Audio
🔊 A Playbook for Smarter AI Agent Skills
📦 mgechev/skills-best-practices
This repository is a practical guide for people building “skills” (task-specific instruction packs) for AI agents. It explains how to organize a skill folder, write clear metadata so agents pick the right skill, keep instructions short to save context space, and move repetitive work into small scripts. It also includes a validation workflow: testing descriptions, simulating agent behavior, probing edge cases, and refining the skill structure. The problem it solves is unreliable or bloated agent skills that trigger at the wrong time or miss important steps. It is useful because it turns trial-and-error into a repeatable process for making agent skills more accurate and maintainable.
🔗 https://github.com/mgechev/skills-best-practices
📰 https://news.ycombinator.com/item?id=47155888
📢 @hackernewsgithubprojects
📦 mgechev/skills-best-practices
This repository is a practical guide for people building “skills” (task-specific instruction packs) for AI agents. It explains how to organize a skill folder, write clear metadata so agents pick the right skill, keep instructions short to save context space, and move repetitive work into small scripts. It also includes a validation workflow: testing descriptions, simulating agent behavior, probing edge cases, and refining the skill structure. The problem it solves is unreliable or bloated agent skills that trigger at the wrong time or miss important steps. It is useful because it turns trial-and-error into a repeatable process for making agent skills more accurate and maintainable.
🔗 https://github.com/mgechev/skills-best-practices
📰 https://news.ycombinator.com/item?id=47155888
📢 @hackernewsgithubprojects
Audio
🔊 Tree-sitter for Go, Minus the C Headaches
📦 odvcencio/gotreesitter
gotreesitter is a Go library that helps software understand source code, making it useful for editors, linters, search tools, and other developer apps. It re-creates the Tree-sitter parsing engine in pure Go, so teams do not need C libraries or a system compiler to build or run it. That solves a common setup and portability problem, especially when shipping tools across different environments or to WebAssembly. It can reuse existing language grammars and supports fast re-parsing after small edits, along with features like syntax highlighting, code queries, and symbol tagging. In short, it makes code-aware features easier to build and deploy in Go.
🔗 https://github.com/odvcencio/gotreesitter
📰 https://news.ycombinator.com/item?id=47155597
📢 @hackernewsgithubprojects
📦 odvcencio/gotreesitter
gotreesitter is a Go library that helps software understand source code, making it useful for editors, linters, search tools, and other developer apps. It re-creates the Tree-sitter parsing engine in pure Go, so teams do not need C libraries or a system compiler to build or run it. That solves a common setup and portability problem, especially when shipping tools across different environments or to WebAssembly. It can reuse existing language grammars and supports fast re-parsing after small edits, along with features like syntax highlighting, code queries, and symbol tagging. In short, it makes code-aware features easier to build and deploy in Go.
🔗 https://github.com/odvcencio/gotreesitter
📰 https://news.ycombinator.com/item?id=47155597
📢 @hackernewsgithubprojects
Audio
🔊 One CLI, Many AI Coding Teammates
📦 bradygaster/squad
Squad is an open-source command-line tool that helps developers run a “team” of AI coding assistants together, including GitHub Copilot Coding Agent, OpenAI Codex CLI, and Claude Code. Instead of switching between tools by hand, it sets them up, coordinates their work, and combines their output while you code in your own project folder. It solves the problem of managing multiple AI helpers and gives you a repeatable workflow for planning, building, reviewing, and comparing results. This is useful for faster experimentation, better code quality checks, and learning how different AI tools approach the same task.
🔗 https://github.com/bradygaster/squad
📰 https://news.ycombinator.com/item?id=47157294
📢 @hackernewsgithubprojects
📦 bradygaster/squad
Squad is an open-source command-line tool that helps developers run a “team” of AI coding assistants together, including GitHub Copilot Coding Agent, OpenAI Codex CLI, and Claude Code. Instead of switching between tools by hand, it sets them up, coordinates their work, and combines their output while you code in your own project folder. It solves the problem of managing multiple AI helpers and gives you a repeatable workflow for planning, building, reviewing, and comparing results. This is useful for faster experimentation, better code quality checks, and learning how different AI tools approach the same task.
