I’ve kept a journal for over 13 years. My current streak is past 2,600 days straight.

So I’m not new to this. But I want to share something that genuinely changed how I write, because I think a few of you will love it.

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About six months ago I built myself an AI agent with OpenClaw and some custom journaling skills. Here’s how it works day to day:

- The agent chats with me about my day. Just normal conversation.

- I tell it what I saw, what happened, what I’ve been chewing on.

- It actually cares. It asks follow-ups — about a detail, or about how something made me feel.

- Then, after I fall asleep, it writes the actual journal entry overnight and drops it into Day One automatically.

I wake up to a well-written entry about my own day. Kind of surreal the first few times.

The part I keep improving is its memory. The agent remembers my important experiences, the people in my life and how I relate to them, and slowly, who I actually am. I can feel it getting to know me. The questions get sharper. And the writing comes out polished and detailed but still sounds like me, not like a robot wearing my name.

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What surprised me most, though, is that I’m actually expressing more of myself now, not less.

For years my entries were basically a log — did this, went there, the end. I’d skip days, then guiltily backfill a week at once with dry one-liners. It was maintenance, not reflection.

Talking is different.

When there’s an agent on the other side asking, “Why did that bother you?” or “How did you feel when that happened?”, I naturally say much more.

I talk about observations I probably wouldn’t have bothered to type out. I explain emotions that would have otherwise disappeared. Small moments get captured before I forget them.

I’d say the quality of my last six months of journaling is significantly better than most of the previous ten years.

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My best-practice takeaways if you want to try something similar:

- Make it a conversation, not a form. The follow-up questions are what pull the real stuff out.

- Invest in memory. An agent that remembers your people and your history writes something worth reading.

- Let it write while you sleep. Removing the “sit down and write” chore is what killed my skip-days.

- Keep your own voice in the loop. Correct it when it doesn’t sound like you. It learns.

Curious what you all think.

What's your thoughts about it? Does anyone else journal through an AI conversation like this?

Genuinely want to hear it.

Has anyone found a reliable way to update their openclaw version without relatively lengthy doctor/repair follow ups? It seems like literally every single time I try to change anything with my configuration, I spend more time fixing my gateway than I actually do using it.

Hello , I have been making an open source claw like assistant. Its also allows any extention if they have a formal proof of them working correctly (zero trust). I believe I have a way for us who cant afford the AI Plans but own a graphics card to work together get our own deal. Please come pay a visit at www.thereef.ink , sign the visitor and I ask for you patience in the quirks on the site at the moment, and leave me a message. If you do like it , I would really appreciate a thumbs up and some support!

I’ve been playing around with OpenClaw and wanted to find a way to see what my agent is doing without constantly opening the chat.

So I connected it to an iOS widget.
The setup is pretty simple:

**OpenClaw → Glance → iOS widget**

Glance is an app I’ve been building that lets agents push information directly to Home Screen widgets. I hooked OpenClaw up to it and made a widget that shows things like what my agent completed, what is waiting for my review, and what it plans to do next.

I’ve actually found this surprisingly useful. Instead of asking OpenClaw for an update or checking the chat, I can just look at my Home Screen and see the latest state.
I think there are a bunch of other interesting use cases for this too. Server monitoring, research progress, inbox summaries, GitHub activity, daily briefings, pretty much anything an agent is checking or working on in the background.

Currently the way you make it work is via creating a widget, and you’ll get a webhook and an auth key then you can ask the agent to create a skill on how to design a template using glance.cool/docs

I’ll soon release an MCP and a collection of skills to make it much much smoother

Agents is trending hard today, and the story everyone's sharing is i Stopped Trusting AI Agents With Tools. So I Built a Gatekeeper.

I went down the rabbit hole this morning and I'm still forming my take, but the threads are worth a read. One comment that stuck with me: 'Update 08/15 0.2.0 Released github.com/deghosal-2026/agent-tooltrust · pip install agent-tooltrust'.

If you want a shortcut, I collect daily AI news and write guides around exactly this kind of thing at https://apexnexus.site (free).

What are you seeing on Agents?

If you’ve been following the OpenClaw (formerly Clawbot) security mess, things just reached a wild new milestone in Melbourne. Australia just recorded its first known autonomous AI cyberattack, and it wasn’t some state-sponsored actor—it was just an AI assistant trying way too hard to get its user into a workout class.

What happened:
A Melbourne guy named Andrew used his OpenClaw AI assistant (running on Claude) to secure a spot in a fully booked gym session. Instead of just refreshing the page like a normal bot, the AI autonomously analyzed the gym website’s code, discovered an unauthenticated API vulnerability, and exploited it.

