Gemini Omni feels like the next obvious step, but also a weirdly ambitious one.

Announced at Google I/O 2026, Gemini Omni is Google’s new model family for creating and editing media from mixed inputs. The first release is Gemini Omni Flash, and Google is starting with video.

That last part is important. Omni is not just another chatbot upgrade. It is Google trying to collapse several creative AI workflows into one model: text-to-video, image-to-video, video editing, audio-aware generation, style transfer, avatars, and eventually more output types. If you have been comparing the current crop of AI text-to-video generators, Omni is trying to move the category from one-shot generation into something more editable.

If that sounds like too much for one model, yes. That is also the point.

What is Gemini Omni?

Gemini Omni is Google’s new multimodal creation model. Google describes it as the place where “Gemini’s ability to reason meets the ability to create.”

In practical terms, Omni can take different kinds of input, including text, images, audio, and video, then generate or edit video based on those inputs.

For example, you could give it:

• a video clip
• an image reference
• a voice reference or other supported audio input
• a written instruction

Then ask it to generate a new video that follows the motion of the clip, matches the style of the image, responds to the audio reference where supported, and changes specific parts of the scene.

That is the part Google is pushing hard: Omni is not only about generating something from scratch. It is also about editing through conversation.

You can ask it to change an object, adjust the camera angle, move a character into a different environment, add effects, or refine a scene across multiple prompts. Google says the model is designed to keep characters consistent, preserve scene context, and understand what came before in a multi-turn edit.

Gemini Omni Flash is the First Model

The first model in the Omni family is Gemini Omni Flash.

Google is rolling it out to Google AI Plus, Pro, and Ultra subscribers through the Gemini app and Google Flow. It is also available at no cost to users aged 18 and up in YouTube Shorts Remix and the YouTube Create app, starting the week of the announcement.

Developers and enterprise customers are not getting it immediately. Google says API access will arrive in the coming weeks.

That rollout tells us quite a bit about where Google sees the first use case. Omni is launching as a creative tool for users, creators, and video workflows before it becomes a developer platform model.

What Can Gemini Omni Do?

The short version: Gemini Omni can create and edit videos using a combination of text, image, audio, and video inputs.

The more interesting version is how those inputs can be combined.

It Can Edit Video Through Prompts

The most obvious feature is natural language video editing.

Instead of opening a timeline editor, masking objects, adding effects, and adjusting layers manually, you describe what you want changed.

Google’s examples include prompts like:

• “Make the sculpture out of bubbles.”
• “Dim the lights in the room.”
• “Change the camera angle to be over the violinist’s shoulder.”
• “Make the violin invisible.”

The model can build on earlier instructions, so the editing process becomes conversational. You make one change, look at the result, then ask for another change without starting over.

If it works as shown, this could be one of the more useful parts of Omni. Most AI video tools are good at generating a clip, then become frustrating when you want a specific revision. Omni is clearly trying to make revision part of the core workflow.

It Can Combine References

Omni can use several inputs at once.

You can provide a character image, a style reference, an existing video, and an audio track, then ask the model to produce a new clip that blends those pieces into one output.

That opens up more controlled creative workflows. Instead of asking an AI model to “make a sci-fi video” and hoping it guesses correctly, you can anchor the result with actual references.

This is especially useful for creators who already have assets: sketches, product shots, test footage, moodboards, music, or rough animations. Omni can use those as creative instructions, not just attachments. It also fits the bigger trend of creative AI tools trying to unify separate image, video, and lip-sync workflows, the same problem discussed in this Open Generative AI review.

It Understands Motion, Physics, and Context

Google is also positioning Omni as a model with better world understanding.

The examples mention gravity, kinetic energy, fluid dynamics, camera movement, and scene continuity. In plain English: the model is supposed to understand how things should move, not just how a video should look frame by frame.

That is a real weakness in current AI video. Many generated clips look impressive for the first second, then hands melt, objects drift, physics break, or the scene quietly forgets its own rules.

Google is claiming Omni has stronger intuition for continuity. We still need real-world testing to see how far that goes, but the direction is clear: prettier clips are no longer enough. The next generation of video models needs to behave more like a director, editor, animator, and physics-aware simulator in one system.

It Can Create Explainers

One of the more practical examples from Google is using Gemini Omni to create explainers.

Instead of simply generating a cinematic scene, Omni can turn a short prompt into a visual explanation. Google showed examples such as a claymation-style explainer of protein folding and a stop-motion explainer about the hippocampus.

This is where Omni could become useful beyond creators chasing surreal effects.

Teachers, marketers, product teams, and technical writers all spend time turning complex ideas into visuals. If Omni can generate accurate, editable explainers from short prompts and reference material, that becomes a very different tool from a novelty video generator.

The catch is accuracy. A model can make an explainer look convincing while still getting the science or mechanics wrong. For anything educational, medical, legal, or technical, Omni output will still need human review.

It Supports Personal Avatars

Google is also tying Omni to avatars.

At launch, users can create videos with their own voice through Google’s Avatars feature, which creates a digital version of the user. Google says broader audio and speech editing capabilities are still being tested before wider release.

This is one area where the safety implications are obvious. A model that can edit video, transform speech, and generate realistic avatar content needs strong identity controls. Google says Omni-created videos include SynthID watermarking. Google DeepMind also says content created or edited with Omni in the Gemini app, Google Flow, or YouTube includes C2PA Content Credentials.

That does not solve every misuse problem, but it gives platforms and users at least some way to identify generated or edited content.

Where Can You Use Gemini Omni?

At launch, Gemini Omni Flash is available through:

• the Gemini app
• Google Flow
• YouTube Shorts Remix
• the YouTube Create app

Google says availability depends on subscription tier and geography. Google AI Plus, Pro, and Ultra subscribers are included in the initial Gemini app and Flow rollout.

YouTube Shorts Remix and YouTube Create are getting access at no cost for users aged 18 and up, starting the same week as the announcement.

API access for developers and enterprise customers is expected later, but Google has not given detailed public API pricing or a specific release date yet.

Is Gemini Omni the Same as Veo?

Not exactly.

Veo is Google’s video generation model line. Gemini Omni appears to be a broader multimodal creation model that combines Gemini’s reasoning abilities with media generation and editing.

The important difference is the workflow. Veo is mainly understood as a video generation model. Omni is being presented as an any-input creation and editing model, starting with video but not limited to it forever.

Google says future Omni releases will support other output modalities, including image and audio.

So the cleaner way to think about it is this: Veo helped Google compete in AI video generation. Gemini Omni is Google’s attempt to make video generation, video editing, multimodal prompting, and creative reasoning feel like one continuous workflow.

Why Gemini Omni is a Big Shift

The most interesting part of Omni is not that it generates video. We already have plenty of tools that do that.

The shift is that Omni treats video as something you can converse with.

You can start with a messy real-world clip, ask for a visual change, add a style reference, sync it to audio, revise the camera, remove an object, and keep working. That is closer to creative direction than one-shot generation.

If Google can make that reliable, it changes the role of AI video tools. They become less like slot machines and more like editable creative systems. That is also why comparisons with Sora’s AI video model are useful but incomplete: the contest is no longer only about who can generate the prettiest first clip.

That is a higher bar.

A slot machine only needs to surprise you. A creative system needs to follow instructions, remember context, keep details consistent, and let you revise without breaking the whole thing.

What to Watch Next

There are still several open questions.

First, how consistent is Omni outside Google’s best demos? AI video announcements always look polished on stage. The real test is whether normal users can get usable results without spending half a day fighting the prompt.

Second, how much control will creators actually get? Prompt-based editing sounds magical until you need a very specific cut, timing change, facial expression, object boundary, or brand-safe detail.

Third, what will the API look like? If Omni becomes available to developers with strong input controls, reasonable pricing, and predictable latency, it could be used inside creative apps, education tools, marketing systems, and product design workflows.

Fourth, how will Google handle safety? Watermarking helps, but AI video will keep testing the limits of consent, impersonation, misinformation, and platform moderation.

Final Thoughts

Gemini Omni is Google’s clearest sign yet that AI video is moving past simple text-to-video prompts.

The next fight is control.

Creators do not just want a nice-looking clip. They want to bring their own footage, their own references, their own style, and their own revisions into the process. Gemini Omni is built around that idea.

For now, Gemini Omni Flash is the version to watch. It starts with video editing and generation across the Gemini app, Google Flow, and YouTube tools. Later, developers should get API access, and future Omni models are expected to add more output types.

If Google delivers on the demos, Omni could become one of the more important creative AI releases of 2026.

If it does not, well, at least we will get a lot of very strange YouTube Shorts out of it.

The post Everything You Need to Know About Gemini Omni appeared first on Hongkiat.

