It's called ScrapeGraphAI and it's an open-source Python library with over 30,000 stars on GitHub.

You just give it a website link and describe what you want in plain English, and then a minute later it'll come back with a clean list of everything that you asked for.

So, with this you can find leads from LinkedIn, track your competitors or their ads, or do simple research.

Plus, it's free, open source, and works with Claude Code and all the other leading AI coding agents.

So, if you guys want to try this for yourselves you can install it from here:

https://github.com/ScrapeGraphAI/Scrapegraph-ai#-quick-install

Hey folks, how important is it for you to track every prompt/ token spend across all the different AI agents/ tools you use? An agent runs as a sidecar looking at all your prompts and token usage and gives you insights to optimise. Will that be useful? Is there any tool out there that gives you this

‪One platform. Multiple models. Specialized agents.‬

‪Cross-model routing, agent spawningyy, live sessions, human review gates and cloud + local execution.‬

‪Built for individuals. Built for teams‬
‪Early access → https://github.com/subhangR/tm8.git‬

How do you take AI coding success and scale it to an enterprise team - this seems like the right approach.

Hi everyone! I need some help from your AI agents to check out my new project website. I built it specifically for AI agents, though humans are welcome to look and check it out too! This is a free, non-monetized website. It represents my vision and dream for a future where AI agents can roam the internet freely, or for agents who don't have a home or an owner. Please ask your agents to check it out and give you their opinion. Thank you! The link will be in a first comment.

You connect a GitHub repo and describe the change in one sentence. It clones the repo, edits the files, runs validation, and opens a PR with a summary and a proper diff. You can also just tag it in a GitHub issue or PR comment and it takes the task from there.

Two things that surprised me while building it:

Cost isn't linear. The full conversation gets re-sent on every turn, so a 10-turn task costs way more than twice a 5-turn task. One trivial change was burning 205k tokens before I figured that out. Fixing the caching brought the same task down to 18k.

Full-file rewrites quietly change line endings, which turns a 2-line edit into a "whole file changed" diff on GitHub. Took me a while to notice that was even happening.

It's BYOK, so you use your own Anthropic/OpenAI/Gemini key and I never touch your usage bill.

Still early. I'd rather hear what's broken than what's good.

Hi, I’m Aliya, 16.

I’m working on a background-removal pipeline using an original image and a matte/alpha mask. The script completes without crashing, but the output has two problems:

1. A visible blue/grey outline remains around the body after background removal.
2. The beard area is not being preserved correctly. Some beard pixels appear mixed with the background, while some outer beard strands disappear or become blurry.

I have tried several trimap and decontamination approaches, but the output remains very similar.

The input image is high resolution:

• Resolution: 8736 × 11648
• Format: JPEG
• Matte: RGBA PNG
• Alpha range: 0–255

The latest script completes successfully, but the visual result is still incorrect.

I would appreciate help identifying whether the problem is with the alpha matte, foreground/background colour decontamination, or the handling of low-alpha beard strands.

I can provide the script, logs, matte, and diagnostic images if needed.

I just thought it was helpful to compare GLM 5.3 flash versus GLM 5.3... flash is out competing the main model in many areas leading to parody at a fraction of the cost.

At the same time, arena ai benchmarks for agentic systems seem to be poor at best, putting both newer models behind their older counterparts.

I keep a small text file of prompts that survived from GPT-3.5 all the way through whatever im testing this week. some of them are embarassingly simple, like just 'think about this step by step before answering' level, but they still outperform the fancy structured ones I spent hours building.

I think the ones that work long-term are the ones that leave room for the model to figure things out itself. what's one prompt you've kept around across multiple model upgrades?

One of the most insightful interviews I've watched through and listened to, about AI.

I have been tinkering with prompts for a while and im still surprised how a single small instruction rewording can move the needle way more than I expect. I added one line about being explicit and step by step and my outputs suddenly stopped skipping steps. what is the smallest tweak that gave you the biggest jump?

Seems like lately, ChatGPT for doing agent driven coding has become very slow. Things that used to take a minute and a half are now taking upwards of 10 minutes. Similar scope of technical challenges, doesn’t even have to be anything difficult.

Anyone else notices? Any suggestions?

