Firefly AI Assistant is Adobe’s attempt to turn Creative Cloud into an agent-driven workflow layer. Instead of bouncing between Photoshop, Premiere Pro, Illustrator, Lightroom, and Express, users describe the result they want, and Firefly handles the app hopping in the background.

That is a much bigger move than adding another AI button inside one Adobe app. It extends the direction Adobe has already been pushing with features like Photoshop with AI, but at a broader workflow level.

What Adobe Announced

At the center of the announcement is a simple idea: one prompt should be able to trigger a multi-step workflow across several Creative Cloud apps while preserving context between sessions. Adobe laid that out in both its official blog post and its newsroom release.

The pitch is straightforward: spend less time figuring out which app, panel, or workflow to use, and more time describing the end result.

Adobe is positioning the assistant to work across apps including:

• Photoshop
• Premiere Pro
• Express
• Lightroom
• Illustrator
• additional Creative Cloud apps over time

It will also ship with prebuilt Creative Skills, reusable task flows for common jobs such as retouching portrait photos with consistent presets or generating content across social channels. Users will also be able to create their own skills, which is where this starts to feel less like a chatbot and more like a customizable automation layer for creative work.

The Real Shift is the Interface

The assistant itself is only part of the story. The more interesting move is Adobe’s push toward prompts as the control surface, with the app stack acting as the execution layer underneath.

That lowers the barrier for less technical users inside Creative Cloud. Instead of knowing exactly where every tool lives or how to chain actions manually, users can ask for an outcome and step in when they want to refine it. If you have run into Adobe’s current AI limitations before, including common Generative Fill issues in Photoshop, that shift is easy to understand.

Firefly AI Assistant is being framed as a guided agent, not a one-shot black box. It can ask contextual follow-up questions, surface suggestions, and let users adjust outputs while the workflow is still in motion.

It is also meant to learn a user’s preferences over time, including aesthetic choices, preferred tools, and workflow habits. If Adobe executes well, repeat tasks could get much faster for solo creators and teams doing similar work every week.

Where It Could Actually Help

One of Adobe’s examples is editing a set of product photos shot in a forest.

Instead of rebuilding the scene manually, the assistant could expose a simple control, such as a slider to increase or reduce trees and foliage around the subject. That turns a job that would usually take several manual edits into a faster guided adjustment.

That is the practical promise here. Not generative AI for show, but a shorter path through tedious creative work.

Frame.io is Part of It

Adobe is also extending this idea into Frame.io, which makes the announcement more relevant for teams.

In Frame.io, the assistant is meant to help package materials for presentations, share them with collaborators, collect feedback, and even apply requested changes automatically.

It is an ambitious claim, but it fits Adobe’s broader direction. Creation, review, and revision are starting to blur into one connected workflow instead of staying as three separate stages.

Adobe Wants It to Work Beyond Its Own Apps

Another detail worth watching is Adobe’s work with Anthropic.

Adobe is also pushing Firefly AI Assistant beyond its own interface. Compatibility with Claude would let creators tap Adobe workflows from outside Creative Cloud itself, and Adobe has already signaled that more third-party integrations are coming.

That suggests Adobe knows people are no longer working inside one software silo all day. If Firefly AI Assistant can show up where people already work, it has a better shot at becoming part of a real workflow instead of a demo feature. For anyone already weighing Adobe’s ecosystem costs and tradeoffs, this Creative Cloud plan breakdown for Photoshop users is a useful companion.

New Firefly Features Shipping Sooner

Firefly AI Assistant is still headed for public beta in the coming weeks, so there is nothing to try yet. Adobe also paired the announcement with a broader batch of Firefly upgrades, including the changes detailed in its separate post on new Firefly video tools and Premiere changes.

Firefly Video Editor Updates

Firefly Video Editor is getting:

• audio upgrades, including Enhance Speech, noise reduction, reverb reduction, and better balancing for speech, music, and ambience
• color controls for exposure, contrast, saturation, temperature, and other image adjustments
• Adobe Stock integration, giving editors access to licensed media assets inside the workflow

Adobe is also expanding the list of third-party models available in Firefly. The lineup now includes Kling 3.0, Kling 3.0 Omni, Veo 3.1, Runway Gen-4.5, Luma Ray3.14, FLUX.2 [pro], ElevenLabs Multilingual v2, Topaz Astra, and Adobe’s own Firefly models.

New Image Editing Controls

On the image side, Adobe also introduced two new editing features:

• Precision Flow, which lets users generate multiple image variations from one prompt and move through them with a slider
• AI Markup, which lets users brush, mark, or guide where edits happen using direct visual input and reference images

Both point in the same direction: less prompt-only trial and error, more controlled editing.

Why This Is Worth Watching

A lot of AI product demos look impressive right up until you try to use them.

This stands out because Adobe is not just bolting another isolated AI trick onto one app. It is trying to connect generation, editing, collaboration, and revision inside one assistant-led workflow.

If Adobe can make that workflow feel reliable, fast, and editable, Firefly AI Assistant could become one of the more practical uses of AI in pro creative software.

If it feels slow, vague, or too eager to take over, creatives will ignore it and go back to the tools they already trust.

That is the balance Adobe has to get right.

For now, Firefly AI Assistant looks less like a minor feature update and more like Adobe’s clearest attempt yet to make AI the operating layer across Creative Cloud.

The post Adobe’s Firefly AI Assistant Wants to Run Creative Cloud for You appeared first on Hongkiat.

You know the one: the address you use for throwaway signups, the inbox attached to an old domain, or the backup Gmail account you set up because it seemed smart at the time, then quietly forgot existed.

It feels harmless to ignore it, but if that email is tied to recovery for any of your important accounts, it is not a side account anymore. It is infrastructure, and neglected infrastructure is where problems like to hide.

The Problem Is Not the Inbox. It Is What the Inbox Unlocks.

A secondary inbox is easy to dismiss because the account itself often looks quiet. No real conversations, no daily workflow, no reason to check it unless something goes wrong. That is exactly why it gets ignored.

But a recovery email is not valuable because of what is inside it. It is valuable because of what it can open.

If a service can send password reset links, sign-in approvals, verification codes, or security alerts there, then that inbox has leverage. It may not be your main account, but it may still be part of the path back into your bank, cloud storage, social accounts, shopping logins, or work tools. A guide on How to Make Your Gmail Account Safer is a useful companion if you have never checked your recovery settings closely.

That changes the math.

A Forgotten Backup Account Can Fail in More Than One Way

Most people think of email neglect as just not reading messages.

The real issue is broader than that.

Sometimes an old inbox goes inactive long enough that the provider starts treating it like abandoned property. Sometimes the password is ancient. Sometimes two-factor authentication was never turned on. Sometimes the account is still signed in on a laptop you sold, a phone you reset, or a browser profile you no longer control.

And sometimes the inbox is technically still fine, but you have trained yourself never to look at it, which means you also never see the warning lights: password reset attempts, new logins from unfamiliar devices, security notifications, even recovery email changes. All the little signs that would have told you something was off are just sitting there unread in the quietest inbox you own.

This Is Why Attackers Like Quiet Accounts

A noisy primary inbox gets attention. A neglected backup inbox does not, and that makes it a softer target.

Not necessarily because it is easier to break into, though sometimes it is. More because it is easier to exploit without being noticed quickly. A broader roundup of online privacy and security tips reinforces the same point: the neglected parts of your setup are often the weakest ones.

If someone gets into a secondary inbox that you rarely check, they may have more time than they should. More time to search for recovery messages. More time to trigger resets. More time to pivot into other accounts. More time before you even realize that particular door was still connected to anything important.

The damage is often indirect. You do not lose the forgotten inbox first. You lose the more valuable account it helps recover.

The Mistake Is Treating Secondary as Unimportant

“Secondary” is a workflow label. It is not a security label.

That is the part many of us get wrong.

We rank accounts based on how often we use them. But account security should be ranked by how much access an account controls.

A backup inbox you never open might still matter more than a social app you check every day. If it sits in your recovery chain, it deserves the same seriousness as your primary email. Maybe more, because you are less likely to notice when something is wrong.

What to Do Instead

This does not need a giant life overhaul. Just a small cleanup pass with some common sense.

Log in on Purpose

If an email account can recover another account, do not wait until an emergency to test it.

Open it occasionally. Make sure it still exists. Make sure you still know the password. Make sure it is not asking you to confirm anything you missed six months ago.

Secure It Like It Matters

Use a strong unique password. Turn on two-factor authentication. Review recovery methods. Check active sessions and connected devices. The same basics show up in this guide to Facebook account security too, because the pattern is the same across almost every account that matters.

The boring checklist still wins here.

Audit Where It Is Being Used

This is the step most people skip because it is annoying. Go through your important accounts and see which email addresses are listed for recovery, alerts, and verification. Chances are, at least one service is still pointing to an inbox you have mentally retired.

That is worth fixing before you need it.

