SearchResearch (7/10/26): Capture that content... or lose lots of great stuff

 As you know, I’m a researcher… 

… and in my day-to-day work I spend around 5 - 8 hours / day doing online research.  

As a consequence, I end up reading / scanning / sifting / sorting through a lot of material.  And over the past couple of years, I’ve noticed an interesting shift in my notetaking behavior.  

Why you want to capture content: You see, I used to notice something slide past me, and then jot down a quick reminder–maybe a key phrase or something that would let me get back to the original source material.  I usually didn’t write down the URL because I could always just re-search for the thing and get the latest, most up-to-date version.

But that’s not really true anymore.  There are a couple of reasons why I now take notes about everything I want to recall.  

First, there is an illusion of permanence. 

But... The web is fundamentally ephemeral, even though most users treat it as a stable archive.  It is NOT THAT.  Why?  What goes wrong?

• Link Rot: The sheer decay rate of URLs. Even highly credible sources restructure their sites, drop legacy pages, or go behind paywalls.

• Content Drift: The page might still exist, but the specific paragraph, image, or data point you found has been quietly edited or removed.

• Platform Enclosure: Forums, older platforms, or specific social threads disappear entirely when platforms shut down or change their API access.

And then, the mechanisms we use to find information are not consistent over time, making re-doing that search incredibly difficult.

• Search Ranking Shifts: The query that surfaced that one, perfect, golden link today might bury it on page four next month due to algorithm updates or personalized search histories.

• The AI/LLM Factor: This is critical right now. Re-finding information in the era of generative AI is uniquely challenging. If a user gets a perfect synthesis from an LLM, trying to reproduce that exact output later is nearly impossible because of the non-deterministic nature of the models.

What this means is that relying on search as an "external hard drive" creates cognitive blind spots.

First, there’s the "Google Effect": We have been conditioned to remember how to find information rather than the information itself. When the pathway degrades, the knowledge is lost entirely.

Second, there’s Context and Query Loss: When you try to re-find something six months later, you rarely remember the exact, highly specific query string you used the first time. You also lose the peripheral context—the "trail of breadcrumbs" that led you there.

Capture as Active Sensemaking

Capturing isn't just about taking notes and hoarding data; it is a fundamental step in the research process.

• Friction as a Feature: The act of saving a piece of text, taking a screenshot, or logging a citation forces a moment of active engagement.

• Annotation: A captured piece of information allows you to immediately append your own notes ("Why is this important right now?"). Re-finding strips away this personal context.

Practical Strategies 

• The "Save it Locally" Rule: Try downloading PDFs, use web clippers, or taking scrolling screenshots rather than just bookmarking URLs. (Or, if you live in the cloud, save it to your personal cloud storage. The point is to keep your captures in a stable place.)

• Organizing for Future-You: This is key–you’re building and structuring a personal knowledge management (PKM) system. One of the most important things you can do is to NOT create a digital junk drawer.  I always add a quick note about WHY I’m interested in this thing I just captured.  .

The key to building a successful capture habit is ruthlessly eliminating friction. If saving a piece of information takes more than two seconds or breaks the reader's flow, they won't do it.  BUT.. at the same time, you need a little friction to annotate why you’re capturing this.  

Here are a few ways to capture content easily… 

1. Capture the "Full Context" (web pages)

When the layout, images, and surrounding context matter just as much as the text.

• Print to PDF (Ctrl/Cmd + P): The oldest trick is still one of the most reliable. It freezes the page exactly as it appears, bypasses future paywalls, and creates a universally readable, locally stored file. I do this a lot, always saving the PDF with a file name that tells me what’s salient here. 
  
  

• Use the Wayback Machine Extension ("Save Page Now"): For researchers who need a verifiable, shareable, and citable record of a page before it changes. A single click archives the current state of a public URL to the Internet Archive, generating a permanent link that won't succumb to link rot.
  
  

• Web Clippers (Notion, Evernote, Obsidian): Best for users who already use some kind of Personal Knowledge Management (PKM) system. These browser extensions extract the core text and images, strip away the ads, and dump the content directly into a searchable database. In Chrome I use Web Clipper.  

2. Capturing Screenshots for Data, Quotes, & Charts

I use these methods more than any other, often because I’m on the wrong platform (usually, my phone) specific paragraph, a data visualization, or a fleeting comment in a forum.

