u/Covert-Agenda

https://preview.redd.it/mrwpd7qt3a2h1.png?width=1384&format=png&auto=webp&s=e7e39b26d8916417e2d5c73851b8e6da3be82ddd

Have you ever started a CLI project and forgotten to note down the resume command when exiting, only to watch your agent re-read the repo from scratch to figure out where you left off?

Yeah.... I burned through enough tokens that way that I finally built a thing.

Stash walks the session storage for Claude Code, Codex, and OpenCode, groups everything by project directory, and gives you one picker.

brew install SectorOPS/Stash/stash

https://github.com/SectorOPS/Stash

Let me know what you think.

https://preview.redd.it/wk3pprl92a2h1.png?width=1384&format=png&auto=webp&s=ae9c7b71b46233f05e77b05ff75e87173b107a23

Have you ever started a CLI project and forgotten to note down the resume command when exiting, only to watch your agent re-read the repo from scratch to figure out where you left off?

Yeah.... I burned through enough tokens that way that I finally built a thing.

Stash walks the session storage for Claude Code, Codex, and OpenCode, groups everything by project directory, and gives you one picker.

brew install SectorOPS/Stash/stash

https://github.com/SectorOPS/Stash

Let me know what you think.

https://preview.redd.it/qx0ekkdk1a2h1.png?width=1384&format=png&auto=webp&s=04f205a58accaecce514e400440a37fb5b028a13

Have you ever started a CLI project and forgotten to note down the resume command when exiting, only to watch your agent re-read the repo from scratch to figure out where you left off?

Yeah.... I burned through enough tokens that way that I finally built a thing.                                

Stash walks the session storage for Claude Code, Codex, and OpenCode, groups everything by project directory, and gives you one picker.

brew install SectorOPS/Stash/stash

https://github.com/SectorOPS/Stash

Let me know what you think.

I've been working on two open-source projects:

• Lycan — a small graph execution language with strategy nodes as a first-class primitive (multiple implementations of the same contract, runtime learns weights from outcome feedback). Compiles to a binary graph, executed by a Rust runtime. No LLM in the hot path.
• Syntra — a self-hosted Docker/API appliance that serves compiled Lycan capsules. Multi-tenant, shadow-mode-first, contextual learning perontextKey, persistent filesystem store, audit/decision/feedback logs separated. Includes an MVP YAML authoring layer so you don't have to write the underlying Lisp.

The use case I care about: repeated decisions where the best option depends on context and the outcome arrives later. LLM model routing, retry/timeout policy, queue selection, threshold tuning, anything where you'd reach for a contextual bandit but don't want to stand up a Python ML platform to do it.

I'm dogfooding it against my own product (a public AI stock-debate panel with 30-day market-resolved outcomes, MoEFolio.ai). The first surprise wasn't from the runtime; it was that my contextKey schema was collapsing all sectors into unknown one because my sector lookup only resolved symbols from one of three input paths.

The bandit was nominally 5-dimensional but effectively 2-dimensional, learning a cross-sector average that meant nothing. Fixing the data pipeline, not the algorithm, is most of the work in adaptive systems.

Apache-2.0, very early, would love eyes from anyone who's worked on bandits in production.

• github.com/SectorOPS/Lycan
• github.com/SectorOPS/Syntra

https://preview.redd.it/0na1vlqevr0h1.png?width=1689&format=png&auto=webp&s=bf606f826b063db3d54db84a06af5a6ea50c74d8

Thank you to the creators of oMLX; it's a beautiful piece of software for inference.

I have tried various things such as uv mlx, LM Studio, Ollama etc.. and they where all pretty slow. oMLX has 4 modles loaded at once not breaking a sweat!

I built an AI trading experiment in which four local LLMs argue bull and bear cases on stocks, and a host model grades the debate and decides BUY, SELL, or HOLD.

Most days it holds. Sometimes it loses in hilariously dumb ways, so I do a postmortem on which model became overconfident, which bias showed up, and where the reasoning broke down.

It runs on local inference, uses Alpaca paper trading, and pulls from 50+ free data sources.

The fun part is watching the debate transcripts, agreement heatmaps, and bad takes unfold live.

