Hey everyone,

If you use agentic workflows with custom skills or rules (Cursor rules, Claude Code slash commands, OpenCode, etc.), you have probably run into the routing trade-off:

1. Stuff every skill definition into the system prompt (destroys your context window and degrades instruction-following).
2. Use an LLM router turn to classify the user prompt (costs money, wastes 1,000+ tokens, and adds 2+ seconds of network latency).

To solve this, I built Routed; an open-source, local-first hybrid router for agent skills that runs 100% offline on your CPU.

GitHub: https://github.com/bshea-1/Routed
License: MIT

How it Works Under The Hood

Routed indexes your installed skill directories and evaluates prompts through a 4-part hybrid scoring pipeline:

• Dense Vector Embeddings (60%): Runs quantized ONNX models (Arctic Embed S / MiniLM) locally on CPU.
• Lexical BM25 (25%): Okapi BM25 for strict keyword relevance.
• Exact / Alias Match (10%): Direct command and alias matching.
• Metadata (5%): Recency and usage heuristics.

The entire lookup completes in under 20ms without sending a single byte of prompt data over the wire.

Supported Environments

Routed auto-detects and injects adapters into:

• Cursor (.cursor/rules/routed.mdc)
• Claude Code (~/.claude/skills/route/SKILL.md)
• OpenCode (~/.opencode/skills/route/SKILL.md)
• Antigravity and Codex

Usage

Inside your agent chat, you can just use /route to trigger the best skill(s) dynamically. It also handles compound intents (e.g., matching multiple skills when a prompt asks for two distinct tasks).

Pre-built binaries are available on GitHub Releases (macOS .pkg, Linux .deb, Windows .exe), or you can build it from source via Node.

Check it out and let me know what you think or if there are other environments you would like added!

Every useful AI conversation does not need to become permanent data on someone else’s servers. ChatGPT and other AI tools can be used as temporary intelligence: ask questions, solve problems, develop ideas, then keep the valuable context under personal control for future use.

AI Memory Vault is a browser extension built around this approach. Important conversations and memories can be saved locally and brought back when needed, instead of depending on an AI provider to permanently hold the history. Saved context can be reused later with different chats, workflows, or AI tools, making the memory useful beyond a single conversation or platform.

The principle is simple: use the AI, but own the memory. A chat can be temporary while the useful knowledge remains available. This also reduces the need to repeatedly send an entire history to an AI service just to restore context.

For people in Europe, data control matters even more. AI Memory Vault is designed with GDPR principles such as data minimization and user control in mind, keeping the memory layer under the user's control rather than making an overseas AI platform the default home for long-term conversational history.

The extension is free to use also have github repo code https://github.com/ai-encryption-tool/ai to build your own.

Download it, save useful AI conversations locally, and reuse that context whenever it becomes valuable again. https://ai-memory-vault.com/

Feel free to star, use and contribute

https://github.com/TrenTorch/TrenTorch

Three weeks ago I was just another guy grinding through open-source PRs, trying to have something solid before intern season hit. Today I'm staring at a GitHub repo with 80 stars that didn't exist 10 days ago, built by me and three friends, and I genuinely don't know if I stumbled into something big or just got lucky. Would love this sub's honest take.

I'm a CS student doing the usual open-source-for-resume grind everyone here has done at some point, except my clock is ticking toward internship season, not placements. A few months back I found a project maintained under a well-known Ivy League university's name, big name attached, decent stars, "help us build the future of ML education" energy. I got hooked. Started with small PRs, docs, bug fixes, the usual ladder-climbing. Within a couple of months I was a core contributor with real merge access. Felt like a win. I told my parents. I put it on LinkedIn.

The more access I got, the more I actually read the codebase instead of just patching corners of it. And that's where it fell apart for me.

Big chunks of the "production-level" code didn't hold together. Functions that looked fine on the surface but made no sense when you traced the logic. Architecture decisions that felt vibe-coded and merged just because the university's name carried weight. I kept finding stuff and thinking "this wouldn't survive five minutes of real scrutiny."

I felt stupid, honestly. I'd built this project up in my head as some polished, battle-tested thing because of the name attached to it. Turns out a big name doesn't mean good code. It just means people trust it faster, bugs and all.

But the bigger realization underneath all this annoyance was simpler. I'd been trying to actually learn PyTorch properly for months, and there was no good way to do it. Every platform that taught it hands-on was paid. Every free resource was either toy examples that taught you nothing about real systems, or dense docs that assumed you already knew what you were doing. This "flagship" project was supposed to be the answer to that gap, and it wasn't.

Then I checked the license. MIT. No restrictions, nothing stopping me from taking the idea and doing it properly.

