Like plenty of things invented in the 20th century, badge engineering was awful but I kind of miss it. Car companies typically American, but not exclusively so, as the image above indicates would take a vehicle from their arsenal, make minor visual tweaks, and sell it under a completely different make and model. Sometimes that would result in more premium features, like leather seating, for example. Other times it would be the exact same damn car. In either case, nobody was ever really fooled. Today, were polling the room for the worst badge-engineered cars in the history of the industry.

There are a couple ways you could answer this question. Maybe youll choose a car that was bad already, whose badness was multiplied as it was marketed under multiple brands. Or you could pick a car that was really just mediocre, but sold in far too many incarnations, flooding the zone for no deserved reason. For the latter example, Im thinking of the Chevrolet Uplander/Buick Terraza/Pontiac Montana/Saturn Relay crew. The same bar of soap, four times, sold concurrently in the same market.

For my choice, though, Ive gone with a car that wasnt necessarily bad, just wrong. The only reason Aston Martin ever bought up a bunch of Toyota iQs and turned them into Cygnets was to slip right under the European Unions average fleet emissions rules. Thats sad on its own, but even if youre not aware of the Cygnets reason for existing, seeing one sort of sullies the brand. And it seems everyone felt that way, because Aston only sold 150 examples of the damn thing over two years the company had hoped to move 4,000 units annually. Apparently, Aston did build one with a V8, though. Thats badge engineering done right, friends.

Enough from me. What, in your eyes, is the most egregious poster child of this industry marketing phenomenon? Tell us right down there in the comments.

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What's the Worst Example of Automotive Badge Engineering? - Jalopnik

Melody Meckfessel, ex-VP of Engineering at Google and Co-founder and CEO of Observable, sat down with Advancing Women in Tech (AWIT) to share her perspectives. Observable is a collaborative web-based platform that empowers data scientists, data analysts, developers and decision makers to uncover new insights and make better data-driven decisions.

Observable is a collaborative web-based platform that empowers data scientists, data analysts, ... [+] developers and decision makers to uncover new insights and make better data-driven decisions.

Melody began her career as a software engineer for OpenOrders, transitioned to working for Sapient as their Director of Technology and then began a 15 year career at Google which would eventually lead her to become the Vice President of Engineering prior to departing to launch Observable. Melody has taken her extensive background working in tech and with data to inform the mission and work of Observable, a cloud-based platform for visualizing and analyzing data that has raised $46M in funding from notable funds including Menlo Ventures, Sequoia Capital, and Acrew Capital (founded by women VC luminaries Theresia Gouw and Lauren Kolodny).

In this founders-focused conversation with Sydney Umeri of Advancing Women in Tech (AWIT), Melody shares whats on her mind as she builds a business, her experience fundraising during a pandemic, and where she believes the future of data visibility is headed.

Umeri: In doing the research for this interview, I saw a slide you used that said, "more developers and more collaboration, more complexity." Can you please speak to how Observable solves that problem?

Melody Meckfessel is co-founder and CEO of Observable. Her 15-year career at Google led her to ... [+] become the Vice President of Engineering prior to departing to launch Observable.

Meckfessel: There are more developers in the world than there were last year and the year before. The current predictions are 70 million developers in the world. It's one of the top jobs in terms of career paths, meaning the complexity is increasing. Companies are using more open-source software. I saw an estimate that 50% of the world's software has some connection or dependency on open-source software. Those are more developers working together to collaborate out in the world and then reusing each other's code. That's a great thing to be able to have a starting point or to not write something yourself, but that complexity starts to amplify. The same is true for data. Companies have to juggle multiple formats, multiple environments, multiple data infrastructures in their workflow, and you can see how the software and the data complexity start to compound.

How can we help developers work together effectively? How can we help them see where there are dependencies in the applications that they're writing? How can we help them see those insights in their data faster by doing it together? How can we include them to collaborate in writing code, analyzing data, and using visualizations to help see those surprises and work with each other more effectively?