🔗 https://github.com/bradygaster/squad
📰 https://news.ycombinator.com/item?id=47157294
📢 @hackernewsgithubprojects
Audio
🔊 3D Scenes, Right in Your Terminal
📦 buildoak/tortuise
Tortuise is an open-source tool that lets you view 3D scanned scenes inside a terminal window using text-like symbols and color. It reads common Gaussian splat files (a modern way to store detailed 3D captures) and gives you keyboard controls to move around the scene. The main problem it solves is access: most 3D viewers need a full graphics app and a decent GPU, while Tortuise is designed to run on the CPU and work in ordinary terminals, even on modest hardware. That makes it useful for quick previews, demos, remote sessions, and anyone who wants a lightweight, surprisingly capable way to explore 3D content.
🔗 https://github.com/buildoak/tortuise
📰 https://news.ycombinator.com/item?id=47151927
📢 @hackernewsgithubprojects
📦 buildoak/tortuise
Tortuise is an open-source tool that lets you view 3D scanned scenes inside a terminal window using text-like symbols and color. It reads common Gaussian splat files (a modern way to store detailed 3D captures) and gives you keyboard controls to move around the scene. The main problem it solves is access: most 3D viewers need a full graphics app and a decent GPU, while Tortuise is designed to run on the CPU and work in ordinary terminals, even on modest hardware. That makes it useful for quick previews, demos, remote sessions, and anyone who wants a lightweight, surprisingly capable way to explore 3D content.
🔗 https://github.com/buildoak/tortuise
📰 https://news.ycombinator.com/item?id=47151927
📢 @hackernewsgithubprojects
Audio
🔊 MCP Fusion Makes AI Tool Backends Safer
📦 vinkius-labs/mcp-fusion
MCP Fusion is an open-source TypeScript framework for building services that AI assistants can call through MCP (a standard for connecting AI to tools and data). It solves a common problem: many AI tool integrations send raw app data directly, which can leak sensitive fields, overwhelm the AI with too much information, and cause wrong or costly retries. MCP Fusion adds a structured layer between your data and the AI that validates responses, removes undeclared fields, attaches clear rules, and suggests what action to take next. That makes AI-driven apps more reliable, safer, and easier to maintain, while letting developers focus on core business logic instead of repetitive plumbing.
🔗 https://github.com/vinkius-labs/mcp-fusion
📰 https://news.ycombinator.com/item?id=47157454
📢 @hackernewsgithubprojects
📦 vinkius-labs/mcp-fusion
MCP Fusion is an open-source TypeScript framework for building services that AI assistants can call through MCP (a standard for connecting AI to tools and data). It solves a common problem: many AI tool integrations send raw app data directly, which can leak sensitive fields, overwhelm the AI with too much information, and cause wrong or costly retries. MCP Fusion adds a structured layer between your data and the AI that validates responses, removes undeclared fields, attaches clear rules, and suggests what action to take next. That makes AI-driven apps more reliable, safer, and easier to maintain, while letting developers focus on core business logic instead of repetitive plumbing.
🔗 https://github.com/vinkius-labs/mcp-fusion
📰 https://news.ycombinator.com/item?id=47157454
📢 @hackernewsgithubprojects
Audio
🔊 Stop Clicking, Start Testing with Venom
📦 ovh/venom
Venom is an open-source command-line tool for checking whether the parts of a software system work together correctly. Instead of clicking through tests by hand, teams write test scenarios in simple YAML text files, then Venom runs steps such as scripts, website or API requests, email checks, and database queries, and compares the results to expected outcomes. It solves the problem of slow, repetitive integration testing by making tests easy to read, reuse, and run automatically in build pipelines. This is useful because it catches breakages earlier, documents expected behavior, and helps developers and operations teams share the same test process.
