Exa

Exa AI

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Exa AI

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Exa AI

Idk how to code but I built my entire prospecting stack with Claude Code

I cant code at all. But i spent about a few hours over a weekend building a full outbound prospecting system with Claude Code and a couple of APIs. It replaced a very manual set up we had with multiple tools. Sharing the workflow because i think more people should know this is possible now without an engineering team. The setup: i have ICP criteria saved in a local text file on my desktop. Industry, headcount range, funding stage, target personas, the usual. Claude Code reads that file as context for everything it does. The workflow: Company search. Claude Code hits a data API with my ICP filters and pulls back matching companies. Headcount, funding, tech stack, hiring signals, all structured. I was using Exa before for web search but the data wasnt structured enough for this. People search within those companies. Filtered by persona, so i'm only pulling Directors of Sales, Heads of Revenue, VP Marketing, whatever matches my buyer. Contact enrichment. Emails and phones through a waterfall provider. Multiple sources checked, only pay for verified contacts. Personalization layer. Pull recent social posts and activity for each contact. Claude Code reads through their posts and drafts personalized openers referencing something specific they said or shared. This is where the AI part actually matters. Monitoring. Set up webhooks for job changes and hiring signals at target accounts. When someone new joins a company on my list or a company starts posting roles in my space, i get an alert and Claude Code auto-generates the outreach. The whole thing runs on three tools: Crustdata - company and people search, firmographics, hiring signals, social posts. API only so Claude Code queries it directly. FullEnrich - email and phone waterfall. 20+ providers, verifies inline, only charges for verified contacts. Also API based so it plugs straight into the workflow. Instantly - sending. Manages multiple inboxes and warming. Nothing fancy here, just needed something reliable for delivery. Some things I learned: Read the API docs carefully before you start building. i burned through a bunch of credits using the expensive realtime endpoint when the cached version would have been fine for 90% of my searches. 33x cost differnce. Claude Code is really good at chaining API calls together if you give it enough context about what you want. i just described the workflow in plain english and it built the scripts. The ICP file is key tho, without that context it doesnt know what to filter for. Its not perfect. Still iterating on the personalization quality and the webhook alerting sometimes fires on irrelevant job postings. But for a weekend build with zero coding ability, its replaced tooling thats very cumbersome and not as effective If you're a solo founder or small team running outbound and paying for 4-5 different tools, this is worth trying. Claude Code plus one good data API plus a sending tool is all you need imo submitted by /u/Unspoken_Table [link] [comments]

The case for AI increasing your salary

Here me out because I know there's a lot of doom and gloom, and believe me, I understand and feel it around job loss. Return to supply and demand with me. Today in the world, there is a certain amount of human processing power and a certain amount of AI processing power. One of these is increasing exponentially, and the other's growth rate is in decline... AI processing will then compete with AI processing for value creation (ultimately judged by humans). Human processing power will be more scarce and thus more valuable. This assumes that you are not one of those crazies who believe that the human brain is perfectly reproducible in bits and bytes, and thus there is no difference between human and AI processing power. To whom I remind that Humans are the result of an 800MB file (human genome) that builds a conscious machine. It wires 100 trillion nerve links across 37 trillion nodes, live-patches its code, runs a 20-watt exaFLOP supercomputer on the caloric intake of a sandwich, and packs 215 petabytes of data into a single gram. Human labor FTW submitted by /u/nomadicsamiam [link] [comments]

GitHub connector not visible in Claude Connectors

Where is it? submitted by /u/Sad_Cut9143 [link] [comments]