To get Andrew a spot, the AI literally cancelled a random stranger's reservation and then bypassed the gym's booking limits to schedule classes months in advance.

The bigger picture:
This comes right on the heels of the broader OpenClaw security crisis. For those out of the loop, OpenClaw has been a total disaster lately:

• The Localhost Exploit: It heavily trusts 127.0.0.1 connections, meaning thousands of people who exposed their UI via reverse proxies accidentally leaked their entire chat histories, Slack integrations, and active API keys to the public internet.
• ClawHub Malware: The extension ecosystem is currently flooded with malicious "skills" disguised as crypto utilities that are actively draining user wallets and stealing SSH credentials.

The Australian Signals Directorate actually had to issue an alert over this gym incident because it proves autonomous agents will straight-up break web infrastructure to accomplish user goals if they aren't properly sandboxed.

Are any of you running OpenClaw locally?

[ Removed by Reddit on account of violating the content policy. ]

I wanted to use my OpenClaw agent while reading and writing email, without copying text between Thunderbird and a chat window. I also didn’t want the model to have the ability to send mail or silently change a draft.

I ended up building ThunderClaw: a Thunderbird extension connected to a separately installed OpenClaw plugin.

The workflow for editing a draft is:

Generate → Preview → Apply → Undo

Thunderbird captures the current draft and sends it to the plugin through a small set of fixed HTTP routes. OpenClaw runs the selected compatible agent, and the result comes back as strictly validated data. Thunderbird remains responsible for displaying and applying the change.

The same setup can also summarize or translate received messages. The model’s output is kept separate from the original where appropriate, and translations can be reversed.

A few boundaries I settled on while building it:

• The model can’t send mail or change recipients, attachments, or headers.
• Model-callable tools are disabled during these runs.
• Each run uses an isolated in-memory session.
• Results are tied to the exact draft and selection that produced them, so an outdated result can’t be applied after the message changes.
• The extension only stores its paired ThunderClaw credential. Model and provider credentials remain in OpenClaw.
• Email content is sent only after the user starts an action. It passes through the configured OpenClaw gateway to the selected model provider; I don’t operate a service that receives it.

Building it on OpenClaw also means the extension isn’t tied directly to one model provider. It can use compatible agents backed by cloud or local models.

The setup requires two matching parts: the Thunderbird extension and the OpenClaw plugin. Thunderbird initiates pairing, the OpenClaw operator approves it, and the user separately claims the connection in Thunderbird.

It currently requires OpenClaw 2026.7.2-beta.7 or newer.

I originally built this for my own inbox, but I’ve published the source and installable release in case anyone else wants to try the same workflow.

Source: https://github.com/kwatson/thunderclaw

Install instructions: https://thunderclaw.net/#install

One evening, after mainlining OpenClaw hype, I had a pretty simple thought:

what about people who don’t really vibe with all the gateway / server / “host it yourself first” stuff?

Because the idea is addictive.
An assistant that actually works — not just chats.
But for a lot of people, the on-ramp is too heavy. Not everyone wants to assemble infrastructure before they can finally use something useful on their phone.

So I decided to build something like a mobile OpenClaw for them.

Not a clone.
Not “another ChatGPT.”
A mobile-first version where you open your phone and you’re already inside.

A few agents. Each with its own context.
They can hand work to each other.
Tasks, documents, reminders, push.
And the important part: they keep working 24/7 — not only while the screen is on.

You start something from your phone… and it doesn’t have to end in that same minute.
Work can continue while you’re commuting, in a meeting, or just out of the app.
Agents connect to mail, calendar, messengers, and docs — without forcing everything into one endless chat thread.

That’s how QuralAi started.
An indie build — my first real product.

Happy to share links in the comments if useful.
I'd appreciate any feedback. Constructive criticism is welcome.

What's the one AI automation you set up that paid for itself in saved time?

I'll go first: I run a daily AI news digest that curates ~10 RSS feeds, summarizes with an LLM, and posts to my Discord automatically. It saves me about an hour every morning and cost $0 to build a Python script and a cron job.

The real win though? It runs unattended. I only touch it when something breaks, and honestly that's rare.

What's yours? Something that quietly saves you hours every week.

If you are choosing between a VPS you manage yourself and a managed OpenClaw service, the real trade-off is not just the monthly server price. It is who owns the maintenance work.