🔗 https://github.com/ovh/venom
📰 https://news.ycombinator.com/item?id=47157496
📢 @hackernewsgithubprojects
📦 ovh/venom
Venom is an open-source command-line tool for checking whether the parts of a software system work together correctly. Instead of clicking through tests by hand, teams write test scenarios in simple YAML text files, then Venom runs steps such as scripts, website or API requests, email checks, and database queries, and compares the results to expected outcomes. It solves the problem of slow, repetitive integration testing by making tests easy to read, reuse, and run automatically in build pipelines. This is useful because it catches breakages earlier, documents expected behavior, and helps developers and operations teams share the same test process.
🔗 https://github.com/ovh/venom
📰 https://news.ycombinator.com/item?id=47157496
📢 @hackernewsgithubprojects
Audio
🔊 Pantalk: One Chat Bridge for AI Agents
📦 pantalk/pantalk
Pantalk is an open-source tool that helps AI assistants talk to people across many chat apps from one place. Instead of building a separate connection for Slack, Discord, Telegram, WhatsApp, and others, developers run Pantalk locally and use one simple interface to send messages, read history, and watch new events. It also stores conversations so an agent can keep context after restarts. This solves the messy problem of juggling different chat APIs and connection rules. Pantalk is useful because it lets teams put one AI agent on multiple platforms faster, with less custom setup and fewer moving parts to maintain.
🔗 https://github.com/pantalk/pantalk
📰 https://news.ycombinator.com/item?id=47158300
📢 @hackernewsgithubprojects
📦 pantalk/pantalk
Pantalk is an open-source tool that helps AI assistants talk to people across many chat apps from one place. Instead of building a separate connection for Slack, Discord, Telegram, WhatsApp, and others, developers run Pantalk locally and use one simple interface to send messages, read history, and watch new events. It also stores conversations so an agent can keep context after restarts. This solves the messy problem of juggling different chat APIs and connection rules. Pantalk is useful because it lets teams put one AI agent on multiple platforms faster, with less custom setup and fewer moving parts to maintain.
🔗 https://github.com/pantalk/pantalk
📰 https://news.ycombinator.com/item?id=47158300
📢 @hackernewsgithubprojects
Audio
🔊 Turn Your Mac Into a Smarter Workspace
📦 asmvik/yabai
yabai is a free tool for macOS that automatically arranges app windows into a clean, organized layout, so you spend less time dragging and resizing everything by hand. It acts like a smarter window manager: you can control windows, virtual desktops (Spaces), and multiple monitors from the keyboard, and set custom shortcuts for quick moves and switches. This solves the common problem of desktop clutter when many apps are open, especially on smaller screens or busy setups. It is useful because it helps people stay focused, work faster, and create a consistent workspace that matches how they prefer to work.
🔗 https://github.com/asmvik/yabai
📰 https://news.ycombinator.com/item?id=47158333
📢 @hackernewsgithubprojects
📦 asmvik/yabai
yabai is a free tool for macOS that automatically arranges app windows into a clean, organized layout, so you spend less time dragging and resizing everything by hand. It acts like a smarter window manager: you can control windows, virtual desktops (Spaces), and multiple monitors from the keyboard, and set custom shortcuts for quick moves and switches. This solves the common problem of desktop clutter when many apps are open, especially on smaller screens or busy setups. It is useful because it helps people stay focused, work faster, and create a consistent workspace that matches how they prefer to work.
🔗 https://github.com/asmvik/yabai
📰 https://news.ycombinator.com/item?id=47158333
📢 @hackernewsgithubprojects
Audio
🔊 Turn AI Coding Plans Into Clickable Reviews
📦 backnotprop/plannotator
Plannotator is an open-source tool that opens AI coding plans in a browser so people can review them visually before the AI starts making changes. Instead of reading long plans in a terminal and sending vague feedback, you can highlight text, add comments, suggest replacements, compare revised versions, and approve or request changes with one click. It also supports sharing reviews by link and reviewing code changes, not just plans. This helps individuals and teams catch mistakes earlier, give clearer feedback, and stay in control of what an AI assistant does next.
