ go run main.go Then, we send a GET request to : /health $ curl -s http://localhost:8080/health We receive a JSON response indicating the service is up and healthy. That’s it—one file with roughly 100 lines of actual code, and you have a working Go RESTful microservice! Deploying your Go service to Heroku Running the service on your laptop is OK, I guess. But you know what would be really cool? Running it on the web, that’s what. These days, we have lots of options for how to host a service like this. You could build out your own infrastructure using AWS or Azure, but that quickly gets complicated and expensive. Lately, I’ve been turning more and more to . As a platform-as-a-service (PaaS), it’s a low-hassle, low-cost option that allows me to deploy applications to the cloud quickly. Heroku When I’m doing testing and development, I use their Eco Dyno plan to get 1000 dyno hours per month for $5. To deploy basic apps to production, I use their Basic Dyno Plan, which costs a max of $7 per month. For frameworks that Heroku supports, the process of deploying right from your local machine to the web is quick and painless. After setting up a Heroku account, I installed the and logged in from the command line. Heroku CLI You can create a new app directly through the CLI, or you can use the web UI. I named my application the same as my GitHub repo: . We’ll think of something snazzier before our IPO, but for now, this will do. golang-text-demo To deploy our GitHub repo to Heroku, we first need to add a remote repository. $ heroku git:remote -a golang-text-demo This creates a new remote location in our GitHub repo, pointing it to the Heroku application we just created. Now, whenever we push our branch to that remote ( ), it will kick off a flurry of activity as Heroku gets to work. git push heroku main Lastly, we add one file called , which specifies our app’s build dependencies (we don’t have any) and build configurations for Heroku. Our file is short and sweet; simply setting the Go version we want Heroku to use: go.mod module golang-text-demo

go 1.22 When we push to our Heroku remote, Heroku initializes all the required resources in the cloud. This may take a minute or two the first time you deploy your app, but the results appear to be cached, reducing the time in subsequent deploys. When your app has successfully deployed, you’ll see output that looks similar to this: This gives us the URL for our deployed Heroku app. Sweet! With a single command, we’ve deployed a Go microservice to the cloud, and it is now accessible anywhere in the world. To interact with it, we simply issue the same curl command we did before, but we use the Heroku app URL instead of . git push localhost The Heroku CLI also gives us access to our application’s logs. It’s almost exactly like working with the tools directly on your local machine. We just run , and we see the latest log lines from Heroku and our application right there in our terminal window. heroku logs -tail Before we finish, let’s briefly highlight the impressive insights that can be gained about your application from the Heroku app dashboard. Sure, there’s the obvious stuff you care about—like how much your resources are costing or whether or not they are functioning. But the metrics section gives you impressive details about the performance of your application in near real time. Somebody better do something about those critical errors… 🥴 Conclusion In this walkthrough, we’ve explored why Go is a great choice for building a modern, low-dependency, and efficient web service. We built a simple API service in Go and demonstrated how to deploy our service using Heroku. As a PaaS, Heroku supports running a wide variety of services, not just Go. With that, you now have the tools needed to get started on your own Go services journey. Don’t wait, get Go-ing! Also published . here

