惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

罗磊的独立博客
Apple Machine Learning Research
Apple Machine Learning Research
The Cloudflare Blog
WordPress大学
WordPress大学
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 叶小钗
博客园 - 聂微东
阮一峰的网络日志
阮一峰的网络日志
腾讯CDC
博客园 - 三生石上(FineUI控件)
V
V2EX
有赞技术团队
有赞技术团队
V
Visual Studio Blog
小众软件
小众软件
Jina AI
Jina AI
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - Franky
量子位
T
Tailwind CSS Blog
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
P
Palo Alto Networks Blog
Cisco Talos Blog
Cisco Talos Blog
I
Intezer
Project Zero
Project Zero
A
Arctic Wolf
P
Privacy International News Feed
V
Vulnerabilities – Threatpost
L
Lohrmann on Cybersecurity
S
Securelist
C
Cybersecurity and Infrastructure Security Agency CISA
C
CXSECURITY Database RSS Feed - CXSecurity.com
T
Tor Project blog
Hacker News - Newest:
Hacker News - Newest: "LLM"
S
Security @ Cisco Blogs
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Google DeepMind News
Google DeepMind News
N
News and Events Feed by Topic
TaoSecurity Blog
TaoSecurity Blog
L
LINUX DO - 热门话题
G
GRAHAM CLULEY
Help Net Security
Help Net Security
N
News | PayPal Newsroom
W
WeLiveSecurity
G
Google Developers Blog
Microsoft Security Blog
Microsoft Security Blog
Engineering at Meta
Engineering at Meta
MongoDB | Blog
MongoDB | Blog
C
Check Point Blog

Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - AronDaron/dataset-generator: No-code desktop app for generating high-quality synthetic datasets to fine-tune LLMs — plan-then-execute pipeline, LLM-as-judge, HuggingFace upload. GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace GitHub - Nyquest-ai/nyquest-rust-fullstack-pub: Nyquest — Semantic Compression Proxy for LLMs. 350+ rules, local LLM stage, 15-75% token savings. Full Rust stack. GitHub - TheoV823/mneme: Enforce architectural decisions in AI-assisted development. GitHub - klemenvod/TokenBrawl: A 1v1 Bomberman-style game where two LLM agents play autonomously against each other. No human plays — you watch the AIs fight. Each agent receives a text description of the board state, reasons about it, and outputs a move as JSON. The game engine executes it. Introducing the Common AI Provider: LLM and AI Agent Support for Apache Airflow Power Circuit AI: Designing Power Electronic Circuits for Motor Drives with Generative Artificial Intelligence Ask HN: How to program with IDE and LLM on CPU locally? Show HN: Agent-cache – Multi-tier LLM/tool/session caching for Valkey and Redis Bonsai 1-bit WebGPU - a Hugging Face Space by webml-community The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows Ask HN: Simple tooling for local LLM code critique without IDE integration? Can a General LLM Diagnose a DICOM Slice? A 10-Case Public Benchmark Charts-of-Thought: Enhancing LLM Visualization Literacy (PDF, 2026) GitHub - Mesh-LLM/mesh-llm: Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat. GitHub - seamus-brady/springdrift: A persistent runtime for long-lived LLM agents Writing an LLM from scratch, part 32k -- Interventions: training a better model locally with gradient accumulation Ask HN: Which LLM model and agentic CLI are you using for local development? GitHub - wayneColt/modelcascade: Route local. Escalate smart. Never overspend. Open-source multi-model cascade routing for autonomous agents. LLM pricing is 100x harder than you think GitHub - asakin/llm-primer: Pre-warmed Claude Code sessions in tmux. No startup wait. GitHub - EggerMarc/chat-rs: A multi-provider LLM framework for Rust. GitHub - SynapseKit/SynapseKit: Minimal, async-first Python framework for production LLM apps- 2 hard deps, no magic, no SaaS. A Claude Skill that Makes LLM Paragraphs More Bearable Does Gas Town 'steal' usage from users' LLM credits & paid services to improve itself? What's Claude Code Actually Doing? Open the Black Box with the Arthur Engine Milla Jovovich's New Open Source LLM Memory App and the Dark Code Problem Your intuition of LLM token usage might be wrong Show HN: Bloomberg Terminal for LLM ops – free and open source GitHub - 0xchamin/mcptube: Transform YouTube videos into a compounding knowledge base with transcripts, vision analysis, and agentic search. Works as an MCP server for Claude, Copilot & more. Show HN: Open KB: Open LLM Knowledge Base Your LLM is a compiler, not a runtime GitHub - sapountzis/Unslop: A Web Feed That Deserves You crates.io: Rust Package Registry Beyond Karpathy's LLM-Wiki: The Necessity of Cognitive Governance GitHub - amitshekhariitbhu/llm-internals: Learn LLM internals step by step - from tokenization to attention to inference optimization. GitHub - parallem-ai/parallem: An expressive library for running agents with the Batch API. GitHub - stfurkan/pi-llm LLM-Wiki Show HN: Formal – Formal verification for AI-generated code using Lean 4 LRTS – Regression testing for LLM prompts (open source, local-first) LLM Wiki Skill: Build a Second Brain with Claude Code and Obsidian I built an LLM Wiki and RAG solution: here's a demo for a security KB The biggest advance in AI since the LLM Predict-Rlm: The LLM Runtime That Lets Models Write Their Own Control Flow the-synthetic-library/the-synthetic-mind at main · joshferrer1/the-synthetic-library GitHub - yisding/reviewwiggum GitHub - Donnyb369/mcp-spine: Context Minifier & State Guard — Local-first MCP middleware proxy GitHub - Beledarian/wgpu-llm: A from-scratch LLM inference engine that uses wgpu (the cross-platform WebGPU implementation) to dispatch WGSL compute shaders for every math operation a Transformer needs. No CUDA. No Python. No massive framework dependencies. Just Rust, raw shaders, and your GPU. GitHub - anitiue/Hindsight: An experience-driven self-improvement framework for LLM agents — 基于经验的 LLM Agent 自我改进框架 GitHub - stef41/lmscan: 🔍 Detect AI-generated text and fingerprint which LLM wrote it. Open-source GPTZero alternative. Zero dependencies, works offline. GitHub - alainnothere/AmdPerformanceTesting: Amd Performance Testing Ask HN: Is a purely Markdown-based CRM a terrible idea? Optimized for LLM agents Context Engineering - LLM Memory and Retrieval for AI Agents | Weaviate little_helper_tui/letter.md at main · sleepyeldrazi/little_helper_tui GitHub - EvanZhouDev/umr: The Unified Model Registry for all your local AI apps. GitHub - JordanCT/VigIA-Orchestrator Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain A Taxonomy of RL Environments for LLM Agents Llama LLM Network Feture GitHub - genedeng-ca/ai-mac-migration: AI-powered Mac-to-Mac migration tool - replace Apple Migration Assistant with intelligent, selective transfer using local LLMs GitHub - lunargate-ai/gateway: High-performance self-hosted AI gateway (OpenAI-compatible) with routing, retries, and streaming GitHub - AuthBits/webmcp: A lightweight, prompt-driven MCP web research server for high-quality LLM powered information extraction. Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception High-Stakes Personalization: Rethinking LLM Customization for Individual Investor Decision-Making From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents HUOZIIME: An On-Device LLM-enhanced Input Method for Deep Personalization TIDE: Token-Informed Depth Execution for Per-Token Early Exit in LLM Inference Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users
Wattfare — Connect your AI budget
bstrama · 2026-06-16 · via Hacker News - Newest: "LLM"