🔗 https://github.com/backnotprop/plannotator
📰 https://news.ycombinator.com/item?id=47159020
📢 @hackernewsgithubprojects
📦 backnotprop/plannotator
Plannotator is an open-source tool that opens AI coding plans in a browser so people can review them visually before the AI starts making changes. Instead of reading long plans in a terminal and sending vague feedback, you can highlight text, add comments, suggest replacements, compare revised versions, and approve or request changes with one click. It also supports sharing reviews by link and reviewing code changes, not just plans. This helps individuals and teams catch mistakes earlier, give clearer feedback, and stay in control of what an AI assistant does next.
🔗 https://github.com/backnotprop/plannotator
📰 https://news.ycombinator.com/item?id=47159020
📢 @hackernewsgithubprojects
Audio
🔊 Replay What Slowed Your Postgres Down
📦 nikolays/pg_ash
pg_ash is an open-source tool for PostgreSQL that keeps a lightweight history of what your database was doing over time. PostgreSQL usually shows what is happening right now, but not what happened during a slowdown an hour ago. pg_ash fixes that by taking small, frequent snapshots and storing them in tables you can query with normal SQL. It is especially useful on managed cloud databases because it installs as SQL scripts, without special server extensions or restarts. That makes it easier for teams to investigate performance problems, spot heavy queries, and understand bottlenecks, while keeping storage and maintenance needs low.
🔗 https://github.com/NikolayS/pg_ash
📰 https://news.ycombinator.com/item?id=47159132
📢 @hackernewsgithubprojects
📦 nikolays/pg_ash
pg_ash is an open-source tool for PostgreSQL that keeps a lightweight history of what your database was doing over time. PostgreSQL usually shows what is happening right now, but not what happened during a slowdown an hour ago. pg_ash fixes that by taking small, frequent snapshots and storing them in tables you can query with normal SQL. It is especially useful on managed cloud databases because it installs as SQL scripts, without special server extensions or restarts. That makes it easier for teams to investigate performance problems, spot heavy queries, and understand bottlenecks, while keeping storage and maintenance needs low.
🔗 https://github.com/NikolayS/pg_ash
📰 https://news.ycombinator.com/item?id=47159132
📢 @hackernewsgithubprojects
Audio
🔊 Build AI Assistants in Ruby Without the Plumbing
📦 adham90/ruby_llm-agents
ruby_llm-agents is an open-source Ruby on Rails toolkit for building and running AI assistants inside a web app. Instead of wiring together model calls, logging, error handling, and cost tracking by hand, it gives developers a ready-made framework with guardrails. It can retry failed requests, switch to backup models, track usage and spending, and show activity in a built-in dashboard. It also supports conversations, file inputs like images and PDFs, and different AI providers. This is useful for teams moving from AI demos to real products because it makes AI features more reliable, easier to monitor, and safer to operate.
🔗 https://github.com/adham90/ruby_llm-agents
📰 https://news.ycombinator.com/item?id=47160192
📢 @hackernewsgithubprojects
📦 adham90/ruby_llm-agents
ruby_llm-agents is an open-source Ruby on Rails toolkit for building and running AI assistants inside a web app. Instead of wiring together model calls, logging, error handling, and cost tracking by hand, it gives developers a ready-made framework with guardrails. It can retry failed requests, switch to backup models, track usage and spending, and show activity in a built-in dashboard. It also supports conversations, file inputs like images and PDFs, and different AI providers. This is useful for teams moving from AI demos to real products because it makes AI features more reliable, easier to monitor, and safer to operate.
🔗 https://github.com/adham90/ruby_llm-agents
📰 https://news.ycombinator.com/item?id=47160192
📢 @hackernewsgithubprojects
Audio
🔊 Turn One Goal Into a Working App
📦 sandgardenhq/sgai
Sgai is a local tool that helps people build software by describing the result they want instead of managing every coding step. You set a goal, and a group of AI agents (like a builder, reviewer, and designer) plans the work, asks follow-up questions, writes code, and runs checks such as tests before marking it done. A visual dashboard shows the plan and progress, so the process is not hidden. It solves the problem of messy AI coding chats and unclear outcomes. It is useful because people stay in control, can verify work actually passes checks, and keep their code on their own machine.