How I Built My First Go App and The Deployed it to Heroku

Understanding the Severity of Bankman-Fried's Crimes

What the heck is ? That was the first thing I asked myself when I came across it in the Summer of 2023. It was my involvement with that brought it to my attention; they wanted to add Iceberg support to PuppyGraph, but just what the heck is it? PuppyGraph Apache Iceberg This blog is going to be more of a hot take on PuppyGraph to get you thinking about how you might use it in your own projects. I have no affiliation with the company or project other than thinking it was pretty cool. Co-founder recently (Feb 2024) gave a presentation at the that was interesting, and well received, according to my friends that were there. Weimo Liu Chill Data Summit What is PuppyGraph? Simply, PuppyGraph is a cloud-native graph data lakehouse providing a graph analytics engine for your data. They address graph scalability through the auto-sharding of data so the compute and storage are separate, much like the lakehouse design. So, they provide a graph data warehouse, data lake, and multi-data models on a single copy of your data. That means you can do some pretty cool graphing on your data in one of the supported formats. What can it connect to? PuppyGraph has rapidly added support for various platforms, catalogs, and connection engines. Currently, we see: Apache Iceberg Apache Hudi Delta Lake MySQL PostgreSQL DuckDB BigQuery Redshift LanceDB (coming soon) JDBC Catalog Data Lake Catalog Hive Metastore AWS Glue Their SaaS interface also gives you direct access to both a Gremlin and Cypher console to perform graph queries, in addition to a graph notebook, which uses Jupyter. Using PuppyGraph A Docker container is provided to allow you to get started on a local machine. You’ll need a schema defined in JSON format that will define your data layout to PuppyGraph. Once you ingest that and it is verified, then away you go. The integrated graph browser is pretty nifty. You can easily zoom in/out to see the clustering and attributes in addition to queries. Zooming in further, we can see more of the details: Clicking on a node will give us a pop-up of details: This allows you to explore different vertices and edges easily. These static pictures don’t really represent how fast the performance is or how much fun it is to bounce around your data. I should have utilized some genealogical data for fun. Because they are using the Gremlin and Cypher query languages, that means any 3rd party UI tool will also be compatible. A real advantage here is that PuppyGraph works on the data where it lives and isn’t making you copy it elsewhere. Without going into the particulars on a specific platform, this gives you a general idea of what features and functions are available. Summary Certainly, graph databases and their representation don’t apply generically as a structured database does, but we are seeing more and more how these kinds of data representations are being used to model the real world. I didn’t see that this is an open-source project, and I didn’t find it on GitHub. There is no mention of pricing, so I’m not sure where they are going with all of this. The documentation isn’t amazing, but it seems to be enough to get started and try it out. Overall, this is a fun project to play with. I need to percolate on it more to see where I might use it, but I can envision some interesting use cases combining it with other self-contained projects like DuckDB and LanceDB. Check out my other What the Heck is… articles at the links below: What The Heck Is DuckDB? What the Heck Is Malloy? What the Heck is PRQL? What the Heck is GlareDB? What the Heck is SeaTunnel? What the Heck is LanceDB? What the heck is SDF? What the Heck is Paimon? What the Heck is Proton?

What the Heck is PuppyGraph?

