Author: Bob Stark

I'm a software developer and entrepreneur with a strong passion for intelligent systems, innovation, and collaboration.

A Lifelong Struggle with Reality

From a young age, I have had to find ways to process my frustration when information is presented in a fragmented or confusing manner. In order to avoid overwhelm or even a mental shutdown, I started building tools and processes that help me hold onto coherence, meaning, and safety.

The outline below shows my sensemaking process — how I’ve learned to understand what’s happening externally that enables me to experiment with narratives that better meet my needs, especially after they’ve so often been misunderstood by others. I’m still finding structures for this, but sharing it feels like one way to open the door to conversation.

Datagotchi Sensemaking Methodology

This framework outlines the four-step process of turning personal anxiety and fragmented information into a clarified, actionable understanding of the world.

  1. The Triggering Event: The initial moment of dissonance or chaos because your reality and external reality do not align.
  2. Externalization: The act of turning internal, chaotic thoughts and feelings into a concrete, external document or system so as to not forget and reference again in the future.
  3. Causal Analysis: The process of connecting the dots and revealing the underlying system at play. It’s where you find the causes of the original triggering event.
  4. Collective Action: The application of your newfound understanding through sensemaking for a greater purpose. Your personal insight becomes a building block for the collective.

Does this process resonate with how you make sense of things? What does your own version of sense-making look like?

This framework was born from a personal struggle to make sense of a fragmented world. You can read the full story of my journey and the philosophy behind my social impact research lab (Datagotchi Labs) here: https://datagotchi.net/2025/08/28/from-personal-struggle-to-collective-rebellion-the-why-behind-datagotchi-labs/.

From Personal Struggle to Collective Rebellion: The Why Behind Datagotchi Labs

From Personal Struggle to Collective Rebellion: The Why Behind Datagotchi Labs

Datagotchi Labs is a rebellion. While our mission was born from a deeply personal struggle—my lifelong journey to make sense of a fragmented and overwhelming world—it has evolved into something more. My personal path has now become our moral mission: to build a new digital economy that empowers people with information to increase their clarity, empathy, and solidarity.

This is the story of how my personal need became the foundation of a neurodivergent rebellion.

The Problem, From the Inside Out

For years, I grappled with mental and emotional states that are a direct response to infant trauma. This internal struggle created a profound sense of powerlessness—a feeling that the world was chaotic and unpredictable, and I was just a passive observer. It manifested as chronic anxiety, which often felt like a crushing information overload that left me in a constant state of fight-or-flight. My lack of trust and disinterest in socializing stemmed from an inability to feel truly safe—a feeling that manifested as a profound sense of being a “perpetual outsider” who was constantly freaked out by that confusion.

This personal, neurodivergent struggle is the microcosm of a much larger societal problem. Today, the average person is a passive consumer in a digital economy that preys on this exact same sense of confusion and isolation. The Digital Oligarchy—a small elite who seized control of platforms after 2008—extracts “rent” from us by feeding us fragmented, filtered, and manipulated information. The result is widespread digital fatigue, a sense of disempowerment, and, ultimately, a broken collective will. Social problems are experienced as individual burdens, making collective action nearly impossible.

This is the very same problem I’ve been fighting my entire life, just on a grander scale. I’ve realized that I am not just a software developer or a founder; I am a neurodivergent integrator. My entire life has been a journey of synthesizing fragmented information from different disciplines and connecting the dots that others miss.

The Digital Oligarchy’s Biggest Lie: The False Promise of Meritocracy

The Digital Oligarchy and its followers—e.g., venture capitalists and their funded startups—echo these lies, because they are selling a vision, not a reality. We, on the other hand, are “pulling back the curtain” on the whole show. We’re here to reveal the truth and show what is real. That truth is that meritocracy is a myth—the lie that our success is a direct result of our individual talent and effort, and that our failure is entirely our own fault. The current billionaires in our society got their money and connections from their parents! It’s a narrative that keeps us from questioning the system itself.

They use the very tools of technology to lie to us, promising individual freedom while deliberately breaking our collective will. They feed us a constant stream of fragmented information, turning our lives into a frantic attempt to grab a piece of the truth—the same way the blind men in the ancient parable tried to describe an elephant based only on the part they could touch. One feels the trunk and says it’s like a snake; another feels the leg and says it’s a tree. No one can see the whole elephant.

