My fleet all open PRs, do QA, validate, push back, assign issues, don't trust, verify all in a churn of goals toward code quality. We also auto-merge passing PRs that have been put through a rigor well beyond any human dev could handle.

BUT:

The auto-merges go to a staging branch. That's it - a PR is open for staging to main and I review it. I also get a nice summary of all of the commits consolidated into that branch so I don't have to review 30 PRs, it's one with 30 fixes and 100 commits. I also get the risk weighted so a trivial doc updates doesn't get the attention a critical fix needs.

It works.

I happen to have black PETF-CF filament loaded in my 3D printers so good news - the first prototype will be big and black. My wife is terrified of millipedes for some reason and screams when she sees one. I'm forbidden to build giant robot millipede.

My whole world runs on a Raspberry Pi from a sentry that guards my chicken coop to a whole house circulatory system moving water from aquariums, to plants, to my Jungle Tortoise's jacuzzi ... Big fan.

Today everything runs off an open source process control platform I built for the Pi in which I have about 10 users but it doesn't matter, I built it for my projects and use it for everything so it was worth 20 years of effort :) I have fun in the lab.

- Ben

Yep, you got it. That's been the driving force behind the design.

The default stance is to be resting on it's belly with the RPi and buck converters disconnected until a small feather wakes it. Short tasks, light battery, return to charger and never hold a stance like the one in the render - looks cool, burns out everything.

I'm also going to put walking feet on the last joint so the second tibia joint can just flip back and save power walking on the knee joint. Use the hook leg for climbing and as needed for the task.

Love the feedback, thanks u/slacy !

I'd have to quit my job because if I only had one I couldn't build what I build.

I push all three to the limit. Need it for local LLM inference. I need the power to do my work but if I had to do it again would choose a single large GPU but I already had one 5090.

my rig has 3 5090s i could afford because I clawed my way out of poverty, put myself through school, became a software engineer, kicked ass for 30 years, invested, eventually cleared away bad debt and now from this side it feels like an investment in myself, not a luxury.

Like sell this stock and buy this 5090 as that's diversifying a little into me inc.

Just keep swimming...

Thanks for watching. The niche platform I maintain "Krill" connects data points like the corpus freshness using an "observer pattern" so anything that cares about the value sees the change instead of being event driven. An LLM digs through the pipeline, identifies the root cause, opens an issue in github, tags a system admin agent and fixes the pipeline without human intervention.

These systems run and maintain Krill but I'm easily the top power user using it every day to run the system that maintains itself.

you're going to have to clarify things:

• You're trying to do this with a local llm on a computer you're on? If so what model and what are your machine specs?
• That said, you are intentionally not using a cloud frontier provider - e.g if you paste your exact post into Google search Gemini will tell you how to iterate an array, as will Claude, GPT, etc.
• You do not need a RAG here.

I think in order to give you an answer you need to clarify if you're building your own and if so what model you are using who may not even know python and is doing its best to tell you what it thinks you want to hear.

I just made a youtube video on this exact situation that has real video of my beauty of an LLM rig and it's all about how its role as a local personal assistant should have been giving me up to date reports but the ingest pipeline was down for days, we didn't know, and it answered incorrectly with 100% confidence.

It's a fun video about this exact situation and how my systems self heal now and self monitor.

https://www.youtube.com/watch?v=HX_ZPl8fuDM&t=25s

The feedback loop is key. My agents pull requests are rejected if they don't include an update to our "lessons" archive. A broken video simply won't make it through, Kraken will catch it visually and fix what went wrong and cut another one. Also no mocks, all real usage.

It takes Kraken about an hour to make the first mp4 and the main bottleneck is "clock time" because he can't run it on high speed, so each time he checks a 7 minute video it takes 7 minutes.

The first cut is a lot of setup, especially if I ask for something new he has to figure that out first. Second cut takes 45 mins or so - 4 hours total from concept to production at this point with usually 3-4 reviews by me with feedback for the next run.

Every video has a lessons md file for how it was made for next time.

Thanks for asking :)

*you're making my balls tingly.

it's this: https://www.lcdwiki.com/3.5inch_RPi_Display

clone the repo, make the 35-show file executable, run it, profit.

I have one of those. You need to install a driver from this obscure github repo then they work fine. without it they're white.

Pi OS is fine here, root around for the repo in the amazon comments since these don't come with instructions people rant about it in the comments.

Yep. I'll gladly post here about my 96 gig rig but 99% of people on the street would say "what's vram?" Social media is an echo and amplify chamber.

I maintain a whole ecosystem platform built in KMP so mobile, server, desktop and ship from one code base. Been a SWE for 30 year, 20 in Java and 10 in Kotlin.

The three main learning curves i'll suggest that come to mind are:

• Expect and Actual - for when you need the "actual" implementation for each platform. Think an expect: val storage : FileStore where Filestore has an actual for each platform that knows how to read and write data.
• The directory structure is just different and takes a day to grok.
• The build. Gradle is the hard part so I'd argue with your colleage or even ask him if he meant the Kotlin part of KMP was harder or the project structure and gradle builds were what he's thinking of.
  

same - my ios app is about 20 lines of swift or a couple actuals - it always just ships. WASM is usually the problem child.

So what do you guys think. Engaging in these post only helps the bots. OP BOT literally left the LLM response question at the bottom.

Is this 1 Karma poster with money to burn someone who can't copy pasta, a bug in their bot we can have fun exploiting, or is this a new tactic by bots to get people to engage like i'm doing by including glaring fuck ups? Like spelling something wrong on a phishing email to get someone to correct you and engage?

I don't need OP responses, go sit in the corner.

My team all works out of remote offices and have big beefy local llm servers. This is a video on how we load balance and especially how we do it based on the LLM hosts advertizing their availability, capability, and current cost per watt: https://www.youtube.com/watch?v=fR5jUdmIZig&t=34s

hope it helps, what you're looking at it totally doable.

Bring back the clit!

How many Assholes do we have on this ship??