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IoT, Computer Science, Software Architecture, Messaging, Esp32.
than known approaches; be it horse back, runner, carrier pidgin, or smoke signals. Before assuming emperor, General Napoleon saw the vision of the Telegraph’s use for military endeavors and in 1792 helped get funding to complete a new European wide Optical Networking system. Towers were built that | medium | 4,383 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
could relay a message up and down a path of similar towers; say from the heart of Paris to a battlefield in Italy. A protocol was developed to encode messages and send them by signaling with the tower: configuring the moving arms above the tower into different angles that encoded the alphabet | medium | 4,384 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
characters. A tower knob was used to pick the characters which moved the arms to match. This accuracy ensured almost perfect message transmission from one location, say Point A, to a destination (Point B). This accessibility and speed meant that simple messages could travel long distances in hours | medium | 4,385 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
versus days; with a bonus of delivery confirmation. Now the “attack at daybreak” command from Napoleon could be sent from the comforts of Paris; weighing the political tensions better. A language for remote concurrent command was enabled but was not the end-all solution. What were the security | medium | 4,386 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
concerns? Could it scale to other routes or could multiple concurrent users overlap their messages in different directions? What about usability at night or in bad weather? Would funds and manpower allow for deploying and eventually maintaining new towers as his empire grew? Napoleon’s Optical | medium | 4,387 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
Networking system: Telegraph — or Distance Communication. 1792–1830 These systems all share common concerns and it’s the task of architects and engineers to design and describe solutions. Computer Science is the field that brings these technical concepts together through collaboration between users | medium | 4,388 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
and various computing devices. This science is entering a new era — the Internet of Things (IoT) era. Note all the terms in italic in above narrative. They form the basis for a formalized Software Architecture criteria: Discovery, Binding, Scalability, Security, Performance, Language, Concurrency, | medium | 4,389 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
Maintainability, Deployment, Usability, Routing, Protocols, and Dissemination. See my Max Headroom meets IoT writeup: https://medium.com/@knowledgeshark/the-internet-of-things-iot-meets-max-headroom-d62629f067b1 Common architecture approach 1830, Morris and others designed a new “electrical” | medium | 4,390 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
signaling device. If built, it would perform almost identical functions as the existing Telegraph systems. Message transmission was getting very efficient, new lines were showing up all over Europe and run independently by the countries who were often at war. Morris had to convince some US senators | medium | 4,391 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
that his system had all the potential to be much better. He had a hard time making that case, and it took better selling. He eventually provided a great demo. A well understood formalism to describe and compare different systems was needed. Morris had to contrast his proposed system to that already | medium | 4,392 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
understood. An architecture blueprint is a tool to help ease some of these concerns, but usually is a set of defining important features of a system. This might be that wires could be strung long distances, and the electrical current would allow messages to be sent at night — while the existing | medium | 4,393 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
telegraph couldn’t. Hiding messages to only those with decoding machines stopped eavesdropping by the enemy that can see the same tower and read the “attack at dawn” message. Selling his idea to congress could have been a litany of these “improvements” — but those stakeholders getting the briefing | medium | 4,394 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
might be lost and unable to relate to what they know. Providing context relating the previous and new system is needed; one that is concise, formal and visual; one that makes it easy to jump back and forth, comparing as you go. This formalism could provide consistency at different phases, such as | medium | 4,395 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
estimating, building, maintaining, or using. With a common checklist, an architecture could address these important issues. An architecture “rubix” could even contrast systems across generations, from 1792 to 1830 — or from 1989 to 2015; or across computing technologies from mainframes, to laptops, | medium | 4,396 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
to smart phones, to embedded or mobile devices, bluetooth networks, and now the Internet of Things (IoT). One part of that architecture approach is to provide an Elevator Speech. And example is below. Follow-on discussions will dive into the architecture “rubix” helping formalize architecutes. | medium | 4,397 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
These follow many of the known IEEE Views and Perspectives. Next time.. See my My Five (5) Computer Science Genre’s writeup: https://medium.com/@knowledgeshark/my-five-5-computer-science-genres-d7ba52ed1cb4 Also, I’ll show how remote messaging is the lifeblood of these IoT frameworks and describe | medium | 4,398 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
powerful architecture solutions, including embedded ESP32 based devices (with Bluetooth and WIFI), MQTT and node-red messaging support. Fun stuff. Elevator Speech (4 parts) Example of an elevator speech in terms of this architectural rubix. What is the problem? Unbounded complexity of new | medium | 4,399 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
generation of environment sensing devices (IoT). We are surrounded by an ever expanding litany of smart devices, continually sensing the environment providing feedback and control in our daily life. Be it a heart rate monitor, smart shopping list, home automation, or remotely monitoring animals — a | medium | 4,400 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
flood of technology, called The Internet of Things (IoT) is a reality. This is becoming an unbounded challenge rivaling the most complex solutions imagined (think of the planet sized “Death Star” from the Starwars movie). 2. What do we provide? Custom Architecture leveraging IoT devices. “Do what I | medium | 4,401 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
mean”. We provide an Architecture customized to leverage your smart devices; a “Do what I mean” solution. 3. How is this approach different? “Rubix” Matrix Architecture App; Automatic Programming, not brute force. By looking beyond brute force coding solutions, we provide an approach for | medium | 4,402 |
IoT, Computer Science, Software Architecture, Messaging, Esp32.