Our Solution: A New Digital Economy

Instead of fighting the Digital Oligarchy on their own terms, we will build a new digital economy from the ground up. Our mission is to empower a new class of rebels to reclaim the information commons—the original vision of an open, decentralized internet—and inspire collective solidarity. We’re doing this with a phased roadmap that leverages the very technology that was used to exploit us.

Phase 1: Empower the Rebels Our core product, Inspect, is the foundational tool for this rebellion. It’s a distributed, peer-to-peer tool for verifiable truth. Inspect is a free, open-source tool available on platforms like GitHub. It empowers Insight Authors (independent journalists and activists) to:

  • Create Insights: Connect disparate information into valuable insights, revealing that many problems are systemic, not personal failings.
  • Collaborate on Insights: Share and build upon these insights to create a shared, collective understanding—the foundation of solidarity.

Phase 2: Empower the Vassals & Build the Commons In the feudal system, serfs paid tribute to their lords. Today, the Digital Oligarchy acts as the new lords. Once we’ve proven our model with the rebels, we will expand our mission to empower businesses and brands—the new vassals—that are currently paying tribute to the Digital Oligarchy. We will help them reclaim their data and build new, more direct relationships with their customers.

The Tools of the Rebellion, Born from a Personal Need

Every one of our products is a direct answer to a personal problem I’ve faced:

  • Inspect: This is the ultimate expression of my personal mission. Starting as a mobile app to share news articles with friends and family members, it’s now a broader tool to transform confusing, fragmented information into clear, verifiable insights. It’s a direct solution to that profound feeling of being overwhelmed and disconnected from a world that doesn’t seem to make sense.
  • PMBoard: Born from my consulting experience for technology companies, PMBoard was created to manage the overwhelming, fragmented demands of product stakeholders. It mirrors my personal need to organize and unify complex information.
  • Collaborative Copilot: This tool is an extension of my core need to synthesize diverse perspectives into a single, cohesive narrative. It’s a system for integrating automation and data-driven tools like AI, designed to bridge the gap between individual understanding and a shared, collective understanding, allowing teams to move from fragmented ideas to unified action.
  • Counteroffer: This project was born from my frustrating experience in a broken job market. It reflects my desire to empower others and restore a sense of control in a system that deliberately disempowers you.

Our Uncompromising Moral Compass

Our business model is a direct extension of our mission. Our core product, Inspect, is free. The revenue from our spinoff projects funds the rebellion, ensuring we can build a more humane digital future without compromising our values.

This approach is guided by a hybrid moral framework that is both rule-based and practical, grounded in universal principles while remaining responsive to the unique details of each community. It’s a commitment to act with prudence, always seeking to do the greatest good for the most people, in a way that respects the dignity of every individual and is grounded in the lived experiences of our users.

This is more than just a company; it’s an authentic rebellion. It’s a belief that our collective well-being is not a byproduct of the system, but its very foundation. We will use the very tools of technology to organize outside the system, build our own parallel institutions, and create a more equitable future.

Version 3: Sensemaking

Sensemaking is a research lens about making sense of the world or part of it when we are surprised by work interruptions or more misinformation published online.

Persist Online Information

  • By making it easier to save news articles
    • Enable adding new links directly within insights (because my initial approach of saving news articles as they appear online to later be put into insights is a ton of work)
      • New URL field on Add Evidence dialog

Improve Understanding/Sensemaking

  • By showing how I am/others are affected/why they might care with tag visualizations
    • Enable red problem tags
      • Show 2 buttons to add problem or solution tag; summarize problem and solution tags at the top of an insight
    • Share top tags along other meta data
      • Voting on/ranking tags in top table
      • Emoji reactions to tags
      • Include top tags in social media shares
  • By enabling sensible/evidence-based narratives
    • Show visualizations connected to the evidence
      • Select citation(s), create visualization
  • By enabling constructions of sensible narratives
    • Enhance insights with visualizations of red problem tags as plausible agreements (things that affect me & probably other people)
      • Voting on/ranking them in top table
      • Emoji reactions to them showing in top table
  • By visualizing related insights together to show how they are related or why anyone should care
    • Enable specifying connections between insights
      • Click button to open dialog to link to other insight on same site or specify URL for insights on other sites; show rich text
      • Auto-create insights from new comments (if desired via checkbox/textbox for the insight name)
      • Improve the look of hyperlinks vs just blue underlined text: icons for insights, (saved) links, unsaved links

