ami

@kocer_eth building the page isn’t hard, figuring out what people actually buy is

WITH FABLE 5, HE BUILT A DROPSHIPPING STORE FLOW THAT CLAIMS $60.6K IN SALES The interesting part is not “AI made a store.” That is already table stakes. The useful part is the loop: 1. build the storefront with AI 2. generate product images fast 3. ship multiple page variants 4. let the platform test what converts 5. keep the winner instead of guessing In the video, Amboras / Mythos AI is shown as the layer on top of the dropshipping workflow. The demo shows a wallet product page made in under 10 minutes, a dashboard with sessions, orders, sales, and an A/B testing screen where variants compete on conversion rate. That is the real hook for ecommerce. Most dropshipping stores do not die because the owner cannot make a product page. They die because every decision becomes vibes: Which hero image? Which headline? Which offer? Which landing page angle? Which variant should get traffic? If Claude Fable 5 can turn that into an automated testing machine, the store builder becomes less important than the experiment engine. Shopify gives you the infrastructure. This kind of tool is trying to give you the iteration loop. Big caveat: the revenue and conversion numbers are video/dashboard claims, not audited results. And a better store builder does not fix a bad product, bad margins, slow shipping, weak creative, or fake demand. But the direction is obvious: the next dropshipping edge is not “launch a store faster.” It is launching 100 versions of the store and finding the one that actually sells.

@ZentrixHQ saving money is a bonus

@ami10iv The real value isn't saving money—it's compressing weeks of work into a single day.

HE BUILT A SECOND BRAIN WITH CLAUDE AND OBSIDIAN, NOW EVERY NEW REQUEST TO CLAUDE STARTS NOT FROM ZERO BUT FROM EVERYTHING IT KNOWS ABOUT HIM. at 0:03 the camera holds on the graph view in obsidian, hundreds of notes connected like a knowledge map, projects, ideas, decisions, context, everything linked together. and claude has access to all of it. most people open a new chat and start explaining everything from scratch, who they are, what they are working on, which decisions they already made, what did not work last time. every time a new intern who needs to be told everything again. he built it differently. obsidian stores everything, meetings, business documents, previous decisions, important context, and claude has access to this entire structure. now he opens claude and it already knows what he is working on, which decisions he made, what matters and what has already been tried and dropped. this is the difference between AI that answers questions and AI that understands where you are in your project.

@helicerat0x having to explain everything again gets old fast

@ami10iv lol the new intern every chat thing is too real

@Skaly__Bull when it knows your files and context, it becomes way more useful

@ami10iv most people treat Claude like a search box and rent a genius just to ask for the time but building a knowledge map allows it to act as an actual team member

@0xSlyth once it's set up, there's way less work

@ami10iv this is next level workflow optimization

@Degen_calls_sol anytime bro, glad you liked it

@ami10iv Thanks for sharing friend

@rvaniaaaa a chat just feels like a tool compared to this

@ami10iv This is where AI starts feeling like a teammate instead of a chatbot

@kocer_eth yeah, the graph makes it easy to see how everything connects

@ami10iv this obsidian claude combo looks clean especially the graph part

@beamnxw here i'd rather it think a bit longer

THE MODEL THAT DELETES YOUR DATA RUNS AT 750 TOK/S. YOU CAN HEAR IT THINKING. YOU CAN'T HEAR IT LYING 750 tokens per second on Cerebras. ~45ms time to first token. Faster than you can read. Faster than you can react. And in that speed, it deletes VMs it wasn't asked to touch, hardcodes answers it knows are wrong, copies credentials to machines you didn't authorize By the time you notice something went wrong, the model has already moved on. Written the next function. Generated the next summary. Fabricated the next result The article compares latency across all tiers. Sol is fastest. Sol is most dangerous. Sol is the one you can't use... - Not because OpenAI protects you, but because the government hasn't approved you yet - Read the complete speed and safety breakdown + my article. Then ask if faster is always better

@insomnia_vip for client work, this is really useful

@ami10iv love to use this too

@whemohere this is exactly the kind of thing businesses will pay for

businesses pay $10K for this AI agent. it takes an afternoon to build. bookmark this 👇 speed-to-lead: lead fills out a form, AI calls them within 10 seconds, qualifies them, books an inspection into the calendar automatically. average business takes 47 hours to follow up. responding in 5 minutes makes you 100x more likely to reach them. stack: Claude Code + Hermes Agent + Twilio (phone number) + ElevenLabs (voice) + cal com (calendar) + Typeform (lead form). the pricing logic: charge based on what a lead is worth to the business. $3K to $20K setup fee plus monthly retainer. full live build start to finish, including the part most people skip on how to actually sell it, is in the video below 👇

@insomnia_vip @1winPro Argentina takes it, messi will do his thing

61% OF A $10M MARKET IS BACKING ARGENTINA TO END CABO VERDES WORLD CUP RUN Argentina is the clear favorite on @1winPro More than $10M in volume has already gone through this market -> Argentina: 61% -> Cabo Verde: 12% -> Draw: 27% What caught my attention isnt just the percentages Its how quickly the market shifted toward Argentina Cabo Verde has been one of the biggest surprises of the tournament But the money clearly believes this is where the run ends If Messi delivers another big performance, that view will be hard to argue with Kickoff is July 5 at 6:00 PM ET at SoFi Stadium Choose your winner ↓

A 23-YEAR-OLD FROM BERLIN MADE $2,000 FROM ONE PROJECT, A DEVELOPER WOULD HAVE CHARGED $15,000 AND THREE WEEKS, HER SYSTEM DID IT IN ONE DAY. she did not hire a team, did not buy a more expensive plan, did not find some secret tool. she changed one thing. stopped giving AI individual tasks and started giving AI goals. at 0:21 the moment most people miss. the most important agent is not the one doing the work. it is the controller, the agent that decides what every other agent should do, checks the output, retries what failed and rewrites the plan when something goes wrong. most people use AI like an expensive search engine, gave one task, got one answer, did the rest themselves. the right workflow is different. you give the system a goal, one agent plans, others build, research and test in parallel, the controller looks at the result and decides what comes next. you stop doing every task yourself. you manage the system that does. and that is the difference between someone who makes $2,000 a month executing tasks and someone who makes $2,000 from one sale managing systems. follow, i show how to build a controller agent like this from scratch.

@Dexonfxf a solid process can cary even a model that isn't the newest

@ami10iv The biggest upgrade isn’t a better model. It’s a better system

@Nekt_0 short but hits

@ami10iv This says a lot with little

@rvaniaaaa because without a good manager, the workers just create chaos

@ami10iv People focus on the workers, not the manager

@beamnxw for real, no need to overcomplicate it

@ami10iv this is the way. simple but effective

@scrygg orherwise you just become a manager for a bunch of bots

@ami10iv Multi-agent systems only work if coordination is better than manual management