automatically programming the dynamic connections between the environment and all the smart IoT devices. This is captured in our “rubix”” Matrix Architecture “app” — comparing and addressing your stakeholders concerns. The result is your custom architecture that adapts to the smart devices you | medium | 4,403 |
Logging, Python, Fastapi, Web Development, API.
Implementing Logging in FastAPI Applications Hey there fellow Python enthusiasts! Today, I’m super excited to dive into one of the most fundamental yet often overlooked aspects of backend development — logging. Specifically, we’ll be exploring how to set up Python logging in the blazingly fast | medium | 4,405 |
Logging, Python, Fastapi, Web Development, API.
FastAPI framework. But wait, hold up! Before we dive into the nitty-gritty details, let’s take a moment to understand why logging is so crucial in any application, regardless of its complexity or scale. Why Logging Matters Picture this: You’ve built this amazing FastAPI application, and everything | medium | 4,406 |
Logging, Python, Fastapi, Web Development, API.
seems to be running smoothly. But then, one fine day, a user reports a strange bug that’s causing the application to crash unexpectedly. Now, without proper logging in place, tracking down the root cause of the issue becomes akin to searching for a needle in a haystack. Logging, my friends, is like | medium | 4,407 |
Logging, Python, Fastapi, Web Development, API.
having a trusty sidekick that diligently records every significant event and detail within your application’s lifecycle. It not only helps in debugging issues but also provides valuable insights into its behavior, performance, and user interactions. Setting Up Python Logging in FastAPI Alright, | medium | 4,408 |
Logging, Python, Fastapi, Web Development, API.
let’s roll up our sleeves and get our hands dirty with some code! Setting up logging in FastAPI is a breeze, thanks to Python’s built-in logging module. First things, let’s import the required modules — logging for, well, logging, and of course, fastapi to work our magic. Step 1: Import the | medium | 4,409 |
Logging, Python, Fastapi, Web Development, API.
Necessary Modules import logging from fastapi import FastAPI Step 2: Create a FastAPI App Next up, let’s create a FastAPI application instance. app = FastAPI() Step 3: Configure Logging Different log levels DEBUG: Use this level for detailed information useful for debugging purposes. It’s like | medium | 4,410 |
Logging, Python, Fastapi, Web Development, API.
wearing your detective hat and delving deep into the inner workings of your application. INFO: This level is perfect for general information about what’s happening within the application. Think of it as your application’s way of saying, “Hey, everything’s running smoothly!” WARNING: Uh-oh, | medium | 4,411 |
Logging, Python, Fastapi, Web Development, API.
something doesn’t seem quite right. Use this level to indicate potential issues that could lead to problems down the road. ERROR: Houston, we have a problem! Use this level to signify errors that need immediate attention but won’t necessarily crash the application. CRITICAL: Brace yourselves; | medium | 4,412 |
Logging, Python, Fastapi, Web Development, API.
things are about to hit the fan! Reserve this level for critical errors that could potentially bring your entire application crashing down. Now comes the fun part — configuring our logging settings. We’ll set the logging level and add a handler to specify where our logs should go. | medium | 4,413 |
Logging, Python, Fastapi, Web Development, API.
