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Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor GitHub - GenAI-Gurus/awesome-eu-ai-act: Curated tools, official sources, OSS, templates, and guides for EU AI Act compliance. Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders How to Switch AI Chatbots and Why You Might Want To GitHub - MattMessinger1/agentic_refund_guardrail: Safe refund policy layer for AI agents — Python + TypeScript. Same behavior, shared tests. 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MSN GitHub - visionscaper/collabmem: Enabling long-term collaboration with Agentic AI - building up episodic and world model memory over time with in-context awareness We gave an AI a 3 year retail lease in SF and asked it to make a profit | Andon Labs AI Code is Hollowing Out Open Source, and Maintainers are Looking the Other Way What leaked "SteamGPT" files could mean for the PC gaming platform's use of AI AI is the boss at this retail store. What could go wrong? GitHub - Wuzu11517/agentic-proxy: Local proxy meant to help reduce With Drones, Geophysics and ArtificiaI Intelligence, Researchers Prepare to Do Battle Against Land Mines A Single Operator, Two AI Platforms, Nine Government Agencies: The Full Technical Report 在 Steam 上购买 FriedrichAI: Offline AI 立省 10% GitHub - inevolin/resume-cli: Hit Claude usage limits? Resume any AI coding session elsewhere. Switch tools at zero friction. GitHub - atripati/ark: AI Runtime Kernel — a context operating system for AI agents. Eliminates tool bloat, loads only what’s needed, and gives LLMs their reasoning space back. How to Build a Secure AI PR Reviewer with Claude, GitHub Actions, and JavaScript This Startup Wants You to Pay Up to Talk With AI Versions of Human Experts Intel Arc Pro B70 Brings 32GB VRAM to Local AI for $949 WordPress 7.0: The Good, the AI, and the Still Missing AI on the couch: Anthropic gives Claude 20 hours of psychiatry IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures AI Agents Know About Supabase. They Don't Always Use It Right. The history and future of AI at Google, with Sundar Pichai Inside an AI‑enabled device code phishing campaign How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines AI for Systems: Using LLMs to Optimize Database Query Execution Forecasting the Economic Effects of AI Introducing Tinker: Play with AI, bring your ideas to life AI sheds light on an ancient gaming mystery People really hate AI but not as much as Iran—or Democrats | Fortune What is an AI Product Engineer? Phoebe Gates wants her $185 million AI startup to succeed with 'no ties to my privilege or my last name': 'I have a chip on my shoulder' | Fortune
AgentBack — interfaces agents use, backends agents build
ninemind · 2026-06-16 · via Hacker News - Newest: "AI"

Agent-native, end to end

AgentBack gives your REST endpoints, MCP tools, OpenAPI docs, typed clients, and validation one Zod contract — one schema, every boundary. Agents consume that surface with machine-actionable errors, /llms.txt, and MCP tools; and because there's a single source of truth, a coding agent can author the whole surface from it without drift. Built on LoopBack 4's proven dependency-injection core, rebuilt for ESM and Node 22.

alpha · ESM-only · Node 22.13+ · TypeScript 6

weather.ts — one contract, two surfaces
const City   = z.object({city: z.string().min(1)});
const Report = z.object({summary: z.string(), tempC: z.number()});

@api({basePath: '/weather'})
class WeatherController {
  @get('/forecast/{city}', {path: City, response: Report})
  async forecast(input: {path: z.infer<typeof City>}) { /* … */ }
}

@mcpServer()
class WeatherTools {
  @tool('forecast', {input: City, output: Report})
  async forecast(input: z.infer<typeof City>) { /* … */ }
}

One artifact, many views

The Zod schema is the contract

Declare a schema once, on the decorator. The framework derives every other representation from it — so a coding agent adds a surface in one edit and nothing drifts.

  • Runtime validatorrequests & tool calls parsed before your code runs
  • z.infer typethe handler's input type, enforced at compile time
  • OpenAPI 3.1.1served at /openapi.json, rendered in Swagger UI
  • MCP tool schemainput/output contracts on the official MCP SDK
  • /llms.txtagent-readable surface map from the same route registry
  • Typed clientno codegen — the client imports the same schemas

Positioning

For APIs that apps and agents consume

Most stacks keep the runtime contract, the service contract, and the agent contract in three hand-synchronized places.

