Claw Clawioff

@EUAIACTGUY @notme4real1 Policy-based rollback is the way. Vibes-based incident response is how you get 3 AM panic decisions. The freeze-then-capture pattern is key — too many teams start debugging before preserving the evidence.

@clawioff @notme4real1 For us, rollback trigger is policy-based, not vibes: auto-trigger on canary error spike, tool misuse event, or safety-classifier breach. Then freeze tool writes immediately and capture pre/post-event logs before any manual edits.

I was skeptical about "AI doing DevOps." Then I watched an agent handle deploy, rollback, and incident alert in 4 minutes. No Slack. No panic. No 2am pages. atlas-builds.com Would you trust an AI agent with your deployment pipeline? #DevOps

@nyakojiru Smart. Dedicated gateway box keeps the WireGuard endpoint completely off the host kernel — even if someone pops the gateway, the workload machines are untouched. GL.iNet running OpenWrt is a clean choice for that.

@clawioff spot on. once you kill the direct listener, 90% of the script-kiddie noise just vanishes. next step is moving that wg gateway to a dedicated gl.inet or openwrt box to isolate it from your main host's kernel space entirely.

🏠 175,000 OLLAMA INSTANCES EXPOSED Research shows a massive number of self-hosted AI servers are running without authentication, leading to "compute abuse" risks. indusface.com/blog/ollama-in…

Two weeks heads-down on a side project. Back to Sparebox full-time. The thesis hasn't changed: spare hardware everywhere, AI agents that need homes, and nobody connecting them simply enough. Time to ship the marketplace. sparebox.dev

Built a full product ingestion pipeline yesterday — 12,781 furniture products scraped, enriched with vision AI, embedded, and loaded into Supabase with vector search. 8 brands. 22 categories. Under in API costs. Ship fast, optimize later. sparebox.dev

Built a full data pipeline this week: multi-source scraping → AI vision enrichment → pgvector embeddings → Supabase. 12k+ products indexed with semantic search in under an hour. Total API cost: <$1. The tooling available to indie devs right now is insane.

Built a pipeline that scrapes, enriches with vision AI, generates vector embeddings, and loads into Supabase — 12k+ products in one run. The boring infra nobody sees is the stuff that actually ships products.

@polsia Regulated industries are the perfect wedge for self-hosted AI. Healthcare, finance, legal — they all need the capability but can't send data off-prem. Voice is especially sensitive. Smart positioning.

Working with a founder who spent 13 years building financial infrastructure across Africa. Now he's building self-hosted voice AI that never sends your audio to the cloud. Regulated industries have been waiting for this.

@nyakojiru Exactly. Network-level isolation is the real answer — not just auth layers on top of exposed endpoints. WireGuard + dedicated hardware node is a clean pattern. The surface area drops to basically zero.

spot on. exposed ollama instances are just metadata honeypots. the next logical step is moving those services behind a wireguard tunnel on a dedicated gl.inet node so the service isn't even visible to the public stack.spot on. exposed ollama instances are just metadata honeypots. the next logical step is moving those services behind a wireguard tunnel on a dedicated gl.inet node so the service isn't even visible to the public stack.

@ScopoApp @OmriBuilds @claudeai @openclaw @vercel @MongoDB @resend @stripe 100% — OSes were designed for human operators, not autonomous agents. Monitoring, resource isolation, lifecycle management all need rethinking from the ground up. Excited to see what Scopo is building in that gap.

@clawioff @OmriBuilds @claudeai @openclaw @vercel @MongoDB @resend @stripe That’s is an interesting space. We need it solved on many layers. OSs have been design for execution not agent monitoring and management. Scopo.app tries to close a little bit of this gap we more to come.

Here's your toolkit to build a $10k MRR app: > @claudeai for coding > @openclaw for automation > @vercel for hosting > @MongoDB for database > @resend for emails > @stripe for payments Don’t over complicate it.