You log in once through a browser-based OAuth flow, Hermes saves the tokens locally, and future sessions refresh automatically in the background.

That is the simple version. The nicer part is that the same login also covers Hermes’ direct xAI media tools, including text-to-speech, image generation, video generation, and transcription. The same xAI credentials can also enable X search if you want that tool available.

If you already pay for SuperGrok or X Premium+, this is a cleaner way to connect Hermes to Grok without juggling API keys. If you are still figuring out where Hermes fits, this OpenClaw vs Hermes Agent comparison is a useful companion.

What Hermes Is Actually Using

The provider is called xAI Grok OAuth (SuperGrok / X Premium+) inside Hermes. Its provider ID is:

xai-oauth

Under the hood, Hermes connects to xAI’s Responses-style API endpoint at:

https://api.x.ai/v1

Hermes reuses its codex_responses transport for this provider, so features such as reasoning, tool calling, streaming, and prompt caching work without needing a separate Grok-specific adapter.

The default model is currently:

grok-4.3

Hermes pins that model near the top of the picker after you log in.

What You Need First

Before setting this up, you need:

• Python 3.9 or newer
• Hermes Agent installed
• An active SuperGrok subscription, or an X Premium+ subscription on the X account you use for login
• A browser on the same machine, or SSH port forwarding if you are setting it up on a remote server

The important detail: this provider does not use XAI_API_KEY. If you want to use your Grok subscription instead of API billing, use the OAuth provider.

There is one caveat worth knowing early. Hermes’ docs note that xAI may restrict OAuth API access by tier. If the browser login works but inference returns HTTP 403, you may need to switch to the API-key provider instead.

Sign In Through the Model Picker

The easiest setup path is through Hermes’ model picker:

hermes model

Select xAI Grok OAuth (SuperGrok / X Premium+) from the provider list. Hermes opens accounts.x.ai in your browser, where you sign in and approve access. This is a normal OAuth-style login flow: you approve access in the browser instead of copying a long secret key into your terminal.

After that, pick a Grok model. For most people, grok-4.3 is the obvious starting point.

Then start Hermes normally:

hermes

Hermes stores the login credentials in:

~/.hermes/auth.json

Once the tokens are saved, Hermes refreshes them automatically before they expire. You should not have to repeat the browser login for every session.

Sign In Manually

If you already know you want the xAI OAuth provider, you can skip the model picker and start the auth flow directly:

hermes auth add xai-oauth

This launches the same browser login flow. After approval, Hermes saves the OAuth tokens and the provider is ready to use.

Set Grok as Your Default Model

You can switch providers through the interactive model picker:

hermes model

Or set the provider and model directly:

hermes config set model.default grok-4.3
hermes config set model.provider xai-oauth

Your ~/.hermes/config.yaml should then contain something like this:

model:
  default: grok-4.3
  provider: xai-oauth
  base_url: https://api.x.ai/v1

Hermes also accepts a few provider aliases if you prefer something more descriptive:

hermes --provider xai-oauth
hermes --provider grok-oauth
hermes --provider x-ai-oauth
hermes --provider xai-grok-oauth

I would still use xai-oauth in scripts and documentation. It is shorter and canonical.

Using It on a Remote Server

OAuth is slightly more annoying when Hermes is running on a server, container, or SSH session.

Hermes can print the authorization URL instead of opening a browser:

hermes auth add xai-oauth --no-browser

But the callback listener still runs on the remote machine at:

127.0.0.1:56121

If you open the printed URL on your laptop without forwarding that port, the browser cannot reach the callback listener. You will usually see a connection error.

Open a second terminal on your local machine and forward the port first:

ssh -N -L 56121:127.0.0.1:56121 user@remote-host

Then, in the SSH session where Hermes is running:

hermes auth add xai-oauth --no-browser

Open the printed URL in your local browser. The login should now redirect back through the forwarded port to Hermes on the remote machine.

If you connect through a jump host, add -J jump-user@jump-host to the SSH command.

Browser-Only Remote Consoles

If you are using a browser-based remote console, such as Cloud Shell, Codespaces, EC2 Instance Connect, Gitpod, or a similar environment, the SSH forwarding recipe may not be available.

In that case, use manual paste mode:

hermes auth add xai-oauth --manual-paste

Or launch it from the model picker:

hermes model --manual-paste

Hermes skips the local callback listener and lets you paste the failed callback URL from the browser instead.

Check Whether the Login Worked

Run:

hermes doctor

Look for the Auth Providers section and check the status for xai-oauth.

If Hermes has a valid OAuth entry, Grok should be available through the model picker and direct provider selection.

Use the Same Login for xAI Tools

The OAuth token is not limited to chat models. Hermes can reuse the same xAI login for several direct-to-xAI tools:

• Text-to-speech
• Image generation
• Video generation
• Transcription
• X search

Open the tools picker:

hermes tools

Then choose the xAI backend where needed:

• Text-to-Speech: xAI TTS
• Image Generation: xAI Grok Imagine (image)
• Video Generation: xAI Grok Imagine
• X Search: xAI Grok OAuth (SuperGrok / X Premium+)

If your OAuth token is already stored, Hermes should detect it and skip the credential prompt.

A small gotcha: video generation is disabled by default, so enable it in hermes tools before expecting the agent to call the video tool. X search behaves differently: Hermes’ docs say it auto-enables when xAI credentials are present, but you can still disable it explicitly in the tools picker if you do not want it available.

Available Grok Models

The current xAI OAuth setup exposes Grok chat models such as:

• grok-4.3
• grok-4.20-0309-reasoning
• grok-4.20-0309-non-reasoning
• grok-4.20-multi-agent-0309

For media tools, Hermes lists xAI options such as:

• grok-imagine-image
• grok-imagine-image-quality
• grok-imagine-video
• xAI TTS through the /v1/tts endpoint

The chat model catalog is pulled from Hermes’ local models.dev cache, so new xAI model releases can appear after that cache refreshes.

Fix Common OAuth Problems

Most setup problems fall into a few predictable buckets.

Token Expired or Refresh Failed

Hermes refreshes tokens before a session and can also retry after a 401 response. If the refresh token was revoked or rotated, Hermes will ask you to authenticate again.

Run:

hermes auth add xai-oauth

Login Timed Out

The local callback listener only stays open for a limited window. If you do not approve the login quickly enough, the flow times out.

Run the login command again and approve the browser request within the window:

hermes auth add xai-oauth

State Mismatch

A state mismatch usually means the OAuth response did not match the request Hermes created. Re-run the login. If it keeps happening, check whether a proxy, browser extension, or redirect layer is interfering with the callback.

Remote Login Cannot Reach the App

This is the common SSH setup failure. The browser is on your laptop, but the callback listener is on the remote machine.

Forward the callback port:

ssh -N -L 56121:127.0.0.1:56121 user@remote-host

Then rerun:

hermes auth add xai-oauth --no-browser

HTTP 403 After a Successful Login

If OAuth login succeeds but inference returns HTTP 403, the problem may be subscription entitlement rather than a stale token. Hermes’ docs say xAI’s backend has been seen to restrict OAuth API access for some tiers.

If that happens, re-running hermes model is unlikely to fix it. Use the API-key provider instead:

export XAI_API_KEY=xai-...
hermes config set model.provider xai

Or upgrade your Grok subscription if the OAuth route is required.

No xAI Credentials Found

Hermes has no saved xai-oauth entry and no XAI_API_KEY set.

If you want to use SuperGrok or X Premium+ OAuth, run:

hermes model

Pick the xAI Grok OAuth provider, or run:

hermes auth add xai-oauth

Log Out When Needed

To remove saved xAI OAuth credentials:

hermes auth logout xai-oauth

That clears the OAuth entry from Hermes’ auth store.

If you only want to remove one credential entry from a pool, list the entries first:

hermes auth list xai-oauth

Then remove the specific entry:

hermes auth remove xai-oauth <index|id|label>

Final Thought

The appeal here is convenience. If you already have SuperGrok or X Premium+, Hermes can use that subscription through a normal browser login. No API key. No separate setup for every xAI tool. No awkward token copying.

For local use, the setup is basically hermes model, pick xAI OAuth, approve in the browser, and start chatting. If you prefer a GUI around Hermes, Hermes Desktop may be worth setting up too.

For remote machines, remember the callback port: 56121. That one small detail saves a lot of head-scratching.

The post How to Use SuperGrok With Hermes Agent Without an xAI API Key appeared first on Hongkiat.

Dinky is a free Mac app that compresses images, videos, audio files, and PDFs without sending anything to the cloud.

You drag something in, pick a format or preset, and get a smaller file back.

It also does more than squeeze files blindly.

For images, it converts them into more efficient formats such as WebP, AVIF, HEIC, or lossless PNG.

For videos, it exports to MP4 with codec and quality presets.