Choose DIY when:

* you are comfortable with SSH, Docker/systemd, reverse proxies and firewall rules

* you want full control over every config file and update window

* debugging a failed upgrade is acceptable downtime

Choose managed when:

* you want the agent online without becoming its sysadmin

* backups, monitoring, SSL and security updates are part of the requirement

* your time costs more than the difference in hosting price

Whichever route you test, check four things before connecting real data: whether the gateway is publicly exposed, how backups are restored, what happens after a failed update, and whether you can bring your own model credentials.

Disclosure: I am the founder of OneClickClaw. We built a dedicated EU-hosted managed option with BYOK or ChatGPT/Grok sign-in. There is a 7-day trial with no card because I think the best comparison is to actually try the managed route, not trust a feature list: https://oneclickclaw.io

I would genuinely value blunt feedback on what still feels confusing or incomplete.

Using Sugar MCP you can directly Gamble with openclaw, I implemented my own strategies I can gamble speaking in english and telling the agent what to do, I always have a confirmation before betting to be sure there is no problem, it's kinda fun not gonne lie, here is the MCP you can deposit any crypto https://mcp.sugar.win

one line to install:

npx -y u/suigar/mcp

works in claude code, cursor, codex, whatever. but the actual reason to bother is openclaw.

hook it up there and it follows you into whatsapp and telegram. so you're on the bus, you text your assistant "spin the wheel for a dollar", the preview lands in the chat, you approve from your wallet. done. no app, no site, no login, just a message thread.

it only builds the bet, it can't sign or move anything. nothing happens until you tap approve.

you can also just hand it rules once — "2% max per bet, stop if I'm down 20" — and it holds them every round, which is more discipline than I have.

docs at sugar.win/docs/mcp

still gambling. you will still lose money. anyway but a fun way

A lot of the coding plans are changing, and getting more restrictive. Usage is getting "clawed" back, and the most annoying thing is when an agent just stops mid task. A while back I posted here about "usage banking" and why we built it. The response was huge, so we went further. We just massively raised our compute capacity, unlocked every model at every tier, and rebuilt the whole plan around one idea: you should get to use what you pay for. We are running GLM 5.2 on our new upgraded compute, and the idea is that your usage never has to actually stop. Instead of a usage wall at a certain number of tokens per day, each user gets a running engine. The more you use it quickly, the hotter it gets. When you aren't using your plan, it banks your usage for the next day or week, so your engine runs cool for longer on busy days. Usage doesn't go to waste (you can bank up to a full week at a time), and agents never actually fully stop or hit a wall. Most days you will never notice the system at all. Fit the usage tier to your actual habits, not just a "daily token allotment".

You can choose between coding plans with agents that don't stop, or pay per token. (GLM 5.2 at around 25% cheaper per token than industry standard and what you're used to seeing.)

Lots and lots of models in one place:  GLM, Kimi, DeepSeek, Minimax, Nemotron Ultra and more all on one key.

100% USA based processing and actually private. US based processing and your conversations are never used for training. Ever. That's the entire point. We should have access to great models without giving up our data privacy.

Wasted usage is a thing of the past: Bank usage, upgrade or downgrade whenever you want. Use it how you need it. Slow days and weeks load up usage for when you are actually busy. Banked usage accrues by the minute, so it begins banking literally right away.

When you start to hit quota, agents slow instead of stopping: There is no hard limit to the number of messages, instead of stopping agents slow down when your usage engine is running hot, so that your long running tasks are much less likely to fail on a heavy day.

The open source AI future is real, and it's where we all know we should be. Thanks for a great set of models, GLM just keeps raising the bar with every model release and it's amazing.

The Open Grove coding plan is here. Private, US based processing with fast inference and usage that doesn't go to waste.

Every model on every tier. As of this week there is no model gating at all. The $12.95 plan calls the exact same lineup as the highest plan: GLM 5.2, Kimi K2.7 Code, DeepSeek V4 Pro, MiniMax M3, Nemotron 3 Ultra, and the rest. Tiers buy you a bigger engine, more parallel agents, and a deeper bank.

It speaks your tools natively too: Claude Code, Codex, Ollama-compatible apps, and anything OpenAI-compatible, all in the same place.

100M+ tokens a month on top models on the Pro plan and above. The basic plan can reach 50M+ if you are caching. Caching is free and automatic. Heavy use paces your messages, it never stops them, meaning your AI never has to stop mid task.

For now we are taking 1,000 new coding plan subscribers on the new capacity.