logging.basicConfig(level=logging.DEBUG, filename='app.log', filemode='a', format='%(asctime)s - %(levelname)s - %(message)s') Here’s what each parameter does: level=logging.DEBUG: This sets the logging level to DEBUG, meaning all log messages will be captured. filename='app.log': This specifies | medium | 4,414 |
Logging, Python, Fastapi, Web Development, API.
the name of the log file. You can choose any name you like. filemode='a': This sets the file mode to append, so new log messages will be added to the end of the file. format='%(asctime)s - %(levelname)s - %(message)s': This defines the format of the log messages, including the timestamp, log level, | medium | 4,415 |
Logging, Python, Fastapi, Web Development, API.
and message. In this logging configuration, we can log all the levels of logs because we set it to the lowest level. Step 4: Define Your FastAPI Endpoints Let’s define some simple FastAPI endpoints to demonstrate logging. @app.get("/") async def read_root(): logger.debug("Root endpoint accessed") | medium | 4,416 |
Logging, Python, Fastapi, Web Development, API.
return {"message": "Hello World"} @app.get("/items/{item_id}") async def read_item(item_id: int, q: str = None): logger.info(f"Item {item_id} requested") return {"item_id": item_id, "q": q} Step 5: Run Your FastAPI App Finally, let’s run our FastAPI application and see our logging-in action! if | medium | 4,417 |
Logging, Python, Fastapi, Web Development, API.
__name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000) Putting It All Together Here’s the complete code for our FastAPI application with logging: import logging from fastapi import FastAPI app = FastAPI() # Configure logging logging.basicConfig(level=logging.DEBUG, | medium | 4,418 |
Logging, Python, Fastapi, Web Development, API.
filename='app.log', filemode='a', format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) @app.get("/") async def read_root(): logger.debug("Root endpoint accessed") logger.info("Testing Info") logger.warn("Testing Warning") logger.error("Testing Error") return | medium | 4,419 |
Logging, Python, Fastapi, Web Development, API.
{"message": "Hello World"} @app.get("/items/{item_id}") async def read_item(item_id: int, q: str = None): logger.info(f"Item {item_id} requested") return {"item_id": item_id, "q": q} if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000) To run the app please type the | medium | 4,420 |
Logging, Python, Fastapi, Web Development, API.
command in the terminal mentioned below. python main.py After hitting the API's endpoint logs we will generate in app.log file as you see below. 2024-03-16 20:55:16,756 - DEBUG - Root endpoint accessed 2024-03-16 20:55:30,827 - INFO - Item 1 requested 2024-03-16 20:57:15,592 - DEBUG - Root endpoint | medium | 4,421 |
Logging, Python, Fastapi, Web Development, API.
accessed 2024-03-16 20:57:15,592 - INFO - Testing Info 2024-03-16 20:57:15,592 - WARNING - Testing Warning 2024-03-16 20:57:15,592 - ERROR - Testing Error Task: How to set up logs in Python with many levels of logging, each with its own log file. First, try putting it into practice. If you have any | medium | 4,422 |
Logging, Python, Fastapi, Web Development, API.
issues, I’ve attached a link to the solution on GitHub. Feel free to message me or leave a comment. How to implement log for different levels of log and Reference: https://fastapi.tiangolo.com/ https://docs.python.org/3/library/logging.html Thank you for reading. If you find something wrong or | medium | 4,423 |
Design, User Experience, Artificial Intelligence, AI, UX.
How Generative AI advances are helping shape the future of feature design, from enhancing content interaction to more informed decision-making Order among chaos (Credit: Dall-E) The ChatGPT revolution has been unfolding for over a year now. While the specific chatbot may not represent a seismic | medium | 4,425 |
Design, User Experience, Artificial Intelligence, AI, UX.
technical shift, the profound change in perception it has engendered within the tech community continues to send shockwaves. Initially, teams across various industries scrambled to mimic and refine the success of chatbots, leveraging the underlying models of GPT to produce solutions that, while | medium | 4,426 |
Design, User Experience, Artificial Intelligence, AI, UX.
similar to ChatGPT, were customized for specific experiences and trained on unique datasets. As the novelty wears off and the real potential of Generative AI becomes clearer, innovative teams move beyond mere chat functions. Notion AI emerged as a beacon of how AI advancements can be integrated | medium | 4,427 |
Design, User Experience, Artificial Intelligence, AI, UX.