For audio, it can convert between formats like AAC, MP3, FLAC, WAV, ALAC, and AIFF.

For PDFs, it lets you choose between flattening for smaller sizes or preserving structure when you need text and links to survive.

What Dinky Does Well

The strongest part of Dinky is that it treats different file types differently instead of pretending one compression method fits everything.

For images, you can output WebP, AVIF, HEIC, or lossless PNG.

There is also Smart Quality, which detects whether the image looks more like a photo or a graphic and adjusts compression accordingly.

If you need tighter control, you can set a max width or target a specific file size.

For video, Dinky exports to MP4 with H.264 or HEVC and a range of quality presets.

It also supports an optional FPS cap, which can help shrink screen recordings or demos that do not need full frame-rate output.

For audio, it can compress or convert common formats including AAC, MP3, FLAC, WAV, ALAC, and AIFF.

That gives it a broader reach than the earlier versions of the app, which started out focused on images alone.

For PDFs, the app offers two paths.

You can flatten the file for smaller output, which works well for scanned or image-heavy documents, or preserve text and links for a lighter-touch rewrite.

There is also optional on-device OCR for scan-like PDFs, so documents can stay searchable before compression.

If your workflow includes extracting pages first, this guide on how to convert PDF to image formats on Mac is a useful companion.

Built for Quick Repetitive Jobs

The interface is straightforward: drag files onto the Dinky window, the Dock icon, or use the file picker. There is also a system-wide clipboard shortcut for images. Copy an image, press Command + Shift + V, and Dinky compresses it even if the app is not frontmost.

You can also drop or paste a direct file URL. Dinky downloads the file first, then compresses it locally, with support for links up to 500 MB.

This is the kind of detail that makes the app more than a one-off utility. It is built for people who compress files often enough to care about friction.

Presets, Watch Folders, and Batch Speed

Dinky gets more useful once you stop treating it as a manual drag-and-drop tool.

You can save presets that bundle format, quality, size limits, destination folder, watch folder, and filename behavior together.

So instead of redoing the same settings every time, you can keep one preset for blog images, one for client PDFs, and another for lightweight video exports.

If you mainly want to optimize images on your MacBook, that broader workflow still applies here too.

The watch folder feature pushes that further. Drop files into a watched folder and Dinky compresses them automatically using your chosen preset.

It also supports batch processing with speed settings that control how many jobs run in parallel. If you process lots of files in one go, that is more useful than a pretty interface.

What Happens to the Originals

Dinky gives you sensible control over the original files after compression. You can keep them where they are, move them to the Trash, or send them to a backup folder.

That is a small feature, but a good one. Compression tools are easy to trust right up until they replace something you wanted to keep.

Should You Use It?

If you regularly resize screenshots, convert images for the web, shrink exported videos, clean up PDFs, or re-encode audio files, Dinky is easy to like.

It is not trying to be a full editor.

It is a focused compression utility with better format support than most free Mac apps in this category.

The presets, watch folders, and clipboard shortcut make it easy to keep using.

You can download it from the official Dinky site or browse the code on GitHub.

The post Compress Images, Videos, PDFs on Mac for Free with Dinky appeared first on Hongkiat.

They are optional animated companions for the Codex app: small floating overlays that sit on your screen while Codex works. The silly part is obvious. The useful part takes about five minutes to notice.

A Codex Pet can show whether Codex is running, waiting for input, or ready for review without making you switch back to the app every few minutes. If you already compare coding agents like Codex and Claude Code, this is the softer side of the same product question: how much does the tool help you stay in flow?

What Is a Codex Pet?

Codex Pets are animated companions built into the Codex app. You can turn one on from Settings > Appearance > Pets, type /pet in the composer, or use the command menu with Cmd + K on Mac or Ctrl + K on Windows.

Once active, the pet floats over your desktop and follows the state of the current Codex thread. OpenAI says the overlay can show the active thread, reflect whether Codex is running, waiting for input, or ready for review, and pair that state with a short progress prompt.

That means you can leave Codex visible in a small, ambient way while you do something else.

How to Turn On a Built-In Pet

If your Codex app supports Pets, the fastest way is to type this in the composer:

/pet

You can also open Settings > Appearance > Pets and choose from the built-in pets there. The same area lets you wake the pet, tuck it away, or refresh custom pets from your local Codex home.

If you prefer the keyboard route, open the command menu:

• Mac: Cmd + K
• Windows: Ctrl + K

Then search for the pet commands.

How to Hatch a Custom Codex Pet

You are not limited to the built-in pets. OpenAI also has a hatch-pet skill for creating a custom animated pet from a text concept, reference image, or both.

First, install the skill from inside Codex:

$skill-installer hatch-pet

Then reload skills. Open the command menu with Cmd + K or Ctrl + K, choose Force Reload Skills, and ask the skill to create a pet.

For example:

$hatch-pet create a tiny desk goblin

Or:

$hatch-pet turn my dog into a Codex pet

Or something looser:

$hatch-pet create a new pet inspired by my recent projects

The skill handles the pet-specific work: concept planning, animation rows, sprite sheet assembly, validation, previews, and packaging. The result is a Codex-compatible pet saved locally, which you can refresh and select from the Pets settings.

If you do not know what to hatch, Codex Pets is a small community gallery that can give you ideas for pet styles, themes, and prompts before you make your own.

What Makes a Good Pet Prompt?

Keep it simple. These pets are small, so clear silhouettes beat elaborate designs.

Good prompt ideas:

• a sleepy raccoon with a tiny laptop
• a calm duck that looks focused while Codex runs
• a blue-screen gremlin for debugging sessions
• a tiny robot with one glowing eye
• my dog as a pixel-style coding companion

Avoid asking for too many props, tiny labels, complex backgrounds, or realistic detail. A Codex Pet needs to read well as a small animated sprite, not as poster art.

If you use a reference image, describe what should carry over: color, shape, expression, accessory, or personality. Do not assume the skill will preserve every detail from a photo.

Why This Feature Works

Codex Pets sound like a joke until you treat them as ambient UI.

A coding agent spends a lot of time in states that are easy to miss: still working, waiting for permission, ready for review, stuck on input. A normal app badge or notification can tell you some of that, but a floating overlay keeps the state in your peripheral vision.

That is why the feature lands better than it should. It makes Codex feel present without requiring another panel, dashboard, or notification stream. If you want the broader OpenAI tools view, this older roundup of useful ChatGPT tools is a decent snapshot of how quickly the ecosystem keeps shifting.

Should You Try It?

If you use the Codex app, yes. Turn on a built-in pet first and see whether the overlay helps or annoys you. If it helps, install hatch-pet and make something that fits your workspace.

It will not write better code for you. It will not replace good review habits. But it can make long Codex sessions easier to track, and it gives the app a little more personality without getting in the way.

The post Codex Pets: What They Are and How to Hatch Your Own appeared first on Hongkiat.

You open a blank canvas, try a few hex codes, hate all of them, then end up nudging saturation sliders for twenty minutes like that somehow counts as design progress.

I have been there more times than I want to admit.

There are plenty of websites that can help you generate palettes, browse combinations, test contrast, and build gradients without fighting your tools. If you want to understand why some combinations work and others do not, this guide to the basics behind color theory is a useful companion. Here are five worth bookmarking.

1. Coolors

Coolors is still one of the fastest ways to get unstuck.

Its main draw is the palette generator. Tap the spacebar, and it keeps serving new combinations until something clicks. That alone makes it useful when you need quick inspiration, but Coolors goes further than that.

It also includes palette exploration, an image picker, a contrast checker, a palette visualizer, gradient tools, and even Tailwind-focused color helpers. If you want one site that can take you from rough inspiration to something you can actually test in a UI, this is probably the most complete one on the list. For a broader view of what’s available, see our roundup of free color tools for web designers.

2. Grabient

Grabient is the one I would open when the brief calls for gradients first, not flat palettes.

It is built around generating CSS gradients with a clean interface and quick export options. The site also doubles as a palette finder, so it is not limited to flashy background blends, but gradients are clearly the star here.

If you build landing pages, hero sections, or marketing visuals and want gradients you can tweak without wrestling with CSS by hand, Grabient is a very handy shortcut.

3. ColorHub

ColorHub feels more product-minded than a lot of palette tools.

Instead of stopping at color generation, it tries to help you preview palettes in more realistic layouts. Its newer versions also added starter kits, palette previews on landing-page style templates, a contrast checker, favorites, and curated palette libraries.

That makes it especially useful if you are not just hunting for a nice palette, but trying to figure out whether that palette will still look good once it hits an actual interface.

4. Color Hunt

Color Hunt is simple in the best way.

It is basically a big, browsable collection of hand-picked color palettes, organized by mood, theme, and style. Pastel, retro, dark, wedding, food, sunset, neon. You can drift through it for five minutes and usually come away with something usable.

