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@jumperz • 14,774 subscribers

UX UI designer , most of what you will find here is about tech, ai & Web3 advisor @CrunchDAO

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Anya Rossi

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this was made from a single prompt btw…yeah, no wonder kimi k3 is sitting at #1 for frontend. sure, i could’ve spent more time polishing it, but it already nailed >the scroll pacing >oversized text reveals >smooth number transitions >layout shifts >color inversions and the way each section flows into the next. the whole thing moves like an actual designer touched it... pretty ridiculous for one prompt.

this was made from a single prompt btw…yeah, no wonder kimi k3 is sitting at #1 for frontend. sure, i could’ve spent more time polishing it, but it already nailed >the scroll pacing >oversized text reveals >smooth number transitions >layout shifts >color inversions and the way each section flows into the next. the whole thing moves like an actual designer touched it... pretty ridiculous for one prompt.

206,614 次观看 • 1 个月前

I refuse to believe that I built this website from a single prompt for $0.82 using DeepSeek V4 Flash 0731. > cost: $0.82 > 20.2M tokens > 310 requests for transparency, I built the prompt using GPT Sol... its currently the best model for writing a very detailed prompt still absolutely ridiculous...this is with 0 polish, btw.. I can’t imagine how good it would look if I actually polished it..

I refuse to believe that I built this website from a single prompt for $0.82 using DeepSeek V4 Flash 0731. > cost: $0.82 > 20.2M tokens > 310 requests for transparency, I built the prompt using GPT Sol... its currently the best model for writing a very detailed prompt still absolutely ridiculous...this is with 0 polish, btw.. I can’t imagine how good it would look if I actually polished it..

67,584 次观看 • 18 天前

since everyone woke up talking about loops, this article talks about domain chips, which i think are super underrated compared to loops.. a chip is a small specialist that's only good at one job QA, trading, content, research whatever ... and not one giant brain trying to do everything.. you wrap it in three things: >a workflow (same shape every time, so it's stable) >the tools that job needs >a benchmark that says what good looks what makes this special is the benchmark; without it, a loop just changes things randomly, when with it, the chip can score every pass and keep only what actually wins.. the problem with loops is they can spin 1000× and still not improve when a domain chip not only focuses on the domain you delegate to them, but they actually improve themselves with every spin. you can start with domain chips here this repo will help you build your first chip, the workflow, the benchmark pack, and the autoloop policy, with guardrails so it can't fool itself: below you can read the article fully + i put a good example of the difference between having a normal loop and a domain chip..

since everyone woke up talking about loops, this article talks about domain chips, which i think are super underrated compared to loops.. a chip is a small specialist that's only good at one job QA, trading, content, research whatever ... and not one giant brain trying to do everything.. you wrap it in three things: >a workflow (same shape every time, so it's stable) >the tools that job needs >a benchmark that says what good looks what makes this special is the benchmark; without it, a loop just changes things randomly, when with it, the chip can score every pass and keep only what actually wins.. the problem with loops is they can spin 1000× and still not improve when a domain chip not only focuses on the domain you delegate to them, but they actually improve themselves with every spin. you can start with domain chips here this repo will help you build your first chip, the workflow, the benchmark pack, and the autoloop policy, with guardrails so it can't fool itself: below you can read the article fully + i put a good example of the difference between having a normal loop and a domain chip..

36,559 次观看 • 2 个月前

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.

34,181 次观看 • 6 个月前

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