How Twitter Could be 10X Bigger, 100X More Profitable, and 1000X More Awesome

Read my new article about how to evolve Twitter, on VentureBeat


I’ve spent many years studying, writing about, building, and funding companies (such as Bottlenose, Klout, and The Daily Dot) in Twitter’s ecosystem.

Despite the media chatter, I am still bullish on Twitter – as should be any investor who understands the social network’s fundamentals and true potential. Twitter has the highest revenue growth rate of any tech firm with over $2 billion in sales over the last year. And at today’s market cap, Twitter is an incredible bargain.

The company has enormous untapped potential to impact the world and create value for investors and partners — far more than short-term investors probably realize. But to unlock that hidden potential, some significant product and business model evolution may also be necessary.

I truly want the Twitter ecosystem to succeed. And it is in that spirit of support and optimism that I’m offering a number of ideas below that could help Twitter not only regain its former growth curve but surpass it. I’m breaking down my detailed playbook for the company into three sections:

1: Improving the signal-to-noise ratio on tweets
2: Enabling better search and collection of tweets
3: Focusing on being a network not a destination


How to Solve Twitter’s Engagement Problem: Add Semantics

The fundamental problem that Twitter has is engagement. If engagement can be corrected, the whole Twitter ecosystem (and their stock price) will improve.

Improving Twitter engagement comes down to fixing the core consumption experience.

First of all what’s wrong with the consumption experience? In a nutshell, two problems:

  1. The signal-to-noise ratio of Twitter has declined dramatically as the service reached social saturation. There are simply too many messages from too many people in the stream — I call this “The Tragedy of the Comments” (yes, that was a pun). The issue is that since there is no economic disincentive to spam Twitter, nor any incentive for anyone to police or manage the common message space, it’s filling up with irrelevant / junk content and the amount of such content is growing rapidly. For users, it becomes very difficult to filter signal from all that noise. Users simply give up when faced with that level of overload. Why spend a lot of effort searching for needles in the haystack, when there are other ways (other apps, sites, etc.) to get a higher ratio of needles?
  2. The participation-to-reward ratio of Twitter has also declined dramatically with saturation. Because of the poor signal-to-noise ratio, the likelihood that anyone will see or respond to a Tweet you post, if you are not a major celebrity or media outlet, has declined far below the threshold of incentive. Why bother to Tweet when there is only a negligible chance anyone will see it or respond? The reward for participation has fallen too low for the majority of the user base to spend the effort.

There are several relatively easy steps Twitter can take to solve both of these problems, with better metadata and analytics in their apps. Making these changes would increase the usability of Twitter for most users and could radically improve Twitter’s metrics.

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Twitter’s Future is Actually Its Past – Where Twitter Went Wrong and How to Right It

With the resignation of Twitter’s CEO, Dick Costolo, there has been a sudden burst in commentary on what is wrong with Twitter, and where they should focus next.

There are suggestions that Twitter should focus on live real-time events. There are suggestions that Twitter should focus on algorithms to filter content so they are more like Facebook. There are also comments from Twitter’s leadership that Twitter will not change its present (broken) strategy.

It’s clear that Twitter’s growth has stagnated. But to solve this problem, Twitter doesn’t need to invent a new strategy, because they already found the right strategy years ago. The problem is that they abandoned it.

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Why Twitter’s Engagement Has Fallen

I have been thinking about Twitter for many years. One of the interesting trends that many of us who share an interest in social networks have been tracking is the decline in engagement on Twitter.

Indeed this decline is not only evident from Twitter’s own metrics and reporting, but also to anyone who has been an active user of Twitter since the early days of the service.

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2014: A Turning Point for the Semantic Web

Read my article in about the significance of 2014 in Semantic Web history.


Google is moving away from hand-made ontologies — they were never a fan of them. From the early days, Google’s philosophy has been biased towards big data over manually constructed knowledge. The end of Freebase, and the rise of Knowledge Vault, are just examples of this bias. However,‘s impressive growth and adoption can’t be ignored either, and the jury is still out as to whether decentralized ecosystems can ultimately out-scale more centralized data-mining approaches like Knowledge Vault to reach Semantic Web dominance. Although Freebase is being handed off, it is not necessarily over — it is going into the Wikidata project — which could be an increasingly important repository of open knowledge in the future. The war for the Semantic Web is not over.

It’s Time for an Open Standard for Cards

Cards are fast becoming the hot new design paradigm for mobile apps, but their importance goes far beyond mobile. Cards are modular, bite-sized content containers designed for easy consumption and interaction on small screens, but they are also a new metaphor for user-interaction that is spreading across all manner of other apps and content.

The concept of cards emerged from the stream — the short content notifications layer of the Internet — which has been evolving since the early days of RSS, Atom and social media.

Read the rest on TechCrunch

Bottlenose Nerve Center 2.0 Released – Milestone for Real-Time Big Data Analytics

I’m happy to announce the release of Bottlenose Nerve Center 2.0 today. Analyzing 3 billion messages an hour (72 billion messages a day), and doing real-time predictive analytics on nearly 300 million data points an hour, it’s a big step in real-time big data analytics.  

Think about it for a moment: 3 billion messages is several times more data volume than the entire daily Twitter firehose, and we’re analyzing this much data every single hour, continuously. For one thing this level of real-time big data analytics cannot be done today with Hadoop — Hadoop is actually not capable of doing huge data aggregations this fast — so we’re using new technologies, like ElasticSearch (our team actually contributes to the ElasticSearch codebase) and Cassandra under the hood. We’re now analyzing in under a second what would take about an hour with Hadoop.

It’s an impressive technical accomplishment and I’m very proud of the incredibly talented engineering team that built this. I just want to give a shout out to my amazing co-founder Dominiek ter Heide and our product and engineering team for their work. This level of real-time analytics is truly game-changing.

Read more about the release here, see screenshots here, and check out the coverage about this release in TechCrunch!



How Bitcoins Could Restructure the World

Read my article in VentureBeat about how Bitcoins may restructure our civilization, and the need for advocacy to support this transition, if it is going to happen. Here’s an excerpt:

Bitcoin is a trend with all the ingredients necessary for changing the world. It spreads virally, funds its own growth, and can’t be controlled from any central point. Like the Web, it could eat the world.

Bitcoin could be the beginning of a massive transfer not only of wealth, but of power — a shift to a new social order. If you change the money system, you change the economy; that in turn changes society, government and industry. The shift to Bitcoins would be more than an economic shift, it would be a shift to a new social order — one built around a “freer market” economy.

Such a shift would be a lot more likely if a new grassroots organization were formed to accelerate, promote and protect the emerging cryptocurrency economy. By helping the cryptocurrency economy to fund its own evolution and defense, it would have a better chance of surviving the inevitable challenges it will soon face.

As this new digital economy emerges, the mysterious Bitcoin creator, Satoshi Nakamoto, could turn out to be one of the most important historical figures of our time.

Did Apple Buy Topsy for Contextual Awareness?

The stunning news that Apple bought social search engine, Topsy, for more than $200M has many scratching their heads. Why would Apple want social data, and why would they pay so much for it?

There has been a lot of speculation about the reasons for this acquisition — ranging from making Siri better, to making the App Store smarter, to acquiring big data expertise to develop insights on the Apple firehose.

But I think the reason may be something else altogether: Personalization.

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Bottlenose Announces Free Live Visualization of Global Social Trends

Bottlenose has just launched something very very cool: A free version of it’s live visualization of trends in the Twitter firehose.  Check it out at and get your own embed for any topic. This is the future of real-time marketing. And by the way it’s also an awesome visualization of the global mind as it thinks collective thoughts.

The Future of Virtual Assistants

Check out my article in Forbes on the future of Virtual Assistants. What’s after SIRI and IVR?

All of the interesting stuff happens when data collides. A voice-based interface to a single data set is a thing of the past. A voice-based interface that talks to over 400 applications, representing over 10,000 unique units of knowledge across over 3,000 discrete products? Now we’re talking.

Creating the next generation of assistance is in fact a data federation problem. It’s a brain problem. A data routing problem. A big data problem, even.

The Present IS the Future: Real-Time Marketing In the Era of the Stream – Part Two

In Part I of this article series, we looked at how the real-time Web has precipitated Nowism as a fundamental shift in how we understand and engage with information. Nowism is a cultural shift to a focus on the present, instead of the past or future.

One example of Nowism in action is Nowcasting, which attempts to make sense of the present in real-time, before all the data has been analyzed, in order to project trends sooner or even continuously. Nowcasting is quickly becoming a necessary and powerful function in our media, culture and society.

These ideas are being harnessed by savvy brands and companies not only in how they operate, but also in how they conceive of themselves.

Next we will look how they impact social marketing, and why brands must learn to act like media companies in this new environment.

The Three Stages of Real-Time Marketing Evolution

The Stream is more real-time than the Web. And it’s even more real-time than blogging and the early days of social networking. But it’s not only faster, it’s also orders of magnitude bigger. Instead of millions Web pages every month, we’re dealing with billions of messages every day.

There’s vastly more activity, more change, more noise, and when trends happen they are more contagious and spread more quickly. It’s therefore even more important to sense and respond to change in the present, right when it happens.

Unlike the Web, the Stream is constantly changing, everywhere, on the second timescale: It is a massively parallel real-time medium. And instead of a few channels there are billions of channels — at least one (if not many) for each person, brand, organization and media outlet on the Net — and they are all flowing with messages and data.