Version 2: Datagotchi Labs

As I founded my R&D company, Datagotchi Labs, I decided to work on PMBoard again with the belief that new technologies should be created if and only if they provides users with value. However, startups are incentivized by people with money (investors and paying customers) because they need to be able to pay their employees. Therefore, most startups offer free apps/services to end users so they can turn them into the products to be sold to advertisers.

Instead, the needs and desires of all product stakeholders need to be understood and synthesized when creating and improving products. Therefore, my new approach is to visualize stakeholder UX research in the form of empathy maps and product visions as journey maps created from those empathy maps.

The empathy maps used means-ends hierarchies from cognitive work analysis that I learned in my government R&D days, which map high-level objectives to goals to achieve the objectives, to activities to achieve the goals, tasks to achieve the activities, and resources/constraints to achieve the tasks:

I have started on a widget to create journey maps that cite these empathy maps, but have not finished it yet, therefore it’s still in progress (no image available at this time).

Version 1: Social Ergonomics

There is so much information involved in the creation of new products, as well as through minimum viable product (MVP) iteration and eventually growth. However, startups and R&D teams rarely collaborate enough to effectively harness this information.

Therefore, a friend of mine and I founded Social Ergonomics, a consulting firm for startups and other companies in the San Francisco Bay Area in California, to enable them to act as Integrated Product Teams (IPTs), a concept I learned from my government R&D days.

IPT members need to do:

  • Stakeholder research to deeply understand the problems and the stakeholders affected by them so that they can make smart decisions when the answers aren’t obvious — over time as stakeholders and their contexts change.
  • Market research to be able to explain why their solution is better than all other possible solutions.
  • MVP iteration to commercialize their product / find problem-solution fit and product-market fit
    • To be able to evaluate whether users resonate with their solution enough to evangelize it to others (p-s fit)
    • To be able to evaluate whether people are willing to pay for their solution, and keep using it and paying for it over time as it remains useful (p-m fit)

To support these needs, we created a tool we called PMBoard and made it open source on GitHub.com: https://github.com/bobness/pmboard

  • For stakeholder research, it includes a widget to link Google Documents and tag them with insights.
  • For market research, we envisioned a widget to link research insights with user journeys.
  • For MVP iteration, we envisioned a widget to link research insights and user journeys to designs and software prototypes and user analytics data.

However, we never got around to the last two widgets. So the tool to tag user research looked like this:

Version 2: Web App

Based on the findings in the version 1 mobile app, I made another version to mitigate news information overload:

Persist Online Information

  • Because people don’t want to download yet another mobile app, especially for something they do not do very often, web apps are more accessible than mobile apps
    • Create a web app
      • Create a Next.js app from the Node.js backend in version 1
  • By saving links to online content in the cloud
    • Manually add links to the web app
      • Copy URL from browser > add link
  • By manually creating insights as facts I want to back up with evidence and share
    • Create a blank one from a new name
      • Prompt for new insight name -> add to insights

Improve Understanding

  • By combining several content links into insights (because people are still overloaded by several news articles published every day)
    • Select existing links to put into an insight
      • Click Add Evidence -> Select links from a list in a dialog to put them into an insight
    • Enable publishing of insights to the public
      • Select insight from list -> click Publish
  • By improving the UX for adding links to existing insights
    • Add new link to an insight from the list of insights
      • Click Save Link button -> select insight(s) to add it to
    • Add existing links inside an insight
      • Click Add Evidence button -> select links from dialog -> submit
    • Add cited links to other insights
      • Select citation(s) from an insight -> click Add to Other Insight(s) -> select insights and submit
  • By enabling discovering new insights from links
    • Select links in an existing insight to add to a new insight
      • Checkboxes in insight evidence items / Create Insight button
  • By commenting on content links that bubble-up to insights
    • Comment on what the linked content is actually about
      • Click Comments (n) -> type in comment -> submit
  • By tagging insights themselves & content links that bubble-up to insights
    • Tag top-level insight comments
      • Create tag by highlighting comment text
    • Tag comments on insight citations / content links
      • Create tag by highlighting comment text