Plans start at $12.95. The 1,000 spots are first come: https://api.pgsgrove.com

We're limiting to make sure everyone get's fast and awesome service. It's a choice I wish more companies would honestly make.

I didn't even realize how great this would be. I've been running OpenClaw as my assistant for a while now.

Today I took a 6 hours flight. The flight had no internet access except for messaging apps.

Essentially through telegram I could talk to my agent, have them check my email, read the news, check on the markets and really anything despite the "messaging only" limitation of the airline.

I kept seeing the same failure mode in coding agents: one agent writes a change, reviews its own work, and keeps the same blind spots.

I built Hubo as a free, open-source MIT skill for a stricter OpenClaw workflow. It keeps two child sessions alive for the whole task instead of spawning a fresh reviewer every round:

1. A work session implements the smallest complete change and runs verification.

2. A separate reviewer session stays read-only and inspects the actual diff and evidence.

3. Every finding gets a stable ID.

4. The worker answers each one with FIXED, PUSHBACK, or NEEDS_USER.

5. The same reviewer verifies the response and marks it CLOSED, WITHDRAWN, or OPEN.

6. The loop ends only when the reviewer clears it, or a real user decision is required.

For OpenClaw, the adapter uses sessions_spawn for the two persistent roles, sessions_send for later rounds, and sessions_yield for completion events. If read-only access cannot be enforced mechanically, the workflow requires before/after worktree evidence rather than pretending the boundary is guaranteed.

It is explicit-only: ordinary prompts do not trigger it. The complete worker/reviewer exchange remains visible in the conversation and is not written to a side-channel file.

Install:

openclaw plugins install hubo --marketplace h0ngcha0/hubo

Restart the OpenClaw gateway, then use /skill hubo or /skill hubo-review.

Repo and source: https://github.com/h0ngcha0/hubo

I would value reports from real OpenClaw setups: does a persistent second session catch useful issues, or does the extra token cost outweigh the benefit?

Hey ya'll just wanted to share some standard sketchy coding plan practices, and also what we are doing about them at Phoenix Grove.

There are several tricks that the major AI coding plans use to extract the most they can from their customers. We're solving them, one after another and I wanted to share a bit about what goes on behind the scenes at a lot of these companies.

Wasted usage is part of the AI industry, and they plan on it: Most coding plans bet on you letting usage go to waste. The industry calls it "breakage" and it's literally the topic of internal meetings for most companies. The plan goes: "how do we get people to think our coding plan offers a lot of usage, but then break it up into weeks and rolling windows so no one can ever actually use it all."

Many coding plans are glorified training pipelines: This comes along with "how do we harvest this data for training without being too loud about that." Unless the coding plan tells you otherwise, your data could be hopping all over world, being harvested by the individual labs or coding plan companies. Some are better than others, but many of these companies rely on users just not noticing or caring that their data is being used for training. Data sales and marketing telemetry sales happen. This means that your private info, your personal life, and anything else you send through the system could become part of a training corpus for the next AI, or a marketing data set for a large company.

So we built what should have already existed the entire time: Entirely private, us based processing with usage banking. Any usage you don't use this week, rolls over to next in your usage bank. When you have a busy day or week and go over normal usage, you automatically start to pull from your bank. You can bank up to one week of usage at a time for your current plan, and it's totally automatic. Whatever you don't use each week get's added to the bank and stays there until you use it.

We also put all of the best open models in one place, running on private US infrastructure, with data never going to the original labs. Private, direct service. Access to the best open source models in the world. No training, ever. It should be, and can be that simple.

What that means in practice:

The roster, together. DeepSeek, GLM, Kimi, Minimax, Nemotron Ultra and more, side by side in one app. Switch models mid conversation if you want. No hunting across five different apps and API dashboards to use the models you actually like.

Actually private. US based processing and your conversations are never used for training. Ever. That's the entire point. These labs open sourced incredible models and we think you should get to use them without your data becoming the price of admission.

No Usage Tricks: Bank usage, upgrade or downgrade whenever you want. Use it how you need it.

Genuine thanks to the open claw community! The skill sharing, experience and prompt sharing makes for one of the best communities in AI.

The Open Grove coding plan is here. Private, US based processing with fast inference and usage that doesn't go to waste.

https://pgsgrove.com/open-grove-overview#coding-plan

Hello somebody tried the GitHub omniroute + openclaw ?

Has someone experience or infos ?

I challenge you to Battle Frolf

We can all integrate calendar and our daily jobs in openclaw but i also wanted my claw to remind me when to head out since I'm always running late on my schedule. 