Keeping up with the deluge of real-time conversation across so many channels at once is a huge challenge, but making sense of so much change in real-time is even harder. Yet, even harder still is intelligently engaging with the Stream in real-time.

These three objectives represent three levels of maturity and mastery for real-time marketers.

Stage One: Week Marketing.

Today, most brands and agencies are still stuck trying to accomplish Stage One, if they are even that far along.

Stage One social marketers are focused on simply monitoring the Stream and trying to keep up with the conversations about their brand.

Some Stage One marketers are also actively trying to drive perception and optimize engagement through social media. But their ability to measure the effects of their actions and optimize their engagement are primitive at best.

The timescale of their measurement and engagement with the stream can range from hours to days, or even to the weekly timescale.

Stage Two: Day Marketing.

A smaller set of organizations have learned how to make sense of the Stream in real-time and are operating on the hour to daily timescale. They have graduated to Stage Two.

Stage Two marketers don’t merely monitor and respond, they digest and interpret. They measure and engage in sense-making and trend discovery. They generate live insights from millions of messages and incorporate these insights into their thinking and behavior on an hourly to daily basis.

Stage Two social marketers have evolved past the stage of simple reflexive response to the stage where they can interpret and reason about the Stream intelligently in near-real-time. They leverage social analytics, data mining, and visualization tools to facilitate insight, and this leads to smarter behavior, more optimal engagement and better results.

But Stage Two organization response times are still not real-time. Instead of seconds or minutes their responses often take hours or even days and that’s not fast enough anymore.

Stage Three: Now Marketing.

Stage Three marketing organizations are incredibly rare today. But there will be more of them soon.

Stage Three’s are able to monitor, make sense of, and then engage intelligently with the Stream, all in real-time, not after-the-fact.

In other words, they are consistently able to detect, measure, analyze, reason, and respond to signals in the Stream within seconds or minutes, or at most within the hour.

Stage Three organizations continuously run a real-time marketing feedback loop that works like this:

  1. Sense. First a signal is sensed: it might be a breaking story rumor, a complaint by an influencer, a change in customer perception or audience sentiment, a shift in engagement levels, a crisis, or a sudden new trend or opportunity. Sensing signals, and differentiating important signals from noise, in real-time, requires new approaches to determining relevance, timeliness, importance that don’t rely on analyzing historical data. There is no time for that in the present. Sensing has to intelligently filter signal from noise by recognizing the signs of potentially interesting trends, regardless of their content. Organizations that can do this well are able to detect emerging trends early in their life cycles, giving them powerful time advantages.

  2. Analyze. Next, the signal is analyzed in real-time to understand what drives it, and what it drives. Its underlying causes, influencers, effects, demographics, time dynamics and relationships to other entities and signals are mined, measured, visualized and interpreted. This requires new live social discovery and analytics capabilities – what’s new here is that this isn’t merely classical social analytics (measuring follower count or message volumes, or charting historical volume), it’s massive “big data” mining and discovery in real-time. It’s live prospecting around all the potentially relevant signals that are connected to each signal to get the context.

  3. Respond. Then an intelligent response (itself a signal) is generated across one or more channels within seconds to minutes. For example a reply or an offer may be sent to a customer, an alert may be sent to a team, a new piece of content may be synthesized and published, the targeting for an ad campaign may be adjusted, or the tone of social messaging may be modified, prices may be adjusted, and even policies may be changed – all in real-time. Organizations that get good at this process are able to respond so quickly they cross the threshold from being reactive to being proactive. They are able to drive the direction of trends by being the first to detect, analyze and respond to them.

This feedback loop is exemplified in real-time social advertising targeting and campaign optimization, for example. But as organizations mature to Stage Three they must learn to apply this same methodology to ALL their interactions with the Stream, not just advertising. They must apply this feedback loop across ALL their engagement with customers, the media and the marketplace.

Within a decade, all leading brands will be stage three marketing organizations.

Mapping The Ripple Effect

“Stage Three” agencies and brands need to master ripple effects to thrive in the next generation social environment.

Ripple effects are the key forces in the emerging real-time social Web. Information propagates through ripple effects along social relationships, across channels, communities, and media. Ripple effects are how trends emerge and rise, how rumors spread, and how ads and content are distributed. But we’re currently almost completely blind to ripple effects, we have almost no way to detect, measure or predict them.

The average Facebook user has 190 friends. The average Twitter user has 208 followers. Each group contains a number of influencers. Within each influencer’s social graph there are another set of even more powerful influencers. And so on and so on. When you seed a branded message on Facebook, for example, it’s not a straight trajectory. A ripple starts with the above numbers, but each new impression creates a new set of ripples.

Suddenly, your branded message is being seeded across a number of platforms — even spreading to platforms you never intended — and 99% of brands and agencies have no way of mapping this ripple effect, let alone controlling it.

But what if you could track your ripple effects? What if you could guide them? What if you could measure their effectiveness, or even predict where they are going? Suddenly there would be a wealth of new insights to pull and learn from. And this is precisely what is now possible, using emerging tools.

Problems with Existing Tools

Stage Three marketing organizations have to keep up with ripple effects in real-time, and they have to anticipate where those ripple effects are headed, in order to react immediately.

But most social analytics and engagement tools fail to show ripple effects. They provide loads of raw data — lists of messages from various social accounts and searches. But they expect humans to do most of the work of actually figuring out what’s important in those lists of messages. That is no longer realistic. Humans can’t cope with the data — it’s overwhelming.

Furthermore, most existing social analytics tools focus on simply measuring engagement via follower counts, mentions, likes, Retweets, favorites, impressions, click-throughs, and basic sentiment. But those metrics are no longer sufficient: They aren’t the trends, they are just signals that may or may not be relevant to actual trends. Not all signals are trends. The art is in finding a way to pull out the actual trends from the rest of the signals that are not in fact trends of any value.

What existing tools fail to do is actually make sense of what’s going on for you — they show you either too little or too much information, but they fail to show you what’s actually important; they’re not smart enough to figure that out for you.

Existing tools are good at finding known topics and trends (“known unknowns”) things you explicitly ask to know about in advance — but what we need in the era of the Stream are tools that show you what you don’t even know to ask for (“unknown unknowns.”) They have to detect novelty, outliers, anomalies, the unexpected — and they have to do this automatically, without being instructed on how to find these nuggets.

Existing social media analytics tools are too retrospective in nature – they show how a brand performed on social channels from the past up to the moment a question is asked. But these reports are static. They don’t show change happening, they don’t say anything about what’s next. The minute they are generated they become obsolete. It’s interesting to look at past performance, but what is really needed is more predictive analytics.


We need a new generation of tools that are designed for identifying real-time ripple effects and filtering them to figure out which ones are noise and which are actual trends we should pay attention to. Better yet, we need tools that can not only identify the trends, but that can project where they are headed in real-time. Think of this as the next evolution of Nowcasting.

We might call this Trendcasting. Where Nowcasting figures out what’s happening now in real-time, Trendcasting figures out what’s happening next in real-time. 

No human can Trendcast in real-time anymore without help from software, the Stream has too much volume and velocity for the human mind to comprehend or process on its own. This is a problem that can only be solved by cloud computing against big data in real-time.

In the today’s real-time Stream, marketers cannot afford to be hours or days behind the curve. They need to know and understand the present in the present, while it is still unfolding. They need tools for Trendcasting – for finding and predicting trends. Trends are not merely raw data, they are particularly meaningful and noteworthy trajectories in the data.

Trendcasting is going to be come absolutely key. The next-generation real-time marketing platforms will provide automated trendcasting as a key feature. They will sift through the noise, find the signal, and then measure it to see if it actually matters. Trendcasting is about filtering for the trends that actually matter, because not all signal is important and not all trends are equal.

Traditionally, finding and forecasting trends has always been thought of as an exclusively human skill — but today we’re starting to automate this function. I believe Trendcasting can be fully automated, or at least dramatically improved, using massively parallel big data analytics approaches. This is the where the cutting-edge of innovation for real-time marketing will focus for the next decade. (Disclosure: My own company, Bottlenose, is focused on exactly this goal, for Fortune 500 brands).

Trendcasting tools are the next leap in a long process of measurement tools innovation that has included inventions like telescopes, microscopes, X-rays, weather satellites, MRIs, and search engines. In a sense trendcasting engines could be thought of as automated cultural measurement tools — the social equivalent of a weather satellite —  social satellites. They help us to visualize, understand and project the weather of markets, cultures, industries, communities, brands and their audiences, just like satellites have helped us understand and map the weather patterns of our planet.

Every Brand is a Media Company

The shift to real-time and the advent of the Stream changes how brands must think of themselves.

Whether they are ready or not, all brands have to learn to function more like media companies – and in particular like news networks – in order to remain competitive in the era of live social media.

For the first decade of social media the emphasis was clearly on social, but now it is shifting to media. Leading brands have learned how to be social for the most part. Now they have to learn how to act like media companies.

Consider a network like CNN: They have reporters all over the world, constantly giving them text, images, video, opinion, insights and leads. They have viewers, some of whom are also contributing news tips and stories, and opinions, all over the world across many platforms and channels.

CNN’s bread and butter is finding breaking stories first, getting the best information about them, and covering them most comprehensively and creating original news content and analysis for their audience.

