tcdent

Harsh opinion: but if you just want to pay the bills this is not for you. Software engineering is a career, not a job.

Far too many people focus on passing an interview and then let legal protections keep their position, all while draining resources that could have been better allocated to someone who is actually passionate. They saw my generation do reasonably well and came for the paycheck. I got into this because there were very few people who could rise to the occasion of problem solving that the industry needed. I did not walk into the room with all of the skills or an idealized salary, I walked into the room with a mentality.

Perhaps the "chaos" being introduced is simply a long overdue market correction.

I'll let the downvotes tally how pervasive this is.

Didn't say that to refute it. I chose that to make the point that the degree is not the destination.

> It's been a huge learning curve.

I have been doing this professionally for over twenty years, and I still learn something every day. Over the past couple years, with AI augmentation, I learn even more, move faster and go deeper into the systems that I work on.

A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door.

AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain. From someone who is technically minded (and self-taught), all I have ever wanted is to keep learning.

"TCP stack" versus "Linux TCP stack" is an OOM in terms of relevance.

Claude has been building me a bespoke inference and training stack (optimized kernels and all), feeding traces from my previous sessions directly into a LoRA adapter. Zero friction.

I am US based, with defender level cyber access.

I have looked into acquiring an ipv4 block many times but never came across this. Thanks for the lead!

Seriously. This idea that humanity has not spent millennia analyzing these concepts is pervasive in the modern world, and the only thing I can think to attribute it to is a lack of education. Not because I want to be insensitive, but because as soon as you start absorbing material and integrating the real timeline, you can't unsee it.

Not that they didn't start their research and development with a bipedal robot, but you can notice that over time they went from more abstract leg concepts to far more humanoid leg concepts.

That tells us that the latter (for their intended use) is a superior design.

I don't understand why this is considered a security incident. Human behavior incident, sure, but this is not a vulnerability.

Exactly. Being able to organize your code in a way that provides distinct responsibilities per crate is just good practice.

And then the ability to enforce a formalized layer between crates and the way that they assemble into your application (i.e. the public API) is powerful for comprehension and maintainability. I go as far as documenting each crate with its own README that presents it as a standalone component with usage instructions, much like an open source library, even if it's only ever used in a single system.

I recently used it for a Rust profiler which writes Parquet files, so DuckDB then gives an easy SQL interface for things like summarization, analysis, and joins across runs.

Aren't you really just talking about the level of abstraction being applied? If there are infinite possibilities between zero and one, then it's just our way of relating to it that imposes the perception of a limitation.

The far easier way and actually more secure way is to just inject sensitive credentials through a proxy service that ideally lives outside of the box in which the agent runs.

Because even if you're doing this in-memory replacement, the content still lives in they system's memory. So, it makes it less obvious to leak a credential, but it certainly doesn't prevent it.

Synthetic data, distilled data, etc., does not necessarily mean data coming from outside of an organization. When we see performance increases with models like Haiku, it is because they are able to leverage the knowledge of larger architectures.

I fully expect that we will see increased performance from smaller architectures (to a point) simply because they are refined using larger ones.

> The problem is if we can pack a Fable in Qwen 4 27B who will pay subscription prices?

This is available to everyone developing these systems. Prices per relative unit of intelligence will continue to decrease. And market economics will continue to drive the cost down.

I agree with you with one caveat. Professionally, your obligation is to be qualified for your job and ship work that you stand behind. Complete comprehension is absolutely necessary.

Personally, with side projects and tooling, go nuts. There's no reason to read all of that code.

The recent DeepSeek release used Opus 5 as a reference point as well. I don't understand it. It's not like they're referencing superseded releases of other open models.

CSRs that seemed like they had to be handled and approved by humans, and thousands of dollars in fees. We have come a long way.

What does it mean to "go into" math?

I'm all about the concepts you're presenting, but care to elaborate on how you think the corals relate?

He basically rebranded himself as an AI commentator/journalist following the playbook of people like Scoble over the last couple of years. I assume his previous endeavors paid off well enough to grant him the freedom to do as he pleases. People (and algorithms) like this type of content so it's well received.

Nobody's saying it should take care of the entire process, but the idea of banning it from all parts of your process is an emotionally charged, short-sighted decision.

Meta did this from the very beginning with the original Llama's. Open weights, but you have to use an account with them and agree to an EULA.

Traditionally, computing and especially the web has used structured formats to designate placement of how information is interpreted, and even the level of trust which is given to it. But as we see with things like HTTP headers in this article, when an LLM interprets content, it's all just text.

Cabinetry stuff is still rough, but here's an example of the methodology applied to PCB layout: https://github.com/tcdent/netspec

To me, this sounds like it's less about the technology, whether it's open source or not, and more about the people in the room that are influencing the design, direction, and implementation of it. All of which are very likely to have previous positions at major companies responsible for the products mentioned.

Perhaps if there is any ambiguity, it's intentional on the part of caution around intellectual property provenance.

For realizing designs in physical space, I create a Python library with a DSL that allows us to express all of the components and how they relate with each other. The most important thing this provides is a deterministic way to test the assembly. Then, you take the parameterized relationships between the components and export them into things like three-dimensional views or cut lists, rather than trying to manipulate three-dimensional software as your source of truth. (Although Claude is pretty effective at driving Blender via Python, too.)

You're talking about Starshield, which is operated by the US Government via services contracted, not personally regulated by Musk himself.

Starlink, the commercial product, was always off limits for military operations (the type that require connectivity at 100+ MPH).

So, no, this is not one person dictating the usage.

Starlink’s Acceptable Use Policy explicitly prohibits:

- Remotely controlling or piloting any aerial drone or unmanned vehicle (unless otherwise contractually permitted by SpaceX).

- Engaging in any military activity other than approved morale/welfare/recreation programs for service members.

I particularly enjoy the narrative around video game decompilation because it's a continuation of what we see in the emulation communities. People that are passionate about certain experiences and want to learn more about the way they're implemented underneath and how it relates to hardware they have access to.

It traditionally involves investing incredible amounts of time and effort and intelligence, but what I think AI does most encouragingly is allows people to access more complex, more modern, more high-performance technologies and interact with them in the same creative and explorational way.

Over the weekend, I was curious about the state of open source touch-based HID support as it compares to the state of proprietary market leaders. I tasked an agent with decompiling and analyzing firmware blobs from the systems I was interested in, and now I have an early alpha version of the most robust source-available multi touch implementation.

There are obviously some legal and perhaps even ethical considerations worth reviewing before considering publishing anything like this, but we have reached the point that proprietary software is essentially open source.

Hadn't seen this one yet, but that's exactly the type of sensibility I personally prefer in a monospace (Menlo has remained my daily driver for years).