Reduce the Friction

If checking multiple inboxes is why the account gets ignored, route the important stuff somewhere visible.

Forward messages. Import mail. Set up filters. Whatever makes that account harder to forget.

The goal is not to admire your inbox architecture. The goal is to avoid being blindsided by a forgotten dependency.

The Better Mental Model

Do not think of these accounts as extras. Think of them as hidden support beams. You do not stare at support beams every day either, but you do care a lot when one quietly rots.

That is what a neglected recovery inbox is: invisible right up until it becomes structural.

Final Thought

A lot of digital security advice focuses on the flashy stuff. This is not that. This is maintenance, quiet, boring, low-status maintenance, but it matters.

If an email account helps you recover anything important, it is an important account. Full stop.

So log in to your secondary email accounts, not because they are busy, but because they are trusted.

The post Why You Should Still Log In to Your Secondary Email Accounts appeared first on Hongkiat.

That is the job Sherlock handles. It gives you a quick way to check public username trails before doing deeper manual research.

Sherlock is an open source command-line tool that checks whether a username appears across hundreds of websites and social platforms. Instead of testing one site at a time, you give it a username and let it do the repetitive work for you.

If you want a quick way to see whether the same handle shows up on GitHub, Reddit, Instagram, TikTok, or a long tail of smaller platforms, Sherlock is one of the easiest tools to keep in your kit.

Sherlock in a Nutshell

Sherlock is a username discovery tool maintained by the sherlock-project team.

It checks a given username against a large list of supported sites and returns the profiles it finds. According to the project documentation, it supports checks across 400+ websites and social networks.

It is an approachable OSINT tool. You do not need API keys, browser extensions, or a complicated setup to start using it.

What Sherlock Does

Sherlock automates a task that is dull, repetitive, and easy to get wrong by hand.

Instead of opening site after site and testing the same username manually, it builds the likely profile URL for each platform, checks the response, and reports what it finds.

In practice, Sherlock can help you:

• find where a username is active online
• check several usernames in one run
• save results to text, CSV, or XLSX
• limit searches to specific sites
• route lookups through a proxy or Tor
• open found profiles in the browser
• load usernames from JSON for bulk checks

Sherlock does not bypass privacy settings, unlock private accounts, or reveal hidden information. It works by checking publicly visible usernames and public profile patterns.

Username reuse is common. Many people keep the same handle, or a close variation of it, across multiple platforms. That makes Sherlock useful to journalists, OSINT researchers, security researchers, recruiters, investigators, developers, creators, and ordinary users who want a quick starting point for mapping a public online presence.

Getting Started

Sherlock is a Python-based CLI tool, and the project offers a few installation options.

Install With pipx

This is one of the cleanest ways to install Sherlock at user level:

pipx install sherlock-project

If you do not use pipx, you can install it with plain pip instead.

Install With pip

pip install --user sherlock-project

Run With Docker

If you would rather avoid installing Python packages directly:

docker run -it --rm sherlock/sherlock

Other Package Sources

The project also mentions community-maintained packages for platforms such as Debian, Ubuntu, Homebrew, Kali, and BlackArch. Those packages are not maintained directly by the Sherlock Project, so the pipx route is still the safer default recommendation.

How to Use Sherlock

Once Sherlock is installed, the basic pattern is simple: run it with a username, then add options depending on how broad or narrow you want the search to be.

Search for One Username

sherlock user123

That checks user123 across supported sites and saves the results to a text file named after the username.

Common Options

• sherlock user1 user2 user3 to check several usernames in one run
• sherlock user123 --print-found to only print matches
• sherlock user123 --print-all to print found and not found results
• sherlock user123 --site GitHub --site Instagram to limit checks to specific platforms
• sherlock user123 --csv or --xlsx to export results
• sherlock user123 --output results/user123.txt to save to a custom file
• sherlock user123 --proxy socks5://127.0.0.1:1080 to run through a proxy
• sherlock user123 --tor to run over Tor
• sherlock user123 --browse to open found results in the browser

Example Workflow

Suppose you want to check whether the username johndoe exists across major platforms. Start with:

sherlock johndoe --print-found

If the first pass looks useful and you want something you can review later, export it:

sherlock johndoe --print-found --csv

If you already know which platforms matter, narrow the search instead of checking everything:

sherlock johndoe --site GitHub --site Reddit --site Instagram

That is usually the better approach when you care more about a few relevant platforms than maximum coverage.

What Sherlock Is Good At, and Where It Falls Short

Sherlock is strong at speed and coverage. It checks a public username across a large number of sites and fits easily into a lightweight research workflow.

Its limits matter just as much:

• it depends on public profile patterns and site responses
• it can only check the sites it knows about
• it may miss accounts if a platform changes how profile detection works
• a matching username does not prove identity

That last point is the important one. If coolhandle99 exists on five platforms, that does not automatically mean all five accounts belong to the same person. Sherlock gives you leads, not proof.

Final Thoughts

Sherlock does one job well: it helps you find where a username appears across a wide range of websites without doing the same checks by hand.

That makes it a practical tool for OSINT work, username research, and general online footprint checks. Test it with a few known usernames and you will quickly see whether it belongs in your toolkit.

The post Find Usernames Across Social Networks with Sherlock appeared first on Hongkiat.

Until now, using Gemini on a Mac mostly meant keeping a browser tab around. It worked, but it never felt like something built for the desktop. This new app changes that. In Google’s official announcement, the company pitches Gemini for macOS as a faster, more integrated way to use its AI on the desktop, with global shortcuts, window sharing for context, and quick access without bouncing back to a browser.

That alone makes it easier to compare with ChatGPT and Claude, especially now that tools like ChatGPT with macOS app integrations already feel at home on the desktop.

What the Gemini Mac App Actually Does

At the core, this is a native macOS app that lets you call up Gemini from anywhere with a keyboard shortcut.

The main shortcut is Option + Space, which opens Gemini quickly without forcing you to switch tabs. Google also says Option + Shift + Space can open the full chat view, and both shortcuts are customizable.

That part feels important. The whole pitch here is speed. If you need to check a formula, summarize a document, draft an email, or ask a quick question while working, the app is supposed to stay out of your way.

Google also built in window sharing, which is one of the stronger desktop-specific features. Instead of pasting content manually, you can share a specific window and let Gemini respond based on what is visible on screen. Google’s examples include summarizing charts, helping with documents, and answering questions about what you are currently looking at.

Beyond that, the app covers the usual Gemini tasks:

• writing and drafting
• brainstorming
• summarizing long content
• coding help
• image analysis

Google is also leaning into creative tooling here. Its Mac landing page says the app can tap image creation with Nano Banana and video generation with Veo, which suggests Google wants Gemini on macOS to be more than just a text box with a shortcut.

The Extra Details Google’s Own Docs Reveal

This is where the official support pages add more value than the launch write-ups.

To run Gemini on Mac, Google says you need:

• macOS Sequoia 15.0 or later
• at least 8 GB of RAM
• at least 200 MB of free disk space
• a stable internet connection

That requirement alone will knock out some older Macs.

Google also says the app is available globally in all languages and countries where Gemini itself is supported, and it is available at no cost. The app is available to users aged 13 and up. If you want a second point of comparison on how desktop AI apps are being packaged for Mac users, this Grok on Mac guide shows how quickly that category is filling up.

There are also a few usage details that did not get much attention in the early coverage:

• You can launch Gemini from the menu bar, Dock, or keyboard shortcuts.
• If you want Gemini to respond to what is inside a browser page more fully, you may need to enable Accessibility permissions in macOS.
• Chat history and memory sync across devices as long as you are using the same Google account.

That last part is useful. It means the Mac app is not a separate side experience. It is another front end for your existing Gemini account and history.

Why This Launch Feels Bigger than Just Another App

A native Mac app does not automatically make Gemini better, but it does remove friction.

That was the weak spot before. Browser-based AI is fine until you use it all day. Then every extra step starts to show. Opening a tab, dragging content over, copying text around, and losing context gets old fast.

The new app is clearly aimed at that problem. Shortcut-first access and window sharing are both about keeping Gemini close enough to use casually.

That also explains why Google is framing this as a desktop workflow tool rather than just a chatbot app. The more useful Gemini becomes while you are already doing something else, the more often it gets used.

What is Still Missing

Google’s launch messaging is polished, but there are still a few open questions.

For one, Google has not framed this around deeper Mac-native automation in the way power users may want. At least from the launch materials, the app looks focused on fast access, contextual help, and creation tools rather than system-level workflows.

There is also a difference between sharing a window and being deeply integrated into the OS. The first is useful. The second is where desktop AI starts to feel like infrastructure.

Google says more features are on the way, which is easy launch-day language, but in this case it probably depends on how quickly the app expands beyond quick chat and contextual assistance.

The Real Takeaway

The Gemini Mac app does not reinvent AI on the desktop. It fixes a practical problem.

