
Zoju
Language feedback that remembers your mistakes
What it does
Zoju is a feedback system for serious language learners, starting with IELTS. Learners practise Reading, Listening, Writing, and Speaking, get feedback on their answers, and see the mistake patterns they keep repeating across sessions. Most tools grade one attempt. Zoju remembers the learner and turns recurring mistakes into focus areas for what to practise next.
Does the same job
all alternatives →- AJA Japanese learning app focused on efficient vocab/grammar acquisition2022 · lessons.nihongo-app.com · ▲161
Hey HN, it’s been 8 years since I posted on here about the launch of my Japanese dictionary app Nihongo [0] and I’m finally back with a new app: Nihongo Lessons! Think of Nihongo Lessons as the textbook to Nihongo’s dictionary. It’s an app for learning Japanese, specifically made for learners who are serious about becoming fluent, and want a guided set of content that will help them efficiently acquire vocabulary and grammar. The project came about when Adam Shapiro of Japanese Level Up (Jalup) [1] announced last April that he was shutting Jalup down. As a fan of Adam’s work I was bummed to…


- LJLearn Japanese contextually while browsingDec 2025 · lingoku.ai · ▲92
Hi HN, Just wanted to share a tool i've been working on to help with my own study routine. It’s a browser extension called Lingoku. The idea is simple: we spend hours browsing the web in English every day. This tool replaces some of the english words with Japanese vocabulary based on your japanese level (Similar to Toucan, but with a better user experience). It’s basically an attempt to make the "i+1" method actually passive, you understand the sentence because it's mostly english, but you pick up Japanese words naturally from the context. It uses an LLM in the backend to make sure the…
- IBI built an AI language teacher to get you speaking2023 · gliglish.com · ▲82
Hello Hacker News, When learning foreign languages, I made the most progress by speaking them throughout the day, every day. So I made a site where you can *speak* to an AI language teacher to practice both listening and speaking. # The product *What I have now:* * Multilingual speech recognition: You can ask a question in English and get an answer in your target language. * Feedback on your grammar. * Suggestions: See examples of what to say next to keep the conversation flowing. * Speed: Choose a lower speed for beginners or a faster one for advanced levels. * Translations: Click to see a…

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.
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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