The hottest Engineering Substack posts right now

And their main takeaways
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LLM coding autopilots still need a steering wheel

Speculative Inference • 1 HN point • 10 Sep 24
🕹 Technology AI Software Automation Engineering Innovation
  1. Self-driving cars still need steering wheels because complete automation is very difficult to achieve. Experts thought we would have fully autonomous cars by now, but there are still many challenges to overcome.
  2. Software engineering is even harder to automate than driving. As we create tools that simplify coding, the demand for software will only continue to grow, rather than decrease.
  3. Small tools that help human engineers will likely be more valuable and widely adopted than fully autonomous coding systems. They make the coding process easier without completely changing how we work.

How We Drive Developer Productivity at Yelp | with Kent Wills, Director of Engineering, Engineering Effectiveness at Yelp

platocommunity • 2 HN points • 25 Jan 24
🕹 Technology Engineering Developer Productivity Metrics Automation
  1. Engineering Effectiveness at Yelp aims to boost engineering capacity through organizational efficiency, working on projects to enhance workflows and systems, like 'paved paths' inspired by Netflix.
  2. Yelp dealt with challenges like transitioning from a monolith to a service-oriented architecture, focusing on issues such as maintaining consistent styles, testing across service boundaries, and facilitating migrations.
  3. The current state of Yelp's Engineering Effectiveness involves supporting web development, improving language support, automating code migrations, and prioritizing better observability of debt and engineering value.

Incident Post-Mortem: Backdoor exposed in popular sex robot operating system leaves millions of men sexless.

Davidovits! • 1 HN point • 05 Apr 24
🕹 Technology Security Engineering Software Authentication Robots
  1. An unintended vulnerability in a core library led to widespread sexbot malfunctions.
  2. Prolonged eye contact was found to reduce startup time for older users and increase customer satisfaction.
  3. The incident post-mortem revealed a critical backdoor compromise that affected millions of sex robot users.
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✳✴❇ Behavioural Science, Better Data Engineering & Timnit Gebru’s New Institute

Sector 6 | The Newsletter of AIM • 19 implied HN points • 05 Dec 21
🕹 Technology Data science Machine Learning Behavioral Science Analytics Engineering
  1. Behavioral science can improve how data engineering is done. Understanding how people think and behave helps create better tech solutions.
  2. There’s a new hackathon for data scientists featuring a challenge to predict loan defaults. It has already attracted over 1,000 participants.
  3. A conference for machine learning developers will be held in-person in Bangalore. It's a great opportunity to learn and connect with others in the field.

Things I learned at Anthropic

sémaphore • 2 implied HN points • 16 May 24
🕹 Technology AI Engineering Research Culture Product Development
  1. A team's success depends a lot on how quickly they make decisions and how willing they are to take risks.
  2. When building models, you might hit problems that stem from the data you used. It’s important to dig deep and understand these issues.
  3. Sometimes the simplest solution is the best one. You often find clear answers after thoroughly exploring a problem.

The “Bus Test” Considered Harmful

Rethinking Software • 2 HN points • 03 Sep 24
🕹 Technology Software Engineering Innovation Corporate culture Management
  1. The 'bus test' checks if a company can function without a specific person. If they can't, their idea is often rejected. But this test can stifle creativity and good ideas.
  2. Believing every employee is replaceable can hurt a company's innovation. Unique contributions should be valued, as they help a company grow and stay competitive.
  3. Encouraging unique ideas instead of over-standardizing processes can motivate employees. When people feel appreciated for their creativity, they are less likely to leave the organization.

I left AI Research to build an AI Startup. Here is what I learned.

Oleksii Sidorov • 10 HN points • 14 Feb 23
🕹 Technology AI Startups Engineering Algorithms Models
  1. In real life, business cares more about whether your AI solution solves a problem than about complex models or theories.
  2. Simplicity often wins in AI solutions - using what you understand well and can deploy quickly can be more effective than complex algorithms.
  3. Understanding the problem domain deeply and focusing on impact rather than endless research is crucial for successful AI projects.

