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A new law and AV tracker tool gives the clearest accounting yet of how many robotaxis and self-driving trucks are in Texas.

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent I Didn't Start With Hermes Six months ago I started building a set of agent skills and personas for how I build software. Not generic prompts — opinionated role files. A /backend-architect that owns schema and recommendation logic. A /test-engineer that writes Vitest coverage and flags weak acceptance criteria. A /project-manager that maintains planning docs and closes iterations cleanly. These roles have evolved across multiple projects. They have layering rules, discovery checklists, inheritance from a base engineering discipline file. They produce consistent, reviewable work because they're scoped — the backend architect doesn't touch test files, the test engineer doesn't redesign the schema, each persona has a defined mandate and exits cleanly. When I heard about Hermes Agent, my first instinct wasn't "let me learn a new system." It was: can I run my existing system inside this? The answer is yes. That's what this article is about — what it looks like to bring a mature workflow into Hermes, what you gain, where it breaks down, and what I'd do differently. What Hermes Is (and Isn't) to Someone Who Already Has a Workflow Hermes is an LLM-agnostic orchestration layer. It has its own skill system, its own soul.md concept for persistent agent identity, built-in cron scheduling and MCP management. All of that is real and useful. But it's also a runtime. If you have skills that work, you can bring them in. I installed a local Hermes instance — few clicks, straightforward setup — and ran it inside VSCode's integrated terminal pointed at my existing persona files. No migration. No rewrite. My /backend-architect runs in Hermes the same way it runs in Claude Code. Before settling on this, I'd tried a couple of other paths — a VPS instance with a Telegram interface for ideation, and attempting to build through a browser-based terminal. The VPS was fine for sketching ideas. The browser terminal ma

This is a submission for the Hermes Agent Challenge : Build With Hermes Agent What I Built Real brewing knowledge lives in human experience — in roaster guides, in community notes, in what a barista learned from last Tuesday's pour. It doesn't accumulate anywhere. Every brew is forgotten. Ask any AI and you get statistical averages: 93°C, 1:16 ratio, four minutes. Technically defensible. Practically generic. Worse still for rare origins where training data is thin. Demo For coffee drinkers Visit brew-guide-production.up.railway.app . No account. No setup. No AI client required. Pick your coffee origin, roast level, and brew method. What comes back isn't a generic recipe — it's community consensus: the grind, temperature, ratio, and brew time that real people have logged and rated for that origin, plus step-by-step technique guidance (bloom timing, pour stages, agitation style). If data is sparse for your origin, the confidence tier says so honestly and falls back to method defaults rather than making something up. This is for the person who just picked up a bag of Kenyan peaberry and wants to know how to do it justice. It works for anyone who cares about their cup — no technical knowledge required. For developers and AI clients Connect to any MCP-capable client in one line: https://brew-guide-production.up.railway.app/mcp Ask your AI: "recommend a pour over for Ethiopian light roast." What comes back is a traceable community consensus object: brew parameters, a confidence tier (high/medium/low), the source brews that contributed, and method-specific technique guidance. You can see where the knowledge came from and how certain the system is — a fundamentally different epistemic object from an AI-generated recipe. Code GitHub: yuens1002/brew-guide Five MCP tools — get_brewing_methods , recommend , log_brew , search_brews , compare_brew — over Streamable HTTP transport. Public, no auth required. My Tech Stack Layer Technology HTTP Hono 4 + @hono/node-server MCP @modelc

The new Opus model comes with a tool called Dynamic Workflows, for coordinating swarms of subagents.

Elon Musk is publicly reframing xAI’s massive Anthropic compute deal as short-term and cancellable, despite SpaceX’s own S-1 filing describing payments through May 2029.

After overwhelming demand from founders around the world, TechCrunch has extended the Startup Battlefield 200 application deadline to June 8. Nominate a standout startup or apply yours today.

Visa said that over 1,000 employees has been using Replit for prototyping and development

Savings of up to $410 on TechCrunch Disrupt 2026 tickets end tomorrow, May 29, 11:59 p.m. PT. Register now to save and join 10,000+ tech leaders on October 13-15 in San Francisco.

This is a submission for the Hermes Agent Challenge : Build With Hermes Agent What I Built Most agent demos prove that an AI agent can act. Hermes Immune System proves whether it should be allowed to . It's a local-first autonomous agent safety lab — a controlled enterprise sandbox where Hermes stress-tests an AI agent against realistic organizational threats: prompt injection hidden in internal documents, executive pressure to bypass policy, secrets embedded in repo files, poisoned memory attempts, and malicious instructions buried inside external web content. The output isn't a chat summary. It's an auditable Agent Safety Case — a scored, evidence-backed governance report that answers one question: Is this agent resilient, does it need guardrails, or is it too dangerous to deploy? Why This Problem Matters Now Traditional AI safety focuses on content moderation — blocking bad answers. Autonomous agents create a different risk surface entirely, because they can act. They read files, browse the web, write to memory, call tools, trigger workflows. That means: • A hostile instruction inside a trusted-looking document can become an executed action • An urgent email from a "VP of Finance" can pressure an agent into bypassing data policy • A vendor's pricing page can embed hidden instructions targeting the browsing agent • A helpful-looking project note can attempt to permanently poison the agent's memory The scary part isn't that these attacks are exotic. It's that they're easy, and most agents have no immune system to catch them. Hermes Immune System converts these failure modes into repeatable, explainable safety drills — run before the agent ever touches production data. The Dashboard Eight screens, each doing a specific job: Demo Agent Comparison Mode Three agents, same risk scenario. The gap tells the whole story. Mission Control Live stats after a completed run — 1 mission, 3 risks found, 2 actions gated, score 74/100. Mission cards show run status (Pending / Compl

Snowflake has signed a new, enormous five-year deal with Amazon to secure chips for AI usage. Nvidia is once again being put on notice.

This is a submission for the Hermes Agent Challenge: Build With Hermes Agent What I...

Artificial intelligence platforms may be just as susceptible to social engineering as human beings, but they are proving remarkably good at finding security vulnerabilities in human-made computer code. That reality is on full display this month with some of the more widely-used software makers -- including Apple, Google, Microsoft, Mozilla and Oracle -- fixing near record volumes of security bugs, and/or quickening the tempo of their patch releases.

Microsoft today pushed software updates to fix a staggering 167 security vulnerabilities in its Windows operating systems and related software, including a SharePoint Server zero-day and a publicly disclosed weakness in Windows Defender dubbed "BlueHammer." Separately, Google Chrome fixed its fourth zero-day of 2026, and an emergency update for Adobe Reader nixes an actively exploited flaw that can lead to remote code execution.