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9. Among the actions the Commission took was to approve Commission Order 29 for special big game license-tag seasons for mule deer, white-tailed deer, pronghorn, elk, turkey, javelina, bighorn sheep, bison, black bear and Mountain Lion for the 2024-2025 ...

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How I Grew My Instagram: Sharing My Journey as a Jewelry Designer

Authors: (1) Reza Ghaiumy Anaraky, New York University; (2) Byron Lowens; (3) Yao Li; (4) Kaileigh A. Byrne; (5) Marten Risius; (6) Xinru Page; (7) Pamela Wisniewski; (8) Masoumeh Soleimani; (9) Morteza Soltani; (10) Bart Knijnenburg. Table of Links Abstract & Introduction Background Research Framework Methods Results Discussion Limitations and Future Work Conclusion & References Appendix 5 Results 5.1 Sample Characteristics Across the older adult participants, there were 15 females and 29 males. Their average age was 68.8 years (min = 65, max = 77, sd = 3.23). The young adult sample had 87 males, 71 females, and 4 individuals who did not self-identify as female or male. Their average age was 20.30 (min = 18, max = 25, sd = 2.18). The dataset also measured participants’ Facebook usage frequency on a 9-point scale from “Never” coded as 0 to “Almost constantly” coded as 9 and the time they spend on Facebook in each session on a 5-point scale from “A few minutes” coded as 1 to “Several hours” coded as 6. On average, older adults used the Facebook platform more frequently (mean = 5.708, sd = 1.519) compared to younger adults (mean = 5.402, sd = 2.055). However, both age groups spent an average time of 30 minutes on Facebook ( older adults: mean = 2.054, sd = 1,194 and young adults: mean = 2.119, sd = 1.275). In the following, we present our analyses with regards to our hypotheses. Table 2 summarizes the hypothesis tests. 5.2 Effects on Tagging Decision In contrast to H1 and in line with the privacy paradox, we did not find any significant relationship between privacy concerns and disclosure decision (p>.05). Therefore, H1 is rejected. However, we found support for H2 and H3 suggesting both framing and default significantly influence tagging decisions. Users who see the positively framed option of “Tag me in the photos” were 31.9% more likely [2] to use the tagging feature compared to those who see the negatively framed option of “Do not tag me in the photos” (p < .001). Furthermore, those users who were being tagged by default were 19.4% more likely to use the tagging feature (p<.01). In addition, in contrast to H4, the various justifications did not influence tagging decision differently (χ2 (4) = 0.823, p > 0.05). Finally, in line with what we hypothesized in H9, older adults were 19.8% more likely to use the tagging feature compared to young adults (p<.05). 5.3 Effects on Privacy Concerns In contrast to H5 and H6, we did not find any significant effects of framing and default manipulations on privacy concerns (p > .05). However, we did find a significant main effect of justifications on privacy concerns (χ2 (4) = 9.827, p < 0.05). Subsequent planned contrast tests revealed a significant effect of justification valence (p < 0.05), supporting H7 and suggesting that negative justification messages lower users’ privacy concerns compared to positive justifications by 0.132 standard deviations. However, this effect was negated by a marginal interaction between justification type and valence, meaning that the difference between positive and negative justifications mostly hold for normative justifications (see Figure 5). Finally, in contrast to H8, older and younger adults did not have significant differences in terms of their overall level of privacy concerns (p > 0.05). Our results show that in contrast to our H8, older and younger adults have the same levels of privacy concerns. Furthermore, while having the same levels of concern, older adults are more open to disclosure. We further unpack the effects of age by studying moderation effects of age and dark pattern designs. We therefore run a saturated path model and report it below. Fig- ure 6 and Table A1 summarize our results.. 5.4 Moderating Effects of Age Group on Tagging Decision We found age group to moderate the effect of framing on tagging decision; the effect of framing was stronger for older adults than for young adults: older adults who were exposed to a positively framed option were 53.1% more likely to use the tagging feature while young adults who were exposed to a positively framed option were only 33.1% more likely to use the tagging feature (p < .05). Figure 7 depicts this effect. We found a similar moderation effect for defaults suggesting that older adults were more likely to keep the default option and use the tagging feature compared to young adults. However, this effect did not reach significance (p = .062). Similarly, age did not moderate the effect of justifications on the tagging decision (χ2 (4) = 4.822, p > 0.05). Lastly, while the effect of privacy concerns on the tagging decision was stronger for older adults than for young adults, this effect was not significant (p>.05). 5.5 Moderating Effects of Age Group on Privacy Concern The effect of defaults on privacy concerns were moderated by age group: an opt-out default increased older adults privacy concerns by 0.255 times standard deviation compared to a negative default, while an opt-out default increased young adults’ privacy concerns by only 0.066 times standard deviation compared to a negative default (p < 0.01). Figure 8 depicts this effect and suggests that an opt-out default only significantly increases the privacy concerns for older adults. Age group did not moderate the effects of any other variable on privacy concerns(p > 0.05). [2] To present the results in a comprehensive manner, we convert the log odds-ratio to percentages. For example, the effect of framing on disclosure is 0.277 (table A1), which is a log odds-ratio. Therefore, disclosure in the positive framing group is e0 .277 = 1.319 times higher than in the negative framing group, i.e., a 31.9% difference in the odds of disclosure This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

Exploring Differences in Privacy Concerns and Tagging Behavior Among Older and Younger Adults