Gemini now has a proper place on the Mac instead of living inside a browser tab. That means faster access, better context, and a setup that makes more sense if you use AI throughout the day.

If you already use Gemini, this is an easy upgrade. And if you are already in Google’s AI ecosystem, getting started with Gemini CLI is the other obvious companion route for terminal-heavy work. If you do not, the app at least gives Google a more credible desktop answer to ChatGPT and Claude on macOS.

And honestly, that was overdue.

The post Google’s Gemini App Is Now on Mac, and It Finally Feels Like a Desktop Tool appeared first on Hongkiat.

ASCII Magic is one of those.

You drop in an image or video, and it turns it into ASCII art in real time. The fun part is not the effect itself, but how much control you get over the result. It feels more like a live visual playground than a one-click filter.

That makes it easy to test odd ideas fast. For designers, that alone gives the tool a reason to exist. If you enjoy finding creative tools worth bookmarking, this fits neatly into that pile.

What ASCII Magic Can Do

ASCII Magic turns images and videos into text-based visuals built from characters. What makes it useful is the amount of control it gives you over the conversion.

Here’s what the tool can do:

1. Real-Time ASCII Conversion

ASCII Magic updates the output as you change the settings. That instant feedback is what makes it fun to use.

If you are trying to find a look for a poster, social post, motion loop, or strange visual experiment, real-time control helps a lot.

2. Works With Both Images and Videos

It does not stop at still images. It also handles video, so you can turn moving footage into animated ASCII visuals without building a messy workflow across multiple apps.

That opens the door to things like:

• retro motion graphics
• experimental social content
• music visual loops
• landing page visuals
• stylized promo assets

3. Adjustable Character Sets and Density

ASCII art lives or dies on character choice.

ASCII Magic lets you change the character set and the density of the output. That changes the look fast. Dense character maps hold more detail, while lighter ones feel cleaner and more graphic.

For designers, this is one of the main controls. It gives the tool range instead of locking everything into one look, much like other design tools for designers that stay useful because they leave room to shape the result.

4. Edge Detection and Blur Controls

Edge detection and blur help decide how much structure survives the conversion.

Edge controls can pull out outlines and separate subjects more clearly. Blur can soften noise before the image is translated into characters. Together, they change whether the output feels rough, crisp, abstract, or readable.

This is more than a simple image-to-text trick. It is image processing with a distinct visual style.

5. Color Support

ASCII art is usually tied to monochrome output, but ASCII Magic also supports color.

That pushes the result away from terminal nostalgia and closer to something you could use in a poster, motion asset, or web visual. With color, the output looks more like a deliberate treatment and less like a novelty.

6. PNG and MP4 Export

Export options are where tools like this either become useful or stay as toys.

ASCII Magic exports to PNG and MP4, which covers the obvious use cases. PNG works for stills, mockups, and thumbnails. MP4 makes the tool more useful for short loops, animated posts, and lightweight video assets.

Why Designers Might Love It

The best tools for designers are often the ones that help you find a look quickly.

ASCII Magic does that well. It is fast, visual, and easy to experiment with. You can throw in an image, move a few controls, and see right away whether the result is worth keeping.

That makes it useful for:

• concept exploration
• moodboard generation
• visual experimentation
• social media asset creation
• motion design play
• web visuals with a retro-computing edge

It also has a quality many designers like: simple input, lots of variation. If the retro-computing angle is part of the appeal, this piece on retro and vintage web design inspiration is a useful companion.

The Real Appeal Is Not Nostalgia

It is easy to frame ASCII Magic as a nostalgia tool, but that sells it short.

The terminal look is part of the appeal. The bigger draw is that it turns ordinary media into something constrained, graphic, and expressive. That kind of limitation can lead to stronger visual ideas because it pulls you away from polished defaults.

ASCII Magic feels like the kind of tool designers will keep around for the days when clean and expected is not the goal.

The post ASCII Magic Turns Images and Videos Into ASCII Art appeared first on Hongkiat.

You find a model that looks promising, pull it into Ollama or llama.cpp, then realize it is too slow, too large, or just wrong for your machine. By the time you figure that out, you have already wasted bandwidth, storage, and a chunk of your afternoon.

That is the problem llmfit is built to solve.

Created by Alex Jones, llmfit is a terminal tool that checks your hardware, compares it against hundreds of models, and recommends the ones that are actually practical for your setup. Instead of guessing whether a model will fit into RAM or VRAM, it ranks options by fit, speed, quality, and context so you can make a smarter choice before downloading anything.

If you run local models often, this is one of those tools that feels immediately sensible.

What is llmfit?

llmfit is a local model recommendation tool for people who run LLMs on their own hardware.

It detects your machine specs, including RAM, CPU, and GPU, then ranks models based on what your system can realistically handle. It supports both an interactive terminal UI and a standard CLI mode, so you can browse visually or script it into your workflow.

According to the project README, it works with local runtime providers such as Ollama, llama.cpp, MLX, Docker Model Runner, and LM Studio.

In plain English, llmfit answers one very practical question:

Which LLM should I run on this machine?

What does llmfit do?

At its core, llmfit helps you stop guessing.

It can:

• detect your CPU, RAM, GPU, and available VRAM
• check model size and quantization options
• estimate which models will run well, barely run, or not fit at all
• suggest models by use case, such as coding, chat, reasoning, or embeddings
• simulate hardware setups, so you can test imaginary builds before upgrading or buying anything
• estimate what hardware a specific model would need

That last part matters more than it sounds.

Most local AI tools tell you what exists. llmfit tries to tell you what is practical.

Why llmfit Is Useful

There are already plenty of places to browse models.

What is usually missing is a clear answer to whether a model makes sense on your machine.

A 7B model might technically run, but if it crawls, that is not much use. A quantized model might squeeze into memory, but leave too little headroom for the context length you want. llmfit tries to bridge that gap by combining hardware detection, model scoring, and runtime awareness.

If you are new to running local models, setting up a local LLM launcher is a useful first step before narrowing down the right fit with llmfit. The tool is useful for a few different kinds of users:

• people new to local LLMs who do not know where to start
• developers comparing Ollama, MLX, or llama.cpp setups
• anyone planning an upgrade and wanting to know what more RAM or VRAM would unlock
• teams running local AI across different machines and needing a quick way to compare fit

How to Install llmfit

The project offers a few install options.

Homebrew

If you are on macOS or Linux with Homebrew:

brew install llmfit

MacPorts

If you use MacPorts:

port install llmfit

Windows with Scoop

scoop install llmfit

Quick Install Script

For macOS or Linux, the project also provides an install script:

curl -fsSL https://llmfit.axjns.dev/install.sh | sh

If you want a user-local install without sudo:

curl -fsSL https://llmfit.axjns.dev/install.sh | sh -s -- --local

Docker

You can also run it with Docker:

docker run ghcr.io/alexsjones/llmfit

Build from Source

If you prefer building it yourself:

git clone https://github.com/AlexsJones/llmfit.git
cd llmfit
cargo build --release

The binary will be available at:

target/release/llmfit

How to Use llmfit

The easiest way to start is to just run it.

llmfit

That launches the interactive terminal UI.

Inside the interface, llmfit shows your detected hardware at the top and a ranked list of models below it. You can search, filter, compare, and sort models without leaving the app.

Several useful keys from the project documentation:

• j / k or arrow keys to move through models
• / to search
• f to filter by fit level
• s to change sort order
• p to open hardware planning mode
• S to simulate different hardware
• d to download the selected model
• Enter to open model details
• q to quit

If you prefer command-line output instead of the TUI, use CLI mode:

llmfit --cli

Here are a few commands worth knowing.

Show Your Detected System Specs

llmfit system

List All Known Models

llmfit list

Search for a Model

llmfit search "llama 8b"

Get Recommendations in JSON

llmfit recommend --json --limit 5

Get Coding-Focused Recommendations

llmfit recommend --json --use-case coding --limit 3

Estimate Hardware Needed for a Specific Model

llmfit plan "Qwen/Qwen3-4B-MLX-4bit" --context 8192

Features Worth Calling Out

Hardware Simulation

This is one of the smarter parts of the tool.

Inside the TUI, pressing S opens simulation mode, where you can override RAM, VRAM, and CPU core count. That lets you answer questions like:

• What if I upgrade from 16GB to 32GB RAM?
• What if I move this workload to a machine with more VRAM?
• What could I run on a smaller target machine?

It is a practical way to plan hardware without leaving the app or doing the math manually.

Planning Mode

Planning mode flips the normal question around.

Instead of asking what fits your current machine, it asks what hardware a specific model would need. That is useful when you already know the model you want and need a quick sense of whether your machine can run it comfortably.

Web Dashboard and API

llmfit is not limited to an interactive terminal.

It can also start a web dashboard, and it includes a REST API through llmfit serve. That makes it more useful for scripting, automation, or folding into a larger local AI setup.

