Show HN: UserAlertX – let your AI agent text you (only you) https://ift.tt/kEjg6zq

Show HN: UserAlertX – let your AI agent text you (only you) https://useralertx.com October 9, 2026 at 02:56AM

Show HN: Spectra – A drop-in optimizer that cuts LLM training VRAM by 50% https://ift.tt/ZQSIA2s

Show HN: Spectra – A drop-in optimizer that cuts LLM training VRAM by 50% https://ift.tt/sGgk4JS October 9, 2026 at 12:51AM

Show HN: Long term Memory and 50M token window for LLM https://ift.tt/MYgQ7IP

Show HN: Long term Memory and 50M token window for LLM https://ift.tt/cvF2DIG October 8, 2026 at 11:35PM

Show HN: Singularity – memory that makes coding agents cheaper on repeat work https://ift.tt/PAExuho

Show HN: Singularity – memory that makes coding agents cheaper on repeat work Coding agents start every session from zero. Ask Claude Code for a change like one it made last week and it reads the same files again, falls into the same test traps again, and you pay for all of it again. Singularity learns from your finished sessions (change committed, tests passing): where each kind of change goes in this repo, how it gets checked, and the mistakes made along the way. When the next task starts, a hook hands the agent the workflows that fit, pointed at the code as it is today. Picking them is plain text matching with no model call. It stores where to edit, never the code itself. Results, medians of 3–4 runs per side, hidden tests decide pass/fail, every run passed. On excalidraw: - A long task with four changes, all of kinds memory had learned: 1.9M → 423k tokens, $0.81 → $0.30, 28.5 → 9.5 turns. - The same kind of task, but two of the four changes were bugs it had never seen: 39% fewer tokens, 38% lower cost. The unseen bugs cost the same with or without memory. - Where it doesn't help: small tasks in validator.js and one-off bug fixes. The difference there is about zero, within noise. Each measurement was written down before it was run. The plans and results are in the repo. Caveats: 3–4 runs per side, mostly one repo. Only Claude Code sessions are learned from; Codex, Gemini CLI and Droid get the hand-over through their hooks. Learning uses your Claude account (about $0.25 a round, capped at $1 a day, and only if you opt in). Everything is stored locally in ~/.singularity. Install (Windows PowerShell and more options in the README): curl -fsSL https://ift.tt/JGS5ax0 | sh I'd love to hear where it breaks on your repos, and what memory should carry for one-off work like bug fixes. https://ift.tt/ZcPbrfD October 8, 2026 at 01:47AM

Show HN: Coding Agent with Cost Saving Built-In https://ift.tt/ek95Uso

Show HN: Coding Agent with Cost Saving Built-In Hey we just launch Wiyomi, coding agent with built-in cost-saving strategy. We want to help developer and engineering team reduce AI waste and build more. I would appreciate if you can try it and give me feedback! thank you. https://www.wiyomi.com/ October 8, 2026 at 12:46AM

Show HN: Opposable – Computer Use for iPhone and Android https://ift.tt/nFIvNr2

Show HN: Opposable – Computer Use for iPhone and Android hey! we built Opposable (named because it gives your AI thumbs to use a smartphone) to bring the computer use revolution to smartphones in a easy-to-use and not too hacky way. most of our life is already on our phones, this enables you to do a lot of things while the phone is in your back pocket. some things /are/ hacks, like using a flashable bluetooth dongle to play as a keyboard+mouse to control iPhones.. but it works! https://ift.tt/9qCXSgt October 7, 2026 at 11:47PM

Show HN: Every number in a tiny GPT, from one forward pass to one step of RLHF https://ift.tt/RmUufjA

Show HN: Every number in a tiny GPT, from one forward pass to one step of RLHF Source: https://ift.tt/u6qgYwr Was taking a course on reasoning LMs with a 50% project component. Shortly after I started, I realised I didn't fully understand how these work. I know the architecture and the theory and steps but still there are stages where either the purpose, or the functioning was unclear to me and so I wanted an interactive walkthrough of one token's generation. So here it is, made with Opus 5.5. It's a step by step walkthrough of a toy GPT-2 like model. Every matrix fits on screen, every number is computed live in the browser, and hovering over any cell shows the exact math of that step. It covers inference -- following the generation of one token from tokensiation to sampling, one training step of pretraining, and one PPO iteration of RLHF for post-training. Inspired in part by Brendan Bycroft's LLM visualization ( https://ift.tt/LUQd2HC ) https://llm.manogya.dev October 7, 2026 at 01:44AM