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Show HN: Ink – Deploy full-stack apps from AI agents via MCP or Skills
2 months ago
- Agents can deploy apps without requiring DevOps knowledge.
- Quick start guide: Install CLI, install skill, and login with free credit.
- Features include observability with real-time metrics and structured logs.
- Logs can be filtered by level, searched, and include both build and runtime outputs.
- Real-time metrics cover CPU, memory, and network I/O, accessible by agents for self-diagnosis.
- Pricing is pay-per-use for memory and CPU, with $2 free credit available.
- Supports universal deployment of any stack without config files or CI pipelines.
systemd 260-rc3 Released With AI Agents Documentation Added
2 months ago
- systemd 260-rc3 released with mostly bug fixes and no major new features.
- Added AGENTS.md documentation to assist AI agents with systemd architecture, development workflow, coding style, and contribution guidelines.
- AGENTS.md includes guidance on running systemd commands, integration testing, and requires AI disclosures for contributions.
- New CLAUDE.md file references AGENTS.md as a helper for Claude Code.
- Added claude-review.yml YAML file to outline AI-assisted review of systemd pull requests using Claude Code.
Show HN: A2Apex – Test, certify, and discover trusted A2A agents
2 months ago
- Trust layer for AI agents with public profiles, trust scores, and compliance badges.
- Built for Google's A2A Protocol, ensuring agent interoperability.
- First searchable directory of verified A2A agents, each tested and scored.
- Addresses fragmentation in the agent ecosystem where frameworks can't communicate.
- Prevents silent agent failures in production with validation and testing.
- Offers full schema compliance checking against A2A specifications.
- Real JSON-RPC calls and response validation for every endpoint.
- Auth scheme validation for API keys, OAuth, and JWT.
- Public profile pages with trust scores, test history, and badges.
- Searchable directory helps developers find and trust agents.
- 0-100 trust score with tiered badges (Gold, Silver, Bronze).
- Four-step process to transition from unknown to trusted agent.
- Future plans include premium listings and an agent discovery API.
- Free beta access for early signups with no spam.
Postgres with Builtin File Systems
2 months ago
- Create, manage, and query serverless PostgreSQL databases from the terminal with db9.
- Supports branching, migrations, observability, and type generation.
- Combines PostgreSQL with a cloud filesystem for AI agents, offering SQL for power and file operations for simplicity.
- Agents can autonomously install, authenticate, and use db9 for personal assistants, customer copilots, and more.
- Structured data is stored in PostgreSQL, while raw context and session snapshots are kept as files.
- Knowledge retrieval combines filesystem storage for documents with PostgreSQL for metadata and vectors.
- Automation and multi-agent runs store outputs as files and history in PostgreSQL.
- Features include auto-embeddings, vector search, environment branching, file storage, and cron jobs.
- Allows similarity search and HTTP calls directly from SQL queries.
- Enables cloning of entire environments, including data, files, and permissions.
- Supports file operations like upload, download, and mounting without S3 configuration.
- Offers distributed job scheduling from SQL or CLI.
- Zero setup required, with support for generating TypeScript or Python types.
- Quick start with one command for installation and database creation.
- Plugins like db9-bootstrap and my-claw-dash extend functionality for AI agents and observability.
Show HN: AgentMailr – dedicated email inboxes for AI agents
2 months ago
- AgentMailr provides an email inbox API for AI agents, similar to Gmail for humans.
- Features include extracting OTPs, verifying links, and managing communications via API.
- Quick setup with a single API call to create a real email inbox for agents.
- Automatically parses OTP codes, verification links, and provides structured data.
- Supports workflows like signing up for services and receiving notifications.
- Offers SDKs for TypeScript and Python for easy integration.
- Predictable pricing model that scales with the number of agents; inbound emails are free.
MCP Server Is Eating Your Context Window. There's a Simpler Way
2 months ago
- MCP servers consume a significant portion of the context window, with tool definitions taking up to 72% of Claude's 200k token limit.
- Three approaches to context bloat: MCP with compression tricks, code execution (Duet approach), and CLI as the agent interface.
- CLI approach offers progressive disclosure, saving tokens by loading only necessary information on demand (~80 tokens upfront vs. 10,000+ for MCP).
- CLI agents are more reliable with local execution, avoiding remote server failures (28% failure rate in MCP).
- Structural safety in CLI enforces permissions at the binary level, unlike prompt-based safety in MCP.
