More on Web3 & Crypto
Miguel Saldana
3 years ago
Crypto Inheritance's Catch-22
Security, privacy, and a strategy!
How to manage digital assets in worst-case scenarios is a perennial crypto concern. Since blockchain and bitcoin technology is very new, this hasn't been a major issue. Many early developers are still around, and many groups created around this technology are young and feel they have a lot of life remaining. This is why inheritance and estate planning in crypto should be handled promptly. As cryptocurrency's intrinsic worth rises, many people in the ecosystem are holding on to assets that might represent generational riches. With that much value, it's crucial to have a plan. Creating a solid plan entails several challenges.
the initial hesitation in coming up with a plan
The technical obstacles to ensuring the assets' security and privacy
the passing of assets from a deceased or incompetent person
Legal experts' lack of comprehension and/or understanding of how to handle and treat cryptocurrency.
This article highlights several challenges, a possible web3-native solution, and how to learn more.
The Challenge of Inheritance:
One of the biggest hurdles to inheritance planning is starting the conversation. As humans, we don't like to think about dying. Early adopters will experience crazy gains as cryptocurrencies become more popular. Creating a plan is crucial if you wish to pass on your riches to loved ones. Without a plan, the technical and legal issues I barely mentioned above would erode value by requiring costly legal fees and/or taxes, and you could lose everything if wallets and assets are not distributed appropriately (associated with the private keys). Raising awareness of the consequences of not having a plan should motivate people to make one.
Controlling Change:
Having an inheritance plan for your digital assets is crucial, but managing the guts and bolts poses a new set of difficulties. Privacy and security provided by maintaining your own wallet provide different issues than traditional finances and assets. Traditional finance is centralized (say a stock brokerage firm). You can assign another person to handle the transfer of your assets. In crypto, asset transfer is reimagined. One may suppose future transaction management is doable, but the user must consent, creating an impossible loop.
I passed away and must send a transaction to the person I intended to deliver it to.
I have to confirm or authorize the transaction, but I'm dead.
In crypto, scheduling a future transaction wouldn't function. To transfer the wallet and its contents, we'd need the private keys and/or seed phrase. Minimizing private key exposure is crucial to protecting your crypto from hackers, social engineering, and phishing. People have lost private keys after utilizing Life Hack-type tactics to secure them. People that break and hide their keys, lose them, or make them unreadable won't help with managing and/or transferring. This will require a derived solution.
Legal Challenges and Implications
Unlike routine cryptocurrency transfers and transactions, local laws may require special considerations. Even in the traditional world, estate/inheritance taxes, how assets will be split, and who executes the will must be considered. Many lawyers aren't crypto-savvy, which complicates the matter. There will be many hoops to jump through to safeguard your crypto and traditional assets and give them to loved ones.
Knowing RUFADAA/UFADAA, depending on your state, is vital for Americans. UFADAA offers executors and trustees access to online accounts (which crypto wallets would fall into). RUFADAA was changed to limit access to the executor to protect assets. RUFADAA outlines how digital assets are administered following death and incapacity in the US.
A Succession Solution
Having a will and talking about who would get what is the first step to having a solution, but using a Dad Mans Switch is a perfect tool for such unforeseen circumstances. As long as the switch's controller has control, nothing happens. Losing control of the switch initiates a state transition.
Subway or railway operations are examples. Modern control systems need the conductor to hold a switch to keep the train going. If they can't, the train stops.
Enter Sarcophagus
Sarcophagus is a decentralized dead man's switch built on Ethereum and Arweave. Sarcophagus allows actors to maintain control of their possessions even while physically unable to do so. Using a programmable dead man's switch and dual encryption, anything can be kept and passed on. This covers assets, secrets, seed phrases, and other use cases to provide authority and control back to the user and release trustworthy services from this work. Sarcophagus is built on a decentralized, transparent open source codebase. Sarcophagus is there if you're unprepared.
Henrique Centieiro
3 years ago
DAO 101: Everything you need to know
Maybe you'll work for a DAO next! Over $1 Billion in NFTs in the Flamingo DAO Another DAO tried to buy the NFL team Denver Broncos. The UkraineDAO raised over $7 Million for Ukraine. The PleasrDAO paid $4m for a Wu-Tang Clan album that belonged to the “pharma bro.”
DAOs move billions and employ thousands. So learn what a DAO is, how it works, and how to create one!
DAO? So, what? Why is it better?
A Decentralized Autonomous Organization (DAO). Some people like to also refer to it as Digital Autonomous Organization, but I prefer the former.