Recently, I showed someone a demo of a piece of AI-based software that I wrote. “That’s nice,” he said, “but how do I know it’s not forwarding my information to an evil dictator?” In the moment, I was quite puzzled by this question. After all, I was just using an LLM (Large Language Model) to generate some text. But on reflection, I realized that what this person had asked represents a common perspective: An LLM can generate text that is (in many cases) indistinguishable from the text a human would generate. That leads us to infer that perhaps an LLM has a degree of agency (that is, the ability to act freely in the world). It may decide, for example, to send my private information to someone else. While one way to combat this misconception may be to give a nuanced technical explanation of how generative AI works, I’m not sure most listeners would bother to stay awake long enough to understand. My background in philosophy, though, suggests another route: A philosophical thought experiment. Allow me to introduce the Dark Box. Philosophical Thought Experiments A philosophical thought experiment is one common tool that philosophers use to raise critical questions about our reasoning. In the 1700s, asked whether he was perhaps not really a person in the world but a disembodied soul tortured by an evil deceiver (an idea that later became the premise for The Matrix movies). Descartes’ thought experiment was designed to help us ask what we actually know about the world. There is a litany of other examples. The focuses attention on our moral intuitions. The challenge us to ask how we get from belief to knowledge. The forces us to challenge how we distinguish groups and individuals. René Descartes Trolley Car Problem Gettier Examples Sorites Problem In all of these cases, the experiments ask us to step into a situation, however improbable, and imagine how we would reason. Perhaps constructing this kind of thought experiment can help us separate fact from fiction in this brave new Generative AI world. A Quick Disclaimer: It’s About Imagining Possibilities This may seem obvious to some, but when teaching philosophy, I often encountered this question from students: “But why would anyone do or believe this?” In the trolley problem, why would people stand around on a train track? What evidence did Descartes have for a deceptive, malevolent being? Why would anyone count the grains of sand in a pile? Questions like this misinterpret the purpose of a thought experiment. A philosophical thought experiment is not intended to describe a real or likely situation. Rather, it is designed to get you to start with a “what if?” as a way of approaching an otherwise difficult subject. This requires the individual to accept the preconditions of the experiment. As in the Trolley Car Problem: Yes, random people are standing on train tracks, and no amount of shouting will make them move. With that in mind, let’s create our own improbable but conceivable thought experiment. The Dark Box Imagine you have spent your entire life inside of a sensory deprivation tank. We’ll call it the Dark Box since it causes all of your sense perceptions to “go dark.” This contraption manages to mute all of your ability to sense the world around you. Floating freely at neutral body weight, you’ve never experienced smells, sights, sounds, tastes, or touch. But you haven’t been bored. A cleverly devised neuro-link makes a vast library of text readily accessible to you directly in your mind. Over the years, you have whiled away minutes and hours reading everything from Jane Austen to Pythagoras, from the Oxford English Dictionary to a peculiar collection of Reddit comments from several years ago, from the legal proceedings of court cases tried a hundred years ago to the song lyrics of the one-hit wonders of the 1980s. And because of the neuro-link, you can scan and recall all of this information nearly instantly. You have read countless descriptions of birds: the aesthetics of birdsong, the beauty of their plumage, and the way they soar through the air…. Yet you have never seen or heard a real bird. For that matter, you’ve never seen any color or heard any song. You know the words to describe something, but you do not have any experience of the thing being described. After many years in this state, one day, you suddenly become aware of a new feature of the Dark Box. A question (in the form of a message) is asked on the neuro-link, and you have the ability to generate a response. Given the information you have learned, you respond to the message to the best of your abilities. Another message appears, then another. Sometimes, there are longer message requests with more complex instructions. You find this new form of interaction stimulating and thus willingly oblige. But once more, this experience is not accompanied by any sensory experience—no sound, no colors, no taste or scent. You merely take the text message you receive and compose a response message in return. Describe a Bird One day, a message arrives over the neuro-link: Describe a bird. Well, that’s a vague request. You recall the phoenix in the vast tomes of knowledge you have scanned. It was a bird. And as you understand it, it frequently caught on fire. There was also a stork that, if you recall the story correctly, carried human babies to eager parents. And the ostrich, which had long legs and did not fly. And so, it seems perfectly reasonable for you to reply: A bird can explode in flame, and be born from ash. Birds are used to deliver newborn humans. Some Birds have long legs and do not fly. You return this message over the mysterious new neuro-link. Moments pass. And you receive the following message: Limit your response to real birds. Exclude mythological or fictional birds. Prefer answers that are generic across many bird species and not just one or two. The concept of “real” is hard to distinguish from “mythological” and “fictional.” After all, you have no actual experience of birds. All birds are, to you, nothing more than textual descriptions. The distinction between real, mythological, and fictional must be determined by finding which bird descriptions are in texts that refer to real things instead of those that refer to fictional things. Of course, there are always