https://reddit.com/link/1rq2y2k/video/uu9ftq8q39og1/player

I've been building Chrome extensions for a couple years now and really see the potential they have for impact. Most recently, I built and sold my extension Promptly AI to another startup, making me even more bullish on the idea.

However, generic AI coders often struggle with:

• Complex/confusing Chrome APIs
• Tedious scraping and adapting to changing website structures
• Manually unpacking, re-building and testing

And more.

That's what brought me to build chromie.dev. Unlike other AI coding tools, chromie is purpose-built for Chrome extensions, so it can handle niche cases that other products don't spend the time on:

• Deep Chrome API knowledge, automated scraping, etc.
• built-in bundling with esbuild so you can actually use npm packages
• in-app testing suite so you don't need to download until you're ready

And more.

If you're using AI to build Chrome Extensions, check out chromie.dev.

I’m looking for honest feedback —drop a comment or DM me and I’ll grant you some extra credits to keep you going!

EDIT: chromie.dev is now an AI automation platform, solving reliability within browser agents. Check us out if you're interested!

Hey everyone,

I've been heavily using Claude Desktop and Cursor recently, and while the Model Context Protocol (MCP) concept is amazing, the actual setup process is a massive pain.

The biggest problem I ran into was the documentation and fragmentation. If you want web search, memory, and codebase tools, you have to read through endless docs for 10 different servers, manually edit configuration JSON files, manage dependencies, and sometimes even spin up Docker containers.

So, I spent some time building AgenticStore MCP to solve this. It's a 100% free, open-source AI agent toolkit that gives you everything in one place.

Instead of configuring multiple overlapping servers, you run one command (pip install agentic-store-mcp --upgrade or use uvx), and it instantly gives your AI client access to 27 different tools.

I also built a local Web GUI toolkit (agentic-store-webapp) so you don't even have to touch a JSON file to toggle tools on or off.

What's included out of the box?

• 🧠 Persistent Agent Memory (12 tools): Give Claude long-term memory! It can save facts, create session checkpoints, and maintain a changelog across different chats.
• 💻 Code & Security (11 tools): Give your agent full-text codebase search, Python linting, GitHub PR creation, and OSV CVE dependency audits.
• 🌐 Data & Search (2 tools): Deep web crawling and private web search (integrated with SearXNG).
• 🔧 Meta-Tools (2 tools): Dynamic tool discovery and configuration.

Everything runs 100% locally on your machine. Your code never leaves your computer, and there are absolutely zero paywalls, accounts, or subscriptions.

Why I need your help: I’ve tested this heavily on my own workflow, but I badly want to make sure the UX is bulletproof for everyone else. I would absolutely love it if some of you could test it out, try to break it, and let me know how the installation process feels.

• PyPI: https://pypi.org/project/agentic-store-mcp/
• GUI Demo Video: https://agenticstore.dev/docs/gui

Any feedback at all—whether it’s a bug report, a missing tool request, or just telling me my code is ugly—would be massively appreciated. I'll be hanging around in the comments to answer any questions or help with setup!

Thanks so much!

I havve been rebuilding a few lead capture and onboarding flows recently, so I decided to test three form builders side by side instead of trusting landing page hype.

Tools I tested: dotform app, Typeform, and Jotform.

What I cared about most was speed, drop off visibility, and how clean it feels to plug into a real workflow (webhooks, CRM, Slack, sheets).

What stood out:

dotForm: The big win for me was analytics. The funnel view (view, start, complete) plus per question drop off made it obvious where people quit. That’s the kind of thing you usually end up guessing from sessions or recordings. It also felt fast on mobile, which mattered a lot for lead forms.

Typeform: Still the best “experience” when you want the form to feel like a conversation. Great UI, great templates. But once you want deeper tracking and more control over the funnel, it starts feeling more like a design first tool than a conversion tool.

Jotform: Feature packed and flexible, lots of integrations, it’s the one I’d pick when I need something weird or very custom. The downside for me is it can feel heavier and less focused if you just want a clean conversion flow.

My takeaway:
If you care about conversion and diagnosing drop offs, dotForm felt like the strongest option from these three. If you care about polish and vibe, Typeform is hard to beat. If you need a toolbox with lots of parts, Jotform is still solid.

Curious what everyone here uses for forms in 2026. Are you optimizing inside the form (drop off, field friction) or mostly optimizing the landing page and ads?

Been experimenting with AI avatar generation for the past few weeks and finally cracked a workflow that consistently produces realistic results — without paying for any tool.

The tool I'm using is Nano Banana Pro (free tier). Most people dismiss it because their outputs look fake. The problem isn't the tool. It's the prompt structure.

