Death by a Thousand Notifications

Dozens of notifications.
All day.
Most of them insignificant.
Each one asking.

Bad UI.
Misfires.
The hand moves before thought.

Time leaks.
Attention thins.

You click the wrong one.

You are dead.

As The World Goes Away •
I Sit By You •
Holding Your Hands •
Kissing You •
Telling You How Much I Love You •
Telling You How Much We Love You •


I Can Feel The World Moving Still • 
But I Can Feel Your World Fading • 
And With It • My World Fades Too • 
As The Lights Grow Soft • 
And The Noise Begins To Fade • 
As Our World Goes Away • 
I Know You Can Still Hear Me • 
I Know You Can Still Feel Me • 
I Tell You Softly • 
How Grateful We Are • 
How Much We Love You • 
How Much We Will Miss You • 


We Tell You That All Will Be Fine •
We Tell You Not To Worry • 
We Tell You That The Kids Will Be Fine • 
The Grandkids Will Be Fine • 
The Family Will Be Fine • 
Luna • Lyra • And Chica Will Be Fine • 
That I Will Be Fine •

I Thank You • Sarylda •
We All Thank You •
For Your Smile • Your Laugh •
Your Energy And Your Love •
For Your Kindness —
For Making Every Place You Were In Just Better •
Thank You For Everything You Gave Us •

You Know • Sarylda — You Are My Rock •
The Love Of My Life • My Other Half •
Together Since Childhood —
Two Souls • Two People —
Who Grew As One •

As You Go Away •
As Our World Together Goes Away •
I Stand In A Spectacular Quiet •
Now I Am Lost •
I Am Broken • Heartbroken •
I Don’t Know What To Do Without You • Sarylda •

My Beautiful And Lovely Wife — Sarylda •
Thank You For Being With Me •
Thank You For Having Me •
Thank You For Your Patience •
And Your Kindness •
For Your Love •
Your Laughs •
And Your Strength •
For Every Day Together • For Every Year • Every Decade •
Thank You For Being There •

Thank You For The Kids •
For The Laughter •
For The Hard Times •
For The Light • And For All Your Guidance •
For Loving Me As You Did •
You Were Always There For Me • And For The Family •

I Want To Hear Your Voice Again •
I Want To Feel Your Skin Against Mine •
I Want To Kiss You •
I Want To Be Wrapped In Your Warmth •
To Feel Your Love • The Way Only We Knew It •
I Want To Tell You That I Love You •

I Want You •
I Love You •
Forever •

As Your World Goes Away •
As My World Goes Away •
You Still Breathe In My Dreams •
You Still Smile In My Dreams •
In My Dreams • You Still Love Me •
You Still Whisper Through My Tears •
All A Lucid Dream •

Our Story Is Not Over •
It Has Only Entered A New Phase — Crossed The Horizon •

I Am Still With You •
Always With You •
You Are Still With Me •
Always With Me •
In The Quiet Between Heartbeats •
In The Place Where Love Does Not End •
In My Lucid Dreams Of You •

You Are And Always Will Be My Rock •
My Foundation •
My North Star •

As Our World Goes Away •
There Is So Much To Be Said •
And So Much To Be Done •
As Our World Together Goes Away •
I Miss You So Much • Sarylda •
I Will Miss You Forever •
I Will Love You • Sarylda • Forever —
You Are The Love Of My Life •
My Beautiful • Loving Wife • Sarylda •

You Will Always Be With Me • Sarylda •
I Will Love You Forever •

—

For Sarylda, the love of my life…

by Carlos Enrique

(Inspired in part by Christoffer Franzén / Lights & Motion music)

The Love of My Life — Sarylda Ortiz (1964 – 2025)

In Loving Memory of Sarylda Ortiz (1964 – 2025)

It is with profound sadness that we announce that on October 31, 2025, at the age of 61, Sarylda Ortiz, beloved wife, mother, grandmother, daughter, sister, and friend, passed away peacefully, surrounded by family and love.

In quiet moments of reflection, amidst the memories of love, togetherness, and family, and even the sadness of her absence, we shall carry Sarylda’s memory with us, a guiding light illuminating the path ahead. May her journey be filled with peace and serenity, and may she find rest in the eternal embrace of love.

A private service will be held for close family and friends to celebrate Sarylda’s life and the immeasurable love she leaves behind. Visitation will be held on Thursday, November 13, 2025, from 11:00 AM to 1:00 PM at Beck Funeral Home (15709 Ranch Rd, Austin, TX 78717). A reception will follow from 1:00 PM to 3:00 PM at the same location.