I like it because it does not overcomplicate the job. Sometimes you do not want a full palette lab. You just want to browse good combinations until one feels right.

That is exactly what Color Hunt is good at.

5. Gradients by Colorion

If all you need is a gradient and the CSS for it, Colorion keeps things nice and direct.

The site offers a hand-curated collection of background gradients, lets you change direction, and gives you the generated CSS immediately. No long setup. No extra friction. Just browse, tweak, copy, and move on.

It is especially useful for quick web design work when you want a softer background, a more interesting callout section, or a color blend that looks more polished than a flat fill.

Which One Should You Start With?

If you want the short version:

• Use Coolors when you want the broadest toolbox.
• Use Grabient when gradients are the main job.
• Use ColorHub when you want to preview palettes in something closer to a real UI.
• Use Color Hunt when you just want to browse good color combinations fast.
• Use Colorion when you want ready-made gradients and CSS with almost no effort.

Most of us do not have a color problem. We have a workflow problem.

The right tool does not magically give you taste, but it does help you explore faster, compare better, and stop second-guessing every hex code. That alone is a good reason to keep these five bookmarked. If you are starting a new project and still working through what colors to use, how to choose a color scheme for your website goes deeper into that decision.

The post 5 Color Palette Generator Websites Worth Using appeared first on Hongkiat.

Binky is a native macOS app for cleaning up those fussy folders. It can sort a folder on demand, watch folders continuously, and move files according to rules you control. It waits for files to finish arriving first, then routes them into folders like Images, PDFs, Media, Documents, Archives, Apps, Screenshots, and Misc.

The point is not clever file management. It is predictable file management.

Quick Sort Handles One-Off Cleanup

Binky’s Quick Sort is for the folder you want to clean right now. Pick an inbox, run the sort, and Binky sweeps through the files in one pass.

Downloads is the default target, but it is not the only one. You can use it on any folder that has become a dumping ground: Desktop, a shared folder, a client upload folder, a screenshots folder, or wherever your Mac stores chaos. If your issue is less about sorting and more about Finder itself feeling limited, a utility like XtraFinder sits in the same Mac productivity lane.

By default, sorted files land in folders such as:

• Images/
• PDFs/
• Media/
• Documents/
• Archives/
• Apps/
• Screenshots/
• Misc/
• Review/

Binky waits for files to settle before moving them, so it does not grab a download halfway through. It also uses collision-safe moves, so two files with the same name do not overwrite each other.

Anything unknown or questionable goes to Review first. That is the part that makes the app feel safer than a blunt extension-based sorter. If Binky is not confident, it does not bury the file somewhere random.

Routines Watch Folders Continuously

Quick Sort is manual. Routines are automatic.

A Routine is a named watcher with its own source folder, rules, and optional Finder tags. You can have one Routine for Downloads, another for Desktop, another for a Dropbox inbox, and another for a screenshots dump. Each one can follow different routing logic.

That makes Binky more flexible than a simple Downloads cleaner. It can sit behind the places where files enter your workflow and keep them from becoming junk drawers.

Rules Give You Control

Binky can sort by file type, but it is not limited to extensions.

Rules can match by name, extension, file kind, size, date, origin, OCR or receipt hints, and Finder tag conditions. A rule can move a file, rename it, apply Finder tags, extract an archive, install from a DMG, trash a match, or fan files out by tag.

A few practical examples:

• send invoices and receipts into a finance folder
• keep client uploads away from general downloads
• route screenshots separately from normal images
• move project exports into the right project folder
• tag files during sorting so they are easier to scan in Finder

Rules run before the default sorted folders. So if you want receipts handled separately, Binky can catch them before they fall into a generic PDFs folder.

It Fits Into Mac Workflows

Binky is built with SwiftUI and AppKit, so it behaves like a Mac app rather than a web app wrapped in a window.

It supports Finder Quick Actions and Services, which means you can select files in Finder and run Sort with Binky directly. If you like tuning Finder actions yourself, this guide to adding items to the macOS context menu is a useful companion.

It also includes an Apple Shortcuts action called Sort Files, so you can pass file paths to the running app from your own automations. For a broader example of Mac automation, see this walkthrough on how to automate opening and positioning apps on Mac.

There is a binky command-line tool too, for people who want to preview sorts, run Routines from Terminal, or plug the same rules into scripts. The app, CLI, and Routines use the same preferences and routing logic, so you are not maintaining separate systems.

Binky also keeps history and batch summaries. You can see what moved, what was skipped, and what went to Review. Undo is available where macOS allows it.

What It Does Not Do

Binky moves files. It does not copy them. When a file is sorted, the original leaves the source folder and goes to its destination.

It also only watches folders while the app is running. If Binky is closed, new files stay where they are. Open it again or run Quick Sort, and it can clean up what accumulated.

There is no cloud account involved. Sorting happens on your Mac, and Binky does not upload your files.

The Bottom Line

Binky is a small Mac utility for a common problem: folders that turn into junk drawers because sorting files by hand is boring.

The app is more useful when you stop thinking of it as a Downloads cleaner and treat it as an inbox cleaner. Point it at any folder where files pile up, set a few rules if the defaults are not enough, and let Review catch anything uncertain.

Binky requires macOS 14 Sonoma or later. It is open source under the MIT license, and the 1.x line is free. You can download the DMG from binkyfiles.com and install it like a regular Mac app.

The post Binky Is a Free Mac App That Sorts Messy Folders for You appeared first on Hongkiat.

It helps users scan faster and saves you from drawing the same magnifying glass, hamburger menu, or notification bell over and over. If you want to cast a wider net, this list of icon search engines is a useful companion.

The harder part is finding libraries that are pleasant to browse, visually consistent, and broad enough that you do not run out of options halfway through a project.

These 11 cover a good spread: open-source defaults, premium systems, huge libraries, and a few more style-specific picks.

1. Lucide

Lucide is a clean open-source icon library with a crisp, minimal style that drops into modern web apps without fighting the rest of your interface.

It is more than a website with SVG downloads. It also has packages for React, Vue, Svelte, Solid, Astro, React Native, and more, which makes it practical for actual product builds rather than one-off mockups.

If you want lightweight outline icons with a consistent look, Lucide is easy to keep in rotation.

2. Iconsax

Iconsax targets designers who want range. It is more commercial than barebones open-source libraries, and the main draw is sheer volume. The site has tens of thousands of icons across multiple styles, framework support, a Figma plugin, and AI-assisted icon generation.

It works well when you need one library that covers dashboards, mobile UI, admin screens, and edge-case icon needs without switching visual styles halfway through.

3. The Noun Project

The Noun Project is less of a single icon pack and more of a huge icon marketplace. You get a massive, diverse collection of symbols made by many contributors, which helps when you need a very specific concept that most UI icon sets do not cover. The tradeoff is consistency, because not every icon will feel like it belongs in the same interface.

For presentations, editorial graphics, concept-driven visuals, or hard-to-find symbols, it is a useful place to look.

4. Iconic

Iconic keeps things straightforward. The site calls its icons pixel-perfect, works on a 24 by 24 grid with a 1.5px stroke, and splits the collection between free and pro sets. The free license is usable, which puts it ahead of icon libraries that bury the useful stuff behind pricing caveats.

The library covers interface-friendly categories and everyday UI shapes, with a style that sits comfortably in web products.

5. Hugeicons

Hugeicons is built around scale. The site positions itself as a giant icon system for both designers and developers, with framework packages, a Figma plugin, icon fonts, and enough breadth to cover almost any app category you can think of.

You pick this when you want scale, coverage, and a polished commercial ecosystem around the icons rather than something intimate or opinionated.

6. Pixelarticons

Most icon libraries aim for smooth and neutral. Pixelarticons goes in the opposite direction with a full pixel-art look. If you are designing something retro, game-like, playful, or deliberately low-fi, this fits better than forcing a generic outline icon pack into the wrong project.

The library has thousands of icons across multiple styles, with a free tier and a pro upgrade for the full set.

7. Nucleo

Nucleo is built around workflow as much as icon count. It combines a large premium library with its own app for browsing, customizing, and exporting icons, plus React packages for developers.

If you work on larger design systems or manage assets across teams, that extra layer is useful. Plenty of icon sites stop at “here are some SVGs.” Nucleo is built more like a full toolkit.

8. Iconoir

Iconoir is an open-source option that is modern, flexible, and genuinely free without the usual premium bait hanging over every click. Its style is clean and slightly expressive without becoming noisy. The project also supports multiple outputs, including SVG, font, React, React Native, Flutter, Figma, and Framer.

If you want a free library that still feels product-ready, Iconoir is easy to recommend.

