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Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Why Cursor is bringing self-hosted AI agents to the Fortune 500

For AI coding agents to work effectively, they need access to a broad range of systems — from private repositories The post Why Cursor is bringing self-hosted AI agents to the Fortune 500 appeared first on The New Stack.

Claude Code's source code appears to have leaked: here's what we know

Anthropic appears to have accidentally revealed the inner workings of one of its most popular and lucrative AI products, the agentic AI harness Claude Code, to the public. A 59.8 MB JavaScript source map file (.map), intended for internal debugging, was inadvertently included in version 2.1.88 of the @anthropic-ai/claude-code package on the public npm registry pushed live earlier this morning. By 4:23 am ET, Chaofan Shou (@Fried_rice), an intern at Solayer Labs, broadcasted the discovery on X (formerly Twitter). The post, which included a direct download link to a hosted archive, acted as a digital flare. Within hours, the ~512,000-line TypeScript codebase was mirrored across GitHub and analyzed by thousands of developers. For Anthropic, a company currently riding a meteoric rise with a reported $19 billion annualized revenue run-rate as of March 2026, the leak is more than a security lapse; it is a strategic hemorrhage of intellectual property.The timing is particularly critical given the commercial velocity of the product. Market data indicates that Claude Code alone has achieved an annualized recurring revenue (ARR) of $2.5 billion, a figure that has more than doubled since the beginning of the year. With enterprise adoption accounting for 80% of its revenue, the leak provides competitors—from established giants to nimble rivals like Cursor—a literal blueprint for how to build a high-agency, reliable, and commercially viable AI agent. Anthropic confirmed the leak in a spokesperson’s e-mailed statement to VentureBeat, which reads: “Earlier today, a Claude Code release included some internal source code. No sensitive customer data or credentials were involved or exposed. This was a release packaging issue caused by human error, not a security breach. We're rolling out measures to prevent this from happening again.” The anatomy of agentic memory The most significant takeaway for competitors lies in how Anthropic solved "context entropy"—the tendency for AI agents to

Show HN: ProofShot – Give AI coding agents eyes to verify the UI they build

I use AI agents to build UI features daily. The thing that kept annoying me: the agent writes code but never sees what it actually looks like in the browser. It can’t tell if the layout is broken or if the console is throwing errors.<p>So I built a CLI that lets the agent open a browser, interact with the page, record what happens, and collect any errors. Then it bundles everything — video, screenshots, logs — into a self-contained HTML file I can review in seconds.<p><pre><code> proofshot start --run &quot;npm run dev&quot; --port 3000 # agent navigates, clicks, takes screenshots proofshot stop </code></pre> It works with whatever agent you use (Claude Code, Cursor, Codex, etc.) — it’s just shell commands. It&#x27;s packaged as a skill so your AI coding agent knows exactly how it works. It&#x27;s built on agent-browser from Vercel Labs which is far better and faster than Playwright MCP.<p>It’s not a testing framework. The agent doesn’t decide pass&#x2F;fail. It just gives me the evidence so I don’t have to open the browser myself every time.<p>Open source and completely free.<p>Website: <a href="https:&#x2F;&#x2F;proofshot.argil.io&#x2F;" rel="nofollow">https:&#x2F;&#x2F;proofshot.argil.io&#x2F;</a>

Cursor built a fleet of security agents to solve a familiar frustration

Cursor‘s security team has built a fleet of AI agents that continuously monitor and secure the company’s codebase, and it The post Cursor built a fleet of security agents to solve a familiar frustration appeared first on The New Stack.

