Podcasts about smartvault

Episode 243 FACULTY: Nick Boscia, CPA, EA aka The Balanced CPA CLASS: #Shoperations In this episode, join Scotty as he talks with Nick Boscia, aka the Balanced CPA from New Jersey. Nick shares insights from his journey in accounting, taking over his father's firm, and running a family business with his brother. They explore the importance of balance in work and life, the evolution of their firm with technology, and the implementation of fair client pricing. Nick also discusses the challenges they faced and the aspirations for the future, all while maintaining a balanced approach to life and work. This engaging conversation offers a deep dive into the life of a modern CPA striving for balance and growth.

I get a little ranty in this one!  I hope it's a helpful rant.  xo Jess   Pareto principle: https://en.wikipedia.org/wiki/Pareto_principle Qount: https://www.qount.io/ SecureFilePro: https://securefilepro.com/ SmartVault: https://www.smartvault.com/home/ AAA9 | Phones at Work: https://podcasts.apple.com/us/podcast/aaa-9-phones-at-work/id1476184074?i=1000647254212  

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: A gentle introduction to mechanistic anomaly detection, published by Erik Jenner on April 4, 2024 on LessWrong. TL;DR: Mechanistic anomaly detection aims to flag when an AI produces outputs for "unusual reasons." It is similar to mechanistic interpretability but doesn't demand human understanding. I give a self-contained introduction to mechanistic anomaly detection from a slightly different angle than the existing one by Paul Christiano (focused less on heuristic arguments and drawing a more explicit parallel to interpretability). Mechanistic anomaly detection was first introduced by the Alignment Research Center (ARC), and a lot of this post is based on their ideas. However, I am not affiliated with ARC; this post represents my perspective. Introduction We want to create useful AI systems that never do anything too bad. Mechanistic anomaly detection relaxes this goal in two big ways: Instead of eliminating all bad behavior from the start, we're just aiming to flag AI outputs online. Instead of specifically flagging bad outputs, we flag any outputs that the AI produced for "unusual reasons." These are serious simplifications. But strong methods for mechanistic anomaly detection (or MAD for short) might still be important progress toward the full goal or even achieve it entirely: Reliably flagging bad behavior would certainly be a meaningful step (and perhaps sufficient if we can use the detector as a training signal or are just fine with discarding some outputs). Not all the cases flagged as unusual by MAD will be bad, but the hope is that the converse holds: with the right notion of "unusual reasons," all bad cases might involve unusual reasons. Often we may be fine with flagging more cases than just the bad ones, as long as it's not excessive. I intentionally say "unusual reasons for an output" rather than "unusual inputs" or "unusual outputs." Good and bad outputs could look indistinguishable to us if they are sufficiently complex, and inputs might have similar problems. The focus on mechanistic anomalies (or "unusual reasons") distinguishes MAD from other out-of-distribution or anomaly detection problems. Because of this, I read the name as "[mechanistic anomaly] detection" - it's about detecting mechanistic anomalies rather than detecting any anomalies with mechanistic means. One intuition pump for mechanistic anomaly detection comes from mechanistic interpretability. If we understand an AI system sufficiently well, we should be able to detect, for example, when it thinks it's been deployed and executes a treacherous turn. The hope behind MAD is that human understanding isn't required and that we can detect cases like this as "mechanistically anomalous" without any reference to humans. This might make the problem much easier than if we demand human understanding. The Alignment Research Center (ARC) is trying to formalize "reasons" for an AI's output using heuristic arguments. If successful, this theoretical approach might provide an indefinitely scalable solution to MAD. Collaborators and I are working on a more empirical approach to MAD that is not centered on heuristic arguments, and this post gives a self-contained introduction that might be more suitable to that perspective (and perhaps helpful for readers with an interpretability background). Thanks to Viktor Rehnberg, Oliver Daniels-Koch, Jordan Taylor, Mark Xu, Alex Mallen, and Lawrence Chan for feedback on a draft! Mechanistic anomaly detection as an alternative to interpretability: a toy example As a toy example, let's start with the SmartVault setting from the ELK report. SmartVault is a vault housing a diamond that we want to protect from robbers. We would like an AI to use various actuators to keep the diamond safe by stopping any robbers. There is a camera pointed at the diamond, which we want to u...