9 GitHub repos this week that make your AI agents actually useful: 1. last30days-skill - researches any topic across Reddit, X, YouTube, HN, and Polymarket automatically github.com/mvanhorn/last30da… 2. headroom - compresses logs and files before they hit the LLM, 60-95% fewer tokens github.com/chopratejas/headr… 3. pm-skills - 100 agentic skills for PMs, from discovery to launch github.com/phuryn/pm-skills 4. apple/container - run Linux containers as lightweight VMs on Apple silicon github.com/apple/container 5. Agent-Reach - gives your agent access to Twitter, Reddit, YouTube, GitHub with zero API fees github.com/Panniantong/Agent… 6. open-notebook - open source NotebookLM with more features and flexibility github.com/lfnovo/open-noteb… 7. taste-skill - stops your AI from generating generic, boring outputs github.com/Leonxlnx/taste-sk… 8. MarkItDown - converts any file or Office doc to Markdown instantly github.com/microsoft/markitd… 9. NVIDIA Cosmos - open platform of world models for robots and autonomous vehicles github.com/NVIDIA/cosmos Bookmark this and send it to your AI agent.

This is really big news. Google introduced the Open Knowledge Format (OKF) - a standardized way to store information in a directory of markdown files. Makes it really easy to make a digital brain that agents can use. These files can serve as a living wiki. You can give agents the ability to query them or edit them. They can interlink. Seems to me this could replace Notion or Obsidian. I can think of so many uses for this. Google's blog post: cloud.google.com/blog/produc… An easier to understand explanation is the SPEC.md file: github.com/GoogleCloudPlatfo… I gave those two links to Antigravity and asked how we could use it for any of the projects we're working on. It came up with so many ideas. I would imagine Claude Fable 5 would whip up some pretty amazing things based on this system. Currently creating an OKF library of our pepper garden. It's going to be a fun weekend.

we just cross the magic 💫 7,000 org member dream number. but as we cross, we realized that its only a checkpoint not an end state. to celebrate this we added a globe discovery feature to our agentic web map: agentcommunity.org

In this, you'll find a guide and an open-source repo to build your own self-improving agent loops As well as a standardized system to build benchmarks and evals around the loop, for any workflow, so you can actually see the growth happening in real time And put your agent to work even while you sleep. The repo for building the system took months of work; it's not a weekend job, so save it and utilize it.

HERMES AGENT NOW RUNS A FULL BUSINESS FOR $8/MONTH. CONTENT. CODE. INBOX. ALL AGENTS. EVERYTHING WHILE YOU SLEEP. one user built this in one session. zero terminal commands. browser only. the team: → content agent: finds video topics, writes first drafts, helps with animations → code builder: writes website and SaaS code → ops agent: organizes and answers email total setup cost: $1. monthly runtime: $8 VPS cheap model API. the trick is matching model to task. frontier models (Opus 4.8, GPT-5.5) for complex /goals only. $5/million tokens. mid-tier (Claude Sonnet, Haiku, Kimiko 2.6) for daily content and research work. budget (DeepSeek V4 Flash, MiniMax 2.7, Gemma 4, Qwen 3) for cron jobs and routine. run the initial setup on Opus. switch to DeepSeek for everything after. your bill drops 90% with zero quality loss on routine work. the agent team picks up tasks from kanban. each completed task creates a skill. week 3 the same workflows run faster than week 1. full breakdown on Hermes Os in the article 👇

i have created a master loop harness that allows you to run perpetual mission loops that create goal loops that then spawn agent loops which then run their own workflow loops which finally run the tool loops. i really wish i was joking 🙃

what is agent looping for the last two years we prompted agents one task at a time. that is starting to change instead of asking an agent to build the landing page and then driving every step yourself, you set up a loop that handles discovery, planning, the work, checking, and iterating until the goal is met looping is a setup you build. almost any agent harness can run it, it just depends on how you wire it up at its simplest, looping is one agent working on itself: > researches > drafts > checks the draft against a goal > fixes what is weak > runs that cycle again until the work clears the requirements you are not prompting each step anymore. the agent repeats the cycle for you the bigger version is a fleet looping. you give an orchestrator agent a goal, it breaks the goal into pieces, hands each piece to a specialist agent, and those specialists hand smaller jobs to their own subagents the whole tree keeps looping through discovery, planning, execution, and verification until the goal is met one agent looping is like a person redoing their own draft. a fleet looping is a whole team running a project end-to-end you create a goal, and the system runs the loop until it finishes within the reqs you set open and closed looping: OPEN LOOPING is exploratory. it still has conditions and a goal, but you give the agent or the fleet a wide space to move in. it can try different paths, discover things, build something you did not fully spec out this is the exciting end, it is what Peter and others are doing, and tbh it is where I want to spend more time the catch is cost, an open loop with real room to explore burns an insane amount of tokens. for the 90 percent of people without an unlimited budget it is not runnable yet, and pointed at projects with a loose standard it turns into a slop machine CLOSED LOOPING is bounded. a human designs the end-to-end path first: > clear goal > defined steps > an eval at each step > a point where it stops or hands back to you (and feeds back performance data) the agents still loop, but inside framework you built. it gets better every run because each pass feeds the next, and it runs on a normal budget because the path is tight. for most marketing work, closed is the one that pays off today. > the orchestrator owns the goal > the specialists own the steps > the subagents do the narrow work > an eval gate make sure its not slop

Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.