Authors: (1) Reza Ghaiumy Anaraky, New York University; (2) Byron Lowens; (3) Yao Li; (4) Kaileigh A. Byrne; (5) Marten Risius; (6) Xinru Page; (7) Pamela Wisniewski; (8) Masoumeh Soleimani; (9) Morteza Soltani; (10) Bart Knijnenburg. Table of Links Abstract & Introduction Background Research Framework Methods Results Discussion Limitations and Future Work Conclusion & References Appendix 3 Research Framework In the sections below, we justify information disclosure and privacy concerns as the two outcome variables of interest when examining the influence of dark-pattern designs. We also explain the 2x2x5 experimental design (where the conditions consist of varying defaults, framing, and justifications). Figure 1 shows our hypotheses via our research model. We investigate these hypotheses using a path model. In a path model, variables can function as both dependant variables (DVs) and independent variables (IVs). In our case, privacy concerns is a DV where we study the effects of dark pattern designs on it, and is an IV when we study how it predicts disclosure behaviors. 3.1 Disclosure Behavior Oversharing information on social media can lead to negative consequences for users [65]. Therefore, social media platform users employ a wide range of privacy management strategies to manage their interpersonal boundaries, such as managing their relationship boundaries (e.g., by adding a new friend or unfriending someone) and their territorial boundaries (e.g., by tagging or untagging oneself or someone else in/from photos) [94]. Existing literature regards tagging as a form of disclosure [95], since photo tagging can reveal the tagged person’s online information (e.g. name, social media page) to a broader audience when the photo is shown on friends’ timelines. Tagging other people in ones’ photos is a contentious issue [95]: on the one hand, it can increase group cohesion and build social capital [56, 68], but on the other hand, it can lead to an interpersonal privacy violation if the others prefer not to be tagged [26, 81]. 3.2 Privacy Concerns The privacy literature has not reached a consensus about the relationship between privacy concerns and disclosure behaviors. On the one hand, many studies suggest a negative association between privacy concerns and disclosure behaviors [21, 34, 73]. The general argument in these studies is that a high concern for privacy motivates individuals to refrain from disclosing their data. On the other hand, many studies have found that despite their high privacy concerns, individuals freely give up their personal information—a phenomenon that is so prominent that it has been dubbed the “privacy paradox” [8]. The privacy paradox suggests that privacy concerns have little or no relationship with self-reported or observed disclosure behaviors [57, 79, 83]. For example, Tufekci [87] studied students’ self-reported disclosure behaviors on social network sites. Her results show “little to no relationship” between online privacy concerns and disclosure on online social network sites. Despite these contradictory findings, we pose the following hypothesis reflecting the base expectation that privacy concerns are predictive of disclosure behaviors: H1: Individuals with higher privacy concerns are less likely to disclose. In the following two sub-sections, we build on our RQ1 by posing six hypotheses about the relationships between dark pattern design, disclosure behavior, and privacy concern. We then address our RQ2 by posing two hypotheses regarding the effect of age on disclosure behavior and privacy concerns. 3.3 Behavioral Effects of Dark Pattern Designs: Inducing Disclosure In this section, we discuss the behavioral effects of framing, defaults, and justification messages. Particularly, we draw upon the “privacy dark patterns” literature to hypothesize how these design features are being used to increase disclosure. 3.3.1 Framing and Defaults Framing and default effects have been extensively studied in the privacy domain. Johnson et al. [30] and Lai and Hui [43] independently found framing and default effects to have a significant impact on users’ decisions. In both studies, a positive framing and an opt-out default setting increased the likelihood of users accepting the disclosure requests. In line with these findings, we study two conditions of framing (positive “tag me in the photos” vs. negative “do not tag me in the photos”), and two conditions of the default (opt-out vs. opt-in). We hypothesize the following: H2: A positive framing will increase disclosure. H3: An opt-out default will increase disclosure. 3.3.2 Justification Messages Existing literature suggests that prompting individuals with a message about a product or service can influence their decisions in favor of the message content [27, 39, 45, 92]. Dark pattern designs sometimes use this feature to promote disclosure [12, 54]. For example, they show messages about a product’s popularity in the form of reviews [17] which can sometimes be fake and misleading [80, 84, 91]. Hanson and Putler [27] showed participants a normative justification: an arbitrary integer ostensibly representing the number of downloads of a software program. Participants who saw a higher number were more likely to download the software for themselves. This is arguably due to a herding effect [6], where individuals follow the footsteps of the majority. In addition to such normative justifications, dark pattern designs sometimes use descriptive justification— pushing the benefits of the product—to promote product use or disclosure [12, 54]. For example, in the context of a travel advisor app, designers prompted users with a message of “by signing in, you can download over 300 cities, locations, and reviews to your phone” [12]. Reading about the benefits of the product is arguably a motivating message to sign in and use the app. Having higher quality reviews will increase the likelihood of one buying an online product [39], or talking about the effectiveness (pros) of treatment rather than the cons will increase the likelihood of accepting it [10]. In line with these findings, we study five conditions of justification messages (positive/negative normative, positive/negative rationale-based, none). We pose the following hypothesis: H4: Positive (as opposed to negative) justification messages will increase disclosure. 3.4 Attitudinal Effects of Dark Pattern Designs: Inducing Concerns The effects of framing, defaults, and justification messages on disclosure behavior are evident in the literature [1, 5, 27, 30, 33, 43]. The compliance-inducing effects of such design interventions meet the goal of dark pattern designs. However, research shows that the behavioral effects of these dark pattern strategies may not actually map to their attitudinal effects [7, 38, 41]. In this section, we examine the effects of dark pattern designs on individuals’ state of privacy concerns. Privacy concerns are often measured as an individual trait, rather than a dynamic state [73]. However, this assumption may not be valid. Few studies consider privacy concerns as a dynamic state [67]. We further contribute to the literature by highlighting this perspective and presenting privacy concerns as a dynamic state which can change in response to different design strategies. 3.4.1 Framing and Defaults Online firms have an incentive to collect as much data about their users as they can. Data is an important asset in the industry [13, 29, 78]. For example, having data about customers’ preferences and needs can help firms better target their advertisements to individuals likely to need their product and avoid the unnecessary costs of contacting consumers whose preferences do not match the product [29]. Therefore, some companies adopt dark-pattern design strategies to collect more user data [12]. Framing and default are compliance-inducing mechanisms [4] that are often used in dark pattern designs [25, 53, 54]. Dark pattern default options are designed to encourage sharing of personal information and to maximize online firms’ collected data [12]. Likewise, dark pattern designs use positive framing in the choice statements to endorse disclosure [54]. Whereas the behavioral effect of such dark pattern designs are outlined in Section 3.3, here we note how these interventions can adversely influence users’ attitudes towards the system [12, 47]. For instance, Knijnenburg and Kobsa [41] found that an opt-out default would increase perceived oversharing threats compared to an opt-in default. Therefore, we pose the following hypothesis: H5: A positive framing will increase privacy concerns. H6: An opt-out default will increase privacy concerns. 3.4.2 Justification Messages In the context of dark-pattern design, we argue that while a positive justification message might be intended to encourage individuals to use an app or increase disclosure, it could also increase users’ privacy concerns, especially when they realize that the justification message is used as a dark-pattern strategy. Conversely, users may find a negative justification (i.e., cautioning them about the cons of the product) a sincere message [38] that is indicative of the developer’s benevolence and/or integrity (two of the primary components of trust [55]). For example, communicating potential risks in a study report will increase its perceived trustworthiness [72]. Since trust and privacy concerns are inter-related [19], we argue that a negative justification will result in lower privacy concerns: : H7 Negative (as opposed to positive) justification messages will decrease privacy concerns. Next, we explain the effects relating to age and our second research question. 3.5 Examining Differences between Older Adults and Young Adults Literature suggests a positive association between age and privacy concerns, indicating higher concerns for older users [90]. However, the literature shows mixed findings in terms of the main effect of age on disclosure behaviors. While some studies do not report a significant relationship between age and disclosure [35], other studies show higher disclosure rates for older adults [71]. Despite these mixed findings, we pose the following hypotheses to investigate these effect: H8: Older adults will have higher levels of privacy concerns than young adults. H9: Older adults will disclose more information than young adults. 3.5 Examining Differences between Older Adults and Young Adults Literature suggests a positive association between age and privacy concerns, indicating higher concerns for older users [90]. However, the literature shows mixed findings in terms of the main effect of age on disclosure behaviors. While some studies do not report a significant relationship between age and disclosure [35], other studies show higher disclosure rates for older adults [71]. Despite these mixed findings, we pose the following hypotheses to investigate these effect: H8: Older adults will have higher levels of privacy concerns than young adults. H9: Older adults will disclose more information than young adults. To the best of our knowledge, we are the first to study the difference between older and younger adults responses to the dark pattern designs. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

