synthetic bioengineering provides a really astronomically large option space for new bodies and new minds that don't have 00:04:28 standard evolutionary backstories

For example, productivity and satisfaction are correlated, and it is possible that satisfaction could serve as a leading indicator for productivity; a decline in satisfaction and engagement could signal upcoming burnout and reduced productivity.

• Design and coding. Volume or count of design documents and specs, work items, pull requests, commits, and code reviews. • Continuous integration and deployment. Count of build, test, deployment/release, and infrastructure utilization. • Operational activity. Count or volume of incidents/issues and distribution based on their severities, on-call participation, and incident mitigation.

The SPACE framework provides a way to logically and systematically think about productivity in a much bigger space and to carefully choose balanced metrics linked to goals—and how they may be limited if used alone or in the wrong context.

Global Engineering

engineering blogs focus on problems where the solution is a necessary but not sufficient part of what they do. And, ideally, they focus on problems that are complementary to scale that only the publisher of that post has.

Platform engineering is trying to deliver the self-service tools teams want to consume to rapidly deploy all components of software. While it may sound like a TypeScript developer would feel more empowered by writing their infrastructure in TypeScript, the reality is that it’s a significant undertaking to learn to use these tools properly when all one wants to do is create or modify a few resources for their project. This is also a common source of technical debt and fragility. Most users will probably learn the minimal amount they need to in order to make progress in their project, and oftentimes this may not be the best solution for the longevity of a codebase. These tools are straddling an awkward line that is optimized for no-one. Traditional DevOps are not software engineers and software engineers are not DevOps. By making infrastructure a software engineering problem, it puts all parties in an unfamiliar position. I am not saying no-one is capable of using these tools well. The DevOps and software engineers I’ve worked with are more than capable. This is a matter of attention. If you look at what a DevOps engineer has to deal with day-in and day-out, the nuances of TypeScript or Go will take a backseat. And conversely, the nuances of, for example, a VPC will take a backseat to a software engineer delivering a new feature. The gap that the AWS CDK and Pulumi try to bridge is not optimized for anyone and this is how we get bugs, and more dangerously, security holes.

But the researchers quickly realized that a model’s complexity wasn’t the only driving factor. Some unexpected abilities could be coaxed out of smaller models with fewer parameters — or trained on smaller data sets — if the data was of sufficiently high quality. In addition, how a query was worded influenced the accuracy of the model’s response.

Civil engineers are responsible for developing and creating infrastructure such as roads, bridges, buildings, and water supply systems, as well as resolving environmental and sustainability challenges.

prompt engineer. His role involves creating and refining the text prompts people type into the AI in hopes of coaxing from it the optimal result. Unlike traditional coders, prompt engineers program in prose, sending commands written in plain text to the AI systems, which then do the actual work.

Before deciding to get a bachelor’s or master’s degree in engineering, it is wise to become acquainted with the curriculum. These programs may put you on the path to a prosperous profession and payoff in more ways than one.

In my most recent field (software), the engineer is placated with the delusion that the purpose is to give the customer what ey wants, whether that solves the customer’s problem or not. This is a lazy sort of self-imposed servitude that entirely avoids the actual purpose of engineering.

Including a prompt prefix in the chain-of-thought style encourages the model to generatefollow-on sequences in the same style, which isto say comprising a series of explicit reasoningsteps that lead to the final answer. This abilityto learn a general pattern from a few examples ina prompt prefix, and to complete sequences in away that conforms to that pattern, is sometimescalled in-context learning or few-shot prompt-ing. Chain-of-thought prompting showcases thisemergent property of large language model at itsmost striking.

Dialogue is just one application of LLMs thatcan be facilitated by the judicious use of promptprefixes. In a similar way, LLMs can be adaptedto perform numerous tasks without further train-ing (Brown et al., 2020). This has led to a wholenew category of AI research, namely prompt en-gineering, which will remain relevant until wehave better models of the relationship betweenwhat we say and what we want.

In the background, the LLM is invisiblyprompted with a prefix along the following lines.

In the near future, we will be in possession of genetic engineering technology which allows us to move genes precisely and massively from one species to another. Careless or commercially driven use of this technology could make the concept of species meaningless, mixing up populations and mating systems so that much of the individuality of species would be lost. Cultural evolution gave us the power to do this. To preserve our wildlife as nature evolved it, the machinery of biological evolution must be protected from the homogenizing effects of cultural evolution.

In the near future, we will be in possession of genetic engineering technology which allows us to move genes precisely and massively from one species to another. Careless or commercially driven use of this technology could make the concept of species meaningless, mixing up populations and mating systems so that much of the individuality of species would be lost. Cultural evolution gave us the power to do this. To preserve our wildlife as nature evolved it, the machinery of biological evolution must be protected from the homogenizing effects of cultural evolution.

