willquack

Imagine if Apple made a GENERIC distributed compute framework to leverage idle computing power from Macs for distributed Rendering/Compiling/etc use cases. I'm sure Apple could convince Adobe and others to support this framework for their compute-intensive products and create a really interesting ecosystem for it.

Then universities and business could reduce their need for cloud and have a reason to put that spending towards higher end Macs...

There is a startup working on this called Distributive I used to work for - their compute nodes are based on js/wasm runtimes so you just need a web browser to turn a device into a compute node...

> You cannot successfully orchestrate an AI in a language you don't deeply understand.

This is the primary point and maybe even the later points may be, at least partially, an extension of it.

Coding agents reflect the developer using them, if the developer isn't proficient with the ecosystem they're developing in or problem they're solving, the output will still be bad. If the developer is lazy, the output will reflect that.

This is very similar to what I imagine whenever I get into a deep focus state debugging a program in gdb... I would love to experience debugging a program in VR with graphics like this,,, there would be no better way to learn a codebase. I wonder how good an experience you could achieve by generating a 3d map based on arbitrary code

> If a tool marks 40% of new papers as machine-written but also marks 20% of papers written before ChatGPT existed, the real story is the 20% nobody mentioned.

When 65% of the papers you read have the characteristics of being AI written, whether or not you use AI to write, your writing will be influenced by the AI style. I imagine this must be particularly the case for newbie researchers who are still developing their writing style

I used to work at Distributive (formerly "Kings Distributed Systems") on its DCP compute platform" which is entirely what you're describing. You can deploy a JS/WASM based workload, and it will be "sliced" and served to browser-based compute nodes. With WebGPU you can sort of have inference executing in the browser too. Incredible people there with an awesome project

I added Python execution support via Pyodide (cpython compiled to wasm) and worked on a bunch of other random stuff like WebLLM inferencing during my time there.

Apart from Distributive, there's also the "Golem network", "Salad", "Koii" and various other similar projects.

---

I'm not sure if I'm convinced by the "Uber for compute" use case with compute buyers and compute workers (sellers), but if you're a university and you have 1000 Windows machines across all your computer labs, it'd be nice to leverage that compute for running research or something idk - especially with the price of ram / cloud offerings these days...

Awesome project!

Dumb question: could you run this in frontend js using the browser's js engine and wasm environment similar to WebContainers? Maybe `fs` is just in-memory, and some things like forking are disabled. It'd be cool to have "nodejs" in the web!

I worked with Jason (creator of Om) at my last job. He's awesome!

Thanks for responding!!

I think it's still the same issue where data modified after the VDiff point in time isn't validated before SwitchTraffic. I'm mostly curious how vitess users handle this case, or if any users even care about about this case in the first place?

Is there no demand for continuous data validation similar to what TiDB offers?

Do people who care about 100% correct data validation just accept the downtime required to run a full VDiff before SwitchTraffic?

> you can run an initial VDiff, and then resume that one as you get closer to the cutover point.

VDiff (v2) only compares the source and destination at a specific point in time with resume only comparing rows with PK higher than the last one compared before it was paused. I assume this means:

1. VDiff doesn't catch updates to rows with PK lower than the point it was paused which could have become corrupt, and

2. VDiff doesn't continuously validate cdc changes meaning (unless you enforce extra downtime to run / resume a vdiff) you can never be 100% sure if your data is valid before SwitchTraffic

I'm curious if this is something customers even care about, or is point in time data validation sufficient enough to catch any issues that could occur during migrations?

Why is there an expectation for social media services to have such high uptime? It's not an ISP or cloud provider, why does it matter if it goes down occasionally?

There's nothing more fun than making a DSL, the only annoying part if finding an excuse to make one

It would be cool to encode the chess board state and turn into the URL so you could hurl urls back and forth over slack to play chess just by clicking on it

but there's something charming about the ascii art over slack in this project that would miss

> `seapie.breakpoint()` opens a working `>>>` REPL at the current execution state. Any changes to variables or function definitions persist. Debugger state is exposed via built-ins (e.g. `_magic_`), and stepping/frame control/etc is handled via small `!commands`.

This is largely what `pdb` does already, no? Example:

```

(Pdb) list

  1   something = 100

  2   import pdb; pdb.set_trace()

  3  -> print(f"value is: {something}")

(Pdb) something = 1234

(Pdb) c

value is: 1234

```

I do like that you use `!<cmd>` to avoid the naming collision issue in pdb between commands and python code!!!