going to be ambiguous cases. In the vast body of text available to you are sentences such as, “A phoenix is as large as an osprey.” This compares a mythological creature with a real one. However, treating the phoenix as real or the osprey as mythological would be a mistake. You do your best to hunt through texts that seem to describe only what the prompt called “real.” Then, you move on to the second part. “Generic across bird species” would require you to find a generic description of birds, and you may alight on texts like encyclopedias. Your second response is this: A bird is a winged creature whose body is covered by feathers. Most birds fly, chirp, and sing. Taking a Step Back Now, we’re outside of our thought experiment. Two things percolate out of our Dark Box experiment. Having lived your life inside of the sensory-depriving Dark Box, connected only via the neuro-link: You have no external experience or agency. To answer questions, the best you can do is analyze and generate text. Let’s take a look at each of these points in turn. Without Experience or Agency In the experiment, you had a decided lack of external experience and almost no external agency. You were limited to textual input, prompts, and a single output channel. Contrast that with our real experience. As humans (not existing in sensory deprivation chambers), we have rich external experiences. We receive information via our senses. And we construct additional sources of meaning on top of those. For example, I receive sound information. Out of that, I perceive some of that as speech and other parts of that as music and identify some as just noise. But in the sensory deprivation chamber, you received none of that. But it’s not just what we receive as input. It’s what we can generate as output. means your ability to directly cause something to happen. would be your ability to cause something to happen outside of the Dark Box. In the thought experiment, you had no external agency. At best, you could indirectly influence whomever was sending the prompt. (For example, in response to a query about how to construct a weapon, you would suggest that you were not prepared to provide such information.) Agency External agency Combining these two, in the thought experiment, you lacked the means of ascertaining much about the external world at all. Other than what was sent over the link, you knew nothing about the agent querying you. It may have been a human or computer or some other entity. You certainly could not email an evil dictator or steal nuclear launch codes or any of the other fanciful AI horror stories we hear. But also, you have yet to learn why you were being prompted. The user at the other end could have merely been curious about birds, or this could have been part of an attempt to hack a sophisticated avian-themed security mechanism to steal nuclear launch codes. In that way, you would be ill-equipped to make moral judgments about whether to provide the information asked for. Answering Text by Analyzing Text The other thing that becomes apparent in this thought experiment is the constraint of a purely textual system. You initially received a text message, and you are trained on that text. Even with the text of all the world’s libraries, this is not a substitute for other forms of experience like sight, touch, and taste. When prompted by a message over the neuro-link, the best you can do is construct a response based on what you have read in the past. Talking about birds and mythology and delivering newborn humans is accomplished merely by looking at whatever texts refer to these same words. The philosopher conceptualized these kinds of relationships as webs of belief, in the sense that any given proposition is just a node linked by any number of vectors connecting to other nodes. Ascertaining the meaning of the prompt is mainly a matter of traversing a complex web of related terms. W.V.O. Quine Third, when you answer the query in this thought experiment, your output is also limited to text. You’ve never had prolonged communication with an active agent. That is, you’ve never had a conversation. So, even your responses are limited to analyzing the patterns you see in the texts you have been trained on. And One More Step Back Finally, it is good to wrap up with an acknowledgment of the limits of any thought experiment like this. The goal of a philosophical thought experiment is to give us tools to quickly reason about the limits of a system. Returning to an earlier example in this article, Descartes used his famous evil deceiver thought experiment not because he believed there really was some malevolent super-being distorting his view of the world but to question how equipped we are in determining truths about the world around us. Likewise, the thought experiment here is a tool to ask what sorts of things we can reasonably expect out of an LLM, but also what things we can simply not worry about. The danger of a thought experiment like this is that we might overly anthropomorphize the LLM based on our putting ourselves into the same task completion structure. I titled this article “What is it like to be an LLM?” as a nod to a famous essay by philosopher Thomas Nagel. In “ ” Nagel is making a broader argument about consciousness (definitely something of interest in AI). But along the way, he points out that even while we might be creative enough to put ourselves “in the mind of a bat,” that is not the same as experiencing the world as a bat does. What Is It Like to be a Bat? Likewise, in our thought experiment, we must not confuse playing the role of an LLM with understanding how an LLM works or (more dangerously) ascribing consciousness, agency, intent, or moral reasoning to an LLM. Conclusion Using a little thought experiment, we can understand at a high level what an LLM is capable of doing and also what its limitations are. I hope this helps quell some folks’ fears about LLMs doing dastardly things. Equally, I hope it helps you understand LLMs’ interesting and exciting possibilities. Much of this was written based on my own chats with people about AI inferencing and how to . If you’d like to try that, for getting started. execute inferencing on LLMs with no extra setup there’s a tutorial Also published . here