Deploy engines as separate app instances and have them only communicate over network boundaries. This is something we’re starting to do more.

I always allocate a year: six months to get up to speed on the internal culture, tools, and processes; another six months to get your first performance review as a “ramped-up” engineer.

While you might think that pairing less experienced engineers is a waste of time, every single time I had a less experienced engineer work by themselves, I ended up regretting it.

engineers will get tired, mistakes will happen, and maintenance will get kicked down the road. Teams need buffer as much as systems do.

L’ingénierie est un processus structuré et méthodique qui permet de passer de besoins opérationnels à la mise en œuvre d’une solution.

Cela correspondrait à ne pas faire le travail d’ingénierie, car il n’y aurait pas eu d’analyse.

One example could be putting all files into an Amazon S3 bucket. It’s versatile, cheap and integrates with many technologies. If you are using Redshift for your data warehouse, it has great integration with that too.

It took me a while to grok where dbt comes in the stack but now that I (think) I have it, it makes a lot of sense. I can also see why, with my background, I had trouble doing so. Just as Apache Kafka isn’t easily explained as simply another database, another message queue, etc, dbt isn’t just another Informatica, another Oracle Data Integrator. It’s not about ETL or ELT - it’s about T alone. With that understood, things slot into place. This isn’t just my take on it either - dbt themselves call it out on their blog:

Working with the raw data has lots of benefits, since at the point of ingest you don’t know all of the possible uses for the data. If you rationalise that data down to just the set of fields and/or aggregate it up to fit just a specific use case then you lose the fidelity of the data that could be useful elsewhere. This is one of the premises and benefits of a data lake done well.

Software Product Engineering and SaaS solutions

Continuous remote monitoring of COPD patients—justificationand explanation of the requirements and a surveyof the available technologies

it was clear that the European and US competitors werebenefiting from these changes to the curriculum in advances in commerce, inindustry, and even on the battlefield.

There has been much discussion about “atomic notes”, which represents the key ideas from a person’s research on some topic or source (sources one and two). These are not the kind of thing I am interested in creating/collecting, or at least not what I have been doing. A far more typical thing for me is something I did at work today. I was trying to figure out how to convert the output of a program into another format. I did some searching, installed a tool, found a script, played with the script in the tool, figured out how to use it, then wrote down a summary of my steps and added links to what I found in my search. Since I am not doing research for a book or for writing academic papers, the idea of an atomic note does not fit into my information world. However, capturing the steps of a discovery or how I worked out a problem. is very real and concrete to me. I used to know a fellow engineer who wrote “technical notes” to capture work he was doing (like a journal entry). Maybe that is how I should consider this type of knowledge creation. 

The major benefit of foreign keys is that they guarantee referential integrity. For example, say you have customers in one table that may refer to a number of invoices in another. Without foreign keys, you could delete a customer, but forget to remove its invoices, thereby leaving a bunch of orphaned invoices that reference an customer that’s gone.

If an operator ever queries the database directly they’re even more likely to forget deleted_at because normally the ORM does the work for them.

At the same time, like Harold, I’ve realised that it is important to do things, to keep blogging and writing in this space. Not because of its sheer brilliance, but because most of it will be crap, and brilliance will only occur once in a while. You need to produce lots of stuff to increase the likelihood of hitting on something worthwile. Of course that very much feeds the imposter cycle, but it’s the only way. Getting back into a more intensive blogging habit 18 months ago, has helped me explore more and better. Because most of what I blog here isn’t very meaningful, but needs to be gotten out of the way, or helps build towards, scaffolding towards something with more meaning.

Hire our Engineers to solve your complex hardware engineering problems.

Outline of academic disciplines

Robert Fenton, Electrical and Computer Engineering Professor Emeritus, pioneered the technology for the first wave of self-driving cars.

systems

Much of this sort of information was later reverse-engineered, and cross-browser support for basic operations is actually quite good. (Browsers still vary widely on the details.)

Reverse-engineering and standardizing contentEditable

three steps required to solve the all-importantcorrespondence problem. Step one, according to Shenkar: specify one’s ownproblem and identify an analogous problem that has been solved successfully.Step two: rigorously analyze why the solution is successful. Jobs and hisengineers at Apple’s headquarters in Cupertino, California, immediately got towork deconstructing the marvels they’d seen at the Xerox facility. Soon theywere on to the third and most challenging step: identify how one’s owncircumstances differ, then figure out how to adapt the original solution to thenew setting.

innovation can result in trade-offs that undermine both progress on mitigation and 12 progress towards other sustainable development goals

It was not until the 14th century that scientists created the first sophisticated astronomical clocks.

the first precision-geared mechanism known is a relatively simple—yet impressive for the time—geared sundial and calendar of Byzantine origin dating to about C.E. 600.

