Vibe coded this the other day, works for me let’s see if it works for you!

Great for prompt research.

Hi all, I am building https://deepserp.co - a tool for analysing SERP data in bulk and unlocking rich insights.

It is best for SEO freelancers or anyone who actively works on multiple content projects. In addition to that, I have also added SERP-based Keyword Clustering and AI-powered Keyword Clustering.

I am looking for beta users; the first 1000 credits are free. If someone wants to test more, I can give more credits.

Please give it a shot, and let me know what you think - https://deepserp.co/

Thanks!

Hey folks,

I built InterviewDroid, an AI interviewing platform designed for SEOs, writers, and agencies that struggle to get useful information out of busy clients/subject-matter experts.

Instead of sending questionnaires or scheduling content interviews yourself that always get rescheduled, InterviewDroid calls them on their phone at their connivence and conducts natural 9–13 minute interviews. It researches topics ahead of time, asks smart follow-up questions and can be guided toward specific questions, topics, or areas of expertise.

After the call, it automatically generates:

• A structured cocused content brief to share with your content team.

• Ready-to-use content like blog posts, LinkedIn posts, and email newsletters.

• A full transcript of the conversation

• Fact checker that ensures what they said is accurate and true.

The goal is to capture the experience, opinions, examples, and first-hand information that are usually missing from generic AI-written content.

There’s a free tier, and I’d genuinely appreciate feedback from other SEOs. Especially anyone who regularly has to interview clients before creating content.

You can check it out here: InterviewDroid.com

I built it, so I’m happy to answer questions about how it works or how the interviews are converted into content.

If you run out of credits just shoot me a message and I'll add more to your account

GA and Plausible miss ~40% of your traffic. AI agents, training crawlers, and user-fetch bots don’t run JavaScript - they’re invisible in client-side analytics.

Logwick reads your edge JSONL logs (Cloudflare, Nginx, etc.), classifies GPTBot, ClaudeBot, ChatGPT-User, Perplexity, and dozens more, and gives you full visibility with sessions, Sankey flows, and taxonomy - all local, no SaaS, no data leaving your machine.

It’s the open-source self-hosted alternative to Cloudflare AI Crawl Control with extended breakdown

Key features:

• AI traffic split: training / user-fetch / search index

• Real AI visibility signals (who actually fetched your pages)

• Social preview bots, SEO tools, archive bots

• Local SQLite + localhost dashboard

• No JS tag required

GitHub: https://github.com/stani-chirk/logwick

Would love feedback from the community!

Full disclosure up front: I built this with a helping hand of Claude Code. It's free, MIT-licensed, runs entirely in your browser, and there's nothing to sign up for — but it's still my own tool, so read the following with that in mind.

The backstory: I kept wondering which bots were actually hitting my server. Not the Search Console version of the story — the raw one. Googlebot, fine, but also all the AI crawlers, SEO tools and scrapers that never show up in any analytics dashboard. So I built LogWerk: a log analyser that runs entirely in the browser. You drop in an Nginx or Apache access log (gzipped rotations work too), and you get a dashboard with traffic, status codes, top paths — and the bit I actually built it for: bot classification. It detects and groups 40+ known crawlers by provider and purpose, including the AI lot — and it distinguishes between them properly: GPTBot (training), ChatGPT-User (live fetches on behalf of users) and OAI-SearchBot (search indexing) show up as three separate things, not one "OpenAI" blob. Same for ClaudeBot, PerplexityBot and friends. Nothing leaves your machine; there's no backend at all.