Unveiling the Impact of Dark Pattern Designs on Privacy Concerns and Disclosure Behavior

Almost any web app that contains data needs a way to represent it succinctly and clearly to the user. However, most Python web development solutions do not provide an easy way to do this purely in . Python Thanks to the plethora of existing React components for creating graphs, an implementation for Python can be created to easily solve this problem. In this post, we’ll walk through how to build a web app purely in Python, which contains graphs that strike a balance between flexibility and ease of use. The graphs are easy to build but also easy to customize to your needs. For this project, we are going to use a new open-source dev tool called Reflex. 1. Getting started with your Python Web App If you face any problems here with installation check out more docs here: https://reflex.dev/docs/getting-started/installation/ $ pip install reflex $ reflex init 2. Creating an Interactive Graph In this example, we will create a live-streaming graph that updates every second with random data. We start by defining some initial data for our chart to use, as well as some imports we will use for the project: from typing import Any, Dict, List
import reflex as rx
import random
import asyncio

data = [
    {"name": "A", "uv": 10, "pv": 110, "amt": 210},
    {"name": "B", "uv": 20, "pv": 120, "amt": 230},
    {"name": "C", "uv": 30, "pv": 120, "amt": 240},
    {"name": "D", "uv": 30, "pv": 130, "amt": 210},
    {"name": "E", "uv": 20, "pv": 140, "amt": 230},
    {"name": "F", "uv": 40, "pv": 170, "amt": 250},
    {"name": "G", "uv": 50, "pv": 190, "amt": 260},
] Here stands for unique visitors, stands for page views and stands for amount. uv pv amt They are arbitrary values that we will use to populate our graph. Next, we define a class that will be used to store the data and update it with an event handler: StreamingState class StreamingState(rx.State):
    data: List[Dict[str, Any]] = data
    stream: bool = False

    def stop_stream(self):
        self.stream = False

    @rx.background
    async def start_stream(self):
        async with self:
            self.stream = True
        while self.stream:
            async with self:
                for i in range(len(self.data)):
                    self.data[i]["uv"] = random.randint(0, 100)
                    self.data[i]["pv"] = random.randint(100, 200)
                    self.data[i]["amt"] = random.randint(200, 300)
            await asyncio.sleep(3) Here, we define a method that will stop the stream when called. We also define a method that will start the stream. We use the decorator to run the method in the background. This allows us to update the data without blocking the UI. stop_stream start_stream @rx.background Remember to use when updating the state in a background task. async with self: Finally, we will define our UI using Reflex's new graphing components. We pass the data from our class to the component and reference the data key we want to use in component. We also add a button to start and stop the stream. StreamingState area_chart area The result is a live updating graph that looks like this: The code for this graph is below: def index():
   return rx.vstack(
     rx.recharts.area_chart(
        rx.recharts.area(
            data_key="pv",
            stroke="#82ca9d",
            fill="#82ca9d",
            type_="natural",
        ),
        rx.recharts.x_axis(
            data_key="name",
        ),
        rx.recharts.y_axis(),
        rx.recharts.legend(),
        data=StreamingState.data,
        width="100%",
        height=400,
    ),
    rx.hstack(
        rx.button(
            "Start Stream",
            on_click=StreamingState.start_stream,
            disabled=StreamingState.stream,
        ),
        rx.button(
            "Stop Stream",
            on_click=StreamingState.stop_stream,
        ),
        width="100%",
    ),
    width="100%",
)

