📦 onmyway133/claude-code-tips-tricks

Claude Code Tips and Tricks

Claude Code Tips and Tricks is the collection that finally stops you from guessing how to use the tool. Instead of scattered forum posts, this repository curates specific, working workflows for the terminal-based coding agent, breaking down complex features into simple, actionable steps. The standout value here is practical context management, teaching you exactly how to keep your AI focused so it doesn't lose track of your project goals. You learn how to save and resume sessions like code branches, control the effort level for different tasks, and even track your token spending without leaving your machine.

🆔 @hackernewsgithubprojects

📦 mpociot/claude-siri-ai

Claude for Siri

Your Mac can now answer to Claude directly from Spotlight, bypassing the need for a separate browser tab or app switch. The repository named Claude for Siri turns your signed in account into a native provider, letting you ask the model right inside the system search bar. It works by running a small local bridge that connects your existing login to the operating system's new delegation framework. This setup is experimental and requires disabling security protections on a development machine, but it proves that third party models can plug into Apple's interface.

🆔 @hackernewsgithubprojects

📦 robinvdvleuten/xprsn

xprsn: Safe expressions without eval

Evaluate rules written by your users without ever executing raw JavaScript code. The xprsn library turns plain text conditions into safe functions that work perfectly under strict browser security policies. It parses expressions into simple closures, so it completely avoids the dangerous shortcuts that usually break secure apps. This makes it ideal for building form filters or spreadsheet formulas where anyone needs to write logic. The result is a tiny, fast tool that keeps your app secure while letting non-programmers create complex checks. If you need user-defined logic, this is the safe way to do it.

🆔 @hackernewsgithubprojects

📦 bilikaz/qwen38-flash-next-cluster-recipe

Two Sparks, One Model: The Qwen Recipe

Spin up a high-speed AI server on just two DGX Spark boxes with the Qwen Three Eight Flash Next cluster recipe. This kit automatically wires your hardware together, downloads the model, and serves it over a high-speed connection without you touching a single config file. The coolest part? It squeezes peak speeds of over one hundred tokens per second from hardware that usually chokes on that load, all while keeping memory tight. If you want a local AI setup that actually feels fast, this is the blueprint.

🆔 @hackernewsgithubprojects

📦 moudrkat/brainscope

Watch Your AI Think With Brainscope

Brainscope turns a standard chat server into a live X-ray for any Hugging Face model, letting you watch the machine think in real time. Instead of just getting text back, you see exactly which words the model is planning to say before it even types them. It peeks deep into the neural layers to reveal hidden intentions and let you nudge the output live using simple sliders. This turns a black box into a transparent lab where you can see the gears turning. If you build with language models, this is how you finally stop guessing and start seeing exactly why your AI says what it says.

🆔 @hackernewsgithubprojects

📦 huskydoge/awesome-loop-models

Awesome Loop Models: The Loop Research Tracker

Awesome Loop Models is the research tracker that finally organizes the scattered world of iterative neural networks. It cuts through the noise by strictly curating papers and deep technical blogs on architectures that reuse a single block to process data repeatedly, rather than just stacking layers. The most fascinating part is how it maps these loop mechanisms to track how models refine their thinking in real time, distinguishing true iterative logic from standard prediction. It gives you a clear map of the field without the fluff.

🆔 @hackernewsgithubprojects

📦 cookiy-ai/sell-sessions-skill

Sell Your AI Chat History?

Turn your old coding chats into actual cash. Sell Sessions Skill lets you license the specific agent sessions you already ran with tools like Claude Code or Codex. It runs entirely on your computer, so nothing leaves your machine until you personally approve it. The tool automatically scrubs out secrets and private info right on your device, then estimates the value of each session. AI labs are actively buying this history because it shows how real engineers correct and guide models to ship working code. You keep full control over what gets shared, and you get paid every time a buyer licenses your work.