Stack:

• Mac Studio M3 Ultra running four different LLL model families locally, which are MoE's.
• FastAPI on a Mac Mini, pushing snapshots to the web app so the bot can crash without taking the site down
• ThinkStation PGX for generating the photos, videos and podcasts, etc. It also transcribes YouTube videos to use as data.
• Alpaca paper accounts for now. No real money yet; the goal is real money once it stops losing on dumb stuff
• 50+ free data sources, no paid APIs whatsoever.
• Built with Claude Code.

https://moefolio.ai/

https://preview.redd.it/veq4kfq90p0h1.png?width=1173&format=png&auto=webp&s=5efc9ea65d39c62e793b4001cec7bd484adf6ec3

I built an AI trading experiment in which four local LLMs argue bull and bear cases on stocks, and a host model grades the debate and decides BUY, SELL, or HOLD.

Most days it holds. Sometimes it loses in hilariously dumb ways, so I do a postmortem on which model became overconfident, which bias showed up, and where the reasoning broke down.

It runs on local inference, uses Alpaca paper trading, and pulls from 50+ free data sources.

The fun part is watching the debate transcripts, agreement heatmaps, and bad takes unfold live.

Stack:

• Mac Studio M3 Ultra running four different LLL model families locally, which are MoE's.
• FastAPI on a Mac Mini, pushing snapshots to the web app so the bot can crash without taking the site down
• ThinkStation PGX for generating the photos, videos and podcasts, etc. It also transcribes YouTube videos to use as data.
• Alpaca paper accounts for now. No real money yet; the goal is real money once it stops losing on dumb stuff
• 50+ free data sources, no paid APIs whatsoever.
• Built with Claude Code.

https://moefolio.ai/

https://preview.redd.it/8xfqm34h0p0h1.png?width=1173&format=png&auto=webp&s=ef9f8100ca29274e954e3ee215a2f9cc0c8fb175

I built an AI trading experiment in which four local LLMs argue bull and bear cases on stocks, and a host model grades the debate and decides BUY, SELL, or HOLD.

Most days it holds. Sometimes it loses in hilariously dumb ways, so I do a postmortem on which model became overconfident, which bias showed up, and where the reasoning broke down.

It runs on local inference, uses Alpaca paper trading, and pulls from 50+ free data sources. No real money yet, no paid APIs, no course/newsletter/Patreon etc...

The fun part is watching the debate transcripts, agreement heatmaps, and bad takes unfold live.

Stack:

• Mac Studio M3 Ultra running four different LLL model families locally, which are MoE's.
• FastAPI on a Mac Mini, pushing snapshots to the web app so the bot can crash without taking the site down
• ThinkStation PGX for generating the photos, videos and podcasts, etc. It also transcribes YouTube videos to use as data.
• Alpaca paper accounts for now. No real money yet; the goal is real money once it stops losing on dumb stuff
• 50+ free data sources, no paid APIs whatsoever.
• Built with Claude Code.

Site: https://moefolio.ai

https://preview.redd.it/ym3kbfzo0p0h1.png?width=1173&format=png&auto=webp&s=cf90436f85ef85ddf592f2179cee9802ce6c82eb

I built an AI trading experiment in which four local LLMs argue bull and bear cases on stocks, and a host model grades the debate and decides BUY, SELL, or HOLD.

Most days it holds. Sometimes it loses in hilariously dumb ways, so I do a postmortem on which model became overconfident, which bias showed up, and where the reasoning broke down.

It runs on local inference, uses Alpaca paper trading, and pulls from 50+ free data sources. No real money yet, no paid APIs, no course/newsletter/Patreon etc...

The fun part is watching the debate transcripts, agreement heatmaps, and bad takes unfold live.

Site: https://moefolio.ai

https://preview.redd.it/hkhq95wv0p0h1.png?width=1173&format=png&auto=webp&s=c2a51ab1ce7f3cb97e8b952f948716797e2a6c58

New to the sub, I hope everyone is doing well!

I thought it would be funny to see how AI handles trading, so I built a panel where four LLMs from four different model families argue about stocks.

Two teams:

🐂 Bull team

Bruce — folksy value uncle. Slow to anger, brutal when he gets there. Cites his late friend Chuck. Takes a sip of cola whenever Meryl says “TAM.”

Meryl — evangelical futurist, permanently bullish. Has never met a bear case she couldn’t explain away with “TAM.”

🐻 Bear team

Walter — antisocial drummer in his mid-30s. Has been bearish since the GFC. Mentions 2007 unprompted. Refuses to update priors.

Layla — sharp sceptic with a Spanish accent. The one who calls HOLD when everyone else starts losing their nerve.

Donald hosts the whole thing, grades the panel after each round, and calls out the worst takes.

The setup is split between cloud and local inference.

The verdict logic is deliberately asymmetric: a trade only fires when one team commits, and the other side doesn’t oppose with equal conviction. Otherwise, it’s HOLD. Most days, they hold. They are painfully cautious little machines.

