Gauge
Agent Led Growth: Get written into every customer's codebase
What it does
Don’t use Gauge. Unless you want your tool to get written into every codebase. Agents are leading the next wave of growth for our customers including Supabase, Openrouter, Resend, PostHog, Mintlify, Braintrust, and more. Unlike AI chat, coding agents are implementing products autonomously. They're doing so without ever talking to a sales rep or taking a demo. Gauge runs real coding sessions to identify actions to improve the agent’s preference, and the agent experience once you’re selected.
Track, analyze, and improve your brand's presence across ChatGPT, Gemini, Perplexity, and AI search. Complete toolkit for AI visibility and GEO optimization.
Reach the millions of users exploring ChatGPT, Gemini, Perplexity, Claude Code, and more. Connect with our team for a full deep dive on your current presence in AI. Gauge will pull an in-depth report on the current AI landscape for your brand. See how Gauge can help your team write new content, audit existing pages, target high value affiliates, and engage with social sources like Reddit. Learn how PostHog's Natalia Amorim was tasked with leading AEO across an 11-product suite, and built a content operation from scratch that grew LLM-referred traffic 41x and took PostHog to the most-cited domain in its space. " I went with Gauge for one reason that mattered to me more than anything else:…from withgauge.com
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Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
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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
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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
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Life & fun · 10d ago · louisabraham.github.io
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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