# Add state and page to the app.
app = rx.App()
app.add_page(index)
app.compile() The last three lines define our app, add the graph component to the base route, and then compile our app. Now, we can run it with the terminal command. $ reflex run 3. Multiple Graphs We can add extra components to our chart to show the and data as well. We can also add a an component to show the data when we hover over the chart. area uv amt graphing_tooltip cartesian_grid Keep in mind the child coming first will be displayed in the back, so the order of the components matter. area The code for this graph is below: def index():
  return rx.vstack(
    rx.recharts.area_chart(
        rx.recharts.area(
            data_key="pv",
            fill="#48BB78",
            stroke="#48BB78",
            type_="natural",
        ),
        rx.recharts.area(
            data_key="uv",
            fill="#F56565",
            stroke="#F56565",
            type_="natural",
        ),
        rx.recharts.area(
            data_key="amt",
            fill="#4299E1",
            stroke="#4299E1",
            type_="natural",
        ),
        rx.recharts.x_axis(
            data_key="name",
        ),
        rx.recharts.y_axis(),
        data=StreamingState.data,
        width="90%",
        height=400,
    ),
    rx.hstack(
        rx.button(
            "Start Stream",
            on_click=StreamingState.start_stream,
            is_disabled=StreamingState.stream,
            width="100%",
            color_scheme="green",
        ),
        rx.button(
            "Stop Stream",
            on_click=StreamingState.stop_stream,
            is_disabled=StreamingState.stream == False,
            width="100%",
            color_scheme="red",

        ),
        width="100%",
    )
)

# Add state and page to the app.
app = rx.App()
app.add_page(index)
app.compile() 4. Conclusion So that’s it; in just a few lines of simple code, you’ve created your live streaming graphing web app in pure Python. If you want to learn more about this and how to build graphs with Python check out this . documentation If you have questions, please comment them below or message me on Twitter at or on . Share your app creations on social media and tag me, and I’ll be happy to provide feedback or help retweet! @tgotsman12 LinkedIn Disclaimer: I work as a Founding Engineer at Reflex, where I build the open-source framework.

Python Web Development with Reflex: Creating Dynamic Graphs Easily

ChatGPT has taken over Twitter and pretty much the whole internet, thanks to its power and the meme potential it provides. We all know how being able to generate memes is the best way to conquer the internet, and so it worked. Since you’ve seen numerous examples, you might already know that ChatGPT is an , that you can chat with. It is also called a chatbot, meaning you can interact with it conversationally, imitatting a one-on-one human discussion. AI recently released to the public by OpenAI What you might not know is what it is and how it works... Watch the video to learn more! References ►Try it: ►OpenAI's blog post: ►What is GPT-3: ►What is Reinforcement Learning: ►Join our Discord community: ►Twitter: ►Support me on Patreon: https://chat.openai.com/ https://openai.com/blog/chatgpt/ https://youtu.be/gDDnTZchKec https://youtu.be/C5_NfQy_kZU https://www.louisbouchard.ai/learn-ai-together/ https://twitter.com/Whats_AI https://www.patreon.com/whatsai Video Transcript 0:00 you've seen it everywhere Chad GPT has 0:02 taken on Twitter and pretty much the 0:04 whole internet thanks to its power and 0:06 the meme potential it provides we all 0:08 know being able to generate memes is the 0:11 best way to conquer the internet and so 0:13 it worked since you've seen numerous 0:14 examples you might already know that 0:16 chatgpt is an AI recently released to 0:19 the public by openai allowing you to 0:21 chat with it it's also called a chatbot 0:24 meaning you can interact with it 0:25 conversionally imitating a one-on-one 0:28 human discussion what you might not know 0:30 is what it is and how it works 0:32 chadjupiti is a model based on 0:35 reinforcement learning and the GPT 0:37 series of models from openai I will 0:39 refer you to a video about reinforcement 0:41 learning we recently published with my 0:43 friend Elias to learn more about the 0:46 subfield of AI but quickly reinforcement 0:48 learning is a way to train algorithms by 0:51 trial and error aiming for rewards just 0:54 like humans would do by learning with 0:56 positive feedback more specifically chat 0:58 GPT was built following three steps the 1:02 first was to take an already powerful 1:04 model and fine tune it with supervised 1:06 learning what does this mean it means 1:08 that they took a model specifically its 1:11 GPT 3.5 and improved and up-to-date 1:14 version of gpd3 which they trained once 1:17 more on conversation examples 1:19 specifically instead of being trained on 1:21 pretty much their whole internet as gpt3 1:24 was this means they are trying to narrow 1:26 its potentials strictly to conversations 1:28 making it theoretically better at 1:31 conversing compared to gpt3 since a 1:34 specialist is almost always better than 1:36 a generalist at a specific task if you 1:38 are still not familiar with the GPT 1:40 series of models I would suggest 1:42 watching the short introduction video I 1:44 made covering gpt3 when it came out the 1:47 second step is to add our reinforcement 1:49 learning magic which will allow the 1:51 model to practice and get better as you 1:53 know practice makes perfect more 1:55 precisely in this step we will use the 1:57 model to chat with humans directly have 2:00 it provide multiple possible answers and 2:03 ask the human to rate the answers from 2:05 best to worst this data will then be 2:07 used to train another model called our 2:10 reward model learning to replicate our 2:12 human annotators this leads to our last 2:15 step where our new reward model will 2:18 give feedback to the chat GPT model's 2:20 answers as a reward function to help it 2:22 converge toward the best answers over 2:24 time this final step is to further train 2:27 our algorithm after the initial fine 2:30 tuning step we explained this is why it 2:32 is companies like open AI that release 2:34 those kinds of amazingly powerful models 2:37 it will be unfeasible for universities 2:39 or individuals as it requires way too 2:42 much Computing and time for training 2:44 still what they achieve is quite 2:46 remarkable and I believe they are worth 2:48 doing and worth sharing to Advent 2:50 science and voila after coupling the 2:54 already powerful and most recent GPT 2:56 based language model fine-tuning it to 2:59 conversations and finally using 3:01 reinforcement learning to make it 3:03 practice its conversation skills you 3:05 obtain chat GPD as you have seen before 3:07 the model is quite promising but also 3:10 sometimes a very dumb and doesn't seem 3:12 to have any logic whatsoever it is still 3:15 just an algorithm and far from being 3:17 either intelligent or conscious though 3:20 it will depend on how we Define both it 3:22 definitely has its limitations 3:24 nonetheless the outputs it gives are 3:27 often surprisingly interesting and 3:29 pertinent chatgpt is definitely a step 3:31 forward in conversational Ai and quite 3:34 promising especially working on the 3:36 prompt engineering side of the model to 3:38 leverage its true potential and limit 3:41 failure cases I hope you've enjoyed this 3:43 video and I'd love to see your 3:45 experiments please tag me on Twitter at 3:47 what's AI if you share them or join our 3:50 Discord Community where we created a 3:52 channel specifically for it I will see 3:54 you next week with another amazing AI 3:57 research 4:00 foreign 4:04 [Music]