The resulting web application at https://inspect.datagotchi.net is pictured here:

After publishing this web app and creating some example insights that cite news articles and technical blog posts, I learned a few things:

  • My initial approach of saving news articles as they appear online to later be put into insights is a ton of work.
  • Although I have created some insights as examples, I have a hard time communicating things that are important and surprising to people.
  • Comments and tags are useful for explaining what insights are about, what to do about them, and how articles/other online information inform them, but it’s a lot of text that overloads users.
  • The list of insights and comments/tags inside them has helped me understand the example ones I created, but it does not show how they are related or why anyone should care.

Version 1: Mobile App

Online information overloads us because it is no longer geographically or socially constrained. Since we can no longer rely on many cultural institutions, we will need to make sense of the world ourselves.

Therefore, we need to:

  • Reliably create and share source trust data,
  • Consistently evaluate the truth of claims, and
  • Use true claims to improve source trust data.

To support these needs, I envisioned:

  • An Ontology-Driven Source Evaluator,
  • A User-Centered Claim Evaluator, and
  • Combined, an Iterative Truth Propagation Process.

I created a mobile app with React Native so it worked on iOS (iPhones, iPads, Apple TVs) and Android. Because people have largely converged to only ingesting information that they subscribe to via newsletters, social media, or other niche websites and apps (e.g., Google News, Apple News+, etc.), I focused the app on following authors you know/trust.

I tested this mobile app with friends and family members, and found that:

  • News often takes the form of clickbait headlines and is hidden behind paywalls because the companies are incentivized by profits, not spreading important information.
  • However, this information is still very important for us to make good decisions and live our lives.
  • People are still overloaded by several news articles published every day.
  • People don’t want to download yet another mobile app, especially for something they do not do very often.

Counteroffer: A Public Forum for Improved Candidate-Job Listing Fitness

Problem Space

These days, a new cultural dynamic has emerged between job candidates and recruiters. During the COVID-19 pandemic, many candidates took part in the Great Resignation and quit their jobs to find something better, and, as the pandemic issues reduced over time, candidates started making more demands like working remotely and an improvement in company culture. However, as the pandemic lessened, recruiters have started making more demands, too, like requiring employees to come back to the office. As a result, the candidate-recruiter dynamic of making offers and counteroffers has returned and has taken the form of both sides again having high-but-competing hiring expectations.

Both job candidates and recruiters are still struggling with connecting with one another, too. On one hand, both are overloaded with the number of job opportunities and candidates on the internet, and, on the other hand, both struggle with finding the right job opportunities and candidates. The latter issue is partly because the right fit might not be even posted on the internet, and, if it is, there are limitations in reaching it. For candidates, this takes the form of applicant tracking systems (ATSs) that automatically scan their resume for specific keywords that aren’t necessarily even on the job listing. For recruiters, this takes the form of having to screen and filter through hundreds of applications for a job and trying to control their conscious and unconscious biases when doing so, such as choosing a candidate based on a specific past employer or their name. Therefore, both parties need improved searching and filtering capabilities. 

Throughout the past five years, job candidates have tried many ways and converged to their favored ways to look for jobs, either through personal connections if they are so fortunate, specific online communities, or even search engines. Regardless of their method, they still struggle with a lack of feedback on their applications. Similarly, almost all recruiters are doing hiring online now, so they already have chosen tools like ATSs, but they keep struggling with data management and ensuring their compliance with regulations while trying to ensure a good experience for candidates. For both parties, these issues arise due to the time-consuming application process and can be made even worse by technological glitches during the process. Therefore, they both have the need to make the hiring process go more smoothly. Furthermore, both have needs for added security. Candidates cannot easily present their credentials or verify job listings, and they may have concerns about sharing information online, and recruiters cannot easily check a candidate’s references, and both may struggle with language barriers. Therefore, candidates and recruiters need an efficient and secure way to exchange information during the hiring process.