into traditional product features, sparking discussions and excitement about the broader applications of this technology. This was followed by companies like Grammarly and Figma, who have introduced many features that transform content creation and modification, setting a new benchmark for | medium | 4,428 |
Design, User Experience, Artificial Intelligence, AI, UX.
practical AI integration. Fast forward to this year, and it seems every forward-thinking company has unveiled a roadmap dotted with AI-enhanced features. Product leaders are now under intense pressure to weave Generative AI into their offerings — not only to deliver genuine value but also to | medium | 4,429 |
Design, User Experience, Artificial Intelligence, AI, UX.
compete in a market where AI capabilities are fast becoming a yardstick for innovation. While chatbots and straightforward text manipulation tools remain popular for their proven value, the landscape is evolving rapidly. Startups in every sector are pushing the boundaries of what Generative AI can | medium | 4,430 |
Design, User Experience, Artificial Intelligence, AI, UX.
achieve, from creating synthetic users for conjoint analyses to agents that can act as junior developers. This surge of creativity is expanding the Overton window, showcasing a future where traditional products are imbued with what seems like a touch of magic. All are powered by the increasingly | medium | 4,431 |
Design, User Experience, Artificial Intelligence, AI, UX.
abstracted applications of underlying LLM technologies. Emerging AI-Enhanced Features As I observe the ongoing evolution of Generative AI, several distinct patterns begin to crystallize. There are many ways to look at these patterns, and some have done so through more traditional design elements | medium | 4,432 |
Design, User Experience, Artificial Intelligence, AI, UX.
seen in AI Features. Others have done so by looking at patterns in the user experience. The trends I have begun to notice focus on how Generative AI is being used to make the product more effective at its intended purpose. These patterns emerge either as a cascade of mimicry — where one company’s | medium | 4,433 |
Design, User Experience, Artificial Intelligence, AI, UX.
innovation spawns a host of imitators, each tweaking the concept for their unique user bases — or as a convergence around a genuinely transformative capability of Generative AI. These standout applications are now staples on the roadmaps of many companies, representing the real value this | medium | 4,434 |
Design, User Experience, Artificial Intelligence, AI, UX.
technology brings to the table. Recall the advent of mobile technology: initial mobile apps were often mere extensions of existing desktop services, adapted — not always gracefully — to fit the mobile format. Many were little more than clunky mobile renditions of company websites. At the time, | medium | 4,435 |
Design, User Experience, Artificial Intelligence, AI, UX.
these solutions merely extended what was already available rather than unlocking the new value potential inherent in the mobile platform. However, as companies grew bolder in their experiments with the capabilities of handheld devices, coupled with the expansion of high-speed internet, truly | medium | 4,436 |
Design, User Experience, Artificial Intelligence, AI, UX.
innovative solutions began to emerge. A prime example is Uber, which leveraged mobile access's ubiquity to revolutionize how people summon and share rides. This breakthrough wasn’t just an adaptation but a complete reinvention, creating unprecedented value by fully harnessing the platform’s | medium | 4,437 |
Design, User Experience, Artificial Intelligence, AI, UX.
potential. Just as mobile technology found its footing by embracing its unique capabilities, the LLM revolution is now poised to transform how we interact with digital environments in equally groundbreaking ways. Rewriting Content is a Natural Starting Point Content Rewriting: One of the most | medium | 4,438 |
Design, User Experience, Artificial Intelligence, AI, UX.
impactful uses of LLM technology lies in content rewriting, which naturally capitalizes on these systems’ robust capabilities for generating and refining text. This application is a logical fit, helping users enhance their content while engaging with a service. Early implementations included | medium | 4,439 |
Design, User Experience, Artificial Intelligence, AI, UX.
Notion’s feature, allowing users to transform brief inputs into structured templates, and Grammarly’s expansion of its editing tools to enable comprehensive content rewrites. These integrations were a natural progression for products designed to streamline and improve written content. Notion AI has | medium | 4,440 |
Design, User Experience, Artificial Intelligence, AI, UX.
a collection of content rewriting features that work on any content a user creates. Message Personalization: As more companies have adopted this application, we see more innovative ways to assist users with rewriting content. In the sales field, both Hubspot and Salesforce have created systems to | medium | 4,441 |
Design, User Experience, Artificial Intelligence, AI, UX.