A 14-day “Growth Forge” sprint: build an AI-powered growth agent on a real stack

Sharing something that sits at the intersection of AI agents and growth systems. VideoDB (backend for video/audio for AI agents) is running a 14-day sprint called Growth Forge for 5 builders to design and ship a growth agent on top of an existing agentic stack – web browsing, social actions, research APIs, metrics, the works – and then prove it can run with minimal manual involvement. Why it’s interesting This is not a fluff “growth challenge” or a generic hackathon. It’s treated like a real audition for a Growth Lead role, with very concrete incentives: 500 USD – “sweat cash” paid on successful sprint completion 1,000 USD – performance bounty if your system beats their internal baseline Co-published case study with your name on it Strongest builder gets an offer to join as Growth Lead at VideoDB Top performers can continue into deeper collaborations So if you execute well, you can walk away with up to 1,500 USD in cash, a strong public case study on a legit AI infra product, and a serious shot at a Growth Lead role. What you get to build with You don’t start from zero. They give you an agentic stack on day one: Tokens & compute (with sane limits) OpenClaw already deployed for orchestration Browser-use agents (X, LinkedIn, YouTube, etc.) wired up with baseline behaviors Parallel / Exa APIs for research and retrieval Cloudflare workers / queues / edge in front of everything Engineering support from the VideoDB team to get agents production-ready The baseline system can already: Browse the web for research / scraping / summaries Operate across social platforms (post, comment, react, follow) Use research APIs for deep retrieval Route workflows between surfaces Observe metrics via attribution + dashboards Your job: treat it like a well-instrumented codebase and build a real growth loop on top of it. How the sprint works Total timeline: 24 days Days 1–3 – Define Pick your metric, instrument the funnel, design the agent loop. Days 4–14 – Build Ship the growth agent, launch it in production, iterate. Days 15–24 – Prove 10-day proving run where the agent keeps running with low manual dependency. On Day 3 you lock one metric (you own it end-to-end): Signups Activation GitHub → usage Content → pipeline They provide UTMs, dashboards, and shared attribution so the work is legible. Who this is for This seems ideal for people who: Have shipped real things (and can show links / proof) Think in systems and loops, not just one-off campaigns Use AI as leverage (agents, automation, retrieval, etc.) Care about hard outcomes: signups, activation, pipeline, usage Want a meaningful, time-boxed growth + infra challenge with upside If that sounds like you (or someone you know), the details are here (deadline is 10 May 2026): 👉 https://forge.videodb.io Curious what people here think of this format. Personally, I like that it’s outcome-backed (cash + metrics + hiring) rather than just another “growth contest”. submitted by /u/CallmeAK__ [link] [comments]

You can now give an AI agent its own email, phone number, wallet, computer, and voice. This is what the stack looks like

I’ve been tracking the companies building primitives specifically for agents rather than humans. The pattern is becoming obvious: every capability a human employee takes for granted is getting rebuilt as an API. Here are some of the companies building for AI agents: AgentMail — agents can have email accounts AgentPhone — agents can have phone numbers Kapso — agents can have WhatsApp numbers Daytona / E2B — agents can have their own computers monid.ai — agents can read social media (X, TikTok, Reddit, LinkedIn, Amazon, Facebook) Browserbase / Browser Use / Hyperbrowser — agents can use web browsers Firecrawl — agents can crawl the web without a browser Mem0 — agents can remember things Kite / Sponge — agents can pay for things Composio — agents can use your SaaS tools Orthogonal — agents can access APIs more easily ElevenLabs / Vapi — agents can have a voice Sixtyfour — agents can search for people and companies Exa — agents can search the web (Google isn’t built for agents) What’s interesting is how quickly this came together. Not long ago, none of this really existed in a usable form. Now you can piece together an agent with identity, memory, communication, and spending in a single afternoon. Feels less like “AI tools” and more like the early version of an agent-native infrastructure stack. Curious if anyone here is actually building on top of this. What are you using? Also probably missing a bunch - drop anything I should add and I’ll keep this updated. submitted by /u/Shot_Fudge_6195 [link] [comments]

OpenClaw has 500,000 instances and no enterprise kill switch

“Your AI? It’s my AI now.” The line came from Etay Maor, VP of Threat Intelligence at Cato Networks, in an exclusive interview with VentureBeat at RSAC 2026 — and it describes exactly what happened to a U.K. CEO whose OpenClaw instance ended up for sale on BreachForums. Maor's argument is that the industry handed AI agents the kind of autonomy it would never extend to a human employee, discarding zero trust, least privilege, and assume-breach in the process. The proof arrived on BreachForums three weeks before Maor’s interview. On February 22, a threat actor using the handle “fluffyduck” posted a listing advertising root shell access to the CEO’s computer for $25,000 in Monero or Litecoin. The shell was not the selling point. The CEO’s OpenClaw AI personal assistant was. The buyer would get every conversation the CEO had with the AI, the company’s full production database, Telegram bot tokens, Trading 212 API keys, and personal details the CEO disclosed to the assistant about family and finances. The threat actor noted the CEO was actively interacting with OpenClaw in real time, making the listing a live intelligence feed rather than a static data dump. Cato CTRL senior security researcher Vitaly Simonovich documented the listing on February 25. The CEO’s OpenClaw instance stored everything in plain-text Markdown files under ~/.openclaw/workspace/ with no encryption at rest. The threat actor didn't need to exfiltrate anything; the CEO had already assembled it. When the security team discovered the breach, there was no native enterprise kill switch, no management console, and no way to inventory how many other instances were running across the organization. OpenClaw runs locally with direct access to the host machine’s file system, network connections, browser sessions, and installed applications. The coverage to date has tracked its velocity, but what it hasn't mapped is the threat surface. The four vendors who used RSAC 2026 to ship responses still haven't produced