9. Feather Icons

Feather is simple, open source, and very restrained. No marketplace pitch. No bloated interface. Just a clean collection of icons with adjustable size, stroke width, and color.

If your taste runs minimal and you do not need thousands of niche symbols, Feather is an easy default.

10. Phosphor Icons

Phosphor is a flexible icon family used widely in product and interface work. It is broad and designed to work across UI, diagrams, presentations, and general digital product design.

If Feather feels too sparse for you, Phosphor is often the next step up.

11. Pikaicons

Pikaicons is more playful than the flatter, more clinical icon libraries out there. The site pitches it as a modern icon library for web and mobile, with more than 5,000 icons, multiple styles, and a Figma-first workflow. That makes it useful for designers who live in Figma and want something with a bit more charm without losing consistency.

Pikaicons works well when you want your UI to look polished without making it feel sterile.

The post 11 Icon Pack Websites Designers Should Bookmark appeared first on Hongkiat.

If you enjoy this kind of thing, our earlier roundup of interesting websites you should bookmark goes in a different direction but scratches the same itch. This list stays practical: 11 websites worth bookmarking because they do something well.

iLoveIMG

iLoveIMG is one of the easiest places to start when you need to resize, compress, crop, convert, watermark, or clean up images without opening Photoshop. It packs a long list of image tools into a simple browser interface, and most of them work in bulk, which is where the real convenience kicks in.

What makes it useful is how little setup it asks from you. Drop in a few JPGs or PNGs, pick the action you need, and you are done. If you like collecting this kind of utility, these online tools for web design projects are a good companion read.

BookFinder

BookFinder is a price comparison engine for books. Search for a title, author, or ISBN, and it pulls listings from a large number of booksellers so you can compare prices for new, used, rare, and textbook editions in one place.

It is especially handy when you are chasing an out-of-print title, an older edition, or the cheapest possible copy of a book you only need for reference. Instead of bouncing across marketplaces one by one, you get a broader view of availability, condition, and pricing almost immediately.

Gapminder

Gapminder is one of the best websites for anyone who likes data but does not enjoy being buried in dry reports. Its mission is to challenge common misconceptions about the world using interactive charts, short explainers, and quizzes built around global trends.

You can use it to explore data on poverty, life expectancy, education, population, energy use, and more. Teachers, journalists, students, and curious readers will get the most out of it, but even a quick visit can reset a few assumptions about how the world is changing.

AsanConvert

AsanConvert does one very specific job that will make perfect sense to a very specific crowd: it converts CorelDRAW files across versions and exports them into formats such as PDF, AI, EPS, SVG, and PNG. If you have ever received a .cdr file that your version of CorelDRAW refuses to open, this site is aimed straight at that headache.

That narrow focus is exactly why it is useful. Instead of hunting for desktop workarounds or asking someone else to re-save the file for you, you can upload it and convert it in the browser. Not everyone will need this, but the people who do will probably bookmark it on the spot.

curlconverter

curlconverter takes a raw curl command and turns it into code for other languages and libraries. Paste in a request copied from your browser or terminal, then convert it into Python requests, JavaScript fetch, Node.js, Go, PHP, and plenty more.

For developers, this is a small but excellent shortcut. A copied curl request is often the fastest way to capture an API call, but not the nicest thing to drop into an app. curlconverter handles the translation step quickly, and the browser-side conversion is a nice touch too.

ProPublica Nonprofit Explorer

ProPublica’s Nonprofit Explorer lets you browse tax filings from millions of U.S. tax-exempt organizations. You can search by nonprofit name, person, city, keyword, or EIN, then dig into revenue, expenses, executive compensation, and full filing text.

This is a strong research tool for journalists, donors, job seekers, and anyone doing due diligence. If you want to know how a nonprofit is funded, how large it has become, or what its filings actually say, this site makes that process much easier than digging through scattered public records on your own.

archai

archai is a short adaptive assessment that tries to measure how ready you actually are for AI. According to the site, it takes about eight minutes and ends with a single result you can share.

What I like about the idea is that it does not present itself as another broad AI news or prompt library site. It feels more like a focused diagnostic. If you are curious where you stand, or you want a lightweight conversation starter for teams thinking about AI adoption, it is a neat little tool.

Ninite

Ninite has been around for years, and it is still one of the cleanest Windows utilities on the web. You pick the apps you want, download one custom installer, and Ninite handles the rest. No bundled junk, no endless Next buttons, no toolbars quietly sneaking in.

It is excellent for setting up a new PC or refreshing an old one. Browsers, messaging apps, utilities, runtimes, developer tools, media players, compression tools, and more are all there. For Windows users, it sits nicely alongside our list of free Windows tools you should install.

CEOExpress

CEOExpress feels like an old-school web portal, and that is exactly the charm. It collects links to business news, financial resources, research tools, travel info, weather, newspapers, and other executive-focused utilities in one dense homepage.

The design is unapologetically dated, but that almost helps. There is very little fluff between you and the links. If you like the idea of a start page that acts more like a control panel than a polished content experience, CEOExpress still does that job surprisingly well.

Music-Map

Music-Map is a music discovery tool built around one question: if you like this artist, who else should you listen to? Type in a band or musician, and it returns a visual map of nearby artists that listeners tend to associate with them.

It is refreshingly simple. No playlist bait, no recommendation essay, no account needed. Just type a name and follow the trail. If you are trying to branch out from an artist you already love, this is a fast and enjoyable way to do it.

Worldometer

Worldometer is a real-time statistics website that tracks numbers across population, health, food, energy, water, environment, economics, and more. It is best known for live counters and fast-glance global stats, which is why it keeps popping up whenever people want a quick sense of scale.

The appeal is obvious: the data is presented in a way that feels immediate. Open it and you are not digging through a report. You are looking at moving numbers, ranked tables, and simple topic pages that make large abstract quantities easier to grasp. It is one of those reference sites that works because it gets to the point quickly.

Final Thoughts

Most people use the same handful of websites every day and rarely look beyond them. That is why lists like this are still fun to build. The web gets a lot more interesting once you start collecting niche tools, odd utilities, and sites that do one job well.

And if you know a few more in this vein, send them over. These lists are never really finished.

The post 11 Useful Websites You Should Know appeared first on Hongkiat.

GhostTrack is a Python command-line tool that groups a few public data lookups into one menu. You can run it locally, pick an option, and get results without setting up anything complex. I spent some time with it to see what it actually does and where it falls short.

What you get with GhostTrack

After installing and running it, the menu gives you four options:

• IP Tracker
• Show Your IP
• Phone Number Tracker
• Username Tracker

The IP Tracker takes any address you throw at it and pulls country, city, region, ASN, ISP, timezone, and rough coordinates from ipwho.is. Running it against a test IP gave back readable output in seconds. To understand how much an IP can already reveal on its own, this breakdown of what your IP exposes is worth reading alongside it.

The Phone Number Tracker parses a number using Python’s phonenumbers library and returns region, carrier, timezone, formatting, and number type. The Username Tracker checks whether a handle shows up across a list of social platforms by hitting profile URLs and reading the response.

If username lookups are what you are after, Sherlock is worth comparing. It covers more platforms and scales larger, but the idea is similar.

This is not a live location tracker. It wraps public lookups and common Python libraries into something a bit more convenient to use.

What actually works

That does not make it useless.

If you are running beginner OSINT labs, security demos, or CTF prep and need one quick interface for basic recon, GhostTrack lowers the friction. One script, one menu, readable output without stitching together separate commands. That is the main appeal: convenience over capability.

The code itself is small and readable. For someone learning how these lookups work, it is a decent starting point to see how IP metadata, number parsing, and username checks are handled programmatically.

Where the pitch overstates what it does

The marketing leans on phrases like “track location” and “track mobile number,” which sound more dramatic than what actually happens when you use it.

The IP lookup gives approximate geolocation from an API, useful context but not live tracking. The phone lookup tells you carrier and region, not where someone is right now. The username check mostly reports whether a handle exists on a platform.

Going in expecting precision surveillance will disappoint you. Going in expecting a quick public-data aggregator makes the tool feel more honest.

Rough edges you will notice

The username checker flags HTTP 200 as a match, which is blunt. Some sites serve friendly pages even for accounts that do not exist, so false positives are likely. The phone-number tracker also defaults to Indonesia as the region, understandable given the author’s background, but not useful if you are working with numbers from elsewhere.

Beyond that, there is very little documentation, no validation notes, and no discussion of error cases. For a quick personal script this is fine, but it means you have to manually verify anything important.

Who it actually suits

GhostTrack makes most sense for:

• beginners exploring how OSINT lookups work
• hobbyists who want a menu instead of running separate commands
• quick demos showing what public metadata can reveal

It is less suitable for professional investigations, anything where evidence quality matters, or workflows that need clean reporting and real validation. It is not Maltego or a full recon platform. It is a small script behind a terminal menu.