OAuth for AI spend · Developer preview

Your users bring
their own
AI budget.

Wattfare drops a “Connect AI budget” button into your app. Users connect, set a spending cap, and you call any model through one SDK — charged to them, not you. You stop eating the inference bill.

OpenAI-compatible Works with the Vercel AI SDK ~5-min integration

Think “Sign in with Google”, but for AI spend. One button connects a user's inference budget to your app — metered, capped, and revocable. You never store a key. They never overspend.

The problem

AI costs are the one line item you can't predict.

Every AI app makes the same uncomfortable bet: price high enough to survive your power users, ration usage so nobody hurts you, or quietly lose money on the heavy ones. Tokens scale with usage — your subscription price doesn't.

Today You pay for their tokens

  • You eat unpredictable inference costs every month.
  • Your price = margin + a budget you guessed each user would burn.
  • Power users wreck your unit economics; free tiers bleed.
  • Side projects need a credit card you're scared to attach.

With Wattfare They bring their own budget

  • Inference is funded by the user, within a cap they set.
  • You charge for the product — your margin, nothing padded.
  • Power users fund themselves. Free tiers cost you nothing.
  • Ship anything. The worst case is their budget, not your bill.

Napkin math illustrative — drag the slider

Monthly active users 1,000 Usage profile

Your inference bill, today $750/mo

With Wattfare $0/mo

Users fund their own usage inside caps they set. Your COGS stops scaling with their curiosity.

How it works

Three steps. Your app code barely changes.