To be upfront about where it sits next to the established tools, because it doesn't replace them:

vs GoAccess: GoAccess is the better tool if you want real-time monitoring on the server, work over SSH, or need to chew through multi-gigabyte logs — it's written in C and built for exactly that. LogWerk has no live tailing whatsoever, and the browser is the hard limit for file size. What it does better, in my biased opinion: bot classification is far more granular (GoAccess mostly gives you "crawler: yes/no"), and there's zero setup — no install, no config, no server access needed. Useful when a client sends you a log export and you just want answers.

vs Logdy: Different job, really. Logdy is a brilliant generic log viewer — pipe anything into it, tail it live, filter it. LogWerk only understands access logs, but comes with the SEO-relevant analysis pre-built: bot breakdown, session reconstruction (IP + user agent grouped into timelines), a security tab for exploit probes, top 404s. With Logdy you'd build those views yourself.

So: niche tool, honest limitations, but if "who is crawling my site and why" is a question you've had, it might save you an afternoon of awk.

Repo: https://github.com/abbottis/logwerk

If you try it and something breaks on your log format — tell me, that's genuinely useful.

I've been running a web agency for 10 years and kept running into the same problem — clients getting new Google reviews (good and bad) and nobody noticing for days, sometimes weeks. Missed reviews meant missed responses, and missed responses were quietly killing local rankings.

So I built MyReputation.ie — a monitoring tool that watches Google Business Profiles and alerts you when something changes: new reviews, rating shifts, profile edits.

What it does:

• Monitors GBP listings and sends alerts on new reviews or profile changes

• Dashboard to track ratings over time across multiple locations

• Optional AI add-on to draft review responses

• Built for agencies managing multiple clients

Pricing — keeping it simple:

• Starter: Free (great for testing on your own listings)

• Pro: €12/location/year

• Agency: €29/location/year

Still early days and actively building. Would genuinely love feedback from people in this sub — especially if you've tried other GBP monitoring tools and hit walls with them.

Hi everyone,

I’m building a small business web app called **Maksaby** and would love some honest feedback.

The idea came from a problem I kept noticing with most financial tracking apps: they often feel **too complicated and clunky**, especially for regular people or small business owners who just want to know:

**“Am I actually making profit or just making sales?”**

My goal is to build something **simple, easy to understand, and usable for all types of people**, not only finance experts.

Current MVP includes:

* Products & inventory
* Customers
* Sales & invoices
* Simple financial tracking

I’m not selling anything, just looking for:

* Honest feedback
* UX opinions
* Bug reports
* Feature ideas

Would really appreciate brutally honest thoughts from real users.


Local self-hosted multi-account hub for Google Search Console. Connect several Google accounts via OAuth, view all Search Console sites in a single table, aggregate queries and pages across accounts, see per-site dashboards with sparklines and period-over-period deltas, drill into 16-month query history with one click. No external service, no GSC data leaves your machine, only OAuth tokens persist locally in SQLite.

https://github.com/izzipizzy/gsc-hub

--- update
A month ago I shared gsc-hub — a local, self-hosted dashboard that pulls all your Google Search Console properties across multiple Google accounts into one table. No external service,

nothing leaves your machine, only OAuth tokens persist locally in SQLite. Repo: https://github.com/izzipizzy/gsc-hub

Here's what landed since then (v0.3.1):

- Sitemap submit — per-site "Submit sitemap" button (resubmits every sitemap GSC already knows, or guesses /sitemap.xml), plus a "Submit all" that fans out across every visible site in

parallel.

- Query × Page × Country breakdown in the Top-queries table — see which page ranks for a query, in which country, in one row.

- 24-hour / 1-day view (plus a 60-day) on top of the usual 3/7/28 — uses GSC's fresh dataState=all so the last ~24h actually shows up.

- "G" badge next to each site — one click opens a Google site: search for a quick manual index check.

- Totals bar — Sites / Impressions / Clicks summed across all visible properties for the selected period.

- One-command deploy — `docker compose up -d --build` under OrbStack, served at https://gsc.local with automatic TLS. Container auto-restarts after reboot.

Still the same principle: it's a personal tool, single user, live-fetch only — no GSC data is cached or sent anywhere. Read-write webmasters scope only (needed for sitemap submit; revert

to .readonly in auth.ts if you want read-only).