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It is great when your company scales in size so that you can hire more employees. But you have to remember that scalability leads to difficulties in team management. Instead of 10 people, you have to manage a team of 50+ employees. During 6 years, I have come over different stages and learned the things that help me today to share my experience with you. In this article, I’d like to give some recommendations concerning team management when your company grows from 10 to 50+ workers. The Beginning of Cadabra Studio Cadabra Studio started in 2015. We had a team of 4 designers ready to create functional design solutions. In a short time, our team grew up to 12 people. Since then, we could consider our company as a small-sized business. One day we realized that we couldn’t implement our pixel-perfect handoffs and designs in the way we expected during the development stage when working with outsourcing development companies. Our solution was to open a development department to provide only high-quality products. We worked a lot trying to find our place under the Sun. And we succeed. Nowadays, Cadabra Studio is a software development company that works with dozens of clients worldwide. Our digital solutions are implemented in the Healthcare sector, Insurance, FinTech, Educational business, etc. We make websites with exclusive designs and premium quality CRM systems. I owe this success to well-planned team management, proper communication, and delegation. We didn’t have unrealistic expectations. On the contrary, my team and I were ready to face the challenges that business scalability can bring. In the following passage, I’ll provide you with tips that help you to overcome the difficulties. Tips on Dealing With the Changes When Your Company Grows In this passage, I’d like to provide you with the relevant tips I constantly use. They are based on simplicity, logic, and my personal experience. I’m sure many entrepreneurs will find them useful. The CEO of a company is directly involved in negotiations and the delegation of tasks. If you feel unconfident about these two points, take training courses, ask for advice from business coaches, and read relevant literature. Be confident in your skills. Any business can't be successful without fostering company culture, no matter how excellent the product is. Great businesses happen on purpose. A proactive leadership team molds the culture from the very beginning. It is crucial when you grow the team. Always invest in the company culture. It is more profitable than you may think. Invest in company culture. Trust the people you hire. It becomes harder to control all processes in a middle-size company. That's why it's essential to delegate different tasks to department heads and team leads. Delegation is a must. Here I mean the creation of guidelines for the company's operations and keeping the hierarchy. It helps solve any issues during one working day and avoid situations when one department blocks another's work. Make business processes clear and automated. When Cadabra Studio became medium-sized, I realized that I was no longer a decision-maker. As a CEO, I need to stand in line with other department heads. At least, this is how I feel, and this position helps our company grow. Otherwise, what's the point of hiring people if you think above all others? CEO is not a single controlling head — it is one of the heads. Do not limit yourself to manual labor — buy subscriptions to relevant services that will help optimize your colleagues' work. Monitor the process optimization. Organize psychological training, webinars, and involve them in joint leisure activities. Pay attention to the mental health of your employees. The more extensive your company gets, the more needs appear. For a proper balance between a company's needs and your wishes, always find the points you don't need. The word "NO" helps to find a middle line. Learn how to accept the word "NO." Final Words So, what to expect when your company grows from 10 to 50+ people? My advice: don’t wait for the pitfalls, start delegating your tasks, trust your team, and always be honest with them. Be very precise in hiring the heads of departments because the workspace tone and atmosphere depend on them. Consider them as key members of your business but don’t forget to praise the rest of the team. All of you are working for the same goal, so try to convey this message to every team member. I hope the above-listed tips help you to go boldly forward, grow your company from 10 employees to 50 and stay strong in the meantime. If you have any questions or want to share your experience, feel free to share them online and tag me! I’ll be glad to read about your approach.

