You missed the setup.
Not because it wasn't there.
Because you were asleep.
The market doesn't care about your schedule.
That's why automation exists.
LyonX Labs develops AI-powered trading systems designed to monitor opportunities, follow rules, and execute without hesitation.
New multicenter 🇪🇸 study: interpretable #MachineLearning models improve survival prediction in extremely preterm infants and outperform CRIB I/II using only birth variables
Thanks to Paula Sol and Paula Petrone for leading this outstanding work. 👏
... sciencedirect.com/science/ar…
Still 96% from ATH ($0.000012560). The asymmetry is real.
Quick alpha:
$QUBIC's approach to AI is unique:
Mining secures the network AND trains AI simultaneously.
→ No idle compute (every cycle is productive)
→ No single entity owns the models
→ Quality validated by Quorum, not one company
→ Open source from day one
This is what decentralized intelligence looks like.
@_Qubic_#QUBIC#Aigarth#Web3#MachineLearning
You'll remember this tweet.
🔗 Learn more: qubic.org | Not financial advice — DYOR.
If you're trying to break into AI Evals, this is one of the best resources I've come across.
I recently went through @HamelHusain ' s guide on Inspect AI and learned a lot about how real-world LLM evaluation systems are built.
Some things that stood out:
• What Inspect AI actually is and why many teams use it for evaluating AI systems
• The core concepts: Datasets, Solvers, and Scorers
• How to evaluate agents, tool calling, reasoning, and coding tasks
• Running reproducible benchmarks instead of relying on vibes and cherry-picked demos
• Logging and tracing model behavior to understand why systems fail
One thing became very clear:
Building AI products without evals is like building software without tests.
Most people focus on prompts, models, and agents. The strongest AI teams invest heavily in evaluation because that's how you know whether your system is actually improving.
Highly recommend this if you're building AI agents, RAG systems, or just want to understand how modern AI teams evaluate their applications.
hamel.dev/notes/llm/evals/in…#AI#AIEvals#LLM#AgenticAI#MachineLearning
Generation after generation, young Indians were given to believe that Sanskrit was a dead language. To be kept strictly within the confines of Sanskrit departments!
A belief gone wrong is a dangerous thing!
The proposal for the Dartmouth Research Project submitted by the young mathematician, John McCarthy, who taught at a liberal arts college, the Dartmouth College in New Hampshire, USA, had this to propose:
"to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."
Of course this hypothesis that a machine can think was only the beginning of the AI revolution that we are seeing today.
But the moot point is, we had a language given to us by our ancestors that basically worked like precise and accurate coding. Sanskrit is not a language in the usual sense, therefore. It is a model of intelligence. It reveals linguistic abstraction of how the human brain works. It is highly algorithmic. These facts have been established for quite sometime now.
But in the year 1956 when AI was coined in this workshop, we had already abandoned Sanskrit from the mainstream education and learning ecosystem, and sadly for political reasons.
While the language in itself would not produce cutting-edge science, but the logical and symbolic systems of Sanskrit engrained in our education system would have us as the frontier culture of innovation!
#AI#ArtificialIntelligence, #MachineLearning#GenerativeAI#Anthropic#ClaudeFable5#Bharatinnovates#IKS#decolonisation@narendramodi@PMOIndia@dpradhanbjp@EduMinOfIndia
Not every customer is the same, so why treat them the same?
Built SmartCart, an e-commerce customer segmentation project using unsupervised ML to uncover customer groups and drive smarter business strategies.
#MachineLearning#DataScience#Ecommerce