3
points
colonCapitalDee
Joined 1,261 karma
- colonCapitalDeeGreat news. I was just starting to explore creating a goto-definition skill for CC, glad I don't have to figure that out now :)
- Before the first loop iteration, the harness sends a message to the LLM along the lines of.
<Skills>
</Skills><Skill> <Name>postgres</Name> <Description>Directions on how to query the pre-prod postgres db</Description> <File>skills/postgres.md</File> </Skill>The harness then may periodically resend this notification so that the LLM doesn't "forget" that skills are available. Because the notification is only name + description + file, this is cheap r.e tokens. The harness's ability to tell the LLM "IMPORTANT: this is a skill, so pay attention and use it when appropriate" and then periodically remind them of this is what differentiates a proper Anthropic-style skill from just sticking "If you need to do postgres stuff, read skills/postgres.md" in AGENTS.md. Just how valuable is this? Not sure. I suspect that a sufficiently smart LLM won't need the special skill infrastructure.
(Note that skill name is not technically required, it's just a vanity / convenience thing).
- It certainly reads like it
- Good luck! Building the next uv is certainly ambitious, but I love ambitious projects :)
- 1 point
- I don't know why that matters? The city selection and routing is a part of the overall autonomous system. People get to where they need to be with fewer deaths and injuries, and that's what matters. I suppose you could normalize to "useful miles driven" to account for longer, safer routes, but even then the statistics are overwhelmingly clear that Waymo is at least an order of safer than human drivers, so a small tweak like that is barely going to matter.
- Blind leading the blind, but my thinking is this:
1. Use the tools to their fullest extend, push boundaries and figure out what works and what doesn't
2. Be more than your tools
As long as you + LLM is significantly more valuable than just an LLM, you'll be employed. I don't know how "practical" this advice is, because it's basically what you're already doing, but it's how I'm thinking about it.
- 7 points
- 3 points
- Great article. Can confirm, writing performance focused C# is fun. It's great having the convenience of async, LINQ, and GC for writing non-hot path "control plane" code, then pulling out Vector<T>, Span<T>, and so on for the hot path.
One question, how portable are performance benefits from tweaks to memory alignment? Is this something where going beyond rough heuristics (sequential access = good, order of magnitude cache sizes, etc) requires knowing exactly what platform you're targeting?
- Well that's your problem. Here's a tip: just because someone says something doesn't mean you have to listen to them
- No. This is far beyond the capabilities of current AI, and will remain so for the foreseeable future. You could let your model of choice churn on this for months, and you will not get anywhere. It will be able to reach a somewhat working solution quickly, but it will soon reach a point where for every issue it fixes, it introduces one or more issues or regressions. LLMs are simply not capable of scaffolding complexity like a human, and lack the clarity and rigorousness of thought required to execute an *extremely* ambitious project like performant CUDA to ROCm translation.
- 2 points
- Yep, just checked, 2 GB RAM. That CX23 sounds like a great deal, 20 TB of free outgoing is ridiculous. But I live in west us and I rarely hit my 1 TB free bandwidth cap anyway, so the added latency isn't worth it
- I have a tiny Hetzner VPS (2 vCPUs, 2 Gb RAM) in their west us datacenter that costs me $5.59 a month. I get 1 TB a month free outgoing bandwidth and unlimited incoming bandwidth, plus additional outgoing bandwidth at a rate of $1.20 per TB. I host my personal project's git LFS server there, a file server, and a Caddy instance that proxies over Tailscale to a more powerful box in my apartment. It's a great homelab architecture and I couldn't be happier with it. Thanks Hetzner!
- 2 points
- 7 points
- I thought this was informative: https://minusx.ai/blog/decoding-claude-code/
- Ok, maybe I oversold this a little bit. It's running smooth now, getting it to run smooth was not easy. I'm on Ubuntu. I spent a few days in a debug loop. Run steam from the terminal to get a log stream, keep an eye on CPU and GPU utilization and temperature, and futz around in the training range or vs AI bots (more "realistic" than training range). Identify which components of the system aren't performing up to spec. CPU running hot? GPU not being utilized? Steam emitting warning messages? If hardware all looks good, it's probably a software problem somewhere. Identify, then fix. Rinse and repeat until linux performance is in the same league as Windows performance.
Things I'd try:
1. Check in game graphics settings
2. Update graphics drivers to the recommended version (may be non-trivial, I had to update my kernel version)
3. Experiment with different proton versions, including proton GE
4. Experiment with different Direct X versions (in game option)
5. Make sure CPU cooler is running
6. Make sure GPU is being used
7. Use gamescope to configure a virtual monitor that exactly matches the capabilities of your physical monitor