Sarylda was the love of the life of her husband, Carlos Enrique, with whom she shared 44 beautiful years, a partnership built on pure love, care, devotion, adventure, laughter, and resilience through life’s trials. She was humble, down-to-earth, firm, deeply loved. Together they built a life filled with family, joy, love, and purpose. Sarylda was his rock, his foundation, his north star, and the heart of their home.

Sarylda was and is a beautiful person and soul, radiant inside and out. She was beautiful, strong, active, independent, loving, patient, unique, organized, and, at times, stubborn in the most endearing way. Her beauty, smile, laughter, and energy could brighten any room, and her love, warmth, kindness, and strength touched everyone who knew her.

Sarylda had a deep love for her family and friends. She loved her animals. She loved nature and going hiking. She loved cooking. She poured her heart into everything she did — as a wife, mother, grandmother, daughter, sister, friend, and as a business owner. From her years as a dental assistant and teacher to her pet-sitting business, she loved what she did for others, and treated every pet she cared for as her own and her clients as friends. Her generous spirit and steadfast heart were a source of comfort and love to all who entered her life.

Sarylda leaves behind her devoted husband Carlos Enrique, her beloved children Sarylda, Diana, and Damián Enrique, her cherished grandchildren Anaya, Abriana, Kaylynn, Amadeus, and Samora, as well as her beloved pets Luna, Lyra, and Chica, and a loving family who will forever carry her light within them.

Though her physical presence has left this world, her love will remain eternal, guiding, protecting, loving, and inspiring all those whose lives Sarylda touched.

Family and friends are welcome to plant a tree in her name, and/or send flowers or share condolences on her memorial page to honor Sarylda and a life that was beautiful, selfless, and filled with love.

So much to be said, and so much left undone.

We will miss and love Sarylda for eternity.

—

I will love you forever, Sarylda, you are and will be the love of my life.

Sarylda Ortiz (1964 – 2025)

In Memory of Eric Maginniss

Eric Maginniss will be deeply missed.

What a sharp mind, what a gifted software engineer. In my ~30 years in this field, I’ve had the privilege of working with some incredibly bright individuals, and Eric was absolutely one of them.

There is something remarkable about watching someone tackle a truly hard problem, and then witnessing solutions come to life—the beauty of software, the art of creating systems from nothing. Eric embodied that.

I had the honor of working with him on several projects:

• Shango – Together with Chris Murphy, a dear friend of Eric, we built systems for managing the life-cycle of telephone numbers (yes, telephone numbers have a life-cycle) and the services tied to them. Think of phone numbers as digital identities—our work involved extensive VoIP integration and complex service orchestration.

• QMC Health – At QMC Health, we developed a sophisticated platform for managing lab results. This included blockchain for immutable ledgers, computer vision for reading lab test strips, and web and mobile apps for both lab technicians and patients.

• Stronghold Resource Partners – At SRP, we built complex systems that automated the calculations and management of land-title/ownership graphs, streamlined landman and legal workflows, and incorporated financial models and workflows to speed-up the management and trading of oil & gas (3D geo-spatial) assets.

Every time, he listened, designed, built, and delivered. He never minded my pickiness about software design specs and development—and always produced solid designs and reliable code.

If I needed a soundboard for ideas and their implementation, Eric was always there.

Eric had the rare ability to single-handedly design and build sophisticated systems from the ground up. He was, in every sense, a true 10x Developer—a “ninja” engineer who could solve end-to-end complex software problems. He was great and fun to work with.

Rest in peace, my friend. QEPD.

My condolences to his wife and family.

/carlos enrique

We have entered a new era of software development where natural language, AI, and intuition replace traditional coding.

The new programming language? English.
The new compiler? Prompts and Large Language Models.
The new IDE? Natural language conversation.

We’re moving from spec-driven, syntax-first coding to vibe-coding—
Where creativity leads and syntax follows.
Where intuition meets inference.
Where inference fuels iteration.

This is a new era of software engineering:
Where individuals can build at the speed of thought.
Where flow replaces friction.
Where inference augments intelligence.
Where the barrier to creation is lower than ever—
and the potential for innovation is higher than ever.

Welcome to the future where code is optional—
and ideas are everything.

— Credit to Jack Dorsey for the original inspiration.

I shared this with my team. There are many valuable skills that aren’t taught in school or in life, and proactiveness is one of them. For some, it comes more naturally than for others. However, it requires practice for everyone. Proactiveness is a top skill I look for in engineers, and it’s beneficial for anyone to have.