Who Should Use llmfit?

llmfit makes the most sense for:

• developers who run local LLMs regularly
• people choosing between Ollama, MLX, and llama.cpp
• anyone tired of trial-and-error model downloads
• hardware tinkerers planning a RAM or GPU upgrade
• teams that want fast recommendations for different machines

If you only run one or two models and already know what works on your system, you may not need it.

But if you experiment often, compare runtimes, or keep asking, “will this model actually run well here?”, llmfit starts looking genuinely useful.

Final Thoughts

llmfit is not another model launcher.

It is closer to a fit advisor for local LLMs.

That sounds modest, but it solves a real problem. Local AI is full of model lists, leaderboards, and download buttons. What most people need first is a faster way to narrow that list to models that actually make sense on their machine.

That is exactly where llmfit looks useful.

Install it, let it inspect your hardware, and see what it recommends before downloading your next model.

The post llmfit Helps You Pick the Right Local LLM for Your Machine appeared first on Hongkiat.

But the default setup still isn’t how I’d leave a Mac I use every day.

A fresh macOS install usually leans a little too pretty, a little too noisy, and a little too willing to spend battery on things I don’t care about. If you’re on a MacBook, that means shorter runtime and more battery wear over time. If you’re on a desktop Mac, it mostly means extra visual clutter and background behavior you probably didn’t ask for.

These are the settings I’d change first. Some help battery life, some make the Mac feel faster, and some simply make the whole thing less annoying to live with.

1. Change Low Power Mode So It Actually Helps

MacBook only

Low Power Mode is worth setting deliberately instead of leaving battery behavior to whatever macOS thinks is best for you.

Go to System Settings > Battery.

If your priority is battery life, set Low Power Mode to Only on Battery. That gives you the battery-saving behavior when unplugged without slowing things down when your MacBook is connected to power.

If you want maximum power savings all the time, you can set it to Always. Most people probably won’t want that.

The other two options are Never and Only on Power Adapter, but for most MacBook users, Only on Battery is the sensible middle ground.

2. Reduce Transparency or Use a Tinted Interface

MacBook and desktop Mac

Liquid Glass looks nice, but it also adds visual noise in places where I’d rather have clarity.

You’ve got two useful options:

• System Settings > Accessibility > Display > Reduce Transparency
• System Settings > Appearance > Liquid Glass style > Tinted

I like this setting for two reasons: text is easier to read, and the interface feels calmer.

On a MacBook, it can also shave off a bit of unnecessary GPU work. Not massive, but real.

3. Shorten Display Sleep Time

MacBook and desktop Mac

This is boring advice, but it works.

Go to System Settings > Lock Screen.

A good starting point:

• MacBook on battery: 3 to 5 minutes
• On power adapter: 10 to 15 minutes

If you walk away from your machine often, this saves power and reduces the number of times your display sits there glowing for no reason.

Also turn on the password requirement after the screen saver or sleep kicks in. It’s basic hygiene.

4. Turn On Auto-Brightness and Lower Manual Brightness

MacBook only

If you want a quick battery win, start here.

Go to System Settings > Displays.

Then:

• Turn on Automatically adjust brightness
• Keep brightness around 40% to 60% indoors

Display brightness is still one of the biggest battery drains on a MacBook. No clever tweak beats simply not blasting your screen at full brightness indoors.

5. Clean Up Login Items and Background Apps

MacBook and desktop Mac

A lot of apps assume they deserve to launch at startup and quietly stay alive forever. Most of them do not.

Go to System Settings > General > Login Items & Extensions.

Then check two things:

• apps that launch at login
• apps allowed to run in the background

Disable what you don’t actually need.

Slack, Discord, browser helpers, menu bar utilities, cloud sync tools, and random updater processes are common offenders.

If you want to see the real energy hogs, open Activity Monitor > Energy and look at what’s actually costing you battery.

6. Trim iCloud Syncing

MacBook and desktop Mac

If you already use Dropbox, Google Drive, Synology Drive, or another sync setup, you may not need iCloud syncing everything too.

Go to System Settings > Apple ID > iCloud.

Review what’s enabled and turn off anything you don’t need syncing constantly. This is especially worth checking for large folders and apps that generate frequent background activity.

7. Tame Spotlight Search and Indexing

MacBook and desktop Mac

Spotlight is useful, but it’s also happy to index more than most people need.

Go to System Settings > Spotlight to review search result categories and hide the ones you never use.

For indexing, add folders to Search Privacy if they don’t need constant indexing.

After a major OS upgrade, Spotlight often reindexes a lot of content anyway. Let it finish first, then trim it down.

8. Set Up Hot Corners

MacBook and desktop Mac

This one is more productivity than battery, but it earns its place.

Go to System Settings > Desktop & Dock > Hot Corners.

A setup I like:

• Top-left: Mission Control
• Top-right: Show Desktop
• Bottom-left: Put Display to Sleep

That last one is especially handy. Once it becomes muscle memory, it’s one of the fastest ways to save power when you step away for a minute.

9. Clean Up the Dock and Reconsider Stage Manager

MacBook and desktop Mac

Tahoe gives you more ways to manage windows, but more options does not automatically mean a better setup.

In System Settings > Desktop & Dock, I’d start with these:

• reduce Dock size
• turn off magnification
• turn on Automatically hide and show the Dock
• test whether Stage Manager genuinely helps your workflow

I’d also spend a bit of time with Tahoe’s window tiling before reaching for a third-party app. It’s better than many people think.

10. Reduce Motion

MacBook and desktop Mac

Go to System Settings > Accessibility > Motion and turn on Reduce Motion.

This is one of those settings that makes the Mac feel faster even when the speed gain is modest. Less animation, less visual drag, less unnecessary movement. I usually prefer that.

11. Use a Static Wallpaper

MacBook and desktop Mac

Dynamic wallpapers are nice in the same way animated lock screens are nice: pleasant for a moment, easy to forget, still doing work in the background.

If battery life or simplicity matters, use a static wallpaper instead in System Settings > Wallpaper.

This is a small gain, but a clean desktop with less motion is usually worth it anyway.

12. Audit Location Services and Analytics

MacBook and desktop Mac

Go to System Settings > Privacy & Security > Location Services.

Turn off location access for apps that don’t need to know where you are.

Then review Apple’s analytics and improvement sharing options too. If you don’t want the extra background chatter, disable them.

This won’t transform your Mac overnight, but it’s part of a better default setup.

13. Use Shortcuts for Smarter Automation

MacBook and desktop Mac

Shortcuts keeps getting better, and macOS Tahoe makes it more useful for small quality-of-life automations.

A few practical ideas:

• when battery drops below 30%, enable Low Power Mode
• when you connect to home Wi-Fi, set your preferred battery charge limit
• when a Focus mode starts, adjust display behavior or other related settings

This is where the Mac starts feeling personal instead of generic.

My Top 5 Changes for Most MacBook Users

If you don’t want to do all 13, start here:

1. set Low Power Mode to Only on Battery
2. turn on auto-brightness
3. shorten display sleep time
4. reduce transparency
5. clean up login items

Those five usually give you the biggest improvement with the least effort.

A Few Fast Bonus Wins

A few extra tweaks that still matter:

• use Safari over Chrome or Edge if battery life matters
• keep macOS and apps updated, especially point releases like 26.4
• disconnect unused drives and USB accessories
• check Battery Health once in a while if you’re on a MacBook

Final Thoughts

macOS Tahoe is polished, but the default setup still tries to be a little too everything-at-once.

A bit more visual flair, a bit more background activity, and a bit more battery use than necessary.

The good news is most of that is easy to fix.

You don’t need to tweak every setting on this list. Even changing five of them can make your Mac feel cleaner, quieter, and more efficient.

If you want a quick way to verify the impact, keep an eye on Activity Monitor > Energy for a few days after making changes. That’s where the vague feeling of “my Mac seems better” turns into something you can actually measure.

The post 13 macOS Tahoe Settings for Better Productivity and Battery Life appeared first on Hongkiat.

If that sounds familiar, there is one built-in setting worth turning on.

What macOS gives you is not a manual slider where you pick 80%, 85%, 90%, or 95%. Instead, Apple uses Optimized Battery Charging to learn your charging routine and reduce the time your MacBook spends sitting fully charged. If you are still setting up a new Mac or cleaning up your defaults, this is one of the easier battery-health settings to turn on early.

It looks small, but it is one of the better low-effort settings for a MacBook that stays plugged in most of the day.

Why This Setting Exists

Batteries do not enjoy sitting full for long stretches.

When a lithium-ion battery stays near 100% all the time, it tends to wear down faster. You will still have a working MacBook, but over time the battery holds less of its original capacity.

That is the trade-off this setting is trying to fix.

If your machine spends most of its time near a wall socket anyway, charging to 100% every day is not buying you much. It is just keeping the battery under more stress than necessary.

How It Actually Works

Apple does not currently give MacBook users a simple percentage selector for charging. You cannot just choose 80%, 85%, 90%, or 95% and leave it there.