That's what we're trying to do at Observable. We're trying to create a place where people can come together and reuse code, so you can import an example that you find on the platform, but you can also collaborate, and through that collaboration, we think we're more powerful together.

Umeri: Many founders come to this idea of wanting to start or launch a business, and sometimes there's an acute pain point for them, and it was a moment where they're like, "We cannot move on without what I want to build." Or sometimes it's over the course of years. Was there an acute moment for you? Or was it just, "No, I've done this long enough, and I just know we need it?"

Meckfessel: That's a great question because pain is real. When I was at Google, I had a tremendous opportunity to work in multiple areas of the infrastructure. One of the things that I saw was we were really focused on how to help Google engineers, and also engineers using Google Cloud, be productive. This is the advent of DevOps. If you think about the tools, the processes, the culture that brings people together to write software and to maintain it effectively, I really believe in that, but I kind of saw data being left out in the cold a bit. I would work with data analysts, data scientists and developers within Google, but there really wasn't a way to bring people together. There were individual tools for tasks that a data scientist had to do. Then you had developers that were working with them to build data apps, essentially dashboards and reports to surface data. I just kept seeing them left out of the picture. I wasn't seeing something come together around how to help them collaborate.

I continue to be inspired by what GitHub did for open-source software development. So for me, I kept seeing that, and then I looked out in the market, and I thought there's a lot of pain that these analysts, scientists, developers, decision-makers, collaborators, feel from not being able to work together. For example, if you've ever been in a meeting where you've been looking at data, and someone has a question, typically, you have to write that question down, and someone has to figure out the answer. What if you could do that exploration in real-time together? That's what we do in Observable.

Umeri: That's filling a huge need in the market. I want to pivot a little bit and talk about scaling the business. It's one thing to have an idea. It's one thing to start building it. But scaling is something completely different. I would love to get your thoughts on what has been the most fun part about scaling. What you've really enjoyed.

Meckfessel: I love to be part of a team that's building things. I use the word things in an inclusive general way. Building technology that's in service of helping people. Building places where people can come together and share what they know, it could be code, it could be a great, interesting, new visualization. Building and growing a team of people that are in service of that mission. This idea of building and creating together is where I have the most fun. We are a data collaboration platform, and we collaborate a lot internally. We pair programs; we collaborate on dashboards using Observable. We're always working together.

Especially in the pandemic, this idea of how do you have fun in your work, especially working with data, is present. Most people wouldn't describe working with data as a fun experience. But what if you did bring fun to it? Visualization plays such a critical role because it taps into our human visual system, and the intuitive parts of our brain, to be able to ask questions and see things in a way that we can't, working with numbers on a screen. That has been the most fun, and I would also share that I am grateful for the community at Observable. The community is creating such expressive work and sharing it with the world. That's what gets me up every day. Being able to build and create with the team at Observable and in collaboration with the community.

Umeri: Many founders who fundraised during the pandemic have their own stories to share on the process. You recently completed a $35M raise - can you share your experience in fundraising during this time?

Meckfessel: It was an interesting journey to navigate. We closed on our Series A at the end of 2019, and the pandemic hit. We definitely adapted throughout the pandemic. Then, we were looking at growth in the platform and the community, and I was very intentional about the folks we were meeting with that could be new partners. I was looking for what we're going to need next. I was trying to identify investors and their value systems that matched our mission and where we were going. It is about matching your company's values and mission with who you will be talking with every other day. Investors are not just members of your board. They're members of your team. In fact "Menlo Venture's team-based approach matches with our collaborative culture and is just the help needed as we continue growth in our customer community."

Our board members mentor leaders within the company, and they're available to talk through challenging issues. They are in it with us. Having that match in terms of values of really believing in the possibilities of our mission, not just for the next year or two years, but five to 10 years from now, was really important. There were a lot of investors that I talked to that I really wanted to ensure they bought into our company culture and how we were trying to show up in the world, our ethics, our values, the fact that we want the company to represent the world that we're trying to create, which is data practitioners from all backgrounds, all educational levels, all role definitions, and you need to match that to be successful.