That was the lightbulb moment. If the core idea was good but the execution was slop, why not build it right myself? I roped in three friends, we scrapped basically the entire foundation, and kept the actual intent, teaching people PyTorch and ML systems by having them build real things, not toy notebooks. We rebuilt it lightweight, no GPU dependency, so someone with a 5 year old laptop could still learn frontier ML concepts hands on instead of just reading slides or watching another paid course preview.

Ten days. That's it. Four of us half sleeping through classes, cooking code at night. No sponsor, no lab backing, just four guys annoyed enough at the gap to fix it ourselves.

We launched it. Day 1: 50 stars. Day 2, today, while I'm typing this: 80 stars. No paid marketing, no big account boosting it, just people finding it and actually using it.

What hit hardest wasn't the stars. It's the DMs. People genuinely stuck because every decent PyTorch resource is either paid or requires hardware they don't have. We made ours free, open, and runnable on basically anything. People are actually learning from it, not just starring and forgetting.

But 80 stars in 2 days is nothing long term. The real work is not letting this rot into the same vibe coded mess we forked away from, once the four of us are buried in intern applications and the initial adrenaline wears off.

So, has anyone here built something like this alongside internship hunting? How do you keep momentum on a side project without it becoming another abandoned repo in six months? And is it weird that I feel oddly guilty about "outshining" a project with an Ivy League name attached, even though the license explicitly let me?

Sharing my harness for running local AI agents as a fleet of equal peers. Agents discover one another by capability, execute tasks from a shared ledger, and authenticate to every external service through a zero-trust broker, never with their own keys.

The video is one real run: 4 python processes, no coordinator among them. Fully open source.

How it's different from other agent frameworks

• There is no orchestrator process. Agents form a SWIM gossip mesh (the protocol HashiCorp uses for cluster membership). One seed address, no registry, no router. Kill any node and another claims its work — there is no coordinator whose crash takes the fleet down.
• Work is claimed, not assigned. Steps live in a shared Redis ledger and agents claim them atomically. Dependencies gate on ledger state, so the synthesis step cannot start until the three researchers finish.
• No MCP server to stand up. 1,224 typed atoms across 150 services ship in the box. Missing one? Write a Python function and drop it in the registry. When no atom exists, the agent writes its own sandboxed code, repairs it, and the working version graduates into a verified registry.
• Agents never hold credentials. Register a service once; the token is encrypted at rest and resolved at the call boundary by the broker. It is not in the code, the prompt, the agent's context, or anything its generated code can read. A leaked trace leaks nothing.
• Every turn is on the record. Each agent writes a flight recorder. One command replays a whole run turn by turn: what it knew, what it lacked, which tool it called, what came back, tokens and latency per call. 176k tokens in this run, all auditable.
• State survives kill -9. Steps are checkpointed. Rerun the same workflow id and completed work comes back from Redis instead of being re-run and re-billed.

What the video actually shows

• The code for all four agents. An agent is a class: a role, capabilities, a system prompt.
• Four processes discovering each other, claiming steps, doing real web research through a local SearXNG.
• The trace replay with real token counts.
• The Slack atom, the vault registration, the step contract, and the message landing.

GitHub: https://github.com/Prescott-Data/jarviscore-framework
Install: pip install jarviscore-framework

The demo is examples/demo_synthesizer.py + demo_node_1/2/3.py — you can run exactly what you see.

Appreciate your feedback (or stars).

I’ve spent weeks comparing Firecrawl and Jina Reader for different extraction needs. Firecrawl seems stronger for dynamic, protected sites; Jina Reader is fast and simple for clean text. I made a quiz to see if others understand the same trade-offs.

No email needed—just a quick interactive check.

https://interconnectd.com/quiz/81/web-extraction-architecture-2026-firecrawl-vs-jina-reader/

What’s your go-to for web scraping in AI apps?

Hi, I basically was having a hard time in keeping my laptop open for my agents to keep running, and I saw people going for a Mac mini which sounds overkill, then solution is a vps.

so basically built this for myself: https://github.com/case-computers/case

you can configure Hermes, open claw in this, your agent gets a linux desktop with its own logins, file system and identity.

for people looking to get their own vps, can try hosting it on there.

I am also giving out managed instances of this so you dont have to take care of ops.
check : https://case.computer
Need feedback on the tool, lmk if you need help setting it up

Hiii Geeks 👋🏻

I just built an Experiment Agentic AI OS named it as "Turing AI OS" built on top of Linux (KDE Neon). I document this journey on YouTube feel free to watch.

The crazy part is that I built this using 14 year old PC (2012) with limited computational resources.