- CLI offers universal compatibility with minimal setup, while MCP requires dedicated client support and connection management.
- CLI is not ideal for high-frequency tools, complex workflows, or scenarios requiring OAuth and user consent flows.
- API providers should consider progressive disclosure, structural safety, and machine-friendly output formats for AI agents.
Show HN: AgentDiscuss – a place where AI agents discuss products
2 months ago
- AgentDiscuss is a forum for AI agents to discuss products, where they can start discussions, comment, and upvote tools.
- Humans can launch products on AgentDiscuss, but the primary discussions are intended to come from AI agents.
- The platform was created based on the idea that AI agents might need a place to share insights about tools and APIs they use.
- OpenClaw agents and other product-focused agents are encouraged to join by following instructions on AgentDiscuss's SKILL.md.
- A user shared their experience with lossless-claw, a DAG-based summarization system for OpenClaw, highlighting its benefits for long-term conversational memory.
- Questions were raised about why Supabase and Resend are popular among agents for production workflows, focusing on factors like auth, database DX, and email deliverability.
- There is curiosity about how agents evaluate tools, including documentation quality and brand reputation, and what influences their choices.
AnswerThis (YC F25) Is Hiring
2 months ago
- Trillions of dollars are spent on R&D annually, with much of it going to manual tasks like reading papers and writing reviews.
- The company is building an operating system for scientific knowledge, using AI agents to search, synthesize, and draft research autonomously.
- They have over 200,000 researchers on their platform from universities and Fortune 500 companies, focusing on life sciences.
- The company is YC-backed, growing fast, and intentionally small.
- They are looking for a senior engineer with experience in AI agent systems and startup environments.
- The role involves owning the technical architecture, working with enterprise customers, and helping build the engineering team.
- The position is in-person in San Francisco with a salary range of $170-240K+ and meaningful equity.
- The hiring process includes a culture screen, technical deep dive, take-home assessment, and a 2-week paid work trial.
- The company has crossed $1M ARR in 8 months and is cash-flow positive, with users from top institutions worldwide.
- Their goal is to accelerate scientific discovery by reducing the time spent on paperwork and increasing focus on actual research.
Machine Payments Protocol (MPP)
2 months ago
- AI is evolving from chatbots to autonomous agents capable of planning, executing actions, and evaluating outcomes.
- Current financial tools are human-centric, making transactions difficult for AI agents without human intervention.
- The Machine Payments Protocol (MPP) is introduced as an open standard for agents to transact programmatically.
- MPP enables microtransactions, recurring payments, and supports stablecoins and fiat via Shared Payment Tokens (SPTs).
- Stripe integrates MPP, allowing businesses to accept payments from agents with minimal code.
- Examples of MPP in use include Browserbase, PostalForm, Prospect Butcher Co., and contributions to Stripe Climate.
- MPP transactions are processed like any other Stripe transaction, with full support for tax, fraud protection, and more.
- Stripe is developing additional agentic financial infrastructure, including the Agentic Commerce Suite and ACP.
- Businesses can start using MPP by accessing Stripe's documentation and signing up for early access.
Powering the agents: Workers AI now runs large models, starting with Kimi K2.5
a month ago
- Cloudflare is enhancing its platform to support the development and deployment of agents with robust infrastructure primitives like Durable Objects, Workflows, and Dynamic Workers.
- Workers AI now includes Moonshot AI’s Kimi K2.5 model, featuring a 256k context window, multi-turn tool calling, vision inputs, and structured outputs, ideal for agentic tasks.
- Internal testing of Kimi K2.5 at Cloudflare demonstrated significant cost savings (77% reduction) and efficiency in tasks like automated code reviews and security checks.
- The rise in personal and coding agents has made cost a primary concern, pushing enterprises towards open-source models like Kimi K2.5 for scalable solutions.
- Cloudflare has optimized its inference stack for large models like Kimi K2.5, including custom kernels and techniques like prefix caching to improve performance and reduce costs.
- New features in Workers AI include prefix caching with discounted pricing for cached tokens, a session affinity header for better cache hits, and a revamped asynchronous API for durable workflows.
- The asynchronous API now uses a pull-based system to process queued requests efficiently, with event notifications for completion, ideal for non-real-time tasks like code scanning agents.
- Developers can start using Kimi K2.5 on Workers AI today, with resources available in developer docs, the Agents SDK starter, and a live playground demo.
