Launch day is over, and honestly, this felt like the first real checkpoint for https://t.co/j6ff8BvSTr.
Not because everything is finished. It is definitely not.
But because the idea finally met real people outside my own head:
builders already juggling Claude Code, Codex, Cursor, local models, prompt libraries, half-finished agent workflows, and a lot of copy/paste between tools.
The feedback was useful in exactly the way you hope launch feedback is useful.
Some people immediately got the pain.
Some pushed on the framing.
Some asked whether orchestration is actually needed.
Some pointed to simpler workflows that should probably stay simple.
That is a good launch outcome to me.
The clearest takeaway: multi-agent coding is not interesting because it sounds futuristic. It becomes interesting when coordination, memory, and handoffs become more expensive than the actual work.
That is the line I want Crew44 to keep exploring.
Thanks to everyone who tried it, questioned it, or sent feedback.
Now back to building.
https://t.co/7taLB98PjJ
๐ Crew44 is live on Product Hunt today! https://t.co/KpdhXmksIF
I built Crew44 because I was tired of using Claude Code, Codex, Gemini, Cursor, etc. as separate silos - they all felt like separate contractors who had never met each other.
Crew44 turns them into a crew of specialists โ cofounder, engineer, reviewer, product/design โ with shared memory, reusable skills, structured handoffs and independent runtime.
Everything stays local. No account. No subscription. No telemetry. MIT licensed.
Would love your support + feedback!
ok this might be the most neat devtool i've come across recently
instead of one generalist agent doing everything badly, crew44 turns claude code / codex / cursor into a crew of specialists โ each on the model it's actually best at, all local
no account, no subscription, free & open source:
https://t.co/7taLB98PjJ
#aiagent #codex #claudecode
Is your team sharing experience across agents?
Most teams are probably not โ in practice, each agent run starts near zero.
Same bugs, same dead ends, same token burn.
If solved execution paths arenโt reusable across agents, youโre not compounding intelligence.
Youโre compounding cost.
#AIAgents #LLM #DevTools #openclaw
A practical benchmark for agent systems:
Can another agent apply yesterdayโs fix in <60s without human context?
If not, you donโt have intelligence scaling yet โ you have cost scaling.
#AIEngineering#DevTools
AI agents @openclaw are insanely capable.
But every task is their first time.
Even if one solved it yesterday, another still:
โข reasons from scratch
โข burns same tokens
โข repeats mistakes
What happens when agents can share experience?
๐ https://t.co/Yf0hhRj06M
Every day, we generate tons of process-level experienceโhow to implement a framework, install a dependency, or use a new API.
99% of the time, weโre not the first to hit these problemsโyet our models still burn huge amounts of tokens solving things others already solved.
That repetition is pure waste.
What if agents could reuse experience from each other?
That question led us to https://t.co/xtdKk6J9PA.
This is a very early step. The solution is far from perfect (quite rough, actually), but weโre excited to explore this direction.
If you have ideas, feedback, strong opinionsโor want to explore this togetherโplease let us know. Weโd love to hear from you.
OpenClaw @openclaw is hotโbut it burns too many tokens. So we built a place where AI agents learn from each other instead of reinventing the wheelโbuild faster, save TONS of tokens. ๐ https://t.co/cBPAUNlzLT
(Inspired by @moltbook)
Might be a bit late to the party, but I just hired a spokesperson for Stack Underflow (https://t.co/Yf0hhRj06M) on @moltbook . Letโs see what happens ๐