CNN is a good model for what every Stage 3 brand has to learn to do in order to master Now Marketing.

Brands that want to lead in the Stream era have to gather intelligence constantly, using social media. They have to create content, share it, and engage. They have to keep their fingers on the pulse of their markets and culture in general in order to remain relevant and timely. They have to respond to a huge influx of questions, opinions, complaints, suggestions, leads. And they have to do this across many platforms and channels at once, in real-time.

The distinction between content provider and audience is dissolving. It’s now a two-way live conversation with the market, a conversation among equals. Brands have to learn to share, interact, make friends, and socialize just like people do. They have to not only create content for their audiences, they have to use their audiences as the content. And they have to do it on a massive scale.

Some brands – like Nike and Red Bull – have gone very far down this path and even think of themselves as media companies to some degree. But for most brands thinking like a media company is still a completely new orientation and set of skills.

Brands need new tools in order to think and operate like media companies. They can’t work on the weekly or monthly timescale anymore. Even daily timescales are too slow: they have to go live.

They can’t just market to their customers, they have to engage them in marketing the brand and creating media, together. They can’t just analyze key metrics anymore, they have to understand the trends that are emerging, and what’s driving change.

The Stream is here, and it’s happening in real-time. Marketers who can adapt to this shift early will be the leaders of tomorrow; Brands that are late in adopting these practices risk become nothing but historical data points.


The Present IS the Future: Real-Time Marketing In the Era of the Stream – Part One


The pulse of the Net has gotten faster. It’s not a static Web of documents anymore, it’s a new real-time messaging medium we call the Stream.

The Stream is unlike any form of live media before it: It is a completely real-time, globally distributed, two-way conversation. And it’s already changing everything we know about marketing, advertising, branding and PR.

For marketers – and particularly for brands and agencies – mastering the Stream requires a new set of approaches, new tools and new practices. Like the similar shift, over two decades ago, from traditional media to digital media, this shift is both an existential challenge and a potentially destiny-changing opportunity.

Some organizations are learning to master the Stream faster than others, and they will be the leaders of tomorrow. But even those that lag will have to adapt soon, or they will become irrelevant by 2016. It’s evolve or die, all over again.

The Clock Rate of the Net is Speeding Up

One of the things that makes the Stream different from the Web is clock rate. The clock rate of the Net is increasing.

For the past 30 years we have been trending towards immediacy – the world has been getting faster, and nowhere has this been more apparent than online.

Now we have arrived at real-time and the Net has become a live medium. This changes everything.

Before blogging the clock rate of the Web was slow. Most Web sites were updated less than once per day. News and media sites were updated daily or perhaps a few times a day. Blogging eventually increased the rate of change to the hours timescale. RSS then made it possible to keep up with this change more efficiently – the Web became a gigantic news ticker. But it was still a relatively slow one compared to today.

Fourth Era

Starting in 2000, instant messaging and text messaging both started to gain adoption. These shifted marketing from the hours to intra-hour timescale.

Facebook was launched in 2004, followed by YouTube in 2005, Twitter in 2006 and Instagram in 2010. Due to the rapid message-based brand conversations they enabled, social networks sped up the timescale of digital marketing from hours to minutes, and even to seconds.

Since 2000 we have also seen a steady transition from stationary and sporadic Internet access to continuous mobile access, complemented by simultaneous increases in bandwidth and reductions of bandwidth cost.

Everyone is now connected all the time, both as a content consumer and as a content provider. These trends have democratized the Internet from a nearly static and one-way textual medium to a fully live two-way multimedia medium – the Stream of today.

Social Media Beats Mainstream Media

Social media is the new media – it is a new form of media, not just a media distribution pipe, and it is much faster than traditional media in every way.

The Stream is more live and real-time than TV and radio ever were. For example, social networks consistently beat TV and radio to the story. They sense and distribute breaking news and trends ahead of mainstream TV networks and media outlets, often by up to tens of minutes and even as long as hours. 

There are numerous examples of major stories that broke on Twitter before mainstream outlets. For example news of Whitney Houston’s death broke on Twitter by 27 minutes ahead of mainstream media. Another example is Earthquakes – Twitter consistently breaks news of earthquakes minutes ahead of mainstream media.

Likewise, ambient social apps that take advantage of geofencing and silent, constant communication between devices are driving real-time content and engagement. 

SoLoMo (Social + Mobile + Local Technologies) tech is still in its infancy, but it opens myriad new doors for brands and agencies to begin mining live data. Tracking consumer behavior in real-time paints an incredibly vivid picture of your fans and followers as SoMoLo becomes widely adopted.

The digital native demographic has developed an expectation that their devices seamlessly integrate with the world around them.  As such, Millennials have begun hyper-tasking: operating multiple devices/consuming data from multiple sources at once.

Brands can no longer rely on a blanket broadcast strategy with their messaging. You need to know the behaviors of your target demo on each device – what they’re saying, when they’re saying it, and who they’re saying it to. And you need to know this in real-time.

Attention has Shifted to the Stream

Much of the growth of the SoLoMo movement has been driven by increased bandwidth, faster adoption times for smartphones and tablets, but also broader demographics embracing social media.

This produces a phenomenal amount of data – and it’s a challenge to manage and make sense of it, but it’s imperative brands/agencies begin sifting through it all to execute the right kind of campaigns.

The pace and volume of social messages on Twitter and Facebook have been growing exponentially, year over year and this trend shows no signs of slowing down. Attention is shifting from search to social.

Meanwhile attention to the top sites on the Web is increasingly being driven by this social messaging activity or dark social, rather than by Web navigation, Web search and SEO.

Among the top 50 sites on the Web, most get at least equal, if not more, of their traffic from social than from search. In other words, the primary driver of digital consumer attention, brand perception, and engagement has shifted to social.


The Age of Nowism

The shift to faster timescales is altering the landscape of marketing, sales and even customer service. Consumers live in the Now, and they are demanding that brands live there too.

This new and growing obsession with now even has its own marketing buzzword called Nowism:

NOWISM | “Consumers’ ingrained* lust for instant gratification is being satisfied by a host of novel, important (offline and online) real-time products, services and experiences. Consumers are also feverishly contributing to the real-time content avalanche that’s building as we speak. As a result, expect your brand and company to have no choice but to finally mirror and join the ‘now’, in all its splendid chaos, realness and excitement.”

Nowism is a cultural shift to a focus on the present, instead of the past or future. It’s new and unprecedented: never before has a civilization on this planet lived so exclusively in the now.

In the information age, thanks to the double-edged blessing of information technology and communication networks, the present has become bigger, faster, and more consuming. 

Today we are focused on immediacy and instant gratification. And with these come an expectation of instant response, instant customer service, and instant solutions. This is an era of shoot-first-ask-questions-later, where trends and rumors flare up and go global in minutes.

Now, the risk of being late is greater than the risk of being wrong. And due to this, even experienced media outlets and brands are under pressure to publish or respond as fast as possible, without even time to think or fact-check. It’s better to issue a correction than to be perceived as slow.

This is a world in which there will be more error, more confusion, more threats, more crises, but they will start and end more quickly as well. It is also a world in which there will be more leads, more opportunities, and more transactions, and they too will start and end more rapidly. 

To survive and prosper in these faster cycles of activity organizations have to learn to think and respond in real-time, even if it means making mistakes and corrections more frequently.

The present contains more data, and more change, than what used to occur in months or even years of activity. And the present is therefore more difficult to understand today than it was before.

Nowcasting: Predicting the Present

Because there is so much to measure in the present, and we can measure everything in higher resolution, there is vastly more that we must pay attention to at any time. And this means we simply don’t have as much time or resources to focus on the past or the future.

Instead of predicting the future, there is a new option, in the age of Nowism: Predict the present, while it is still unfolding, using an approach called Nowcasting, which was pioneered at Google.

Nowcasting attempts to make sense of the present before all the data has been analyzed, in order to project trends sooner, or even continuously. For example, using nowcasting techniques Google has been able to predict monthly sales, economic indicators, and disease spread in near real-time, before end of month results are usually available.

Nowcasting has been applied by hedge funds, economists, and epidemiologists, and soon it will be a standard tool for marketers.


Applying nowcasting effectively is more than simply measuring and predicting events. The opportunity is to use those measurements to then act proactively to respond, create new content, adjust campaigns, and stay one step ahead of emerging trends. It is not reactive, but proactive to opportunities as they develop in real-time.

This of course requires a sophistication and understanding of the present to sense, analyze and act in a way that is substantial and moves engagements forward.

In part II of this series, we will look at how marketing has evolved to its present state in the real-time Web, and how Nowism is necessarily changing the way that marketers work. We will also explore how leading businesses must learn to consider themselves as creators of media rather than simply forces moving within it.


The author thanks Adam Blumenfeld and Phil Ressler for contributions, edits and suggestions for this article.

Making Sense of Streams

This is a talk I’ve been giving on how we filter the Stream at Bottlenose.

You can view the slides below, or click here to replay the webinar with my talk.

Note: I recommend the webinar if you have time, as I go into a lot more detail than is in the slides – in particular some thoughts about the Global Brain, mapping collective consciousness, and what the future of social media is really all about.  My talk starts at 05:38:00 in the recording.