🔗 https://github.com/sandgardenhq/sgai
📰 https://news.ycombinator.com/item?id=47153941
📢 @hackernewsgithubprojects
📦 sandgardenhq/sgai
Sgai is a local tool that helps people build software by describing the result they want instead of managing every coding step. You set a goal, and a group of AI agents (like a builder, reviewer, and designer) plans the work, asks follow-up questions, writes code, and runs checks such as tests before marking it done. A visual dashboard shows the plan and progress, so the process is not hidden. It solves the problem of messy AI coding chats and unclear outcomes. It is useful because people stay in control, can verify work actually passes checks, and keep their code on their own machine.
🔗 https://github.com/sandgardenhq/sgai
📰 https://news.ycombinator.com/item?id=47153941
📢 @hackernewsgithubprojects
Audio
🔊 SimpleTalk: A Friendlier Way to Build Interactive Ideas
📦 dkrasner/simpletalk
SimpleTalk is an experimental project that explores a friendlier way to build software, inspired by older systems like HyperCard and Smalltalk. Instead of focusing on polished apps first, it provides an authoring environment where people can create and organize interactive pages (called stacks) and save them as regular HTML snapshot files. This helps address a common problem in personal computing: many tools make creating things feel technical and fragmented. SimpleTalk is useful as a research prototype because it shows how documentation, examples, and the working environment can live together, making ideas easier to test, teach, and improve.
🔗 https://github.com/dkrasner/Simpletalk
📰 https://news.ycombinator.com/item?id=47160684
📢 @hackernewsgithubprojects
📦 dkrasner/simpletalk
SimpleTalk is an experimental project that explores a friendlier way to build software, inspired by older systems like HyperCard and Smalltalk. Instead of focusing on polished apps first, it provides an authoring environment where people can create and organize interactive pages (called stacks) and save them as regular HTML snapshot files. This helps address a common problem in personal computing: many tools make creating things feel technical and fragmented. SimpleTalk is useful as a research prototype because it shows how documentation, examples, and the working environment can live together, making ideas easier to test, teach, and improve.
🔗 https://github.com/dkrasner/Simpletalk
📰 https://news.ycombinator.com/item?id=47160684
📢 @hackernewsgithubprojects
Audio
🔊 Where CSS Rules Are Written
📦 w3c/csswg-drafts
This repository is the working home for the W3C CSS Working Group’s draft specifications, the documents that define how CSS features should behave in web browsers. Instead of scattered notes and private discussions, it gives browser makers, web developers, and standards editors one shared place to write proposals, review changes, report problems, and track decisions. That helps reduce inconsistencies between browsers and makes new CSS features easier to understand and test before they are widely released. It is useful because the rules behind everyday web styling are developed openly here, which improves compatibility and makes the web more reliable for everyone.
🔗 https://github.com/w3c/csswg-drafts
📰 https://news.ycombinator.com/item?id=47160864
📢 @hackernewsgithubprojects
📦 w3c/csswg-drafts
This repository is the working home for the W3C CSS Working Group’s draft specifications, the documents that define how CSS features should behave in web browsers. Instead of scattered notes and private discussions, it gives browser makers, web developers, and standards editors one shared place to write proposals, review changes, report problems, and track decisions. That helps reduce inconsistencies between browsers and makes new CSS features easier to understand and test before they are widely released. It is useful because the rules behind everyday web styling are developed openly here, which improves compatibility and makes the web more reliable for everyone.
🔗 https://github.com/w3c/csswg-drafts
📰 https://news.ycombinator.com/item?id=47160864
📢 @hackernewsgithubprojects
Audio
🔊 One Toolkit for Every Document Task
📦 adithya-s-k/omnidocs
OmniDocs is an open-source Python toolkit that helps people and teams pull useful information from documents like PDFs, scans, and images using one simple interface. Instead of stitching together separate tools for OCR, layout detection, table parsing, and structured data extraction, OmniDocs lets you swap models or cloud providers without rewriting your code. It supports local GPU setups and cloud APIs, so it can fit both experiments and production workflows. This is useful for building apps that read invoices, reports, forms, and research papers faster, with more consistent outputs and less engineering work spent on integration.
