Tamped
AI-powered coffee journal for home baristas
What it does
Tamped is a coffee journal built for home baristas. Log every brew with precise recipes and timers. Track your bean shelf and equipment. Rate shots with flavor radar charts. Discover roasteries on an interactive atlas and log cafe visits. Share favourite cafes with friends in private circles. Whether you're dialing in espresso or experimenting with pour-over, Tamped helps you brew better, one cup at a time. Available on iOS and Android for beta testing.
Does the same job
all alternatives →
- WRWe're two coffee nerds who built an AI app to track beans and recipes2025 · beanbook.app · ▲60
It’s available on iOS now: https://itunes.apple.com/app/id6499280064 We got into specialty coffee during COVID and, like many others, fell deep down the rabbit hole. Along the way, we ran into the same frustrations: - A drawer full of empty coffee bags. - No simple way to track grind size, rest dates, notes—by bean. - My coffee history scattered across photos, screenshots, notebooks, and half-memories. - The unique traits, people, and stories behind each coffee disappearing from the internet once it sold out (since coffee is an agricultural good) - In our opinion, no…
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, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com