A claim like that needs receipts — so here's the whole integration, as the diff you'd ship.

feat: let users bring their own AI budget +9 −1

1Drop in the button frontend · +4

@@ main.tsx @@+ <WattfareProvider publishableKey="pk_live_…" session={getToken}>    <App />+ </WattfareProvider>@@ chat.tsx @@  const ai = useChat();+ const { connect, connected } = useWattfare();+ if (!connected) return <button onClick={connect}>Connect AI budget</button>;

2Mint sessions server · +4

+ const wf = new Wattfare({ secretKey: process.env.WATTFARE_SECRET_KEY });+ app.post("/api/ai-session", (c) =>+   c.json(wf.createSession(c.var.userId, { requestLimit: { monthlyUsd: 10 } }))+ );

3Swap the model chat route · +1 −1

  const result = streamText({-   model: openai("gpt-4o"),                                    // billed to: you+   model: wf.user(userId).model("anthropic/claude-sonnet-4"),  // billed to: them    prompt,  });  // the deleted line is the one where you were paying.

01 Publishable key in the browser. The secret key stays on your server and mints short-lived session tokens.

02 Consent happens on Wattfare's domain — a popup, a cap, ~30s. State lives with us, keyed by your existing user ids.

03 The returned model is AI-SDK-compatible. Stream like you already do — every token metered against their cap.

Two sides, one button

Good for the people who build it. Better for the people who use it.

Every mechanism in the middle is read from both ends. Same line, two balance sheets.

Inference is funded by the person using it. Your AI line item drops toward zero, and margins stop fighting compute.

the budget

One budget covers every connected app — no more paying five different markups for the same tokens.

Your worst case is their cap, never your card. The front page of HN stops being a financial event.

the cap

You pick a monthly number. It's enforced upstream — requests stop at your cap, not at an honor system.

No provider keys to collect, encrypt, rotate, or leak. Connection state lives on Wattfare, keyed by your user ids.

the key

You never paste a raw API key into a stranger's app. Consent happens on Wattfare's domain, like OAuth.

Disconnects surface as typed errors — the not-connected path is a normal flow state, not a 3am page.

the switch

Revoke any app in one click. The spending stops immediately, for that app only.

One SDK, hundreds of models, AI-SDK native. Same code in dev and prod — test keys auto-approve.

the models

Your prompts proxy straight through to the model. Wattfare meters cost; it isn't where conversations live.

Freemium that costs you nothing — give the whole product away and let usage fund itself.

the free tier

Try new AI apps in 30 seconds without a new account, card, or subscription each time.

Enterprise spend controls — per-seat budgets, finance-grade reporting — are on the roadmap.

OpenAI-compatible

Standard wire format, proxied to OpenRouter. No protocol to learn.

AI SDK native

Drop-in model for the Vercel AI SDK — stream as usual.

Real from day one

Connect your own budget on localhost — same consent flow your users see.

Edge-fast

Cloudflare Workers, streamed untouched, typed errors — no buffering.

FAQ

The honest answers.

Isn't this just BYOK with extra steps?

BYOK makes the user paste a raw provider key into your app. Wattfare is an OAuth-style consent flow: the user owns the budget, sets a cap, and can revoke it — and your app never sees or stores a key. Usage is metered for them.

How is this different from OpenRouter's OAuth or “Sign in with ChatGPT”?

Same conviction — users should fund their own inference — different layer. Provider sign-ins tie your app to one vendor's accounts and hand you a per-user key to store, scope, and babysit. Wattfare keeps all state on its side, keyed by the user ids you already have, and adds the parts apps actually need: monthly caps, usage status, one-click revocation, and an AI-SDK-ready model(). Inference currently routes through OpenRouter under the hood; the connection layer is provider-neutral by design.

Which models can I use?

It's an OpenAI-compatible proxy to OpenRouter, so hundreds of models across providers — Anthropic, OpenAI, Google, open-weights — all behind one AI-SDK-compatible model() call.

Do you see my prompts?

Inference is proxied through to read the final usage so we can meter cost — Wattfare isn't a place your conversations are meant to live. As an early preview, treat it accordingly; full data terms land before general availability.

How is the spending cap enforced?

The user picks a monthly cap. Wattfare meters usage against it, and the underlying provider key carries a hard ceiling as the real backstop — so requests stop at the cap, even mid-stream.

What does it cost?

Wattfare is in developer preview and free to build on while we shape it. Pricing for the hosted service will be simple and announced well before it kicks in.

What's the stack?

Cloudflare Workers + Hono on the edge, KV for connection state and soft metering. The SDK is a tiny TypeScript package with server, client, and react entry points.

Developer preview · free to start

Add a “Connect AI budget” button today.

Install the SDK, wrap your app, and let your users fund their own AI. Five minutes to your first capped, metered request.