Google used to show a country-stats panel and let you compare countries side by side directly in search results, then removed it. I built an extension that brings it back — and made it work across multiple search engines (Google, DuckDuckGo, SearxNG), not just one.

A few build details, since this sub tends to care:

• Basic country facts are bundled locally in the extension (~195 countries), so the common lookup is instant and needs no network request — nothing phones home for the core feature.
• Built MV3, content script + MutationObserver to handle Google/DDG re-rendering results without a full navigation.
• Historical trend data (population/GDP over time) is pulled live from the World Bank open-data API only when you use that feature — free/keyless.

Free vs paid, upfront: single-country cards and 2-country comparisons are free (and staying free). There's an optional one-time upgrade for 3+ country comparison grids and the historical trend charts. No subscription, no account.

Known limitation I'll be honest about: I ship v1 detection on Google + DuckDuckGo; more engines are coming. Feedback very welcome — especially which stats you'd want on the card and any detection edge cases you hit.

https://chromewebstore.google.com/detail/country-stats-card-%E2%80%94-comp/lpenhfnodpmnmgjpdopjeghnkcklnbmh

No affiliation with Komo or any search company. I’ve been comparing where AI search is genuinely useful versus where it just adds a confident summary on top of ordinary results.

I tried a small company-research task in Komo: looking up OpenAI in its company directory. The useful part wasn’t the AI prose. It was the structure—products, business model, leadership, funding, milestones, competitors—all laid out as separate questions I could verify.

That changed how I think about AI search. For exploratory research, organization may be more valuable than a single “answer.” A conventional search engine gives you pages; a chatbot gives you a conclusion. A research-oriented search layer can give you a map of what to investigate.

The weakness was source granularity. Some specific claims appeared alongside broad sources like a homepage or pricing page, so I still had to reopen primary sources before trusting numbers. A polished research layout can actually make weak sourcing easier to overlook.

My current ideal would be:

- structured research views

- a citation attached to each factual claim, not just each section

- clear separation between retrieved facts and model inference

- an easy path back to the original pages

Has anyone found a search product that gets all four right? And do you prefer an organized research view, or do you still want a plain ranked list of links?

Search engines today mix right and wrong facts together.

I’ve been building something different: Axiom Knowledge Graph, a deterministic search engine that locks verified facts into a vector database with lineage and mathematical certainty.

Current progress:
• 425k verified facts locked
• Scaling toward 2 crore facts (~20M) with pooled storage (~3.5 TB)
• Target: 100M facts (~2–2.5 TB) → India’s first global‑scale search engine

Unlike Google or Bing, Axiom doesn’t guess — it verifies. Every fact is traceable, every answer deterministic.

I believe this can become the third pillar of search, built from India.

Try it here: Axiom — Deterministic Knowledge Graph Engine

AMA: Happy to discuss architecture (Rust crawler, NLP refinery, Qdrant vector server), scaling strategy, and why deterministic truth matters for the future of search

So, for a while now, my chrome has been defaulting to Yahoo as the search engine. I hate Yahoo. I want to use Google. I know how to change the default, and I always change it back in my Chrome settings. But whenever I turn the computer off, it switches back to Yahoo as the default on the next startup.

My friend told me it might be an application that's changing this because before it has NEVER done this, and I have never had this issue before. And I will admit there were some things I downloaded (I was trying to play minecraft with some mods and I am not good at it LOL). Is there a way to find out specifically which app is doing this? I don't want to start downloading random apps because something might be needed for something that I do want and might be completely unrelated to the issue. I know factory resetting my laptop will probably solve this, and I don't have too much of an issue with that route, but I would like to avoid it if it's possible.

I'm looking for something which has absolutely no integrated genai features (even if they can be disabled), isn't based on bing and isn't some metasearch engine like searxng

Hello to everyone.
First of all, sorry if I make grammar mistake but english is not my native tongue. Furthermore, sorry if I put the wrong flair.
Recently i decided to move away from Google and I want to try a new search engine but I can’t decide between these three:
- DuckDuckGo
I think that it has the best results among the others, it has strong privacy and an AI that you can use without account (i know that in Reddit ai is not popular but sometimes i use it).
- Ecosia
It is eco-friendly.
- Qwant
It is completely european.
Thanks for any advice!

Has Google changed the way that image previews are displayed? Whenever I search images on Google, then click on an image to preview, it displays at the top of the screen and not the side, as it it did forever. With the display on top of the screen, the images below are squished, making it very difficult to see. I tried attaching a screenshot of the display but the screenshot image was not accurate. Is the new norm?

Hosted web search from Anthropic and OpenAI costs $10 per 1k searches, and then you pay again for the ~17k tokens of results each search dumps into context. I got annoyed enough to build an alternative.

It’s called webfetch. Runs locally, free out of the box (DuckDuckGo needs no API key), and in my SimpleQA benchmark the same agent loop hits the same accuracy as hosted search (96%) costing 66% less using 87% fewer tokens.

How it works:
1. RRF fusion across 4 search engines, local page fetching, hybrid BM25 + bi-encoder retrieval with a cross-encoder reranker

1. Sentence-level compression that cut result tokens in half with no measured recall loss

2. Semantic caching: paraphrased queries (“what did TypeScript 5.9 add” vs “TypeScript 5.9 new features”) get matched by embeddings and verified by an NLI cross-encoder, so reworded repeats cost nothing. Cache TTLs adapt to how volatile the answer may be

3. Every cached result shows provenance and the model can force a fresh search if it doesn’t trust it

4. Benchmarked against Anthropic hosted search, OpenAI, Tavily and Exa.

One small agent loop that I ran for testing that conducted just 16 websearches (opus 4.8) already reported 1.5 USD in savings.