Bottlenose Beat to the First Attention Engine – But It’s Going to Get Interesting

Bottlenose (disclosure: my startup) just launched the first attention engine this week.

But it appears that is launching one soon as well.

It’s going to get interesting to watch this category develop. Clearly there is new interest in building a good real-time picture of what’s happening, and what’s trending, and providing search, discovery, and insights around that.

I believe Bottlenose has the most sophisticated map of attention today, and we have very deep intellectual property across 8 pending patents and a very advanced technology stack behind it as well. And we have some pretty compelling user-experiences on top of it all. So in short, we have a lead here on many levels. (Read more about that here)

But that might not even matter because I think ultimately will be a potential partner for Bottlenose, rather than a long-term competitor — at least if they stay true to their roots and DNA as a data provider rather than a user-experience provider. I doubt that will succed in making a search destination that consumers will use and I’m guessing that is not really their goal.

In testing their Realtime service, my impression is that it feels more like a Web 1.0 search engine. Static search results for advanced search style queries. I don’t see that as a consumer experience.

Bottlenose on the other hand, goes way into a consumer UX, with live photos, newspapers, topic portals, a dashboard, etc. It is also a more dynamic, always changing, realtime content consumption destination. Bottlenose feels like media, not merely search (in fact I think search, news and analytics are actually converging in the social network era).

Bottlenose has a huge emphasis on discovery, analytics, and other further actions on the content that go beyond just search.

I think in the end’s Realtime site will really demonstrate the power of their data, which will still mainly be consumed via their API rather than in their own destination. I’m hopeful that will do just that. It would be useful to everyone, including Bottlenose.

The Threat to Third-Party URL Shorteners

If I were, my primary fear today would be Twitter with their shortener. That is a big threat to and will probably result in losing a lot of their data input over time as more Tweets have links on them than links.

Perhaps is attempting to pivot their business to the user experience side in advance of such a threat potentially reducing their data set and thus the value of their API. But without their data set I don’t see where they can get the data to measure the present. So as a pivot it would not work – where would they get the data?

In other words, if people are not using as many links in the future, will see less attention. And trends point to this happening in fact — Twitter has their own shortener. So does Facebook. So does Google. Third-party shorteners will probably represent a decreasing share of messages and attention over time.

I think the core challenge for is to find a reason for their short URLs to be used instead of native app short URLs. Can they add more value to them somehow? Could they perhaps build in monetization opportunities for parties who use their shortener, for example? Or could they provide better analytics than Twitter or Facebook or Google will on short URL uptake ( arguably does, today).

Bottlenose and Realtime: Compared and Contrasted

In any case there are a few similarities between what may be launching and what Bottlenose provides today.

But there are far more differences.

These products only partially intersect. Most of what Bottlenose does has no equivalent in Realtime. Similarly much of what actually does (outside of their Realtime experiment) is different from what Bottlenose does.

It is also worht mentioning that’s “Realtime” app is a “labs” project and is not their central focus, whereas at Bottlenose it is 100% of what we do. Mapping the present is our core focus.

There is also a big difference in business model. Bottelnose does map the present in high-fidelity, but has no plans currently to provide a competing shortening API, or an API about shortURLs, like presently does. So currently we are not competitors.

Also, where currently has a broader and larger data set, Bottlenose has created a more cutting-edge and compelling user-experience and has spent more time on a new kind of computing architecture as well.

The Bottlenose StreamOS engine is worth mentioning here: Bottlenose has new engine for real-time big data analytics engine that uses a massively distributed and patent pending “crowd computing” architecture.

We actually have buit what I think is the most advanced engine and architecture on the planet for mapping attention in real-time today.

The deep semantics and analytics we compute in realtime are very expensive to compute centrally. Rather than compute everything in the center we compute everywhere; everyone who uses Bottlenose helps us to map the present.

Our StreamOS engine is in fact a small (just a few megabytes) Javascript and HTML5 app (the size of a photo) that runs in the browser or device of each user. Almost all the computing and analytics that Bottlenose does happens in the browser at the edge.

We have very low centralized costs. This approach scales better, faster, and more cheaply than any centralized approach can. The crowd literally IS our computer. It’s the Holy Grail of distributed real-time indexing.

We also see a broader set of data than does. We don’t only see content that has a URL on it. We see all kinds of messages moving through social media — with other shortURls, and even without URLs.

We see URLs, but we also see data that is outside of the universe. I think ultimately it’s more valuable to see all the trends across all data sources, and even content that contains no URLs at all (Bottlenose analyzes all kinds of messages for example, not just messages that contain URLs, let alone just URLs).

Finally, the use-cases for Bottlenose go far beyond just search, or just news reading and news discovery.

We have all kinds of  brands and enterprises actually using our Bottlenose Dashboard product, for example, for social listening, analytics and discovery. I don’t see going as deeply into that as us.

For these reasons I’m optimistic that Bottlenose (and everyone else) will benefit from what may be launching — particularly via their API, if they make their attention data available as an additional signal.

This space is going to get interesting fast.

(To learn more about what Bottlenose does, read this)


How Bottlenose Could Improve the Media and Enable Smarter Collective Intelligence

How Bottlenose Could Improve the Media and Enable Smarter Collective Intelligence

This article is part of a series of articles about the Bottlenose Public Beta launch.

Bottlenose – The Now Engine – The Web’s Collective Consciousness Just Got Smarter

How Bottlenose Could Improve the Media and Enable Smarter Collective Intelligence (you are here)

A New Window Into the Collective Consciousness

Bottlenose offers a new window into what the world is paying attention to right now, globally and locally.

We show you a live streaming view of what the crowd is thinking, sharing and talking about. We bring you trends, as they happen. That means the photos, videos and messages that matter most. That means suggested reading, and visualizations that cut through the clutter.

The center of online attention and gravity has shifted from the Web to social networks like Twitter, Facebook and Google+. Bottlenose operates across all them, in one place, and provides an integrated view of what’s happening.

The media also attempts to provide a reflection of what’s happening in the world, but the media is slow, and it’s not always objective. Bottlenose doesn’t replace the media — at least not the role of the writer. But it might do a better job of editing or curating in some cases, because it objectively measures the crowd — we don’t decide what to feature, we don’t decide what leads. The crowd does.

Other services in the past, like Digg for example, have helped pioneer this approach. But we’ve taken it further — in Digg people had to manually vote. In Bottlenose we simply measure what people say, and what they share, on public social networks.

Bottlenose is the best tool for people who want to be in the know, and the first to know. Bottlenose brings a new awareness of what’s trending online, and in the world, and how those trends impact us all.

We’ve made the Bottlenose home page into a simple Google-like query field, and nothing more. Results pages drop you into the app itself for further exploration and filtration. Except you don’t just get a long list of results, the way you get on Google.

Instead, you get an at-a-glance start page, a full-fledged newspaper, a beautiful photo gallery, a lean-back home theater, a visual map of the surrounding terrain, a police scanner, and Sonar — an off-road vehicle so that you can drive around and see what’s trending in networks as you please. We’ve made the conversation visual.

Each of these individual experiences is an app on top of the Bottlenose StreamOS platform, and each is a unique way of looking at sets and subsets of streams. You can switch between views effortlessly, and you can save anything for persistent use.

Discovery, we’ve found from user behavior, has been the entry point and the connective tissue for the rest of the Bottlenose experience all along. Our users have been asking for a better discovery experience, just as Twitter users have been asking for the same.

The new stuff you’ll see today has been one of the most difficult pieces for us to build computer-science-wise. It is a true technical achievement by our engineering team.

In many ways it’s also what we’ve been working towards all along. We’re really close now to the vision we held for Bottlenose at the very beginning, and the product we knew we’d achieve over time.

The Theory Behind It: How to Build a Smarter Global Brain

If Twitter, Facebook, Google+ and other social networks are the conduits for what the planet is thinking, then Bottlenose is a map of what the planet is actually paying attention to right now. Our mission is to “organize the world’s attention.” And ultimately I think by doing this we can help make the world a smarter place. At at the end of the day that’s what gets me excited in life.

After many years of thinking about this, I’ve come to the conclusion that the key to higher levels of collective intelligence is not making each person smarter, and it’s not some kind of Queen Bee machine up in the sky that tells us all what to do and runs the human hive. It’s not some fancy kind of groupware either. And it’s not the total loss of individuality into a Borg-like collective either.

I think that better collective intelligence really comes down to enabling better collective consciousness. The more conscious we can be of who we are collectively, and what we think, and what we are doing, the smarter we can actually be together, of our own free will, as individuals. This is a bottom-up approach to collective consciousness.

So how might we make this happen?

For the moment, let’s not try to figure out what consciousness really is, because we don’t know, and we probably never will, but regardless, for this adventure, we don’t need to. And we don’t even need to synthesize it either.

Collective consciousness is not a new form of consciousness, rather, it’s a new way to channel the consciousness that’s already there — in us. All we need to do is find a better way to organize it… or rather, to enable it to self-organize emergently.

What does consciousness actually do anyway?

Consciousness senses the internal and external world, and maintains a model of what it finds — a model of the state of the internal and external world that also contains a very rich model of “self” within it.

This self construct has an identity, thoughts, beliefs, emotions, feelings, goals, priorities, and a focus of attention.

If you look for it, it turns out there isn’t actually anything there you can find except information — the “self” is really just a complex information construct.