create tailored outreach to prospective customers utilizing information stored within their systems. Adding this capability creates a demand for high-quality data, further reinforcing the need to invest in building their respective CRMs. UpWork, known for matching contractors with work, has a | medium | 4,442 |
Design, User Experience, Artificial Intelligence, AI, UX.
system that enhances job postings to attract more qualified candidates. On the consumer front, Bumble recently released a tool to help create icebreaker messages. Bumble’s AI generated an icebreaker prompt. In all examples, the feature moves beyond basic content rewriting to increase the message’s | medium | 4,443 |
Design, User Experience, Artificial Intelligence, AI, UX.
effectiveness. Some do this by improving a user's quality of content based on best practices. Others do this by identifying unique characters that can be included in the message based on additional variables collected within the system. It even shows up as fewer words overall, increasing the | medium | 4,444 |
Design, User Experience, Artificial Intelligence, AI, UX.
message's efficiency. Summarizing Content Overload Summarization: While content rewriting remains a prevalent application of Generative AI, extracting actionable insights from voluminous content has rapidly gained traction as a vital use case. The most straightforward method is summarization, where | medium | 4,445 |
Design, User Experience, Artificial Intelligence, AI, UX.
significant amounts of data are processed to discern patterns and key points. LinkedIn’s article summarization feature exemplifies this, transforming lengthy posts and articles into digestible snippets, making complex information accessible at a glance. Identifying Highlights: Similar to | medium | 4,446 |
Design, User Experience, Artificial Intelligence, AI, UX.
summarization but incorporating an element of judgment, features like Microsoft Team CoPilot’s call transcript summaries distill extensive discussions into essential bullet points, spotlighting pivotal moments or insights. Rippling AI and Greenhouse, both HR technologies, have released features | medium | 4,447 |
Design, User Experience, Artificial Intelligence, AI, UX.
that do the same with interviews. They can take the transcripts of interviews combined with the notes written by the interviewer to create summaries while highlighting the positives and negatives of each candidate. The same is being done in the B2B space with ServiceNow, which creates a collection | medium | 4,448 |
Design, User Experience, Artificial Intelligence, AI, UX.
of features to summarize and highlight patterns among support tickets. From there, they are expanding into two more emerging feature sets: report creation and advanced search. Microsoft’s CoPilot creates summaries based on calls. Report Creation: With summarized data, a natural next step, | medium | 4,449 |
Design, User Experience, Artificial Intelligence, AI, UX.
especially in SaaS products, is the creation of reports. By identifying the most important variables or common patterns, a product can auto-generate a deliverable for users to build upon. ServiceNow does this for customer service, just as Hubspot does for sales data. Tableau takes it further by | medium | 4,450 |
Design, User Experience, Artificial Intelligence, AI, UX.
automatically creating summaries based on trending data. Not only in B2B products, but Priceline has also recently launched a few GenerativeAI features, with the automatic creation of an itinerary being a leading example. This feature builds a plan for a user to adhere to during an upcoming trip. | medium | 4,451 |
Design, User Experience, Artificial Intelligence, AI, UX.
While still an early feature, there is much potential for further expansion and value-creation opportunities. Tableau creates a daily pulse based on relevant metrics. Advanced Search: The ability to ‘understand’ nuanced language through summarization extends naturally into advanced search | medium | 4,452 |
Design, User Experience, Artificial Intelligence, AI, UX.
functionalities. ServiceNow does this by enabling customer service agents to search tickets for recommended solutions and to dispel jargon used by different agents. ZenDesk does something similar by pulling up relevant information related to a service ticket. Concur takes the same format of pulling | medium | 4,453 |
Design, User Experience, Artificial Intelligence, AI, UX.
up relevant documents to an expense report, enabling employees to file their expense reports faster. In a more traditional search fashion but with much more value, given the ability to rapidly summarize contents are the new features offered by Dropbox. Users can ask questions about the documents | medium | 4,454 |
Design, User Experience, Artificial Intelligence, AI, UX.
within their storage systems, quickly extracting insights without opening a variety of documents. Glean does the same by using an interactive chatbot so people can continue their line of questioning. The most innovative end of this spectrum allows for the inclusion of quantitative data, which | medium | 4,455 |
Design, User Experience, Artificial Intelligence, AI, UX.