Bazel: Aspirations and Realities

Subsack • 3 HN points • 22 Feb 24
🕹 Technology Software Development Startups Programming Engineering
  1. Bazel can be amazing for bigger projects, but setting it up takes a lot of time, which startups often don't have. It's crucial to focus on building a product quickly before diving into complex systems.
  2. Using Bazel with languages like Python and JavaScript can be tricky because they aren't as well supported. It can lead to a lot of wasted effort if you're not careful about the tools you choose.
  3. While Bazel has great potential, it's often not the right choice for startups due to the time and resources needed. It's better to find a simpler solution until you have a stable system.

Can You Hear the Pain?

Rethinking Software • 2 HN points • 14 Aug 24
🕹 Technology Software Development Agile Management Engineering
  1. Many developers are unhappy with Agile and Scrum. They feel it leads to more stress and micromanagement instead of helping them.
  2. There's a strong desire among developers for more autonomy in their work. They want to solve problems themselves rather than follow strict daily tasks.
  3. People are questioning the effectiveness of Agile methods. If developers are not thriving under these processes, it might be time for a change.

Understanding The Monte Carlo Method

Photon-Lines Substack • 6 HN points • 20 Jul 23
🔬 Science Simulation Mathematics Physics Engineering Risk Analysis
  1. The Monte Carlo method uses random sampling to estimate complex mathematical results or simulate probabilistic events.
  2. It is applicable in various fields like finance, physics, engineering, risk analysis, environmental modeling, manufacturing, and artificial intelligence.
  3. The method involves generating a large number of random samples to approximate outcomes when exact solutions are difficult to obtain analytically.

Stick to boring architecture for as long as possible.

Elevate • 1 HN point • 19 Feb 24
🕹 Technology Software Development Innovation Engineering
  1. Stick to well-established, 'boring' technologies at the start of a project and only use new, exciting tech when it significantly adds value.
  2. Avoid the Fear of Missing Out (FOMO) in technology decisions - prioritize solutions that solve specific problems and enhance your product.
  3. Focus on delivering value with software by keeping user needs at the forefront, rather than getting lost in the latest tools and technologies.

Wonders of our World #3: Undersea Cables

Of All Trades • 6 implied HN points • 22 Jun 23
🕹 Technology Engineering Global Infrastructure Economy Society
  1. Undersea cables are critical for global communication, commerce, and collaboration, carrying majority of international data traffic.
  2. The evolution of undersea cables from telegraph to fiber-optic technology has significantly increased data capacity, reliability, and speed.
  3. The undersea cable network is a remarkable feat of human engineering, showcasing global interconnectedness and the importance of unseen digital infrastructure.

Potential impacts of Large Language Models on Engineering Management

Stuff on Engineering • 4 implied HN points • 30 May 23
🕹 Technology AI Management Engineering Models Automation
  1. Large Language Models can help managers analyze team members' activities and provide insights for improvement.
  2. Artificial intelligence models can assist in assigning tasks tailored to individual team members' needs for growth.
  3. Performance reviews may become automated, but managers need to ensure data quality and avoid biases in the process.

Why engineers need to be bored.

On Engineering • 3 HN points • 05 May 23
🕹 Technology Engineering Innovation Productivity Automation Creativity
  1. The Pareto principle applies to engineering work and problems, with a small group often responsible for a majority of the outcomes.
  2. Innovation and creativity in engineering often stem from incorporating boredom into the workday.
  3. Encouraging free-form boredom time can lead to increased creativity, innovation, and unique solutions in engineering teams.

KAKHOVKA DAM DESTRUCTION

Black Mountain Analysis • 1 HN point • 08 Jun 23
🔬 Science Military Engineering
  1. The dam collapse was likely caused by a combination of human factors and events such as shelling, water pressure buildup, and vibrations.
  2. The destruction of the dam had significant strategic implications for both Ukraine and Russia, impacting military operations, civilian populations, and environmental factors.
  3. The technical aspects of dam design, including the sluice gates, were critical in understanding how the dam was damaged and the potential consequences of the breach.