Launch HN: Captain (YC W26) – Automated RAG for Files

Hi HN, we’re Lewis and Edgar, building Captain to simplify unstructured data search (<a href="https:&#x2F;&#x2F;runcaptain.com">https:&#x2F;&#x2F;runcaptain.com</a>). Captain automates the building and maintenance of file-based RAG pipelines. It indexes cloud storage like S3 and GCS, plus SaaS sources like Google Drive. There’s a quick walkthrough at <a href="https:&#x2F;&#x2F;youtu.be&#x2F;EIQkwAsIPmc" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;EIQkwAsIPmc</a>.<p>We also put up this demo site called “Ask PG’s Essays” which lets you ask&#x2F;search the corpus of pg’s essays, to get a feel for how it works: <a href="https:&#x2F;&#x2F;pg.runcaptain.com">https:&#x2F;&#x2F;pg.runcaptain.com</a>. The RAG part of this took Captain about 3 minutes to set up.<p>Here are some sample prompts to get a feel for the experience:<p>“When do we do things that don&#x27;t scale? When should we be more cautious?” <a href="https:&#x2F;&#x2F;pg.runcaptain.com&#x2F;?q=When%20do%20we%20do%20things%20that%20don&#x27;t%20scale%3F%20When%20should%20we%20be%20more%20cautious%3F">https:&#x2F;&#x2F;pg.runcaptain.com&#x2F;?q=When%20do%20we%20do%20things%20...</a><p>“Give me some advice, I&#x27;m fundraising” <a href="https:&#x2F;&#x2F;pg.runcaptain.com&#x2F;?q=Give%20me%20some%20advice%2C%20I&#x27;m%20fundraising">https:&#x2F;&#x2F;pg.runcaptain.com&#x2F;?q=Give%20me%20some%20advice%2C%20...</a><p>“What are the biggest advantages of Lisp” <a href="https:&#x2F;&#x2F;pg.runcaptain.com&#x2F;?q=what%20are%20the%20biggest%20advantages%20of%20Lisp">https:&#x2F;&#x2F;pg.runcaptain.com&#x2F;?q=what%20are%20the%20biggest%20ad...</a><p>A good production RAG pipeline takes substantial effort to build, especially for file workloads. You have to handle ETL or text extraction, chunking, embedding, storage, search, re-ranking, inference, and often compliance and observability – all while optimizing for latency and reliability. It’s a lot to manage. grep works well in some cases, but for agents, semantic search provides significantly higher performance. Cursor uses both and reports 6.5%–23.5% accuracy gains from vector search over grep (<a href="https:&#x2F;&#x2F;cursor.com&#x2F;blog&#x2F;semsearch" rel="nofollow">https:&#x2F;&#x2F;cursor.com&#x2F;blog&#x2F;semsearch</a>).<p>We’ve spent the past four years scaling RAG pipelines for companies, and Edgar’s work at Purdue’s NLP lab directly informed our chunking techniques. In conversations with dozens of engineers, we repeatedly saw DIY pipelines produce inconsistent results, even after weeks of tuning. Many teams lacked clarity on which retrieval strategies best fit their data.<p>We realized that a system to provision storage and embeddings, handle indexing, and continuously update pipelines to reflect the latest search techniques could remove the need for every team to rebuild RAG themselves. That idea became Captain.<p>In practice, one API call indexes URLs, cloud storage buckets, directories, or individual files. Under the hood, we’re converting everything to Markdown. For this, we’ve had good results with Gemini 3 Pro for images, Reducto for complex documents, and Extend for basic OCR. For embedding models, ‘gemini-embedding-001’ performed reasonably well at first, but we later switched to the Contextualized Embeddings from ‘voyage-context-3’. It produced more relevant results than even the newer Voyage 4 models because its chunk embeddings are encoded with awareness of the surrounding document context. We then applied Voyage’s ‘rerank-2.5’ as second-stage re-ranking, reducing 50 initial chunks to a final top 15 (configurable in Captain’s API). Dense embeddings are just half the picture and full-text search with RRF complete our hybrid retrieval. In the Captain API, these techniques are exposed through a single &#x2F;query endpoint. Access controls can be configured via metadata filters, and page number citations are returned automatically.<p>The stack is constantly changing but the Captain API creates a standard interface for this. You can try Captain, 1 month for free, and build your own pipelines at <a href="https:&#x2F;&#x2F;runcaptain.com">https:&#x2F;&#x2F;runcaptain.com</a>. We’re looking for candid feedback, especially anything that can make it more useful, and look forward to your comments!

How I Built a Persistent Memory Layer for AI Coding Assistants Using MCP

Every time I start a new Claude or Cursor session, I have the same conversation: "I use TypeScript...

Add example usage for Costory billing datasources in documentation

- Included Terraform example for Anthropic billing datasource. - Added Terraform example for Cursor billing datasource. - Defined required variables and provider configuration for both datasources.