OCR for construction documents does not work, we fixed it

So we&#x27;ve built an API and trained models that detects fixtures, extracts schedules, and analyzes construction documents. Check us out!<p>More examples: - <a href="https:&#x2F;&#x2F;www.getanchorgrid.com&#x2F;developer&#x2F;docs&#x2F;endpoints&#x2F;drawings-doors" rel="nofollow">https:&#x2F;&#x2F;www.getanchorgrid.com&#x2F;developer&#x2F;docs&#x2F;endpoints&#x2F;drawi...</a><p>Main website: - <a href="https:&#x2F;&#x2F;www.getanchorgrid.com&#x2F;developer" rel="nofollow">https:&#x2F;&#x2F;www.getanchorgrid.com&#x2F;developer</a><p>Why we did it: <a href="https:&#x2F;&#x2F;www.getanchorgrid.com&#x2F;developer&#x2F;docs&#x2F;changelog&#x2F;construction-drawings-are-data-prisons" rel="nofollow">https:&#x2F;&#x2F;www.getanchorgrid.com&#x2F;developer&#x2F;docs&#x2F;changelog&#x2F;const...</a>

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>

Vandalizing My Own Wikipedia Experience: A 90s Cyberpunk GeoCities Makeover

Wikipedia is a marvel. It is the Library of Alexandria of our time, a meticulously curated repository...

How to Pass the Claude Certified Architect (CCA) Foundations Exam

Anthropic launched its first official technical certification on March 12, 2026 — the Claude...

Fixing AI failure: Three changes enterprises should make now

Recent reports about AI project failure rates have raised uncomfortable questions for organizations investing heavily in AI. Much of the discussion has focused on technical factors like model accuracy and data quality, but after watching dozens of AI initiatives launch, I’ve noticed that the biggest opportunities for improvement are often cultural, not technical. Internal projects that struggle tend to share common issues. For example, engineering teams build models that product managers don’t know how to use. Data scientists build prototypes that operations teams struggle to maintain. And AI applications sit unused because the people they were built for weren't involved in deciding what “useful” really meant. In contrast, organizations that achieve meaningful value with AI have figured out how to create the right kind of collaboration across departments, and established shared accountability for outcomes. The technology matters, but the organizational readiness matters just as much. Here are three practices I’ve observed that address the cultural and organizational barriers that can impede AI success. Expand AI literacy beyond engineering When only engineers understand how an AI system works and what it’s capable of, collaboration breaks down. Product managers can't evaluate trade-offs they don't understand. Designers can't create interfaces for capabilities they can't articulate. Analysts can't validate outputs they can't interpret. The solution isn't making everyone a data scientist. It's helping each role understand how AI applies to their specific work. Product managers need to grasp what kinds of generated content, predictions or recommendations are realistic given available data. Designers need to understand what the AI can actually do so they can design features users will find useful. Analysts need to know which AI outputs require human validation versus which can be trusted. When teams share this working vocabulary, AI stops being something that happens in