8 Tips for CEOs When Teams Grow from 10 to 50+ People

TechBullion.com has a budget for approaching authors on Substack. Hey! TechBullion! I just gave you a wonderful, higher-ranking dofollow than the ones you're offering to people! Congrats! Seriously, read on. This is the first automail I'd ignored: When they hit me with a 2nd one, I answered just to see what the vig on this action was: Ok... you ready for this? What angers me most is that the offer and pricepoint mean that some beleaguered, under-represented writers out there are falling for this trap and literally handing over money for... well, here's my drafted reply that I've decided to just write here on Hacker Noon, as an open letter for people in this predicament: In this other automated mail from you folks it mentions 'promotion to a social audience of over 10 million'. Can you elaborate on that? Is this social reach included in this DIY package? Can you provide actionable stats like clickthroughs and conversion ratios? Does the package support me enabling remarketing through GTM or FB pixel? I'm already a published author on Hacker Noon, having been moderated by their editorial team, and successfully publishing 45 stories there - many with dofollow links. Their alexa siterank is a full 60 000 ahead of techbullion's. They publish me for free, a half dozen times so far putting my stories on the front page, and even entering me in the running for three of Hacker Noon's annual Noonies awards... I won first in Trading, and came 2nd place in both Automation and Mental Health Advocate of the Year. They have the automation in place to Tweet my stories and @ tag me three times during the course of the social promotion campaign, sometimes an additional hand-written tweet from their lead blockchain editor and big wig bizdev guy, Utsav. Hacker Noon even shares some of their . monetization with me for great content I contribute Let alone, the leads I still get regularly in my inbox, from evergreen content I wrote for Hacker Noon, years ago. countless (Medium also lets me publish for free, and I'm in a half dozen publications, driving me even more leads - and I don't lie when I say I charge $2000 for 20 hour's work, upfront, and usually end up charging more to complete the scope of the project - from easy-peasey short pieces .) like this one I've been approached by countless publishers to pitch a featured (paid) piece. I've done far more as an authority in this space for up-and-coming crypto projects, who paid me a decent sum to get their name out there on some of the publications I work with, as well as my personal email list. Can you tell me what exact benefits I, myself, personally, would receive from this DIY package that I can't do better - for free, or even by being paid myself - elsewhere? Remember, always always DYOR.