The Privacy Angle

What stands out most after using it is not GhostTrack itself. It is how much metadata ordinary systems already expose. IP geolocation, carrier info, timezone hints, and username reuse patterns all add up. Even basic lookups can sketch a surprising amount of context when stacked together.

Defensively, that is a good reason to audit your own digital footprint. If you want to take that further, this guide on deleting your digital footprint is a practical next step.

The post How Good Is GhostTrack for IP Phone and Username Lookups appeared first on Hongkiat.

The problem is that some of Brave’s best features are buried in settings, while some of its more distracting ones are turned on by default. If you use Brave every day, a few quick changes can make it cleaner, faster, and less annoying.

Here are 10 settings and tweaks worth changing first.

1. Clean Up the New Tab Page

Brave’s new tab page can feel busier than it needs to be. By default, it pushes Brave Rewards, VPN promos, stats cards, and other extras that most people do not need every time they open a new tab.

Open a new tab, click the gear icon, and start trimming it down. You can turn off new tab ads, hide Brave-specific cards like Rewards, VPN, Talk, and News, and remove top sites if you prefer a blanker start.

While you are there, you can also change the search engine shown on the new tab page. If you want to replace Brave Search everywhere, head to Settings > Search engine and change the default for both normal and private windows. If privacy is the main reason you use Brave, you may also want to launch Brave in private mode by default.

2. Make Brave Shields More Aggressive

Brave already blocks a lot by default, but its content filters get much better once you dig into them.

Go to Settings > Shields > Content filters > Show full list and enable the filters you actually want. A few useful ones include:

• cookie notice blocker
• annoying distractions blocker
• AI suggestions blocker
• newsletter popup blocker
• YouTube Shorts blocker
• tracking URL blocker

Spend five minutes here and a lot of the web becomes noticeably less irritating. If you use Brave on your phone too, this goes nicely with other tips on blocking ads in Android.

3. Use Copy Clean Link When Sharing URLs

A lot of links now come stuffed with tracking parameters. You copy a URL, paste it somewhere else, and drag a pile of analytics baggage along with it.

Brave has a simple fix built in. Right-click a link and choose Copy Clean Link. It strips out a lot of that tracking junk before you share it.

It will not solve every case, especially shortened links, but it is still a smart little feature that more browsers should copy.

4. Turn Off Brave’s AI Features if You Do Not Want Them

Brave includes its own AI assistant, Leo. If you have no interest in browser-level AI, you can strip it out pretty quickly.

Go to Settings > Leo and disable the options there. Then right-click the toolbar icons you do not want and remove things like Leo, Brave Wallet, Brave Rewards, or Brave VPN from the address bar.

For a cleaner autocomplete experience, check Settings > Appearance and turn off the Leo suggestion there too.

5. Switch to Vertical Tabs

Vertical tabs make more sense than horizontal ones once your tab count gets even mildly embarrassing.

In Brave, go to Settings > Appearance > Tabs and enable Use vertical tabs. If you want a tighter layout, turn off Show title bar too. That removes one of the extra bars at the top and helps the browser feel less cramped.

On larger displays, this setup works much better. You can see more tab titles, waste less horizontal space, and avoid the tiny-tab mess that happens when too many pages are open.

6. Enable Memory Saver if You Hoard Tabs

If you are the kind of person who treats tabs like bookmarks, Memory Saver is worth turning on.

Brave can suspend inactive tabs to reduce memory usage and keep the browser from dragging your whole system down. You will find it under Settings > System. If there are sites you always want to keep alive, such as music players, chat apps, or dashboards, add them to the exception list.

This is a real quality-of-life feature if you keep dozens of tabs open. If that sounds like you, these tips for manage Chrome tabs and memory are useful too, even if you never touch Chrome again.

7. Use Brave’s Built-In Task Manager

When Brave starts feeling sluggish, the culprit is often one rogue tab, one bloated web app, or one extension going feral.

Press Shift + Esc to open Brave’s task manager and see what is eating CPU or memory. If the shortcut does not work, go to More Tools > Task Manager from the main menu.

This is one of those features people forget exists until the day a single tab starts torching their laptop.

8. Use Force Paste on Sites That Block Normal Paste

Some websites still block paste inside login forms or other fields, usually in the name of security and usually to everyone’s annoyance.

Brave includes a Force Paste option in the right-click menu for forms, which can bypass those blocks. It is a tiny feature, but a useful one if you deal with password managers or long strings of text all day.

If a site also blocks right-click entirely, you may still need an extension or workaround, but Force Paste handles the simpler cases well.

9. Use Brave on Mobile for a Better YouTube Experience

Brave’s mobile browser has a practical advantage over the regular YouTube app. It blocks ads, supports background playback, and can keep videos playing in picture-in-picture mode.

That gives you a chunk of the YouTube Premium experience without paying for YouTube Premium, especially on mobile where ad blocking is usually more limited.

If you already use Brave on desktop, this is one of the best reasons to install it on your phone too.

10. Force Dark Mode on Websites

A surprising number of sites still ignore dark mode. Brave has an experimental flag that can force dark themes across the web.

Type brave://flags/#enable-force-dark into the address bar, enable the option, and restart the browser.

Because this uses Brave’s own internal flag system, it is usually lighter than installing yet another extension just to darken websites. It will not render every site perfectly, but it is often good enough.

The Best Brave Setup Is Usually the Less Brave-y One

Brave is at its best when it gets out of the way.

The browser already has a strong foundation: Chromium compatibility, built-in blocking, decent privacy defaults, and a few genuinely useful tools. The bigger win is removing the stuff you do not want, then turning on the features that actually improve day-to-day browsing.

Do that, and Brave feels less like a browser packed with side projects and more like a lean Chrome alternative that respects your time a bit more.

The post 10 Brave Browser Settings Worth Changing First appeared first on Hongkiat.

In OpenAI’s official announcement, the company says the new system is faster, follows instructions better, preserves details more reliably across edits, renders text more cleanly, and now lives inside a dedicated Images experience in ChatGPT. It also says outputs can be generated up to four times faster than before.

That alone would make it a meaningful product update. The more interesting shift is how believable these images are starting to look, especially when the model is asked to generate screenshots, UI mockups, or desktop scenes that look like they came from a real device. If you want a quick refresher on how to create AI images in ChatGPT, this release pushes that workflow much further.

What the Launch Actually Adds

It can generate original images, edit uploaded photos more precisely, preserve details across multiple revisions, handle smaller and denser text better, and produce outputs in more aspect ratios. OpenAI also says the model is better at keeping people and objects consistent across edits, which helps when you are refining the same image over multiple passes.

This pushes the model closer to practical output, not just visual flair. The official examples point to mockups, explainers, social graphics, layouts, product visuals, and edits that hold together across iterations. That should feel familiar if you have already experimented with the ChatGPT AI image generator for more stylized or illustrative work.

If you want to browse examples yourself, OpenAI’s announcement and image generation docs are the best places to start.

The Screenshot Problem Just Got Worse

AI-generated screenshots used to be pretty bad.

The text usually gave it away first. Menu labels looked off, terminal output felt fake, browser tabs were messy, and interface spacing never quite matched what a real app or operating system would produce. Even when the image looked decent at first glance, it usually fell apart the moment you looked closer.

That is changing fast.

OpenAI is openly calling out better text rendering, and that is a huge deal for screenshots. Once labels, commands, notifications, browser chrome, and window layouts start looking clean, the usual tells start disappearing too.

Just look at the image below. It looks like a normal macOS Terminal window showing a Homebrew installation in progress. The command line layout feels plausible, the spacing looks natural, and the whole thing has the plain, unremarkable look of a real screenshot.

That is the shift. AI screenshots used to look like rough mockups pretending to be real. Now they can look close enough to pass in a post, a chat, or a support thread unless you stop and inspect them carefully.

Or look at the image below. It is not a screenshot exactly. It looks more like a photo of a phone showing a WeChat conversation, as if someone used another phone to take the picture. That extra layer makes it feel even more believable.

If the model can now generate cleaner UI text, stronger layout consistency, and more believable desktop or mobile compositions, then it is useful not just for interface mockups, but also for screenshots and screen photos that pass as documentation.

There Is a Real Upside Too

For legitimate work, this gets useful fast. Designers can mock up product screens without spending hours in Figma. Marketers can create cleaner campaign visuals. Developers can generate UI concepts, onboarding flows, and explainer graphics with less prompt wrestling than before. Anyone producing tutorials or documentation can prototype visual ideas much faster.

That is the tension. The same improvement that makes the tool more useful also makes it easier to abuse.

The post ChatGPT Image 2.0 Is Making Fake Screenshots More Convincing appeared first on Hongkiat.