In comparison, the average household energy use for gas heating in Belgium – which has a moderate climate – is 20,000 kWh per year. Assuming that the average Belgian heating system is used for six months per year, daily energy use corresponds to 109.6 kWh per day. This energy could heat roughly 900 water bottles per day – enough to keep the whole neighbourhood comfortable.

Graham Allison and Niall Ferguson have called for an “applied history” movement, to better draw lessons from history and apply them to real-world problems, including through the advising of political leaders.

When a product manager trusts that the engineers on the team have the interest of the product at heart, they also trust the engineer’s judgment when adding technical tasks to the backlog and prioritizing them. This enables the balanced mix of feature and technical work that we’re aiming for.

Applying Systems Engineering to Policy

I didn't have to think too much of what’s good for the company - I usually assumed that whatever I’m doing is what the business needs.

The Beloit-based billionaire has publicly pushed for tax breaks and said she wants to stop the U.S. from becoming “a socialistic ideological nation.”

That's going to be extremely ugly. Nothing about this makes sense. Your JSON schema should just have one object that has {"is_enabled":true}, or something like this {"name":"change","is_enable":true}.

My advice is if you are looking for a quick and accurate answer ask to have the trouble ticket elevated immediately and to speak with an engineer that will recognize your knowledge and speak with you on your level.

I typically request to speak with an engineer when I find myself detecting an inexperienced support person.

In one of my internship, I got to befriend a level 2 tech support, so learned a couple thing of how it worked (in that company). Level 1 was out-sourced, and they had a script to go from, regularly updated. From statistics, this took care of 90% of issues. Level 2 was a double handful of tech people, they had basic troubleshooting tools and knowledge and would solve 90% of the remaining issues. Level 3 was the engineering department (where I was), and as a result of level 1 and 2 efficiency less than 1% of issues ever got escalated. The process worked!

Which brings us back, once again, to the question with which we began: why does it matter who gets to be seen as a prominent “tech critic”? The answer is that it matters because such individuals get to set the bounds for the discussion.

Coordination: More environments require more coordination. Teams need to track which feature is deployed to which environment. Bugs need to be associated with environments. Every environment represents a particular ‘state’ of the codebase, and this has to be tracked somewhere to make sure that customers & stakeholders are seeing the right things;

Sorry you’re surprised. Issues are filed at about a rate of 1 per day against GLib. Merge requests at a rate of about 1 per 2 days. Each issue or merge request takes a minimum of about 30 minutes (across at least 2 people) to analyse, put together a fix, test it, review it, fix it, review it and merge it. I’d estimate the average is closer to 3 hours than 30 minutes. Even at the fastest rate, it would take 3 working months to clear the backlog of ~1000 issues. I get a small proportion of my working time to spend on GLib (not full time).

System architects: equivalents to architecture and planning for a world of knowledge and data Both government and business need new skills to do this work well. At present the capabilities described in this paper are divided up. Parts sit within data teams; others in knowledge management, product development, research, policy analysis or strategy teams, or in the various professions dotted around government, from economists to statisticians. In governments, for example, the main emphasis of digital teams in recent years has been very much on service design and delivery, not intelligence. This may be one reason why some aspects of government intelligence appear to have declined in recent years – notably the organisation of memory.57 What we need is a skill set analogous to architects. Good architects learn to think in multiple ways – combining engineering, aesthetics, attention to place and politics. Their work necessitates linking awareness of building materials, planning contexts, psychology and design. Architecture sits alongside urban planning which was also created as an integrative discipline, combining awareness of physical design with finance, strategy and law. So we have two very well-developed integrative skills for the material world. But there is very little comparable for the intangibles of data, knowledge and intelligence. What’s needed now is a profession with skills straddling engineering, data and social science – who are adept at understanding, designing and improving intelligent systems that are transparent and self-aware58. Some should also specialise in processes that engage stakeholders in the task of systems mapping and design, and make the most of collective intelligence. As with architecture and urban planning supply and demand need to evolve in tandem, with governments and other funders seeking to recruit ‘systems architects’ or ‘intelligence architects’ while universities put in place new courses to develop them.

The Quality Engineering team is focused on creating a culture of testing, increasing test coverage, and helping the company ship high-quality features faster. We encourage all our developers to write and own end-to-end (E2E) tests. In turn, Quality Engineering (QE) is responsible for the frameworks used and provides best practices for writing reusable, scalable, and maintainable tests.

“Functional programming language” is not a clearly defined term. From the various properties that are typically associated with functional programming I only want to focus on one: “Immutability” and referential transparency.

Mathematical Methods for Physics and Engineering: A Comprehensive Guide 3rd Edition by K. F. Riley (Author), M. P. Hobson (Author), S. J. Bence (Author)

I'm okay with an overall design that allows people to plugin the parts they need in order to be able to generically support a compile-to-javascript language, but to bake in support for one singular solution because its popular is simply bad engineering.

