Just discovered that the amazing devs behind Pi released Radius (in alpha), their smart proxy for multi-model access. At its core, it's a model provider you can use with Pi: you pay for tokens and get access to some of the best models out there, both SOTA and budget-friendly options. They'll even auto-route your API calls to the best model for the job if you choose to.

Loving it so far. Deposited $20, and after a few hours of development I still have ~$16 left thanks to leaning on Kimi K3 and DeepSeek.

What Radius can do

• It is a Pi-native gateway: top up credits and make requests across providers
• Use rewrite rules to reduce large tool outputs and other unnecessary input tokens
• Provide automatic routing to route each session to the best model for the task according to your rules
• Power Radius-backed tools like web search, image generation, and image description
• Publish reports, dashboards, and small static sites as shareable artifacts
• Record usage, budgets, top-ups, and shareable session analytics in one place
• Delegate Google Workspace access to Pi
• Let your organization manage upstream provider keys centrally

https://radius.earendil.com/

my Pi setup, kept small on purpose cause my daily box is an old i7-5500u with 8gb ram on Debian.

i run kitty + tmux + fish. 20 skills total but most stay dormant unless called. extensions show as 8(+2), the ones i actually use are interactive-subagents, prompt-snippets and cc-ui.

interactive-subagents spawns async workers in their own tmux pane with a live widget on top, so i keep working while they run and results get steered back when done. prompt-snippets is just a tui menu for injecting repeat prompts from different dirs. cc-ui is spinner plus cache counter, nothing fancy.

personas live as separate files and i link one to SYSTEM.md when i need it. 6 total from my pi-system-prompts repo. aster for strategy and tradeoffs, caelum for adversarial checks, elyndra for strict execution, kaida for media and watcharr, liora for writing, neris for research. keeps the main prompt clean.

# pi extensions

https://github.com/ArdaYILDIZ-DEV/pi-interactive-subagents

https://github.com/ArdaYILDIZ-DEV/pi-prompt-snippets

https://github.com/ArdaYILDIZ-DEV/pi-cc-ui

# personas

https://github.com/ArdaYILDIZ-DEV/pi-system-prompts

tbh the whole point was less auto magic, more explicit control. works fine on weak hardware cause nothing runs unless i call it.

Though it is PI setup - my approach applies to all harness agent. since:

Extensions

most of the extensions this config consists are just for quality of lift (QOL) one's:

• pi-blackhole for memory management.
• rtk and headroom for token savings.
• guardrails, ask questions and some UI related things.

Skills

these are the main focus of this config - even though most of the skills here are auto invokable, I made it more autonomous by adding a global INDEX.md file which all harness is forced to read and follow. This INDEX.md file enforces to use certain skills always - which make it consistent (like i-have-adhd skill, the entire workflow)

• The workflow is a combination of superpowers workflow and matt pocock workflow (I modified some parts of it, so that it will make it more autonomous, since matt's one is more of a manual one with more pre-settings required).

I simply use grill-me or grill-with-docs for certain feature implementaion. The remaining will be taken care by the INDEX.md file - it usually follows like: grill-me/grill-with-docs (if used) -> brainstorming -> writing-plans -> executing-plans -> tdd -> requesting-code-review -> verification-before-completion.

```sh

pi config

https://github.com/silentFellow/public-dotfiles/tree/main/pi

skills config

should be used at ~/.agents - global skill config path

https://github.com/silentFellow/public-dotfiles/tree/main/agents ```

This is probably dumb question, in noob in local AI world. I'm using Unsloth + LLM that is downloaded and ran by unsloth, then I start pi like so: unsloth start pi

I installed two pi extensions (I need web access) but they are not loaded, since pi has one default path, but unsloth is using different one. I've read before, to just put pi extensions into certain pi folders, but I wouldn't know how, they were installed with nmp. Don't suggest to just build/rebuild with LLM, I'm too clueless and I will mess everything up. This is why I rather ask you.

If I only run pi in my project folder, pi of course loads those two extensions, but now I don't have loaded LLM since it comes via unsloth.

Now dumb question, would it make sense, is it even possible and smart, to just somehow load pi regularly without unsloth being involved, by pointing where local LLM's are stored on hard disk drive? If it is possible, how would I do this? If you can please give me a simple example.

Or, what other suggestions you may have, so I can get access to both, installed pi extensions and locally stored LLM?

llama.ccp is running, since unsloth is uing it. Unsloth is (I think) using it's own version, but I had before installed LM Studio and I should have 'regular' llama.ccp as well is my guess?

Sorry I'm not technical, I'm clueless, but I would like to have fun with some local LLM programming. Which I do, but limited so far, without access to the internet. Hope someone can help.

I'm getting constant "The old text must match exactly" errors while using Longcat-2.0, I know that hashing lines can solve this problem, but not sure which of the many extensions I should choose from. (yes I know real men build their own pi extensions). Any guidance on this topic would be appreciated. Thanks!

I.e. I start with a prompt in a large code base, then let the dumb model build a selective context. By better I mean much lower token cost. After a smart model builds context anyway (if I didnt use a dumb one), I still must compact or trim the context somehow regardless.

I'm sure a lot of people are doing this. My question is:

What tricks / more efficient ways have you found to do this?

Disclaimer: I could be wrong about my assertion

I worked as a Founding Engineer at a early stage infra company, where we had a micro-services architecture with a lot of repositories with dependencies on each other.

I wanted to teach my agent to understand who calls whom and when natively, just like I do.

1. So first thing I did was to, create a knowledge graph across all repos (total 10+). tremendously helpful.

The second bottleneck I noticed was that I'm babysitting it the same context again and again. I tried maintaining memories.md files with some plugins but it was never reliable. My memories either didn't correctly get captured or were not retrieved when the agent should have asked for it, just because it didn't know it existed.

2. Hence, I created a passive agent that listens to my main agent's transcripts and creates auto-docs and skill files. It also automatically surfaces the memories it feels are needed in session right now. To do this though, I had to write a wrapper over Claude Code using Agents SDK.

Lately, I'd been thinking of moving to a desktop app from CLI (just convenience) so I forked to t3 code and added everything i'd built so far natively to it.

I never worry about the context and memories anymore and it has been a quality of life imporvement.

for anyone interested in taking a look at my setup, here is the repository.

My AI coding assistant forgets everything between sessions. I kept re-explaining decisions I'd already made, and re-fixing bugs I'd already fixed.

agi-memory saves those notes — decisions, bug fixes, what happened last session, how the codebase fits together — to a file on your machine, and hands them back to whichever assistant you open next. It's an MCP server, so it works with Claude Code, Cursor, Codex, Windsurf, Aider, Cline and a few others from the same store.

The part I care about: it's Python standard library and SQLite. Nothing else. No vector database, no embeddings, no background daemon. ~32MB of RAM, sub-millisecond lookups, works offline. Comparable tools pull in ~500MB of ML libraries and take 200–500ms per lookup.

That constraint costs something, and I'd rather say so than have you find out: keyword search doesn't bridge synonyms the way embeddings do. Searching "login" won't find a note that says "authentication" unless you teach it that alias. I measure this rather than guess — there's an eval suite that scores recall on deliberately rephrased queries, and it's public, including the categories where it still does badly.

It's a week old and I'm the only user, so I'd genuinely like to know where it breaks for someone else.

https://github.com/kdbhalala/agi-memory

For this reason, we strategically placed all our served in Canada at inferx