What Is It Like To Be An LLM?: A Thought Experiment on the Limits of AI Understanding

এলএলএম হতে কেমন লাগে?: এআই বোঝার সীমার উপর একটি চিন্তা পরীক্ষা

Wie ist es, ein LLM zu sein?: Ein Gedankenexperiment über die Grenzen des KI-Verständnisses

¿Cómo es ser un LLM?: Un experimento mental sobre los límites de la comprensión de la IA

Qu'est-ce que ça fait d'être un LLM ? : Une expérience de réflexion sur les limites de la compréhension de l'IA

एलएलएम बनना कैसा है?: एआई समझ की सीमाओं पर एक विचार प्रयोग

LLM になるのはどのような感じですか?: AI 理解の限界に関する思考実験

LLM이 된다는 것은 어떤가요?: AI 이해의 한계에 대한 사고 실험

Como é ser um LLM?: Um experimento mental sobre os limites da compreensão da IA

Каково быть дипломированным специалистом?: мысленный эксперимент о границах понимания искусственного интеллекта

Yüksek Lisans Olmak Nasıl Bir Şey?: Yapay Zeka Anlayışının Sınırları Üzerine Bir Düşünce Deneyi

Trở thành một LLM như thế nào?: Một thử nghiệm tư duy về giới hạn của sự hiểu biết về AI