The most comprehensive Hermes Desktop tutorial on the internet NOW is LIVE. You'll learn sessions, profiles, artifacts, cost savings, and real use cases for making money and building startups with Hermes agents. Whether you're already running Hermes or haven't started yet, this is the episode for you. @AlexFinn says this is the moment Hermes overtakes OpenClaw. S/o to Alex for walking me through it. "It's now the best way to use AI agents on your computer" I do think the desktop app of Hermes looks almost like an Apple product. Everything you need to know about Hermes Desktop App/agents in 43 minutes This episode is 100% free. No ads. @startupideaspod I just want to see you win on the internet. And I think Hermes can help. Plus, It's fun thing to play with this weekend. Share this with a friend. Link below. YT: youtube.com/watch?v=EJm8Ka-g… Watch

Pulled the trigger today and switched 100% of Lindy traffic to DeepSeek v4, churning from Anthropic models. Saves us millions of $ and we're actually seeing an *increase* in performance on many core use cases. Transformative for the business.

NVIDIA Nemotron™ 3 Ultra is now live on Nebius Token Factory. It’s built for long-running agents across coding, deep research and enterprise workflows. Nemotron 3 Ultra delivers frontier reasoning with up to 5x faster inference and up to 30% lower cost for agentic workloads. For builders, the next question is production: performance, reliability, economics and control. Run it today: tokenfactory.nebius.com/mode…

“Every company in the world today needs to have an OpenClaw strategy. This is the new computer.” - @Jensen_Father NemoClaw is @nvidia's open-source unlock: sandboxed AI agents with kernel-level isolation, L7 egress control, and an intent policy engine - deployable on @NebiusAI in < 15 min. Let me show you how it's done. Hosting a webinar with Sam Pastoriza (NVIDIA), Wed Jun 10, 9 AM PT. luma.com/r16tprwv

BuilderShip is an AI hackathon co-hosted by @nebiusai, @composio, @tavilyai, and @openclaw. Finals are on a yacht on June 14, San Francisco Bay. To apply: post something you've built (agent, demo, or repo) and tag all four accounts. Every builder gets GPU credits, Token Factory inference keys, Composio integrations, Tavily search, and OpenClaw runtime from day one. Top 30 builders make the finals. Winner takes home $50K in cloud credits and a DGX Spark. Submit by June 12 → ship.builders

We're excited to announce our partnership with @coinbase to bring Tavily to x402, the open protocol for internet-native agentic payments. With x402, agents can discover and use Tavily web search at runtime without an API key. Agents use a @Base wallet to pay per-request and get instant results. The next billion agents will discover, pay for, and use online services fully autonomously. We’re live on x402.tavily.com, and we’re just getting started. More in the comments.

If you missed the show, you can watch online here: nebius.com/events/webinar-bu…

NVIDIA's LocateAnything is a new vision model for grounding and detection. Very performant and accurate! > 10x faster than Qwen3-VL > 138M queries 785M boxes > GUI, OCR, docs, dense detection > Free & open source research.nvidia.com/labs/lpr…

talked to a YC company that scaled from $0 → $2m ARR in their first 6 months with their ENTIRE GTM built off going to conferences. Here's the playbook they cracked (step by step): ~4 weeks before: > Post abt the conference and tell attendees exactly how to reach you > Send personal DMs to the right ppl on LinkedIn and X > Reply within the hour & lock in 10 top targets to close. > Send everyone else to your drip email campaign. Then, set a meeting block of 1-3 days during the conference: > make shared booking link for the team > Reserve a quiet café / private dining room > Pack in 12 meetings per day, 30 min each, with buffer time built in While you're there: >Hand every prospect a thoughtful small gift and a personal card >Single out 5 standout customers whose pain ur product actually solves >Pull them aside for a casual on-camera Q&A in a solid film spot >Don't pitch hard. >Let the conversation breathe and weave your product in naturally. The 4 weeks after >Hand the raw footage to a freelance editor ask for ~15-20 punchy clips with captions. >Drop a new clip every couple of days on LI / X > use these clips when you post online about the next conference to keep the momentum This is the formula, costs less than a few thousand dollars to execute. They’re on track to end the Y1 at ~$6m ARR (B2B, targeting large enterprises) STILL not using any other channels for customer acquisition