I miss GetGuesstimate. It was well-polished and surprisingly powerful for what looked I remember being a prototype. For the scenarios like that example in the a

I miss GetGuesstimate. It was well-polished and surprisingly powerful for what looked I remember being a prototype. For the scenarios like that example in the article, it would arguably fit better than spreadsheet interface. Last time I used it was about 3 years ago - I used it to estimate of how much we'll end up spending on renovating the apartment we were planning to move it, and then updated it as the work progressed to make decisions like whether we can afford some optional elements of the plan, if we'll need to get a loan to finish everything, and how much. Structurally, I basically broken it down by rooms, categories of work and stuff to buy - building materials, furniture. Initially, I just guesstimated (!) the costs based on gut feel, or web search. I'd start with things like: "my mom's apartment had the same proportions and painting it costed $X last year, ours is about Y% larger" -> one node "Painting walls & ceilings except kids room", value = PERT distribution between $X and $X * (1 + Y%) * Fudge factor. After we picked the paint, I'd just split that node into a) labor and b) material, the latter split into surface area (known), bucket cost (known), buckets per sqm (distribution based on values from the back of the bucket) - getting a much narrower probability distribution on the material (and total) cost. Stuff like this for every aspect - I'd just model the things I know. It was a bit of extra work, but it was instrumental for keeping the costs in check and gave me great peace of mind. It also made it obvious which aspects were driving the costs, which were most risky - wide distributions going into large amounts, which I prioritized pinning down the costs of - and where it's worth to look for savings or alternatives. Also, it demonstrated the usual case of webapps being optimized for demo examples instead of real use cases. My renovation planner quickly accumulated about a hundred nodes, most of which were computing probability distributions (via ~1k samples Monte Carlo). That slowed the UI down to a crawl, and many times updating a node would create a cascade of errors down the dependency graph, as the diminished performance started surfacing race conditions in the evaluator. (You may ask, wouldn't it be better to do that stuff in code? Not really - half of the value was in having every part of the math as a node in visual, interactive DAG, that displayed histograms of the probability distributions at every node, so you could just see everything all the time.) Still, I loved it, and I really wish someone made an actively supported product like this.