Technical Requirements

Because both job candidates and recruiters again have high-but-competing hiring expectations, there needs to be a way for both of them to explicitly outline these expectations. The current approach to this involves phone calls and zoom meetings, but these are not recorded, and, even if they were, they would have to be processed manually—or, if by automation or crowdsourced to people in a third world country, the transcription would still need to be fixed manually. Therefore, these interactions should be based on text. For candidates’ requirements, this means including it on or adjacent to their resume, and for recruiters’ requirements, it means including them in job listings. Then, candidates will need the ability to tag their resume with job listing requirements before or when applying to the job, and recruiters will need the ability to answer to candidate requirements and save the answers for the business’s records. Therefore, a solution is needed to enable taggable and savable requirements document interactions. 

Even though job candidates and recruiters also need improved searching and filtering capabilities, most modern search systems are based on keywords or n-grams (a series of keywords in the same order). Instead, also search for synonyms and frequently-used-together keywords—fuzzy searching—is needed. Similarly, fuzzy filtering based on candidate-job listing fitness is also needed instead of direct keyword matching. Because the previous component will make job listings and resumes use tags, searching and filtering will not need to create new tags. However, results of the searching and filtering will need to be savable for future reference and sharing with others. Together, that means a solution is needed to enable fuzzy tag search and fitness filtering with savable results. 

To help job candidates and recruiters use an efficient and secure way to exchange information during the hiring process, existing chat tools need to be enhanced significantly. First, most are not secured end-to-end largely because some third party in the process wants access to the data, and this is a data security requirement for both candidates (for their sensitive portfolio materials) and recruiters (for their business data). Second, existing chat systems do not enable saving information once you get it from the other party. They do usually support copying text and pasting it elsewhere, but this is a very manual process that does not necessarily capture the information in its desired. Therefore, such a system must enable tagging information entities by the sender and saving this information to third-party systems. Together, a solution is needed to enable taggable and savable E2E-encrypted chat. 

Our Solution

For our solution to enable taggable and savable requirements document interactions, how to tag and save the documents need to be decided. To tag the documents, having the creator highlight text and choose to tag it seems the most straightforward. However, how to save the tags locally and how to present them to the other party are not straightforward. Ultimately, our solution will save them in a database, but this database will not be publicly available. So, we will use a markup language as a solution to saving them locally, transferring them, and rendering them locally by the other party. That enables copying text that contains tags and using them elsewhere if desired.

For candidates, these documents will be surveys for recruiters to fill out to see if they can satisfy the candidate’s requirements, along with their resume. When recruiters supply answers, they will be stored in our solution for the candidate to list and compare multiple job opportunities, and both will be stored in our solution for the recruiter and can be sent to ATSs to track applications to this job. 

For recruiters, these documents will be job listings for candidates’ resumes to match the job requirements. They will be saved in our solution and exportable to other job boards. When applying, candidates can rearrange their resume sections and add tags from the job listing requirements. Then, the resumes will be stored in our solution for candidates to reuse and also to export to PDF to apply to jobs outside of our solution, and again in our solution for recruiters and to be exportable to ATSs.

The resulting components will be called Publishable Requirements Web Pages with the same text tagging, saving, and exporting features features, and the ability for creators to start with drafts and publish them when desired. These will be accomplished with making them high-quality HTML documents, partly for the public search feature mentioned below. Also, both will have user- or organization-editable standards to show in the UIs so certain standards will not be forgotten. For candidates, these standards might include best practices in their domain, and for recruiters this might be business standards or metrics, and the fact that keywords used by your ATS should be included in the job listing. On this standards display, eventually some technological automation will also be added to suggest requirements from the users’ past documents and perhaps other public documents on the web. 

For our solution to enable fuzzy tag search and fitness filtering with savable results, how we implement fuzzy search and filtering, as well as saving, need to be determined. For fuzzy search, we will start with using Google’s tool (https://programmablesearchengine.google.com/about/) that lets you put their search engine on your website. This means that the resumes with surveys, and the job listings, will be searched on the public web, which is acceptable because they have been explicitly published by their authors. Filtering search results is a different matter because those search results will be updated by incrementally-changed filters, and they are private to the searcher. Therefore, we will use one of the many fuzzy matching libraries based on the results from the Google search. For recruiters, we will use the job listing requirements as possible filters, thus making the filtering based on candidate-job listing fitness and acting as a sort of standards that the recruiter should use to filter on. For candidates, we will save filters they used from other job opportunities so they can be listed and compared to each other to decide on which ones they prefer. For saving, search queries and filters wille be saved in our solution, of course, making the searches a sort of draft being saved by the user. Then, the search and filter results will also be exportable to external systems. The resulting component will be Exportable Job Findings. 

