andrewingram

I had the opposite experience. Clef was worse in accuracy, and an order of magnitude slower.

The notable thing that these LLM-generated ports aren't doing is _rewriting in Go_. TypeScript 7 is a lot faster than the JS/TS-based version 6, but memory use is a major bottleneck.

However, it mostly represents a mechanical port of the old codebase to Go. What I haven't seen anyone do is try a true rewrite in Go optimized for performance (whilst keeping a strong emphasis on readability).

> If I added a few vibecoded projects to my resume, the gaping maw of every startup, enterprise company, and FAANG behemoth would open to me.

I don't know how true this is. I've got a handful of vibe-coded projects that are quite interesting to me, but the project that is by far the most interesting to talk about (and most impressive) is the one that I did back in 2021 before I ever used an LLM.

The common wisdom in this industry is that ideas are cheap, and if that's still true, there's now a pretty high bar for a vibe-coded project to be considered impressive. You need to either execute to an unusually high level of polish, or be building in a space where even having an idea would require a high level of experience.

Of course, I might be wrong. Maybe all these vibe-coded projects really are recruitment fodder?

It's anecdata, but I ported a Jev-based prototype i've been working on to Clef, and it ended up being significantly slower and the results were worse.

And I said that they do this in my last paragraph. But you need an execution environment for this, and you don’t get this automatically when just interacting with models via their API

The value is that rather than an agent chaining together tool calls itself (which means each step sends the result back to the agent for it to analyse and work out what to do next), it writes a script for the harness to execute that chains together all the calls. The major benefits are:

* speed - much fewer hops back to the LLM

* fewer tokens - intermediate execution steps in the script don't leak into context, only the final result does.

* repeatability - if the LLM needs to repeat work, it can reuse a script it wrote last time.

If you have a harness that has access to a full shell and knows how to use bash or python, you'll often see it writing little scripts. For setups that don't (ie normal model API requests with tool calls), you can give it an lightweight secure execution environment like just-bash, or quickjs.

Over the last week or so on Twitter, I've seen a lot of people talking about how various ways of using MCP don't compose, but nobody seems to be clarifying what that means with a concrete fashion; which leads me to try and read between the lines.

In this very post:

> While a lot of things have improved about MCP, quite a few have not. The biggest issue with MCP continues to be that it’s hard to compose. Even with codemode, which is just a neat little sandbox to allow composing of tool calls, MCP doesn’t fully deliver on this. But that at this point is less the problem of MCP but the MCP servers out there and different approaches of harnesses to work with them.

Can we get a bit more clarity on this? With codemode, what's the gap? I've also been investigating the search+execute MCP server pattern evangelised by Cloudflare (it uses codemode inside the MCP server to bypass the need to expose a large number of individual tools), but i've seen people say that doesn't compose well either.

Only one i'd trust less is SpaceX, and they're other major player in this space...

Yeah, I kept looking for the first place it was defined in the article and... nothing

I had a lot of fun with Snowboard Kids. Not quite Diddy Kong Racing, but one of my favourite N64 racers.

I play quite a few games on my Mac Mini via Crossover, but rarely anything particularly graphically intensive. Though I do see occasional buzz about improvements in the game porting toolkit leading to significant perf improvements when they make their way into Crossover.

I probably wasn’t clear with my experience. I was trying to get Claude to present my ideas, with a lot of steering and refinement, but it always seemed to be plagued by Claudisms. Even after getting rid of most, it took me rewriting all the prose to get something which people felt comfortable reading.

The published post reminded my of where I got to after several rounds of review and cleanup.

The article reads eerily like the Claude-authored drafts I did for some internal explanations of Relay’s design philosophy (it’s a GraphQL client I’ve been advocating for). I’m not one to make accusations of AI authorship, but it was a particularly striking impression with this one.

I had to rewrite the drafts very heavily in my own voice in order for people to enjoy reading them —- not that I’m a great writer, but I am human and people tend to appreciate that.

Are you paying? I see an endless stream of AI or bot-like posts in the replies of basically every tweet I see with any traction, but i'm not a paying user so I may have the bad version of the platform.