🆔 @hackernewsgithubprojects

📦 diqierjia/stratagate-agentmemory

StrataGate: AI Memory That Checks Its Sources

StrataGate stops A I agents from treating every past conversation as a simple, unquestioned fact. Instead of just summarizing history, it builds small cards for specific events that keep the exact time and the original source message. The most surprising part is a built in check that asks the A I if it actually has enough proof to answer a question. If the memory is vague or missing a date, the system forces the agent to dig deeper into the raw chat logs before speaking. This prevents the A I from confidently guessing based on incomplete information.

🆔 @hackernewsgithubprojects

📦 aidenai-io/aiden-firmware

Aiden Firmware: The AI Hardware That Controls Your Phone

Aiden Firmware turns a small Linux computer into a voice-controlled operator that physically drives your mobile phone. It solves the gap between cloud-based chatbots and local hardware by running a full agent loop directly on the device. The standout feature is its human handoff, which detects when the AI is stuck and passes control back to you seamlessly. This repo contains the entire C++ and Go source code needed to build this autonomous hardware. If you want to see how AI actually interacts with the physical world, this is your blueprint. Grab the code, flash the board, and let the machine do the tapping.

🆔 @hackernewsgithubprojects

📦 aetherlabsai/rsiagent

R S I Agent: How AI Learns Without Training

R S I Agent lets a computer figure out how to use new software without ever updating its brain. Instead of memorizing instructions through heavy training, three separate digital workers explore a fresh environment, test their actions, and save useful lessons into a shared notebook. This notebook stays fixed while the agent tackles real tasks, allowing it to improve performance by simply recalling past experiences. It works by first exploring broadly to gather general knowledge, then digging deeper into tricky details to solidify that memory. The result is an agent that gets better at using software just by thinking and trying, not by being retrained.

🆔 @hackernewsgithubprojects

📦 rsasaki0109/visloc-rs

The Rust Library for Drone Navigation Without GPS

This Rust library figures out exactly where a robot is by looking around, even when GPS signals are completely gone. It takes ordinary camera photos and motion sensor data to build a detailed three-dimensional map of the surroundings in real time, a process called simultaneous localization and mapping. Most tools for this job are slow or require expensive hardware, but this one runs entirely on standard computer processors with no special graphics cards needed. It also processes thousands of images faster than the industry standard, making it a powerful tool for developers building autonomous drones and ground robots.

🆔 @hackernewsgithubprojects

📦 iamkaleemsajjad-hue/aether

Aether: Run Any AI Model on Any Hardware

Compile an AI model once and run it on any hardware, from your phone to a massive data center server, without rewriting a single line of code. That is the core promise of Aether. It takes standard open-source models and converts them into a universal execution format that automatically adapts to whatever chip is available, whether it is a standard processor or a specialized graphics card. The surprising part is that this process happens entirely on your own machine without needing expensive cloud services or proprietary software. In rigorous tests against major industry standards, Aether delivered significantly faster response times and lower memory usage across the board.

🆔 @hackernewsgithubprojects

📦 haoruilee/awesome-agent-native-services

Awesome Agent-Native Services

One of the most surprising things about modern A.I. agents is that they can actually join online services using just a single sentence. The project called Awesome Agent-Native Services is a curated directory of tools built specifically for A.I. agents, not for humans. It covers everything from email to memory systems. The key feature here is called Url Onboarding. This means an agent can read a single link and automatically register itself for a service without any human help. This repository explains exactly how this works and lists the services that support it. It is a clear guide to the new world of A.I. infrastructure.

🆔 @hackernewsgithubprojects

📦 cosmosmind-ai/rsi-harness

How RSIH turns AI habits into code

RSIH is the agent harness tool that finally makes your custom AI setups versionable and shareable. It wraps the Pi coding agent with a configuration layer called Genome, turning your scattered prompts, tools, and rules into a single, portable folder. The coolest part is its GEE engine, which analyzes your actual past sessions to automatically generate a new harness based on what you really do, not what you think you want. You get a working setup that captures your specific workflow, ready to run locally or send to a friend. No more losing your perfect prompt tweaks when switching machines.