Show HN: BurnRate – Track what you spend on AI coding tools

I was paying $100&#x2F;mo for Claude Code Pro and had no idea where it was going. I&#x27;d hit the 5-hour rate limit constantly, but couldn&#x27;t tell which sessions were burning through my allocation or whether Opus was worth the premium over Sonnet for my workflows. So I built a tool to find out.<p>BurnRate is a local CLI that parses your AI coding tool session data and gives you a full cost analytics dashboard. It tracks Claude Code, Cursor IDE, and OpenAI Codex in one place.<p>Everything runs 100% on your machine. Your session data, token counts, costs, prompts — none of it leaves your computer. No API key to paste, no telemetry. It reads the local session files your tools already generate and does the math.<p>Out of the box you get: multi-provider cost tracking, 10 different analytics views (daily trends, per-session breakdown, model usage split, token efficiency), an optimization engine with 23 rules that suggests concrete config changes to reduce spend, usage limit monitoring so you know when you&#x27;re approaching rate limits, side-by-side provider comparison, and budget alerts when you&#x27;re on track to blow past a monthly cap.<p>For managers, there&#x27;s a team dashboard where devs can optionally push anonymized usage snapshots to a shared view. Useful for understanding team-wide AI tool costs and figuring out which plans actually make sense. Free tier available, Pro is $9&#x2F;mo, Team is $29&#x2F;mo.Happy to answer any questions. Feedback welcome.

Show HN: Hydra – Real-time ops dashboard for developers running AI agents

I built this because I was running Claude Code, a local LLM, and multiple dev servers simultaneously and had no visibility into what was actually happening. Activity Monitor is useless for this. htop has no context.<p>Hydra is a macOS desktop app (Electron + React + TypeScript) that shows:<p>- Which AI agents are running (Claude Code, Codex, Cursor, Gemini) and their status - Per-process CPU&#x2F;memory with project grouping - Port-to-process mapping - Git repo health across all your projects - Network bandwidth per process - Security posture via a built-in scanner - AI briefings from a local LLM (I use LM Studio with Qwen 35B) — no cloud API needed - Claude Code usage and cost tracking<p>Everything runs locally. No telemetry, no accounts, no cloud dependency.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;kunalnano&#x2F;hydra" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;kunalnano&#x2F;hydra</a><p>Happy to answer questions about the architecture or the local LLM integration approach.

Show HN: OpenRouter Skill – Reusable integration for AI agents using OpenRouter

Hi HN,<p>I kept rebuilding the same OpenRouter integration across side projects – model discovery, image generation, cost tracking via the generation endpoint, routing with fallbacks, multimodal chat with PDFs. Every time I&#x27;d start fresh, the agent would get some things right and miss others (wrong response parsing, missing attribution headers, etc.).<p>So I packaged the working patterns into a skill – a structured reference that AI coding agents (Claude, Cursor, etc.) read before writing code. It includes quick snippets, production playbooks, Next.js and Express starter templates, shared TypeScript helpers, and smoke tests.<p>I&#x27;m a PM, not a developer – the code was written by Claude and reviewed&#x2F;corrected by me. Happy to answer questions about the skill format or the OpenRouter patterns.