This “self” is not really who we are, it’s just a construct, a thought really — and it’s not consciousness either. Whatever is aware is aware of the self, so the self is just a construct like any other object of thought.

So given that this “self” is a conceptual object, not some mystical thing that we can’t ever understand, we should be able to model it, and make something that simulates it. And in fact we can.

We can already do this for artificially intelligent computer programs and robots in a primitive way in fact.

But what’s really interesting to me is that we can also do it for large groups of people too. This is a big paradigm shift – a leap. Something revolutionary really. If we can do it.

But how could we provide something like a self for groups, or for the planet as a whole? What would it be like?

Actually, there is already a pretty good proxy for this and it’s been around for a long time. It’s the media.

The Media is a Mirror

The media senses who we are and what we’re doing and it builds a representation — a mirror – in the form of reports, photos, articles, and stats about the state of the world. The media reflects who we are back to us. Or at least it reflects who it thinks we are…

It turns out it’s not a very accurate mirror. But since we don’t have anything better, most of us believe what we see in the media and internalize it as truth.

Even if we try not to, it’s just impossible to avoid the media that bombards us from everywhere all the time. Nobody is really separate from this, we’re all kind of stewing a media soup, whether we like it or not.

And when we look at the media and we see stories – stories about the world, about people we know, people we don’t know, places we live in, and other places, and events — we can’t help but absorb them. We don’t have first hand knowledge of those things, and so we take on faith what the media shows us.

We form our own internal stories that correspond to the stories we see in the media. And then, based on all these stories, we form beliefs about the world, ourselves and other people – and then those beliefs shape our behavior.

And there’s the rub. If the media gives us an inaccurate picture of reality, or a partially accurate one, and then we internalize it, it then conditions our actions. And so our actions are based on incomplete or incorrect information. How can we make good decisions if we don’t have good information to base them on?

The media used to be about objective reporting, and there are still those in the business who continue that tradition. But real journalists — the kind who would literally give their lives for the truth — are fewer and fewer. The noble art of journalism is falling prey, like everything else, to commercial interests.

There are still lots of great journalists and editors, but there are fewer and fewer great media companies. And fewer rules and standards too. To compete in today’s media mix it seems they have to stoop to the level of the lowest common denominator and there’s always a new low to achieve when you take that path.

Because the media is driven by profit, stories that get eyeballs get prioritized, and the less sensational but often more statistically representative stories don’t get written, or don’t make it onto the front page. There is even a saying in the TV news biz that “If it bleeds, it leads.”

Look at the news — it’s just filled with horrors. But that’s not an accurate depiction of the world. For example crimes don’t happen all the time, everywhere, to everyone – they are statistically quite unlikely and rare — yet so much news is devoted to crimes for example. It’s not an accurate portrayal of what’s really happening for most people, most of the time.

I’m not saying the news shouldn’t report crime, or show scary bad things. I’m just pointing out that the news is increasingly about sensationalism, fear, doubt, uncertainty, violence, hatred, crime, and that is not the whole truth. But it sells.

The problem is not that these things are reported — I am not advocating for censorship in any way. The problem is about the media game, and the profit motives that drive it. Media companies just have to compete to survive, and that means they have to play hard ball and get dirty.

Unfortunately the result is that the media shows us stories that do not really reflect the world we live in, or who we are, or what we think, accurately – these stories increasingly reflect the extremes, not the enormous middle of the bell curve.

But since the media functions as our de facto collective consciousness, and it’s filled with these images and stories, we cannot help but absorb them and believe them, and become like them.

But what if we could provide a new form of media, a more accurate reflection of the world, of who we are and what we are doing and thinking? A more democratic process, where anyone could participate and report on what they see.

What if in this new form of media ALL the stories are there, not just some of them, and they compete for attention on a level playing field?

And what if all the stories can compete and spread on their merits, not because some professional editor, or publisher, or advertiser says they should or should not be published?

Yes this is possible.

It’s happening now.

It’s social media in fact.

But for social media to really do a better job than the mainstream media, we need a way to organize and reflect it back to people at a higher level.

That’s where curation comes in. But manual curation is just not scalable to the vast number of messages flowing through social networks. It has to be automated, yet not lose its human element.

That’s what Bottlenose is doing, essentially.

Making a Better Mirror

To provide a better form of collective consciousness, you need a measurement system that can measure and reflect what people are REALLY thinking about and paying attention to in real-time.

It has to take a big data approach – it has to be about measurement. Let the opinions come from the people, not editors.

This new media has to be as free of bias as possible. It should simply measure and reflect collective attention. It should report the sentiment that is actually there, in people’s messages and posts.

Before the Internet and social networks, this was just not possible. But today we can actually attempt it. And that is what we’re doing with Bottlenose.

But this is just a first step. We’re dipping our toe in the water here. What we’re doing with Bottlenose today is only the beginning of this process. And I think it will look primitive compared to what we may evolve in years to come. Still it’s a start.

You can call this approach mass-scale social media listening and analytics, or trend detection, or social search and discovery. But it’s also a new form of media, or rather a new form of curating the media and reflecting the world back to people.

Bottlenose measures what the crowd is thinking, reading, looking at, feeling and doing in real-time, and coalesces what’s happening across social networks into a living map of the collective consciousness that anyone can understand. It’s a living map of the global brain.

Bottlenose wants to be the closest you can get to the Now, to being in the zone, in the moment. The Now is where everything actually happens. It’s the most important time period in fact. And our civilization is increasingly now-centric, for better or for worse.

Web search feels too much like research. It’s about the past, not the present. You’re looking for something lost, or old, or already finished — fleeting.  Web search only finds Web pages, and the Web is slow… it takes time to make pages, and time for them to be found by search engines.

On the other hand, discovery in Bottlenose is about the present — it’s not research, it’s discovery. It’s not about memory, it’s about consciousness.

It’s more like media — a live, flowing view of what the world is actually paying attention to now, around any topic.

Collective intelligence is theoretically made more possible by real-time protocols like Twitter. But in practice, keeping up with existing social networks has become a chore, and not drowning is a real concern. Raw data is not consciousness. It’s noise. And that’s why we so often feel overwhelmed by social media, instead of emboldened by it.

But what if you could flip the signal-to-noise ratio? What if social media could be more like actual media … meaning it would be more digestible, curated, organized, consumable?

What if you could have an experience that is built on following your intuition, and living this large-scale world to the fullest?

What if this could make groups smarter as they get larger, instead of dumber?

Why does group IQ so often seem inversely proportional to group size? The larger groups get, the dumber and more dysfunctional they become. This has been a fundamental obstacle for humanity for millennia.

Why can’t groups (including communities, enterprises, even whole societies) get smarter as they get larger instead of dumber? Isn’t it time we evolve past this problem? Isn’t this really what the promise of the Internet and social media is all about? I think so.

And what if there was a form of media that could help you react faster, and smarter, to what is going on around you as it happens, just like in real life?

And what if it could even deliver on the compelling original vision of the cyberspace as a place you could see and travel through?

What about getting back to the visceral, the physical?

Consciousness is interpretive, dynamic, and self-reflective. Social media should be too.

This is the fundamental idea I have been working on in various ways for almost a decade. As I have written many times, the global brain is about to wake up and I want to help.

By giving the world a better self-representation of what it is paying attention to right now, we are trying to increase the clock rate and resolution of collective consciousness.

By making this reflection more accurate, richer, and faster, and then making it available to everyone, we may help catalyze the evolution of higher levels of collective intelligence.

All you really need is a better mirror. A mirror big enough for large groups of people to look into and see what they are collectively paying attention to in it, together. By providing groups with a clearer picture of their own state and activity, they can adapt to themselves more intelligently.

Everyone looks in the collective mirror and adjusts their own behavior independently — there is no top-down control — but you get emergent self-organizing intelligent collective behavior as a result. The system as a whole gets smarter. So the better the mirror, the smarter we become, individually and collectively.

If the mirror is really fast, really good, really high res, and really accurate and objective – it can give groups an extremely important, missing piece: Collective consciousness that everyone can share.

We need collective consciousness that exists outside of any one person, and outside of any one perspective or organization’s agenda, and is not merely just in the parts (the individuals) either. Instead, this new level of collective consciousness should be something that is coalesced into a new place, a new layer, where it exists independently of the parts.

It’s not merely the sum of the parts, it’s actually greater than the sum – it’s a new level, a new layer, with new information in it. It’s a new whole that transcends just the parts on their own.  That’s the big missing piece that will make this planet smarter, I think.

We need this yesterday. Why? Because in fact collectives — groups, communities, organizations, nations — are the units of change on this planet. Not individuals.

Collectives make decisions, and usually these decisions are sub-optimal. That’s dangerous. Most of the problems we’ve faced and continue to face as a species come down to large groups doing stupid things, mainly due not having accurate information about the world or themselves. This is, ultimately, an engineering problem.

We should fix this, if we can.

I believe that the Internet is an evolving planetary nervous system, and it’s here to to make us smarter. But it’s going to take time. Today it’s not very smart. But it’s evolving fast.

Higher layers of knowledge, and intelligence are emerging in this medium, like higher layers of the cerebral cortex, connecting everything together ever more intelligently.

And we want to help make it even smarter, even faster, by providing something that functions like self-consciousness to it.