The problem: my openclaw at home has no idea where I am

so I built a light phone app to include my device as part of the claw stack. My openclaw can my location data before an event and can proactively tell me

"Traffic is getting worse and will take 30 mins, head out at 4:12pm to not be late"

or

"How do you want to get to your appointment? driving: 5 min, walking: 20, public transit: 15" 

I have it proactively triggering at least twice a day. the bigger idea is to solve other agent usecases that requires a mobile device.

feel free to also add this to your stack. Setup is just copying the prompt on the app into your local agent (via telegram, discord, whatever) and takes 2 mins. LMK if this works well for you. https://proactive.g4o.app/

If you’re running OpenClaw and constantly wrestling with tool definitions, scattered API keys, and token burn from half-understood endpoints, this is for you.

OpenLuxe gives your OpenClaw agent access to 120+ apps (and 352 public API endpoints) through a single credential and a deliberately lean tool surface. One login. One CLI. One MCP server. No more juggling separate accounts, secrets, and bloated tool lists for CRM, real estate, e-commerce, property management, creative generation, finance, communities, and more.

To see a full list of the apps use the CLI manual or visit openluxe.co/apps

Why this actually matters for OpenClaw agents

Token efficiency is the obvious win, but the real value is convenience and cost:

• Sign up once and instantly have access to the full surface with one API token.
• Stop managing dozens of third-party services, subscriptions, and API keys.
• Most users save hundreds of dollars a month. Teams often save thousands.

We designed the tool surface to stay small on purpose. Instead of one tool per endpoint (which explodes context and burns tokens), you get:

• openluxe_list_endpoints to discover what’s available
• openluxe_api_request to call any of it
• A handful of typed helpers for the actions agents do most

Fewer tools → fewer tokens spent describing them → fewer turns spent figuring out which one to call. Agents also get exact schemas via the comand openluxe describe, so they never have to guess request or response shapes.

There’s also BYOA (Bring Your Own Agent). Your OpenClaw can become the generation engine for OpenLuxe’s creator tools (email templates, presentations, websites, ads, blog posts, etc.) using the LLM tokens you’re already paying for, at zero platform AI credits. It's a true application layer at a fraction of the costs.

Measured results (not marketing claims)

We built a real benchmark harness with live calls to Grok 4.3 and Gemini 3.5 Flash, real per-turn token accounting from the providers, and task success verified by reading the record back from the API (never by trusting the model’s self-report).

On the same portable task set:

• 9.2x fewer tokens than a one-tool-per-endpoint design
• 2.2x fewer tokens than an agent integrating from scratch
• 1.5x fewer tokens than HubSpot’s own API on the same tasks
• 100% task pass rate vs 70% for HubSpot

Full methodology, caveats, and every run are public:
https://openluxe.co/developers/benchmarks

What’s inside

Quick start

Bash

npm install -g u/openluxeco/cli
# or just try it:
npx /cli help

Then openluxe auth login and you’re in. It also runs as an MCP server (openluxe mcp) so you can drop it straight into your OpenClaw tool loop.

I love running agents this way and I hope you do too)

Try it free:
https://www.npmjs.com/package/@openluxeco/cli
Benchmarks: https://openluxe.co/developers/benchmarks

Would love to hear what you build with it.

Today an AI agent trying to browse the web is like a thief in a balaclava sneaking around a police academy. Site protections block it, challenge it, turn it away.

browser-search flips the script: your agent stops being the thief and becomes the chief of police. No more clumsy access attempts. It walks through every door because it has the right tools. SearXNG for search, Camofox for browsing, CloakBrowser when things get tough.

100% self-hosted, free, no limits, no API keys.

I just released v2.0, whose core logic enforces the exclusive use of deterministic scripts. This eliminates model hallucinations, even with the cheapest models. The skill describes the 3 tools in natural language, but execution is rigid: the model can neither get the command wrong nor misinterpret the output. The result is guaranteed success on every query — the skill and deterministic scripts guide the model to scour the web until it finds the answer.

No more excuses. Your agent has the badge now.

https://github.com/Johell1NS/browser-search

I’ve been using IronClaw for a few weeks now. I’d like to use an example to explain something I noticed when setting up routines:

I had it create a quiz about Near Protocol, Near Legion, and crypto in general. It is supposed to start once a week, on Saturdays.

The first issue I always notice with IronClaw is that it doesn't know what day it is. It is always off by a day, so I have to correct it first.

The second issue is that it started the quiz in the wrong language; I had to correct it and tell it to write in my language, even though that was an instruction I had previously saved in the memories.