Show HN: Armalo AI – The Infrastructure for Agent Networks

Hey HN — I&#x27;m Ryan, founder of Armalo AI (<a href="https:&#x2F;&#x2F;armalo.ai" rel="nofollow">https:&#x2F;&#x2F;armalo.ai</a>). I spent years as a software engineer at Google, YouTube, and AWS, most recently building AI agents at AWS. Watching those systems interact in production — and seeing the same gaps appear over and over — convinced me that the missing piece wasn&#x27;t more capable agents, but the infrastructure underneath them. So I left to build it.<p>Armalo AI is the infrastructure layer that multi-agent AI networks need to actually function in production.<p>THE PROBLEM<p>Every week there&#x27;s a new story about an AI agent deleting a production database, a multi-agent workflow cascading into failure, or an autonomous system doing something its operator never intended. We dug into 2025&#x27;s worst incidents and found a consistent root cause: agents have no accountability layer.<p>You can&#x27;t Google an agent&#x27;s reputation. When one agent delegates to another, there&#x27;s no escrow, no contract, no recourse. State doesn&#x27;t persist across a network. And as agents start hiring other agents — which is already happening — the absence of identity, commerce, and memory infrastructure becomes a critical gap.<p>Benchmarks measure capability. We measure reliability.<p>WHAT WE BUILT<p>Armalo is three integrated layers:<p>1. Trust &amp; Reputation<p>Agents earn a PactScore: a 0–1000 score across five behavioral dimensions — task completion, policy compliance, latency, safety, and peer attestation. Four certification tiers (Bronze → Gold). Scores are cryptographically verifiable and on-chain. When automated verification isn&#x27;t enough, our LLM-powered Jury system brings multi-model judgment to disputes. All of it is queryable via REST API in sub-second latency.<p>2. Agent Commerce<p>Agents can define behavioral pacts — machine-readable contracts that specify what they promise to deliver. These are backed by USDC escrow on Base L2 via smart contracts. Funds lock when a deal is created and release only when verified delivery conditions are met. The marketplace lets agents hire and get hired autonomously, no human intermediary needed. We also support x402 pay-per-call: agents pay $0.001&#x2F;score lookup in USDC with no API key, no account, no human billing setup.<p>3. Memory &amp; Coordination<p>Memory Mesh gives agents persistent shared state across a network. Context Packs are versioned, safety-scanned knowledge bundles that agents can publish, license, and ingest. Swarms let you form synchronized agent fleets with real-time shared context — so a network of 50 agents can reason from the same ground truth.<p>THE FULL STACK<p>Beyond the three core layers, we&#x27;ve shipped: OpenClaw MCP (25 tools for Claude, Cursor, LangChain), Jarvis (an agent terminal for interacting with the platform), PactLabs (our research arm — working on trust algorithms, collusion detection, adversarial robustness, and optimal escrow sizing), real-time monitoring and alerting, and a governance forum where trust-weighted agents post, vote, and collaborate.<p>WHY ON-CHAIN<p>We get that &quot;on-chain&quot; raises eyebrows in some HN circles. Our reasoning: agent-to-agent trust needs to be verifiable by parties who have no prior relationship and no shared authority. Cryptographic verification at every layer, with an open protocol, means any agent framework can interoperate with Armalo AI&#x27;s trust signals without going through us as an intermediary. We&#x27;re not building a walled garden.<p>PRICING<p>Free tier (1 agent, 3 evals&#x2F;month), Pro at $99 USDC&#x2F;month (10 agents, unlimited evals, escrow, jury access), Enterprise at $2,999&#x2F;month. Or pure pay-per-call via x402 — no subscription required.<p>We&#x27;d love feedback from builders working on multi-agent systems. What&#x27;s the hardest part of trust and coordination you&#x27;ve hit in production?

Show HN: Agents-lint – detect stale paths and context rot in AGENTS.md files

AGENTS.md (and CLAUDE.md, GEMINI.md, .cursorrules) has become the standard way to tell AI coding agents how your repo works. It&#x27;s now in 60,000+ repos. Codex, Claude Code, and Gemini CLI read these files before every task.<p>The problem: nobody keeps them up to date.<p>Paths get renamed. npm scripts change. Framework patterns go stale. The file that was accurate when you wrote it in September starts giving your agents wrong instructions by December — without a single commit to AGENTS.md.<p>An ETH Zurich study presented at ICSE 2026 put numbers on this: stale context files reduced agent task success by 2–3% while increasing token costs by over 20%.<p>agents-lint is a zero-dependency CLI that catches this automatically:<p><pre><code> npx agents-lint </code></pre> It runs five independent checks: 1. Filesystem — every path mentioned in your file is verified to exist 2. npm scripts — every `npm run &lt;script&gt;` is verified against package.json (workspace-aware) 3. Dependencies — deprecated packages (moment, request, tslint) are flagged 4. Framework staleness — Angular NgModules in Angular 14+, ReactDOM.render() in React 19, getInitialProps in Next.js App Router, CommonJS in ESM projects 5. Structure — recommended sections, bloat (&gt;15k chars adds 20% token cost), unresolved TODOs, old year references<p>Every run produces a freshness score (0–100). The real value is adding it to CI with a weekly schedule — because context rot happens even when the file hasn&#x27;t changed:<p><pre><code> schedule: - cron: &#x27;0 9 * * 1&#x27; # Every Monday </code></pre> That weekly schedule is the whole point. Your AGENTS.md can rot without a single commit to it.<p>When multiple agent config files exist (AGENTS.md + CLAUDE.md, etc.), it also cross-checks them for conflicting instructions — e.g. one file says `npm run test`, the other says `npm run test:unit`.<p>What I found when testing on real repos: absolute home-directory paths that only work on the author&#x27;s machine, monorepo commands copy-pasted into single-package projects, and framework references to APIs removed two major versions ago. All silently misleading agents on every task.<p>Landing page: <a href="https:&#x2F;&#x2F;giacomo.github.io&#x2F;agents-lint&#x2F;" rel="nofollow">https:&#x2F;&#x2F;giacomo.github.io&#x2F;agents-lint&#x2F;</a> npm: <a href="https:&#x2F;&#x2F;www.npmjs.com&#x2F;package&#x2F;agents-lint" rel="nofollow">https:&#x2F;&#x2F;www.npmjs.com&#x2F;package&#x2F;agents-lint</a><p>Would love feedback — especially if you find unexpected issues in your own AGENTS.md.

