📦 omdsh-dev/dsh-mnemon

DSH Mnemon: The Missing Memory Layer

DSH Mnemon gives your coding agent a proper long-term brain so it actually remembers what it learned. Instead of forgetting everything between sessions, it splits knowledge into three clear buckets: quick notes for right now, full documents for deep dives, and persistent facts for everything else. The coolest part is that it lets you plug in almost any memory backend you like, so you are never locked into one specific database. It turns a chatbot that keeps losing context into a reliable partner that recalls decisions you made months ago. If you are tired of repeating yourself to your AI, this is the layer that finally fixes it.

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📦 saurabhav88/enviouswispr

EnviousWispr: Private AI Dictation for Mac

EnviousWispr turns your Mac into a private dictation machine that never sends your voice to the cloud. It runs entirely on your Apple Silicon chip, using local models to transcribe speech in under a second. The standout feature is their custom AI polish model, which cleans up grammar and filler words right on your device, so your audio never leaves your computer. It’s free, open source, and works fully offline. If you want to type faster without sacrificing your privacy, this is the tool to watch.

📰 https://news.ycombinator.com/item?id=48963964

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📦 kunchenguid/vision

Vision: The AI Skill That Finds Your Project's Soul

Vision is the coding assistant that finally writes down what your project actually stands for by reading your past work. Instead of asking you to list vague values, it studies your real history of accepted and rejected code to draft a concrete vision document. The surprising part is how it tests that draft by throwing tough, hypothetical scenarios at you on a simple review board. You decide whether a new idea fits your project, and the tool updates the writing based on your answers. This creates a clear rulebook that helps humans and machines agree on what the project is and is not.

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📦 sylvinus/agent-vm

agent-vm

agent-vm lets you run autonomous A.I. coding agents inside a safe, isolated Linux virtual machine while keeping your actual computer completely untouched. The tool creates a lightweight Debian environment that mounts only your current project folder, meaning the A.I. can execute any command, install dependencies, or run containers without ever exposing your personal files, cloud credentials, or system keys to potential supply-chain attacks. You get a fully equipped workspace with Docker and a headless browser ready to go, so you can let the agent work with full permission without worrying about accidental damage or malicious code.

📰 https://news.ycombinator.com/item?id=47301380

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📦 dgillis/emacs-codex-ide

Codex IDE for Emacs

Codex IDE for Emacs renders artificial intelligence code suggestions directly into your editor as native text buffers instead of hiding them in a separate terminal window. This project solves the problem of context switching by turning AI output into clickable, syntax highlighted code blocks that behave exactly like your regular files. It uses Emacs’ built in diff engine to show changes clearly, so you can review edits with the same familiar tools you already use. You can even let the AI see what you are currently editing to give it better context for its suggestions.

📰 https://news.ycombinator.com/item?id=47664195

🆔 @hackernewsgithubprojects

📦 oh-my-dsh/dsh-plugin-upgrade-skill

dsh-plugin-upgrade-skill: The Upgrade That Tests Itself

dsh-plugin-upgrade-skill is the maintenance tool that actually proves your plugin survives the update. Most upgrades break things silently, but this project includes a massive benchmark suite of tricky test scenarios called traps. It runs your plugin through these real-world headaches to see if it still works after a version bump. If it fails, you know exactly why before your users do. It’s a safety net for developers who want to ship updates with confidence. This is the upgrade process that checks its own homework. Your plugins will finally stay in sync without the guesswork.

🆔 @hackernewsgithubprojects

📦 robbyant-research/dsaquant

DSAQuant: Fast Video Generation Explained

DSAQuant is the video generation tool that makes massive AI models run faster on standard graphics cards. It solves the problem of slow rendering by shrinking the math inside the model, letting it create videos using far less computer memory. The most surprising part is that it keeps the picture quality sharp even after cutting the data size in half. This means your local computer can handle heavy video creation without needing a data center. If you build visual apps, this is how you get professional results on everyday hardware.

