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- 📈 Supervised Learning: The Foundation of Predictive Modeling
📈 Supervised Learning: The Foundation of Predictive Modeling
🦾Plus: 💼 OpenAI Testing ChatGPT Jobs
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Hey folks! Let’s get into Big Data and AI craziness…
In today's edition: What's Shaping the Future of Data?
🏗️Modern Data Engineering Services in 2026
🏥 OpenAI Rolls Out Hospital-Ready Healthcare Platform
📊 Artificial Analysis Shifts Benchmarks to Agentic & Coding Tasks
🧪 MIT & Sakana AI Study LLMs Evolving Attacking Programs
💰 a16z Raised $15Bn With $7Bn For Growth Investments
💡 AI Tutorial:How to practice case interviews with feedback using Claude
🤖 AI Tools and Data Tools to checkout
Supervised learning is widely regarded as the foundation of predictive modeling in machine learning. But why? At its core, it is a learning paradigm in which a model is trained on labeled data — examples where both the input features and the correct outputs (ground truth) are known. By learning from these labeled examples, the model can make accurate predictions on new, unseen data.
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Traditional data engineering focused on structured data movement using rigid batch ETL processes. However, this approach no longer meets the needs of dynamic enterprises. In 2026, organizations are adopting flexible, event-driven data architectures.
Healthcare teams already experiment with general LLMs, but regulation, reliability, and integration slow adoption. The key change here is that OpenAI evaluates these models on real medical tasks, not generic QA tests. Healthcare work creates a specific problem: clinicians need fast synthesis of evidence and policies, but tools must operate inside strict compliance boundaries.
New year, new Artificial Analysis Intelligence Index! Announcing Intelligence Index v4.0: incorporating 3 new evaluations, further aligning to real-word use and reducing saturation
Large language models (LLMs) are increasingly being used to evolve solutions to problems in many domains, in a process inspired by biological evolution. However, unlike biological evolution, most LLM-evolution frameworks are formulated as static optimization problems, overlooking the open-ended adversarial dynamics that characterize real-world evolutionary processes.
Q4 is the perfect window to turn this year’s numbers into a clear, actionable forecast aligned with your goals. Set your business up for a stronger 2026 with BELAY’s new guide.
👨💻 Data Tools, Libraries
docker-pgautoupgrade
A PostgreSQL Docker container that automatically upgrades your database.
Lets-Plot
An open-source plotting library for statistical data.
pg_embedding
Hierarchical Navigable Small World (HNSW) algorithm for vector similarity search in PostgreSQL.
dt
duct tape for your unix pipes.
AI News:
OpenAI is supposedly developing a dedicated job search agent inside ChatGPT. The internal feature looks like it’ll offer resume tips, role matching, and career guidance in one place.
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Venture capital firm Andreessen Horowitz (a16z) has raised $15 billion to bolster bets across an array of areas including infrastructure, health and what co-founder Ben Horowitz calls “American Dynamism.” The bulk of the capital, $6.75 billion, will go toward a growth fund and two $1.7 billion funds, with one focused on apps and the other on infrastructure.
Another win for Anthropic. The AI research lab announced a deal with German global insurance conglomerate Allianz to bring “responsible AI” via its large language models to the insurance industry. The financial terms of the deal weren’t disclosed.
OpenAI and SoftBank announced plans to invest $1 billion in SB Energy as part of a strategic partnership that will support OpenAI’s artificial intelligence infrastructure buildout. The partnership is part of the $500 billion Stargate commitment between OpenAI, SoftBank, and Oracle and will result in SB Energy building and operating OpenAI’s 1.2 gigawatt data center site in Texas.
Meta might be one of the largest corporate buyers of nuclear power in American history. The tech giant just signed a series of multi-gigawatt nuclear energy contracts to power its sprawling network of artificial intelligence data centers that could supply more than 6 gigawatts of electricity – enough to power a city of roughly 5 million homes.
AI Tutorial
How to practice case interviews with feedback using Claude
Upload your case study so Claude can extract all key data. (Enable ‘Extended Thinking’ for deeper analysis ).
Tell Claude your goal and ask it to build a spreadsheet model with formulas and core frameworks.
Sample Prompt: I'm practicing for consulting interviews. Set up this practice scenario for me using the case I've uploaded, extract the data into a spreadsheet model with formulas and frameworks. Then, tell me what questions I should focus on. After I finish my analysis and write my recommendation, I'll share it for feedback. Review it like a senior partner would: check my numbers, evaluate my logic, and tell me specifically what needs improvement.
Let Claude generate your practice guide: key questions, required analyses, and what to focus on.
Once done, ask follow-ups to refine your approach, compare alternative solutions, dive into specific frameworks, or request a tighter review of your work.
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Have cool resources to share? Submit AI tool
A.I. Generated Image of the Day
👀 The Kitchen Was
a Continent
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