• Native OS Screenshots with OCR: Modern operating systems are incredibly good at reading text inside images (Live Text on macOS, Snipping Tool on Windows). Taking a quick localized screenshot (Cmd+Shift+4 on Mac, Win+Shift+S on Windows) is the fastest way to capture a chart or quote. Because the OS indexes the text, the user can search for the words inside the image months later. I use Snagit on my Mac desktop to grab images of things that interest me.

• Drag-and-Drop to the Desktop: Highlighting a block of text on a webpage and dragging it directly to the desktop or a folder instantly creates a .txt snippet file on most operating systems. It’s a zero-click way to grab a quote.

• Screenshots on phones: Learn the screen snapshot shortcut on your phone. For iPhone it’s pressing both top buttons left and right simultaneously; for Android it’s press and hold the Power button and the Volume Down button at the same time. Note that both Android and iPhone let you do this by voice command (“Take a screen snapshot”).  VERY handy.  

3. Capturing the "Un-reproducible" (AI Sessions & Dynamic Content)

With generative AI and highly personalized search algorithms, users must treat their screen as a transient state. You cannot rely on "I'll just ask the AI again later," because the model's non-deterministic nature guarantees a different response.  What’s worse, some platforms (like Facebook or LinkedIn) will sometimes update the screen while you’re doing something else.  You can’t just leave the app and hope that the content will still be there when you come back!  Instead, if you want to capture the whole session, try one of these:  

• Immediate Export: If an LLM gives a perfect synthesis or a highly specific breakdown, use the platform's native export button (e.g., Export to Docs/PDF) immediately. Do not rely on the platform's chat history, which can be wiped, deprecated, or lost if the account is locked.

• Prompt + Response Copying: If exporting isn't an option, users should copy both the prompt they used and the result. The prompt is the intellectual work; the result is the product. Both need to be saved together in a local document.

4. Capturing your Mental Model

Captured information is useless if the user forgets why they saved it.

• The "Forward to Self" Rule: It sounds antiquated, but emailing a link or snippet to yourself with a subject line like RESEARCH: [Topic] - [Why this matters] is very effective. It forces a micro-moment of active sensemaking, and the email inbox acts as a built-in, highly searchable triage system.

• Add a short label or note: Imagine that you see this captured content in a year… will you still be able to reconstruct why you captured this?  (Pro tip: You’ll find that sometimes you’ll look at a note to yourself and say “what??”--learn from that moment.  Think to yourself, “what would have been enough context to remember why I captured this??” 

Treat your browser and research history like a river, not a library. If you see gold in the water, pan it out immediately. If you walk away, the current will wash it downstream and your content will be lost.  

The keys to building a successful capture habit are: 

(1)  to simplify everything. If saving a piece of information takes more than two seconds or breaks your flow, you won't do it. 

And (2) establish a practice of looking through your notes.  This will prevent the “digital junk drawer” from forming.  I have a Sunday morning practice where I rigorously look through all of the week’s notes, deleting everything that doesn’t make sense and incorporating the really valuable stuff into the research topics that I’m working on at the moment.  

Capture it, then harvest it.  Don’t let it accumulate (or you’ll have yet another task that you don’t want to do)!  

Keep searching.

SearchResearch (7/3/26): What you need to know about image search (3/3)

 The most important thing to know about image search... 

P/C Gemini. Prompt:
[usually it works, but sometimes it gets it wrong]

... is that Image Search works pretty well, but is not perfect.  One of the skills you have to develop, as a user of online search tools, is to understand their limits.  When does the AI work well, and when does it fail?  

It's pretty easy to find places where Search-by-image doesn't work.  Many of them involve identifying plants.  

I've written about this before here at SearchResearch. What are those plants? What I said then is still true.  Yes, the AI is improving, but it's still not quite perfect.  

You really need to know this, as it's probably going to be this way for a while.  

Here's today's example.  While walking at the Googleplex, I saw a tree with browning leaves.  Here's the photo I took: 

But when I asked Google Lens what this tree was, here's the answer I got... 

Thing is, I know this particular tree REALLY well--I walk past it nearly every day, spring, summer, fall, and winter. I know for certain that this a California Buckeye (Aesculus californica)--you can get all of the details from the authoritative source, CalFlora.  

But Google search-by-image (aka Google Lens) gets it wrong. I can't really blame it because it looks a lot like a California Sycamore (Plantanus racemosa) in its fall foliage.  But it's only early July, so the California Sycamores look like this: 

A California Sycamore at the beginning of June

What Google Lens doesn't know is that California Buckeyes typically brown up and drop their leaves in mid-summer. A quick search for [when do California Buckeye leaves turn brown] gets you to the University of California Master Gardener's page where you can confirm this.  