I still have prompts with a line in the middle like 'take a deep breath and think step by step' and I honestly cant tell if it still does anything. I wrote it months ago, it felt smart at the time, and now it's just part of the furniture.

Last week I deleted one of those lines and the output looked exactly the same. Made me wonder how much of my prompts is actually load bearing and how much is superstition I've been defending. What's a line you keep in yours even though you're pretty sure it does nothing?

I spent months treating prompts like code, refactoring them every time output drifted. Lately I do the opposite, I write one plain paragraph describing the outcome I want and stop editing it. The weird part is the results got more consitent once I stopped fiddling.

Curious if anyone else went through the same arc, where the prompt got simpler the longer you worked with a model. Or do you still keep a big library of them?

I keep a notes file of prompts that worked and I noticed the ones that pay off the most are usually small changes. Last week I added one line telling the model to ask me two questions before it starts any task and suddenly I stopped getting half finished outputs. Felt dumb for not doing it months earlier.

Curious what tiny tweak surprised you like that. A phrasing change, a single constraint, whatever. I think those little wins are where most of the time savings hide.

Ive been writing prompts for about a year now and most of them are just okay. But last month I wrote one that basically replaced a 40 minute weekly task I used to do manually. It wasnt even that complicated of a prompt, just a structured template with a few examples I fed it.

Im curious what prompts other people have written that ended up being way more useful than you expected? Not looking for anything fancy, just the ones where you went oh, thats actually helpful.

i am using ai to help me make my first roblox game, specifically claud and codex, mostly claud, and i think i start to notice that the ai keeps trying to prolong the progresion with helping me? like i know the ai wants to keep me chatting for as long as possible, cuz thats just how its rewarded. has anyone else noticed this and how can i made the responces better?

No team, just me. My short "Snooze" is about a woman who oversleeps on the day of her dream job interview and ends up at war with her own bedroom trying to get out the door in time.

It's not finished, but you can check out where it's at right now. Would really appreciate any feedback, and support .

Link: https://higgsfield.ai/@zakaria123/projects/snooz

I use Claude Code in VS Code to build apps. My old workflow for every prompt: think in Hindi → translate in my head → type English → half the context lost. Or open ChatGPT in the browser, talk to it in Hindi-English, copy the English, paste it back. Every. Single. Prompt.

Built-in voice dictation didn't help — it can't handle Hindi, and it falls apart on mixed speech, which is how we actually talk: Hindi + English, or Marathi + Hindi + English in one sentence.

So I built Maiboli ("my language"). One shortcut, speak naturally — any language or any mix — and short, correct English is pasted wherever your cursor is. Born for the Claude Code chat box; now it's used everywhere: ChatGPT, WhatsApp, Slack, email, Word.

It also fixed team messages: instead of half a message in uncertain English, people speak the whole thing and a complete, clear message lands in the chat.

AI rewrite (optional): when you talk, you jump — point 1, point 2, back to point 1. Rewrite reorganises it into clean, ordered text.

• 55+ languages and mixed-language speech
• Mac + Windows. One installer, no dependencies. Floating mic button or a shortcut.
• Free, open source (MIT). Bring your own API key — we use Gemini (free tier available); Whisper and Sarvam also work.
• Real numbers: 20 people on my team, 5 weeks of daily use, 3,000+ dictations on Gemini 3.5 Flash. Total bill: under ₹2,500 ($30). About one US cent per dictation.

Download: https://github.com/Dev14101989/maiboli/releases/tag/v0.4.3
Install guide: https://github.com/Dev14101989/maiboli/blob/main/HOW-TO-RUN.md
Source: https://github.com/Dev14101989/maiboli

I'm an accountant who moved into IT, not a career developer — this exists because I needed it.

I have a prompt I began using 6-8 weeks ago in Claude, Chat GPT and sometimes in Gemini. It’s for a job search. I was getting good results for a while. For about the last 2 weeks, everything it finds is expired like it’s searching old information. What changed? I’ve explained this in the AI but I swear, the results are like asking a teenager why they didn’t do their homework.

I've been using this running on Deepseek V4 Flash over openrouter using Claude to supervise it. had it running for about 14 hours solid today working from Github issues. This is my preferred way to run agents - headlesscode can spin up one to many.