For good use and visualization of the timeline design - we’re providing 2 small tools - one for the Project Tasks - and one for Proposed Schedule: Linear calendars disguised as gsheet formulas. Video Preview The input data is the originar source of busyness - crowded calendar and too many commitments in too little time, no space to breathe. ...

SchedulerPro: Simple calendar editing in a spreadsheet The video unfolds as follows: Applying a filter for keyword: a spreadsheet action. We get a tiny subsection of the full schedule We add a column to compute a shift in project timeline of +2days And paste that colum back over the original timeline. Then delete it We apply to the calendar. Only the visible part of the table gets applied We changed our minds: it had bad schedule. So we wipe it out and tewak it again - and redeploy. Once edits are done (bringing past time blocks to this weekend) - we send to calendar

Video Preview SchedulerPro: simplest use case, illustrated. 1. Filling the project list Run the initialization for SchedulerPro - it creates the template tabs for the spreadsheet. Add Projects, tasks, timelines, estimates. Add priorities if they matter. 2. Run the scheduling engine Make sure you have the target calendar set in the Settings tab. ...

9. From HMC to NUTS: Automating the Hamiltonian In the previous post we visited the (simplified) version of a Bayesian estimator of the $GARCH(1,1)$ volatility model parameters distributions using the Hamiltonian Monte Carlo technique. We suggested then, that implementing the No U-Turn Sampler (NUTS) would provide an added bonus of better convergence and faster run times. This article serves as the second part of that series - and will number the sections accordingly. ...

1. The Model: GARCH(1,1) We assume a time series of returns $y_t$. We model them as: $$y_t = \sigma_t \epsilon_t \quad \text{where} \quad \epsilon_t \sim \mathcal{N}(0, 1)$$ The variance $\sigma_t^2$ evolves according to the GARCH(1,1) recursion: $$\sigma_t^2 = \omega + \alpha y_{t-1}^2 + \beta \sigma_{t-1}^2$$ Constraints for Stability: $\omega \gt 0, \alpha \ge 0, \beta \ge 0$ (Positivity) $\alpha + \beta \lt 1$ (Stationarity/Mean Reversion) 2. The Bayesian Objective (Log-Posterior) In HMC, we don’t just want the “best” parameters; we want to sample from the posterior distribution $P(\theta | y)$. By Bayes’ Theorem: $$\log P(\theta | y) = \log P(y | \theta) + \log P(\theta)$$ ...

Introduction We had several attempts at using AI—either for physics problems, for trading signals, or for general-purpose fuzzy automations. They worked well. We’ll skip those here. We put significant emphasis on understanding the inner workings of the systems we build—whatever “understanding” might mean algorithmically speaking (a concept that, between Charles Peirce and logicians, remains up in the air). We also admire the recent advances in LLMs, and we plan to deploy them whenever and wherever they are truly relevant. ...

She’s a street cat who understands human speech, negotiates company perks, and has a very specific corporate contingency plan if we ever run out of food.

Problem Statement Suppose we have busy lives - and some parts are bolted with calendar appointments. And that has nothing to do with growth - but mostly with maintenance. Some growth seeps through - barely to make a dent. Not to mention the sustained effort required to make an idea into a product. Now, suppose there’s two hours every day where you could work at the growth part - pushing the project forward, connecting all the disparate parts. ...