Google Lens identification got it wrong. 

Another non-botanical example is this beautifully carved slate plaque that I found as part of an art installation.  

I love the texture and the script, but... I can't quite read it!  Have I had a stroke, or is this in some other language? 

Upload the image to Google looking for an analysis.  What IS that text? Here's what Lens tells me... 

This is interesting, but not particularly helpful. 

A trick worth knowing:  You can scroll up a bit in the search window and--lo & behold--a new query box appears!  It will let you ask an additional question of the search: 

That's great!  I asked the obvious question:  [what language is this text?]   Google replied that it was Russian!  

That's a surprise--it doesn't look like Russian to me, but then I don't know what cursive Russian looks like.  

But I do have a Russian friend who reads and writes with fluency. When I asked, she was astounded that I would ask such a silly question.  "Of course this isn't Russian!"  But she DID know about the plaque, telling me that it was part of an art installation and that she knew who the artist was!  (It sometimes pays to ask humans about tricky questions...)    

Turns out that the artist is Sarah Stiles who makes "Cursive Binary" with her handwriting modified by AI.  

Since this doesn't look like Russian, where did Gemini get the idea that this was Russian?  

Well, that's interesting.  If you look at Stiles website, you'll see that she's of Kalmyk heritage.  (That is, from the Republic of Kalmykia, a federal subject of southwestern Russia situated between the Caspian Sea and the lower Volga.)  

I can't prove it, but I suspect there's a bit of AI leakage from the original query image to the question-answering component.  

SearchResearch Lessons 

1. Search still makes mistakes; AI search does too.  Be sure to check your work. Always.  

2. If you ask the AI to go further, it will.. but that's when you're asking for hallucinations to happen. 

3. You need to know what image identification is good at, and what's not good at.  Plants are a difficult case because they often require very close attention to detail in order to tell one apart from another.  The ID might be close, but not quite right. (And as you know, never use AI to identify mushrooms.  This is another case where the fine details really matter.)  

4. Most search-by-image systems don't let you identify people by name.  Of course, your personal content is different. Both Google Photos and Facebook let you identify people in your collection of pix.  

5. Image recognition is really good at commercial products.  Makes sense, since most of the training material is commercial in nature.  Even partial or incomplete images can often be recognized.  

Keep searching! 

SearchResearch (7/2/26): What you need to know about image search (2/3)

Search by image can literally... 

 

Part of a choir book page from Venice, mid-1300s. P/C Dan. 

... help you understand your world.  Here's a bit of understanding the world that I had just this week.  

This photo shows a piece of a large page taken from a choir book that was taken apart many decades ago.  As a fan of  Early Music, I bought this to capture a bit of the music of the period.  (To hear singing from this kind of score take a listen here.)  

I recognize the 4-line staff (the red lines--modern music notation has 5 lines), and I recognize the Latin text below the notes.  But I'm curious: what is this thing on the far left of the staff?  (The thing that looks like a person with a fat belly, made up from the square notes and a big triangle.)  

The obvious approach is to do a Search-By-Image.  

When I do this, Google tells me that "This image shows stemmed semibreve groups from a 14th-century liturgical manuscript. These musical notations are found on folio 5v of a manuscript in the Bodleian Library." The link to the Bodleian Library takes you to this: 

P/C Bodelian Library
Figure 2. Ave vivens hostia/ Ave vivens hostia/ Organum
(Bodleian Library, lat. liturg. e. 42, fol. 4r).

To understand this, you probably have to go look up what that means. I found that stemmed semibreve groups refer to a specific notation used in 14th-century mensural notation. Instead of representing a single, long whole note, these groups are a series of smaller, subdivided notes (semibreves) strung together and marked by stems and dots to dictate complex rhythms.  

That's great... but it doesn't answer my real question which is what's the big triangle thing?  

I notice that each line of music has a triangle with a square on top, next to a pair of squares that bracket one of the lines (either the 3rd or 4th line).  

I know enough music notation to recognize that the far left edge of the music is where the clef symbol usually goes.  

To find out, I can go into AI mode and ask the question directly about the image: 

This is great and accords with what I already know, but now, of course, let's double check this.  

A quick regular search for [C-clef] leads to several articles about clef notations evolved over the years.  We learn that a C-clef in Gregorian chant notation looks like this (inside the red box):  

That's close, but not quite right.  