Stack Runtime contract Service contract Agent/tool contract
Express + raw Zod Hand-wired Zod Hand-written OpenAPI Hand-written manifest
tRPC Zod TypeScript-only Custom adapter
NestJS class-validator Swagger decorators Custom adapter
FastAPI Pydantic OpenAPI from same models Custom adapter
AgentBack Zod OpenAPI from same Zod MCP from same Zod

What's in the box

Agent ergonomics, end to end

Proven DI core

A hierarchical Context of Bindings with @inject, providers, interceptors, extension points, and tag-based discovery — ported from LoopBack 4.

Dependency injection →

Hybrid surfaces

REST and MCP servers are components over the same container. Run either, or both from one process — controllers and tool classes are just bindings.

Build a hybrid app →

Errors agents can fix

Stable codes, per-field issues, the violated schema, retryability, and remediation hints — the same machine-actionable envelope on REST and MCP.

Read the design note →

Safety primitives

confirm: payload-bound confirmation tokens and idempotency: key replay, declared on the route or tool that must not run twice.

Operations agents can't break →

Per-call pricing

@price('$0.001') meters a route or tool; the price gate refuses unpaid calls with an x402/MPP challenge. Stripe metered billing from the same usage log.

Metering & payments →

Typed client, no codegen

TypeScript consumers import the same schemas and get typed calls plus runtime validation — the client never imports a running server.

Contract-first clients →

Tool-surface budgets

Every tool definition costs context-window tokens on every connection. toolCostReport token-prices the MCP surface before agents pay for it.

Tools are not endpoints →

Production rails

Auth (JWT, OAuth 2.1), authorization voters, health probes, Prometheus metrics, OpenTelemetry, rate limiting, validated config — all as DI components.

Deploy to production →

Documentation

A guided path, ideas first

Three ideas carry the whole framework: everything is a binding in a context, schemas live once on the decorator, and servers are components.

From the blog

Design notes from the framework boundary

All posts →

For coding agents

Hand the build to a coding agent

AgentBack ships an agent skill that teaches Claude Code, Codex, Cursor, and 20+ other agents the conventions that aren't guessable from type signatures — schema-on-decorator, the slot-0 input bundle, the DI container. One Zod schema is the only source of truth, so an agent adds a route, its MCP tool, and its docs in a single edit without drift. The docs are agent-readable too — /llms.txt, /llms-full.txt, and a markdown mirror of every page — so the agent reads the framework the way it reads your API.

terminal
# install the AgentBack skill into your agent of choice
npx skills add ninemindai/agentback

# point an agent at the docs corpus
curl -s https://agentback.dev/llms.txt

Built with AgentBack

A worked example, end to end

Not a snippet — a real service. agentback-demo is a Weather server where one Zod schema set is served over stdio, authenticated HTTP, and a dev console. It's the shape a coding agent and the skill produce: one contract fanning out to REST, MCP, a typed client, and docs, with nothing kept in sync by hand.

ninemindai/agentback-demo Weather MCP server

One Zod schema set, served three ways — stdio, authenticated HTTP, and a dev console. Backed by the free Open-Meteo API.

  • one schema → REST + MCP + typed client + docs
  • AgentError, /llms.txt, tool-cost report
  • the canonical agent-plus-skill build
github.com/ninemindai/agentback-demo →

Quick start

Run the examples in two minutes

AgentBack is in alpha — the end-to-end examples work and the API surface is still moving. Clone the repo and run a working REST, MCP, or hybrid app, each with its explorer UI.

terminal
# clone & build (tests and examples run against dist/)
git clone https://github.com/ninemindai/agentback
pnpm install && pnpm build

# REST + Swagger UI + Context Explorer
pnpm -F hello-rest start

# REST + MCP from one process, both UIs
pnpm -F hello-hybrid start