One of the primary tasks of engineers is to minimize complexity. JSX changes such a fundamental part (syntax and semantics of the language) that the complexity bubbles up to everything it touches. Pretty much every pipeline tool I've had to work with has become far more complex than necessary because of JSX. It affects AST parsers, it affects linters, it affects code coverage, it affects build systems. That tons and tons of additional code that I now need to wade through and mentally parse and ignore whenever I need to debug or want to contribute to a library that adds JSX support.

Linux Memory Management at Scale

The timescales on which a system’s processes run have critical consequences for its ability to predict and adapt to the future.

We also know that if individuals are bad at collecting good information – if they misinterpret data due to their own biases or are overconfident in their assessments – an aggregation mechanism can compensate.

market engineers introduced what’s called a ‘circuit breaker’ – a rule for pausing trading when signs of a massive drop are detected.

Elon Reeve Musk FRS (/ˈiːlɒn/; born June 28, 1971) is an engineer

An Idiom is a low-level pattern specific to a programming language. An idiom describes how to implement particular aspects of components or the relationships between them using the features of the given language.

A Design Pattern provides a scheme for refining the subsystems or components of a software system, or the relationships between them. It describes a commonly recurring structure of communicating components that solves a general design problem within a particular context.

Building blocks are what you use: patterns can tell you how you use them, when, why, and what trade-offs you have to make in doing so.

patterns are considered to be a way of putting building blocks into context

A "pattern" has been defined as: "an idea that has been useful in one practical context and will probably be useful in others"

Engineering Execute development

Fifth, the engineer is not selfish and thinks for the good of the product and the team. If you can’t work in a team, you have a harder chance to succeed nowadays.

Second, the engineer is able to apply the right amount of solution to the problem. Start with the simplest approach that solves the problem

Third, the engineer has a high sense of ownership to the problem he’s solving. This way, the engineer will pay attention to the big picture as well as the details of the problem.

Fourth, the engineer is able to understand how his teammates think. Let’s face it, engineering is a team sport

propelled by a “water plasma” engine. Solar panels generate electrical power, which the vehicle then uses to generate microwaves, which superheat the water up to Sun-surface temperatures. That produces a plasma that shoots out a nozzle, propelling Vigoride forward.

which they estimate to be $230,000 per year.

You do not process your projects through an Institutional Review Board, nor you are equipped to deal with persons who express trauma to you.

ericb 12 days ago | unvote [-] * Better googling. Time-restricted, url restricted, site restricted searches. Search with the variant parts of error messages removed.* Read the source of upstream dependencies. Fix or fork them if needed.* They're better at finding forks with solutions and gleaning hints from semi-related issues.* Formulate more creative hypothesis when obvious lines of investigation run out. The best don't give up.* Dig in to problems with more angles of investigation.* Have more tools in their toolbelt for debugging like adding logging, monkey-patching, swapping parts out, crippling areas to rule things out, binary search of affected code areas.* Consider the business.* Consider user-behavior.* Assume hostile users (security-wise).* Understand that the UI is not a security layer. Anything you can do with PostMan your backend should handle.* Whitelist style-security over blacklist style.* See eventual problems implied by various solutions.* "The Math."

My suspicion is, a good KPI for a knowledge tool is minimum threshold of time required to make a negentropic update to it, with every halving of the threshold increasing its capacity to hold positive-interest-rate knowledge repos by an order of magnitude.

This btw is the maker time/manager time problem pg wrote about. Making needs 4 hour chunks because anything less tends to increase entropy rather than decrease it in any non-trivial knowledge work project. So anything that lowers that lower limit is a big win.

many instructional designers and others adjacent to the field have responded swiftly with critiques that range from outright rejection of the term, to general skepticism about the concept, to distrust for its advocates and their support of learning analytics and outcomes-based learning.

the standard AES17 dynamic range measurement

INVEST

Questions for our first 1:1

Unless you need to push the boundaries of what these technologies are capable of, you probably don’t need a highly specialized team of dedicated engineers to build solutions on top of them. If you manage to hire them, they will be bored. If they are bored, they will leave you for Google, Facebook, LinkedIn, Twitter, … – places where their expertise is actually needed. If they are not bored, chances are they are pretty mediocre. Mediocre engineers really excel at building enormously over complicated, awful-to-work-with messes they call “solutions”. Messes tend to necessitate specialization.

Questions about the inclusivity of engineering and computer science departments have been going on for quite some time. Several current “innovations” coming out of these fields, many rooted in facial recognition, are indicative of how scientific racism has long been embedded in apparently neutral attempts to measure people — a “new” spin on age-old notions of phrenology and biological determinism, updated with digital capabilities.

We can’t force two people to become friends, nor should we want to.