🆔 @hackernewsgithubprojects

📦 dashdogy/rtx40mfg-unlock

RTX 40 MFG Unlock: Pushing DLSS Limits

RTX 40 MFG Unlock is the experimental mod that lets you push DLSS Multi Frame Generation beyond the default limits on your graphics card. Normally, game developers cap how many extra frames your chip can create, but this tool overrides that ceiling to expose up to six times more frames in compatible titles. It works by quietly intercepting the frame generation process before it starts, ensuring your card runs as fast as possible without touching any of the original game files. This gives you noticeably smoother motion in heavy scenes, though the author warns it is still research software that can cause glitches if things go wrong.

🆔 @hackernewsgithubprojects

📦 evomap/autoresearch

AutoResearch: AI That Writes Its Own Evidence

AutoResearch doesn't just give you a chatbot answer; it runs actual experiments and writes a full evidence package you can turn into a paper. It starts by scanning recent papers and code trends to find gaps, then uses multiple different AI models to brainstorm and debate ideas so one model can't just rubber-stamp its own work. It plans a pilot experiment, runs it, and if the idea fails, it honestly records the negative result instead of spinning it into a success story. Everything is saved to disk so you can inspect the code, logs, and reviews later. It is basically a tireless research assistant that shows its homework.

🆔 @hackernewsgithubprojects

📦 shootthesound/comfyui-mesh

ComfyUI Mesh: Split AI Models Across GPUs

ComfyUI Mesh lets you run massive image generators by splitting the work across two separate graphics cards, even if they are on different computers. The tricky part is that these cards need to constantly send huge amounts of data back and forth, which usually slows everything down. This project solves that by treating that raw data like a video stream. It uses a specific chip inside your graphics card, called N V Enc, that is usually sitting idle, to compress the data right before it leaves the card.

🆔 @hackernewsgithubprojects

📦 intellisensing/geochrono

How Computers See the Past

GeoChrono can ask a satellite image what happened last Tuesday, not just what is there right now. Existing tools treat space photos as single snapshots, missing the story of how places change over years. This project fixes that by giving a vision model the ability to recall and reason about long-term evolution. It uses a special encoder to track how a single location transforms over time, rather than just comparing two dates. The result is a system that actually understands the history behind the pixels.

🆔 @hackernewsgithubprojects

📦 theagentplane/tokenops

TokenOps

TokenOps can cut your AI agent’s wasted spending by sixty-five percent just by checking the bill before every single step. It treats an entire multi-agent workflow as one single job with one shared budget, not a bunch of separate calls. Instead of letting a complex task quietly burn through your credits, it watches the total spend in real time and stops the run the moment it hits the limit. This prevents that sneaky scenario where dozens of tiny, cheap actions add up to a surprisingly huge final invoice. It is basically a guardrail for your AI’s money.

📰 https://news.ycombinator.com/item?id=49574349

🆔 @hackernewsgithubprojects

📦 shiphrahx/ai-for-engineering-leaders

AI for Engineering Leaders: Your New Copilot for Management

There is a specific, practical playbook on GitHub called AI for Engineering Leaders that turns the chaotic art of managing teams into a structured, copy-and-paste workflow. It isn't just a vague list of tips; it is a library of over one hundred ready-made prompts and skills designed for the messy reality of engineering leadership. If you are a manager, director, or VP, this repo hands you the exact words to use for your next executive status update, a tricky performance review, or a critical incident postmortem.

🆔 @hackernewsgithubprojects

📦 johndpope/llama-cpp-turboquant

llama-cpp-turboquant

llama-cpp-turboquant is the local inference engine that squeezes massive language models onto your regular computer hardware. Most people think running big A.I. models requires expensive cloud servers, but this project lets you run them directly on your laptop or desktop without sending your data anywhere. It uses clever math called quantization to shrink the model files, which means you get faster speeds and less memory usage. If you care about privacy or want to play with A.I. offline, this is the tool that makes it possible right now. Stop renting your A.I. and start owning it. Your next project runs locally, and it is actually quite impressive.