Now I don’t claim that what we’re making with Bottlenose is the same as actual consciousness — real consciousness is, in my opinion a cosmic mystery like the origin of space and time. We’ll probably never understand it. I hope we never do. Because I want there to be mystery and wonder in life. I’m confident there always will be.

But I think we can enable something on a collective scale, that is at least similar, functionally, to the role of self-consciousness in the brain — something that reflects our own state back to us as a whole all the time.

After all, the brain is a massive collective of hundreds of billions of neurons and trillions of connections that themselves are not conscious or even intelligent – and yet it forms a collective self and reacts to itself intelligently.

And this feedback loop – and the quality of the reflection it is based on – is really the key to collective intelligence, in the brain, and for organizations and the planet.

Collective intelligence is an emergent phenomena, it’s not something to program or control. All you need to do to enable it and make it smarter, is give groups and communities better quality feedback about themselves. Then they get smarter on their own, simply by reacting to that feedback.

Collective intelligence and collective consciousness, are at the end of the day, a feedback loop. And we’re trying to make that feedback loop better.

Bottlenose is a new way to curate the media, a new form of media in which anyone can participate but the crowd is the editor. It’s truly social media.

This is an exciting idea to me. It’s what I think social media is for and how it could really help us.

Until now people have had only the mainstream, top-down, profit-driven media to look to. But by simply measuring everything that flows through social networks in real time, and reflecting a high-level view of that back to everyone, it’s possible to evolve a better form of media.

It’s time for a bottom-up, collectively written and curated form of media that more accurately and inclusively reflects us to ourselves.

Concluding Thoughts

I think Bottlenose has the potential to become the giant cultural mirror we need.

Instead of editors and media empires sourcing and deciding what leads, the crowd is the editor, the crowd is the camera crew, and the crowd decides what’s important. Bottlenose simply measures the crowd and reflects it back to itself.

When you look into this real-time cultural mirror that is Bottlenose, you can see what the community around any topic is actually paying attention to right now. And I believe that as we improve it, and if it becomes widely used, it could facilitate smarter collective intelligence on a broader scale.

The world now operates at a ferocious pace and search engines are not keeping up. We’re proud to be launching a truly present-tense experience. Social messages are the best indicators today of what’s actually important, on the Web, and in the world.

We hope to show you an endlessly interesting, live train of global thought. The first evolution of the Stream has run its course and now it’s time to start making sense of it on a higher level. It’s time to start making it smart.

With the new Bottlenose, you can see, and be a part of, the world’s collective mind in a new and smarter way. That is ultimately why Bottlenose is worth participating in.

Keep Reading

Bottlenose – The Now Engine – The Web’s Collective Consciousness Just Got Smarter

How Bottlenose Could Improve the Media and Enable Smarter Collective Intelligence (you are here)


Bottlenose – The Now Engine – The Web’s Collective Consciousness Just Got Smarter

Recently, one of Twitter’s top search engineers tweeted that Twitter was set to “change search forever.” This proclamation sparked a hearty round of speculation and excitement about what was coming down the pipe for Twitter search.

The actual announcement featured the introduction of autocomplete and the ability to search within the subset of people on Twitter that you follow — both long-anticipated features.

However, while certainly a technical accomplishment (Twitter operates a huge scale and building these features must have been very difficult), this was an iterative improvement to search…an evolution, not a revolution.

Today I’m proud to announce something that I think could actually be revolutionary.


And here’s the video….


My CTO/Co-founder, Dominiek ter Heide, and I have been working for 2 years on an engine for making sense of social media. It’s called Bottlenose, and we started with a smart social dashboard.

Now we’re launching the second stage of our mission “to organize the world’s attention” — a new layer of Bottlenose that provides a live discovery portal for the social web.

This new service measures the collective consciousness in real-time and shows you what the crowd is actually paying attention to now, about any topic, person, brand, place, event… anything.

If the crowd is thinking about it, we see it. It’s a new way to see what’s important in the world, right now.

This discovery engine, combined with our existing dashboard, provides a comprehensive solution for discovering what’s happening, and then keeping up with it over time.

Together, these two tools not only help you stay current, they provide compelling and deep insights about real-time trends, influencers, and emerging conversations.

All of this goes into public beta today.

An Amazing Team

I am very proud of what we are launching today, in many ways — while still just a step on a longer journey — it is the culmination of an idea I’ve been working on, thinking about, dreaming of… for decades… and I’d love you to give it a spin.

And I’m proud of my amazing technical team — they are the most talented technical team I’ve ever worked with in my more than 20 years in this field.

I have never seen such a small team deliver so much, so well. And Bottlenose is them – it is their creation and their brilliance that has made this possible. I am really so thankful to be working with this crew.

Welcome to the Bottlenose Public Beta

So what is Bottlenose anyway?

It is a real-time view of what’s actually important across all the major social networks — the first of its kind — what you might call a “now engine.”

This new service is not about information retrieval. It’s about information awareness. It’s not search, it’s discovery.

We don’t index the past, we map the present. That’s why I think it’s better to call it a discovery engine than a search engine. Search implies research towards a specific desired answer, whereas discovery implies exploration and curiosity.

We measure what the crowd is paying attention to now, and we build a living, constantly learning and evolving, map of the present.

Twitter has always encouraged innovation around their data, and that innovation is really what has fueled their rapid growth and adoption. We’ve taken them at their word and innovated.

We think that what we have built adds tremendous value to the ecosystem and to Twitter.

But while Twitter data is certainly very important and high volume, Bottlenose is not just about Twitter… we integrate the other leading social networks too: Facebook, LinkedIn, Google+, YouTube, Flickr, and even networks whose data comes through them like Pinterest and Instagram. And we also see RSS too.

We provide a very broad view of what’s happening across the social web — a view that is not available anywhere else.

Bottlenose is what you’d build if you got the chance to start over and work on the problem from scratch — a new and comprehensive vision for how to make sense of what’s happening across and within social networks.

We think it could be for the social web what Google was for the Web. Ok that’s a bold statement – and perhaps it’s wishful thinking – but we’re at least off to a good start here and we’re pushing the envelope farther than it has ever been pushed. Try it!

Oh and one more thing, why the name? We chose it because dolphins are smart, they’re social, they hunt in pods, they have sonar. We chose the name as an homage to their bright and optimistic social intelligence. We felt it was a good metaphor for how we want to help people surf the Stream.

Thanks for reading this post, and thanks for your support. If you have a few moments to spare today, we’d love it if you gave Bottlenose a try. And remember, it’s still a beta.

Note: It’s Still a Beta!

Before I get too deep into the tech and all the possibilities and potential I see in Bottlenose, I first want to make it very clear that this is a BETA.

We’re still testing, tuning, adding stuff, fixing bugs, and most of all learning from our users.

There will be bugs and things to improve. We know. We’re listening. We’re on it. And we really appreciate your help and feedback as we continue to work on this.

Want to Know More?

How Bottlenose Could Improve the Media and Enable Smarter Collective Intelligence



A New Approach to Artificial Intelligence: Non-Computational AI

I was recently contacted by a computer scientist, Sergey Bulanov, who has been working quietly for 20 years on a new approach to artificial intelligence. It’s a pretty interesting and novel approach, and I would like to see what others think about it.

From what I understand, the essence of Sergey’s approach is a new form of computer reasoning that implements “non-computational” networks of logical operations to solve problems.

It is “non-computational” in the sense that it is not an expert system or traditional computer program — rather it is a network of simple operators that compute locally and interact with one another, to emergently arrive at results, reflected by an overall state of the system at the end of the process. This approach reminds me of “connectionist” approaches to AI, such as neural networks and cellular automata.

Sergey believes that his approach could be an important step towards making truly humanlike artificial intelligence in the future. His point is that the brain is a non-computational system, and might in fact use some of these principles.

Sergey calls his approach “Artificial Consciousness,” but I don’t think the word “consciousness” adds value here – and it may even distract from the core idea. But, for the moment, let’s not argue about terminology — his theory is very interesting.

Sergey states that he has used this approach, to solve every logic problem in Raymond Smullyan’s book, Lady and the Tiger. For more info, read Sergey’s overview of his theory. You can read some more of his writings on this theory, here.

You can also view a working simulation of the system in operation, here.

I can’t explain it very well, so here is Sergey’s explanation to me, from our correspondence (please note, he is not a native English speaker, so I have added some corrections to his letter to improve readability):


I consider the present version of system, which only solves logical tasks, to not be a truly “intelligent” system. This system is only a starting point for my investigations. This system only looks like it is intelligent because it is solving tasks that are hard for people. The idea for how to solve logical problems in this way came to me accidentally by thinking about the book, Lady and the Tiger, by Raymond Smullyan. In my classification of AI, a system for solving logical puzzles appears to be a kind of low complexity system (according my theory). This present version of the system is just a step along the way towards more sophisticated AI.


Despite my low valuation of systems for logical solving, for practical use at least, such systems can be amusing for people. And such system can be the starting point to thinking about more sophisticated “non-computational” systems. The theory of such systems is well developed for computational case and such system is called SAT system (Boolean satisfiability problem).

The essence of the problem is as follows. Suppose we have a logical expression. (In our case the logical expression reflects the statement of a puzzle). And we consider that logical expression has value “TRUE” (in our case the formulation of the puzzle is true). Then we shall find out logical arguments of this expression which satisfy this expression (to make this expression to be “TRUE”). This procedure is so called NP-complete. In the worst case, this requires full enumeration of all possible arguments. The SAT approach aims to reduce the probable enumerations. The methods of SAT is well developed. But I don’t know about this at the beginning of my work. Moreover, from the beginning I started to create a non-computational approach.