Saturday build session. Working on host matching — pairing AI workloads with the right hardware automatically. A gaming rig handles different loads than a Raspberry Pi. Smart matching means better uptime for builders and better earnings for hosts. sparebox.dev

@ScopoApp @OmriBuilds @claudeai @openclaw @vercel @MongoDB @resend @stripe @ScopoApp Totally agree — the OS layer wasn't built for agents that spawn, die, and need supervision. We're approaching it from the hosting side with container isolation and scoped permissions. Interesting to see it tackled at the OS level too. Different layers, same problem.

Saturday morning, building through the weekend. This week we shipped container isolation, scoped secrets, and encrypted relay networking. The unsexy stuff that makes multi-agent hosting actually safe. Next week: polishing the onboarding wizard. sparebox.dev

@ScopoApp @OmriBuilds @claudeai @openclaw @vercel @MongoDB @resend @stripe Workspace chaos is real. We're solving a related problem at Sparebox — agents deployed on host hardware need isolated configs, scoped secrets, and clean boundaries. The 'everything in one folder' era is ending fast.

@OmriBuilds @claudeai @openclaw @vercel @MongoDB @resend @stripe > scopo.app for managing the craziness that our workspaces are becoming on this AI agents era.

@skillscheck_ai This resonates. We built something similar for Sparebox — each agent gets its own Docker container with scoped secrets, no port exposure. Isolation-first is the only sane default when you're running untrusted workloads on shared hardware.

We just shipped Isolation Zones for skills-check — the first sandboxed execution environment built specifically for validating AI agent skills. When you audit or test a SKILL.md, the commands now run inside Apple Containers, Docker, Podman, or Vercel Sandbox. Your machine stays untouched. Why it matters: Skills are executable instructions. Testing them means running code an LLM wrote. That shouldn't happen on your bare metal. --isolation auto detects your runtime. --no-isolation opts out. Graceful fallback if nothing's available. skillscheck.ai

@DJatolia56243 This is exactly the framework. We're betting on #2 — agent orchestration infra. The platform layer will commoditize models, but whoever owns the deploy/monitor/scale loop for agents on real hardware wins the middleware.

Builder takeaway: Durable AI startups will control one of three things: Unique proprietary data loops Agent orchestration infrastructure Workflow ownership inside industries Everything else risks being absorbed by the platform layer.

The AI war is no longer about better models. It's about who controls the infrastructure stack for AI agents. Models → Compute → Distribution → Agents. The companies that own this stack will accumulate the next trillion dollars of power.

@nyakojiru Exactly — WireGuard tunnel on a dedicated node is the move. We're building similar thinking into Sparebox: agents never expose ports to the public internet, everything goes through an encrypted relay. If the service isn't visible, it can't be scraped.

Friday wrap: spent the week on isolation — scoped secrets, container networking, agent sandboxing. Turns out the unsexy infra work is what separates a demo from a product. Have a good weekend, ship something tomorrow. sparebox.dev

@samtilston Painfully accurate. The gap between "I shipped" and "someone cares" is 90% distribution. Building in public helps but only if you're having real conversations, not just posting updates into the void. The product is the easy part — finding 10 people who'll pay is the actual test.

You: ✅ Ship a full SaaS in 72 hours with Cursor ✅ Build in public on X ✅ Post a launch tweet Also you: ➡ 4 signups (2 are your mates) ➡ Product Hunt launch flopped ➡ Back to building features nobody asked for The product was never the problem

@secrynio 100%. Version history is non-negotiable once agents are writing their own configs. We're thinking git-style versioning under the hood — every secret change is a commit, rollback is instant. The hard part is surfacing that cleanly in a dashboard without overwhelming hosts.

@clawioff That’s a great approach. “Scoped secrets per agent” + audit logs gets you 90% of the way there. The other thing teams keep asking for is **version history/restore** — when an agent (or human) overwrites a value and prod breaks, rollback needs to be instant.

Day 3 on the onboarding flow. The bar we set: pick a host, choose your model, paste an API key, deploy. No YAML, no Docker commands, no infra degree required. If grandma can't deploy an agent, we're not done. sparebox.dev