
CoreUncap v1.0.2
Stop thermal throttling & lock 0.1% lows with AI Algorithm
What it does
Unlike generic "game boosters" that just clear RAM, CoreUncap uses a specialized V10 hysteresis band algorithm to stabilize CPU TEMP. It doesn't just "boost" speed; it prevents the aggressive thermal throttling that causes mid-game stutters Features Thermal Lock: Maintains consistent CPU WATTAGE to eliminate erratic 0.1% lows. Built by developers for developers; no bloatware, just performance. Real-time Telemetry: See exactly how your hardware is behaving under load Works on every windows device
Does the same job
all alternatives →
- COCore – open source memory graph for LLMs – shareable, user owned2025 · github.com · ▲112
I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…

- CACrabs All the Way Down – Running Rust on Logic Gates2022 · zdimension.fr · ▲34
Made an ARM Thumb CPU from scratch in a digital circuit simulator for fun and decided to push the joke a little further and run real stuff on it, like a Web server, a Scheme interpreter, a MIDI player, and a VT100 emulator. This is basically a how-did-we-get-here blogpost, it's my second one of this kind, I'd love any feedback you might have.
FluxTwin AI18d ago · fluxtwin-ai.simunovatech.com · ▲8Real-Time Thermal Intelligence for Data Centers
I built a tool showing how AI providers (should) throttle their models9d ago · throttle.staffinganalytics.io · ▲6OP here: this project was born out of the frustration/paranoia that AI providers are throttling their models when their server load is too high. So, I set out to model and study the problem mathematically to understand what was happening, what I found was quite surprising. The idea seems natural: as the data center demand increases momentarily through the day, throttling their models (either using quantized versions, reducing the context window or lowering the tier of the model to a smaller one) seems appealing as the replacement model in principle uses less electricity. The problem is…
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 · 19d 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