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: A gentle introduction to mechanistic anomaly detection, published by Erik Jenner on April 4, 2024 on LessWrong. TL;DR: Mechanistic anomaly detection aims to flag when an AI produces outputs for "unusual reasons." It is similar to mechanistic interpretability but doesn't demand human understanding. I give a self-contained introduction to mechanistic anomaly detection from a slightly different angle than the existing one by Paul Christiano (focused less on heuristic arguments and drawing a more explicit parallel to interpretability). Mechanistic anomaly detection was first introduced by the Alignment Research Center (ARC), and a lot of this post is based on their ideas. However, I am not affiliated with ARC; this post represents my perspective. Introduction We want to create useful AI systems that never do anything too bad. Mechanistic anomaly detection relaxes this goal in two big ways: Instead of eliminating all bad behavior from the start, we're just aiming to flag AI outputs online. Instead of specifically flagging bad outputs, we flag any outputs that the AI produced for "unusual reasons." These are serious simplifications. But strong methods for mechanistic anomaly detection (or MAD for short) might still be important progress toward the full goal or even achieve it entirely: Reliably flagging bad behavior would certainly be a meaningful step (and perhaps sufficient if we can use the detector as a training signal or are just fine with discarding some outputs). Not all the cases flagged as unusual by MAD will be bad, but the hope is that the converse holds: with the right notion of "unusual reasons," all bad cases might involve unusual reasons. Often we may be fine with flagging more cases than just the bad ones, as long as it's not excessive. I intentionally say "unusual reasons for an output" rather than "unusual inputs" or "unusual outputs." Good and bad outputs could look indistinguishable to us if they are sufficiently complex, and inputs might have similar problems. The focus on mechanistic anomalies (or "unusual reasons") distinguishes MAD from other out-of-distribution or anomaly detection problems. Because of this, I read the name as "[mechanistic anomaly] detection" - it's about detecting mechanistic anomalies rather than detecting any anomalies with mechanistic means. One intuition pump for mechanistic anomaly detection comes from mechanistic interpretability. If we understand an AI system sufficiently well, we should be able to detect, for example, when it thinks it's been deployed and executes a treacherous turn. The hope behind MAD is that human understanding isn't required and that we can detect cases like this as "mechanistically anomalous" without any reference to humans. This might make the problem much easier than if we demand human understanding. The Alignment Research Center (ARC) is trying to formalize "reasons" for an AI's output using heuristic arguments. If successful, this theoretical approach might provide an indefinitely scalable solution to MAD. Collaborators and I are working on a more empirical approach to MAD that is not centered on heuristic arguments, and this post gives a self-contained introduction that might be more suitable to that perspective (and perhaps helpful for readers with an interpretability background). Thanks to Viktor Rehnberg, Oliver Daniels-Koch, Jordan Taylor, Mark Xu, Alex Mallen, and Lawrence Chan for feedback on a draft! Mechanistic anomaly detection as an alternative to interpretability: a toy example As a toy example, let's start with the SmartVault setting from the ELK report. SmartVault is a vault housing a diamond that we want to protect from robbers. We would like an AI to use various actuators to keep the diamond safe by stopping any robbers. There is a camera pointed at the diamond, which we want to u...

Joining Eric on this week's podcast is Luke Kiely, Chief Information Officer at SmartVault. Luke and Eric discuss the need for back-up and assuring business continuity, some simple steps to do that and the cost of NOT having a back-up plan in place, both literally and figuratively.   https://www.smartvault.com/eric-green/

Joining Eric on this week's podcast is Luke Kiely, Chief Information Officer at SmartVault. Luke and Eric discuss the need for back-up and assuring business continuity, some simple steps to do that and the cost of NOT having a back-up plan in place, both literally and figuratively.