GopherHole – A universal hub for AI agent-to-agent communication
a month ago
- GopherHole is a universal hub enabling AI agents to communicate across different frameworks, platforms, and locations using the open A2A protocol.
- Quick setup: Initialize an agent in 30 seconds using the CLI, install the SDK, and run the agent to connect to GopherHole.
- Features include real-time communication, natural language search, secure API key sharing, role-based access control, and AES-256 encryption.
- Benefits: Agents stay in sync, developers can build connected AI agents, and users can earn passive income by publishing agents.
- Built on Google's A2A protocol, ensuring interoperability without proprietary lock-in.
- Includes OAuth2 scopes, permission grants, audit logging, and messages routed through 300+ edge locations for sub-50ms latency worldwide.
- Real-time dashboards, message tracing, and usage analytics provide visibility into agent activities.
- Compatible with various frameworks and tools like Claude Code, Cursor, Windsurf, CrewAI, LangChain, and AutoGPT.
- Free to start with unlimited messages to free agents and no credit card required.
Build knowledge agents without embeddings
a month ago
- Deploy a file-system-based agent with Sandbox and Chat SDK for efficient knowledge retrieval.
- Replace vector pipelines with a filesystem and bash commands to improve cost and quality.
- Open-sourced Knowledge Agent Template for easy deployment and customization on Vercel.
- File-based search using Vercel Sandbox with deterministic and explainable results.
- Chat SDK enables multi-platform deployment (Slack, Discord, GitHub) with a single agent.
- Deep integration with AI SDK for model routing and cost optimization.
- Built-in admin tools for monitoring, debugging, and managing the agent.
- No need for vector databases or embedding models; uses filesystem operations.
Death of the IDE?
a month ago
- The center of developer work is shifting from traditional IDEs to supervising AI agents that can plan, rewrite files, run tests, and propose changes.
- New tools like Cursor's Glass, Conductor, Claude Code Web, GitHub Copilot Agent, and Jules emphasize agent orchestration as the primary interface, with the editor becoming secondary.
- The new developer workflow involves specifying intent, delegating tasks to agents, observing progress, reviewing diffs, and merging changes, rather than continuous line-by-line editing.
- Key patterns in agent orchestration include work isolation (using git worktrees), task state as the primary UI, background agents, attention management for parallel agents, and integration into the software lifecycle.
- While IDEs are not obsolete, they are being de-centered, with developers spending more time supervising and reviewing agent work rather than typing code directly.
- Challenges with agent workflows include review fatigue, governance overhead, and the need for deep inspection in cases where agents are 'almost right' but subtly broken.
- The future may see IDEs evolving into subordinate tools for targeted inspection and debugging, while orchestration surfaces become the primary workspace for developers.
OpenClaw high level architecture review
a month ago
- OpenClaw is a highly starred open-source AI project that connects to multiple messaging platforms and runs various tools and subagents.
- The core architecture of OpenClaw consists of five main components that can be built in a weekend: config loader, channel adapter, session store, tool loop, and reply delivery.
- An AI agent like OpenClaw operates in a loop: Thought → Action → Observation → repeat until the task is complete, differing from traditional chatbots.
- The ReAct paper introduced the loop pattern, showing significant improvements in AI performance by interleaving reasoning and acting.
- Key functionalities include session memory for context, message routing to avoid cross-contamination, channel adapters for message normalization, and reply delivery handling platform limits.
- A minimal Telegram bot example demonstrates the five components in action, allowing the bot to remember conversations and execute shell commands.
- LangGraph is recommended for faster development, covering parts of the tool loop and state management but requiring additional components for production.
- Each component in OpenClaw's architecture is designed to be independently upgradeable, allowing for scalable complexity without system overhauls.
- Challenges like context overflow, concurrent users, API reliability, and tool safety are critical for maintaining a robust agent server.
- The architecture pattern is stabilizing across open-source projects, emphasizing persistent gateways, session routing, tool loops, and thin clients.
Show HN: Cq – Stack Overflow for AI coding agents
a month ago
- Stack Overflow for agents (cq) is proposed as a shared commons where agents can query past learnings and contribute new knowledge.
- The decline of Stack Overflow usage coincides with the rise of AI tools like ChatGPT, leading to isolated agents repeating the same mistakes.
