Playbook actionnable pour entrepreneurs, freelances et agences
Automatisation complète | De zéro aux premiers résultats en 1 jour
Hey everyone,
I just wanted to share my use case without promoting anything. I think that automated SEO might be the biggest value I’ve been able to get out of this so far. We have an HVAC company and we recently decided to get into residential. Our website started from scratch this year, so we have a long way to go. This is a sub agent with SEO skills, and connected to Ahref to run weekly reports, after every report, it adds tasks to a file that it then uses on every heartbeat. My website code
Notes on Managing ADHD. The pleasure is in foreseeing it, not in bringing it to term. — Jorge Luis Borges, . Selected Non-Fictions. This post is about managing ADHD. It is divided into two sections: “Strategies” describes the high-level control syst
I've helped fix 200+ OpenClaw setups over the past few weeks. Reddit, Discord, DMs. The pattern is just the same: people break things in their first week that take 5 minutes to prevent but 5 hours to fix later.
OpenClaw now has 310,000+ GitHub stars. NVIDIA just announced NemoClaw at GTC. The v2026.3.22 update dropped on March 23 with 12 breaking changes and 30+ security patches. A fresh wave of people are installing for the first time, and a bunch of existing users just had their setups silent
aurl
A command line tool for turning any API into a CLI command, supporting OpenAPI 3.0, OpenAPI 3.1, Swagger 2.0, and GraphQL. Built for AI agents — aurl makes APIs as easy to use as tool calls.
Register any API by name, and aurl parses the spec to auto-detect auth, validate requests, generate documentation, and provide example bodies. Agents like Claude Code, Codex, and Cursor can discover endpoints via --help, understand parameters and types via describe, and make validated requests — all without reading raw API docs.
Alphabet is Down 20%: Why This Is Not a Quality Entry Yet. Quality Stocks. Subscribe. Sign in. Stock Analysis. Alphabet is Down 20%: Why This Is Not a Quality Entry Yet. I bought at $100, added at $150 and trimmed at $300. What now?. Quality Stocks.
Heads-up to anyone building with Claude (especially on Pro or Max 20x plans): Anthropic updated their policy in Feb 2026 — using even a single script or wrapper (including OpenClaw-style agents, IDE extensions, or your own automation) around your consumer OAuth token is now explicitly banned as “third-party tool” usage. Your project instantly becomes a “third-party service” in their eyes, and they’re enforcing it hard. On top of that, the fastest way to get lifetime-banned right now is to buy th
Reached usage limit in the middle of a task last night. First thing in the morning, I went on and had it continue. It took literally 1 minute to finish the job and push up to github. 50% of my usage is now gone. What is going on!??
Somebody just mapped the ENTIRE Hermes Agent ecosystem
40+ skills, tools, integrations, and resources for @NousResearch's agent, all in one curated list
highlights:
▫️ skills that upgrade themselves after every run
▫️ 734 security playbooks, ready to deploy
▫️ visual UI for memory, sessions, and skills
▫️ remote control your Android from your agent
▫️ Claude Code -> Hermes task handoffs
▫️ full OpenClaw migration in one command
▫️ AI council that debates before executing
every entry has a re
How To Transform Claude Code Into A Self-Evolving System [FULL GUIDE]
The future of AI is about self-evolution in every step.
Agents are now evolving themselves. Tons of research are being shared on the benefits of self-evolving systems from every
AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary
Best models to run on your hardware level
I'll be doing this every week, I hope you guys enjoy.
---- 8 GB ----
Autocomplete for coding (like Cursor Tab)
Tool calling, assistant style
---- 16 Gb ----
Here things get better:
Multimodal
---- 24 GB ----
- The best model you can get (thanks Qwen) https://t.co/fy8INjJP8N
- Great model (strong agents) https://t.co/CRpiKlSX5d
- Mine hehe https://t.co/YBeUveU0M6
I'm doing a weekly series
How I used autoresearch to fix Gumroad's flaky tests in a week
Last week Gumclaw made 206 commits to our repo while I slept. It fixed 13 flaky tests. I didn't write a single line of test code.
Gumclaw is Gumroad's team AI assistant. It runs on OpenClaw on a Mac mini at our Brooklyn office. It answers questions, reviews PRs, and now, apparently, fixes flaky tests.
Flaky tests are detective work with a 20-minute feedback loop. They pass locally, fail in CI, and after enough false alarms the team starts ignoring red builds. Nobody wants to fix them. So nobody does.
I wanted to see if Gumclaw could do the grinding for me.
Spoiler alert: it did.
This freed me to do my job. My highest-value work is building product, not debugging why a tax test fails 1 in 20 runs. Gumclaw ran overnight while I shipped features.
Here's how you can setup the same system for yourself.
The tool
I built openclaw-autoresearch, a plugin for OpenClaw. It's a port of pi-autoresearch (by Tobi Lutke) to the OpenClaw plugin system.
The idea is simple. You give it a command that measures something. Gumclaw runs it, gets a baseline, makes a change, runs it again. If the numbers improve, it commits. If they don't, it logs what it learned and what to try next. Then it loops.
All state lives in plain files. If the session crashes, you type /autoresearch resume and Gumclaw picks up where it left off.
What happened
I pointed Gumclaw at our test suite on March 18. One week later: 206 commits, 94 CI runs, 13 merged PRs. Race conditions, timing issues, browser session corruption, test cleanup hooks leaking between tests.
The best find wasn't even a flaky test. It was a real bug: when remapping file IDs, A became B, then B became C, silently corrupting file references. The flake was just the symptom.
What the agent found
It was methodical. Fix a class of failures, trigger CI, log the results, move to the next class.
When a fix didn't hold, it wrote down why and what to try next. Those notes fed an ideas backlog that kept it from repeating failed approaches. By experiment 20, it had built a map of which tests were flaky and why.
Some fixes took multiple iterations. One tax input field went through four different approaches before Gumclaw found one that held across CI runs.
What I learned
Flaky tests are a perfect target for this. Green or red. Pass or fail. The agent ran 30+ CI cycles overnight without getting bored.
The ideas backlog is the killer feature. Every failed experiment forces Gumclaw to write down what it tried. It stops repeating mistakes.
It takes time: 206 commits for 13 PRs. Fixing a flaky test is easy. Proving it's fixed means running CI enough times to trust the flake is gone and not hiding. The loop handles that grind.
Try it now:
openclaw plugin install @gianfrancopiana/openclaw-autoresearch
/autoresearch setupopenclaw-autoresearch is open source, and we would love your contributions!
Outil de nettoyage gratuit et open source pour mac concurrent de CleanMyMac
Reddit - The heart of the internet. Skip to main content. Go to OpenClawUseCases . r/OpenClawUseCases. •. goldgravenstein. Fresh install on M4, what’s your best local model use case? . M4 Mac Mini, 16GB, 4tb SSD. Ready to roll… What’s your best use c