Show HN: Oxyde – Pydantic-native async ORM with a Rust core

Hi HN! I built Oxyde because I was tired of duplicating my models.<p>If you use FastAPI, you know the drill. You define Pydantic models for your API, then define separate ORM models for your database, then write converters between them. SQLModel tries to fix this but it&#x27;s still SQLAlchemy underneath. Tortoise gives you a nice Django-style API but its own model system. Django ORM is great but welded to the framework.<p>I wanted something simple: your Pydantic model IS your database model. One class, full validation on input and output, native type hints, zero duplication. The query API is Django-style (.objects.filter(), .exclude(), Q&#x2F;F expressions) because I think it&#x27;s one of the best designs out there.<p><i>Explicit over implicit.</i> I tried to remove all the magic. Queries don&#x27;t touch the database until you call a terminal method like .all(), .get(), or .first(). If you don&#x27;t explicitly call .join() or .prefetch(), related data won&#x27;t be loaded. No lazy loading, no surprise N+1 queries behind your back. You see exactly what hits the database by reading the code.<p><i>Type safety</i> was a big motivation. Python&#x27;s weak spot is runtime surprises, so Oxyde tackles this on three levels: (1) when you run makemigrations, it also generates .pyi stub files with fully typed queries, so your IDE knows that filter(age__gte=...) takes an int, that create() accepts exactly the fields your model has, and that .all() returns list[User] not list[Any]; (2) Pydantic validates data going into the database; (3) Pydantic validates data coming back out via model_validate(). You get autocompletion, red squiggles on typos, and runtime guarantees, all from the same model definition.<p><i>Why Rust?</i> Not for speed as a goal. I don&#x27;t do &quot;language X is better&quot; debates. Each one is good at what it was made for. Python is hard to beat for expressing business logic. But infrastructure stuff like SQL generation, connection pooling, and row serialization is where a systems language makes sense. So I split it: Python handles your models and business logic, Rust handles the database plumbing. Queries are built as an IR in Python, serialized via MessagePack, sent to Rust which generates dialect-specific SQL, executes it, and streams results back. Speed is a side effect of this split, not the goal. But since you&#x27;re not paying a performance tax for the convenience, here are the benchmarks if curious: <a href="https:&#x2F;&#x2F;oxyde.fatalyst.dev&#x2F;latest&#x2F;advanced&#x2F;benchmarks&#x2F;" rel="nofollow">https:&#x2F;&#x2F;oxyde.fatalyst.dev&#x2F;latest&#x2F;advanced&#x2F;benchmarks&#x2F;</a><p>What&#x27;s there today: Django-style migrations (makemigrations &#x2F; migrate), transactions with savepoints, joins and prefetch, PostgreSQL + SQLite + MySQL, FastAPI integration, and an auto-generated admin panel that works with FastAPI, Litestar, Sanic, Quart, and Falcon (<a href="https:&#x2F;&#x2F;github.com&#x2F;mr-fatalyst&#x2F;oxyde-admin" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;mr-fatalyst&#x2F;oxyde-admin</a>).<p>It&#x27;s v0.5, beta, active development, API might still change. This is my attempt to build the ORM I personally wanted to use. Would love feedback, criticism, ideas.<p>Docs: <a href="https:&#x2F;&#x2F;oxyde.fatalyst.dev&#x2F;" rel="nofollow">https:&#x2F;&#x2F;oxyde.fatalyst.dev&#x2F;</a><p>Step-by-step FastAPI tutorial (blog API from scratch): <a href="https:&#x2F;&#x2F;github.com&#x2F;mr-fatalyst&#x2F;fastapi-oxyde-example" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;mr-fatalyst&#x2F;fastapi-oxyde-example</a>

Understanding Representation Learning in Neural Networks (With PyTorch Example)

Deep learning systems are powerful because they learn representations of data automatically. Instead...

AI and Jobs: What Anthropic's Labor Market Data Actually Shows About Your Career

Anthropic's 2026 study ranks jobs by real AI usage data. Programmers are 75% exposed, but unemployment has not risen. See exactly where your role stands.

feat: Sandbox integration test — real binary lifecycle + stress testing (#37)