Umeri: Is there anything that stood out to you from a gender perspective regarding fundraising?

Meckfessel: Yes, I was very intentional about finding partners that were going to be supportive. Jim Goetz at Sequoia is an incredible partner, and the network within Sequoia is extremely supportive. I was also looking for a member of the board who was a woman and was in the space that we were in. I met Theresia Gouw, who leads Acrew Capital. She's incredible. She's been a tremendous mentor. Her work to diversify cap tables and support diverse founders with her organization is really an inspiration for all of us. I was very intentional about wanting that on the board. I wanted someone who was going to challenge me to go big, and Theresia goes big.

As I was evaluating other investors for the company, it was really important to me that their teams were diverse and that the folks that our team was going to work with were kind of a mirror representing us as much as possible. There were folks I met with that were incredible venture firms, but when I asked very direct questions about how much of your portfolio is led by women or underrepresented founders, I didn't get an answer. Not a great sign.

Umeri: I want to pivot and talk about how you guys diversified internally early. When people think of startups, it's kind of like, everyone wears all these hats. But you ended up putting people into silos internally, and it ended up working well for you. Why did you decide to do that early on?

Meckfessel: I learned through my experiences, often just mistakes that I made at Google, of not investing in product education, advocacy, early design, and engineering collaboration; we missed on several really important features, important engagement with the community. When I was looking at what we needed to build and create, a data collaboration platform, it was really important to me to bring that cross-functional, cross-domain perspective in from the beginning. I didn't go out and hire 25 software engineers. I hired designers, user experience researchers, and product educators. I grew a community team to invest in community programs. You have to think about what humans need. If we're going to build features that are user-centric, we need to have design and engineering working together from the beginning, and we need research to listen to the community.

Umeri: I'd love to know where you think data visibility will be in the next five to 10 years and the role that Observable will play in championing that.

Meckfessel: I don't have a crystal ball; I wish I could predict what's going to happen. I will say that what we see at Observable and in the data community is an ever-present role of interactive, real-time visualizations. When I say real-time, I mean those visualizations are connected to live versus static data.

That means that you can have confidence in it. You have confidence that you're looking at something, that when you have that insight or that aha moment, you can count on it. Interactivity is important. If we're all different and have different expertise, we want to explore and interact with the data in different ways. The ability to have a slider to explore, a search field, or different filters that you can walk through the data with, that's supporting our exploration and our collaborative approach, and we can then build on it. Data observability will continue to be a standard across the industry in everything because of the richness of the exploration that we have. It's just different from a standard static report.

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Melody Meckfessel, ex-Google VP Of Engineering, Aims To Make Data Collaboration Accessible To Everyone - Forbes

We live in a progressively more digital world. As digitalisation ramps up in various industrial sectors, so does the need for companies to rethink how they implement products, services, and engagement strategies. For over two decades, industry 4.0 has been on the horizon, and then in its infancy. This transition had previously been gradual. Engineering firms were discussing their future digital transformation initiatives. If they already had roadmaps, they were mapped out across anywhere from a year to over a decade.

Accelerating the evolutionary process Over time, evolution has accelerated significantly with the recent rapid change in engineering practices, a key example. For many years, the different engineering disciplines operated alone, focusing only on their specific fields. And that worked! But recently, market dynamics have forced process engineers to look at the bigger picture, to optimise their process by integrating different engineering disciplines and improving collaboration.

Across the globe, firms have identified millions of dollars in lost opportunities that can be attributed to a lack of collaboration. Enhanced workflows that enable better communication and sharing of information between process engineers, project engineers, estimators, and safety and energy specialists deliver the potential to capture those opportunities. Local initiatives such as the UAEs Fourth Industrial Revolution Strategy aims to improve the UAEs position as a global hub and to develop its contribution to the economy, by advancing modernisation and future technologies.