What you do get is Optimized Battery Charging. It learns your charging routine and tries to reduce the time the battery sits at full charge, especially when your MacBook spends long stretches plugged in.

That means the behavior is managed by macOS, not by a fixed manual limit you control yourself.

So the real advice here is simpler than the earlier version of this post: do not go looking for a manual percentage cap on MacBook. Turn on Optimized Battery Charging and let macOS handle that part.

Who Should Turn It On

This setting is best for people whose MacBook acts like a desk machine most of the week.

If your laptop is usually docked, connected to a monitor, or left charging on the same table every day, there is little reason not to enable it.

If you move around constantly and depend on every bit of battery life, macOS may still charge more aggressively based on your usage pattern. Even then, keeping the feature enabled is still the sensible default. If you are often away from power for long stretches, something like these portable batteries for your MacBook may help more than obsessing over battery percentages.

How to Turn It On

On MacBook, the path is straightforward:

1. Open System Settings
2. Click Battery
3. Click Battery Health
4. Click the Info icon
5. Enable Optimized Battery Charging

That is the setting you are looking for.

A Good Default for Desk Setups

If your MacBook rarely leaves the desk, enabling Optimized Battery Charging is the obvious default.

You do not have to micromanage charge percentages or guess at the perfect ceiling. You just let macOS reduce unnecessary time spent at full charge.

For people who use their MacBook like a half-laptop, half-desktop machine, this is one of those settings that quietly makes more sense the longer you think about it. Pair it with a few other basic Mac tips and tricks and the machine tends to feel better looked after overall.

It is a small setting, but a sensible one.

The post Why You Should Enable Optimized Battery Charging on Your MacBook appeared first on Hongkiat.

That is fine when you are talking to family or coworkers. It gets awkward when you want to message someone new, join a community, sell something, or keep your personal number to yourself.

Usernames fix that. WhatsApp has already said officially that usernames are coming, noting in its contact-management update that they will eventually let people connect without sharing a phone number. According to WABetaInfo, the feature has now started a very limited rollout.

Why This Helps

On Telegram, Signal, and most other messaging apps, usernames are normal. On WhatsApp, the phone number has always been the anchor.

A username gives WhatsApp a cleaner middle layer: a way to share contact access without handing over your number immediately.

It does not make WhatsApp anonymous, and it does not replace phone-number registration. It does make first contact feel less invasive, which is a clear privacy win.

What We Can Actually Verify So Far

WhatsApp does have an official post mentioning usernames, but not a full public launch post with rollout details. So the clearest picture still comes from beta tracking.

What looks reasonably consistent so far is this:

• usernames are in limited rollout, not broad public release
• WABetaInfo reports the feature appears in profile settings for some users
• WhatsApp has already said usernames are meant to let people connect without exposing their phone number so directly
• WABetaInfo reports the system is being built with Android, iOS, Windows, and web support in mind

That is enough to say the feature looks real and is moving forward. It is not enough to treat every detail as final.

How WhatsApp Usernames Are Expected to Work

If the feature reaches your account, you should see a username option in profile settings.

From there, the idea is simple: create a unique username and let people find or message you through that instead of relying only on your phone number.

The reported username rules are fairly standard:

• only lowercase letters, numbers, periods, and underscores
• at least one letter is required
• cannot start with www.
• cannot end with domain-style endings like .com
• length appears to be between 3 and 35 characters

The Meta Account Angle

One interesting wrinkle is the reported link between WhatsApp usernames and Meta’s wider account system.

According to that report, a username may need to be available across Meta services, or verified through Accounts Center if it already exists on Instagram or Facebook.

That could make things convenient, but it also creates a privacy tradeoff. If your WhatsApp username matches your public Meta identity, it becomes easier for strangers to connect those accounts.

There May Also Be a Username Key

Another reported detail is an optional four-digit username key that could act as an extra gate when someone messages you for the first time.

If that ships as described, it could be one of the smarter parts of the system. A username makes discovery easier, while the extra key could give users more control over who can actually reach them.

What This Does Not Change

Even with usernames, WhatsApp is still a phone-number-based service.

You still need a phone number to register, and the platform is not turning into an anonymous messenger overnight. So this is not a total privacy reset. It is a much-needed privacy layer that should have existed years ago.

The post WhatsApp Usernames Are Coming appeared first on Hongkiat.

That is where Hermes Agent fits.

Hermes adds the layer OpenClaw is missing: persistent memory, skill synthesis, failure review, and a feedback loop that can improve how your setup works over time. Put them together and you get a split that makes sense. OpenClaw does the work. Hermes watches, reviews, and helps the system improve. If you want background on either side first, start with the OpenClaw docs and the Hermes docs.

This guide shows how to set that up.

Why the Pairing Works

OpenClaw is good at operational work. It handles messaging, scheduled jobs, skills, and reliable task execution.

Hermes is better suited for the meta layer. It can learn from previous runs, turn repeated behavior into reusable skills, compress memory, and act more like a supervisor than a worker.

That split is the whole appeal. You let OpenClaw stay focused on execution while Hermes handles oversight.

What You Need

• A working OpenClaw installation with your ~/.openclaw config, skills, and gateway already running
• Linux, macOS, or WSL2
• Python 3.11 or newer
• Access to at least one model provider such as OpenRouter, Anthropic, OpenAI, or Ollama
• A VPS or dedicated machine if you want both systems running full-time

Install Hermes Agent

Run this:

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

Then reload your shell:

source ~/.bashrc   # or ~/.zshrc

Check that it installed correctly:

hermes --version

If you want the official walkthrough, use the Hermes docs.

Migrate Your OpenClaw Setup

Hermes includes a migration path for OpenClaw. If you want to inspect the project itself first, check the Hermes GitHub repo.

hermes claw migrate

Run it with --dry-run first if you want to see what will be imported.

If you would rather start fresh, skip the migration and go straight to hermes setup.

Run the Setup Wizard

hermes setup

A few choices matter more than the others.

Provider: Use the same model family you trust for OpenClaw. Keeping both systems reasonably aligned makes behavior more predictable.

Messaging adapters: Turn on the same channels OpenClaw already uses, such as Telegram or Discord. That makes it easier for Hermes to observe what OpenClaw is doing in shared spaces.

Gateway: Enable the Hermes gateway service so it can stay available in the background.

Profiles: Create a dedicated supervisor profile with:

hermes profile create supervisor

This keeps your oversight instance separate from any other Hermes profiles you may want to run.

Decide How Hermes Should Supervise OpenClaw

There are three practical ways to do it.

Chat-Based Supervision

This is the simplest setup.

Put Hermes and OpenClaw in the same Telegram group, Discord server, or private control channel. Then give Hermes a standing instruction like this:

You are my OpenClaw supervisor. Monitor @openclawbot. Review every task it completes. Respond with [ACK] to approve, [REJECT] plus a reason to stop, or [IMPROVE] plus suggestions. Log everything to memory and synthesize new skills when patterns emerge.

That gives Hermes a clear role. OpenClaw does the task. Hermes reviews the result.

Programmatic Control Through MCP or API

If you want tighter control, Hermes also ships with MCP support and an OpenAI-compatible API server.

Start Hermes with MCP enabled:

hermes gateway start --mcp

Then expose OpenClaw through its gateway, API, or Mission Control dashboard. From there, you can give Hermes a tool that calls OpenClaw directly.

A typical delegation prompt looks like this:

Use your OpenClaw control tool to delegate this task to the OpenClaw instance, then review the output and learn from it.

This setup is better if you want Hermes acting like an actual manager instead of just a second bot reading a group chat.

Hybrid Group Orchestration

You can also keep things simple:

• Hermes acts as the manager
• OpenClaw acts as the worker
• Hermes reviews failures, stores what it learns, and suggests updates to your OpenClaw skills

That model is less formal, but it works well if most of your workflow already lives in chat.

Turn on Learning and Monitoring

Hermes usually enables its learning loop during setup. Once it is running, a few commands are worth checking:

• hermes skills list to see what Hermes has synthesized
• hermes memory search "openclaw review" to inspect what it has learned from reviewing OpenClaw activity

For long-running setups, keep Hermes in the background with profiles and use either hermes cron or OpenClaw’s scheduler for recurring checks.

A Simple Example

User: Research the latest AI papers and summarize them.

Hermes: Delegates the task to OpenClaw, reviews the result, responds with approval or feedback, and turns the workflow into a reusable skill if the pattern repeats.

A Few Things to Watch For

Resource usage: Hermes can get heavy during active learning. Give it enough memory, or use model compression if the machine is tight on RAM.

Security: Hermes creates filesystem snapshots before risky changes. That is useful when you are letting an agent modify files or settings. If you plan to expose either system beyond your laptop or LAN, spend a few minutes reading the deployment and security sections in the docs first.

Migration confusion: If Hermes behaves like it is still OpenClaw after migration, reset the profile and run the migration again.