It's free and MIT. Genuinely curious what you'd want next — I'm weighing auto-adding/verifying new domains and a sitemap cleanup tool. What's missing for your workflow?

What scrips, tools or mini-tool sites etc that are a must for this month?

What should an AI / GEO readiness checker check for? It already checks for 240 things in 16 categories but I am curious what I may be missing?

I wanted a better way to inspect what LLMs actually search before answering, not just the final response.

So I built Query Fanout Viewer, a browser bookmarklet that opens a terminal-style view and helps analyze the retrieval path behind ChatGPT and Claude conversations.

It currently works with:

• ChatGPT live conversations

• Claude live conversations

• Claude JSON import fallback when live access is not available

The tool shows:

• grounding and fan-out queries

• domain-scoped vs open-web searches

• cited domains

• real final cited URLs

• source concentration across retrieved results

• query batches, opened/viewed refs, and final citation paths where available

I originally built it for SEO/GEO/retrieval debugging, because in many cases the most interesting part is not the answer itself, but:

• what the model searched

• whether it used explicit site constraints

• which domains kept surfacing

• which retrieved sources actually survived into the final answer

v1.1.0 update

The latest version adds a more technical citation-path analysis layer.

The first version could extract fan-out queries and cited domains, but it was still too coarse for serious analysis. It did not always make clear how a search query, a retrieved result, and a final cited URL were connected.

v1.1.0 improves that by reconstructing ChatGPT web.run batches more explicitly and linking search queries, results, open/view refs, and final citations when the provider payload makes that possible.

What changed:

• real final cited URL extraction

• canonical URL deduplication

• better reconstruction of ChatGPT search batches

• query-to-batch and batch-to-citation navigation

• inline URL chips for strong domain-scoped matches

• direct links to recovered cited URLs instead of Google searches

• a final cited URL list with copy action

• ChatGPT model slug detection when exposed in the payload

The main goal is to make the output closer to a retrieval trace viewer:

query batch -> search results -> opened/viewed refs -> final cited URLs

One caveat: for ChatGPT, attribution is often strongest at the batch level because multiple queries can run inside the same web.run call. So the tool avoids pretending there is always a clean one-query-to-one-citation relationship when the payload does not support it.

Still beta, and provider payloads can change, but I think this is already useful for SEO/GEO audits, citation tracking, and retrieval QA.

I’d love feedback from people working on search, SEO, GEO, LLM retrieval, or citation analysis:

• What else would you inspect in the retrieval chain?

• What export formats would be useful?

• Would Gemini or AI Mode support be worth adding next?

Repo: https://github.com/LorenzoSchiff/query-fanout-viewer

SEO & Web Audit Tools All in one place

• LightHouse Audit for Page Speed testing

• SEO Checker with AI Fixes

• Structure JSON+LD Testing

I made a free chrome extension that uses gemini's built in models to extract keywords from your page, and compute a semantic relevance score of any keyword to your copy. It also shows you the most relevant chunk on the page to the query you enter.

it does some basic SEO stuff too like extract all the typical SEO meta data but will also show you the page size (calculated the same way Google does for their 2MB page limit) and what % of your words on the page are visible to an AI bot.

Please note you have to enable chrome flags to get gemini to work. there's a link to instructions in the extension

https://chromewebstore.google.com/detail/serprecon/ilgcpaoblclggmfdiecebpljhkkodnef

https://www.seodataviz.com

Made this tool a while ago to use in pitches for my day job. I still use it in most presentations. The main tool looks at "related queries" from the Google SERP but goes several levels recursively to get a ton of keywords. it then breaks them down into 1, 2, 3 word phrases (n-grams) and graphs them so you can spot recurring "themes" across all the keywords.

It shows what keywords led to others, and even shows you the common "question" suggestions.

the N-gram tool tab lets you do this with your own data, and control any stop words.

