Deep in a session, right when I understood the work best, I wrote down what to build next as a short structured capsule. Then I cleared the context. A fresh session with none of that understanding read the capsule cold and built it to spec. It did not know it was finishing its own work. Handoff notes fail two ways: they ROT (you forget) or they CANNOT TRAVEL (a fresh session can't read your mind). And terse notes silently drop the two things that matter most: WHERE the work goes and what DONE looks like. I measured it. Labeled-field capsules scored 10/10 on intent fidelity vs terse prose at 9.67, and prose's weakest spots were exactly WHERE and ACCEPTANCE. The honest part: the model still ran ~31% of malformed input instead of refusing it, so that check is deterministic code, not a model call. It understands the capsule. It does not get to decide the capsule is safe. Free CLI and Claude Code plugin, MIT. Try authoring one capsule next time you are deep in something, then clear and let a fresh session build it. github.com/gtsbahamas/intent-capsule

OpenClaw, NanoClaw, ClaudeClaw. An assistant in your chat that reads your inbox, remembers everything, and gets things done. Lean in. The people getting the most out of them aren't the most careful. They just wear a seatbelt, so they can drive faster: - Read-only to start. Let it earn more. - It asks before it sends, deletes, or pays. - Its own space, not your main account. That's most of the protection, at zero cost to what it can do. Full playbook, plain English, no login. Plus a prompt your agent runs to audit itself: https://shipsafe.franklabs.io/agent-safety Which one are you running, and what have you got it doing?

Hi all, I kind of dropped off from the community during last few months as I got a good gig at a large bank to build an AI AML platform for them. But I will be dropping in again .. Sharing how my SDLC agentic harness look like and how I make sure quality of code I produce is high. I am still doing all this inside Claude Code. Can give a demo or presentation if community is interested.

Hey guys! I just sat down with @Patrick Chouinard for one of the coolest deep dives we've done in the community. Patrick has become our community's go-to AI expert and one of the most helpful resources we have. He's quietly been building something most teams pay a vendor $50k a year for. He's turning every community call we've ever recorded (two and a half years of two to three hour conversations) into a queryable "community brain." You'll soon be able to ask it anything that's been discussed and get a real answer with citations. Here's the part that broke my brain. Standard RAG completely falls apart on transcripts. The question gets asked at minute 6, the conversation drifts, and the real answer shows up at minute 41. A normal chunker has no idea those two moments belong to the same idea. Patrick solved it by adding an LLM analysis layer BEFORE chunking that restructures the transcript into self-contained units of knowledge. You can watch the full breakdown above! Here's everything we covered: ✅ Why traditional chunk-and-embed fails on non-linear data ✅ The LLM analysis layer that turns raw transcripts into RAG-ready knowledge ✅ How Patrick picks the cheapest model that's still smart enough for each step (Kimi K2.5 for restructuring, Sonnet 4.6 for the signal extraction) ✅ Why he chose LanceDB over Pinecone (and when you'd flip that decision) ✅ Running the whole thing locally with Ollama, Open Web UI, Gemma 4 4B, and gpt-oss:20B for more complex retrieval ✅ Using Claude Code to build a custom chunker instead of fighting with a library ✅ Real cost math. About 40 cents per two hour episode and under $100 to process the entire archive The wildest part is the price. Once the embeddings are built, querying is free forever because everything runs on your own machine. No SaaS, no per-token cost, no IT review. Patrick is also planning to open source the full pipeline once a few rough edges are ironed out. So if you've been wanting to build something like this for your own team, agency, or client, you'll have a working blueprint to start from.

Everybody’s building automations. Nobody’s asking which tasks should even be automated. I run a 50-person company. We sat down with every department and asked one question about every recurring task: does this need a human? Not “can AI do this?” That’s the wrong question. The right question is “does a human add any cognitive value here?” Take accounts payable. Bill comes in as a PDF email. Someone opens it on one screen, copy-pastes it into the accounting system on another screen, then emails it to the bank for payment. That’s three screens. Zero thinking. The system should be doing that. You should be upset that you’re doing it. That’s bucket one. Digital. 42% of our tasks landed there. Bucket two is judgment. Things where AI can prep the work but a human has to make the call. Vendor disputes. Ambiguous invoices. HR issues. 35% of tasks. Bucket three is the point of all this. Contributor. The stuff your people DON’T do right now but COULD do. Ideas. Process improvements. One of my team members suggested attaching plain-English FAQs to our technical quotes so customers actually understand what they’re buying. Simple. Nobody thought of it because everyone was too busy copy-pasting invoices. I published the whole framework. 90-day rollout, the psychology behind why people resist, daily playbook, real case study with numbers. https://3buckets.ai What’s the most mind-numbing task in your business that a human is still doing for no good reason?