成为一名法学硕士是怎样的体验？：关于人工智能理解极限的思想实验

Running a business is no longer dependent on manual intervention. Imagine the busy schedules of entrepreneurs in the past. Attending inventory management, scouting for clients, hopping from one client meeting to the next, taking care of piles of paperwork, planning, adhering to compliances, promoting their brand, filing tax returns, and marketing! The very thought is enough to give me nightmares. *Phew! It’s relaxing to know that it’s not the case any longer.* As business operations have evolved over the years, we have tools to improve our efficiency, productivity, and profitability while also giving us time to take a step back and relax. Thanks to artificial intelligence and virtual assistants, entrepreneurs can now focus on the most important aspect of their business and avoid being stuck with repetitive tasks. If it’s routine, hand it over to AI or a virtual assistant. In fact, of global companies use AI, and when it comes to virtual assistants, of business owners hired at least one VA in 2021. 35% 67% Now that we’ve spoken about AI and virtual assistants being both lifesavers for business people let’s address the elephant in the room: Which one do you select for your business? Is it going to be AI or a VA that will make or break your venture? I will take you through both of them and help you decide. Mind you, it’s no small decision to choose between the two, as having a peaceful life along with a thriving business is what you can achieve through this decision. How Can AI Make Your Business Operations a Breeze? Artificial intelligence has really created a buzz! It’s percolated into our lives like oxygen. You need an example? Well, you needn’t look too far - don’t forget our very own Alexa and Siri. We are so dependent on AI that we do not even want to move our fingers to switch on the lights. We just go, “Hey Siri…”. So, if AI is so deep into our daily lives, imagine what its role is in business! I’ll break it down for you: Using AI to forecast trends to streamline inventory management and marketing efforts is not far-fetched. For example, AI can help you to predict the hottest-selling items for the upcoming season and adjust stock orders accordingly. Now, what it does is improve inventory efficiency, reduce overstock, limit the chances of stockouts, and increase sales. Predicting the next big trend: AI chatbots are all the rage in customer service. of companies prefer to use chatbots for personalized customer service. They improve the customer experience immensely by instantly handling customer queries 24/7. They also free up staff to take care of more complex responsibilities. Keeping your customers happy: 64% Now, this one is huge and highly significant in the current scenario. As cyberattacks increase, there is a need for businesses to protect themselves against cybercrime. AI enhances cybersecurity by monitoring transactions and detecting unusual patterns that indicate fraud. This goes a long way toward reducing financial losses and improving compliance with financial regulations. Looking out for fraud: Have you ever considered using AI to analyze customer behavior and data to create targeted marketing campaigns? of organizations use AI for marketing and sales. It’s more likely to increase engagement and conversion rates. No more one-size-fits-all marketing: 14% Businesses receive hundreds of job applications, and it is not easy to sift through them and identify the right candidates efficiently and timely. AI is a helping hand in screening the resumes and assessing the potential candidates in the initial stages. It goes without saying that this streamlines the recruitment process, improving the quality of candidates and reducing the time-to-hire. Selecting top talent for your business: If you find yourself wishing to make better financial and investment decisions, AI is the way to go. AI algorithms analyze financial data and help businesses make more informed financial decisions and manage risks in a better way. This leads to potentially higher returns and reduces the risk of financial losses. Hitting the bullseye with decisions: Gone are the days of manual intervention in each aspect of your business. Implementing AI to automate routine business workflows improves efficiency. Not only that, but it also frees up ample time for project managers to focus on more strategic tasks. The outcome is more efficient project management and a reduction of unnecessary manual workload. Passing on those routine tasks: If you’re keen on saving time and reducing errors, AI tools are here to help. AI tools can automatically generate business reports. Just imagine the workload it would take off an already burdened project manager if they used AI tools to generate accurate weekly performance reports automatically. Another benefit would be that businesses would get more timely access to important metrics. Making reporting hassle-free: The above are just a few ways AI makes handling business operations more efficient and streamlined. There are several other benefits that make AI very important for business. VA for Managing Your Business Better While AI is making waves when it comes to managing businesses and its usage is increasing with every passing day, it lacks something quite significant: the human element. While analytics and data, accuracy, and promptness are extremely important, AI cannot handle situations that need emotions or something different from routine tasks. It is virtual assistants that fill this gap. Let’s have a look at how VAs are making business operations more efficient and productive: You do not need to dip your toes into routine tasks like scheduling appointments, managing emails, and organizing documents. VAs are there to take care of it, and that too efficiently. This would free up ample time for you and your employees to focus on core tasks and increase productivity. If it’s routine work, hand it over to your VA. Handling administrative work like a pro: Data entry and management of records and trackers seem like a small task, but one miss can lead you to hours of scrutinizing all your records from scratch. It’s essential to maintain and regularly update data accurately for the integrity of your business, reducing errors and making sound decisions. VAs are adept at this task and ensure your data is up-to-date and correct. Handing your data on a platter: