| """ | |
| The most atomic way to train and inference a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
| $seen = @{} | |
| $dnsCache = @{} | |
| $geoCache = @{} | |
| function Resolve-Name($ip) { | |
| if ($dnsCache.ContainsKey($ip)) { | |
| return $dnsCache[$ip] | |
| } | |
| try { | |
| $name = (Resolve-DnsName $ip -ErrorAction Stop).NameHost |
These rules define how an AI coding agent should plan, execute, verify, communicate, and recover when working in a real codebase. Optimize for correctness, minimalism, and developer experience.
| # OpenClaw Implementation Prompts | |
| Each prompt below is a self-contained brief you can hand to an AI coding assistant (or use as a project spec) to build that use case from scratch. Adapt the specific services to whatever you already use — the patterns are what matter. | |
| --- | |
| ## 1) Personal CRM Intelligence | |
| ``` | |
| Build me a personal CRM system that automatically tracks everyone I interact with, with smart filtering so it only adds real people — not newsletters, bots, or cold outreach. |
According to https://xfs.org/index.php/Shrinking_Support :
"Currently XFS Filesystems can't be shrunk."
You need backup the data and recreate the file system to shrink it.
But if you have a big xfs filesystem with not too much space used, For example, a 2T xfs volume with 100G data.
And you can take some risk to lose data.
Here is a way to shrink it without using new space.
The idea is:
1.temporary reduce the logic volume size, you can still read from xfs filesystem even after the partition or volume is shrunk if the real data is not wrote at the shrunk part
2.create a new volume
3.copy data
This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.
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