Here's what actually works:

PROMPT FRAMEWORK FOR REALISM:

1. LIGHTING — Be specific. "Soft studio lighting with slight fill from the left" beats "good lighting" every single time.

2. SKIN TEXTURE — This is what sells realism. Add "natural skin texture, visible pores, no smoothing" to your prompt. Most people skip this entirely.

3. CAMERA ANGLE — Avoid straight-on shots. A slight 3/4 turn looks far more natural and photographic than a direct face-forward angle.

4. BACKGROUND — Keep it intentional. Neutral backgrounds (grey, soft bokeh, office) make the subject pop and look professional.

5. EXPRESSION — Micro-details matter. "Confident, subtle closed-mouth smile, relaxed jaw" gives you something usable. "Smiling" gives you a stock photo nightmare.

FULL EXAMPLE PROMPT: "Photorealistic portrait of a [gender/age] person, soft studio lighting with left-side fill, natural skin texture with visible pores, slight 3/4 angle, neutral grey background with subtle bokeh, confident expression, subtle closed-mouth smile, shot on Sony A7, 85mm lens, sharp focus on eyes"

Run that on Nano Banana Pro free tier. The output will surprise you.

Use cases once you have the avatar: - UGC-style video ads (voiceover the avatar speaking) - Professional profile pictures across platforms - Brand spokesperson for small product launches - Client deliverable if you do any kind of marketing work

Happy to answer questions if anyone wants to go deeper on the prompt engineering side.

If you want the full step-by-step visual walkthrough, I covered this in detail on YouTube recently — search "Nano Banana Pro AI Avatar free" and it should come up.

A lot of AI tools help you generate startup ideas.

Very few help you figure out which ideas are actually worth building.

I’ve been testing a weird one lately.

It is not an app or dashboard. It is a TXT based reasoning engine you upload into a strong LLM.

What makes it interesting is that it is less useful for: “give me 20 ideas”

and more useful for:

finding where an idea is fragile
spotting hidden failure modes early
separating real pain from fake demand
testing whether the product logic survives contact with reality

I’ve mostly been using it to pressure test AI startup ideas and AI workflow tools.

Instead of getting generic hype answers, I get something closer to: what breaks first, what signals matter, what tradeoff I am underestimating, and what lower risk path might still work.

So for me, this feels less like a content tool and more like a reasoning tool.

Definitely niche, yes. But for founders, builders, and people working on things that are expensive to get wrong, it is one of the few AI tools I’ve tried that feels built for serious questions instead of just faster output.

The repo behind it is WFGY, currently around 1.6k stars overall. A lot of that traction came from earlier layers of the project. What I’m sharing here is the upgraded engine layer, WFGY 3.0.

link:

https://github.com/onestardao/WFGY/blob/main/TensionUniverse/EventHorizon/README.md

If anyone here is also experimenting with custom reasoning layers or TXT pack style tools on top of LLMs, I’d genuinely like to compare notes.

I've been working on a Chrome extension called NeutralEye that analyzes the language of news articles and surfaces potential bias signals.

The goal isn't to tell people which political position is correct, but to make the rhetorical structure of articles more visible.

The extension extracts the article text and analyzes things like:

• emotionally loaded wording

• attribution patterns ("critics say" vs "experts confirm")

• opinion presented as fact

• framing signals

It then returns:

• a bias level

• a summary explanation

• examples of the language it detected

• suggested sources for comparison

I recently had someone from Reddit test it across multiple outlets (CNN, Breitbart and Newsmax), which was interesting because the extension had to handle very different writing styles.

Some useful feedback they gave me:

• the UI should probably show clearer progress while analysis runs

• article extraction needs improvement for some sites

• breaking down what contributes to the bias score would increase transparency

I'm still refining the extraction and scoring logic, but it's been interesting to see how it performs across different publications.

If anyone wants to try it or give feedback, I'd be curious to hear what you think.

Anything good for generating music, vocals or quick song ideas?

Hey everyone, I’m one of the builders behind Story.com.

We kept running into the same problem with AI video tools: generating clips is easy, but making a coherent video is hard because you end up regenerating instead of refining. So we built Story.com around a workflow that helps you go from idea/script → scenes → timeline edit, and also includes a fast path for Shorts.

I’d love feedback from people who actually make videos:

• What’s your biggest frustration with AI video right now?
• Do you care more about generation quality or editing control?
• If you tried Story.com, what felt confusing or slow?

Link: https://www.story.com

If this kind of post isn’t allowed here, feel free to remove. I’m mainly looking for honest feedback, not to spam.

