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🎙️ @1inch deputy general council Maylea Ma says that malicious actors in crypto are constantly getting more sophisticated. "I think it's always a cat and mice game....I do think that some actors are finding novel ways to scrape information, social engineering. And maybe it's not necessarily sophisticated, but just new in ways that you know people hadn't seen before."

Beyond Reading — AI with Eyes 👁️👁️ AI agents can already read the web. Now, we’re giving them eyes. This week, we demoed Semantic Video Search — powered by the UpRock Network. Not transcript search. Not keyword matching. Real multimodal intelligence across video, audio, frames, timestamps, and context. The demo: We asked UpRock to search across 100+ hours of livestream history. In seconds, it found: → the exact moment → the visual context → the timestamp → the story behind Jesse’s heart-shaped glasses 🕶️ This is what AI search is becoming. Drop in a YouTube video, TikTok, X video, earnings call, competitor demo, or 3-hour conference talk. Don’t just get a summary. Get structured intelligence your AI agent can actually reason over. What makes UpRock different is what’s underneath: 3M+ real devices. 190+ countries. Real phones. Real locations. Ground-truth access. No datacenter proxy games. No synthetic traffic. No stale scrape layer. Just the internet as real users see it. Semantic Video Search is coming soon for UpRock business customers. Building AI agents, research workflows, media intelligence tools, or multimodal search products? Get in touch → ⚡

anybody who uses or learns agentic systems, SHOULD READ THIS the install order I run before any new agentic project: 1. PRIVACY: direnv + a real secrets manager install direnv, then plug it into your team's password manager (1Password CLI via op run, doppler, infisical, vault, pick one) what direnv does: loads per-folder environment variables when you cd in, unloads when you cd out. the real move is wiring it into your secrets manager so credentials NEVER live in plain text on disk what this stops: - API keys accidentally committed to git history, the most common AI agent breach pattern in 2026 - credentials leaking from one project into another through your shell history - shared .env files that one teammate quietly backs up to Dropbox - secrets that survive a laptop theft because they were sitting in /Users/you/projects the part nobody mentions: most "my agent got jailbroken" stories actually trace back to one credential the agent had access to that it shouldn't have. scope keys to projects, scope projects to folders, and the blast radius of any single compromise drops dramatically I shipped 2 agents with keys in .env files before switching. the day I plugged direnv into op run I stopped having that whole class of nightmare 2. TOKENS: litellm or portkey as your model proxy one URL that fronts every AI provider (Anthropic, OpenAI, Google, Mistral, local models). all your spend flows through one place what it saves you: - response caching keyed by prompt hash, cuts your bill 30-60% on repeat tasks - automatic fallback on rate limits (Sonnet hits a 429? falls to Opus, then GPT, then your local backup, no broken users) - per-feature and per-user budget caps, block the call before it costs $200 instead of auditing it after - model routing rules, cheap tasks to Haiku, expensive ones to Opus, never the wrong way - PII redaction before requests leave your network, security side benefit the part nobody mentions: every "$4k AI bill" story I've heard ends with "we didn't have a proxy in front." this is where you put guardrails around spend BEFORE the spend happens I built my own router for 2 weeks. it took 20 minutes to replace with litellm. I will be embarrassed about this forever 3. CONTEXT: uv + git commit on every passing eval install uv (the new Python package manager, 10-100x faster than pip+venv, by the Astral team behind ruff). then commit every time an eval suite PASSES, with the model version and pass rate in the commit message what this preserves: - exact dependency set via uv.lock, you always know which packages your agent was using, no nasty surprises from a quiet update - exact prompt + code state, you can reproduce any past run from a single git hash - exact model version paired to exact pass rate, a paper trail when prod breaks weeks later - one-command rollback to a known-working state when a refactor goes sideways - a compliance story, every prompt version tied to a model version in your commit log the security side: when something blows up in prod, you want to say "the prompt was version X, model was Sonnet 4.6.1, last eval pass rate was 94%." not "I think we deployed on Tuesday?" the first is an incident report. the second is a resignation letter I've lost more agents to "I changed 3 prompts in one session and broke something" than to any actual bug 4. VISIBILITY: mitmproxy in front of every LLM call it's basically a wiretap for your agent. install it, point your agent through it, and now you see every conversation your agent has with the model in real time what actually shows up: - every silent retry your SDK sneaks in when a call fails - the full prompt being sent (including any creds you accidentally embedded) - what the model returns BEFORE your code reacts to it - exact token cost per call, per tool, per loop iteration - responses that quietly trigger your code into doing something you didn't intend, this is where prompt injection lives the part nobody talks about: if a website your agent scraped slipped instructions into its data, mitmproxy is how you SEE the moment your agent decides to follow them. without this layer, you're trusting your agent did the right thing, not verifying I shipped 3 agents before adding this. I have no honest idea what they were doing in production 5. EVALS: inspect-ai (the framework the labs actually use) an eval framework is what tells you "this agent works" with numbers instead of vibes. inspect-ai is the one Anthropic, DeepMind, and the UK AI Safety Institute use for the eval reports you read in their papers. open source, MIT licensed what your homegrown version won't have: - run the same task across 5 different models and compare scores side by side - pre-built tests for risky agent behavior (lying, manipulating, misusing tools) - proper structure for evaluating tool-using agents, not just chat - repeatable scoring, the same input always gets graded the same way - reproducible eval seeds, so a flaky test is actually flaky and not just unlucky I wrote my own eval harness 4 times across 4 projects. threw it out 4 times if you ever want to say "my agent passes safety checks" out loud, the check has to come from a framework someone else can re-run. this is that framework the move that ties this together: keep a /lessons.md in every repo. every weird agent behavior, every edge case, every config change you find at 2am, write it down you will not remember it. you'll come back in 3 weeks and the lessons file is the only reason you still know what's going on lock these 5, keep the lessons file, your next agentic system takes 2 days instead of 2 months p.s. half of "AI agent" content online is people who've never run mitmproxy on their own loop. they don't actually know what their agent is doing. they're shipping demo videos. don't be that guy