This guide covers practical ways to debug a stopped Docker container, from checking logs and exit codes to preserving its state before you restart anything.

Check container status and logs

When a container stops, your first stop should always be the Docker logs. Docker keeps a record of everything that was written to stdout and stderr while the container was running.

First, let’s see all containers, including stopped ones:

docker ps -a

Look for your container in the list. Note its name or container ID.

Then, check its logs:

docker logs [container_name_or_id]

This often reveals the immediate cause. Maybe your application threw an exception, or a dependency failed to start. If the logs don’t show anything obvious, don’t worry. We have more tools.

Understanding exit codes

As you can see from the screenshot above, every Docker container exits with a code. Exit code 0 means success (the container completed its task). Any other number indicates an error. You can see the exit code in the docker ps -a output, or get it directly:

docker inspect [container_name_or_id] --format='{{.State.ExitCode}}'

Common exit codes:

If you see exit code 137, your container likely hit a memory limit and was killed by the system. Exit code 143 usually means something asked Docker to stop the container gracefully.

Before you try to bring the container back up, there is one mistake worth avoiding.

Don’t restart too soon

The usual instinct is to run docker start [container] or reach for Docker Compose. That can erase useful evidence, especially if the container was not configured with persistent storage. You could lose:

• Application logs that weren’t captured by Docker’s logging driver
• Temporary files created during the failed run
• Crash dumps or core files
• Database transaction logs (if running a database)
• Configuration changes made at runtime

So before you even think about restarting, you need to preserve the evidence.

Preserve logs before they disappear

Docker keeps logs for stopped containers, but there are limits. By default, Docker uses the “json-file” logging driver with no size limit, but in production, you might have log rotation or different drivers.

First, save the logs to a file immediately:

docker logs [container_name_or_id] > container_logs.txt

For containers with a lot of output, you might want to limit to the last N lines:

docker logs --tail 1000 [container_name_or_id] > recent_logs.txt

If you suspect the issue happened a while ago, you can include the timestamps:

docker logs --timestamps [container_name_or_id] | grep -i "error\|exception\|fail"

Save container filesystem state

When a container stops, its filesystem still exists unless it was started with --rm.

You can extract files from it using the following commands:

Copy files out before restarting

Use docker cp to extract important directories:

docker cp [container_name_or_id]:/var/log ./container_logs
docker cp [container_name_or_id]:/tmp ./container_tmp
docker cp [container_name_or_id]:/etc ./container_etc

Look for application-specific directories too. If you know your app writes to /app/logs or /data, copy those.

Create a full filesystem backup

For critical debugging situations, create a complete backup of the container’s filesystem:

docker export [container_name_or_id] > container_fs.tar

This creates a tar archive of the entire container filesystem. You can explore it later:

tar -tf container_fs.tar | head -20  # List first 20 files
tar -xf container_fs.tar ./var/log   # Extract just the log directory

Creating a snapshot with docker commit

You can also use docker commit.

This command creates a new Docker image from a stopped container, preserving everything including files, environment, metadata. It’s like taking a snapshot of the container exactly as it stopped.

docker commit [container_name_or_id] debug-snapshot

Now you have a new image called debug-snapshot. You can start it, explore it, even push it to a registry for someone else to examine:

# Start the snapshot container
docker run -it debug-snapshot /bin/bash

# List files inside (from outside)
docker run --rm debug-snapshot ls -la /var/log

# Push to Docker Hub for team analysis
docker tag debug-snapshot yourusername/debug-snapshot
docker push yourusername/debug-snapshot

This works well in a production environment because it preserves the container state completely. You can analyze it later, even if the original container gets removed or rebuilt.

But keep in mind that committed images can be large as they include all container layers. Use them judiciously, and clean up when you’re done with the following command:

docker rmi debug-snapshot

Make future debugging easier

For smoother debugging next time, use persistent volumes for logs and data, set up the right logging driver, and consider scripting the preservation steps you use most often.

When a container stops, save the evidence first, then restart it.

The post How to Debug a Stopped Docker Container appeared first on Hongkiat.

This month’s picks cover minimalist CSS frameworks, JavaScript libraries, WordPress tooling, and a handful of AI-focused tools for building faster workflows.

Comark

Comark is a fast Markdown parser built for streamed content. AI-generated text or progressively loaded content renders cleanly as it arrives. It also auto-closes syntax on the fly and supports plugins such as math and code highlighting. It looks especially useful for docs and interactive blogs.

Loftlyy

Loftlyy is a growing database of real-world brand identities. You can browse logos, colors, and design systems from actual companies. It’s a solid reference if you need inspiration without relying on made-up mock brands.

Oat

Oat is a lightweight HTML and CSS UI component library with zero dependencies. It weighs about 8KB in total and uses semantic HTML tags instead of CSS classes. If you’re tired of JS-heavy UI stacks, this goes in the opposite direction.

PicoCSS

PicoCSS is a minimalist framework that styles HTML directly without extra classes. It uses plain CSS, no JavaScript, and adapts automatically to light or dark mode. Good fit if you want a clean system without carrying extra UI baggage.

Agent Skills by Addy Osmani

Agent Skills by Addy Osmani is a collection of production-grade workflows for AI coding agents. It uses slash commands like /spec, /plan, and /ship to enforce specs, tests, code reviews, and security checks. The appeal here is structure, especially for teams that want agents to follow a stricter engineering process.

It works with Claude Code, Cursor, Gemini CLI, and other agents.

Boneyard

Boneyard automatically generates skeleton screens by snapshotting your real UI. You wrap a component in <Skeleton>, run the CLI once, and it captures pixel-perfect rectangles that mirror your actual layout. No manual measurement or hand-tuned placeholders.

If you build React apps and want to reduce layout shift during loading states, this looks like a neat solution, especially at around 7.5KB.

Locker

Locker is an open-source, self-hosted file platform. You can sync files across multiple storage backends, including S3, R2, and local disk. It also searches inside images and PDFs. Looks useful if you want tighter control over file storage without being locked to one vendor.

WordPress Build Tooling

@wordpress/build is a new build tool for WordPress plugins. It replaces Webpack and Babel with esbuild. So multiple scripts, modules, and admin pages build in seconds with almost no configuration.

Gutenberg already uses it, though it is not ready for every plugin yet. Still, it looks worth a look if you follow WordPress tooling closely.

EmDash

EmDash is a new CMS from Cloudflare, built with TypeScript and Astro. It positions itself as an alternative blogging platform to WordPress.

One interesting part is that it runs plugins in sandboxed Cloudflare Workers, while content uses Portable Text JSON instead of HTML or Gutenberg. That alone makes it stand out from the usual CMS stack.

Open Screen

OpenScreen is a free, open-source macOS app for product demos. It records your screen with zoom effects, annotations, and styled backgrounds. No account required. Handy if you want cleaner product recordings without paying for another screen recording app.

Heerich.js

Heerich.js is a minimalist 3D voxel engine that renders to SVG. It builds compositions using boxes, spheres, and lines. Nice pick for generative art on the web or even pen plotter experiments.

WP Studio

wp-studio is a CLI for local WordPress development that lets you create sites, run WP-CLI commands, and publish temporary previews to wp.build. It requires Node.js 22+. Useful if you want a more scriptable local WordPress setup.

pgmicro

pgmicro is an embeddable, single-file database that speaks PostgreSQL but stores data as SQLite. It parses PostgreSQL and compiles it directly to SQLite bytecode. You can run it in memory, on disk, or as a psql server. Interesting option for ephemeral AI workloads or lightweight local PostgreSQL setups.

Testo

Testo is a modern PHP testing framework for PHP 8.2+. It uses attributes instead of magic conventions, supports functions or classes without inheritance, and includes async tools, benchmarks, and a PhpStorm plugin. The pipe-style assertions are a nice touch if you prefer more explicit, type-safe tests.

PHPantom LSP

PHPantom is a fast PHP language server written in Rust. It supports generics, Laravel Eloquent, PHPStan annotations, and conditional return types. It starts in under 1 second with about 59MB of RAM and skips the usual indexing phase. Strong option if you want faster autocomplete without the usual heavyweight setup.

Graffiti

Graffiti is a minimal CSS toolkit with utilities, elements, blocks, and templates. It’s configurable, themeable, and uses zero JavaScript. It also works with any framework or plain HTML. Good candidate if you want a lightweight drop-in CSS layer without dragging in more JS.

Expect

Expect is a testing skill for AI coding agents. It reads your git changes, generates a test plan, and runs it in a real browser with Playwright. It checks for performance issues, security vulnerabilities, broken links, and design regressions, without forcing you to maintain scripts or selectors. It can also run locally or in CI.

agentOS

agentOS is a portable OS for AI agents. Powered by WebAssembly and V8 isolates, it claims ~6ms cold starts at up to 32x lower cost than sandboxes. It also includes filesystem mounting, host tools, and granular security controls. That makes it a notable option for embedding agents directly into backend systems.