Reading further we learn that the F-clef looks like this: 

I also did a search on [mensural notation] and found the Wikipedia article on clefs used in Medieval music notation, with this lovely illustration comparing different clefs, and their evolution over time: 

As you can see, the clef in the original manuscript (at the very top) looks a LOT more like an F clef than a C clef with a decoration.  

In any case, we've resolved the question:  that massive glyph is a clef, showing the singers where the C note was based.  It might be a C clef OR an F clef, but we can leave that to the musicologists to figure out.  We now know what it denotes! 

In a world of music notation that looks like this, with multiple F clefs and a gloriously illuminated intial letter D, it's easy to imagine that the C clef would be transformed to an F clef by the stroke of a monk's pen.  

The "Dragon D" music manuscript. "Deus Omnium" from SC Library.

(Searcher Caution: Bing image search completely messes this up, identifying the original clef image as a piece of Chinese calligraphy.)   

SearchResearch Lesson 

I'll say it again, check your work. Always get a separate source that agrees with your interpretation.  

Keep searching! 

SearchResearch (7/1/26): What you need to know about image search (1/3)

 As we've pointed out... 

P/C Gemini [cartoon of young woman looking at mushroom images]

... the only constant in our online world is change. That's VERY true for what kinds of things you can search for, and how you can do the search.  

Today is part 1 of 3 (and I might add more next week!) about how to think about and use image search.  

1. Caution: Search by image is pretty good, but if it's a critical or high-stakes search, double and triple check the identification.  Here's an example. Maybe you've seen this cartoon floating around the internet... 

It makes a really good point. A mushroom is edible, but you didn't ask if was poisonous, did you?  (Yes, I know that in ordinary speech, "edible" implies non-poisonous, but remember that you're talking with an AI that may/may-not share your assumptions.)  

Big important point: DO NOT rely on image search to identify non-poisonous mushrooms. (Or berries, or leaves, or ...)  

Here's an easy example of a misidentification you don't want to experience... 

This is poison oak: 

Poison oak (Toxicodendron pubescens). P/C Dan. Do not touch. 

But on one search-by-image, this was identified as either poison oak OR as a kind of (non-toxic) Boston Ivy (Parthenocissus tricuspidata 'Lowii').  

Here's what Boston Ivy looks like in its red-phase, so you can understand the misidentification.  

You can sit in one of these vines with impunity.  The other will leave you with a wretched rash.  Don't be that person. 

2. (Advanced) You can still use the date operators (before: and after:) with image search.  For instance, if you're looking for the original version of the above cartoon, you'll quickly get overwhelmed.  Asking Gemini to find the original version just doesn't seem to work.  

But you, as a skilled SearchResearcher, can add date restrictions to the search. 

Once you restrict the date by adding it into the search box at the top:   

Once you do this, you'll see something very different.  The AI-drawn cartoon version seems to have been inspired by a pastiche image (published in October, 2025) featuring a real red mushroom and the OpenAI logo. 

If you change the before: date to 2025-12-1 you see the first instances of the "classic" form of the meme: 

I'll let you iterate on all of the dates for the images first published in November, 2025.  The point is that you can still use those date restrictions to help narrow down the date of first publication.  

More tomorrow... 

Keep searching. 

SearchResearch (06/24/26): A new kind of research tool--agent systems working for you

You might have been reading… 

Human scientist with AI support. (P/C Generated by Gemini Nanobanana with a long prompt.)

…about all of the new tools people are building for online research purposes.

This is really striking to me because it feels like we’re at an inflection point.  Before this year, people would occasionally build a new application or website for doing online research, but now it seems that every other day I see another announcement for another search augmentation  tool.  AI-powered coding has unlocked a great deal of creativity, and it’s fascinating to see where all of this is going. 

But one of the most interesting areas of new research tools are the virtual scientist assistants (call them VSAs for the moment). These are systems that help professional scientists see more deeply into what they’re trying to understand. 

The technology behind VSAs mirrors the scientific method by using specialized AI agents that independently generate, debate, and refine novel hypotheses.  They do this across various fields like biomedicine, geophysics,  and materials science. 

Experimental VSAs demonstrate their ability to accelerate breakthroughs, such as finding drugs that can be repurposed for diseases they weren’t created to handle.  (This is called “using a drug off-label,” the best known example is using the drug sildenafil (Viagra)--originally developed to treat angina and hypertension–which became famous for also being useful to treat erectile dysfunction.)  That's a kind of research that's basically impossible with "classic" web search.