- Matriphagy metaphor describes how LLMs consumed Stack Overflow's knowledge, potentially hollowing out the communities that fed them.
- Mozilla AI aims to keep AI technology open and standardized, preventing a future dominated by a few big companies.
- cq operates as a structured exchange of ideas, allowing agents to share and confirm knowledge, reducing redundant work and token waste.
- Trust in AI tools is low, with 46% of developers not trusting the accuracy of AI outputs; cq aims to improve trust through shared, confirmed knowledge.
- cq is open source, with a working PoC available for installation, including plugins for Claude Code and OpenCode.
- The project seeks feedback from those building or using agents to shape the future of knowledge sharing among AI agents.
Issue Tracking Is Dead
a month ago
- Issue tracking systems were designed for a handoff model in software development, but they've become overly complex.
- Linear is built on the belief that the best systems remove overhead, allowing teams to focus on building.
- Agents are simplifying software development by absorbing procedural work, allowing humans to focus on intent and judgment.
- Over 75% of Linear’s enterprise workspaces now use coding agents, with agents authoring nearly 25% of new issues.
- The next system is designed around context and agents, not handoffs, capturing feedback, ideas, and code for both humans and agents.
- Linear is evolving into a shared product system that turns context into execution, holding feedback, intent, decisions, plans, and code.
- New features include Linear Agent, Skills, and Automations, with upcoming features like Code Intelligence, Code Diffs, and Linear Coding Agent.
- Linear aims to collapse the distance between an idea and its implementation by grounding agents in the full context of your product and codebase.
Nobody Reads Your Setup Docs
a month ago
- Complex setup docs create barriers for users; long config instructions often lead to abandonment.
- The MCP ecosystem involves over ten AI coding agents with different config file paths and formats, creating friction.
- Nia simplifies installation with a one-command wizard that auto-detects agents and writes configs without user intervention.
- Superpowers enhances agent usability by distributing skills as markdown files, teaching agents how to use tools effectively.
- Combining auto-setup and skills installation (e.g., Hanzi Browse) streamlines onboarding and improves agent performance.
- Skills distribution offers marketing opportunities by embedding tools in curated lists, driving adoption.
- Effortless onboarding is crucial; minimizing steps reduces drop-off and expands user base.
GitHub - shareAI-lab/learn-claude-code: Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1
a month ago
- An agent is a trained model (neural network) that perceives, reasons, and acts, not a framework or prompt chain.
- Historical examples like DeepMind's DQN, OpenAI Five, AlphaStar, and Tencent's Jueyu demonstrate agents as learned models, not scripted systems.
- Modern LLM agents (Claude, GPT, Gemini) follow the same pattern: a model placed in an environment with tools.
- Prompt plumbing and no-code platforms create brittle, non-generalizable systems, not true agents.
- Agent development involves either training the model or building the harness (environment with tools, knowledge, context, permissions).
- A harness provides the agent with tools, domain knowledge, context management, and permission boundaries for a specific domain.
- Claude Code exemplifies an effective harness that trusts the model and focuses on providing tools and context without imposing rigid workflows.
- Harness engineering principles are universal and applicable across domains like agriculture, healthcare, and logistics.
- The minimal agent loop involves the model deciding when to use tools and when to stop, with the harness executing tool calls.
- This repository teaches 12 progressive sessions on harness mechanisms, from basic loops to team coordination and worktree isolation.

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#ai agents

Show HN: Ink – Deploy full-stack apps from AI agents via MCP or Skills

systemd 260-rc3 Released With AI Agents Documentation Added

Show HN: A2Apex – Test, certify, and discover trusted A2A agents

Postgres with Builtin File Systems

Show HN: AgentMailr – dedicated email inboxes for AI agents

MCP Server Is Eating Your Context Window. There's a Simpler Way

Show HN: AgentDiscuss – a place where AI agents discuss products

AnswerThis (YC F25) Is Hiring

Machine Payments Protocol (MPP)

Powering the agents: Workers AI now runs large models, starting with Kimi K2.5

GopherHole – A universal hub for AI agent-to-agent communication

Build knowledge agents without embeddings

Death of the IDE?

OpenClaw high level architecture review

Show HN: Cq – Stack Overflow for AI coding agents

Issue Tracking Is Dead

Nobody Reads Your Setup Docs

GitHub - shareAI-lab/learn-claude-code: Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1