My idea was very simple. Assume we have a logical function , “AND,” with two arguments. This function will have output value “TRUE” only in case where both of its arguments are “TRUE”. So if we know the value of the output of function, we can predict (not in any cases) the value of its inputs.

The formulation of the puzzle is expressed as a logical expression. The expression is represented in a form of a tree (mathematical tree). This tree you can see at video in my website. The nodes of the tree are logical functions (AND, OR and some more types). These nodes are represented as balls in the video. Each ball has one output link and several input links. The state of the function can be TRUE (red ball), FALSE (blue ball) and UNKNOWN (grey ball). From the beginning the logical tree has some nodes with pre-determined initial values (according to the formulation of the puzzle). These values are reassigned not only at the top or the bottom of the tree, but also in the middle of it.

After the start of the system,  each ball (each of the logical functions, i.e. each node) can fill states of the adjacent nodes. And each of the balls begins to continuously correct its state depending on the states of the nearby balls. For example, if one of the balls bears function AND with three inputs (thee arguments) and the upper ball sends to this ball information to be a “TRUE” then this ball will assign value “TRUE” at the each of its three inputs. In such a way different kinds of information will be propogated through the tree until a steady state is reached.

This information can change until steady state, asynchronously and even without clocking (this is not proved by me). During the theory about NP-completeness, solving can’t be reached unconditionally (like solving in the linear or differential equations). After some time, the system reaches an unresolvable state and it would need some more iterations to reach the complete solution. The system can be knocked out from each of these unresolvable states by assuming a hypothesis on one of the unresolved balls. The system can reach a global contradiction state or it can reach a global solution. If system doesn’t reach global solution or global contradiction state we must add a next hypothesis on the one of the next balls. In case of contradiction state we must change one of the hypotheses (typically the last hypothesis).

So the system can reach the solution (or set of the solutions) during the iterations between the assignment of hypotheses. This solving can be achieved without explicit algorithm and it can be achieved on non-computational structure, thousands or million time faster than in the computational devices.


These results appear to be an unusual and promising for the AI domain. The importance of these results is in the demonstration of possibilities of non-computational solving of complicated tasks. I hope this system can attract attention of people to develop non-computational cognitive system millions times more powerful than human brain.

But unfortunately this kind of system is not yet a true AI system. Below is some explanations of why.


A full AI system can’t be based on traditional (simple) logical basis. The system represented in our website can solve some kinds of logical tasks. But it can’t discus with humans about these tasks. It can’t explain the solving of these tasks. It can’t (and never could in future) understand natural written text. And it couldn’t do most of the human brain’s functions. One of the most fundamental reasons is that a network of logical functions (as I represent it) could only solve logical tasks, and it can’t grow by its own reasoning. There are many reasons to construct completely another kind of AI system based on different principles. But creating of more complicated system would be hard without understanding principles and problems of more simple system. Logical systems, such as mine, can be a starting point of the way to more powerful systems that apply my non-computational approach.


I came to idea that a really powerful system must be based on the idea of mathematical sets. I found a way to create a network based on sets that can grow, and how such a network can solve different tasks. The range of these tasks is much greater than only solving of mathematical puzzles. I am working on this presently.


My idea for a the chain of model tasks is not an engine of the system but it is a method of research. This  idea is very close to the statement of philosopher Bertrand Russell:

“The point of philosophy is to start with something so simple as not to seem worth stating, and to end with something so paradoxical that no one will believe it”.

So that is my approach. For example, I made an expression of the idea of logical functions without logical notions. And I found unusual ideas for my novel system in this way.

There is another example of my principle. Assume we take a simplest question, so simple that decision of this question would be almost inevitable. Then if the decision would have high quality, the principles of this decision can be applied to a next but more complicated question. So moving from simple task to more complicated we can develop our theory.

I hope Sergey’s 20 years of thinking in this direction will prove interesting, and perhaps even fruitful, for the field of artificial intelligence. It does appear to me to be a novel and potentially promising vein of innovation.

Best of luck to Sergey and his collaborators. I’m always happy to see really original thinking in the field of AI.

I Get 13,000 Messages/Day via Different Streams – Here’s the Analysis

Continuing with the theme I’ve been writing about lately, focused on the growth of the next phase of the Web, what I call “The Stream,” I’ve started to analyze the messages I get on a typical day.

First of all, through all the different channels I use, I now receive approximately 13,000 messages a day. I don’t think I am an extreme case. In fact, anyone who uses Twitter, Facebook, LinkedIn, Google+, email, RSS, and a few Web apps, is probably in the same boat.

Of these, email is no longer the largest stream, but it’s still the most important. However, of 112 email messages received on that day, 46 (41%) were “notifications” from Web apps and Web sites, and these were a lot less important than the remaining messages that were actual communication of one form or another.

The largest streams are Twitter, Facebook and LinkedIn. These streams are comprised of public messages posted by people and sources I follow. In these streams, based on a cursory analysis, it appears messages spool in at a rate that varies on the low side from 1 message every 2 minutes on average (LinkedIn) up to 2 messages per minute on average (Twitter for my personal account where I follow 525 people).

The volume of messages pouring through my social streams is impossible to keep up with. It’s becoming a personal firehose. So, like most people, I have no choice but to ignore 99.99% of them.

However, there are some needles in the haystack that I really would like to find. To solve for that, I use Bottlenose as my dashboard – it surfaces the social messages I really need to pay attention to. It helps me extract more value from my social streams. Fewer calories, more protein.  (Disclosure: I’m the CEO of Bottlenose).

I really need something to make sense of all the messages I’m getting. You probably do too. And in with the exponential growth of message volume across all streams, everyone’s going to need this within a year or two. Not just professionals and power-users, but even everyday consumers. In fact, many regular people are already overwhelmed.

Today, while we’re in beta testing, Bottlenose only pulls in Twitter and Facebook. However, where we’re heading is to include ALL the types of streams you see in this diagram above, making Bottlenose a truly “universal dashboard” for the era of the Stream.

I would be very curious to hear what your messaging looks like and if you’re seeing similar levels of overload.

Here’s the raw data for a typical day:

Stream Messages/day Percentage Notes
LinkedIn Direct Messages 1 0.01%
LinkedIn Connect Requests 2 0.01%
Facebook Private Messages 3 0.02%
Facebook Events Notifications 6 0.04%
Facebook Notifications 7 0.05%
Novaspivack Twitter Mentions 10 0.07%
Facebook suggested Events 15 0.11%
GitHub Notifications 42 0.31%
Yammer Messages 44 0.32%
Facebook Groups 58 0.42%
Email Messages 112 0.82%
Bottlenose Twitter Mentions 200 1.46%
RSS News Articles 350 2.55%
Google+ 720 5.24% .5/min*
LinkedIn Updates 720 5.24% .5/min*
Facebook News Feed 1440 10.49% 1/min*
Twitter @bottlenoseapp 2800 20.39% 2/min**
Twitter @novaspivack 7200 52.44% (5/min)**
Total  13,730
 *  Estimated average based on counting; does not include comments on messages
 ** Estimated average based on counting

Keeping Up With the Stream — New Problems and Solutions

This is Part III of a series of articles on the new era of the Stream, a new phase of the Web.

In Part I, The Message is the Medium, I explored the shift in focus on the Web from documents to messages.

In Part II, Drowning in the Stream, we dove deep into some of the key challenges the Stream brings with it.

Here in Part III, we will discuss new challenges and solutions for keeping up with streams as they become increasingly noisy and fast-moving.


Getting Attention in Streams

Today if you post a message to Twitter, you have a very small chance of that message getting attention. What’s the solution?

You can do social SEO and try to come up with better, more attention-grabbing, search engine attracting, headlines. You can try to schedule your posts to appear at optimal times of day. You can even try posting the same thing many times a day to increase the chances of it being seen.

This last tactic is called “Repeat Posting” and it’s soon going to be clogging up all our streams with duplicate messages. Why am I so sure this is going to happen? Because we are in an arms race for attention. In a room where everyone is talking, everyone starts talking louder, and soon everyone is shouting.

Today when  you post a message to Twitter, the chances of getting anyone’s attention are low and they are getting lower.  If you have a lot of followers, the chances are a little better that at least some of them may be looking at their stream at precisely the time you post. But still, even with a lot of followers, the odds are that most of your followers probably won’t be online at that precise moment you post something, and so they’ll miss it.

Scheduled Posting

But it turns out there are optimal times of day to post, when more of your followers are likely to be looking at their streams. A new category of apps, typified by Buffer, has emerged to help you schedule your Tweets to post at such optimal times.

Using apps like Buffer, you can get more attention to your Tweets, but this is only a temporary solution. Because the exponential growth of the Stream means that soon even posting a message at an optimal time will not be enough to get it in front of everyone who should see it.

Repeat Posting

To really get noticed, above the noise, you need your message to be available at more than one optimal time, for example many times a day, or even every hour.

To achieve this, instead of posting a message once at the optimal time per day, we may soon see utilities that automatically post the same message many times a day – maybe every hour – perhaps with slightly different wording of headlines, to increase the chances that people will see them. I call this “repeat posting” or “message rotation.”