GetBusy management present a detailed insight into the Company, its products and its proven strategy for growth. Dr Miles Jakeman, Non-Executive Chairman 00:16 - Introduction 00:48 - About GetBusy Daniel Rabie, CEO 05:05 - Overview Paul Haworth, CFO 12:51 - The markets GetBusy serve Dania Buchanan, President, SmartVault 18:50 - Overview of SmartVault Tracey Nichols, HFL Accountants 36:30 - A Smartvault customer journey David Owen, Global Co-Head of Virtual Cabinet 00:52:42 - Overview of Virtual Cabinet Deborah Baxter, CEO, Turnkey 01:24:19 - The Insolvency Marketplace and Virtual Cabinet 01:38:57 - Part 1 Q&A Daniel Rabie, CEO 01:50:54 - Emerging products and long term growth Jason Ross, Global Co-Head of Workiro 01:53:30 - Overview of Workiro Paul Hawoth, CFO 02:09:23 - Overview of Certified Vault Daniel Rabie, CEO 02:18:35 - People and Culture Paul Hawoth, CFO 02:35:41 - GetBusy's ambition 2:43:30 - Part 2 Q&A   About GetBusy GetBusy's specialist productivity software solutions enable growing businesses to work securely and efficiently with their customers, suppliers and teams anytime, anywhere. Our solutions can be delivered flexibly across cloud, mobile, hosted and on-premise platforms, whilst integrating seamlessly with a wide variety of other class-leading core business systems. With over 74,000 paying users across multiple market sectors and jurisdictions, GetBusy is an established and fast-growing SaaS business delivering sustained double-digit growth in high-quality recurring subscription revenue over the long term. Further information on the Group is available at www.getbusyplc.com

“Disruption comes in all shapes and sizes.” - Dania BuchananThank you for tuning in to episode 31 of The CUInsight Network, with your host, Lauren Culp, Publisher & CEO of CUInsight.com. In The CUInsight Network, we take a deeper dive with the thought leaders who support the credit union community. We discuss issues and challenges facing credit unions and identify best practices to learn and grow together.My guest today is Dania Buchanan, the President at SmartVault. The core mission at SmartVault is providing businesses with a central document destination that enables the adoption of a fully digital, fully automated document workflow. The SmartVault system is also highly secure and supports regulatory compliance mandates. They focus on reducing or eliminating routine and repetitive tasks associated with document workflows, serving small and mid-sized businesses like credit unions.While she originally wanted to be a journalist, in this episode Dania discusses the inspiration that led to founding SmartVault and taking on the role as President. She shares that product disruption is exciting to her, as her team turns traditional document management on its head. We explore ways that integrating document management into the apps and workflows credit unions are already using can make them more competitive.Dania always works to create a culture where staff can do the best and most vibrant work of their careers. Throughout the episode, we talk through the needs of employees to be able to collaborate whether working from the office, at home, or in a hybrid of the two. Dania expresses how important community service is to her as a leader and how she intentionally builds structures for her whole team to participate. As technology continues to evolve, Dania recommends building the foundation first and leveraging incremental change instead of trying to redo everything at once.As we wrap up the episode, Dania shares a favorite beverage, her go-to podcasts, and the person she thinks of first when she hears the word “success.” Enjoy my conversation with Dania Buchanan!Find the full show notes on cuinsight.com.Connect with Dania:Dania Buchanan, President at SmartVaultdbuchanan@smartvault.com https://www.smartvault.com/ LinkedIn | Twitter | Facebook | Instagram

