There has to be a much better way to share your setup

Yeah, there is... Post your tooling to GitHub and link to it with a human-written description in a place like HN or Reddit.

I don't know why this has to be reinvented?

Ah yes, just what people want, 50,000 posts about your AI setup on HN.

Concentrating certain types of information can be useful. You don't go to an encyclopedia looking for a recepie.

I don't ming a "ASK HN: What's your AI Setup", and then sifting through the 100s of comments on what people are doing. Then I'd scrape all that into my LLM WIKI and start digesting.....

But you do browse through listings to find that car you like.

ShareYourAISetup folks create a skill that user can run locally that collects information re their setup, formats it and sends to dedicated endpoint. Result = fairly uniform reports where people share what they're comfortable sharing.

No requirement from given user to spend their time. Users see what the skill does.

I guess atm they dont know what questions they want to be asking

Any setup that is longer than a sentence or two of plain text is almost certainly overcomplicated.

Not really?

I have some custom skills/MCP servers I use:

one MCP server for using https://usefeyn.com/blog/pulpie-pareto-optimal-models-for-cl... ran locally so that reading web pages is cheaper and uses up less context

One MCP server for doing automated code quality checking, using Valknut, Jscpd, and Lizard to output scores, combine/aggregate them. That's then give to the model so that it can see if there's a mess of code dupe or if there was significant architectural regression. It's allowed to say "This is worth it" but it ground it a bit and stops really stupid, short sighted, hacked-in features

Another uses https://github.com/thejens/id-token-nicer so that the model doesn't bork a big ID without being noticed. It's told to translate in and out of that as necessary.

I had it write it's own skill for making sure to follow some sane commenting/style guides per language, that way it doesn't just give me massive files with no comments.

Finally, a last one lets Antigravity or Codex call out to local models as workers, for long running background tasks where a cheap, dumb model is fine. Saves on overall token usage.

I also have a few skills from the internet setup. https://github.com/AlmogBaku/debug-skill being the most important one.

On top of that, the way people work is notable: Do you use worktrees for agents? Why/why not? Do you have only one subscription? What level? I'm using both Codex at $20/mo and Antigravity at (promo) $5/mo right now. Antigravity's TOS says you MUST use their harness, which informs some of my skill setup/workflow. Do you make your agents.md self modifying, write it yourself, or not use one at all? Do you use a sandbox or YOLO it?

Even just how you prompt matters. On Antigravity/Gemini, I can give it a big list-o-TODOs and have it make a very detailed implementation plan itself, carry it out, and generally do the thing, all unsupervised. Codex, from what I have experienced, isn't as good at that. Doing the planning as a separate step, writing to a file, then telling it to step-by-step it with commits for each step makes it work okay again though.

> I also have a few skills from the internet setup. https://github.com/AlmogBaku/debug-skill being the most important one.

From the README for that skill:

> Install the skill, and Claude debugs your code the way you would — not with print statements.

Jokes on you! I use print statements too!

Doom demo is beyond impressive, even scary

It's very misleading. If I'm actually playing a game I don't get the coordinates of enemies sent back to me so that I can feed into my mouse to snap my crosshair to. It's looking through walls too, because it's working off structured state in text form. You could re-create this whole demo without using AI. Have an LLM generate the state machine for you and no model is required to run it.

The impressive part is that it is low latency enough to serve high quality answers at game speed through the model instead of a pre generated ad-hoc machine.

A pre-generated machine can serve the answers in <1ms. It's a far better strategy.

cool but maybe i was expecting much more from both m5 ultra and the next gen so it feels somewhat underwhelming. Hopefully m6 max is more than just a 10% improvement over m5 max

What was the harness used? And yeah surprised about such prompts not being downright blocked, even with the cybersafety verification"

How do you have access?

OpenAI said in their initial blog post that certain organizations, probably big tech, have early preview access.

They seem to be serving it at 3x the price while also struggling with maintaining uptime on openrouter; while the vercel router advertizes even bigger speeds but has no clear uptime stats

I guess you really do have to try it at least for some time to actually know

yeah and lets not forget codex and glm have subscriptions too, with even more usage per dollar

but they also burn more tokens per task, so in the end, Claude comes out as the more efficient one, despite giving you less tokens.

You've got it backwards. Opus is the token/money burning one https://deepswe.datacurve.ai/

Gpt 5.5 uses a third of the opus 4.8 tokens for the same task and scores higher. Glm 5.2 was worse in quality but used half the tokens - 5.3 is not tested yet but will be higher.

We have notebooklm at home? Is there any comparison between these two, looks nice

Thanks for sharing NotebookLM, I hadn't seen that! I'll take a look and add a comparison to the README if it's compelling.

In my opinion, the coolest thing in NotebookLM is the podcast-episode-generator. Each one sounds like two people having a conversation. It's fun to listen to a podcast episode about some niche topic (e.g. nuclear isomers, or the Weyl curvature tensor) while I'm cooking or driving.

insane levels of slop, so bad it almost feels intentional