On Proactiveness

Being proactive means taking initiative, anticipating challenges, and driving solutions without waiting for instructions or problems to arise. It’s a forward-thinking mindset where you actively seek ways to improve processes, enhance team performance, and achieve better outcomes. Instead of merely reacting to tasks or issues as they come up, or waiting for others to take action, you proactively solve problems by finding the right solutions and identifying opportunities for improvement, then act on them to complete tasks efficiently.

Key aspects of being proactive include:

1. Leadership: Stepping up to guide and inspire others.
2. Effective Communication: Clearly conveying ideas and feedback. Communicate early and often.
3. Decisiveness: Making informed decisions, especially for reversible choices.
4. Assertiveness: Confidently expressing your ideas and standing up for your decisions.
5. Anticipation: Planning for future challenges and opportunities.
6. Preemptive Problem-Solving: Proactively addressing issues before they escalate or before being asked. Don’t wait!
7. Big Picture Thinking: Grasping the broader context and long-term goals.
8. Initiative: Proposing and implementing improvements.
9. Situational Awareness: Understanding what is happening around you.
10. Constructive Disagreement: Learning to disagree respectfully and commit to decisions.
11. Expectations: Clearly set expectations, manage expectations, and communicate early and often any changes to already set expectations.
12. Support and Collaboration: Helping others through brainstorming, design discussions, pair programming, PR reviews, and mentoring.
13. Curiosity: Continuously learning and exploring new ideas.

If we follow the above principles, as an individual, as a Team, we can and will foster a proactive culture that drives innovation and excellence within our team, and we will deliver wonderful results.

In software development, the principles of abstractions and separation of responsibilities and concerns are essential for creating clean, maintainable, scalable, and secure systems. Artificial intelligence (AI) is no exception to these principles. In this essay, I will explore the concepts of AI system (or core) spaces versus application spaces, drawing an analogy from computer operating systems (OS).

In both computer operating systems and artificial intelligence, distinct logical layers separate core low-level functionalities from application-level functionalities and user interactions.

In operating systems, the system space is where the kernel operates, providing core services and maintaining system integrity. Access to this space is typically privileged or restricted, preventing regular users and applications from direct access. The user space, on the other hand, is where applications run, interacting with the system space through system calls (APIs). This separation not only protects the system from potentially harmful user applications but also simplifies the process for developers to write and deploy applications.

Let’s now look at the AI space and an illustration of this layered view of AI System/Core and Application Spaces.

Figure 1 – AI App and System/Core Spaces or Layers

The AI System (or Core) space is analogous to the system space in an operating system (OS). This space encompasses the core models, algorithms, and infrastructure managed by AI engineers. It handles complex operations such as model training and inference, fine-tuning, optimization, and related infrastructure. In contrast, the AI Application (App) space includes the interfaces, APIs, and tools that enable users and developers to interact with and build applications using AI functionalities without needing to understand the underlying complexities. This separation allows for the creation of valuable applications such as chatbots, intelligent agents, and generative AI apps. This separation ensures that AI app developers and users can leverage AI capabilities safely and effectively without needing to delve into low-level complexities, thereby lowering the barriers to entry.

The following table explores the AI System/Core and Application spaces in more detail.

Table  – AI System versus App Space

Next, let’s explore additional examples.

While a large language model (LLM) such as GPT clearly belongs in the AI System/Core space, the placement of Retrieval-Augmented Generation (RAG) might not be as obvious. Let’s explore this further. RAG is a popular technique used to enhance the responses of LLMs by integrating information retrieval systems with these models. AI developers typically work with RAG to extend or augment LLM capabilities.

While we could debate whether RAG serves as a middle-layer or middleware within the application space, in this essay I focus on distinguishing between core low-level complex functionalities and services versus higher-level functionalities and applications. Based on this criterion, RAG belongs to the AI System/Core space due to its intricate integration of information retrieval systems with LLMs to enhance responses by referencing external knowledge bases. This integration involves combining various data sources, potentially through ETL (Extract, Transform, Load) data pipelines, and employing enrichment methods such as semantic enrichments. Additionally, sophisticated algorithms like embedding models, vector search, graph search, or contextual augmentation are used to retrieve accurate and relevant information, which is then merged with the generative capabilities of LLMs.

Once built, the app-specific RAG can be utilized generically by applications within the AI Application space. From this perspective, RAG’s benefits are delivered to users through applications like chatbots and search engines. Developers can access RAG functionalities via APIs, simplifying the integration into user-facing applications.