Multiple instances: Run hermes -p supervisor to keep the oversight profile separate from your main Hermes environment.

Final Thought

This setup does not magically make either agent smarter on day one. What it gives you is a cleaner division of labor. OpenClaw executes. Hermes watches what happened, keeps track of patterns, and helps improve the system over time.

If that is what you want, the pairing is worth setting up.

The post How to Set Up Hermes to Supervise OpenClaw appeared first on Hongkiat.

According to The Verge, the individual plan now costs $15.99 per month, up from $13.99. The family plan is now $26.99, up from $22.99. Premium Lite, the ad-free plan without YouTube Music, goes from $7.99 to $8.99.

This latest hike appears to apply to the US only, while many international users already saw YouTube Premium price increases in late 2024.

That is the headline. The bigger story is how often YouTube has changed the price and the product around it.

From Music Key to YouTube Red to YouTube Premium, the paid offering has been moving in one direction for years.

The 2026 Price Hike

The new monthly US prices are:

• YouTube Premium Individual: $15.99, up from $13.99
• YouTube Premium Family: $26.99, up from $22.99
• Premium Lite: $8.99, up from $7.99

The increase applies immediately for new subscribers, while existing users are being notified ahead of their next billing cycle.

The standard individual plan is now $4 higher than it was at launch under the YouTube Premium name in 2018.

Where It Started

YouTube’s paid subscription history is a little messy because the service has gone through three main eras.

1. YouTube Music Key, 2014

The earliest version was YouTube Music Key, introduced in November 2014.

Music Key beta launched at a promotional price of $7.99 per month, discounted from a stated regular price of $9.99 per month. It focused on music features such as ad-free listening, background play, and offline viewing.

2. YouTube Red, 2015

In October 2015, YouTube expanded the idea and launched YouTube Red in the US.

This was the first version of the broader ad-free YouTube subscription, covering videos across the platform, offline viewing, and background playback. Official launch pricing was $9.99 per month.

3. YouTube Premium, 2018

In May 2018, YouTube rebranded YouTube Red as YouTube Premium.

At launch, the new Premium plan cost $11.99 per month in the US, while YouTube Music Premium by itself cost $9.99 per month.

Every Major US Price Step So Far

How Much More Expensive Is YouTube Premium Now?

If you use YouTube Red in 2015 as the starting point for the full service, the standard monthly price has gone from $9.99 to $15.99.

That is a 60% increase over roughly ten and a half years.

If you use the YouTube Premium launch in 2018 as the baseline, the standard plan has gone from $11.99 to $15.99.

That is about a 33% increase in eight years.

The post YouTube Premium Raises US Prices Again appeared first on Hongkiat.

At first glance, they look like direct rivals. They’re both open-source. They both run on your own hardware or a cheap VPS. They both promise a more useful kind of AI assistant than the usual chatbox.

But after spending time with both, I don’t think the real question is which one kills the other. That framing is lazy.

The better question is this: what job do you want the agent to do?

Because OpenClaw and Hermes Agent are built around different ideas.

OpenClaw feels like a capable runtime for getting things done across apps, channels, and workflows. Hermes feels more like an agent that is trying to become better at being itself.

That difference matters.

The Short Version

If you want a practical assistant that can live in Telegram, WhatsApp, Discord, email, the browser, and your shell, OpenClaw makes a lot of sense.

If you want an agent with stronger memory, a built-in self-improvement loop, and a setup that invites experimentation with lots of models, Hermes Agent is the more interesting bet.

And if you’re deep enough into this space to care about both orchestration and long-term adaptation, the honest answer is probably: run both.

What Each One Is Really Trying to Be

OpenClaw: the runtime layer

OpenClaw is built around orchestration.

Its strength is not just the model. It’s the system around the model: messaging integrations, browser control, shell access, scheduled jobs, skills, automations, and the ability to turn an LLM into something that feels like a persistent digital operator.

That’s why it clicked so quickly with people. You can talk to it inside the apps you already use, wire it into daily routines, and get useful behavior without building everything from scratch.

If you want the official docs instead of secondhand takes, start with the OpenClaw documentation.

It feels less like a research project and more like a practical assistant that already has a body.

Hermes Agent: the learning layer

Hermes Agent is aiming at a different problem.

Its big pitch is that agents shouldn’t just execute tasks. They should learn from experience, write down what worked, refine their own skills, and become more useful over time.

That shows up in three places:

• a stronger memory system
• automatic skill creation and refinement
• broad model support, especially for people running open-weight models

If you want to understand how Hermes frames this, its official docs are worth reading.

So while OpenClaw feels like a well-equipped operator, Hermes feels like a mind that is trying to compound.

That’s not marketing fluff. That design choice changes the entire experience.

The Biggest Difference: Execution vs Improvement

This is the cleanest way I can frame it.

OpenClaw is better when the problem is operational.You want something to monitor, send, fetch, route, schedule, automate, and act across lots of surfaces.

Hermes is better when the problem is developmental.You want something to remember context, learn from repeated work, improve how it handles similar tasks, and become more personalized over time.

That’s why comparisons between the two often feel slightly off. People are arguing over tools that overlap, but don’t point in exactly the same direction.

Side-by-Side Comparison

Here’s the practical version.

Where OpenClaw Wins

1. It already knows how to live in your workflow

This is the thing many people underestimate.

A lot of agent demos look great in a terminal and then fall apart the moment you want them to fit into real life. OpenClaw avoids that by meeting you where you already are: chat apps, background jobs, browser automation, shell commands, and a growing skill ecosystem.

That makes it immediately useful.

You don’t have to imagine what it could become. You can put it to work.

2. The ecosystem gives it a head start

If you need a skill for something obscure, there’s a decent chance someone has already built it or at least built 80% of it.

That matters more than people admit.

A self-improving agent is exciting. A prebuilt skill that solves your problem tonight is also exciting, just in a less theatrical way.

3. It’s better suited to “digital colleague” use

If your ideal setup is an assistant that can:

• send you reminders
• monitor channels
• handle inbox-like workflows
• automate recurring web tasks
• respond inside messaging apps
• run scheduled jobs without babysitting

then OpenClaw is the more natural fit.

It has more of the operational plumbing already in place.

Where Hermes Wins

1. The self-improving loop is the real differentiator

This is the feature that keeps coming up for a reason.

When Hermes completes a task, it doesn’t just move on. It can reflect on what happened, turn successful patterns into reusable Markdown skills, refine old skills, and carry those lessons forward.

That changes the long-term value of the system.

Most agents are still stuck in a loop of “impressive today, forgetful tomorrow.” Hermes is trying to break that.

2. Its memory model feels closer to what people actually want

A lot of users say they want an AI assistant with memory. What they usually mean is not “save a few notes.”

They mean:

• remember my projects
• remember how I like to work
• remember what failed last time
• remember the tradeoffs we already discussed
• stop making me restate the same context every week

Hermes gets closer to that ideal.

3. It’s attractive if you care about open models and experimentation

If you like testing different models, swapping providers, running local setups, or pushing for lower cost over time, Hermes is a strong fit.

It feels built by people who expect users to tinker.

If that matters to you, it also helps to know the broader tooling around it, especially OpenRouter and Ollama, since both make model-switching and local runs more practical.

That’s not always the easiest path, but it is often the more flexible one.

Where Each One Still Feels Weak

Neither of these systems is magic. Both still have rough edges.

OpenClaw’s weak spots

OpenClaw can feel heavy once you start stacking integrations, skills, and background workflows.

That’s the tradeoff of a powerful ecosystem: more moving parts, more places for something to misbehave, and a bigger security surface to think about seriously.

Browser automation is powerful, but power and fragility often travel together.

Hermes’ weak spots

Hermes has a smaller plug-and-play ecosystem right now.

That means if your use case depends on wide integrations and ready-made workflows, you may end up building more yourself. Its browser story also feels less mature if what you want is full-on computer-use style automation rather than API-first workflows.

And while the learning loop is compelling, it also asks you to care about how the system learns, not just what it can do today.

That will be exciting for some people and annoying for others.

So Which One Should You Choose?

Choose OpenClaw if you want:

• an assistant that works across messaging apps and daily workflows
• strong browser, shell, and automation support
• a bigger ecosystem of prebuilt skills
• fast practical value with less experimentation

Choose Hermes Agent if you want:

• stronger long-term memory
• self-improving behavior that compounds
• more flexibility across models and providers
• a lighter, more hackable setup for coding, research, or deep work

The Real Power-User Answer: Run Both

This is where the conversation gets more interesting.

The strongest setup may not be choosing one camp. It may be separating responsibilities.

You can let OpenClaw handle execution. That means messaging, automations, cron jobs, integrations, and web actions. Meanwhile, Hermes can handle adaptation through memory, skill formation, reflection, and more thoughtful long-horizon work.

That combination makes sense because the two tools are opinionated in different directions.

One gives you reach.The other gives you accumulation.