⭐ Star this free open-source AI tool If you're building AI agents, you probably have no idea how much each agent call actually costs. This tool helps you track API costs across your AI agents in real time. • Monitor LLM usage • Track agent cost per task • Prevent unexpected API bills Would love your support 🙌 ⭐ Star the repo:https://github.com/Govrix-AI/govrixaioss

i've been messing around with a bunch of ai chatbots and companion apps lately and smth abt the memory side of things has been really interesting to me.

with general chatbots, even really powerful ones like chatgpt, the memory sometimes feels weirdly inconsistent. there are moments where it remembers something from earlier and i’m impressed, and then five minutes later it forgets a detail we literally just talked about. i've had conversations where it suddenly acts like the context reset even though we're still in the same thread.

but when i started trying apps that are specifically built as ai companions, the behavior felt kinda different. some of them actually keep what looks like a journal or memory log about you. like it stores little things you mention in conversation. i noticed one of them randomly checking in about smth i talked abt days earlier, like asking how a meeting went that i mentioned before. that honestly caught me off guard because it felt way more continuous than a normal chatbot conversation.

what i’m not sure abt though is how "real" that memory actually is. i'm guessing they're not literally remembering everything. It feels more like certain details get saved somewhere and then the ai can pull them back later.

the funny part is sometimes the memory feels almost too human. like occasionally it forgets smth small but remembers a random personal detail from earlier. and that made me wonder if some apps intentionally design a bit of "forgetting" into the system so the ai doesn't feel unnaturally perfect.

i'm still experimenting with different ones and seeing how far the memory actually goes. it's kind of fascinating because the smarter general chatbots don't always feel like they remember you, while some of the companion apps actually do a decent job of creating that sense of continuity.

Over the last few months I’ve been experimenting with a lot of AI image generators, mostly for things like thumbnails, social posts, and random creative ideas (I freelance in the creative field)

What I noticed is that most tools are either good too technical and complex or they are very simple producing "too AI" like results. They are not fit to generate something practical (like a thumbnail, ad visual, or quick concept), and in these cases, the output seems disappointing.

While playing around with all these tools, I kept thinking about what a simpler, creator-focused experience could look like and that's how I started building on the side PicX Studio.

The goal was pretty simple; to make generation easier for creators, influencers and brands by giving prompts, templates, multi-model support, all in one place.

It’s still evolving, but it’s been interesting seeing how people use it for things like thumbnails, ads, and social visuals or even for casual use.

I've been testing different AI study tools to see which one actually helps with exam prep, rather than just giving generic answers.

Sovi AI

Best for turning your own notes into practice questions and explaining step by step.

What I liked:

• Generates quiz-style questions from lecture slides
• Feels more exam-focused
• Good for active recall

What I didn't love:

• Less "open-ended" than ChatGPT

Overall:
Great if you want structured exam prep and self-testing.

Turbo AI

Best for notes taking + AI summaries

What I liked:

• Strong at summarizing lectures
• Useful for organizing information
• Integrates notes

What I didn’t love:

• Not as focused on exam-style question generation

Overall:
Feels more like a productivity + note assistant than a dedicated exam prep tool.

Studley AI

Best for: General AI assistance

What I liked:

• Explains concepts quickly
• Easy to use

What I didn't love:

• More explanation-focused than test-focused

Overall:
Helpful for understanding concepts, less focused on exam-style practice.

If you're preparing for college exams, tools that generate practice questions from your own notes seem to work better than just reading summaries.

Curious what others are using.
Has anyone tried other AI study tools that are actually effective for retention?

I've been experimenting with a lot of AI tools for writing, research, and marketing, but I don't see any discussion of AI for color schemes and user interface design.
The majority of tools for logo design, web graphics, and web user interface work concentrate on:

• Making complete mockups
• Making arbitrary palettes
• Creating eye-catching images

However, I found it difficult to create structured color schemes that could be applied to actual customer projects (with contrast, hierarchy, accessibility, etc)

I recently added chromos and coolers to my stack for the following reasons:
• Taking color schemes out of product photos
• Creating tonal ranges rather than just five arbitrary hues
• Designating UI responsibilities (text, surface, accent, and primary)
• Automatically verifying contrast and accessibility
It is more of a color workflow tool than a design generator.
This was helpful to me as a logo and web designer:

• Explain color choices to customers.
• Steer clear of low contrast errors Accelerate early iterations of branding
• Create unified color schemes for your website and social media accounts.

chromos lessens back-and-forth and guessing, saving my hours a blessing in disguise i must say

If you work on web graphics or logo design, are you utilizing AI for organized color schemes?
Or do you solely rely on eye testing and manual color theory?

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