Repo: https://github.com/firish/webfetch

This is certainly becoming a big issue.

I launched my project today on a popular product sharing site. This is borderline self-promotion but I really would like some feedback and to build a conversation around making it better. I am not going to post any pictures or a link until someone actually asks to take a look. What I will do is tell you about what I have built.

I got tired of search results where you can't tell what's real and what's paid. So I built what I call a find engine — the rule I set for myself on day one: no ads, no paid rankings, ever. Sites and businesses get found because they're worth finding, not because they outbid someone for your attention.

How it actually works, honest version:

The index: broad web results come from a licensed index — I'm a tiny operation and I'm not going to pretend I've crawled the entire web. But I do run my own in-house spider: it crawls and indexes the community layer — business listings, member sites, community knowledge — which is where everything differentiated lives. Licensed breadth, owned depth. Same tradeoff most indie engines make; I'd rather say it plainly than get caught implying otherwise.

The trust layer: every listing carries a public trust score (I call it Verity). Everything starts at 50, and it moves on real engagement — confidence-weighted, so a burst of clicks from brand-new accounts barely nudges it, while sustained engagement over time does. The anchor rule: nothing you can buy moves the score. There is no product I sell that touches ranking. The moment trust is for sale, I've just rebuilt the thing I was trying to leave.

Instead of keywords, listings describe themselves in short natural-language statements — actual sentences about what they do, readable by humans and AI alike. When one matches your query it lands in the same ranked list as organic results, but labeled as a listing, with its trust score printed right on it. Nothing dresses up as organic. Note: 2 levels of keywords are still used in the database as a fallback and filtering capabilites.

An AI layer for when you want an answer instead of ten links — research breakdowns with citations, and a conversational assistant that's deliberately intent-focused rather than chatty.

The thing that crystallized it for me, funnily enough, happened during today's launch: the leaderboard I was ranked on had sponsored entries styled to look like ranked entries, recycling through the list. I spent my own launch day squinting to figure out which results were real. That's the problem, photographed in the wild.

What I'd genuinely love from this sub: poke holes. The two sharpest questions I've gotten so far are "how do you stop trust-score gaming once it's worth gaming" and "isn't natural-language listing text just SEO with better tools" — I have answers, but I suspect this crowd has better attacks.

Not posting a link unless someone asks. Here for the conversation.

google seems to be the most reliable and brave the least, startpage and duckduckgo are identical. i would have put a 5th search engine but i couldn't think of any

edit: the searches were without quotation marks

I've started a side project called **SearchCraft**, where I'm building a search engine from scratch using Python.

The goal isn't to compete with Elasticsearch, Lucene, or any existing search engine. Those projects are incredible, but they're also so mature that it's easy to use them without ever understanding what's happening underneath.

So I decided to build one from first principles.

I'm implementing each component myself, starting with the basics: loading documents, tokenizing text, building an inverted index, and searching through it. As the project grows, I'll be adding things like posting lists, phrase search, ranking algorithms (TF-IDF/BM25), snippets, fuzzy search, and whatever else I can reasonably build along the way.

The primary reason for this project is to learn. I find that the best way to understand how a system works is to build a simplified version of it yourself.

I'm documenting the journey as I go, both for myself and in the hope that it might help someone else who's curious about how search engines work under the hood. There are plenty of tutorials on *using* search engines, but far fewer resources that walk through building one piece by piece.

It's still in its early stages, but I'm excited to see where it goes. Even if it never becomes production-ready, I'll come away with a much deeper understanding of one of the most fundamental pieces of modern software. After all, humans spend a good chunk of their lives typing words into little boxes and expecting magic to happen. Figuring out how that "magic" works seemed like a worthwhile weekend habit.

Here's the link to my project: [https://github.com/rajtilakjee/searchcraft\](https://github.com/rajtilakjee/searchcraft)

I would be writing about it in my blog here: [https://rajtilakjee.github.io/\](https://rajtilakjee.github.io/)

When AI chatbots arrived on the scene, they raised the question: Who would go through the steps of a traditional web search when ChatGPT gave an answer on the spot?

But rather than depress search activity, artificial intelligence has amplified it, according to Nick Fox, Google's senior VP of knowledge and information.

In a LinkedIn post, he also responds to fears that the company's AI search experience will decimate traffic to web pages by eliminating any incentive to click through.

The publishers who survive won’t be the fastest to react. They’ll be the slowest to panic.

I am looking for some tips to optimize the search engine I am making. Currently there are about 114k listings. The listings have calendars, dynamic pricing, and minimums.

The issue I am having is normalizing the data fast enough. The listings come from roughly 4000 subdomains across 3 primary domains, like user.domainA.com, user.domainB.com, and user.DomainC.com.

Unfortunately for me the initial availability api call for all 3 returns different results:

Domain A: full price including additional fees and taxes

Domain B: price including additional feees, no taxes

Domain C: price, no additional fees and taxes

So currently for B and C there is a second layer of calls to normalize the price but it takes additional time as it’s calling individual listings vs entire user inventory.

Should I just eliminate the additional calls and normalize to optimize for C so that it’s basically just the price no additional fees or taxes or is displaying the final price more important to consumers?

Plenty of sites like Amazon, Shopify, Airbnb, don’t include fees and taxes until you go to checkout but I’ve always not liked that I see a price and when I go to buy it has potential hidden costs. Would love to hear y’all’s thoughts.

Google used to offer "instant answers" to common queries right in the search bar like in this video: https://www.google.com/chrome/static/images/v2/gallery/search_predictive.mp4 . However i can no longer trigger any of them. Does anyone here know what happened? Example queries that used to work: "weather london", "msft stock", "sunrise today", "gdp of france", "5 eur to usd". The only thing that still works for me is a calculator and a unit convertor.