Mixpanel is doing with its Spark AI. Users can ask questions about their data, searching for value among metrics. Mixpanel’s Spark AI allows users to ask questions about data visualizations. Insight Recognition: The final dimension of finding value in lots of emerging content is the identification | medium | 4,456 |
Design, User Experience, Artificial Intelligence, AI, UX.
of novel insights. Mixpanel and Tableau are already doing that along with a collection of AI-specific products. Gong, a sales enablement tool, has a new feature that identifies trends in what is working during sales calls. It uses GenAI to identify these patterns across similar but different words, | medium | 4,457 |
Design, User Experience, Artificial Intelligence, AI, UX.
triangulating around a central meaning that appears effective regardless of how it is exactly communicated to the potential customer. On the consumer side, Fitbit has begun to utilize users’ specific actions in combination with the corpus of users’ data to display personalized health insights. | medium | 4,458 |
Design, User Experience, Artificial Intelligence, AI, UX.
Combined with the tailoring of messaging, I imagine the natural next step will be to nudge the user to take personalized actions. A personalized insight is offered by Fitbit. These design patterns demonstrate the multifaceted capabilities of summarization as a fundamental aspect of Generative AI. | medium | 4,459 |
Design, User Experience, Artificial Intelligence, AI, UX.
As experimentation continues, we can expect to see an expansion of these applications, with successful models being refined and replicated across industries. Just as with content rewriting, summarization and its many manifestations are but one of many burgeoning superpowers of Generative AI, paving | medium | 4,460 |
Design, User Experience, Artificial Intelligence, AI, UX.
the way for future innovations. Another superpowers that are just beginning to emerge is the ability to create evidence-backed predictions based on the collective knowledge that the foundational LLMs hold due to their training data. Tapping into Collective Knowledge Despite some controversies and | medium | 4,461 |
Design, User Experience, Artificial Intelligence, AI, UX.
unresolved issues, the undeniable reality is that large language models (LLMs) are trained on a vast corpus of human knowledge, covering a diverse range of data sources and content types. While these systems may not truly “understand” content in the human sense, they excel at recognizing patterns | medium | 4,462 |
Design, User Experience, Artificial Intelligence, AI, UX.
in human behavior. This capability underpins their effectiveness in role-playing specific personas, achieved by crafting prompts that guide the system to assume a desired stance. Organizations are only beginning to explore this collective knowledge's potential in their product features. Rather than | medium | 4,463 |
Design, User Experience, Artificial Intelligence, AI, UX.
merely focusing on content creation or manipulation, emerging applications of these systems provide new perspectives and predict outcomes based on accumulated human experiences. The actual value of these applications lies not merely in enhancing efficiency but in augmenting effectiveness, enabling | medium | 4,464 |
Design, User Experience, Artificial Intelligence, AI, UX.
users to make more informed decisions. While still not widespread, I foresee this approach becoming a mainstay on company roadmaps, representing the next evolutionary step in AI application. Once an LLM generates a novel insight, the next logical question is, “What should I do with this | medium | 4,465 |
Design, User Experience, Artificial Intelligence, AI, UX.
information?” Using Generative AI to inform decisions based on the collective experiences and actions of others offers a powerful new tool for decision-making. Scenario Planning: In professional settings and our personal lives, we frequently encounter situations that feel unique to us. However, | medium | 4,466 |
Design, User Experience, Artificial Intelligence, AI, UX.
others have likely navigated similar challenges. Generative AI can leverage this vast repository of experiences to offer guidance and predict potential outcomes. For instance, BetterUp’s Difficult Conversation Scenario Planner utilizes this technology to help users strategize for challenging | medium | 4,467 |
Design, User Experience, Artificial Intelligence, AI, UX.
interactions. By simulating different conversational paths, the tool reveals the potential effects of various approaches and allows users to anticipate aspects of the dialogue. In a B2B context, companies like AMEX are experimenting with Generative AI to forecast customer behaviors, aiming to | medium | 4,468 |
Design, User Experience, Artificial Intelligence, AI, UX.
refine financial planning processes. BetterUp’s Difficult Conversation Scenario Planner setup screen. Empathy Building: Beyond scenario planning, these systems can deepen understanding between individuals. Our divisions often stem from a lack of understanding, and Generative AI, trained on a broad | medium | 4,469 |
Design, User Experience, Artificial Intelligence, AI, UX.
spectrum of perspectives, can serve as a bridge. For example, an experimental feature I observed allowed users to explore how they would react in a scenario and understand why others might choose differently. While full realization of this technology is on the horizon, LinkedIn’s current feature | medium | 4,470 |
Design, User Experience, Artificial Intelligence, AI, UX.
that suggests questions following posts hints at future possibilities. Users could query, “Why would someone take this perspective?” or “Why would this user feel comfortable sharing this?” Generative AI could then offer predictions that foster empathy among participants. The current state of | medium | 4,471 |
Design, User Experience, Artificial Intelligence, AI, UX.