For our solution to enable taggable and savable E2E-encrypted chat, the tagging and saving will use similar or the same code as our other components. Specifically, survey answers and job applications will be stored in the E2E chat and thus not be publicly searchable, but will be copy-and-pasteable, and the tags will be saved to our solution and exportable to external sources. Like the components above, standards will be listed adjacent to the chat to remind users about business metrics, government compliance, and professional manners, which for this third component will also include reminding recruiters to give feedback to candidates after they have evaluated their applications. Eventually, automation may be added to use the saved chat tags to evaluate the success of the job listing for the recruiter and the success of a resume for the candidate across job listings. 
The E2E chat will be provided by the Signal messaging application because it is the world’s best chat platform that is, it is open source, and it always will be E2E encrypted. It is also created and maintained by the Signal Foundation, a non-profit with the mission to “protect free expression and enable secure global communication through open source privacy technology.” Together, this solution component will be called Protected Job Application Chat.

Inspect: A Multiplatform Social Network for Trustworthy News

Problem Space

As the world becomes more connected and more complex, it is increasingly difficult to know what to believe: events happen far away from us, to other people, and we usually hear about them after the fact. A long time ago, we would get this information from newspapers. More recently, there were also television stations that focused on local news. 

Nowadays, there are so many online sources of information — from newspaper websites to social media posts — that it overloads us and makes it difficult for us to discern what information is important. As a result, many people have started completely ignoring the news. For media literacy in our democracy, those people should still be kept aware of important news. Therefore, we need a new way to get the news that is important to us. 

It is also difficult to know which information sources are reliable. Unreliable sources frequently publish fake news and unverified information that serves political or ideological agendas. As a result, echo chambers and filter bubbles are created that limit exposure to diverse perspectives. This is also very destructive to our democracy. Therefore, we need a new way to determine what news sources are reliable.

Even if a new source is otherwise reliable, they often have pressure to publish quickly, attract clicks, and make more money. As a result, there is less thorough fact-checking and investigative reporting, as well as less context and background information. Even quality news articles are often framed with clickbait headlines and sensationalist content, and are often hidden behind subscription paywalls — even articles that are essential information for our democracy! Therefore, we need a new way to obtain the content and context hidden in articles from reliable news sources. 

Technical Requirements

To help people get the news that is important to us, different people’s conceptions of importance and usage contexts throughout the day need to be supported. For different conceptions of importance, the author can communicate the importance to their followers in a way that they understand it due to their personal connection to the author, or, failing that, in a way that most people in their social groups would probably understand. For different usage contexts throughout the day, a technology can be designed to “fit” into a user’s life with different mediums or platforms they can choose to use at different times of the day. Therefore, a solution is needed to enable social communication of important news throughout the day onto multiple platforms. 

To help people determine what news sources are reliable, critically thinking about sources’ reliability is very taxing, so it should be assisted somehow with crowdsourcing from multiple people or with technological automation. A source can be said to be more reliable if it publishes more true articles. Automation is good at counting things, and crowdsourcing counting seems unnecessary, but judging an article to be true is ultimately a contextual judgment, therefore must be done by humans. Therefore, a solution needs to provide ways for one or more users to decide if it’s true, and can be enhanced with information that suggests the truth of of an article and the reliability of the source. Therefore, a solution is needed to provide information about source reliability and article truth adjacent to a way for users to decide if an article is true.

To help people obtain the content and context hidden in articles from reliable news sources, the the articles need to be summarized and the headlines need to be represent the content of the article. Summarizing news articles can be done manually, but that would mean creating summaries could not be done quickly and easily. To extend the efficiency of creating summaries, it can be assisted by crowdsourcing to other people or automation. Because the content that readers need to broadly understand the article is a human judgment, automated or crowdsourced assistance can be used to suggest content, rather than forcing the author to copy content from the article themselves, but the author still needs to make the final call. Similarly, assistance can mark a headline as likely clickbait and can suggest another headline, but, again, the human author needs to make the final call. Therefore, a solution is needed to assist authors summarizing articles and improving headlines.