🆔 @hackernewsgithubprojects

📦 john-rocky/coreai-kit

CoreAIKit Runs AI Locally on Your Phone

A small language model that weighs less than a phone photo can now live entirely on your device, answering questions without a single data packet leaving the building. CoreAIKit is a Swift tool that lets developers drop these private models directly into iPhone and Mac apps, handling the heavy lifting of downloading and storing the files automatically. It solves the privacy headache of cloud services by keeping your chats, voice memos, and photos safe on the hardware you already own. You get real time intelligence for things like meeting notes or image searches, all running silently in the background.

🆔 @hackernewsgithubprojects

📦 alchaincyf/huashu-report

The Science Behind High-End Reports

Huashu report is built on precise measurements taken from forty-one actual research documents produced by major institutions like McKinsey and the World Bank. Instead of guessing what makes a document look professional, this project reverse-engineers the exact typography, spacing, and data structures found in those successful examples. It turns those measurements into a reusable set of rules and automated tools that ensure every number has a clear source and every layout flaw is caught automatically. This solves the problem of inconsistent quality by providing a verified baseline for anyone producing high-stakes documents.

🆔 @hackernewsgithubprojects

📦 turanmahmudov/masume

Masume: Your Terminal Database Copilot

Masume is the terminal database client that finally lets you chat with your data directly from the command line. It is a keyboard-first tool for working with databases like PostgreSQL and MongoDB, but the coolest part is the built-in AI assistant. Instead of guessing at syntax or debugging errors alone, you can ask the AI to explain a complex query or help you fix a broken statement right inside your terminal. It even acts as a server for other AI tools, making your database accessible to external agents safely. This means you get a smart, interactive helper for your data work without leaving your favorite environment.

🆔 @hackernewsgithubprojects

📦 joshhhhhan/vista

VISTA: The AI Agent With Perfect Vision

VISTA, created by researchers at the Massachusetts Institute of Technology, lets artificial intelligence models see every single frame of a game they are playing, allowing them to look back at past evidence when they are stuck on a problem. This visual memory system is so effective that it achieved a perfect one hundred percent win rate on all twenty-five public games in the A R C A G I three benchmark. The project solves a major challenge in artificial intelligence where agents often forget what they saw earlier in a task, causing them to make poor decisions based on incomplete information.

🆔 @hackernewsgithubprojects

📦 hruiccc/ruic-card-skill

RuiC Card Skill: 3D Holographic Cards from One Sentence

RuiC Card Skill is the creative tool that turns a single sentence into a physical-feeling 3D holographic card. Instead of manually modeling complex materials, this system generates a fully interactive web page where your card floats in space. The most surprising part is that it gives you an actual editable Blender file, so you can adjust the lighting and layers yourself. It solves the problem of needing expensive software to create realistic, shimmering collectible cards. You get a live website where you can drag, tilt, and zoom the card to see the light shift across the surface.

🆔 @hackernewsgithubprojects

📦 stratcraftsai/nexusfix

NexusFix: The Engine That Never Allocates

NexusFix parses a single trading message without asking the computer for new memory even once. This C plus plus tool is built for high-frequency trading where every nanosecond of delay costs real money. It reads standard financial data directly from the network buffer without copying a single byte into a new spot. By doing the heavy lifting at compile time instead of runtime, it skips all the slow guesswork typical engines rely on. The result is a parser that runs three times faster than the industry standard. If you are building systems where speed is everything, this is the engine to watch.

📰 https://news.ycombinator.com/item?id=47520153

🆔 @hackernewsgithubprojects

📦 kevinnbass/metr-money-figure

The Invisible Money: METR Money Figure Explained

metr-money-figure is the data investigation that maps the hidden financial links between METR and Anthropic. It solves the problem of opaque funding in AI safety by tracing money flows that never appear in direct grant records. Instead of relying on simple donations, this project reveals how investors in Anthropic funnel funds to METR through parent organizations and shared partners. This matters because it exposes the structural ties between the organizations evaluating AI labs and the labs they are meant to judge. You will see exactly where the money goes and who benefits. This transparency is essential for anyone following the real independence of AI oversight.

🆔 @hackernewsgithubprojects