They are virtual organizations. In the real world, you have organizations or companies right? These firms have shareholders and a board. Usually, anyone with authority makes decisions. It could be the CEO, the Board, or the HIPPO. If you own stock in that company, you may also be able to influence decisions. It's now possible to do something similar but much better and more equitable in the cryptocurrency world.
This article informs you:
DAOs- What are the most common DAOs, their advantages and disadvantages over traditional companies? What are they if any?
Is a DAO legally recognized?
How secure is a DAO?
I’m ready whenever you are!
A DAO is a type of company that is operated by smart contracts on the blockchain. Smart contracts are computer code that self-executes our commands. Those contracts can be any. Most second-generation blockchains support smart contracts. Examples are Ethereum, Solana, Polygon, Binance Smart Chain, EOS, etc. I think I've gone off topic. Back on track. Now let's go!
Unlike traditional corporations, DAOs are governed by smart contracts. Unlike traditional company governance, DAO governance is fully transparent and auditable. That's one of the things that sets it apart. The clarity!
A DAO, like a traditional company, has one major difference. In other words, it is decentralized. DAOs are more ‘democratic' than traditional companies because anyone can vote on decisions. Anyone! In a DAO, we (you and I) make the decisions, not the top-shots. We are the CEO and investors. A DAO gives its community members power. We get to decide.
As long as you are a stakeholder, i.e. own a portion of the DAO tokens, you can participate in the DAO. Tokens are open to all. It's just a matter of exchanging it. Ownership of DAO tokens entitles you to exclusive benefits such as governance, voting, and so on. You can vote for a move, a plan, or the DAO's next investment. You can even pitch for funding. Any ‘big' decision in a DAO requires a vote from all stakeholders. In this case, ‘token-holders'! In other words, they function like stock.
What are the 5 DAO types?
Different DAOs exist. We will categorize decentralized autonomous organizations based on their mode of operation, structure, and even technology. Here are a few. You've probably heard of them:
1. DeFi DAO
These DAOs offer DeFi (decentralized financial) services via smart contract protocols. They use tokens to vote protocol and financial changes. Uniswap, Aave, Maker DAO, and Olympus DAO are some examples. Most DAOs manage billions.
Maker DAO was one of the first protocols ever created. It is a decentralized organization on the Ethereum blockchain that allows cryptocurrency lending and borrowing without a middleman.
Maker DAO issues DAI, a stable coin. DAI is a top-rated USD-pegged stable coin.
Maker DAO has an MKR token. These token holders are in charge of adjusting the Dai stable coin policy. Simply put, MKR tokens represent DAO “shares”.
2. Investment DAO
Investors pool their funds and make investment decisions. Investing in new businesses or art is one example. Investment DAOs help DeFi operations pool capital. The Meta Cartel DAO is a community of people who want to invest in new projects built on the Ethereum blockchain. Instead of investing one by one, they want to pool their resources and share ideas on how to make better financial decisions.
Other investment DAOs include the LAO and Friends with Benefits.
3. DAO Grant/Launchpad
In a grant DAO, community members contribute funds to a grant pool and vote on how to allocate and distribute them. These DAOs fund new DeFi projects. Those in need only need to apply. The Moloch DAO is a great Grant DAO. The tokens are used to allocate capital. Also see Gitcoin and Seedify.
4. DAO Collector
I debated whether to put it under ‘Investment DAO' or leave it alone. It's a subset of investment DAOs. This group buys non-fungible tokens, artwork, and collectibles. The market for NFTs has recently exploded, and it's time to investigate. The Pleasr DAO is a collector DAO. One copy of Wu-Tang Clan's "Once Upon a Time in Shaolin" cost the Pleasr DAO $4 million. Pleasr DAO is known for buying Doge meme NFT. Collector DAOs include the Flamingo, Mutant Cats DAO, and Constitution DAOs. Don't underestimate their websites' "childish" style. They have millions.
5. Social DAO
These are social networking and interaction platforms. For example, Decentraland DAO and Friends With Benefits DAO.
What are the DAO Benefits?
Here are some of the benefits of a decentralized autonomous organization:
- They are trustless. You don’t need to trust a CEO or management team
- It can’t be shut down unless a majority of the token holders agree. The government can't shut - It down because it isn't centralized.
- It's fully democratic
- It is open-source and fully transparent.
What about DAO drawbacks?
We've been saying DAOs are the bomb? But are they really the shit? What could go wrong with DAO?
DAOs may contain bugs. If they are hacked, the results can be catastrophic.
No trade secrets exist. Because the smart contract is transparent and coded on the blockchain, it can be copied. It may be used by another organization without credit. Maybe DAOs should use Secret, Oasis, or Horizen blockchain networks.