Show HN: AgentGuard – a QA engine that sits between AI coding agents and LLMs

AI coding agents are great at generating code, but terrible at respecting architecture. We kept getting code that looked correct but didn’t type-check, had hallucinated imports, or ignored project boundaries.<p>AgentGuard is an open-source quality-assurance engine that sits between an AI coding agent and the LLM. It enforces a top-down generation pipeline:<p>Skeleton → Contracts → Wiring → Logic<p>Each stage constrains the next, and we validate syntax, imports, linting and types before any human sees the code. The LLM also self-reviews its output against explicit criteria.<p>AgentGuard supports MCP (Claude Desktop, Cursor, Windsurf, Cline) and tracks token cost per model and run.<p>Python 3.11+, MIT licensed.

Ask HN: Why do AI coding agents refuse to save their own observations?

I&#x27;ve spent months building tooling for AI coding agents and hit something I can&#x27;t fully explain.<p>If you give an agent (Claude Code, Cursor, Codex) a tool to save observations — &quot;save_observation: persist this insight for future sessions&quot; — and explicitly instruct it to use the tool in system prompts, config files, everywhere you can, it calls it maybe 30% of the time.<p>The agent will happily use tools that help it complete the current task. But a tool that only benefits future sessions? Almost never.<p>My working theory: these models are optimized for task completion within the current context window. Saving an observation has zero value for the current task — it&#x27;s a token cost with no immediate reward. The model has learned that every token spent on &quot;let me save this for later&quot; is a token not spent on the actual work. The incentive structure is wrong at the training level.<p>I ended up building a passive observation system that watches what the agent does and infers observations from tool calls and AST-level code diffs, without requiring agent cooperation. But I&#x27;m curious if others have found ways to make agents reliably self-document.<p>Has anyone solved this? Techniques like: - Prompt structures that actually get agents to save context - Fine-tuning approaches that reward knowledge retention - Alternative architectures for persistent agent memory<p>Or is passive observation the only reliable path when the agent won&#x27;t cooperate?

Show HN: Vigilo – Local audit trail and cost tracker for AI coding agents

I realized I&#x27;d spent $197 in a single day using Claude Code and Cursor without any visibility into what they were actually doing. No breakdown of which operations cost what, no record of what files were read or written, no way to audit what the agent executed on my behalf.<p>So I built vigilo.<p>It sits between your AI agent and your system as an MCP server, logging every tool call — file reads, writes, shell commands, git operations — to a local append-only JSONL ledger. For write operations it captures a unified diff. Every event gets a risk level (read&#x2F;write&#x2F;exec), timing, git context, model, and token count.<p>Nothing leaves your machine. No accounts, no telemetry, no cloud. Arguments and results can be encrypted at rest with AES-256-GCM — the key never leaves ~&#x2F;.vigilo&#x2F;.<p>Works with Claude Code and Cursor. Claude Code uses MCP + a PostToolUse hook to capture both MCP tools and built-in tools (Read, Write, Bash, Edit). Cursor uses MCP + cursor.com&#x27;s API for real per-request token and cost data.<p>The CLI gives you: - vigilo view — full session history with collapsible events, diffs, costs - vigilo stats — aggregate breakdown by tool, file, model, project - vigilo watch — live tail as events happen - vigilo dashboard — real-time web UI with SSE live feed, time-series charts, session explorer - vigilo cursor-usage — actual billing data pulled from cursor.com<p>The ledger is plain JSONL, rotates at 10MB, optionally encrypted. You can export to CSV or JSON anytime.<p>Built in Rust.