Table 2 – RAG in the System/Core versus the User/App Space

In Summary / Final Thoughts

In both operating systems and AI, the distinction between system space (or low-level AI system/core space) and user space (or AI App space) helps maintain clear separation of concerns and responsibilities.

• The low-Level AI System/Core Space manages core functionalities, concealing complexity while ensuring robust performance and safeguarding critical operations.
• The AI App Space is where AI is applied, where users and developers create and utilize AI applications that interact with lower-level AI layers, without needing to understand the underlying complexities.

This analogy is not about oversimplifying the complexities of AI systems, as AI involves a wide range of components and interactions that may not fit neatly into the System space and App space framework. In AI, the boundaries between different layers can be more fluid compared to OS. For example, we could debate about RAG as a middle-layer, or the fact that models are not as tight from the security perspective, and that AI and data governance are very important aspects to consider. As the AI technologies evolve and security and privacy concerns become more apparent, I do expect the practice of App versus System separation becoming more important.

This separation allows for application innovation and usability, ensuring that end-users and application developers can benefit from advanced technologies via proper abstractions, which in turn hides complexity and helps to clearly approach application functionality, security, stability, and efficiency, while simplifying (without compromising) the overall system integrity.

Carlos Enrique Ortiz (2025)

Musk insists on navigation using computer vision (CV) alone, partly due to cost and partly because he believes that, just as humans rely on vision to drive, machines should be able to do the same with advanced neural networks. His argument is that given sufficient compute power and high-quality data, computer vision should be able to match or exceed human perception. However, achieving this level of reliability is dependent on three key factors: image quality, compute speed, and the accuracy of the neural network models.

Image Quality

Higher pixel count increases image detail but also demands greater computational power for processing. Tesla’s HW4 cameras capture 5-megapixel (MP) images, providing approximately 2592 × 1944 pixels per frame. While this equates to over five million raw pixel values per image, not all pixels are directly fed into the neural network in their raw form. Instead, the image undergoes preprocessing, where it may be cropped, resized, normalized, or converted into feature maps before entering the first layer of the neural network. Additionally, Tesla’s FSD system likely processes multiple frames over time, enabling motion estimation and tracking, further reducing reliance on individual pixel values.

As a side note, in a deep learning pipeline, the first layer is typically a convolutional neural network (CNN), which does not process raw pixels directly but instead extracts low-level spatial features such as edges, textures, and patterns. Instead of each pixel acting as an independent input, small regions of the image (kernels or patches) are analyzed in parallel to detect key features that are passed forward in the network.

Compute Speed

Higher computational speed enables more decisions to be made per second, improving responsiveness and real-time performance. Tesla’s HW4 FSD computer is estimated to have two to four times the performance of HW3. HW3 operated at around 72 TOPS (Tera Operations Per Second), meaning HW4 likely exceeds 150-300 TOPS. Real-time inferencing runs at approximately 50-100 Hz (or every 10-20ms) per camera. Latency remains a limiting factor. Even a few milliseconds of delay in processing and decision-making can mean the difference between avoiding or causing an accident.

As a side note: If an autonomous vehicle needs to react within 100ms, inference should run every 10-20ms to provide a buffer.

Neural Network Accuracy

Like any neural network, the accuracy of Tesla’s models depends largely on how well they are trained to generalize and adapt to real-world driving conditions. Training a neural network for self-driving is inherently data-dependent, meaning it can only learn from what it has been exposed to. This creates a fundamental challenge: “You cannot learn what you haven’t been exposed to.” Rare edge cases—such as an unusual road hazard, an unpredictable pedestrian movement, or an animal crossing at night—are difficult to anticipate and require enormous amounts of real-world driving data to cover all possibilities.

If the system misses just one critical scenario, the consequences can be severe. There have been cases where FSD has failed to recognize a deer in the roadway or misjudged an obstacle, leading to a crash. The only way to improve is through continuous training across millions of miles, capturing as many real-world scenarios as possible. Perhaps in the future, with quantum computing, AI could train on all possible driving situations simultaneously, but for now, training takes time and vast amounts of data collected over years of driving. See Tesla’s Limitations and Warnings page.