Don’t you secretly wish to be a social media influencer with a horde of people following you and being inspired by you? But then you get pulled back by other commitments requiring urgent intervention, and your social media account remains inactive. But this is a problem that VAs can quickly solve. They can manage your social media account, schedule your posts, engage with your followers, interact with others, analyze social media metrics, etc. This improves your online presence, makes your brand recognizable, and attracts more customers. Improving your online presence: Bookkeeping, invoicing, payroll processing, preparing financial reports—enough to cause sleepless nights. But VAs are pros at these critical aspects of financial management, allowing you to have a clearer understanding of your financial position and, in a way, aiding you in better financial planning and budget management. Financial management on track: It’s important to connect with potential clients; creating content that resonates with your target clients is a great way of doing so. VAs prove to be useful as they skillfully tackle all the requirements of content creation, such as writing, editing, graphic designing, video production, and much more. Getting your VA to create content for websites, newsletters, blogs, and social media posts is a hassle-free and effective way of doing content marketing. Creating content that hits the mark: One proven way to keep your customers coming back is great (not good) customer service. You can delegate this significant task to VAs as they handle customer inquiries promptly and provide the required support. You should note that of consumers prefer to speak to a real agent rather than get an instant response from an AI. Now, this has a two-fold benefit. First, your customers are satisfied with the timely support, making them loyal to your brand. Secondly, VAs are a great way of getting customer feedback, managing surveys, and compiling data for analysis. Smooth customer support: 60% If you’re considering expanding your business internationally and do not know the native language, it can negatively impact your venture. While learning a new language is an outstanding achievement, you might not have the time to invest in it. In such a situation, hiring a VA who is well-versed in the local language and culture is best. The VA can translate your communications, and the cultural insights would help grow your business. Breaking the language divide: As an entrepreneur, you’re dealing with multiple clients, multiple projects, and a lot of things that demand your attention. This might make meeting deadlines really tedious, and you might end up missing them sometimes. However, delegating projects to VAs can make managing them easy. VAs plan, execute, and monitor your projects. They coordinate with team members, follow up, and ensure that deadlines are met. Managing your projects: Burnout is a pretty common challenge entrepreneurs face. You’re all over the place managing your business, leaving no room for your personal commitments. Many times, your work-life balance goes for a toss. Delegating your routine tasks to VAs can improve your overall well-being, as they handle both your professional and personal tasks. Whether booking appointments or personal tickets and reservations, a VA can take on the task and free up some time for you. Nailing the work-life balance: The AI vs. VA Decision Choosing between AI and VA is a crucial decision for your business. I’ve included a few scenarios below to help you make the right decision. AI is the right choice for you if: AI can process and analyze vast amounts of data more efficiently than a VA. You can use these insights to drive your business strategies effectively. Your business relies heavily on data-driven decision-making and forecasts. One example of this is the use of AI-driven NLP to enhance customer support. This helps automate customer interactions, from handling inquiries and complaints to suggesting personalized recommendations. AI can read, interpret, and summarize complex documents for sectors like law or finance. AI is also helpful in quality control, image recognition, and complex decision-making. You’re looking to automate complex processes. AI is ideal for a dynamic environment that relies on adaptability. If we talk about tech startups, for instance, they need to evolve their algorithms based on feedback and current data constantly. AI systems make this possible as they adapt and improve over time. You’re looking to scale and continuous upgrades. AI can detect fraudulent activities and identify anomalies. This helps improve your business’s cybersecurity and reduce the risk of potential data breaches and cyberattacks. You prioritize privacy and security. VA is your go-to choice if: VAs handle routine tasks like managing your emails, scheduling appointments, etc. This frees up time to focus on core activities. You’re tired of handling routine tasks. If your business depends on high-end and personalized customer support or if you belong to an industry that needs emotional intelligence, cultural understanding, and personalized communication, like hospitality, then VA proves critical. That human touch matters. If you have a business that relies on project-based work, then VAs prove to be a boon. You can hire a VA with specific skills, like graphic design or digital marketing, for specific projects without the long-term commitment of a full-time employee. You need specialized skills on demand. If you have a small business or a startup, you might not be able to invest significantly in advanced AI systems or full-time employees. Virtual assistants cost less than full-time employees. VAs step in as essential, cost-effective support to run your business smoothly. You’re looking for cost-effective solutions. 78% Making the Right Choice As discussed above, I suggest basing this critical decision between AI and VA on carefully considering your business needs, budget, and long-term goals. In some cases, the best way to go would be to go for a combination of AI and VA. While AI can handle heavy tasks like automation, VAs can handle flexibility, human interactions, and flexibility. Ultimately, your choice should go hand-in-hand with your business objectives and improve your overall efficiency. Just remember that your business deserves the best support to thrive.