In terms of digital transformation itself though, most engineering firms were either initiating or contemplating initiatives to improve engineering productivity and reduce project and financial risk. These initiatives were being undertaken, in large measure, to consolidate and connect their portfolios of engineering software and technology in support of new, streamlined, digitalised workflows spanning departments, disciplines, and offices. Anticipated business benefits include shorter cycle times, lower cost, and higher-quality designs, as well as fewer errors and problems encountered during design, construction, and handover.

The pandemic has acted as a further catalyst for digital transformation in the engineering sector. Supply chains globally were exposed to cracks that led to empty shelves and pandemic-induced panic. Engineering firms had to embrace industrial digitalisation for new ways to maintain customer engagement and recover from supply chain disruptions. Roadmaps from companies that already had a digital strategy underway were shortened drastically. What was once mapped across a year, or more was narrowed down to weeks and days.

Delivering new opportunities through AIToday, a growing number of engineering firms are extending their digital initiatives into new areas to take advantage of escalating business opportunities. Artificial intelligence (AI) often lies at the heart of these opportunities. Indeed, senior executives are now prioritising AI to reduce setup and programming overheads that are commonly required for individual tasks. If reports are to be believed will contribute $96bn in 2030 (13.6 per cent GDP) to the UAE economy. In line with this, we have seen several examples of AI-driven digitalisation delivering benefits across the industry.

The use of AI-driven digital twin technology, in particular, can bring high value to these companies clients, as it captures real-time data of the asset once it is in operation. Additionally, being in a position to leverage digital design and engineering data during handover provides greater potential for offering value-added services during operations and maintenance, making the firm less reliant on capital spending alone.

AI can also play an important role in supporting and outlining an engineering approach to a project, the constructibility of a design, and the planning of how materials, equipment, and labour are organised. This early planning approach has been proven to reduce costs and speed up schedules.

Risk and best-case scenarios evaluationThe use of first-principle models for defining and predicting a projects performance and its outcomes is standard in process industries. However, there are some processes that are more difficult to predict. Often these are managed through less-precise techniques such as operator experience or rules of thumb, but this can lead to less than expected performance levels.

However, AI can simulate thousands of design options which very quickly narrow down the options that not only best meet the owners requirements, but are the safest, most environmentally friendly, and most cost-effective. A new capability, known as multi-case analysis, offers engineering firms the opportunity to transform the way these early decisions are made. Previously, engineers would define these critical parameters using limited data from a limited number of operating cases and conditions. Imagine designing an iPad with such a limited set of data, never mind a bespoke $10bn process plant. Yet that has been standard practice in the past.

Multi-case analysis helps to optimise early design decisions based on the consideration of hundreds or even thousands of operating conditions and cases. Leveraging AI and high-performance computing, either in the cloud or on a desktop, allows designers to rely on a significantly broader set of data to adjust and fine-tune their designs.

From the many different grades of crude oil to varying ambient weather conditions, this improvement in understanding how a potential design would perform in real conditions can result in improvements across the board, from construction materials, and the size of equipment to the type of utilities and even the location of the plant. These decisions will often have a major impact on the plants capital and operating costs, its risk analysis, as well as the overall fit for its intended purpose.

A multi-case analysis is undoubtedly a key area of focus for engineering firms using AI-driven digitalisation today. However, in speaking with customers across all regions, the highest priority area to be addressed under digitalisation is the consolidation of engineering software and technology portfolios, followed by the digitalisation of remaining applications and business processes. Critical to this effort is the ability to find and re-use data across the organisation, and eventually across their ecosystem of vendors, sub-contractors, and consultants.

There is so much to be gained (some companies estimate there is an opportunity for double-digit improvement in engineering and estimating productivity alone) that those who do not move forward risk being less competitive in the future.

Positive prospectsLooking ahead to the future of the engineering sector, digital transformation will inevitably accelerate and bring about significant improvements. While the path of change was already firmly established prior to the arrival of Covid-19, the pandemic has acted as a catalyst to speed this up. For engineering firms to succeed it is imperative that they adopt the advanced technology that is now available to them which includes AI tools. This will ensure they achieve operational efficiencies across the end-to-end value chain, giving them that all-important competitive edge. That, in turn, will position them well as they look to migrate beyond Industry 4.0 to the rapidly emerging fifth industrial revolution, or Industry 5.0, which takes the existing paradigm one step further by highlighting operational excellence and innovation as key drivers for change.