Dear Substack Writers Who've Been Approached by TechBullion

This article will guide you through running ETH2.0 Beacon Node with Lighthouse client on MacOS if you have barely to 0 experiences with Terminal command lines and you want to spin off Lighthouse Beacon node and join the validator pool on ETH2.0 Beacon chain with Lighthouse client. This guide includes spinning off Nethermind Goerli node. The goal of this article is to guide you through becoming a validator without the need to reach out to teams so you can post “ ” kinda tweet when you manage to spin off your Beacon node (make sure to tag me if you use this guide). This guide can’t be used to run other clients as there may be differences when running different ETH1 clients to run Goerli node and other ETH2.0 clients to run Beacon node. This is exciting You may experience running into a bugs. Don’t worry about bugs, make sure to report them to teams if you experience some :) If you will get stuck, reach out to the teams- they’re happy to assist you. Note: Intro Ethereum has recently (July 30th) since genesis block of ETH1 chain was mined. Today, August 4th 1pm UTC ETH2.0 Medalla testnet will go live. This is super exciting moment for Ethereum community as the road to ETH2.0 is going public will start processing attestations and blocks on Proof-Of-Stake mechanism. This is the beginning of the slot zero of epoch zero. Important note — Medalla is just Testnet, which means that you’re going to use Goerli testnet gETH to stake and join Medalla Validator pool. Not a real mainnet ETH. celebrated it’s 5th Birthday If you’re interested to dive more into ETH2.0 I highly recommend you to check out by Danny Ryan The State of ETH2, June 2020 TL:DR of what we are going through in this guide Install Developer Libraries Spin off ETH1 Goerli node Make deposit via Launchpad Pick ETH2.0 client Install Lighthouse Developer Libraries Run Lighthouse Beacon Node Run Lighthouse Validator Client Additional Reading Install Developers libraries📚⌨️ This is just a basic installation of what we need in order to spin off ETH1 Goerli node and make deposit on ETH2.0 chain via d. We will do some installation later, as every ETH2.0 client have different requirements on what needs to be installed in order to run their client. Launchpa You will need to install a few developer libraries at first. In order to have no struggles, neither “red lines” — errors in Terminal window, you should head over to “Security & Privacy” window on your Mac and select “Open Anyway” as you may experience that some apps are not notarised by Apple, which may cause you some headache moments. Use for a better explanation with screenshots. You will need this step to enable Nethermind runner — we will go through this step later. You may need this step to enable Homebrew or other libraries, programming languages. this guide — This is the magic that will help you install and run other programs on your MacOS. This is the basic you need in order to be able to run next programs and help you with installation process. Copy paste this into your Terminal: Homebrew brew install gmp && brew install snappy && brew install lz4 — this is general-purpose cryptographic library that Lighthouse, and I believe other ETH2.0 clients are using. Copy paste into Terminal: OpenSSL brew install openssl I hope that you made it through this important step, it will be just simpler 🙂 Goerli Node Goerli node is well supported across all ETH 1.0 clients, tooling, and infrastructure, and is used to test the ETH 2.0 transition through a deposit contract deployed to Goerli. You need to run Goerli testnet in order to make deposit contract that you upload as “deposit data” to launchpad that will allow you to stake on ETH2.0 Beacon chain. You have more options to pick which client to spin off in order to run Goerli node — the most used is , lighter one is . Geth Nethermind Note: Geth may be easier to spin off, Nethermind is lighter.You may experience some bugs which are not something that you can’t deal with. I found a great . There’s another . guide on how to spin off Lighthouse with Geth node guide on how to spin off Goerli Geth node Request gETH — Goerli ETH You will need gETH or Goerli ETH in order to stake on ETH2.0 Beacon node. There are multiple ways how to request for gETH: Go to and enter your ETH address Goerli Authenticated Faucet Go to that can give you 165 gETH every 6 hours Prysmatic Labs Discord Or ask on for gETH Lighthouse Discord Spinning off Nethermind Goerli node To spin off Nethermind Goerli node head over to downloads page and ETH1 client. Unzip file (click on it). You may experience some struggles as Nethermind is not notarized app by Apple so and enable “Nethermind.Launcher” file. Now paste into Terminal: download the latest version of Nethermind head over this guide rocksdb brew install Let the operation process then paste into Terminal: ./Nethermind.Launcher select Goerli (light Clinque testnet) select sync: Fast sync type: N to not configure eth stats You should be able to see this on your Terminal: 🥳 Now you should be able to get logs and run Goerli node This may take some time, depending on your internet bandwidth. Note: I recommend you to check out this guide by Nethermind on how to run Nethermind node and logs explanation. If you will experience some bugs, just restart the node by Control + C and type/paste into Terminal: ./Nethermind.Launcher And start this process again: select Goerli (light Clinque testnet) select sync: Fast sync type: N to not configure eth stats Feel free to reach out to Nethermind team on Gitter if you are stuck at this point. Make deposit via Launchpad 💸 This is pretty exciting part as you don’t need to deal with Terminal for a while. This is the part where you will deposit 32 gETH (Goerli ETH). Head over to (in your browser). Make sure to read the steps carefully although you’re going to stake just gETH which is testnet ETH (have no real value) but you still should be aware of what are you doing. Make sure to pay attention to Signing Keys (key management). Hit continue button. Open new Terminal window, at this point you should have 2 Terminal windows open — one with Goerli node, one which you will use to make deposit. Just copy paste this line into your second (new) Terminal window: Launchpad website ./deposit — chain medalla Now pick your mnemonic language — it doesn’t really matter which one you choose, you will get mnemonic worlds — randomly generated worlds in different language. English is nice by default but you can pick any from 7 languages. Type the language into your Terminal and make sure to save this mnemonic phrase somewhere offline. Now type the password, be careful there won’t be any appearance of you typing the password so I recommend you to use offline text editor to paste your password — you can use Notes, Word or anything other. Make sure to save this password as well as your mnemonic phrase Paste the password, hit enter. This cute Rhino should appear in your Terminal: Now continue on to the next step in Launchpad. Find “deposit-data- [timestamp — numbers].json file — windows will appear where you will find this file. You can find it in Finder User (your Mac username) → eth2.0-deposit-cli → validator_keys. This are the keystores that generated Rhino. Now you are going to make transaction on Launchpad. Make sure to select Goerli Test Network on your wallet and deposit Goerli ETH not mainnet ETH. Don’t worry about gas fees, this is not real Ether just test gETH and so the gas fees doesn’t matter at this point. Make a transaction and tadaaaa! Pick ETH2.0 Client There’s a by . I highly recommend you to go through it at least to get a big picture of why there are so many ETH2.0 clients so you will pick the one that you like the most. great overview of ETH2.0 Clients Somer Esat Lighthouse 💡🏠 Lighthouse is aiming to be the most secure ETH2.0 Client written in Rust. They provide where you can find which I believe may not be something that you’re familiar with. Lighthouse book a guide on running Validator in Docker In order to run Lighthouse ETH2.0 Client, you will need to: Install Lighthouse Developer Libraries Run Lighthouse Beacon Node Run Lighthouse Validator Client Install Lighthouse Developer Libraries 📚⌨️ You need to install Cmake — building tool and Rust — programming language and clone Lighthouse library in order to spin off — run Lighthouse Beacon Node and Validator Client. — is a building tool which is required by some libraries that are apart of Lighthouse install package that you need to download with extra command line. Copy paste into Terminal: Cmake brew install cmake — it is a programming language that Lighthouse is written in. You need to install rust in order to run Lighthouse client. Copy paste into Terminal: Rust curl — proto ‘=https’ — tlsv1. -sSf https: 2 //sh.rustup.rs | sl Clone Lighthouse library — you need to clone Lighthouse library in order to be able to run Lighthouse Beacon Node and Validator client. Copy paste into Terminal: git ://github.com/sigp/lighthouse.git cd lighthouse clone https Run Lighthouse Beacon Node The beacon node is a program that maintains the current state of the chain — network and talking to other peers — nodes on the network. Now back to your Terminal. You’re a few Terminal lines away — a few Terminal lines close to be ETH2.0 Medalla validator. At this point you should have open 2 Terminal windows — one with Goerli testnet running, one with ETH2.0 deposit client (where you had Rhino). Open new Terminal window and paste: — http — eth1 lighthouse bn This will start your Lighthouse Beacon node which is a program that maintains the current state of the chain by talking to other peers on the network. This is what you should see on your Terminal: Run Lighthouse Validator Client Now you Lighthouse Beacon Chain is running, let’s do the final step — run the Validator Client which will be used to validate blocks on the ETH2.0 network. In this step you will use Terminal with Rhino — eth2.0- deposit-cli. Paste into your Terminal: account validator — directory validator_keys lighthouse import Now you need to paste your password again (password the one word, not mnemonic phrase — many words). Your Terminal will show you many lines starting with: The last step The Validator Client (vc) is a program that holds your keys (one or more depending on how many validators you’re running) and signing messages (blocks) that you send to the network. Now you need to paste last thing into your Terminal, which will kick off the Validator Client. Use Terminal with Rhino — eth2.0-deposit-cli window and paste into this Terminal: lighthouse vc Congratulations ! You’re now ETH2.0 Lighthouse client Validator of Medalla network 🤗 I hope this article was educational and provided you all information you needed in order to join Lighthouse ETH2.0 Beacon node validator. Now you can make a about being apart of the network 😄 Tweet Make sure to save you mnemonic phrase and password in an offline storage. Additional reading There’s so much to read about ETH2.0, here’s a list of what I recommend you to check out. curated by ETH2.0 Newsletter Ben Edgington. ETH2.0 for Dummies If you’re interested in reading more about Launchpad, . check out this article What’s ETH2.0 and what will happen to this cryptocurrency This may be similar to this article but maybe a bit outdated — Using Nethermind to Run a Validator in Eth2 I also curated my own list of articles regarding to ETH2.0 There’s a guide on with Geth client how to run Lighthouse ETH2.0 validator Kudos to Sigma Prime Thank you so much for this opportunity to show you how to run Lighthouse validator for Non tech people. Thank you so much to for technical assistance, answering my questions during this process. It has been a blast to work with team, good luck with the Lighthouse ETH2.0 Client ! 🥳 Mehdi Zerouali Pawan Dhananjay Sigma Prime Thank you to from for helping me out with Goerli node. Tomasz K. Stańczak Nethermind Previously published at https://medium.com/@AnettRolikova/eth2-0-lighthouse-validator-non-tech-guide-on-macos-90cb00f7ba4f