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Vaniver's ELK Submission, published by Matthew "Vaniver" Graves on March 28, 2022 on The AI Alignment Forum. You can see the actual submission (including a more formalized model) here, and the contest details here. I've reordered things to be more natural as a blog post / explain the rationale / intuition a bit better. This didn't get a prize, tho it may have been because I didn't try to fit the ELK format. The situation: we have the capacity to train an AI to predict physical reality quite well. We'd like to train an AI (the "SmartVault") that uses that understanding of reality to protect a valuable diamond inside the vault. Ultimately, we'd like the system to protect the diamond and not deceive the owner of the diamond; proximally, we'd like to make a Reporter that can figure out, from the latent knowledge of the SmartVault, whether it's protecting the diamond or deceiving the owner. I roughly think the proximal goal is doomed, and that the hope needs to be prospective instead of retrospective. [That is, we need to have a system that we can see ahead of time will try to protect the diamond instead of us checking after the fact for deception, because that can just move the meta-level on which deception occurs.] So I'm going to try to propose a solution that de-emphasizes the Reporter, and see how far I get. Setup We can roughly model SmartVault as being composed of some subsystems: the Predictor (which, given a trajectory of actions, estimates their likely result), a Reporter (which, given access to the predictor, answers specific questions about the result), and an Optimizer (which, given a situation, tries to decide which trajectory of actions to take). Obviously, the Predictor and the Optimizer are going to be intimately connected to each other, and the ontology of the Predictor is going to be jointly optimized by reality and the training feedback (i.e. the structure of the model's knowledge will reflect both the underlying reality and the salience landscape of the humans labelling examples as 'good' and 'bad'). I'm going to assume that 'the human value function' is part of the 'latent knowledge relevant to prediction (of training reward)', even if it's implicit instead of explicit. While we'd like to train our machine on (reward, state of reality) pairs, unfortunately we only have access to (reward, human observation) pairs for training; that is, we only know how to communicate the lower-level salience landscape. This means a core challenge is ontological identification, as each ‘human observation' state could mean many possible ‘machine observation' states. Furthermore, let's assume the machine ontology is more detailed and relevant (as it's the one driving actions, including actions that might corrupt sensors). There are two main classes of reporters: the 'human simulator' (which answers the question: "what would the human think if they saw their observations?") and the 'direct translator' (which answers the question: "what would a hypothetical correct human think if they saw my observations?"). Similarly, I think there are two main classes of inferred human value functions, which seem pretty analogous: the 'finger' (which answers the question: "did the human observe things that they approved of?"), and the 'moon' (which answers the question: "did the thing the human approved of happen?", or "would a hypothetical correct human approve of the thing that I infer occurred?"). I am tempted to just call them the 'human simulator' and the 'direct translator', but it's probably better to reserve those terms for reporters and introduce new terms for talking about the inferred value functions, even tho I think they use basically the same mechanisms. [One reporter you could imagine, for example, doesn't answer arbitrary questions but just implements the human value func...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Eliciting Latent Knowledge Via Hypothetical Sensors, published by John Maxwell on December 30, 2021 on The AI Alignment Forum. This is a response to ARC's first technical report: Eliciting Latent Knowledge. But it should be fairly understandable even if you didn't read ARC's report, since I summarize relevant parts of their report as necessary. Here I propose some approaches to the problem ARC outlines which are very different from the approaches they explore. Idea #1: Detecting failure using a hold-out sensor The core challenge in ARC's report is to obtain accurate knowledge about the actual presence / absence of a diamond in a protective "SmartVault", as opposed to accurate knowledge about whether a human observer would think the diamond is present if the human observer had access to data from a sensor I'll call S1. This is difficult because it's hard to get labeled data which differentiates between the two. If the AI protecting the diamond in the vault is optimizing for whether a human observer would think the diamond is present, it will find that corrupting readings from S1 while allowing a thief to take the diamond is a perfectly legitimate strategy. When the AI simulates this diamond-protection strategy, it will receive the maximum score, since score is calculated based on what a human with access to S1 readings would think. Suppose we install an additional sensor S2 in the vault, on a closed-circuit system that isn't present in the AI's model of the vault. It seems reasonable to guess that the AI won't be able to boost the simulated score of its diamond-protection strategy by corrupting readings from S2 in addition to S1. So if ARC has some method they think will get the AI to actually guard the diamond, secretly observing S2 gives them a chance to check if the method is working. This strategy could fail. For example, if (a) S2 ends up being present in the AI's model of the vault despite our best intentions, and (b) the AI's learned scoring function happens to be based on what a human with access to all known sensors would think. (This latter possibility is a bigger risk if humans have been peeking at S2 periodically during the course of iteratively developing the AI. The analogy to train/dev/test splits should be pretty obvious here.) So it's not a perfect detection method. Idea #2: Predicting the readings of hypothetical sensors An implicit assumption of the Eliciting Latent Knowledge report is that the SmartVault AI can make predictions about readings from sensor S1, given the actions it takes to manage the vault and other data as predictor variables. I'm imagining the AI has been trained to be able to do this using supervised learning. We have some features X consisting of actions taken and other data, and a target Y consisting of S1 readings. The AI searches for a model which maps X to Y using e.g. gradient descent. We can modify the training procedure to predict the readings from any hypothetical sensor as follows. In addition to the original features X, we add six additional features corresponding to the latitude, longitude, height, yaw, pitch, and roll of the sensor, resulting in an augmented feature matrix X'. To generate training data, we repeatedly: Use a random number generator to generate random values for those six additional features Place the sensor according to those random values Use whatever data generation method we were using in the original SmartVault scenario to populate Y with readings from the randomly placed sensor If the AI correctly learns a mapping from the augmented feature matrix X' to Y, we've now got a model that can make predictions about the readings we would get from any hypothetically placed sensor. This idea mainly serves as a building block for later ideas, but the basic intuition is that sensor corruption is tougher if t...