Fusion Startup Spotlight: Realta Fusion

Fusion Hub • 1 HN point • 11 Aug 23
🔬 Science Startup Physics Engineering
  1. High temperature superconducting magnets revolutionizing magnetic fusion energy for commercialization
  2. Fusion startup Realta Fusion focusing on industrial process heat market initially, aiming for cost-effective energy production
  3. Realta Fusion collaborating with CFS to use HTS magnets on simple mirror reactor design for cheaper and more efficient fusion energy

Bonus: More details on how Bell Labs operated

FreakTakes • 3 implied HN points • 20 Apr 23
🕹 Technology Innovation Engineering Development Research Systems
  1. Mervin Kelly emphasized the three key groups at Bell Labs: Research and Fundamental Development, Systems Engineering, and Specific Systems and Facilities Development.
  2. Research and Fundamental Development focused on pushing research frontiers, with a balance between research and basic technology.
  3. Systems Engineers played a vital role in integrating new knowledge with existing systems, ensuring efficiency, and guiding the application of research ideas into profitable projects.

libSQL: Diving Into a Database Engineering Epic

Luminotes • 2 HN points • 23 Jul 23
🕹 Technology Database Engineering Replication
  1. SQLite is widely used and respected in critical industries due to strong engineering ethics and a commitment to reliability and backward compatibility.
  2. Forking a project like SQLite, as seen with libSQL, requires competent developers, a clear purpose, and a focus on maintaining high standards to ensure success.
  3. libSQL offers innovative features like different wire protocols, virtual WAL, user-defined WASM functions, and replication to the edge, showcasing the project's evolution and dedication to excellence.

Fixing Java Thread & Database Connection Leaks For Fun & Profit

Synystron Synlogica • 1 HN point • 30 Jan 24
🕹 Technology Programming Engineering Software Databases Code Optimization
  1. Encountered a memory leak with Java threads due to instantiation of threads but never starting them.
  2. Identified a database connection leak in a Java app due to a race condition in a connection pool initialization code.
  3. Fixed the issues by patching code, improving exception handling, and implementing best practices for thread and connection management.

Welcome to the exciting dirigibles era of AI

Artificial General Ideas • 3 HN points • 30 Mar 23
🕹 Technology Innovation Artificial Intelligence Science Engineering Research
  1. The history of flight provides insights into scaling technologies like large language models in AI.
  2. Just like dirigibles were an exciting technology in their time, large language models are similarly groundbreaking today.
  3. While scaling up is important, it's also valuable to explore new directions in AI to truly advance human-like intelligence.

What to do with rotting software?

On Engineering • 1 HN point • 03 Dec 23
🕹 Technology Software Development Open Source Engineering Security Supply Chain
  1. Deprecating and removing open source projects can have major consequences on dependent projects in the software ecosystem.
  2. Maintaining a library may not always be feasible due to various factors like engineering allocations and dependencies.
  3. Forking a project can be a viable option for businesses heavily reliant on a library that is no longer actively maintained.

Data Science Weekly - Issue 233

Data Science Weekly Newsletter • 19 implied HN points • 10 May 18
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Engineering
  1. AI systems can learn from each other by arguing, which might help us understand their behavior better.
  2. In the future, machine learning tools may interact with us more like pets than machines, creating a collaborative experience.
  3. Despite powerful tech companies, skilled programmers can still outperform them in certain AI tasks, showing the value of human creativity.

Twitter Algorithm Review - Part 1 - my criteria and Real Graph

PashaNomics • 2 implied HN points • 02 Apr 23
🕹 Technology Social media Algorithm Transparency Engineering
  1. Good social media algorithms should focus on improving user experience and positive user engagement, rather than just efficiency.
  2. Transparency in algorithm criteria and decision-making is crucial for understanding how the algorithm functions and analyzing its impact.
  3. Algorithm improvements should aim to fulfill user expectations, promote positive behavior, and avoid incentivizing negative interactions, such as hate engagements.

Today’s AI sucks at math, but developing cars and planes with it is still a good idea

Assisted Everything • 2 HN points • 23 Feb 23
🕹 Technology Engineering AI Hardware Simulation Design
  1. Today's AI can assist in engineering tasks and lead to faster and safer product design.
  2. Assisted Engineering involves AI assisting engineers in brainstorming, retrieving information, triggering simulations, reviewing work, system modeling, and documenting.
  3. To ensure safety, AI in engineering should be complemented with math, engineering structure, and proper verification processes.