How To Run An ETH 2.0 Beacon Node Using The Lighthouse MacOS Client

is a free, open-source CSS framework based on and used by more than developers. It's the easiest to learn (my opinion) due to its natural naming of classes and a simple grid system. It's not that popular like Bootstrap, but it got a specific fanbase. Bulma Flexbox 200,000 Today's post is not about Bulma's benefits or usage. It's more specifically about the integration of Bulma and VueJS. How to use Bulma with VueJS There are several ways to with Bulma. You can either: get started use to install the Bulma package (Recommended - Customizable) npm use the to link to the Bulma stylesheet (cannot be customized) jsDelivr CDN 1. Integration Bulma using npm It's my personal recommendation to use the Bulma in VueJS this way, because of some benefits that you'll not get another way around. You can customize colors, breakpoints, type, and custom imports to decrease the file size. This is only possible if we set up a Sass workflow in our project, because of Bulma's nature. So Here we go: Installing Bulma Install these required dependencies (If it already doesn't exist in your project): npm install --save-dev node-sass sass-loader You can also read this about it if you don't understand this or want a more detailed installation. in-depth post Install Bulma through npm/yarn: npm install bulma You can now review your to see the changes. package.json Adding to the project It's super opinionated to import Bulma to the project, but this way works best for me: Create a main.scss in the /assets folder. In this `main.scss` file paste this code: @ ; // Customizations Here (Optional) // Bulma Import import '~bulma' Add this file in main.js of your project like this: ( ); require '@/assets/main.scss' You're all good, now. Just start the local development server and check Bulma working on your website. 2. use the jsDelivr CDN to link to the Bulma stylesheet (cannot be customized) This is the easiest way to integrate Bulma into your project (Vue or HTML/CSS) but you'll not able to anything in this way. You'll get a complete build of Bulma. customize So here is how you can add through CDN: Copy this, or retrieve the : latest version of Bulma from JSDelivr <link href= > "https://cdn.jsdelivr.net/npm/bulma@0.9.0/css/bulma.min.css" Go to the /public directory and paste this in index.html Restart the local development server, and start using Bulma 🙌. Customization (Bonus) This is the bonus chapter of this post, where I will shortly mention the effects of customization in Bulma, and how you can customize it. Bulma is natively written in Sass, so you need a Sass workflow to customize it using Variables which is only possible if you're using the npm Basic Customization @charset ; @ url( ); $purple: # A4D76;
$pink: #FA7C91;
$brown: # ;
$beige-light: #D0D1CD;
$beige-lighter: #EFF0EB; $family-sans-serif: , sans-serif;
$grey-dark: $brown;
$grey-light: $beige-light;
$primary: $purple;
$link: $pink;
$widescreen-enabled: ;
$fullhd-enabled: ; $body-background-color: $beige-lighter;
$control-border-width: px;
$input-border-color: transparent;
$input-shadow: none; @ ;
@ ;
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@ ; "utf-8" // Import a Google Font import 'https://fonts.googleapis.com/css?family=Nunito:400,700' // Set your brand colors 8 757763 // Update Bulma's global variables "Nunito" false false // Update some of Bulma's component variables 2 // Import only what you need from Bulma import "../node_modules/bulma/sass/utilities/_all.sass" import "../node_modules/bulma/sass/base/_all.sass" import "../node_modules/bulma/sass/elements/button.sass" import "../node_modules/bulma/sass/elements/container.sass" import "../node_modules/bulma/sass/elements/title.sass" import "../node_modules/bulma/sass/form/_all.sass" import "../node_modules/bulma/sass/components/navbar.sass" import "../node_modules/bulma/sass/layout/hero.sass" import "../node_modules/bulma/sass/layout/section.sass" : Result This is it, and voilà! You've managed to install and customize Bulma. Thanks for reading this post, if you liked it, Share it on Twitter and tag me. Don't forget to follow me there. Previously published at https://justaashir.com/blog/bulma-vue-js-installation/

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