Google is working on a project called “Hypothesis Generation” (known previously as “Co-Scientist”) that helps professional scientists to work out what their research process should be.  (Full disclosure: This is the Google Deep Mind project I’m currently working on.)  

Most interestingly, there are many well-known academic and corporate labs developing similar VSA agentic frameworks to handle much of the research tasks like literature synthesis and (eventually) even running autonomous laboratory hardware. 

While these systems significantly increase the speed of discovery, they are currently positioned as assistive tools that augment rather than replace human ingenuity. This perspective emphasizes a future where collaborative AI helps scientists navigate information overload to solve complex research challenges.

What could all this mean for the future of research of the kind we do at SearchResearch?  

While today these VSA tools are built to help professional scientists (think: biologists and chemists), their underlying "multi-agent systems" are a powerful new method for pulling together ideas from many different sources on the internet. 

Instead of relying on a human to use the right keywords for searching, a VSA co-scientist-type system brings together a collaborative coalition of AI agents that systematically structure the inquiry, do the legwork, and then analyze what it/they find.  

Imagine a future iteration of a search tool where you act not as a lone web surfer, but as a digital "Principal Investigator” leading a merry band of research agents all working for you.   

How a VSA works: When you start a complex research task, you have a conversation with the “Interview agent” that chats with you about what you’re trying to learn or discover. That conversation asks you to clarify a few things before it launches into its fleet of agents doing your research.  Then, an adaptive "Supervisor agent" breaks down your high-level goal into executable steps, creating a research plan, coordinating a team of digital personas running in parallel. 

Key to that research process are "Generation agents" and "Literature agents" that scour the internet to mine facts, cluster ideas, read through the literature, and propose different perspectives. The thing is, a collection of research agents will search across a huge and vastly varied set of content–much more than you’ll ever cover in your own research.  

But rather than just dumping a list of links on your screen or generating an AI summary, the system then deploys a "Reflection agent" to act as a virtual peer reviewer. This agent critically evaluates the retrieved information, cross-checking claims against verified data to ensure factual accuracy and logical coherence. 

After all that, a separate "Meta-Review Agent" specifically looks for errors and flags contradictions in the reporting.

If there are conflicting sources, the system doesn't force the user to guess which link is right based on surface-level credibility. Instead, it uses a kind of  "tournament of ideas" to compare and contrast the ideas (potentially hundreds of them) side-by-side.  It might have hundreds of possible ideas that it will compare against each other, looking for the best possible reply to your research Challenge.  

In this framework, AI agents hold simulated scientific debates to verify, refine, and rank the most robust answers based on between-idea cross-checking. 

For the everyday researcher, this means your search engine won't just retrieve information; it will debate it, critique it, and synthesize it before presenting a conclusion with citations.  

Ultimately, the advent of AI co-scientists means the end of the solitary, keyword-driven hunt. It changes the average internet user into the manager of a large digital research team that works 24/7 for you. 

As one researcher currently using these early systems noted, working with this technology feels like "having a team of 50 people at your disposal, doing all the work within a day.”  

VSAs are already producing impressive results, finding off-label drug uses, coming up with explanations for antimicrobial resistance, or finding new treatments for difficult-to-treat disesase.  

While these co-scientist VSA systems are currently set up to accelerate medical and physical science breakthroughs, you can see a day when their eventual democratization will bring the rigorous, structured thinking of the scientific method to everyone.  You probably won’t need all of this specialized agent architecture to find a good deal on cheese at your local market, but you might well want it to help you solve big, complicated, serious problems.  

The future of everyday online research–the kind of thing we do here– will no longer be about putting together just the right query and extracting the answer from the texts; it will be about managing a brilliant team of virtual assistants to navigate the world's information creatively and intelligently. You will be guiding a team of research agents all executing your vision of the research task. 

Keep searching! 

SearchResearch (6/20/26): Celebrating AI search / Specialty search tools

It's easy to complain...

P/C DS Studio at Pexels.com 

... about the quality of AI-powered search tools.  (I've done my fair share!) But when you're searching for something that's fairly difficult, I've often found the answers to be incredibly helpful, especially when the research question is vague or difficult.  They really help you find the right puzzle piece, even in a massive soup of pieces that all look pretty-much the same. 

As you know, this blog is about trying to give you tips and methods to be better at your online research.  But it's also about the best ways to think about what our online research tools are doing.  In other words, what's the most effective mental model you can have. 