Are DAOs legally recognized??
In most counties, DAO regulation is inexistent. It's unclear. Most DAOs don’t have a legal personality. The Howey Test and the Securities Act of 1933 determine whether DAO tokens are securities. Although most countries follow the US, this is only considered for the US. Wyoming became the first state to recognize DAOs as legal entities in July 2021 after passing a DAO bill. DAOs registered in Wyoming are thus legally recognized as business entities in the US and thus receive the same legal protections as a Limited Liability Company.
In terms of cyber-security, how secure is a DAO?
Blockchains are secure. However, smart contracts may have security flaws or bugs. This can be avoided by third-party smart contract reviews, testing, and auditing
Finally, Decentralized Autonomous Organizations are timeless. Let us examine the current situation: Ukraine's invasion. A DAO was formed to help Ukrainian troops fighting the Russians. It was named Ukraine DAO. Pleasr DAO, NFT studio Trippy Labs, and Russian art collective Pussy Riot organized this fundraiser. Coindesk reports that over $3 million has been raised in Ethereum-based tokens. AidForUkraine, a DAO aimed at supporting Ukraine's defense efforts, has launched. Accepting Solana token donations. They are fully transparent, uncensorable, and can’t be shut down or sanctioned.
DAOs are undeniably the future of blockchain. Everyone is paying attention. Personally, I believe traditional companies will soon have to choose between adapting or being left behind.
Long version of this post: https://medium.datadriveninvestor.com/dao-101-all-you-need-to-know-about-daos-275060016663
Ashraful Islam
4 years ago
Clean API Call With React Hooks
| Photo by Juanjo Jaramillo on Unsplash |
Calling APIs is the most common thing to do in any modern web application. When it comes to talking with an API then most of the time we need to do a lot of repetitive things like getting data from an API call, handling the success or error case, and so on.
When calling tens of hundreds of API calls we always have to do those tedious tasks. We can handle those things efficiently by putting a higher level of abstraction over those barebone API calls, whereas in some small applications, sometimes we don’t even care.
The problem comes when we start adding new features on top of the existing features without handling the API calls in an efficient and reusable manner. In that case for all of those API calls related repetitions, we end up with a lot of repetitive code across the whole application.
In React, we have different approaches for calling an API. Nowadays mostly we use React hooks. With React hooks, it’s possible to handle API calls in a very clean and consistent way throughout the application in spite of whatever the application size is. So let’s see how we can make a clean and reusable API calling layer using React hooks for a simple web application.
I’m using a code sandbox for this blog which you can get here.
import "./styles.css";
import React, { useEffect, useState } from "react";
import axios from "axios";
export default function App() {
const [posts, setPosts] = useState(null);
const [error, setError] = useState("");
const [loading, setLoading] = useState(false);
useEffect(() => {
handlePosts();
}, []);
const handlePosts = async () => {
setLoading(true);
try {
const result = await axios.get(
"https://jsonplaceholder.typicode.com/posts"
);
setPosts(result.data);
} catch (err) {
setError(err.message || "Unexpected Error!");
} finally {
setLoading(false);
}
};
return (
<div className="App">
<div>
<h1>Posts</h1>
{loading && <p>Posts are loading!</p>}
{error && <p>{error}</p>}
<ul>
{posts?.map((post) => (
<li key={post.id}>{post.title}</li>
))}
</ul>
</div>
</div>
);
}
I know the example above isn’t the best code but at least it’s working and it’s valid code. I will try to improve that later. For now, we can just focus on the bare minimum things for calling an API.
Here, you can try to get posts data from JsonPlaceholer. Those are the most common steps we follow for calling an API like requesting data, handling loading, success, and error cases.
If we try to call another API from the same component then how that would gonna look? Let’s see.
500: Internal Server Error
Now it’s going insane! For calling two simple APIs we’ve done a lot of duplication. On a top-level view, the component is doing nothing but just making two GET requests and handling the success and error cases. For each request, it’s maintaining three states which will periodically increase later if we’ve more calls.
Let’s refactor to make the code more reusable with fewer repetitions.
Step 1: Create a Hook for the Redundant API Request Codes
Most of the repetitions we have done so far are about requesting data, handing the async things, handling errors, success, and loading states. How about encapsulating those things inside a hook?
The only unique things we are doing inside handleComments and handlePosts are calling different endpoints. The rest of the things are pretty much the same. So we can create a hook that will handle the redundant works for us and from outside we’ll let it know which API to call.
