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.  

Changes to Google search post I/O -- don't panic about it

 The only constant is change

"the only constant is change" as a math-y expression, where 
k represents a constant, which is equated the delta symbol
representing change

After the latest Google I/O conference, it’s no surprise to learn that the Google search experience is changing too.  But I’m not sure the changes are quite as Earth-shattering as some people are making it out to be.  

I’m seeing lots of slightly panicked posts about how Google results are going to be all AI all-the-time.  My advice: relax.

What Google did announce at I/O 2026 is a much more AI-forward Search experience: that’s not the same as trashing the organic results. But Google’s own I/O post explicitly says: “You’ll continue to get a range of results from Search, just like you do today.” The new AI search box still returns a range of results, and the AI Search flow includes links to learn more.

Here’s the important distinction that people are over-reacting to:

Standard Search / SERP: organic results are not being removed.  AI Overviews appear when Google thinks they add value, and Google still describes them as part of Search with supporting links. Google Search Central says AI Overviews are shown only when “additive to classic Search” and “often don’t trigger.”

AI Mode: this is more conversational and answer-first. People see this and react to it saying it feels much less like the old SERP. It includes AI-generated responses and supporting links, but it is not the same thing as removing organic results from all Google Search.

On the other hand, the ecosystem is changing as well… 

Publisher / SEO implications: organic visibility may decline for many queries because AI Overviews, AI Mode, generative UI, and agents can satisfy more intent before a click. That is a traffic/CTR displacement problem, not a removal of organic listings. Google’s Search Central guidance still tells sites to follow standard SEO fundamentals to appear in both Search and AI features, and says eligible AI-feature links must be indexed and eligible for Google Search with a snippet.

One of the truisms in the web/search/online world is that people hate change.  In fact, they hate on any visible change… for about 2 weeks. By then, whatever changed will now become the new norm.  

That’s obviously not always true, but it happens often enough that people who have been around the block a few times know that you should basically ignore user feedback of the form “I hate this…” for a while.  (That’s especially true if you’ve done the background research and know that the change is actually an improvement in clicks, time-on-task, accuracy, or whatever user behavior you’re trying to improve.)  

At Google I/O this month (May 19 and 20, 2026), some changes to the core Google search experience were announced.  Here’s a summary of what they said: 

The search box now expands with user query length.  (Finally!)  This is a really-good-thing; now you can enter an arbitrarily long query without losing the front part of your query.  

And the search results page now ALWAYS has the AI Overview present.  It also has AI-powered suggestions for more nuanced question expansion.  It also has vastly improved multi-modal search letting you ask with text (the usual), images, files, or videos.  

There’s a plan to smooth over the differences between AI Overviews and AI Mode.  Right now they’re separate, but you can see why (and how) they’ll integrate them together.  

But Don’t Panic!  The blue links are still around, and you can use all the operators that you like.  (Although I can see a day when some of them will be retired.  Stay tuned: I’ll let you know when I find out.)  

Here’s what the SERP looks like today (May 28, 2026): 

(Click to expand)  

Below the query box (which now happily expands as needed), you see (2) the AI Overview.  Notice that if you click on (1), you’ll switch out of regular search into the “AI conversational mode.”  Some people like it, some don’t.  If you don’t trust it or just hate chatting with an AI, don’t click on it.  

Your regular old search results are just below the AI overview… which has been true for a while now.  

SearchResearch Lessons

1. Don’t Panic. This isn’t the last change to Google search, and we’ll survive this one in great shape.  The right attitude (methinks) is one of how can we take advantage of these changes?  

As always, keep searching! 

Answer: What is this called and why do they do that?

  When you see something out of the ordinary... 

Two fish swimming in close formation 

...the interested SearchResearcher should say "What??"  Or at least, "How's that?"  

As Isaac Asimov is alleged to have said, 

The most exciting phrase to hear in science, the one that heralds the most discoveries, is not “Eureka!” but 'That's funny...'

