Note on how to install caffe on Ubuntu. Sucessfully install using CPU, more information for GPU see this link
###Installation
- verify all the preinstallation according to CUDA guide e.g.
lspci | grep -i nvidia| #!/usr/bin/python | |
| # -*- coding: utf-8 -*- | |
| import web, json, time, mpd, collections | |
| STATIONS = { | |
| "FoxNews" : "mmsh://209.107.209.181:80/D/138/13873/v0001/reflector:24137?MSWMExt=.asf", | |
| "Classic" : "http://radio02-cn03.akadostream.ru:8100/classic128.mp3", | |
| "Jazz": "http://streaming208.radionomy.com:80/A-JAZZ-FM-WEB" | |
| } |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>Radio RPi</title> | |
| <meta name="viewport" content="width=device-width, initial-scale=1"> | |
| <link rel="stylesheet" href="http://code.jquery.com/mobile/1.2.0/jquery.mobile-1.2.0.min.css"/> | |
| <script src="http://code.jquery.com/jquery-1.8.2.min.js"></script> | |
| <script src="http://code.jquery.com/mobile/1.2.0/jquery.mobile-1.2.0.min.js"></script> | |
| </head> | |
| <body> |
| static BOOL PSPDFIsDevelopmentBuild(void) { | |
| #if TARGET_IPHONE_SIMULATOR | |
| return YES; | |
| #else | |
| @autoreleasepool { | |
| // There is no provisioning profile in AppStore Apps. | |
| NSData *data = [NSData dataWithContentsOfFile:[NSBundle.mainBundle pathForResource:@"embedded" ofType:@"mobileprovision"]]; | |
| if (data) { | |
| const char *bytes = [data bytes]; | |
| NSMutableString *profile = [NSMutableString new]; |
| # | |
| # Uncrustify Configuration File | |
| # File Created With UncrustifyX 0.2 (140) | |
| # | |
| # Alignment | |
| # --------- | |
| ## Alignment |
| namespace Alphabet | |
| { | |
| public class AlphabetTest | |
| { | |
| public static readonly string Alphabet = "abcdefghijklmnopqrstuvwxyz0123456789"; | |
| public static readonly int Base = Alphabet.Length; | |
| public static string Encode(int i) | |
| { | |
| if (i == 0) return Alphabet[0].ToString(); |
Note on how to install caffe on Ubuntu. Sucessfully install using CPU, more information for GPU see this link
###Installation
lspci | grep -i nvidia| # import config. | |
| # You can change the default config with `make cnf="config_special.env" build` | |
| cnf ?= config.env | |
| include $(cnf) | |
| export $(shell sed 's/=.*//' $(cnf)) | |
| # import deploy config | |
| # You can change the default deploy config with `make cnf="deploy_special.env" release` | |
| dpl ?= deploy.env | |
| include $(dpl) |
First, you have to enable profiling
> db.setProfilingLevel(1)
Now let it run for a while. It collects the slow queries ( > 100ms) into a capped collections, so queries go in and if it's full, old queries go out, so don't be surprised that it's a moving target...
OpenClaw is useful, but most of the pain people run into comes from letting one model do everything, chasing hype, or running expensive models in places that don't need them.
What worked for me was treating OpenClaw like infrastructure instead of a chatbot. Keep a cheap model as the coordinator, use agents for real work, be explicit about routing, and make memory and task state visible. Cheap models handle background work fine. Strong models are powerful when you call them intentionally instead of leaving them as defaults.
You don't need expensive hardware, and you don't need to host giant local models to get value out of this. Start small, get things stable before letting it run all the time, and avoid the hype train. If something feels broken, check the official docs and issues first. OpenClaw changes fast, and sometimes it really is just a bug.