LinkedIn’s recommended questions could be built upon for empathy building. As we continue to unlock the capabilities of summarization and tapping into collective knowledge, these new generative AI applications not only promise to replicate knowledge but to contextualize and humanize it, potentially | medium | 4,472 |
Design, User Experience, Artificial Intelligence, AI, UX.
transforming how we interact with and understand each other. The Evolution Continues Table of the nine GenAI-Enhanced Design Patterns I have covered nine design patterns for AI-enhanced features emerging across the product landscape. All of this has occurred in less than a year! Looking forward, I | medium | 4,473 |
Design, User Experience, Artificial Intelligence, AI, UX.
am sure some of these will stick, and others will be overtaken by better applications. As entire AI-first products are built, it will be fascinating to see what angles are used and how many of these early features remain. What is sure is that the evolution will continue as teams learn how to build | medium | 4,474 |
Design, User Experience, Artificial Intelligence, AI, UX.
solutions that are fit for this novel technology. In this exploration, I have highlighted nine distinct design patterns for AI-enhanced features that have proliferated across the product landscape — all within the span of less than a year. As we look to the future, some of these innovations will | medium | 4,475 |
Design, User Experience, Artificial Intelligence, AI, UX.
likely become staples while others may fade, surpassed by more sophisticated applications. The rise of AI-first products promises to introduce fresh perspectives and methodologies, potentially reshaping which of these early features remain relevant. What remains certain is that the evolution of | medium | 4,476 |
Design, User Experience, Artificial Intelligence, AI, UX.
AI-enhanced features will persist as development teams continue to refine their approach to integrating this transformative technology. The key to future success lies in the ability to adapt and innovate, ensuring solutions are technologically advanced and deeply aligned with human needs and | medium | 4,477 |
Design, User Experience, Artificial Intelligence, AI, UX.
contexts. As we continue to navigate this exciting terrain, the principles of user-centered design will guide us toward creating functional tools and transformative experiences that redefine how we live and work. The next chapter of product design is being written today, and Generative AI is | medium | 4,478 |
Design, Design Process, Design Thinking, Design Management, UX Design.
How to track and monitor the effort and success of design-related activities that are not easily quantifiable Design KPIs Graphs Why should we measure our performance? After many years of being asked and trying to track efficiently but also truthfully the design processes and efforts of designers, | medium | 4,480 |
Design, Design Process, Design Thinking, Design Management, UX Design.
I came up with this monitoring method. Its main focus is to give the right weight to the process of thinking and elaborating on the outcomes of research and workshops. In my opinion, those are the most important elements of a designer’s work but are also works that require the most effort and | medium | 4,481 |
Design, Design Process, Design Thinking, Design Management, UX Design.
expert knowledge. The deliverables itself more than often do not represent all the hard work that went into the polished final designs which to a less expert eye may seem effortless. Therefore, I propose the following criteria to evaluate the design process, effort, and outcomes. Of course, as with | medium | 4,482 |
Design, Design Process, Design Thinking, Design Management, UX Design.
any KPI or OCR documents, it should evolve as the teams and work objectives evolve. But most importantly it works only if it is regularly tracked and valued. Note: This tracking method is thought for the design team itself or internal (department) monitoring and not for the organizational level | medium | 4,483 |
Design, Design Process, Design Thinking, Design Management, UX Design.
where the success is tracked thru CHURN or customer satisfaction Let’s dive in…. The 4 main criteria: Design activities Complexity scale Deliverables Management level Each of the 4 criteria is divided into 4 dimensions: Small Medium Large Extra Large The values for each category change and are to | medium | 4,484 |
Design, Design Process, Design Thinking, Design Management, UX Design.
be defined in correspondence to the usual teamwork and project type. Design activities Design activities are all the activities designers do to reach a certain outcome. These activities can vary by project or by design teams which can be specialized in some areas more than others. Below I showed | medium | 4,485 |
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