500: Internal Server Error
Here, this request function is identical to what we were doing on the handlePosts and handleComments. The only difference is, it’s calling an async function apiFunc which we will provide as a parameter with this hook. This apiFunc is the only independent thing among any of the API calls we need.
With hooks in action, let’s change our old codes in App component, like this:
500: Internal Server Error
How about the current code? Isn’t it beautiful without any repetitions and duplicate API call handling things?
Let’s continue our journey from the current code. We can make App component more elegant. Now it knows a lot of details about the underlying library for the API call. It shouldn’t know that. So, here’s the next step…
Step 2: One Component Should Take Just One Responsibility
Our App component knows too much about the API calling mechanism. Its responsibility should just request the data. How the data will be requested under the hood, it shouldn’t care about that.
We will extract the API client-related codes from the App component. Also, we will group all the API request-related codes based on the API resource. Now, this is our API client:
import axios from "axios";
const apiClient = axios.create({
// Later read this URL from an environment variable
baseURL: "https://jsonplaceholder.typicode.com"
});
export default apiClient;
All API calls for comments resource will be in the following file:
import client from "./client";
const getComments = () => client.get("/comments");
export default {
getComments
};
All API calls for posts resource are placed in the following file:
import client from "./client";
const getPosts = () => client.get("/posts");
export default {
getPosts
};
Finally, the App component looks like the following:
import "./styles.css";
import React, { useEffect } from "react";
import commentsApi from "./api/comments";
import postsApi from "./api/posts";
import useApi from "./hooks/useApi";
export default function App() {
const getPostsApi = useApi(postsApi.getPosts);
const getCommentsApi = useApi(commentsApi.getComments);
useEffect(() => {
getPostsApi.request();
getCommentsApi.request();
}, []);
return (
<div className="App">
{/* Post List */}
<div>
<h1>Posts</h1>
{getPostsApi.loading && <p>Posts are loading!</p>}
{getPostsApi.error && <p>{getPostsApi.error}</p>}
<ul>
{getPostsApi.data?.map((post) => (
<li key={post.id}>{post.title}</li>
))}
</ul>
</div>
{/* Comment List */}
<div>
<h1>Comments</h1>
{getCommentsApi.loading && <p>Comments are loading!</p>}
{getCommentsApi.error && <p>{getCommentsApi.error}</p>}
<ul>
{getCommentsApi.data?.map((comment) => (
<li key={comment.id}>{comment.name}</li>
))}
</ul>
</div>
</div>
);
}
Now it doesn’t know anything about how the APIs get called. Tomorrow if we want to change the API calling library from axios to fetch or anything else, our App component code will not get affected. We can just change the codes form client.js This is the beauty of abstraction.
Apart from the abstraction of API calls, Appcomponent isn’t right the place to show the list of the posts and comments. It’s a high-level component. It shouldn’t handle such low-level data interpolation things.
So we should move this data display-related things to another low-level component. Here I placed those directly in the App component just for the demonstration purpose and not to distract with component composition-related things.
Final Thoughts
The React library gives the flexibility for using any kind of third-party library based on the application’s needs. As it doesn’t have any predefined architecture so different teams/developers adopted different approaches to developing applications with React. There’s nothing good or bad. We choose the development practice based on our needs/choices. One thing that is there beyond any choices is writing clean and maintainable codes.
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Julie Zhuo
2 years ago
Comparing poor and excellent managers
10-sketch explanation
Choosing Tasks
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carrying out 1:1s
providing critique
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Zuzanna Sieja
3 years ago
In 2022, each data scientist needs to read these 11 books.
Non-technical talents can benefit data scientists in addition to statistics and programming.
As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.
Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.
Ready? Let’s dive in.
Best books for data scientists
1. The Black Swan
Author: Nassim Taleb
First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.
Three characteristics define a black swan event:
It is erratic.
It has a significant impact.
Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.
People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.
Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.
Try multiple tactics and models because you may find the answer.
2. High Output Management
Author: Andrew Grove
Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.
That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.
Five lessons:
Every action is a procedure.
Meetings are a medium of work
Manage short-term goals in accordance with long-term strategies.
Mission-oriented teams accelerate while functional teams increase leverage.
Utilize performance evaluations to enhance output.
So — if the above captures your imagination, it’s well worth getting stuck in.
3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers
Author: Ben Horowitz
Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.
Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.
It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.
Find suggestions on:
create software
Run a business.
Promote a product
Obtain resources
Smart investment
oversee daily operations
This book will help you cope with tough times.
4. Obviously Awesome: How to Nail Product Positioning
Author: April Dunford
Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.
How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.
You'll learn:
Select the ideal market for your products.