Sonali Singh is the VP Product Management at AspenTech

The rest is here:

How AI-driven digitalisation is transforming the engineering sector - Gulf Business

Repairing electronic devices isnt as hard as it used to be. Thanks to the internet, its easy to find datasheets and application notes for any standard component inside your gadget, and once youve found the faulty one, you simply buy a replacement from one of a million web shops assuming you dont end up with a fake, of course. When it comes to non-standard components, however, things get more difficult, as [dpeddi] found out when a friend asked him for help in repairing a Roland Juno-G synthesizer with a broken display.

The main issue here was the fact that the display in question was a custom design, with no replacement or documentation available. The only thing [dpeddi] could figure out from the service manual was the basic pinout, which showed a parallel interface with two lines labelled chip select an indication that the display contained two separate controllers. But the exact protocol and data format was not documented, so [dpeddi] brought out his logic analyzer to try and decode the signals generated by the synthesizer.

After a bit of trial and error, he was able to figure out the protocol: it looked like the display contained two KS0713-type LCD controllers, each controlling one half of the screen. Finding a compatible replacement was still proving difficult, so [dpeddi] decided instead to decode the original signals using a microcontroller and show the picture on a modern LCD driven by SPI. After some intial experiments with an ESP32, it turned out that the task of reading two reasonably fast parallel buses and driving an even faster serial one was a bit too much for the ESP, so [dpeddi] upgraded to a Raspberry Pi Pico. This worked a treat, and thanks to a 3D-printed mounting bracket, the new display also fit snugly inside the Rolands case.

The Picos code is available on [dpeddi]s GitHub page, so if youve also got a dodgy display in your Juno-G you can simply download it and use it to plug in a brand-new display. However, the method of reverse-engineering an existing display protocol and translating it to that of a new one is pretty universal and should come in handy when working with any type of electronic device: say, a vintage calculator or multimeter, or even another synthesizer.

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Reverse-Engineering A Display Protocol To Repair A Roland Synthesizer - Hackaday

From left, Ed Barnabas, Booz Allen Hamilton; Song K. Choi, College of Engineering assistant dean; Kimberly Perez Hults, College of Engineering director of marketing and outreach relations; and Julie Morikawa, president of ClimbHI

The University of Hawaii at Mnoa College of Engineering was recognized as a top business in 2022 in education and workforce development by Hawaii nonprofit ClimbHI.

The inaugural ClimbHI Bridge Awards Brunch was held on October 12. The event recognized top participants on the ClimbHI Bridge online portal, which is used by 560 businesses across the state in an unprecedented public-private partnership. The bridge links educators, students, businesses and organizations to provide educational and workforce development opportunities.

Utilizing the ClimbHI platform this past year has allowed us to expand our outreach efforts to a wider swath of schools and classrooms throughout Hawaii and allow us to efficiently and effectively connect with STEM educators seeking classroom presenters, mentorship, expert judging, and other support for their students, said Kimberly Perez Hults, College of Engineering director of marketing and outreach relations. Our team of student ambassadors, along with other students, faculty and staff, have really stepped up to the plate to meet these requests, having logged many hours engaging in these activities both virtually and in person. We are thrilled to be a ClimbHI top contributor and appreciate everything they are doing to build these critical industry-to-education connections.

UH Mnoas College of Engineering joined 19 others in the top business category, including The Queens Health Systems, YMCA of Honolulu and HMSA.

These awardees and our hundreds of opportunity providers have helped reach nearly 57,000 students and 4,000 educators from 190 Hawaii schools statewide since the launch of ClimbHI Bridge in January 2021, said Julie Morikawa, president of ClimbHI. We are grateful for their continued support as we strengthen educational and career opportunities for our keiki right here in Hawaii.

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UH Mnoa engineering honored for excellence in education and workforce development | University of Hawaii System News - University of Hawaii