AcePoint
Record, learn, improve from your tennis matches
What it does
AcePoint is an all-in-one tennis platform where players can record their matches, gain insights from match videos, and work on their skills by setting training goals and exercises. You keep practicing. So why does every match feel the same? AcePoint analyses your match videos and shows you what to improve. You can set goals, tick off targeted exercises, log match results, and have a competition calendar—all in one place. Built for anyone who wants a clearer idea on what to work next in tennis.
Review match footage, record results, and keep your next training focus in one place with AcePoint.
Keep your matches, movement reviews and next training focus together—so every session starts with a reason. Keep scores, satisfaction, and reflections together, then see monthly win-rate and satisfaction trends. Review body movement from match footage, revisit highlighted windows, and watch them in slow motion. Turn a reflection into a SMART goal and drills you can confirm, customise, complete, or dismiss. Schedule your next match, see the countdown, and revisit match details from any played date. The route is simple. Open a step only when you want the detail. Add the score, match type, satisfaction, reflection, and optional footage you want to keep together. Review rotation, balance,…from acepointtennis.netlify.app
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 · 26d 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