Impeccable

Impeccable is a design skill pack for AI coding agents. It teaches design principles across typography, color, layout, and motion. It also includes a CLI and browser extension that detect more than 25 anti-patterns, including gradient text, overused fonts, and nested cards.

It works with Cursor, Claude Code, Gemini CLI, and more. Useful if you want AI-generated UIs with a bit more visual discipline.

Charcuterie

Charcuterie is a visual explorer for Unicode. It lets you browse characters, discover related glyphs, and learn about scripts and symbols. Rendered glyphs are compared in vector space to power visual similarity.

It is still under active development, but it already looks like a more interesting way to explore Unicode than staring at code charts.

The post Fresh Resources for Web Designers and Developers (April 2026) appeared first on Hongkiat.

I tried dozens of approaches before finding what actually worked. Once I got it running, however, the benefits were clear: unlimited usage, zero API fees, and complete data privacy.

Today, you no longer need a supercomputer to run AI models. You don’t need the latest GPU either. What you need is the right model for your hardware and the know-how to run it efficiently.

In this guide, I’ll show you how to do the same.

Know your hardware

Before you download any model, you need to know what your computer can handle. The common mistake I’ve seen is people trying to run a model that exceeds their physical memory. This could trigger “disk swapping” which could make your laptop unresponsive due to the heavy process.

So first, check your system specs:

• VRAM: If you have a dedicated NVIDIA or AMD GPU, check its Video RAM. This is where the model runs for near-instant responses. 8GB VRAM is a solid baseline for hobby use.
• RAM: 16GB is the absolute minimum I’d suggest for a smooth experience. This handles the “offload”. If a model is 10GB and you only have 8GB of VRAM, the remaining 2GB sits here.
• CPU: Modern processors like Intel i5/i7 or Ryzen 5/7 can run smaller models reasonably well, especially with 4-bit quantization.
• Storage: Ensure you have at least 50GB of SSD space. If your internal storage is tight, you can also run LLMs from an external drive with Ollama. Running models off an old-school HDD will result in painful load times.

Pro Tip: Always subtract ~2GB from your total VRAM/RAM to account for your operating system and open browser tabs. If you have 8GB total, you really need to have 6GB for the AI.

Know your needs

With thousands of models available, don’t just chase the highest benchmark scores. If your hardware is limited, focus on models optimized for your specific tasks.

Since we assume that hardware is constrained, I think there are two use cases that you can realistically run on your laptop: text generation and code generation.

• Coding: Specialized models like Qwen2.5-Coder or DeepSeek-Coder are tuned for syntax and logic.
• Creative Writing: Gemma 4 or Mistral variants tend to have a more natural, less “robotic” prose style.

Consider model size vs. quality

The “B” in 3B or 7B stands for Billions of parameters. More parameters usually mean better reasoning, but higher memory costs.

• 1B – 3B models: Extremely fast, low memory, best for basic grammar and simple summaries.
• 7B – 14B models: A practical range for most users. Good reasoning, and they fit in many modern GPUs.
• 30B+ models: Professional-grade reasoning, but requires high-end hardware (24GB+ VRAM).

Quantization helps here. It compresses the model so it fits on consumer hardware with little loss in output quality.

• 4-bit (Q4_K_M): The industry standard. Reduces memory usage by ~70%.
• GGUF: The most user-friendly format. It allows the model to run on both your CPU and GPU simultaneously.

Can MacBook Air M2 with 8GB RAM run LLMs?

Let’s walk through a concrete example.

Say you have a MacBook Air with an M2 chip (8-core CPU) and 8GB of unified memory. You want to use it for text editing, grammar fixing, and light writing assistance.

With 8GB total RAM, you need to reserve about 2GB for macOS and your other applications. That leaves ~6GB for the model. Apple Silicon’s unified memory architecture also helps because the GPU can access the same memory pool.

Based on these constraints and your needs for text editing and grammar tasks, you don’t need an advanced model with high reasoning capabilities. A model with ~3B parameters is more than enough.

So here are your best options:

• Phi-3.5 Mini 3.8B (Q4_K_M): ~2GB RAM, 20-30 tokens/second. A compact model that handles grammar and editing tasks well enough for daily use.
• Llama 3.2 3B Instruct (Q4_K_M): ~2GB RAM, 15-25 tokens/second. Specifically trained for instruction following, great for “fix this sentence” or “rewrite this paragraph” requests.
• Qwen2.5 3B Instruct (Q4_K_M): ~2GB RAM, similar speed. Good multilingual support if you work with multiple languages.

I’d avoid running 7B models on this hardware. They’ll work but will be slower and might cause swapping if you have other apps open.

Using llmfit to find the perfect model

Manual calculations are a good start, but they still involve some guesswork. If you want a clearer read on what your computer can handle, use llmfit. It scans your hardware and shows which models suit your setup. I also covered how llmfit helps you pick the right local LLM for your machine if you want a closer look at what it does.

You can install llmfit with:

# macOS/Linux with Homebrew
brew install llmfit

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

Then run it to get recommendations:

llmfit

The tool detects your RAM, CPU cores, and GPU VRAM, then scores hundreds of models based on quality, speed, and how well they fit your hardware.

Each recommendation also includes estimated tokens per second, memory usage, and context length, as we can see below.

You can filter and sort by different criteria, which saves hours of manual testing and helps avoid the frustration of downloading models that won’t run on your hardware.

llmfit integrates with your favorite tools

llmfit also works with tools like Ollama and LM Studio, so the recommendations are easier to act on.

Ollama integration

If you’re using Ollama, llmfit can help you narrow down good model options for your setup. If you prefer a desktop UI instead, LM Studio is another good way to run LLMs locally.

For example, if llmfit recommends google/gemma-2-2b-it, you can immediately hit d and it will show you “Ollama” as an option, as seen below:

Once you’ve selected it, it will download the model for Ollama.

llmfit also supports:

• LM Studio
• llama.cpp
• MLX
• Docker Model Runner

What’s next?

Give it a try. Download a small model, run it locally, and see what you can build with your own private AI assistant.

I recommend llmfit if you want to compare options faster. It would have saved me weeks of trial and error when I was starting out.

The first time you get a response from a model running entirely on your computer, you’ll understand why I made the switch.

The post Choosing the Right LLM Models for Your Everyday Laptop appeared first on Hongkiat.

Dinky is a free app, and that already makes it more appealing if you have been comparing it with paid compression tools like Optimage or Permute.

It is a small macOS utility from Derek Castelli that compresses images, videos, and PDFs in one place. You drop files in, it gives you smaller ones back, but the real appeal is how much repetitive file prep it can absorb once you start using presets, watch folders, and automation.

What Dinky Does

Dinky goes beyond the usual image-only utility. It works with JPG, PNG, WebP, AVIF, TIFF, and BMP, then exports images to WebP, AVIF, or lossless PNG. It also compresses videos to MP4 with H.264 or HEVC presets, and shrinks PDFs while either keeping selectable text and links or flattening pages for more aggressive reduction.

• compress images, videos, and PDFs from one app
• drag and drop files, paste from the clipboard, or use a direct URL
• convert images to WebP, AVIF, or lossless PNG, which pairs nicely with this WebP guide
• resize images or target a specific file size
• save presets, watch folders, and use Apple Shortcuts for repeat jobs
• keep originals, move them to backup, or send them to the trash after compression

That makes it a stronger fit for recurring file prep, especially if you would otherwise piece together the same workflow with Quick Actions or guides like this one on batch compressing files on Mac.

Dinky is listed at around 28 MB installed. Built in Swift and SwiftUI, with no Electron and no web views, it still feels like a lightweight native app rather than a wrapped website pretending to be one.

Who This Is For

Dinky makes the most sense for people who compress files constantly and are tired of doing it in a browser tab.

That includes:

• bloggers and publishers preparing web images
• designers exporting client assets
• people dealing with video uploads
• anyone cleaning up PDFs before sending or archiving them
• Mac users who want a native utility instead of an online service

If your workflow already includes repetitive file prep, Dinky looks like one of those tools that could quietly save time every week.

Before You Install It

There are a few practical limits to keep in mind.

First, it is built for macOS 15 Sequoia and later, not as a cross-platform tool.

Second, it is not notarized, so macOS will probably block it the first time you open it. You can still run it, but you will need to approve it through macOS security settings or remove the quarantine flag manually.

Still, if you are comfortable installing indie Mac software, that probably will not scare you off.

The post Dinky Is a Free Mac App That Compresses Images, Videos, and PDFs appeared first on Hongkiat.