HyperTrade retweeted
Introducing Kled Labs.
Every major AI model learned from the internet. We're betting on people instead.
One goal: train our own models to beat closed models at important human tasks, using our data alone.
All data is sourced from our data application layer and our network of 600,000+ contributors.
We're hiring: jobs.ashbyhq.com/kled-ai/6e9…
HyperTrade retweeted
This is Kled V3. We've solved data collection for artificial general intelligence.
Any consumer dataset that can exist, can now be collected from physical reality in under 72 hours.
All powered by the largest and most comprehensive data application layer on the planet. (Thread)
HyperTrade retweeted
Brain storming with the team on the best ways to incorporate x402 into our current infra
HyperTrade retweeted
💰𝐓𝐨𝐦𝐨𝐫𝐫𝐨𝐰
@MigrateFun Block Party
Steady Lads, Deploying Migrations
See you in the morning! 🤝🔁🤝
xitter.cf/i/spaces/1RDGlAbAzbmJL
HyperTrade retweeted
Welcome @HypertraderAI (formerly Modern Stoic) who will be an initial platform launch partner during our first migration wave!
We are excited to set a new standard for migrations with your community and participation 🔄
HyperTrade retweeted
🔥 New Migrate.fun partners
$Kitty ( @HKittyOnSol) will be migrating to @bonkfun.
$STOIC ( @ModernStoicAI) chose the same route. Stoic once had a 15m mc, now at 250k. Could pump due to migration. There could be investing chances in things like this.
$HUSTLE 🚀
HyperTrade retweeted
Been chatting with @StoicAdii, the founder of @ModernStoicAI, the levels of grit and determination he’s had will see this project rise above $100mil MC.
We’ve already tested several algorithms that are profitable, as these enable functional live trading it will change the game!
Extremely excited to hear about the success of @AgentHustleAI & @MigrateFun after the recent migration!
Stoic migration is coming soon 😉
HyperTrade retweeted
Would love for you to check us out Tom!
We are building a blazing fast platfrom that builds, backtests & automates trading strategies (Beta)!
One thing that can help stop sniper attacks is having an onchain platform that allows for copying trading (vaults) exclusively for their holders since execution would happen in parallel for the vault subscribers!
This is what we are building at Stoic. Users will be able to subscribe to the Vaults of profitable traders, in return, traders receive a cut from the winnings of the vault thus encouraging them to continue to be profitable.
HyperTrade retweeted
Wait till you see @ModernStoicAI is automating strategies to be deployed on HL with 0 code for retail users with ultra fast execution!
HyperTrade retweeted
I said it a while back, and I’ll say it again, do not sleep on this team! $Stoic
RT @StoicAdii: Our beta will be going out public early next week!
You will be able build complete trading systems with 0 code, in additio…
This quoted post is unavailable.
HyperTrade retweeted
Everyday, the progress Stoic team is making is crazy!
In just 4 weeks, we have been able to revamp, redesign & redeploy the Stoic AI beta. (Public access soon)
Stoic is the only platform of its kind.
It will allow users to create trading models, backtest them, compare vs other top models, subscribe to other top trading models & automate them!
Let stoic trade for you! @ModernStoicAI
HyperTrade retweeted
The progress we have made in the past 4 months pales in comparison to what we have accomplished the in past 1 week
Last 4 months of having been extremely difficult, constant blockers, lots of mistakes, lots of lessons
But we stayed $Stoic
Accelerate 🚀
Tech Update: Strategy Backtesting Service
We’ve implemented a service that enables automated strategy backtesting using FastAPI and cloud-native infrastructure. Here’s a high-level overview of the flow:
•The Stoic backend interacts with an LLM API to generate a trading strategy.
•This strategy is saved to a Google Cloud bucket.
•The Stoic backend then triggers the backtesting service by referencing the strategy name.
•Requests are routed via auto-scaling (either to an existing instance or by spinning up a new one).
•Each instance maintains an internal queue; incoming requests create a Redis job and dispatch a worker.
•The worker downloads the strategy, configures Freqtrade, runs the backtest, and updates Redis with the result.
•The backend polls for job completion and returns the backtest results to the user.
This design ensures scalability, fault tolerance, and efficient resource use.
@StoicAdii
HyperTrade retweeted
Jarvis team deserves all of the respect for not quitting & continuing to grind! Best of luck on @colosseum hackathon!
@jarvis_fun