🔗 https://github.com/adithya-s-k/Omnidocs
📰 https://news.ycombinator.com/item?id=47154193
📢 @hackernewsgithubprojects
📦 adithya-s-k/omnidocs
OmniDocs is an open-source Python toolkit that helps people and teams pull useful information from documents like PDFs, scans, and images using one simple interface. Instead of stitching together separate tools for OCR, layout detection, table parsing, and structured data extraction, OmniDocs lets you swap models or cloud providers without rewriting your code. It supports local GPU setups and cloud APIs, so it can fit both experiments and production workflows. This is useful for building apps that read invoices, reports, forms, and research papers faster, with more consistent outputs and less engineering work spent on integration.
🔗 https://github.com/adithya-s-k/Omnidocs
📰 https://news.ycombinator.com/item?id=47154193
📢 @hackernewsgithubprojects
Audio
🔊 Learn AI by Building It
📦 deeptrackai/deeplearningcrashcourse
This repository is a free set of chapter-by-chapter Jupyter notebooks that accompanies the book Deep Learning Crash Course. It guides learners from simple neural networks to newer AI methods through hands-on projects, without requiring advanced math or a research background. The main problem it solves is the gap between theory-heavy explanations and practical learning: it shows how to build and test models step by step. With organized chapters covering images, text, forecasting, image generation, game-playing, and more, it gives beginners, students, and working professionals a clear path to practice real code, understand what each approach is for, and build confidence with modern AI tools.
🔗 https://github.com/DeepTrackAI/DeepLearningCrashCourse
📰 https://news.ycombinator.com/item?id=47161572
📢 @hackernewsgithubprojects
📦 deeptrackai/deeplearningcrashcourse
This repository is a free set of chapter-by-chapter Jupyter notebooks that accompanies the book Deep Learning Crash Course. It guides learners from simple neural networks to newer AI methods through hands-on projects, without requiring advanced math or a research background. The main problem it solves is the gap between theory-heavy explanations and practical learning: it shows how to build and test models step by step. With organized chapters covering images, text, forecasting, image generation, game-playing, and more, it gives beginners, students, and working professionals a clear path to practice real code, understand what each approach is for, and build confidence with modern AI tools.
🔗 https://github.com/DeepTrackAI/DeepLearningCrashCourse
📰 https://news.ycombinator.com/item?id=47161572
📢 @hackernewsgithubprojects
Audio
🔊 Apple Intelligence, Now in Python
📦 apple/python-apple-fm-sdk
This repository is Apple’s Python toolkit for using the on-device language model behind Apple Intelligence on a Mac. Instead of building everything in Swift, developers and data teams can script prompts, run many tests, stream responses, and check output quality from Python, which is often faster for experiments and analysis. It also supports guided responses, so apps can ask for answers in a specific format. In plain terms, it helps people prototype and evaluate AI features for Apple apps without sending data to the cloud. That makes it useful for local testing, privacy-sensitive workflows, and comparing model behavior before shipping an app.
🔗 https://github.com/apple/python-apple-fm-sdk
📰 https://news.ycombinator.com/item?id=47161249
📢 @hackernewsgithubprojects
📦 apple/python-apple-fm-sdk
This repository is Apple’s Python toolkit for using the on-device language model behind Apple Intelligence on a Mac. Instead of building everything in Swift, developers and data teams can script prompts, run many tests, stream responses, and check output quality from Python, which is often faster for experiments and analysis. It also supports guided responses, so apps can ask for answers in a specific format. In plain terms, it helps people prototype and evaluate AI features for Apple apps without sending data to the cloud. That makes it useful for local testing, privacy-sensitive workflows, and comparing model behavior before shipping an app.
🔗 https://github.com/apple/python-apple-fm-sdk
📰 https://news.ycombinator.com/item?id=47161249
📢 @hackernewsgithubprojects
Audio
🔊 Before You Build, Get a Reality Check
📦 mnemox-ai/idea-reality-mcp
Idea Reality MCP is a small add-on for AI coding assistants that checks whether a project idea is already crowded before you start building. You give it a plain-language idea, and it searches places like GitHub, Hacker News, npm, PyPI, and Product Hunt, then returns a simple 0-100 "reality signal," examples of similar projects, and suggestions for how to stand out. It solves a common problem: spending hours building something only to learn many versions already exist. This is useful for founders, hobbyists, and teams because it adds a quick reality check inside the coding workflow, helping them research earlier, avoid duplicate work, and choose better ideas or sharper niches.