Our Solution

For our solution to enable social communication of important news throughout the day onto multiple platforms, the types of social communication and the various times and platforms that are supported need to be determined. Nowadays, social communication online is often done with text and emojis, and emojis are now included in unicode text. What is not included in unicode text is images. Therefore, our solution will support communicating news summaries with unicode text and images. For multiple platforms and usage over time, our solution will include: a mobile app for both iOS and Android, web pages representing summaries, email digests, and easy sharing to all social media platforms. We will combine these ideas into a Multiplatform Social Network for News that will start out as the mobile app being used to create summaries and share their web pages on social media, and will hopefully turn into people signing up for Inspect to get email digests, and eventually to download the mobile app, where they will get more immediate notifications of new summaries and create their own summaries. 

For our solution to provide information about source reliability and article truth adjacent to a way for users to decide if an article is true, our solution will use a combination of the human author and crowdsourcing other users. The first component to communicate source reliability and article truth will be snippets taken from the article. These are discussed more in the next paragraph, but for source reliability and article truth, they can communicate the rationale behind the article and any sources that it cites. Not only are the snippets helpful for people who don’t have subscriptions to the news source, they also help avoid legal issues from the news source for plagiarizing their entire articles. Then, when other users see these snippets and understand what the article is about, they can discuss among themselves whether or not they think the article is true. Our solution will use discussion threading like other popular social media platforms do, where users can reply to one another and easily see the discussion history. As they become more confident that they know whether or not the article is true, they will be able to vote on it, and the truth value of the article summary will be whether true or false have more votes. Finally, the news source’s reliability will be recalculated based on how many true articles it has published and have been summarized on Inspect, and it will be visualized with common meta-information methods like color (e.g., red – yellow – green) or opacity. The resulting summaries will be called Human-Evaluated Article Summaries. 
For our solution to assist authors summarizing articles and improving headlines, we will again use article summaries with snippets, but in this paragraph we will discuss how authors can be assisted by crowdsourcing other users and eventually some technological automation. As implied above, the author can insert the snippets, but we will make it so that other users can suggest snippets to the author, too and, eventually, an automated algorithm will also be available to suggest snippets by intelligently parsing the articles. Similarly, other users can mark the headlines as clickbait, notifying the author that they should change it, and also suggest new headlines that are more informative. Eventually, automation will also be able to mark headlines as clickbait and suggest new headlines that are more informative. The resulting component will be called Semi-Automated Snippet and Headline Selection.

Why Startups Need Problem-Solution Fit

To hire, test out product features, and grow, startups need money before they start making profits—they need investment. Investors want to have confidence that a startup they invest in will be able to grow enough so that they get a return on their investment. As a result, a concept was established called product-market fit, which is when a startup’s product is useful to one or more customer market segments and competes effectively with other products in that space. 

However, most startups are not able to achieve product-market fit. There are several reasons for this, but a key aspect of it is that their product does not does not serve the needs of any potential customers. In other words, their product does not solve an actual problem. A term to describe this problem is not having problem-solution fit. This is a bit misleading for products that offer novel interactions rather than solving a problem, but it could be said that not being able to interact with people as much, or as effectively, is the problem such products are solving. 

Therefore, all startups must reach problem-solution fit.

To do so, they need to empathize with users and/or customers in order to understand what they are thinking and feeling, both in their existing work and with a startup’s new product. This can be done with easy or “lean” methods like signup forms on landing pages or created content on blog sites like Medium. Then, if these methods show some promise, more serious or formal research can be done to confirm that they have a problem that a startup’s solution addresses, and then that a large enough number of them have this problem for similar reasons. The first step in formal user/customer research can be done by observing people doing their jobs and interviewing them, while the second step can be done with online surveys or focus groups.

Then, after identifying some potential users that the product solves a problem of theirs, a startup should find a subset of them or their managers that are willing to pay for it (customers). This is the first step toward achieving product-market fit.