On this episode of the Planned Solutions Incorporated Podcast, It remains to be seen whether the recent bout of high inflation will persist longer-term. However, should inflation remain elevated, it is important to understand the impact that it may have on stock prices. This includes the potential for high inflation to lead to an increase in interest rates, which will likely have negative implications for growth stocks, while value stocks and high-dividend stocks may benefit from such an environment. Also, Two of the last pandemic related stimulus programs are scheduled to end in the next couple of months. The expanded child tax credit payments that some households are receiving monthly in advance are scheduled to end on December 31, 2021. Also, the moratorium on federal student loan payments and the suspension of interest payments on those loans are scheduled to end in February 2022. And, Year-end tax planning often involves tax-loss harvesting in which investors sell investments that have a tax loss to capture the tax benefit for the current or future tax years. However, in some years tax-gain harvesting may also be advisable, especially when taxpayers can use past losses to offset the gain, will avoid taxes on the gain due to low income for the tax year, or expect income tax rates on capital gains to increase in future years. Plus a look at the Planned Solutions Incorporated Office Bulletin Board- The 2021 SmartVault files have been created; this can be a good place to store any donation receipts, car registrations taxes, property tax payments, estimated tax payments, etc. while we wait for the tax season to begin. Chase is once again contributing to the Sacramento Business Review (SBR) January economic update event. The SBR is a collaboration between members of the Sacramento Chartered Financial Analysts (CFA) Society and the Sacramento State University School of Business. Chase Armer's book- Financial Planning Insights is now available at: store.bookbaby.com/book/financial-…anning-insights www.amazon.com/Financial-Plannin…1586894022&sr=8-1 To subscribe to the Personal Finance Review (the written form of all the content we discuss on the podcast) please e-mail Katie@PlannedSolutions.com The Personal Finance Review is published and distributed on a biweekly basis by Planned Solutions, Inc. for informational purposes only. Please seek the advice of a qualified financial planner before taking any action. Planned Solutions, Inc. ADDRESS: PHONE: 1130 Iron Point Road, Suite 170 (916) 361-0100 Folsom, CA 95630 (800) 750-2111 E-MAIL: FAX: Shannon@PlannedSolutions.com (916) 361-0191 WEB SITE: www.PlannedSolutions.com #finance #invest #investment #stocks #inflation #deflation #bonds

On this episode of the Planned Solutions Incorporated Podcast, After 18 months of volatility the Federal Reserve Senior Loan Officer Survey appears to be signaling a normalization in the market for loans. Lenders tightened lending standards for both consumer and business loans in 2020 but are now loosening those standards to encourage more borrowers to apply. Meanwhile, demand for mortgage loans increased in 2020 but has since declined while the demand for consumer loans (auto, credit card, and personal loans) has recovered this year. Also, Many people are leaving corporate life for the freedom and flexibility that may come from being self-employed. However, working as an independent contractor rather than as an employee may come with some additional taxes and expenses. It may also reduce access to a number of common employee benefits which should be taken into consideration. And, When someone passes after setting up a trust to handle their financial affairs there are a series of actions that are required by law to protect the interests of all parties involved. Therefore, the successor trustee as well as the beneficiaries or other interested parties should be aware of the legal requirements in order to meet their fiduciary duty and protect their rights. This is especially important when there are disagreements about how the trust should be administered. Plus a look at the Planned Solutions Incorporated Office Bulletin Board- Planned Solutions will soon be sending out our tax engagement letters and organizers in early January. We will also be posting the organizers in SmartVault a few weeks before mailing the organizers. At Planned Solutions, we have been engaged in training about cyber security. Chase Armer's book- Financial Planning Insights is now available at: store.bookbaby.com/book/financial-…anning-insights www.amazon.com/Financial-Plannin…1586894022&sr=8-1 To subscribe to the Personal Finance Review (the written form of all the content we discuss on the podcast) please e-mail Katie@PlannedSolutions.com The Personal Finance Review is published and distributed on a biweekly basis by Planned Solutions, Inc. for informational purposes only. Please seek the advice of a qualified financial planner before taking any action. Planned Solutions, Inc. ADDRESS: PHONE: 1130 Iron Point Road, Suite 170 (916) 361-0100 Folsom, CA 95630 (800) 750-2111 E-MAIL: FAX: Shannon@PlannedSolutions.com (916) 361-0191 WEB SITE: www.PlannedSolutions.com #finance #invest #investment #stocks #inflation #deflation #bonds

