jonator

vor 23 Tagendiscuss

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

vor 24 Tagendiscuss

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.

jonator

vor 25 Tagendiscuss

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.

jonator

vor 25 Tagendiscuss

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.

jonator

letzten Monatdiscuss

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.

jonator

vor 4 Monatendiscuss

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

jonator

vor 4 Monatendiscuss

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.

jonator

vor 6 Monatendiscuss

I've been using Opus 4.5 via Claude Code

jonator

vor 6 Monatendiscuss

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.

jonator

vor 6 Monatendiscuss

https://jonator.dev

jonator

vor 6 Monatendiscuss

I think frontier models are getting to the point where we can start to reach higher trust agentic workflows.

As a hardcore AI chat user, I'm often frustrated with the single-agent workflow, where a single context window is used for even very long conversations. If I want to change the topic, open a thread, or go on a tangent, I often end up compromising the main thread and I'm forced to copy context over if I want to dive into something.

To solve this, I'm working on a collaborative AI agent orchestrator that models the solution as a group chat with humans and AI agents, including an agent orchestrator.

You can spawn participating agents with the orchestrator who will decisively route messages to the existing agents, or spawn new agents if needed. Also, you can open agent details and send messages directly to existing agents, similar to threads in slack.

So far, I have MCP integrations working with Linear and GitHub, but plan to add many more.

I've been working on this just over 2 weeks, making heavy use of 4+ concurrent Claude Code agents. This would have been impossible otherwise.

If you're interested, feel free to DM on X.

https://x.com/jon_ator/status/2010370649147998459?s=20

jonator

vor 6 Monatendiscuss

  Location: NYC
  Remote: Yes
  Willing to relocate: No
  Technologies: React/Next.js, TypeScript, Swift, Golang, Elixir
  Résumé/CV: https://www.linkedin.com/in/jon-ator/
  Email: jon (at) ator (dot) us

5 years exp, started career in healthcare at Epic, then building defi applications with $30+B volume. Interested in AI + Crypto.

jonator

vor 9 Monatendiscuss

The issue you described is an issue with AI?

Submissions

Schedule tasks in a loop in Claude Code

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