NoiseMake
Inject small mistakes so text feels more hand-written
What it does
NoiseMake adds controlled, reproducible imperfections to text: typos, light repetition, spacing glitches, punctuation normalization, and adjacent word swaps. Use it in the browser, from the CLI, or as an npm package to create messy test fixtures for AI evals, text pipelines, localization QA, and robustness testing.
Does the same job
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- FIFixkey is a keyboard-focused AI copilot for writing2024 · fixkey.ai · ▲36
Demo: https://www.youtube.com/watch?v=sRFOWNpb3U4 Imagine having the CMD+K functionality of Cursor.sh but instead of code you edit natural language. *Introducing Fixkey:* - Native Swift macOS app (no Electron) - Keyboard-centric - Select common prompts with keyboard shortcuts - Press one shortcut to select and fix the current paragraph - Create custom prompts just in time - Works in every application on macOS (Apple Notes, Obsidian, Notion, Gmail, Slack…) - Support for local running models (Beta) *Why not using Grammarly?:* I always feel that traditional grammar correction…
- WFWritekin – fine-tune a local LLM on your own writing, on your MacJul 2026 · github.com · ▲6
Hey Hacker News! I built Writekin over the past week because I was tired of AI writing that didn't sound like me, even though I had just used AI to clean it up, rather than wholesale write it. The usual fixes I found online for this were: - Some sort of SKILL.md, or - A system prompt full of rules to strip the generic AI tells (e.g. no em-dashes, none of the stock phrases, varying the sentence length, etc). While those cleaned up the surface a bit, Pangram still came back as ~100% AI written, which was frustrating, as again it was mainly taking my sloppy copy and tweaking it. So when…
More ai this month
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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
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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