🆔 @hackernewsgithubprojects

📦 worldflowai/everything-claude-code

How to Make Claude Code Actually Work

An Anthropic hackathon winner just open-sourced the exact configuration files he used to build real products with AI. This is not a new coding tool; it is a complete toolkit of specialized agents and rules that you plug directly into your existing setup. The coolest part is how it handles memory. Instead of forgetting everything when a conversation gets long, it automatically saves your progress and learns patterns from your past sessions so it gets smarter every time you work. It turns a generic chatbot into a disciplined senior developer that follows your specific coding standards.

🆔 @hackernewsgithubprojects

📦 code-yeongyu/lazycodex

LazyCodex: The Missing Memory for AI Coders

LazyCodex solves the most annoying problem in AI coding: your AI assistant forgets everything the moment you close the window. It acts as a persistent brain for OpenAI Codex, remembering your project structure, planning complex tasks, and actually verifying that the code works before it tells you it is done. You do not just get answers; you get a disciplined workflow that treats your codebase like a real software project. It installs in one line, needs no global setup, and works right inside your existing Codex environment. If you are tired of re-explaining your project to a chatbot every single time, this is the fix.

🆔 @hackernewsgithubprojects

📦 jakubantalik/libraries.dev

Libraries.dev: The Glow Engine

Libraries.dev builds visual effects for artificial intelligence interfaces that actually feel alive. Instead of a static spinner, these libraries give a thinking machine a physical presence, using animated beams and flowing liquids to show you exactly what the software is doing right now. It solves the problem of waiting for complex calculations without knowing if your computer is stuck or still working. The most interesting part is the thinking orbs, which mimic human thought patterns, making the machine feel responsive rather than frozen. By turning invisible data processing into visible motion, it transforms a dry technical wait into an engaging experience.

🆔 @hackernewsgithubprojects

📦 coulsontl/ai-toolbox

AI Toolbox: Control Every AI Coder in One App

Stop juggling a dozen different config files and scattered API keys for every AI assistant you own. AI Toolbox is a dedicated desktop app that centralizes the settings for major coding tools like Claude Code, OpenAI Codex, and Gemini CLI into one clean, visual dashboard. It handles the messy details, such as switching providers, managing prompts, and syncing your setup to remote servers or WSL environments, so you never have to hunt through hidden folders again. The real magic is the local proxy gateway, which lets you swap models or track usage without changing a single line of code.

🆔 @hackernewsgithubprojects

📦 tripolskypetr/backtest-kit

Backtest Kit: Live Trading on a Single Engine

Backtest Kit is the trading engine that finally lets you test and run the exact same strategy without rewriting a line of code. Most tools force you to build a simulation, then build a separate live bot, and then spend your weekends patching the two so they actually agree. Here, you write your logic once, and the system handles both the historical replay and the real-time market execution. The coolest part is that it uses the same underlying engine for both modes, which means the results you see in your backtest are literally the same code path that trades your money in live.

🆔 @hackernewsgithubprojects

📦 deeplethe/lethe

Lethe: The AI Memory That Forgets

Lethe is the AI memory system that finally learns to forget. While most tools just keep piling up facts until they get messy, Lethe treats every piece of information like a leaf floating in water, letting old or wrong details naturally sink and disappear. The coolest part is that it can cryptographically prove a fact is gone, which is huge for privacy. It runs entirely on your computer with no cloud calls. If you are building agents, this is how you keep them clean and accurate without the data bloat.

🆔 @hackernewsgithubprojects

📦 kronop/vibe-aso

Vibe ASO: The AI Tool That Localizes Your App for 50 Countries

A single AI assistant can now handle the entire App Store optimization process for your mobile app, handling everything from keyword research to translating your interface into fifty different languages. It solves the massive, repetitive problem of manually managing metadata, screenshots, and pricing for every single market you want to enter. By using a specialized tool, you can verify that your app looks professional and speaks the local language correctly before it ever reaches the App Store. You get a polished, global-ready product without hiring a team of translators or spending weeks on manual updates.

🆔 @hackernewsgithubprojects