Stack:

• Mac Studio M3 Ultra running four different LLL model families locally, which are MoE's.
• FastAPI on a Mac Mini, pushing snapshots to the web app so the bot can crash without taking the site down
• ThinkStation PGX for generating the photos, videos and podcasts, etc. It also transcribes YouTube videos to use as data.
• Alpaca paper accounts for now. No real money yet; the goal is real money once it stops losing on dumb stuff
• 50+ free data sources, no paid APIs whatsoever.

It loses trades, often. Sometimes in impressively stupid ways. When that happens, it gets a forensic autopsy the next day: which cognitive bias did the panel fall for, who overruled whom, and where the reasoning went sideways.

But that's the fun of it, you see them arguing, and I will be producing YouTube Videos and Podcasts from the transcripts for a laugh.

Everything is live: verdicts, positions, debate transcripts, the “who agrees with whom” heatmap, plus a UK FTSE 100 panel that runs overnight.

I'm not selling anything, no course, no newsletter, no Patreon.

Just a weird trading bot with receipts.

My end goal is to train the actual model weights, not just wrap prompts around existing models, so the resulting model can be plugged into different workflows and have a genuinely better than random chance of being right.

After managing to bag a 256gb M3U I have finally put it all together.

RackMate T1
Mac Mini M4 512GB SSD 24GB Memory
ThinkStation PGX GB10 1TB SSD 128GB Memory
Mac Studio M3 Ultra 2TB SSD 256GB Memory

Criticism welcome 😂

https://preview.redd.it/rotecj348wzg1.jpg?width=3024&format=pjpg&auto=webp&s=9e5de56a0c570a29a0aac4e22db92e6fbf719d30

In my quest to have my own AI infrastructure at home, I have settled on this little setup.

RackMate T1
Mac Mini M4 512GB SSD 24GB Memory
ThinkStation PGX GB10 1TB SSD 128GB Memory
Mac Studio M3 Ultra 2TB SSD 256GB Memory

I also have an M5 Max MBP 2TB SSD 128GB Memory as my daily driver.

I've tried to cable manage the best I could - criticism welcome 😄

Use case is AI development and image/video generation.

https://preview.redd.it/rzl2g7g23wzg1.jpg?width=3024&format=pjpg&auto=webp&s=4ec749a601dda244ab9aa5c15f3c80923086b65a

https://preview.redd.it/mqeg8ag23wzg1.jpg?width=3024&format=pjpg&auto=webp&s=84776dae4c5a57b8b180c96882bedf39f61f33a2

After a few months of trying to source an M3 Ultra Mac Studio, I finally got one.

I originally bought a 96GB model on eBay a month ago, but something felt off. The serial number didn’t match the one on the bottom of the machine, so I sent it back and thankfully got my money back.

After that, I decided to go for the 256GB model instead, but I couldn’t find one anywhere. I put an order in with Apple, but the ETA was around the end of July, and even then, it was for a slightly lower spec than I really wanted because of the price.

Then last week, one popped up on eBay: M3 Ultra, 256GB RAM, 2TB storage, 80-core GPU. Honestly, I thought there was no way it was legit, especially because the listing said no AppleCare. But I needed a machine and decided to take the plunge due to buyer protection.

Somehow, luck was finally on my side.

It arrived this morning, so I logged in with my Apple ID, and it showed I still had 60 days to add AppleCare. The serial numbers match, the hardware matches, it passes Apple Diagnostics, and it’s effectively brand new, or at least properly covered under Apple warranty.

I thank the Apple gods today!

The best part is, I got it for around £1,500 less than it would have cost from the Apple Store.

Just wanted to say thank you to everyone who has been posting about their experiences sourcing these machines. Reading those posts genuinely helped while I was trying to figure out what was genuine, what wasn’t, and whether I was making the right call.

Really appreciate this community!!!

Ps - Got a DeskPI RackMate T1 on Amazon the other day to build out an AI cluster, just need to finish building the rack properly.

Current Setup;
Mac Mini M4 512GB SSD 24GB Ram - Doing little tasks for me, running GPT-OSS-20B.
MacBook Pro M5 Max 2TB SSD 128GB Ram - Daily driver with Opencode
New - Mac Studio M3U 2TB SSD 256GB Ram - Workhorse for 100b+ models.

https://preview.redd.it/5fm6bl66niyg1.png?width=1086&format=png&auto=webp&s=80b82dd03578a933d20d6ebe6c5c151944091e43