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AI solves 80-year-old math conjecture for under $1000

GPT-next solved an 80-year-old Erdős combinatorics conjecture for under $1,000 in compute. That single fact reframes everything else happening this week. The [Erdős unit distance problem](https://www.latent.space/p/ainews-openai-gpt-next-disproves) resisted human mathematicians since 1946. A frontier model closed it at a cost lower than a mid-tier SaaS subscription, which means the boundary between "AI as tool" and "AI as independent discoverer" is no longer theoretical. [Lilian Weng's new deep dive](https://lilianweng.github.io/posts/2025-05-01-thinking/) on test-time compute and chain-of-thought reasoning explains the underlying mechanism: reasoning models are not retrieving known proofs, they are generating novel inference chains at scale. The infrastructure layer is pricing this in faster than most observers realize. [Railway reports $200K+ monthly coding agent spend](https://www.latent.space/p/railway) and 100K signups per week, and is now building own-metal data centers to absorb the load. Daytona hit 850K daily sandbox runs with 74% month-over-month growth, confirming that isolated compute environments are now a first-class primitive, not a niche DevOps concern. Three specialized infrastructure companies, Exa, Modal, and TurboPuffer, reached unicorn valuations simultaneously this week, covering retrieval, serverless GPU, and vector search. When picks-and-shovels companies price in sustained demand at the same moment, it is not coincidence. Every major lab has now repositioned as an agent lab, not a model lab. [ClickUp replacing hundreds of employees with thousands of AI agents](https://techcrunch.com/2026/05/25/what-clickups-mass-layoff-tells-us-about-the-future-of-work/) is the first established tech company to execute that repositioning at the labor level rather than just the product level. The counterweight is that [Salesforce customers remain locked in](https://www.theregister.com/saas/2026/05/26/the-saas-pocalypse-can-wait-salesforce-still-has-customers-where-it-wants-them/5245228) despite the theoretical ability to rebuild on AI-native stacks cheaply. Data gravity and switching costs are buying incumbents time, but ClickUp's move suggests that time is measured in quarters, not years. The governance conversation caught up this week in an unexpected place. [Pope Leo XIV's 42,000-word encyclical](https://simonwillison.net/2026/May/25/encyclical-on-ai/#atom-everything) names specific failure modes including algorithmic control, surveillance capitalism, and autonomous weapons, and will directly shape EU and Latin American regulatory debates. [TechCrunch's read](https://techcrunch.com/2026/05/25/the-popes-ai-encyclical-isnt-really-about-ai/) is that the document's real target is the tech elite's capacity to reshape society outside democratic accountability, a framing that lands harder alongside [new UK research](https://www.theregister.com/off-prem/2026/05/26/big-tech-extracts-retirement-scale-wealth-from-uk-internet-users-research-shows/5246048) quantifying data extraction from consumers as equivalent in value to retirement savings. The Vatican and the empiricists arrived at the same diagnosis from opposite directions. Two structural forces will shape AI infrastructure economics over the next 90 days in ways most deployment teams are not modeling. China flooding global markets with DRAM and NAND will compress inference cluster costs faster than US export controls intended. The EU's sovereign cloud setback has paradoxically clarified the build-domestic mandate, accelerating European AI infrastructure investment independent of US hyperscalers. Security remains the open variable: even Google has no established playbook for prompt injection, model supply chain risk, or agentic authorization at production scale. A second Fortune 500 company will publicly attribute a reduction of more than 500 knowledge-worker roles directly to agentic AI systems before Q3 earnings season, making ClickUp's announcement the start of a visible series rather than an isolated case.

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

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

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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](https://go.videodb.io/1tP0wE8) 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”.

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.