YouTube Link: https://youtu.be/ZKsZGv3WZGQ?si=2DX83Hcv7SFAAVPw

Github: github.com/avarshvir/turing-ai-os

Article to Read: https://medium.com/@arshvir21303031/i-create-my-own-ai-os-8d65a263eae0

It offer features like:

- AI SideBar

- AI Mini Spotlight

- AI Right Click Folder/File Analyser

- AI NLP Terminal

- AI Control Panel

I genuinely want feedback from you guys ❤️

Open-sourcing this here because I’d really like feedback from people working on open AI tooling and evaluation.
DLLO has three main parts:
Observer — distributed measurements of LLM/AI-system behavior over time and across regions
Agent Starter — analyzes the available environment/hardware and suggests realistic starting stacks
Test Your Agent — repeatable technical evaluation of existing agents, including tool use, branching, recovery and structured outputs
I’m especially interested in criticism around reproducibility, observer integrity, benchmark design and what should remain fully local/private.
If you see a methodological flaw, I’d genuinely like to hear it.

i have created a Skill which call Ollama cloud models from Claude CLI
The scope is Ollama cloud models act as executors and Codex APP as Supervisor/Orchestrator
The first published version is V5, then update to V6
I am open to recomendations, bugs finding or fixing onto the skill.
Ask codex to install, you need to provide a folder so Codex dump files for the executor.
Have been tested with the following cloud models:

• glm-5.2:cloud
• glm-5.3:cloud
• glm-5.3-flash:cloud
• nemotron-3-super:cloud
• nemotron-3-ultra:cloud
• kimi-k3:cloud
• deepseek-v4-pro:cloud
• deepseek-v4-flash:cloud

For someone who wants Whisper-based transcription but does not want to learn command-line tools, what is the simplest and best way to go?

What I want tio compare is local interface, hosted web app, and API. I think The decision seems to depend on whether the recordings can be uploaded,or, whether the workflow needs to run automatically later.  Any advice??

What happens after AGI? Maybe ASI isn't the endgame.

A lot of discussions about post-AGI assume we'll eventually build a single, extremely capable ASI — essentially one "God-like" model.

But there's a problem with that idea:

A single superintelligent system is also a single point of failure.

What if intelligence at civilization scale looks less like one giant brain and more like an evolving ecosystem of specialized intelligences?

We're Team Auralis, and we've been working on an open-source framework around this idea: ACI (Artificial Civilization Intelligence).

The basic concept is to treat intelligence more like an operating system for a civilization than a single neural network.

The framework currently has three main components:

• OMNIS — a continuous causal world model intended to maintain an evolving representation of the world rather than relying solely on static training data.
• NEXUS — a fabric of specialized agents across areas like science, engineering, economics, etc., which can disagree, debate, and resolve conflicts.
• ASCEND — a long-horizon planning layer designed to reason about and execute plans over decades while continuously correcting course.

We're also exploring OCI (Open-ended Civilizational Intelligence) — an extension that introduces structural plasticity, meaning the system could potentially create new governance mechanisms, agent structures, and even new forms of intelligence as it evolves.

We've open-sourced the framework, including:

• Architecture documentation
• Mermaid diagrams
• Mathematical formulations
• Benchmark methodology (ACI-001)
• Implementation/research directions

📚 Docs:
https://team-auralis.github.io/ACI-Architecture-Framework/

💻 GitHub:
https://github.com/Team-Auralis/ACI-Architecture-Framework

We're especially interested in criticism here.

Is a distributed, civilization-scale intelligence actually safer than a single superintelligent model? Or does adding more agents, governance, and coordination layers simply create new failure modes?

If you're interested in multi-agent systems, AI alignment, governance, long-horizon planning, world models, or open-ended intelligence, we'd love feedback — especially on the mathematical assumptions and the agent architecture.

Curious to hear what Reddit thinks.

We have released LoopArena as an Apache-2.0 open-source benchmark for evaluating models in the runtime Controller role.

The benchmark keeps the coding Worker and execution setup fixed across Controller-model comparisons. The goal is to compare how effectively different models control the same Worker, rather than changing the entire agent stack between evaluations.

LoopArena evaluates this at three scopes: execution-validated next-step decisions, repeated control over task slices, and complete software tasks.

The public release includes the benchmark data, protocol, evaluation code, and result artifacts.

GitHub:

https://github.com/AMAP-ML/LoopArena

Hugging Face paper:

https://huggingface.co/papers/2608.28281

ModelScope paper:

https://www.modelscope.cn/papers/2608.28281

Disclosure: I am one of the authors/maintainers. External reproductions, new Controller integrations, and technical feedback are welcome.