Choicease
AI-assisted structured decision-making tool
What it does
Choicease is an AI-assisted decision-making tool that helps you think through complex choices, compare options across what matters, and arrive at a clearly reasoned recommendation. Work through one factor at a time, set priorities, and evaluate options. Then see the trade-offs, key drivers, sensitivity, risks, confidence, what-if scenarios, alternative lenses, and robustness behind the result. Export your reasoning as a PDF or editable PowerPoint, and explore decisions in the Decision Gallery.
Work through complex choices one factor at a time. Choicease combines structured thinking and AI support to reveal trade-offs and a clearly reasoned recommendation.
Start by naming the decision. Then work through it one factor at a time. Consider widening the comparison. Three or more meaningful options often reveal a better path than an early either-or choice. You have a broad set of options. That can be useful, but comparing many alternatives takes more effort. Keep only the options that are genuinely viable. Choose the criteria that should determine the decision. Five to seven focused criteria are often enough. You have a large number of criteria. Consider combining overlaps or keeping the factors that genuinely differentiate the options. Rate the importance of each criterion from 1 to 5. The resulting percentage shows how much influence it has on…from choicease.com
Does the same job
all alternatives →
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io
- SA
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com