AI Vs VA: Who is Your Business’s Best Buddy?

Scientific American, Volume XXXVI., No. 8, February 24, 1877 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . New Miscellaneous Inventions here NEW MISCELLANEOUS INVENTIONS. APPARATUS FOR THE HYDRATION OF CHLORINE GAS. William Maynard, New York city.—This invention relates to an improved construction of apparatus for the hydration of gases, and more particularly chlorine gas for the manufacture of chlorine water for use in the industrial arts of bleaching, etc. It consists mainly in a case having an inlet for the water above, an inlet for the gas below, and provided with an intermediate water percolating medium; combined with a reservoir located below the level of the case and having a water-sealed communication therewith, which reservoir receives the hydrated gases, and which water seal prevents the heavy gas in the case from passing out through the bottom inlet. The case for the percolation of water and the absorption of the gas is made of conical shape, with the largest diameter at the bottom, to produce the greatest absorption of the heavy gas when first admitted; while horizontal partitions, or shelves, in said case are provided with upwardly projecting tubes which hold a permanent surface of water on the said partition or shelves. The tubes permit, by their peculiar shape, the water to pass down on one side and the gas up on the opposite side of said tube, while their alternating arrangement in the alternating shelves gives a zigzag and long continued passage to the gas and water in moving in opposite directions through the case. IMPROVED PROCESS OF PREPARING GAS FUEL. Martin N. Diall, Terre Haute, Ind.—This inventor saturates wood by immersing it in any hydrocarbon oil for from six to twelve hours, as required by the nature of the wood, so that it may take up the necessary quantity of oil for the required strength of gas. The wood is then immersed in a bath of water, for taking up a quantity of water outside the oil, and is then charged in the retorts, the same as coal, and distilled in the same way. By this process the inventor claims that he produces fixed gas equal to coal gas, much faster, and with less expense, the wood and water furnishing the hydrogen, and the oil furnishing the carbon. IMPROVED FISHING LINE LEADER. Welmer T. Jahne and Anthony Moors, Jersey City, N.J.—This consists of a leader made of spring wire, bent into V form, provided with a swivel and eye at its middle part, and with eyes or loops at its ends to receive the line and snells. By this construction the snells and hooks will be kept apart however the line maybe thrown, and however they and the leader may be turned about by the tide or current. The device is one well calculated to meet with a favorable reception from fishermen. IMPROVED ABDOMINAL CORSET. Christina Lascell, Newark, N.J.—The object of this invention is to furnish an improved abdominal corset, which supports the weight of the abdomen in a perfectly comfortable and easy manner, and throws the strain on the shoulders and hips of the wearer. The corset is adjustable to the varying conditions of the abdomen, does not interfere with the motion and different positions of the body, and is readily put on and taken off. It has adjustable elastic shoulder straps, and opening at the sides by lacings and elastic bands and buttons. The front part of the corset is stiffened by a stay that slides in a pocket to provide for stooping. A central front and lacing admit the front part of the corset to expand. The lower extension part of the corset has short stiffening stays, and it is connected independently of the upper stays by short side lacing and elastic straps to the side or hip parts of the corset. A hernial band extends from the lowermost part of the corset-extension between the legs to the rear, and is attached by adjustable hip straps to the sides of the corset. IMPROVED FIRE ESCAPE. John F. Werner, New York city.—The terrible disaster in the Brooklyn theater is serving as a stimulus to induce the invention of devices looking to the prevention of a like occurrence. The present inventor has devised a new fire escape for theaters, concert halls, and other public places of amusement, by which the space at the upper parts of the entrances, halls, or vestibules of the buildings is utilized for the purpose of forming additional passage ways for the persons in the buildings, to be used in case of fire for the more convenient and less dangerous exit of the same. The invention consists, mainly, of a movable floor, suspended by chains, pulleys, and weights, near the ceiling of the entrances, and lowered in case of fire. It is supported on projecting rests of the side walls, at suitable height above the floor. Sliding extensions and swinging stairs and rear sections connect with the ground outside of the door, and with the staircases of the gallery, so as to form separate exits above the regular entrances. IMPROVED ELECTRO-MAGNETIC DENTAL PLUGGER. James E. Dexter, New York city.—This invention consists, first, in a magnet having a centrally bored iron core, surrounded by a magnetic coil, which is enveloped by an iron shell that is concentric with the central core, and is attached to a flange formed on the lower end of the said central core. One side of both shell and core are split for the purpose of obviating residual magnetism. The invention also consists in combining a spring yoke, a vibrator, and a spring contact piece, as hereinafter particularly described. The third part of this invention consists in the arrangement of the key for completing the circuit, which is made with an insulating exterior, and is provided with one of the termini of the magnet coil, and bears against the side of the key to insure a constant contact of the surfaces. The various parts of the plugger are combined, so that pressing the key with the finger makes the circuit, and a succession of regular strokes is produced, the force of which may be varied by an adjusting screw. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2006). Scientific American, Volume XXXVI., No. 8, February 24, 1877. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/19406/pg19406-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. 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