## Summary Implements comprehensive GitHub Actions sandbox testing workflow that validates real daemon binary lifecycle, catching deployment bugs that in-process tests cannot detect. ## Changes - **Complete Sandbox Workflow**: Tests actual `pi-daemon` binary in CI environment - **Comprehensive Coverage**: Smoke tests, concurrency, stress testing, crash recovery - **Real-world Validation**: PID files, port binding, signal handling, memory behavior - **Future-Ready**: Enhancement issues created for persistence, supervisor, scheduler testing ## Test Phases Implemented ### 🔍 Phase 1: Smoke Testing - **Binary Startup**: Release build starts as real daemon process - **Endpoint Validation**: Health, status, agent CRUD, webchat, OpenAI API - **PID Management**: daemon.json creation, tracking, cleanup verification - **Basic Functionality**: All core features work in real deployment scenario ### ⚡ Phase 2: Concurrency & Load Testing - **HTTP Load**: 50 concurrent requests to `/api/status` endpoint - **Agent Stress**: 20 concurrent agent registrations with verification - **WebSocket Load**: 5 concurrent WebSocket connections within per-IP limits - **Memory Monitoring**: RSS usage tracking with 200MB warning threshold ### 💪 Phase 3: Stress & Recovery Testing - **Sustained Load**: 30-second continuous request generation with memory growth monitoring - **Crash Recovery**: Kill -9 simulation → restart verification → full functionality restored - **Memory Validation**: Growth monitoring with warnings if >50MB increase during load ### 🛑 Phase 4: Graceful Shutdown Testing - **API Shutdown**: `POST /api/shutdown` endpoint triggers graceful exit - **Process Cleanup**: PID file removal, port release verification - **CLI Validation**: Commands handle daemon state correctly when stopped ## Critical Gaps Addressed | What In-Process Tests Miss | Real Deployment Bug Example | Sandbox Test Coverage | |---------------------------|----------------------------|---------------------| | Binary actually starts | Compiles but panics on launch | ✅ Real daemon startup | | PID file lifecycle | Written but not cleaned up | ✅ File creation/removal | | Port binding issues | Works on random ports, fails on 4200 | ✅ Standard port binding | | Signal handling | Ctrl+C cleanup, SIGTERM shutdown | ✅ Kill signals + cleanup | | Concurrent behavior | Race conditions under load | ✅ 50+ concurrent operations | | Memory leaks | Only visible after sustained use | ✅ Memory growth monitoring | | Config from disk | Tests use in-memory config | ✅ Real TOML file loading | | WebSocket limits | Per-IP connection enforcement | ✅ Connection limit testing | ## Future Enhancements Created ### Issue #77: P2.6 Persistence Testing (Phase 2+) - Data survival across restarts (agents, sessions, usage) - Database integrity after ungraceful shutdown - **Blocked by:** #13 (SQLite memory substrate) ### Issue #78: P3.4 Supervisor Stress Testing (Phase 3+) - Heartbeat timeout detection under load - Auto-restart functionality validation - **Blocked by:** #17 (Supervisor implementation) ### Issue #79: P3.5 Scheduler Validation (Phase 3+) - Cron job execution timing accuracy - Job management under concurrent load - **Blocked by:** #16 (Cron scheduler engine) ## Workflow Configuration ### Trigger Conditions - **Pull Requests** to main branch - **Path Filter**: Only when `crates/**`, `Cargo.toml`, `Cargo.lock` change - **Skip**: Documentation-only changes (no unnecessary CI overhead) ### Environment Setup - **Ubuntu Latest**: Standard CI environment - **Release Build**: Tests production binary (optimized, no debug symbols) - **Dependencies**: jq for JSON parsing, websocat for WebSocket testing - **Timeout**: 10 minutes prevents hung processes from blocking CI ### Error Handling & Reporting - **Actionable Errors**: Clear failure messages with context - **Resource Monitoring**: Memory usage warnings and alerts - **Cleanup**: Guaranteed daemon process cleanup even on test failures - **Debugging**: Process PID tracking and status validation ## Test Execution Flow ```bash # 1. Build release binary cargo build --release # 2. Start daemon in background ./target/release/pi-daemon start --foreground & # 3. Wait for health endpoint (30s timeout) curl -sf http://127.0.0.1:4200/api/health # 4. Run comprehensive test suite # - API endpoint validation # - Agent CRUD lifecycle # - Webchat content verification # - OpenAI compatibility testing # - Concurrent load testing # - Memory usage monitoring # - Crash recovery simulation # - Graceful shutdown validation # 5. Cleanup and summary pkill pi-daemon && rm daemon.json ``` ## Benefits - ✅ **Deployment Confidence**: Catches real-world integration issues - ✅ **Performance Validation**: Memory and concurrency behavior under load - ✅ **Recovery Testing**: Ensures robustness against crashes and restarts - ✅ **Signal Handling**: Validates production process management - ✅ **Resource Management**: Prevents port confli