🔗 https://github.com/mnemox-ai/idea-reality-mcp
📰 https://news.ycombinator.com/item?id=47140335
📢 @hackernewsgithubprojects
📦 mnemox-ai/idea-reality-mcp
Idea Reality MCP is a small add-on for AI coding assistants that checks whether a project idea is already crowded before you start building. You give it a plain-language idea, and it searches places like GitHub, Hacker News, npm, PyPI, and Product Hunt, then returns a simple 0-100 "reality signal," examples of similar projects, and suggestions for how to stand out. It solves a common problem: spending hours building something only to learn many versions already exist. This is useful for founders, hobbyists, and teams because it adds a quick reality check inside the coding workflow, helping them research earlier, avoid duplicate work, and choose better ideas or sharper niches.
🔗 https://github.com/mnemox-ai/idea-reality-mcp
📰 https://news.ycombinator.com/item?id=47140335
📢 @hackernewsgithubprojects
Audio
🔊 AI Agents That Keep Working After You Log Off
📦 rightnow-ai/openfang
OpenFang is an open-source app for running AI assistants as always-on workers, not just chatbots you talk to one prompt at a time. It bundles setup, scheduling, tools, safety checks, and a dashboard into one program, so people can launch agents that research topics, monitor websites, find sales leads, automate browser tasks, manage social posts, or clip videos. It solves the headache of stitching together many separate AI tools, scripts, and services just to automate repetitive work. OpenFang is useful because it gives one place to run and monitor these jobs, with built-in approvals and guardrails for sensitive actions while still allowing customization.
🔗 https://github.com/RightNow-AI/openfang
📰 https://news.ycombinator.com/item?id=47160246
📢 @hackernewsgithubprojects
📦 rightnow-ai/openfang
OpenFang is an open-source app for running AI assistants as always-on workers, not just chatbots you talk to one prompt at a time. It bundles setup, scheduling, tools, safety checks, and a dashboard into one program, so people can launch agents that research topics, monitor websites, find sales leads, automate browser tasks, manage social posts, or clip videos. It solves the headache of stitching together many separate AI tools, scripts, and services just to automate repetitive work. OpenFang is useful because it gives one place to run and monitor these jobs, with built-in approvals and guardrails for sensitive actions while still allowing customization.
🔗 https://github.com/RightNow-AI/openfang
📰 https://news.ycombinator.com/item?id=47160246
📢 @hackernewsgithubprojects
Audio
🔊 PgDog: A Traffic Cop for PostgreSQL Growth
📦 pgdogdev/pgdog
PgDog is an open-source tool that sits in front of PostgreSQL databases and helps them handle more traffic without forcing teams to rewrite their apps. It works like a smart traffic manager: it reuses database connections, spreads requests across servers, and can split data across multiple database machines when one server is no longer enough. This solves common scaling problems like slowdowns, too many simultaneous connections, and painful database growth. It is useful because apps can keep using standard PostgreSQL tools while PgDog handles the routing behind the scenes, improving speed, reliability, and room to grow with less operational effort.
🔗 https://github.com/pgdogdev/pgdog
📰 https://news.ycombinator.com/item?id=47123631
📢 @hackernewsgithubprojects
📦 pgdogdev/pgdog
PgDog is an open-source tool that sits in front of PostgreSQL databases and helps them handle more traffic without forcing teams to rewrite their apps. It works like a smart traffic manager: it reuses database connections, spreads requests across servers, and can split data across multiple database machines when one server is no longer enough. This solves common scaling problems like slowdowns, too many simultaneous connections, and painful database growth. It is useful because apps can keep using standard PostgreSQL tools while PgDog handles the routing behind the scenes, improving speed, reliability, and room to grow with less operational effort.
🔗 https://github.com/pgdogdev/pgdog
📰 https://news.ycombinator.com/item?id=47123631
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