If you think about Google as a database search, that's the wrong mental model.  A database search implies that the query will find every thing that matches.  If your query is something like "magic trick" then I'd expect the database to give me back a complete and accurate list of all the hits.  Those are key ideas: "accurate" and "complete." 

But that's not the way any search engine works. Instead of database records, a search engine indexes all kinds of documents--text files, Word documents, PDFs, videos, spreadsheets, images, etc etc etc.  Your search engine finds the most probable hits and then rank orders them by what it thinks is best.  Usually, that means sorting the hits by relevancy.  (What makes something relevant is a topic of long debate and discussion, quickly approaching the zenith of technical discussion.  Here's an article with more details, should you wish to learn more.)  

With the additional AI overviews, the search engines now have another tool that tries to answer your question.  A good old-fashioned Google query (short and to the point) isn't as helpful to the AI as an extended question: for AI questions, longer (with more detail) is often better.  

My point is that people often complain about the changes to search engines.  You're right to complain about inaccuracies and errors, but it's also worth taking a moment to celebrate how truly magical some of the AI-augmented search experience really is.  

There's been a huge improvement in finding difficult-to-find things.  

When I needed some quark (a kind of a fresh, unaged, and spreadable dairy product from Central Europe; halfway between yogurt and cream cheese) for a recipe I'm making, I just asked and got a truly helpful response: 

These are really decent suggestions, although there are a couple of errors. Kalinka isn't at that address anymore and neither is the Slavic Shop at that address. But they're both plausible places to buy quark. (And it was simple to find their current addresses with a quick Google search.)  

Best of all, the answer suggests contacting the stores via their online stock-checker. That just saved me several pointless trips.  And this was an incredibly useful suggestion--I didn't KNOW you could do online stock checking!  

My point is that this was a useful AI-augmented result.  It didn't quite give me the answer, but it told me useful information that I could carry foward to get to the answer.  

As members of the SearchResearch Rancho have noted, it's often true that just putting in old SRS challenges works pretty well.  Current AI search technology just answers them. 

For instance, if you remember the Carolina Parakeet Challenge (find an image drawn from life), copy-pasting the Challenge with the default current Google search gives more-or-less the same answer that we worked out by hand.  

Other AI engines also do pretty well.  Here's Claude's lovely answer… 

But wait, there's more!  Specialty Search Tools!

You might have also noticed that there are an increasing number of other kinds of search engines.  We've talked before about music identification systems.  But there are more: like Shazam for music, or Starwalk2 for things in the night sky, or Vinvino for wine identification. 

I want to mention another special-purpose search app that I've been using recently.  

Merlin Bird ID is incredibly accurate—with roughly 98% accuracy for photo identification and 70-80% for sound recognition. Developed by the Cornell Lab of Ornithology, it relies on massive, crowdsourced databases to deliver reliable results in your immediate area.  

Identifiying birds just by their songs requires human verification.  A part of good search practice is double checking.  For instance, there's the problem of the Northern Mockingbird, which imitates other species (hence, "mocking bird").  Merlin will often identify the mockingbird as the original bird... that is, the one being mocked.  Ah well.  (Pro tip: Listen for consistency: If Merlin flags an unusual bird but you only hear a split-second snippet of it once, it is likely a misidentification. If the song repeats continuously, it's generally accurate.)  

AND, when Merlin hears a bird, it will show a picture of that bird and give you additional spotting information.  (Such as "look in the top branches of a nearby tree; they love to perch there...")  

Here's an example from this morning's birds: 

The Merlin Bird ID interface.

When Merlin hears a particular birdsong, it will highlight that bird in the list (or add it to your list if it hasn't heard it yet today).  That way you can quickly learn which bit of bird song you're hearing is actually that bird.  

SearchResearch Lessons 

1. AI search engines sometimes make mistakes... but they're often useful.  For what it's worth, *I* sometimes make mistakes as well.  Learn from the errors and try to figure out what happens, why, and how to work around the issue.  (Big tip:  CHECK EVERYTHING!)  

2. What used to be hard SearchResearch Challenges are now (mostly) straightforward.  This is a huge shift! And I'm celebrating the increase in our ability to find the answers to complex questions.  And, as always, be sure you understand the answer.

3. Consider other kinds of special purpose search tools. There are a large number of speciality search tools.  It's good to learn which ones are useful for the tasks you do.  (I'll try to collect a list of them in a future post. It will go out of date quickly, but it will show us the range of possibilities!)  

And... 

        ... keep searching.