The top Hermes integrations to give your agent superpowers: 1. Firecrawl Basically web search built for agents. It's better than the native Hermes web search because it gives you clean web data, so responses come back faster and uses fewer tokens. I keep this on by default. 2. Browserbase Gives Hermes browser access for actually interacting with sites. Logging in, clicking buttons, booking stuff, anything that needs a real browser session. Hermes will automatically pick between Firecrawl and Browserbase depending on what the task needs, so you just plug both in. 3. Google Workspace Gmail, Calendar, Drive, Docs, and Sheets in one connector. If Hermes can't read your inbox, see your calendar, or write to your docs, it can't really work for you. Plug this in first. 4. Reddit The best signal you'll find on what people actually think about any product, niche, or problem (bc its real opinions from real users) Amazing for market research. 5. YouTube transcripts Pulls captions from any video. Long podcasts, tutorials, interviews etc become searchable notes in seconds. Probably the highest-leverage research integration nobody plugs in. 6. Discord I host my business in Discord, so this one's huge for me. I plug Hermes into different channels and have it run specific workflows in each. Example: I have a dedicated customer support channel where Hermes scans my email every morning for support tickets and drops them in organized. 7. GitHub Code, issues, PRs. Turns Hermes into an actual engineering teammate. Non-negotiable if you write code. 8. Stripe Payments, customers, failed charges, refunds. You can just ask "why did this customer churn" and get a real answer. Also can't wait for this...Stripe is releasing agentic payments, so soon Hermes will be able to actually book stuff with your card. 9. Bland (or Twilio) Gives Hermes a voice so it can place real phone calls (like booking reservations etc). I love listening to the recordings haha 10. Apify Pre-built scrapers for X, LinkedIn, Instagram, Google Maps, etc. The way to get X data without paying $5k/mo for the official API. 11. Readwise Every highlight you've ever saved from books, articles, tweets, and podcasts, all queryable. Solves the "dead knowledge" problem. 12. Granola (or Fathom) Searchable transcripts of every meeting you've had. Hermes can answer "what did that client say about pricing last month" instantly. 13. Obsidian For Karpathy LLM wiki second-brain maxxing. If I had to set up only 5, I'd do Firecrawl, Browserbase, Google Workspace, GitHub, and Obsidian. Covers ~80% of what most people need. I use Composio to add these in one click, makes setup basically zero effort instead of messing w technical stuff. Anything I'm missing?? What's in your stack?

AUSTRALIA COULD SCRAP 50% CRYPTO TAX DISCOUNT IN BIGGEST CAPITAL GAINS OVERHAUL IN YEARS The Australian government is set to release its 2027 budget on Tuesday. It will reportedly scrap the 50% capital gains tax discount for Australian crypto investors who hold assets longer than 12 months. Per reports, a full inflation-indexed tax on real gains would replace it. That could double the effective tax rate on long-term holdings from around 23.5% to nearly 47%, per the Australian Financial Review. The change would hit crypto, shares, and commercial property alike. Assets bought before May 10 get partial exemption. New rules take full effect in July 2027, with a one-year grace period for assets acquired after that date.

Scrapling 正在颠覆 Python 网络爬虫领域，拥有 +42.9k stars 且于几天前刚刚更新。 这不仅仅是另一个爬虫工具： • 残暴的自适应解析器：能够从网页变化中学习并自动重新定位元素（彻底告别失效的选择器）。 • 原生绕过 Cloudflare Turnstile + 全面的指纹伪装（真正的隐身模式）。 • 比 BeautifulSoup 快高达 774 倍（在实际基准测试中碾压 PyQuery、Selectolax 和 BS4）。 • 完整的 Scrapy 式框架：并发爬取、多会话（HTTP + Playwright 隐身模式）、自动代理轮换、带检查点的暂停/恢复功能以及实时流式传输。 • 集于一体的库：从简单的请求到大规模爬虫器 + CLI 命令行工具及 AI 集成 (MCP)。 如果你从事数据采集、自动化或严肃的工程项目，这是你的必备神器。

this OpenClaw bot finds restaurants with ugly menus, rebuilds them as live web menus, and mails the owner a postcard...on autopilot. here's how agencies can land recurring contracts with this system: - scrapes every restaurant in a city in real time - filters by review count + rating + last menu update + photo quality - pulls the real menu items from the official site, PDF, or Google reviews - samples the brand palette from the restaurant's own visual identity - renders a 9:16 brand-matched menu, hosted live at a QR-accessible URL - writes a personalized postcard referencing a real reviewer and a real dish - mails it to the registered office addressed to the owner by first name every step from discovery to brand-matching to outreach is automated. reply "MENU" + RT and i'll send you a free guide so you can build this too

作为一个影迷，最近没有电影看是很慌的。 每次院线不作为，断档时，我都会去找韩国片看，只有韩影，才能拯救我这年阅百部的狂热爱好者 话不多说，以及是 Claude Code + XCrawl , 这次难度稍高些。 我们提前做了 XCrawl-Scrape API的封装。封装的逻辑是做技术层面的解耦，也对最终结果更友好。 下次想看印度片，提示词只要把韩国替换成印度

6/ Using the full dataset I mapped out the complete organizational structure of the network, including payment totals per user and group. The interactive org chart can be accessed here: Password: 123456 Note: Data range is Dec 2025 through Feb 2026. Payment totals are derived from scraped transaction data and may vary slightly.