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I tried to make an LLM fail on tabular data. It didn’t, sort of!
8+ hour, 45+ min ago (172+ words) A tabular foundation model, a gradient-boosted classic and a frontier LLM walk into a changing table. Only one of them needed no …...
You Don’t Need to Train a Model to Forecast Your Own Data Anymore.
15+ hour, 4+ min ago (266+ words) Two free foundation models forecast any time series with no training. Both run on a laptop CPU. Here’s how to run each one, plus how to…Continue reading on Medium » You Don’t Need to Train a Model to Forecast Your…...
I Checked Whether My Models’ 90% Confidence Actually Meant 90%. Most Didn’t.
21+ hour, 48+ min ago (31+ words) Another fully real one — no composite scenario. Every number below came out of an actual script run just now …...
Article 21 — Where Motion Is Mistaken for Movement
1+ day, 11+ hour ago (901+ words) Last time, we reached a point where problems were no longer hidden or misunderstood, but fully visible, repeatedly discussed, and quietly accepted, mostly because fixing them required more coordination than living with them. This time, we didn’t accept the problem....
The LLM security failure that doesn't raise an error is the one that costs you
1+ day, 16+ hour ago (438+ words) I am calling these soft failures. They are the expensive kind, because your monitoring never sees them. Here is how I think about them, and how I test for them. A hard failure breaks a contract. The model says no,…...
I Asked AI How to Make More Money. The Answer Wasn’t What I Expected.
1+ day, 15+ hour ago (955+ words) I opened ChatGPT with a simple question: “How can I make more money?” I expected the usual answers. Start a YouTube …...
Why Your Machine Learning Model Struggles to Predict Stock Prices
1+ day, 15+ hour ago (32+ words) You build a Random Forest model. You train it on historical stock data with features like breakout levels, order blocks, CVD. Your …...
Our Recall Was 0.087 and the Model Was Innocent: How Domain-Scoped Replay Doubled It
1+ day, 23+ hour ago (645+ words) CauterRule is an open-source sidecar that learns standing rules from repeated agent failures. It extracts lessons from trajectories, replay-tests them, and tries to separate reusable guidance from noisy overgeneralization. For two field tests, one number refused to move: golden recall…...
Applied Sciences, Vol. 16, Pages 8993: CDTS2: Causal Downstreamer with Causally Disentangled Trend and Seasonality Time-Series Representations
3+ day, 18+ hour ago (411+ words) Time-series representation learning decomposes signals into interpretable factors such as trend and seasonality, and disentangled representation learning assigns these factors to distinct latent dimensions, enhancing interpretability and forecasting accuracy. However, existing methods achieve only statistical disentanglement: an intervention on one…...
AI Integration Without the Hype: A Practical Framework for Adding ML to Systems That Already Work
4+ day, 3+ hour ago (332+ words) Most "AI integration" content falls into one of two camps: breathless hype about transforming your business overnight, or dense academic papers about model architecture. Neither helps you decide whether the customer support ticket triage your team keeps complaining about is…...