Put them together and you get something closer to what people have wanted all along: an agent that can both do things now and get better over time.

Final Verdict

If you’re starting fresh and want the more immediately useful system, I’d lean OpenClaw.

If you’re more interested in where agents are heading next, especially around memory and self-improvement, Hermes Agent is the more exciting project.

And if you’ve been in this space long enough to know there’s no perfect single-agent setup yet, you already know the boring truth:

you probably don’t want one tool.

you want a stack.

That’s where this category is heading.

Not toward one winner that replaces everything, but toward systems that specialize, collaborate, and improve.

That, more than the tribal comparisons, is the part actually worth paying attention to.

The post OpenClaw vs Hermes Agent: Which One Should You Choose? appeared first on Hongkiat.

Install, nodes, and plugins are in the OpenClaw docs if you need them.

Before You Start

You need:

• OpenClaw installed and the gateway running (openclaw gateway status should show it is live)
• A Telegram account
• About 5 minutes

If OpenClaw is not installed yet, go through the official install guide first. Come back here when openclaw gateway status returns clean.

Step 1: Create a Telegram Bot

OpenClaw does not use your own Telegram account. It uses a bot. You create one through Telegram BotFather.

1. Open Telegram and search for @BotFather. Make sure the blue checkmark is there.
2. Send /newbot
3. BotFather asks for a name. Pick something short and readable, like My OpenClaw.
4. BotFather asks for a username. It must end in bot, for example myopenclawbot.
5. BotFather gives you a token that looks like 123456789:ABCdefGHIjklMNOpqrSTUvwxYZ. Copy this now. You cannot retrieve it later without regenerating it.

Keep that token handy. You will paste it into your OpenClaw config next.

Step 2: Configure OpenClaw for Telegram

Open your OpenClaw config file. It lives at ~/.openclaw/config.yaml (or wherever your setup points it). Add the Telegram channel block:

channels:
  telegram:
    enabled: true
    botToken: "PASTE_YOUR_TOKEN_HERE"
    dmPolicy: "pairing"
    groups:
      "*":
        requireMention: true

What each setting does:

• enabled: true turns the Telegram channel on
• botToken is the token BotFather gave you
• dmPolicy: "pairing" by default only people you approve can DM the bot. This is the safe default
• groups.*.requireMention: true in group chats the bot only responds when someone @mentions it. Set to false if you want it to respond to everything in a group

Save the config. Restart the gateway:

openclaw gateway restart

Step 3: Pair Your Telegram Account

By default, OpenClaw blocks unknown users from DMing your bot. You need to approve yourself first.

1. Open Telegram and send any message to your bot (e.g. “hello”)
2. The bot replies with a pairing code, a short alphanumeric string
3. On your machine, run:

openclaw pairing list telegram
openclaw pairing approve telegram <CODE>

The pairing code expires after 1 hour. If it expires, send another message to the bot to get a fresh code.

Once paired, you can chat with your OpenClaw bot directly in Telegram. Ask it something to confirm it works.

Group Chats: Adding the Bot

Want the bot in a group chat? Here is how that works.

Add the Bot to Your Group

In Telegram, go to your group, tap the group name, tap “Add members”, and search for your bot username (the one ending in bot that you set in BotFather).

Permissions the Bot Needs

Privacy mode is what trips most people up. Telegram bots default to privacy mode, which means they can only see messages that @mention the bot or are commands. If you want the bot to read all messages in a group, for example to respond without being @mentioned, disable privacy mode.

To change privacy mode:

1. Go to BotFather in Telegram
2. Send /setprivacy
3. Select your bot
4. Choose Disable. This lets the bot see all messages in groups

After disabling privacy mode, remove and re-add the bot to each group for the change to take effect.

Making the Bot an Admin

Alternatively, make the bot a group admin. Admin bots bypass privacy mode automatically and can see all messages. This also gives the bot ability to pin messages, manage members, and handle other admin tasks.

For most setups, either disable privacy mode OR make the bot an admin, not both.

Allow the Group in OpenClaw Config

By default, OpenClaw blocks all group messages unless you explicitly allow the group. Add the group to your config:

channels:
  telegram:
    groups:
      "-1001234567890":          # your group chat ID
        requireMention: false    # true = @mention required, false = respond to all
        groupPolicy: "open"      # "open" = anyone in group can use it

To get your group chat ID:

1. Add @userinfobot or @getidsbot to the group
2. Forward any message from the group to that bot
3. It replies with the group chat ID (a long negative number like -1001234567890)

Or, read the ID from the logs:

openclaw logs --follow

Send a message in the group while tailing the logs. The chat.id will show the group ID.

Finding Your Telegram User ID

Some configs require your numeric Telegram user ID rather than your username. To find it:

1. DM your bot while running gateway logs:

   openclaw logs --follow

2. Look for from.id in the log output. That number is your Telegram user ID

Alternatively, use the Telegram Bot API directly:

curl "https://api.telegram.org/bot<YOUR_BOT_TOKEN>/getUpdates"

Your user ID shows up in the from object of the response.

Permissions Explained

Here is a quick breakdown of the permissions and policies you are setting:

Keeping the Bot Safe

Do not set dmPolicy: "open" unless you understand the risk. An open DM policy means anyone can send commands to your OpenClaw instance. Depending on what your agent can do, this could let strangers trigger tools, read files, or run exec commands.

Use dmPolicy: "pairing" or dmPolicy: "allowlist" for DMs. Pairing means you explicitly approve each user. Allowlist means only predefined Telegram user IDs can get through. For a personal bot, pairing is the simplest safe choice.

Be careful with group groupPolicy: "open" and requireMention: false. This combination means anyone who adds the bot to a group can talk to it. If your agent has exec or file access, this is a potential attack surface. Use explicit group allowlisting for anything beyond trusted groups.

Do not share your bot token. Treat it like a password. If it leaks, go to BotFather immediately and regenerate it with /revoke, then update your config.

Group admin bot status gives the bot significant powers beyond just reading messages. Only make the bot an admin in groups you fully control.

Keep your OpenClaw gateway updated. Run openclaw update when new versions land. Security and feature releases are on the changelog.

Running the Bot Without a Public IP

OpenClaw uses long polling by default. Your gateway repeatedly asks Telegram for new messages. This works behind a home NAT with no public IP or open ports needed. Telegram never reaches your server; your server reaches out to it.

If you prefer webhooks instead, you can configure that, but it requires a publicly reachable URL. Long polling is simpler for most personal setups.

Common Issues

Bot does not respond in group chats:

• Check if privacy mode is still on in BotFather, disable it, and re-add the bot
• Make sure the group is listed in channels.telegram.groups or you have "*" wildcard
• Check openclaw logs --follow to see why messages are being dropped

Pairing code expired:

• Send another message to the bot to get a fresh code
• Codes expire after 1 hour

Bot not seeing any messages:

• If groups is configured in your config, the group ID must be in the list (or use "*" to allow all)
• Verify the bot is actually in the group and not blocked

What is Next

Once your bot is running, you can:

• Chat with your OpenClaw agent from anywhere on Telegram
• Add it to group chats for shared access
• Use /activation always in a group to have it respond without @mentions
• Configure skills and tools exposed to specific groups or users

Check the OpenClaw docs for channel configuration, multi-agent routing, and advanced Telegram features like forum topics and inline buttons.

The post How to Set Up and Chat With Your OpenClaw Bot on Telegram appeared first on Hongkiat.

Instead of opening a web dashboard every time you want to test a prompt, generate an image, or synthesize speech, you can do it all from the terminal with one command: mmx. If you are still getting comfortable with command-line basics, that alone is a nice shift.

MiniMax CLI is the official command-line tool for the MiniMax AI platform. Once it is installed, you can use it to generate text, images, video, speech, music, run image analysis, perform web search, and manage your MiniMax account settings from the terminal.

This guide walks through the basics: how to install it, how to log in, how to use it, and what each major feature can do.

What MiniMax CLI Is

MiniMax CLI is a Node.js-based tool published by MiniMax-AI. It gives you terminal access to MiniMax models and services through a single command-line interface.

If you have used tools like git, npm, or ffmpeg, the idea is similar. You install it once, then run different subcommands depending on what you want to do.

With MiniMax CLI, those subcommands include text, image, video, speech, music, vision, search, auth, config, and quota.

What You Need Before You Start

Before installing MiniMax CLI, make sure you have two things ready:

• Node.js 18 or newer
• A MiniMax API key with an active token plan

If those are in place, setup is straightforward. If you are new to shell work in general, brushing up on a few basic terminal commands first will make the process feel less awkward.

How to Install MiniMax CLI

For most people, this is the command to use:

npm install -g mmx-cli

That installs the mmx command globally so you can run it from anywhere in your terminal.

The project also documents an agent-oriented install path:

npx skills add MiniMax-AI/cli -y -g

That one is more useful if you are wiring it into an AI agent environment. If you are just learning the tool or using it manually, start with the global npm install.