(I say "alleged" because as the Quote Investigator points out in his analysis, this particular phrase can't be found anywhere in his writings.)  

Regardless of its origin, the phrase rings true: seeing something and saying "that's funny" leads to interesting SRS questions.  That's the case here as well.  

In this case, there's a fishy peculiar behavior that I've seen multiple times.  Two fish of different species will often swim in perfect formation, like two fighter pilots flying in tight formation through the reef. Here's what it looks like in motion: 

I've seen this happen with many different species in different oceans and have always wondered: 

1. What is this kind of behavior called? 

2. Why do they do this? 

3. In the pic above, we have a hogfish being closely followed by a trumpetfish.  What other combinations of fish species might I find doing this?  

A query like this: 

[ two fish of different species swimming together ] 

leads us to quickly learn about schooling and shoaling.  A school of fish is a large number of fish of the same species swimming together in synchronized group.  

Here's an example of large school of Bigeye Trevally (Caranx sexfasciatus), which are frequently seen congregating in these synchronized, metallic-silver formations... 

By contrast, a shoal of fish is a large number of not-so-synchronized swimming fish (possibly of many species) some traveling one direction, some traveling another--like this group of somewhat disorganized fish:  

While that's interesting, it's not what we see in the video above--two fish traveling together in close synchronization.  

I modified my query to be: 

[ two fish of different species swimming together in close synchronization ] 

and learned that this could be called a "a heterospecific school or a mixed-species shoal."  That is, a fish group that has adopted synchronized schooling behavior. 

While schooling is typically defined by fish of the same species, size, and age moving together, mixed-species groupings occur when fish share similar body shapes, colors, or ecological needs, allowing them to gain the safety and energy-saving benefits of a larger school. 

That's interesting, but not quite what I was looking for.  I'm interested in pairs of fish (not schools or shoals) that are moving in harmony.  

When I give the query with the specific case of hogfish and trumpetfish, I get a very different answer: 

[ what do you call it when a spanish hogfish and trumpetfish swim together in sync ] 

The AI overview looks like this: 

This is the answer I was looking for.  A quick search on Scholar for literature about this behavior leads to many article (like this one: Predatory trumpetfish conceal themselves from their prey by swimming alongside other fish) 

So, shadowing or shadow hunting is the term for this kind of behavior between a largish herbivore and a sleek hunter.  

The researchers made a brilliant test rig to check out the idea that trumpetfish can escape detection by hiding behind a larger, bulkier vegetarian fish (like a parrotfish or hogfish).  Here's their video: 

We have our answer. 

BUT WAIT... there's more.  

Here's what I actually saw in the tropical waters on the Tubbataha Reef, the behavior that caused me to say "hey.. that's funny..."  

I saw these trevallys, also swimming in close synchronization with another all-black fish.  

This was striking: two fish swimming in what looks like shadowing, but they're the same size and shape.  Since they're so different in coloration, I just assumed they were different species that were shadowing.  (For example: see Giant Trevally spawing aggregation.) 

But they look identical, except for color.  What's going on? 

Or, as Asimov would say... "that's funny.."  

I grabbed a frame of this video and asked Google Lens to tell me what was going on.  Here's what it told me: 

A quick search to validate showed that this interpretation is correct.  Giant Trevally DO synchronized swimming when in a mating display.  I was lucky enough to see this behavior by diving on the reef within 2 days of the full moon.  

The black fish was a color dimorph (that is, a male that changed to black coloring for mating purposes), swimming close to a female for piscine seduction purposes.  

So... here's another example of very close in-sync swimming that's NOT for hunting purposes, but for mating!  

SearchResearch Lessons

1. Sometimes you need details to get to what you seek.  In this case, we needed the specific fish names to learn that these particular KIND of fish do shadow hunting.  Details matter.  

2. Sometimes what you see is not quite what you think.  Just because two fish are completely different color doesn't mean that they're different species.  