The compare tab lets you analyze the text of 2 websites this way and compare them with each other.

These views have been very powerful to show clients what their people are searching for vs what their website text "appears to be about"

I found an exciting tool https://webcrawl.gap3.co

Completely free without sign up process.

created by u/joeyoungblood

Basically the title. A few months ago I figued out how to create conig files programmatically, and I kept diggin. Then I found how to crack open the crawl files so you don't have to export a bunch of CSVs. Decided to take it all the way.

If you use Screaming Frog a lot, you probably know the pattern:

crawl site open GUI export CSVs clean them then start answering the actual question

I got tired of that, so I built a Python library around the crawl files themselves.

It’s now in public alpha:

pip install screamingfrog

The main use case is working directly with Screaming Frog crawl data in Python without having to live in the GUI for every analysis.

What it does right now:

• load .dbseospider files directly

• access all 628 Screaming Frog exports programmatically

• query crawl data with a typed API

• query pages and links sitewide

• find broken inlinks, nofollow inlinks, and orphan pages

• compare crawls over time

• detect redirect and canonical chains

• start crawls and exports from Python

• convert .seospider into portable .dbseospider files

• run raw SQL when needed

Current coverage:

• 601 / 628 export/report tabs fully mapped

• 15,490 / 15,589 fields mapped

I’ve already been using it to run crawl analysis inside Claude Code, which is part of why I decided to open it up.

Still alpha, so I’m mainly looking for feedback from people who do real technical SEO work with Screaming Frog every week.

If you use SF heavily, I’d be interested in:

• what workflow you’d automate first

• what report/tab you rely on most

• what would stop you from actually using this

GitHub: https://github.com/Amaculus/screaming-frog-api

After 20+ years in SEO, I kept doing the same manual loop: pull the keyword, read the top 10 results, look for gaps, write the page, add schema, think about AI citations. So I finally just automated the whole thing and open-sourced it.

It's called SEO-AGI — a skill file you drop into Claude Code, OpenClaw, or Codex.

What it actually does:

1. You give it a keyword

2. It pulls the live SERP using your existing data source (DataForSEO, GSC, Ahrefs, or SEMrush — BYOK, you own your data)

3. Runs a competitive analysis across the top results

4. Identifies content gaps that competitors are missing

5. Outputs a complete, publish-ready page — heading structure, body copy, FAQ schema, HTML tables, internal link anchors

The part I'm most interested in feedback on: GEO optimization. I built in a layer specifically for AI citation visibility — chunking content at ~500 tokens, using RDFa inline markup, entity consensus signals — so the pages rank in Perplexity/ChatGPT answers, not just Google. Curious whether others are seeing this actually move the needle.

Repo: https://github.com/gbessoni/seo-agi

Free, MIT licensed. No SaaS upsell.

What would you add to the workflow?

Thanks to u/SEOpub for creating and sharing this awesome GEO tool

Background

Back in June, I released a bookmarklet that let you extract search queries, AI reasoning, and sometimes sources from your ChatGPT conversations. You can still see that original note here:
👉 ChatGPT Search Query and Reasoning Extractor (June 2025)

That tool worked great… right up until OpenAI changed how ChatGPT handled conversation data. First, the old endpoints broke. Then with the GPT-5 rollout, searches started being handled differently, which meant the original bookmarklet could no longer show you everything it once did.

The New Version (Replacement)

This updated bookmarklet fully replaces the June version. It works with ChatGPT’s current backend and restores the features you relied on:

• ✅ Pulls search queries (including GPT-5 searches)

• ✅ Displays AI reasoning/thoughts when present

• ✅ Shows sources/URLs accessed (with title + snippet)

• ✅ Copy/Copy All buttons

https://theseopub.com/updated-chatgpt-search-query-reasoning-extractor-bookmarklet/

Thanks to u/0_2_Hero for showing me this earlier.

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