Can Machine Learning Predict the World Cup?
With FIFA set to kick off on Thursday, June 11, 2026, the opening match at the Mexico City Stadium, I think it would be fun to build the best ML model we can to predict match outcomes. To do this, I have brought together several databases—49,000 matches—with data on Elo ratings, match results, and cup […]
ClawHub Security Signals: A Coding Guide to End-to-End Security Signal Analysis and Verdict Classification on the AI Skills Dataset
TEXT_COL = “skill_md_content” NUM_COLS = [“skillspector_score”, “static_finding_count”, “skillspector_issue_count”, “virustotal_malicious_count”] TARGET = “clawscan_verdict” def prep(df): out = df.copy() out[TEXT_COL] = out[TEXT_COL].fillna(“”).astype(str).str.slice(0, 6000) for c in NUM_COLS: out[c] = pd.to_numeric(out[c], errors=”coerce”) return out train_p, test_p = prep(train_df), prep(test_df) get_text = FunctionTransformer(lambda X: X[TEXT_COL].values, validate=False) text_pipe = Pipeline([ (“select”, get_text), (“tfidf”, TfidfVectorizer(max_features=20000, ngram_range=(1,2), min_df=3, sublinear_tf=True)), ]) num_pipe = […]
Increase Recommendation Systems’ Precision with LLMs, Using Python
in American culture is the following: “You can’t have your cake and eat it too.” I find this sentence extremely poetic but also very practical and useful. The message of this saying is straightforward: everything you accomplish is achieved through a tradeoff, as everything has a price. The philosophical discussion is out of scope for […]
Xiaomi MiMo and TileRT Push a 1-Trillion-Parameter Model Past 1000 Tokens Per Second on Commodity GPUs
Inference speed is becoming a competitive metric for large language models. Xiaomi’s MiMo team just released MiMo-V2.5-Pro-UltraSpeed, built in collaboration with the TileRT systems group. It decodes faster than 1000 tokens per second on a 1-trillion-parameter model. Xiaomi team describes this as a first at trillion-parameter scale. Demos show generation peaks near 1200 tokens per […]
How to Keep Quantum Information Alive for Machine Learning
How errors arise in classical and quantum systems Why quantum information is fundamentally fragile Modelling quantum errors through channels and noise The three fundamental quantum errors: X, Y and Z The dilemma of measuring versus detecting quantum errors A first intuition for stabiliser codes Modern machine learning systems perform an extraordinary number of operations every […]
The Practitioner’s Guide to AgentOps
# research_agent.py # Purpose: A research agent with full AgentOps instrumentation. # Every session is logged, replayed, and cost-tracked in the AgentOps dashboard. # # Prerequisites: # pip install agentops anthropic python-dotenv # # Environment variables required (in .env): # AGENTOPS_API_KEY — from https://app.agentops.ai # ANTHROPIC_API_KEY — from https://console.anthropic.com # # How to run: # […]
4 New Techniques to Maximize Claude Code
, I’ll cover some of the newest techniques that I’ve developed and am actively using whenever I code with Claude Code and Codex. Both of these are excellent coding models that I’m using every single day when I program. They’re great models out of the box; however, if you do it correctly, you can get […]
Sequential Fitting: A Different Perspective on the Spectral Bias of Neural Networks
by Conor Rowan and Finn Murphy-Blanchard Introduction with complex tasks such as image classification [1], autonomy [2], and language modeling [3], neural networks are spectacularly good at fitting high-dimensional, nonlinear functions from data. In fact, neural networks have such robust representational capabilities that they can achieve zero training error on images with randomized class labels, […]
The Polynomial That Fixed 30 Years of Cloth Simulation
in the code of almost every 3D animation pipeline ever built. It shows up when a character’s sleeve passes through their own torso, when a skirt clips through a leg mid-walk, when a simulated tablecloth phases through the edge of a table like it’s made of light. High-end studios spend thousands of artist hours hunting […]
Aviva deploys AI to stop £230M in sophisticated insurance fraud
Aviva has uncovered a record £230 million in insurance fraud claims and is using AI tools to counter the growing problem. The battleground has changed, and the culprits are also coming armed with a new generation of tools. We’re now in an environment where AI is being used not just to defend against fraud, but […]