3. Sometimes what looks like the same behavior is NOT the same! Two fish swimming in perfect sync can mean very different things, depending on the species, the time, and the context.  

Keep Searching! 

SearchResearch (5/6/2026): What is this called and why do they do that?

 I've been busy... 

Spanish hogfish (in front), followed closely by a Trumpetfish. Why?  

...not just with work, but with another dive vacation. 

And the vacation causes me to be curious about all kinds of things.  In this case, there's a peculiar behavior that I've seen multiple times.  Two fish of different species will often swim in perfect formation, like two fighter pilots flying in tight formation through the reef. Here's what it looks like in motion: 

I've seen this happen with many different species in different oceans and have always wondered: 

1. What is this kind of behavior called? 

2. Why do they do this? 

3. In the pic above, we have a hogfish being closely followed by a trumpetfish.  What other combinations of fish species might I find doing this?  

Can you help out this scuba diver figure out what's going on with this strange fishy behavior? 

Let us know in the comments.  Be sure to say HOW you figured it out (note that every method is acceptable, including AI--just tell us how you used the AI.)  

Keep Searching! 

SearchResearch (4/23/26): AI image gen tools are great, as long as you don't ask for accuracy

 Every few weeks... 

The wreck of the M/V Oduna on a remote beach on the
southeast side of Unimak Island, Alaska. P/C US Dep. Interior

... there's an advance in AI in one way or another. Maybe the text is smoother, or there are fewer hallucinations.  It's always something. 

Sometimes it's just a wreck.  

This past week it was OpenAI announcing a new and improved image generation model.  (Their press announcement of 2.0)  

And it's true--much has been improved. 

BUT... don't look for accuracy in the images, especially if you're creating a diagram to help you understand how something works.  

The little details count, and all of the AI image gen tools get details wrong.  

Looking back at our "tea kettle" Challenge from Nov, 2023 ("How does it work?")  I asked for a diagram about how a tea kettle operates.  The prompt was simply: [create a diagram of an electric steam kettle showing how it knows when to shut off when the water is boiling]  

Here's what I got from ChatGPT (2.0):  

Which isn't terrible... EXCEPT... there's no thermostat or temperature-sensing device shown.  It says there's a "bimetallic strip assembly," but most tea kettles use a circular bimetallic device to detect shutoff temps.  They look like this with the bimetallic device shown as a gold circle (illustration from above on the left; a side-view on the right).

More annoyingly, the "simplified circuit diagram" is nonsense. That's just two parallel switches side-by-side.  Both of them are open.  It's unclear how this helps the story of the diagram. 

Meanwhile, here's Google Gemini's version--very glossy, but missing in the important details: 

Here, the thermostat is shown in the handle (which is odd, they're usually in the bottom) and it's fairly clunky and cyberpunky in appearance.  

I'm not the only person pointing this out.  Gary Marcus managed to win with the most-ridiculous diagram by this week's OpenAI 2.0 model. Here he asked for a:  

P/C Gary Marcus and OpenAI

We can go on in this vein. 

But here's the big take-away for you:  Do NOT trust diagrams generated by AI systems (at least at the moment).  They often have obvious bugs (and even worse, sometimes subtle bugs).  

And don't get me started on Claude's image generation skills... 

It's good to know that Claude is really good at code generation, it's not going to make it as a technical illustrator.  

Back to our regular programming next week! 

SearchResearch (4/9/26): How Bespoke Tools are Changing the Game for Sensemaking

Bespoke means custom... 

Bepoke tailoring means one that fits you perfectly.. P/C Wikimedia.

... as in a "bespoke" suit--one that's made specifically for you.

For a long time, if you wanted a custom data visualization, you had two tough choices: learn a massive, complex platform or hire an engineering team. It was slow, expensive, and often frustrating. But something big changed in early 2026.

We've entered the era of seriously good "vibe coding."

