Video description, from the Velcro brand YouTube channel:
Our Velcro Brand Companies legal team decided to clear a few things up about using the VELCRO® trademark correctly – because they’re lawyers and that’s what they do. When you use “velcro” as a noun or a verb (e.g., velcro shoes), you diminish the importance of our brand and our lawyers lose their insert fastening sound. So please, do not say “velcro shoes” (or “velcro wallet” or “velcro gloves”) - we repeat “velcro” is not a noun or a verb. VELCRO® is our brand. #dontsayvelcro
100% agree. I am interested in seeing how this will change how I work. I'm finding that I'm now more concerned with how I can keep the AI busy and how I can keep the quality of outputs high. I believe it has a lot to do with how my projects are structured and documented. There are also some menial issues (e.g. structuring projects to avoid merge conflicts becoming bottlenecks)
I expect that in a year my relationship with AI will be more like a TL working mostly at the requirements and task definition layer managing the work of several agents across parallel workstreams. I expect new development toolchains to start reflecting this too with less emphasis on IDEs and more emphasis on efficient task and project management.
I think the "missed growth" of junior devs is overblown though. Did the widespread adoption of higher-level really hurt the careers of developers missing out on the days when we had to do explicit memory management? We're just shifting the skillset and removing the unnecessary overhead. We could argue endlessly about technical depth being important, but in my experience this hasn't ever been truly necessary to succeed in your career. We'll mitigate these issues the same way we do with higher-level languages - by first focusing on the properties and invariants of the solutions outside-in.
An important skill for software developers is the ability to reason about what the effects of their coce will be, over all possible conditions and inputs, as opposed to trial and error limited to specific inputs, or (as is the case with non-deterministic LLMs) limited to single executions. This skill is independent of whether you are coding in assembly or are using higher-level languages and tooling. Using LLMs exactly doesn’t train that skill, because the effective unpredictability of their results largely prevents any but the most vague logical reasoning about the connection between the prompt and the specific output.
I actually think this is one skill LLMs _do_ train, albeit for an entirely different reason. Claude is fairly bad at considering edge cases in my experience, so I generally have to prompt for them specifically.
Even for entirely “vibe-coded” apps I could theoretically have created without knowing any programming syntax, I was successful only because I knew about possible edge cases.
Location: Boston, MA, USA
Remote: Yes (In-office/Hybrid also fine if local; travel is fine)
Willing to relocate: No
Technologies: AWS, Linux, Programming (esp. Ruby, Go, Elixir/Erlang), React, Typescript, Databases (MongoDB, MySQL, Postgres, Redis, etcd, Kafka), Infrastructure as Code, Kubernetes, Data Eng (Spark, Trino, Airflow, Hadoop), Consul, Envoy
Résumé/CV: Shared on request
Email: deplhwa6y@mozmail.com (Note: e-mail mask; will openly share e-mail in response)
14 YOE with experience leading teams. Currently working for a unicorn. Keeping details light here so I am not doxed, but I am happy to share openly in private. Looking to roles working in distributed systems, reliability, or infrastructure (esp. databases, streaming, data eng). Prefer working for companies which are in scale phase and which have some traction. Role must have technical leadership opportunities.
I really wish someone would have shared something like this with me in graduate school. Learning how to be a successful grad student took me too long to learn. In fact, I honestly didn't event learn it until I was done with school.
Makes me wonder if we’ll see more emphasis on loosely coupled architecture as a result of this. Software engineers maintain the structure, and AI codes it chaos at the leaf. Similar to how data engineers commoditized data via the warehouse
Musicians picked Monster because they were reliable and had an excellent replacement policy not because of brand ego. The Darn Tough of cables at least in terms of policies
> They did it within a system of laws and regulations that government admittedly does create for fostering such wealth creation. However, this still often requires strenuous effort by these people for their own ends.
You are confusing two things thinking they aren’t highly related but they are. This statement could otherwise be written “government created a flawed system and motivated individuals achieved wealth by taking advantage of that system”. That implies a flawed system was causal. We don’t need bigger government, we need the right government. No one wants to say that those who worked hard - even by benefitting from a flawed government - should not have high wealth, but by your same argument, what did the wealthy children of these individuals do to justify their wealth? Their children? How long do we believe this chain of inheritance is sensible?
I believe the previous poster was arguing that taxation still exists at a macro level, i.e. money is sucked out of the economy. It doesn’t matter so much who paid the taxes. The wealthy pay the interest.
Part of me wonders if doubling down on taxing transactions (which tariffs is categorically) can thus work. It seems like an elegant way to avoid having to deal with wealth vs income.
