jonator

It will write whatever you prompt using any conventions you want, so you can use it to write great code.

It’s very clear AI writes great code and will even review it for bugs much better and faster than a human can. With cloud agents like Devin, you can spawn an army of these agents at a moments notice. Teams who figure out how to do this will simply out compete any teams who don’t.

Now humans operate at a higher level of abstraction. Our focus area is now ensuring the high-level architecture will accommodate future needs well, ensuring the final product meets requirements, and most importantly, ensuring the final product has been validated. It’s important to use every strategy in the book to test the output via unit tests, smoke tests, integration tests, and end to end tests. On our team, we’ve been investing a lot in setting up full test environments that include the entire stack at a level simply unachievable before AI. Now we can merge code changes at an unprecedented velocity without losing confidence in the system.

Yep, but the code is still reviewed/tested/validated before merging (so -vibe)

Assume reusable spaceflight eventually brings launch cost close to the cost of fuel. This is close to happening.

The overhead of building out grid and power infrastructure on land would then exceed the installation speed and cost relative to space based deployments.

Also assume the compute that does make it to space has a short shelf life anyways so lack of ability to repair is a non issue. As we scale manufacturing on land this will increasingly be the case.

China has already run experiments and served models from space, so we know the heat dissipation equation is solvable.

Finally you’d arrive at a similar model that’s already proven successful with Starlink but applied to serving inference.

The key question is speed to scale new deployments to meet demand. If the markets demand is near infinite, they will choose to fund space based deployments over slower land deployments.

Energy will be the biggest bottleneck to data centers on land. Is not an issue in space. Space is the perfect env for running compute.

90% of my dev workflows have moved to Devin cloud agents.

I don’t miss the days of fumbling around with my local repos across my multiple agent work trees or clones.

I just throw a task at Devin and I get a PR a few moments later.

Then it monitors the PR for any failing CI or review comments without me in the loop.

Now I can have 10+ Devin’s running at any given moment as I walk home from the coffee shop.

It seems like now’s the time to rethink how we do education.

In my personal post academic life, I’ve found LLMs to be an incredible teacher. Almost like the best professor in the world at my fingertips. I use it to generate quizzes on demand to test for my own knowledge gaps.

However, if I use it to speedrun over concepts I should be learning, I may achieve my end goal but I wouldn’t actually learn many of the details.

I think it requires an approach where you have to continuously audit your own understanding as you work with the concepts. You must slow down until you’ve confirmed this. Only once you know the concepts deeply and have retained them in your own memory can you then go all in with the LLM.

Fully agree. Perhaps I should have clarified that it’s primarily for agents now, not just engineers.

I’ve found a good counter to this is having agents visualize and explain the architecture of the system. Then I gain just enough context to figure out what I’m trying to accomplish.

Also, as always, a highly modular codebase is very important. If I only have to reason about a single module then I don’t have to have full context on system.

It seems we’re now in a world where engineers are responsible for creating a good environment where an agent is able to gain context on the architecture and validate its work via tests (e2e, unit, smoke, etc). Then it can get into its own feedback loop and find the correct solution on its own much faster.

I've been using Opus 4.5 via Claude Code

I can attest to everything. Using Tidewave MCP to give your agent access to the runtime via REPL is a superpower, especially with Elixir being functional. It's able to proactively debug and get runtime feedback on your modular code as it's being written. It can also access the DB via your ORM Ecto modules. It's a perfect fit and incredibly productive workflow.