$ONDS reports earnings before the bell tomorrow morning. The implied move for the stock heading into earnings is +/- 10.91% as of right now. Wall St. is expecting quarterly numbers of: - Adjusted EPS of ($0.10) - Revenue of $67.3 million The company will hold a conference call tomorrow at 8:30am ET. Check out $ONDU, the Tradr 2X Long ONDS Daily ETF. It’s a strategy that seeks 200% the daily performance of @OndasHoldings. Leveraged ETFs involve significant risk.

Weekend Market Review: 66 Stocks + 12 Dr. Cat Videos, My Weekly Insights x 2 , Bullish List & Buy Orders – August 17, 2026 Weekend Market Review Date: August 17, 2026 1. 66 Stocks Covered This Weekend NBIS, AEHR, LITE, AAOI, MRVL, ZETA, ABCL, ALAB, AXTI, RKLB, CRDO, CRWV, ASTS, PL, QCOM, TSLA, PLTR, NVDA, AMD, AVGO, TSM, SMH, SOFI, HOOD, SHOP, AFRM, NOW, TEAM, DELL, RBRK, TE, BB, INTC, P, ARM, HIMS, OSCR, NOK, NAK, SPCX, AAPL, AMZN, META, MSFT, GOOGL, NFLX, IREN, CIFR, COIN, MU, SNDK, RDDT, TMC, ONDS, LMND, OUST Major ETFs / Indexes QQQ, SPY, IWM, TNA, DRAM, IGV 2. Dr. Cat’s 12 Video Insights Core Stock: TSLA Software: NOW, TEAM Trending Stock: ABCL SPACE: SPCX and SATL Fintech: SOFI, HOOD and SHOP AI stocks: ALAB and CRDO Big Tech: NFLX, GOOGL, META and MSFT Core Stock: PLTR Crypto related Stocks: IREN and COIN Semiconductors: NVDA, AVGO and AMD Trending AI Stocks: NBIS, AAOI, AEHR, MRVL and LITE Health: OSCR, HIMS and LLY 3. My Weekly Insights x 2 4. Weekend Bullish List 5. My Buy Orders Disclaimer I do not provide any financial advice on buying or selling. The strongest bullish and buy signals are clearly highlighted in the “MUST-READ” posts—their titles speak for themselves. Please take the time to review them carefully. As a reminder: never blindly follow anyone else’s trades or investments. Every investor has different levels of conviction, risk tolerance, and time horizon. My own time frame is a minimum of 3–10 years, and I am fully prepared to handle volatility of 80% or more. @cantonmeow @redfoxryder

Fwiw $BUN mcap on dexscreener / gecko are wrong. Only 20% float rn since ~80% supply is permanently locked So real mcap is 0.2 x price, which currently equals ~$16M instead of $22M on DS has the right numbers

You're in an ML Engineer interview at Apple. The interviewer asks: "Two models are 88% accurate. - Model A is 89% confident. - Model B is 99% confident. Which one would you pick?" You: "Any would work since both have same accuracy." Interview over. Here's what you missed: Modern neural networks can be misleading. They are overconfident in their predictions. For instance, I saw an experiment that used the CIFAR-100 dataset to compare LeNet with ResNet. LeNet produced: - Accuracy = ~0.55 - Average confidence = ~0.54 ResNet produced: - Accuracy = ~0.7 - Average confidence = ~0.9 Despite being more accurate, the ResNet model is overconfident in its predictions. While the model thinks it's 90% confident in its predictions, in reality, it only turns out to be 70% accurate. Calibration solves this. A model is calibrated if the predicted probabilities align with the actual outcomes. For instance, say a model predicts an event with a 70% probability. Then, ideally, out of 100 such predictions, ~70 should result in the event. Handling this is important because the model will be used in decision-making. In fact, an overly confident that is not equally accurate model can be highly misleading. To exemplify, say a government hospital wants to conduct an expensive medical test on patients. To ensure that the govt. funding is used optimally, a reliable probability estimate can help the doctors make this decision. If the model isn't calibrated, it will produce overly confident predictions. Reliability Diagrams are a visual way to inspect how well the model is currently calibrated. More specifically, this diagram plots the expected sample accuracy as a function of the corresponding confidence value (softmax) output by the model. If the model is perfectly calibrated, then the diagram should look like the identity function. That said, it is often also useful to compute a scalar value that measures the amount of miscalibration, called expected calibration error (ECE). One way to approximate the expected calibration error shown above is by partitioning predictions into equally spaced bins and taking a weighted average of the bins’ accuracy/confidence difference. These are some common techniques to calibrate ML models: > For binary classification models: - Histogram binning - Isotonic regression - Platt scaling > For multiclass classification models: - Binning methods - Matrix and vector scaling 👉 If you care about probabilities and both models are operationally similar, which model would you prefer? ____ Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.