How to Log In

Once the CLI is installed, the next step is authentication.

If you already have your API key, run:

mmx auth login --api-key sk-xxxxx

If you prefer a browser-based login flow, use:

mmx auth login

After logging in, these commands are worth knowing:

mmx auth status
mmx auth refresh
mmx auth logout

You can also check account usage and config with:

mmx quota
mmx config show

How to Use MiniMax CLI

MiniMax CLI works through command groups.

You start with mmx, then add a feature area such as text, image, video, speech, music, vision, or search. If you already use a few basic shell commands, the pattern will feel familiar quickly.

The easiest way to learn it is to try one command from each group.

Start With Text Generation

The simplest text example looks like this:

mmx text chat --message "What is MiniMax?"

That sends a prompt and returns a response in the terminal.

If you want the response to stream live as it is generated, use:

mmx text chat --model MiniMax-M2.7-highspeed --message "Write a short intro about CLI tools" --stream

If you want to guide the model with a role or instruction, add a system prompt:

mmx text chat \
  --system "You are a coding assistant" \
  --message "Write a Go function that prints Fibonacci numbers"

And if you are feeding in multi-turn messages from a file or another command, you can pipe them in:

cat messages.json | mmx text chat --messages-file - --output json

That last pattern is useful when you want to plug MiniMax CLI into scripts or automated workflows.

What MiniMax CLI Can Do

Once you are comfortable with the basic command pattern, the rest of the tool starts to make sense.

Generate Text

MiniMax CLI can handle normal text prompts, system prompts, multi-turn chat input, streamed responses, and structured JSON output. This is the part most people will reach for first.

Example:

mmx text chat --message "Explain what an API does in simple terms"

Generate Images

You can create images from text prompts and control things like aspect ratio and batch size.

Example:

mmx image generate --prompt "A cozy desk setup with a glowing mechanical keyboard" --n 2 --aspect-ratio 16:9

Generate Video

MiniMax CLI can submit video-generation jobs, which is useful for longer-running tasks that may complete asynchronously.

Example:

mmx video generate --prompt "A paper airplane flying across a city skyline" --async

You can then check progress later:

mmx video task get --task-id 123456

And download the result:

mmx video download --file-id 176844028768320 --out paper-airplane.mp4

Generate Speech

You can turn text into speech, choose different voices, and even stream playback.

Example:

mmx speech synthesize --text "Welcome to your first MiniMax CLI test" --out welcome.mp3

To see available voices:

mmx speech voices

Generate Music

MiniMax CLI can also generate music from prompts, with support for lyrics and instrumental output.

Example:

mmx music generate --prompt "Lo-fi study beat with soft piano" --instrumental --out lofi.mp3

If you want lyrics too:

mmx music generate \
  --prompt "Indie pop with a light summer feel" \
  --lyrics "[verse] Side streets glowing after rain" \
  --out indie-pop.mp3

Analyze Images With Vision

The vision commands let you pass in an image and ask MiniMax to describe or inspect it.

Example:

mmx vision screenshot.jpg

That makes it useful for quick image understanding tasks without leaving the terminal.

Run Web Search

MiniMax CLI also includes search commands, which can be handy if you want one command-line tool to handle both generation and quick lookup tasks.

Example:

mmx search "best ways to use AI from the terminal"

Manage Account and Config

There are also practical commands for checking quota, viewing config, switching region settings, and keeping the CLI updated.

Example:

mmx quota
mmx config show
mmx update

A Good First Test

If you just installed MiniMax CLI and want a simple way to confirm everything works, start with these:

mmx auth login --api-key sk-xxxxx
mmx text chat --message "Summarize what MiniMax CLI can do"
mmx image "A mechanical keyboard floating in space"

That gives you a quick test of authentication, text generation, and image generation in a few seconds.

After that, you can branch into speech, music, video, or scripting.

Final Thoughts

MiniMax CLI is easiest to understand once you stop thinking of it as a single AI command and start seeing it as a toolbox.

You install it once, learn the mmx command pattern, and then branch into whatever you need: text, image, video, speech, music, vision, or search.

For beginners, that is a nice setup. You do not have to learn five different tools at once. You just learn one CLI, then grow into the rest of it.

The post How to Install and Use MiniMax CLI appeared first on Hongkiat.

And for good reason.

It’s one of the nicest screen recording tools around if you care about presentation. You record your screen, and it handles a lot of the polish for you: cursor-following zooms, smooth motion, clean framing, and a result that looks far better than a raw screen capture usually has any right to.

The problem, of course, is that it’s not free.

If you only make polished demos once in a while, another subscription can feel a bit ridiculous. That’s exactly where OpenScreen comes into play. It aims at the same kind of workflow, but it’s free, open source, and available across macOS, Windows, and Linux.

That alone makes it worth a look.

What Is OpenScreen?

OpenScreen is an open-source desktop app built for turning ordinary screen recordings into cleaner, more watchable demos. It is positioned very clearly as an alternative to Screen Studio, and the overlap is obvious the moment you look at it.

You can record your screen or a specific window, then refine the result with zooms, cursor effects, backgrounds, annotations, and timeline-based edits. In other words, it is not just a recorder. It is trying to solve the more annoying part that comes after recording: making the video look presentable without spending ages in a full video editor.

At the time of writing, the latest release is v1.2.0, and it adds a few meaningful improvements, including microphone and system audio capture, saved projects, a speed track for clips, and configurable keyboard shortcuts.

What Makes Screen Studio Popular

To understand why OpenScreen matters, you have to understand what makes Screen Studio appealing.

Most screen recorders do one job: they capture what happened.

Screen Studio goes a step further. It helps make that recording feel intentional. Cursor movement looks smoother. Zooms land where attention should go. Backgrounds look cleaner. The end result feels closer to a product demo than a plain desktop capture.

That polish is what people are really paying for.

OpenScreen goes after that same value proposition, just without the subscription, license lock-in, or closed-source black box.

What OpenScreen Can Do

Here are the features that make OpenScreen genuinely useful rather than just interesting:

• Record the full screen or a specific window
• Capture microphone and system audio, depending on platform support
• Apply automatic or manual zoom effects around cursor movement and clicks
• Use smooth cursor animations and motion effects
• Add a webcam bubble overlay
• Change the background with wallpapers, gradients, colors, or your own image
• Add annotations such as text, arrows, and images
• Trim clips, crop, resize, and adjust playback speed in the timeline
• Export in different resolutions and aspect ratios, including vertical formats
• Save projects and reopen them later

That is already enough for a lot of creators.

If your usual workflow is “record something, tighten it up, export it, and publish,” OpenScreen covers the core path surprisingly well.

OpenScreen vs. Screen Studio

Here’s the practical comparison.

The short version: OpenScreen handles most of what makes Screen Studio attractive, especially if your priority is polished visuals and faster editing rather than AI extras or platform-specific convenience features.

Where OpenScreen Still Falls Short

This is the part where the open-source glow needs a little realism.

OpenScreen looks promising, but it is still a younger tool. That means rough edges.

A few caveats stand out:

• System audio support can be platform-dependent
• macOS users may hit Gatekeeper warnings because the app is not signed like a commercial Mac app
• Some users have reported bugs around cursor tracking, loading clips, or preview/export consistency
• It does not yet match Screen Studio on things like built-in transcripts, iPhone recording workflows, or more advanced audio cleanup
• The editor is improving, but it is still not as mature as a commercial product that has had more time to sand off the sharp corners

None of that is unusual.

It is just the trade-off. You are giving up some polish and some premium features in exchange for zero cost, full source access, and a tool the community can keep improving in public.

OpenScreen Is Worth Paying Attention To? Yes

What makes OpenScreen interesting is not that it beats Screen Studio across the board.

It doesn’t.

What makes it interesting is that it gets close enough in the areas that matter most for a lot of people.

If all you want is a clean, modern way to record demos with automatic polish, OpenScreen already looks far more capable than the phrase free open-source alternative usually suggests. And if you are the sort of person who likes tools you can inspect, modify, or contribute to, it has an obvious advantage that closed commercial apps simply do not.

For indie makers, educators, developers, startup teams, and anyone tired of stacking monthly subscriptions, that is a compelling pitch.

Final Thoughts

If you need the smoothest, most polished, least-fussy experience on macOS, Screen Studio still has the edge.

But if you want something free, capable, cross-platform, and open source, OpenScreen looks like one of the most credible alternatives out there right now.

That is especially true if you can live without a few premium extras and do not mind the occasional rough edge that comes with a fast-moving project.

In other words: if Screen Studio is the premium answer, OpenScreen is the surprisingly good answer that costs nothing.

And frankly, that is enough to make it a big deal.

If you want to test it yourself, grab it from the official GitHub repository.

If it clicks for you, great. If not, you spent nothing but a few minutes.

That is a pretty good deal.

The post OpenScreen Is the Free Open-Source Alternative to Screen Studio appeared first on Hongkiat.