Connect an audience to the value of your goods right away.
Take use of three positioning philosophies.
Utilize market trends to aid purchasers
5. The Mom test
Author: Rob Fitzpatrick
The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.
Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.
6. Introduction to Machine Learning with Python: A Guide for Data Scientists
Authors: Andreas C. Müller, Sarah Guido
Now, technical documents.
This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.
Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.
If you know machine learning or artificial neural networks, skip this.
7. Python Data Science Handbook: Essential Tools for Working with Data
Author: Jake VanderPlas
Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.
Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.
The only thing missing is a way to apply your learnings.
8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.
The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.
9. Data Science from Scratch
Author: Joel Grus
Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.
The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.
Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.
10. Machine Learning Yearning
Author: Andrew Ng
Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.
The book delivers knowledge and teaches how to apply it, so you'll know how to:
Determine the optimal course of action for your ML project.
Create software that is more effective than people.
Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.
Identifying machine learning system flaws
Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.
11. Deep Learning with PyTorch Step-by-Step
Author: Daniel Voigt Godoy
The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.
It comprises four parts:
Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.
Is every data scientist a humanist?
Even as a technological professional, you can't escape human interaction, especially with clients.
We hope these books will help you develop interpersonal skills.
Jano le Roux
3 years ago
Never Heard Of: The Apple Of Email Marketing Tools
Unlimited everything for $19 monthly!?
Even with pretty words, no one wants to read an ugly email.
Not Gen Z
Not Millennials
Not Gen X
Not Boomers
I am a minimalist.
I like Mozart. I like avos. I love Apple.
When I hear seamlessly, effortlessly, or Apple's new adverb fluidly, my toes curl.
No email marketing tool gave me that feeling.
As a marketing consultant helping high-growth brands create marketing that doesn't feel like marketing, I've worked with every email marketing platform imaginable, including that naughty monkey and the expensive platform whose sales teams don't stop calling.
Most email marketing platforms are flawed.
They are overpriced.
They use dreadful templates.
They employ a poor visual designer.
The user experience there is awful.
Too many useless buttons are present. (Similar to the TV remote!)
I may have finally found the perfect email marketing tool. It creates strong flows. It helps me focus on storytelling.
It’s called Flodesk.
It’s effortless. It’s seamless. It’s fluid.
Here’s why it excites me.
Unlimited everything for $19 per month
Sends unlimited. Emails unlimited. Signups unlimited.
Most email platforms penalize success.
Pay for performance?
$87 for 10k contacts
$605 for 100K contacts
$1,300+ for 200K contacts
In the 1990s, this made sense, but not now. It reminds me of when ISPs capped internet usage at 5 GB per month.
Flodesk made unlimited email for a low price a reality. Affordable, attractive email marketing isn't just for big companies.
Flodesk doesn't penalize you for growing your list. Price stays the same as lists grow.
Flodesk plans cost $38 per month, but I'll give you a 30-day trial for $19.
Amazingly strong flows
Foster different people's flows.
Email marketing isn't one-size-fits-all.
Different times require different emails.
People don't open emails because they're irrelevant, in my experience. A colder audience needs a nurturing sequence.
Flodesk automates your email funnels so top-funnel prospects fall in love with your brand and values before mid- and bottom-funnel email flows nudge them to take action.
I wish I could save more custom audience fields to further customize the experience.
Dynamic editor
Easy. Effortless.
Flodesk's editor is Apple-like.
You understand how it works almost instantly.
Like many Apple products, it's intentionally limited. No distractions. You can focus on emotional email writing.
Flodesk's inability to add inline HTML to emails is my biggest issue with larger projects. I wish I could upload HTML emails.
Simple sign-up procedures
Dream up joining.
I like how easy it is to create conversion-focused landing pages. Linkly lets you easily create 5 landing pages and A/B test messaging.
I like that you can use signup forms to ask people what they're interested in so they get relevant emails instead of mindless mass emails nobody opens.
I love how easy it is to embed in-line on a website.
Wonderful designer templates
Beautiful, connecting emails.
Flodesk has calm email templates. My designer's eye felt at rest when I received plain text emails with big impacts.
As a typography nerd, I love Flodesk's handpicked designer fonts. It gives emails a designer feel that is hard to replicate on other platforms without coding and custom font licenses.
Small adjustments can have a big impact
Details matter.
Flodesk remembers your brand colors. Flodesk automatically adds your logo and social handles to emails after signup.
Flodesk uses Zapier. This lets you send emails based on a user's action.
A bad live chat can trigger a series of emails to win back a customer.
Flodesk isn't for everyone.
Flodesk is great for Apple users like me.