GetBusy's CEO, Daniel Rabie outlines what the business does, the market drivers and the growth strategy through organic growth and acquisition, including the two new acquisitions of Quoters and DocDown. GETB aspire to double their ARR over the next 5 years. 00:21 What Get Busy does 00:55 Market drivers 02:30 The growth strategy 03:10 Acquisitions: 1. Quoters 2. DocDown 04:38 The opportunity GetBusy plc is a United Kingdom-based company. The company is engaged in document management software business. The Company's product offering includes Virtual Cabinet and SmartVault. The Virtual cabinet is document management software focused on the medium size to enterprise size content management (ECM) markets. It is used by accounting firms in the United Kingdom. SmartVault product is document management software targeting the professional small and medium enterprise (SME) market. The Company has operations across the United Kingdom, United States, Australia and New Zealand.

Nicholas Tyson, Director of CS at SmartVault, joins us from Houston to talk about building and scaling customer success. We met Nicholas back In 2019 at the CS100 conference and let me tell you, this guy Is DYNAMIC. His energy and passion Is Infectious so you can hear how excited he gets about what he and the team have been able to accomplish at SmartVault. -- If you want to join the discussion with thousands of other customer success leaders, join Gain Grow Retain: http://gaingrowretain.com/ This podcast is brought to you by Jay Nathan and Jeff Breunsbach... Jay Nathan: https://www.linkedin.com/in/jaynathan/ Jeff Breunsbach: https://www.linkedin.com/in/jeffreybreunsbach

Nicholas Tyson, Director of CS at SmartVault, joins us from Houston to talk about building and scaling customer success. We met Nicholas back In 2019 at the CS100 conference and let me tell you, this guy Is DYNAMIC. His energy and passion Is Infectious so you can hear how excited he gets about what he and the team have been able to accomplish at SmartVault. --If you want to join the discussion with thousands of other customer success leaders, join Gain Grow Retain: http://gaingrowretain.com/This podcast is brought to you by Jay Nathan and Jeff Breunsbach...Jay Nathan: https://www.linkedin.com/in/jaynathan/Jeff Breunsbach: https://www.linkedin.com/in/jeffreybreunsbach

In the midst of the COVID-19 pandemic Eric sits down and discusses the issues around data with Dania Buchanan, head of SmartVault. During the program Dani and Eric discuss the current situation, why the use of technology to protect and move information around a decentralized workforce has never been more critical and the opportunities created by the current situation.

In the midst of the COVID-19 pandemic Eric sits down and discusses the issues around data with Dania Buchanan, head of SmartVault. During the program Dani and Eric discuss the current situation, why the use of technology to protect and move information around a decentralized workforce has never been more critical and the opportunities created by the current situation. Want to check out SmartVault? Click here: www.smartvault.com/ericgreen Want to join the free webinar on 5/4 - Audit Horror Stories? Head over here and register for free: https://gs-lawfirm.com/program-20200504-audit-horror-stories-registration/ Looking to build your Tax Representation practice? Join us inside Tax Rep LLC! www.TaxRepLLC.com

All throughout history, women are taught to conform, to fit someone else’s standards, and to be someone they are not. As the world slowly shifts, women are now reclaiming who they should be on their own terms, living their lives away from the playbook. Dania Buchanan, Head of SmartVault, has lived through the changes of time—moving in to fit the standards of being a woman in tech in the early ‘90s, to becoming the leader that she is now. She shares with us her career journey, of leading her life without a playbook, beginning with her personal story of growing up as a child of a fallen commander in Vietnam and how it has shaped her career and leadership style. Blessed or cursed, everything she has been through has led her to where she is now. All it takes is a change in perspective and mindset to see learning experiences as blessings.