This isn't about writing perfect lines of code for months; it's about using LLMs to rapidly generate functional software in minutes. The barrier to entry hasn't just lowered—it has collapsed. Now, we can create interactive, special-purpose tools for a single task and then just throw them away when we're done. This new technology has completely changed the cost structure of traditional sensemaking tasks. [Russell, 1993]

The New "Floor" for Visualization

I've seen this first-hand in the classroom. Students who used to struggle with syntax are now building professional-grade interactive tools in hours. By offloading the "boilerplate" work to AI, they can focus on the most important part: mapping data to visual meaning. As noted in recent research, data visualization literacy is no longer just for experts; it's a vital workplace skill for everyone. [Börner]

Disposable Sensemaking Tools

In a professional setting, this means we can build "disposable" artifacts. For example, when analyzing conceptual overlaps in academic papers, we don't need a generic network tool. We can vibe-code a "Concept Map" in ten minutes that does exactly what we need: highlight specific connections between papers and topics, then get discarded once the insight is found. Here's one I made in a few minutes. I uploaded a set of papers, have it identify the common concepts between the papers, and then create an interactive visualization so I can probe the relationships between the papers and what's in them. Try it out yourself.

The Challenge: Quality vs. Speed

Is it perfect? Not on the first try. "Zero-shot" AI outputs often have messy labels or weird colors. However, vibe coding is an iterative loop. You aren't a "coder" anymore; you're a "director of design." You spot the flaws, ask for a tweak, and watch the tool evolve in real-time.

There is a catch, though. Research shows that when making moves is too "cheap" and easy, we sometimes fall into mindless trial-and-error instead of thoughtful planning. We have to stay "planful" even when the tools are fast. [O'Hara]

Exploration vs. Explanation

Bespoke tools are amazing for finding the truth (such as when you're doing Exploratory Data Analysis), but they can be terrible for telling the truth to others. For presentations, stick to the classics like bar charts and histograms. You don't want your audience to have to learn a whole new visual language just to understand your point. You can read all about it in my paper from a few years ago. [Russell, 2016]

One way to explore a set of papers is with an interactive, bespoke AI analysis tool. Today, in 10 minutes, I made this bespoke info visualization that reads in a set of PDFs, creates a 25 word summary of the paper, and then lets you ask questions of all the papers, putting the answer to your query/prompt in the third column.

I've asked for short summaries of the papers AND in column 3, asked the tool to extract all of the place names mentioned in each PDF. 

When we did this same task in 2014 on the SearchResearch Rancho, it was a LOT more complicated and not nearly as accurate. Things do improve!

SearchResearch Lessons

The future of sensemaking isn't simply that we'll have one giant software platform that does everything for everybody. It's a series of small, tight, custom-fit tools that are easy to make, and more-or-less disposable. They help us see deeply into our data just when we need to.

What bespoke tool will you vibe-code today? Have you tried vibe coding to build tools that you've needed? Let us know in the comments.

And keep searching...

References

Börner, A Bueckle, M Ginda . Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments.  Proceedings of the National Academy of Sciences, 2019

Knoll, Christian, Torsten Möller, Kathleen Gregory, and Laura Koesten. "The Gulf of Interpretation: From Chart to Message and Back Again." In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pp. 1-17. 2025.

O'Hara, Kenton P., and Stephen J. Payne. "The effects of operator implementation cost on planfulness of problem solving and learning." Cognitive psychology 35.1 (1998): 34-70.

Russell, Daniel M. "Simple is good: Observations of visualization use amongst the big data digerati" Proceedings of the International Working Conference on Advanced Visual Interfaces, pp. 7-12. 2016.

Russell, D. M., Stefik, M. J., Pirolli, P., & Card, S. K. (1993, May). The cost structure of sensemaking. In Proceedings of the INTERACT'93 and CHI'93 conference on Human factors in computing systems (pp. 269-276).