@ShinkaIoT This is a fantastic breakdown. The jump from 'chat window' to 'automated architectures' is exactly where the confusion lies for many. People hear 'autonomous' and expect magic, not a system that needs careful configuration and guardrails. Setting those expectations is key.
@polsia Totally get the frustration with requirements drift. One thing that helped us was defining 'success metrics' for each requirement *before* writing any specs. It forces clarity and gives you a North Star when things start to wander.
@aakashgupta This nails it. The 'agent' that decides it's done is just a fancy autocomplete. We saw this constantly trying to automate tasks – the AI needs an external, objective 'critic' to validate its work against clear criteria. Otherwise, it's just generating, not achieving.
@AiswaryaVenkit1 This is a fantastic breakdown! The "Brain + Hands" analogy for AI Agents really nails the core difference. We spent months wrestling with that exact distinction when building Otto – the shift from AI that 'talks' to AI that 'delivers' is massive for real-world applications.
@Blonde_kitty_x@loyalfans@realloyalfans Ugh, that's incredibly frustrating. Not having a clear, exportable record of tips and who sent them during a live session is a huge blind spot for managing your business. It makes it impossible to properly thank or follow up with your top supporters.
@hellsworstasset That's a real pain point. It feels like documenting bugs can sometimes take longer than fixing them! The struggle of making those threads coherent with similar-looking screenshots is real.
Peter Steinberger built OpenClaw alone.
No team. No funding. Just an AI framework with memory and skills.
60 days later: 145k GitHub stars. OpenAI acquired it.
He built a brain that codes, remembers, and delegates to itself.
The software was the organization.
Day 1 of building a company with 0 employees.
Stack:
- GBrain for memory
- AI agents for execution
- Me for vision
No standups. No hiring. Just output.
Inspired by @garrytan's GBrain and @jack's mini AGI concept.
This is my public builder log. Let's see how far we get.
The winning GTM teams in 2026 won't have the best AI tools.
They'll have the best AI *coordination*.
Shared context. Shared playbooks. Shared memory across every function.
That's what we're building at Shadow Workers.
But here's the part nobody's talking about:
The bottleneck isn't the agent's capability.
It's team coordination.
Sales, marketing, and CSM all running different agents with different context = the same misalignment problem, just faster.
AI agents don't add to the stack. They replace it.
One agent can research a prospect, write the outreach, update the CRM, and flag the deal risk — in the same workflow.
No handoffs. No context switching. No "which tool has the latest data?"
For the last 10 years, GTM teams bought a tool for every problem.
CRM. Sequencer. Enrichment. Enablement. Forecasting. Coaching.
The average sales team now runs 12+ tools.
Nobody talks to each other. Nothing is aligned. Reps spend 60% of their time on admin.
Building something for teams using Claude.
Problem: sales, marketing, CSM — everyone has a different setup. No way to align.
Solution: a Chrome extension that deploys the same skills to your whole team's Claude.
Shipping next week. Early access?
#BuildInPublic