All throughout history, women are taught to conform, to fit someone else’s standards, and to be someone they are not. As the world slowly shifts, women are now reclaiming who they should be on their own terms, living their lives away from the playbook. Dania Buchanan, Head of SmartVault, has lived through the changes of time—moving in […]

Taking charge of your finances as an entrepreneur is one of the hardest things to do when building your business, but it’s the most important!   This week’s episode of Good Girls Get Rich is brought to you by Uplevel Media and CEO and founder, Karen Yankovich discusses her thoughts with special guest Dawn Brolin on how to take charge of your Finances as an Entrepreneur. In this podcast, the discuss many mindsets on how to manage your money as a solopreneur or entrepreneur and give key resources on how to do so.   Dawn Brolin is a CPA, CFE and CEO of Powerful Accounting LLC. Dawn is a nationally recognized accounting tax, forensic and fraud and IRS representation, as well as a quickbooks consulting firm. Dawns list of professional accomplishments include speaking and consulting for prestigious companies. She was named top 100 most influential people in accounting in 2018 by Accounting Today, top 25 most powerful women in accounting from 2012-2017 by CPA practice advisor, top 10 managing partner by elite great accounting firm leader in 2017 by Accounting Today, top 40 under 40 in 2009 by CPA Technology Magazine.     #GoodGirlsGetRich We want to hear your thoughts on this episode! Leave us a message onSpeakpipeor email us at info@karenyankovich.com.   Episode Spotlights: Recap of last weeks episode (1:00) Meet Dawn Brolin, CFE, CPA and CEO of Powerful Accounting LLC. (4:30) What Dawn tells all entrepreneurs and small business owners at the start of their work together (6:31) How to get out of the entrepreneur mindset of being “paycheck to paycheck: (9:46) The Profit First Mindset and Model (14:05) The importance of knowing the numbers of your business model (22:15) What made Dawn decide to be an entrepreneur (27:05) Dawns experience working with fraud (28:51) Ways to securely track and store expenses and documents (33:23) A tool for entrepreneurs to store receipts and store info incase of an IRS audit (38:47) Where to find more information about Dawn (42:42) Resources Mentioned In This Episode: Free LinkedIn TipsSeries By UpLevel Media Profit First by Mike Michalowicz Right Signature, a way to document credit cards Docusign, a way to implement electronic signatures SmartVault, an online document storage and secure file sharing system Receipt Bank, a way to store receipts QuickBooks, the accounting software Dawn implements for clients and herself Dawn’s business Powerful Accountingthat helps service businesses accounting needs All the information you need about Dawn Brolinand how to work with her. Dawn’s upcoming Fight Against Fraudproject to educate business owners Free Facebook Groupto discuss LinkedIn strategy and building your business   Help Us Spread The Word! It would be awesome if you shared the Good Girls Get Rich Podcast with your fellow entrepreneurs on twitter.Click here to tweet some love!   If this episode has taught you just one thing, I would love if you could head on over to Apple Podcasts and SUBSCRIBE TO THE SHOW! And if you’re moved to, kindlyleave us a rating and review.Maybe you’ll get a shout out on the show! Ways to Subscribe to Good Girls Get Rich: Click here to subscribe via Apple Podcasts Click here to subscribe via PlayerFM You can also subscribe via Stitcher Good Girls Get Rich is also on Spotify Take a listen on Podcast Addict

Host Jose Piña is joined in studio by Ivyson Donnie & Leah Martinez of SmartVault and members of I AM KATY TX Networking.I AM KATY TX Networking is a professional networking group that connects those doing business in the Katy, TX and West Houston area. For more information on the organization visit:www.IAMKATYTX.orgwww.FB.com/IAMKATYTX

SmartTalk Radio E08 - SmartCash Web Wallet, SmartVault and SmartShift Hosts Chris "Zaphoid" Karabats and Mark Hakkarinen discuss two new SmartCash web wallet features, SmartVault and SmartShift, with Web Hive Coordinator Lamas. Read more at: https://coinlance.com/2019/01/24/new-smartcash-web-wallet-features-make-crypto-cash-accessible-to-everyone/ Sign up for a web wallet at https://wallet.smartcash.cc https://www.smartcash.cc