Radar as a Redundant Safety Sensor

While self-driving cars are not classified as man-rated applications, redundancy remains a fundamental requirement in any system where failures could lead to fatal accidents. Tesla initially used radar as part of its sensor suite but later removed it, betting on vision-only navigation. The reasoning was that advanced CV models could eventually outperform traditional sensor fusion approaches. However, radar has distinct advantages that vision alone struggles to match. Unlike cameras, radar is generally not significantly affected by poor weather conditions such as fog, rain, or snow. It can detect objects even when occluded, such as a stopped vehicle hidden by another car or a pedestrian emerging from behind a truck. Most importantly, radar provides direct velocity measurements, whereas vision-based depth estimation relies on inferencing, which can introduce errors.

After removing radar in 2021, Tesla has now begun reintroducing “HD Radar” in newer models, suggesting that even they recognize the need for redundancy. The new HD radar likely improves on traditional radar by offering higher resolution and better object classification, making it more useful in complex driving environments.

What About LiDAR?

LiDAR, on the other hand, offers even greater precision than both radar and vision when it comes to depth perception. It can generate highly accurate 3D maps of the surrounding environment, making it an attractive choice for fully autonomous vehicles. However, LiDAR is expensive, power-hungry, and difficult to scale for mass production. Companies like Waymo and Cruise use LiDAR as a central component in their self-driving stacks, but Tesla continues to argue that computer vision will eventually be sufficient on its own.

The Cost of a Pure Vision Approach

While computer vision-based neural networks may eventually reach human-level perception, and I believe it will, today they remain imperfect, mainly due to the vast amount of scenarios to be trained. The cost of vision-only approach is real, and that cost is measured in lives—whether it be deer, pedestrians, or other vehicles involved in accidents that might have been prevented with additional sensor redundancy. For now, it is clear that redundancy in perception is still one of the safest approaches to autonomous driving.

/ceo

The Mission Evaluation Room (MER)

Back when I was a younger software engineer, I had the privilege of writing software for the Space Shuttle onboard systems. I also supported Shuttle missions in real-time as part of the Backroom team in the Mission Evaluation Room (MER). It was an incredible experience where engineers, including myself, provided critical support to the Mission Control Center (MCC), often referred to as the Frontroom, the large room with flight controllers that you see on TV.

The role of Mission Evaluation Room (MER) engineers is to provide engineering support during Space Shuttle missions, for Space Shuttle systems. These engineers are concerned with ensuring that the systems for which they are responsible function reliably, and as intended. The MER is a central facility from which engineers may work, in fulfilling this obligation. Engineers participate in real-time monitoring of shuttle telemetry data and provide a variety of analyses associated with the operation of the shuttle.  — see Source.

The MER had a motto prominently displayed on the wall, right behind the MER leads’ desk area, which shaped the rest of my professional career: “In God we trust; all others must bring data.”

This phrase, originally attributed to W. Edwards Deming (Wikipedia), underscores the importance of empirical evidence in decision-making. Deming, a renowned statistician and quality control expert, emphasized the need for data-driven approaches in business and engineering. The motto resonates deeply in the field of software engineering, emphasizing that trust and credibility are built on the foundation of solid data.

In software development, this principle translates to a rigorous approach to problem-solving. It means conducting thorough research, understanding and quantifying issues, and effectively communicating findings. By relying on data, engineers can make informed decisions, identify potential issues early, and implement solutions that are both effective and efficient.

The application of this motto in my work with the Space Shuttle onboard software was paramount. The complexity and critical nature of space missions necessitated a meticulous approach. Every piece of software had to be tested, verified, and validated with precise data to ensure the safety and success of the mission. This experience instilled in me the importance of data-driven engineering, a principle I have carried throughout my career.

“In God we trust; all others must bring data” serves as a constant reminder that in engineering, intuition and experience are valuable, but they must be backed by data. It fosters a culture of accountability and continuous improvement, driving engineers to seek evidence, validate assumptions, and strive for excellence in their work.

Image generated by DALL-E given the title of this Post

Read this today:

“Occasionally in human history,” Nosanov wrote, “a superpower will choose to abandon a position of leadership, or yield dominance of a frontier, in favor of, or because of, internal or domestic conflict.”

In the case of the NASA layoffs, which impacted about 530 employees and 40 contractors at the agency’s joint facility with Caltech in Pasadena, California, this travesty occurred because Congress is in gridlock over budget issues.

To Nosanov’s mind, that failure of leadership is unacceptable precisely because of how important the JPL is to America’s extraordinary accomplishments in space — and because, unlike the mistakes made by rulers past, our current ones have historical precedent that should cast out their “ignorance.”

Source: https://futurism.com/the-byte/ex-nasa-employee-slams-government