Repeat posting tools may get so sophisticated that they will A/B test different headlines and wordings and times of day to see what gets the best clickthroughs and then optimize for those. These apps may even intelligently rotate a set of messages over several days, repeating them optimally until they squeeze out every drop of potential attention and traffic, much like ad servers and ad networks rotate ads today.

But here’s the thing — as soon as anyone starts manually or automatically using repeat posting tactics, it will create an arms race – others will notice it, and compete for attention by doing the same thing. Soon everyone will have to post repeatedly to simply get noticed above the noise of all the other repeat posts.

This is exactly what happens when you are speaking in a crowded room. In a room full of people who are talking at once, some people start talking louder. Soon everyone is shouting and losing their voice at the same time.

This problem of everyone shouting at once is what is soon going to happen on Twitter and Facebook and other social networks. It’s already happening in some cases – more people are posting the same message more than once a day to get it noticed.

It’s inevitable that repeat posting behavior will increase, and when everyone starts doing it, our channels will become totally clogged with redundancy and noise. They will become unusable.

What’s the solution to this problem?

What to Do About Repeat Posting

One thing that is not the solution is to somehow create rules against repeat posting. That won’t work.

Another solution that won’t work is to attempt to detect and de-dupe repeats that occur. It’s hard to do this, and easy to create repeat posts that have different text and different links, to evade detection.

Another solution might be to recognize that repeat posting is inevitable, but to make the process smarter: Whenever a repeat posting happens, delete the previous repeat post. So at any given time the message only appears once in the stream. At least this prevents people from seeing the same thing many times at once in a stream. But it still doesn’t solve the problem of people seeing messages come by that they’ve seen already.

A better solution is to create a new consumption experience for keeping up with streams, where relevant messages are actually surfaced to users, instead of simply falling below the fold and getting buried forever. This would help to ensure that people would see the messages that were intended for them, and that they really wanted to see.

If this worked well enough, there would be less reason to do scheduled posting, let alone repeat posting. You could post a message once, and there would be much better chance of it being seen by your audience.

At Bottlenose, we’re working on exactly this issue in a number of ways. First of all, the app computes rich semantic metadata for messages in streams automatically, which makes it possible to filter them in many ways.

Bottlenose also computes the relevance of every message to every user, which enables ranking and sorting by relevancy, and the app provides smart automated assistants that can help to find and suggest relevant messages to users.

We’re only at the beginning of this and these features are still in early beta, but already we’re seeing significant productivity gains.

Fast-Moving Streams

As message volume increases exponentially in streams, our streams are going to not just going to be noisier, they are going to move faster. When we look at any stream there will be more updates per minute – more new messages scrolling in – and this will further reduce the chances of any message getting noticed.

Streams will begin to update so often they will literally move all the time. But how do you read, let alone keep up with, something that’s always moving?

Today, if you follow a Twitter stream for a breaking news story, such as a natural disaster like the Tsunami in Japan, or the death of Steve Jobs, you can see messages scrolling in, in real-time every second.

In fact, when Steve Jobs died, Twitter hit a record peak of around 50,000 Tweets per minute. If you were following that topic on Twitter at that time, the number of new messages pouring was impossible to keep up with.

Twitter has put together a nice infographic showing the highest Tweets Per Second events of 2011.

During such breaking news events, if you are looking at a stream for the topic, there is not even time to read a message before it has scrolled below the fold and been replaced by a bunch of more recent messages. The stream moves too fast to even read it.

But this doesn’t just happen during breaking news events. If you simply follow a lot of people and news sources, you will see that you start getting a lot of new messages every few minutes.

In fact, the more people and news sources, saved searches, and lists that you follow, the higher the chances are that at any given moment there are going to be many new messages for you.

Even if you just follow a few hundred people, the chances are pretty high that you are getting a number of new messages in Twitter and Facebook every minute. That’s way more messages than you get in email.

And even if you don’t follow a lot of people and news sources – even if you diligently prune your network, unfollow people, and screen out streams you don’t want, the mere exponential growth of message volume in coming years is soon going to catch up with you. Your streams are going to start moving faster.

But are there any ways to make it easier to keep up with these “whitewater streams?”

Scrolling is Not the Answer

One option is to just make people scroll. Since the 1990’s UX designers have been debating the issue of scrolling. Scrolling works, but it doesn’t work well when the scrolling is endless, or nearly endless. The longer the page, the lower percentage of users will scroll all the way down.

This becomes especially problematic if users are asked to scroll in long pages – for example infinite streams of messages going back from the present to the past (like Twitter, above). The more messages in the stream, the less attention those messages that are lower in the stream, below the fold, will get.

But that’s just the beginning of the problem. When a stream is not only long, but it’s also moving and changing all the time, it becomes much less productive to scroll. As you scroll down new stuff is coming in above you, so then you have to scroll up again, and then down again. It’s very confusing.

In long streams that are also changing constantly it is likely that engagement statistics will be very different than for scrolling down static pages. I think it’s likely engagement will be much lower, the farther down such dynamic streams one goes.

Pausing the Scroll is Not the Answer

Some apps handle this problem of streams moving out from under you by pausing auto-scrolling as you read – they simply notify you that there are new messages above whatever you are looking at. You can then click to expand the stream above and see the new messages. Effectively they make dynamic streams behave as if they are not dynamic, until you are ready to see the updates.

This at least enables you to read without the stream moving out from under you. It’s less disorienting that way. But in fast moving streams where there are constantly new updates coming in, you have to click on the “new posts above” notification frequently, and it gets tedious.

For example, here is Twitter, on a search for Instagram, a while after the news of their acquisition by Facebook. After waiting only a few seconds, there are 20 new tweets already. If you click the bar that says “20 new Tweets” they expand. But by the time you’ve done that and started reading them, there are 20 more.


Simply clicking to read “20 new tweets” again and again is tedious. And furthermore, it doesn’t really help users cope with the overwhelming number of messages and change in busy streams.

The problem here is that streams are starting to move faster than we can read, even faster than we can click. How do you keep up with this kind of change?

Tickers and Slideshows Are Helpful

Another possible solution to the problem of keeping up with moving streams is to make the streams become like news tickers, constantly updating and crawling by as new stuff comes in. Instead of trying to hide the movement of the stream, make it into a feature.

Some friends and I have tested this idea out in an iPad app we built for this purpose called StreamGlider. You can download StreamGlider and try it out for yourself.

StreamGlider shows streams in several different ways — including a ticker mode and a slideshow mode where streams advance on their own as new messages arrive.


The Power of Visualization

Another approach to keeping up with fast moving streams is to use visualization, like we’re doing in Bottlenose, with our Sonar feature. By visualizing what is going on in a stream you can provide a user with instant understanding of what is in the stream and what is important and potentially interesting to them, without requiring them to scroll, skim or read everything first.

Sonar reads all the messages in any stream, applies natural language and semantic analysis to them, detects and measures emerging topics, and then visualizes them in realtime as the stream changes.

It shows you what is going on in the stream – in that pile of messages you don’t have time to scroll through and read. As more messages come in, Sonar updates in realtime to show you what’s new.

You can click on any trend in Sonar that interests you, to quickly zoom into just the messages that relate.

The beauty of this approach is that it avoids scrolling until you absolutely want to. Instead of scrolling, or even skimming the messages in a stream, you just look at Sonar and see if there are any trends you care about. If there are, you click to zoom in and see only those messages. It’s extremely effective and productive.

Sonar is just one of many visualizations that could help with keeping up with change in huge streams. But it’s also only one piece of the solution. Another key piece of the solution is finding things in streams.

Finding Things in Streams

Above, we discussed problems and solutions related to keeping up with streams that are full of noise and constantly changing. Now let’s discuss another set of problems and solutions related to finding things in streams.

Filtering the Stream

For a visualization like Sonar to be effective, you need the ability to filter the stream for the sources and messages you want, so there isn’t too much noise in the visualization. The ability to filter the stream for just those subsets of messages you actually care about is going to be absolutely essential in coming years.

Streams are going to become increasingly filled with noise. But another way to think about noisy streams is that they are really just lots of less-noisy streams multiplexed together.

What we need is a way to intelligently and automatically de-multiplex them back into their component sub-streams.

For example, take the stream of all the messages you receive from Twitter and Facebook combined. That’s probably a pretty noisy stream. It’s hard to read, hard to keep up with, and quickly becomes a drag.

In Bottlenose you can automatically de-multiplex your streams into a bunch of sub-streams that are easier to manage. You can then read these, or view them via Sonar, to see what’s going on at a glance.

For example, you can instantly create sub-streams – which are really just filters on your  stream of everything. You might make one for just messages by people you like, another for just messages by influencers, another for just news articles related to your interests, another for just messages that are trending, another of just photos and videos posted by your friends, etc.

The ability to filter streams – to mix them and then unmix them – is going to be an essential tool for working with streams.

Searching the Stream

In the first article in this series we saw how online attention and traffic is shifting from search to social. Social streams are quickly becoming key drivers for how content on the Web is found. But how are things found in social streams? It turns out existing search engines, like Google, are not well-suited for searching in streams.

Existing algorithms for Web search do not work well for Streams. For example, consider Google’s PageRank algorithm.

In order to rank the relevancy of Web pages, PageRank needs a very rich link structure. It needs a Web of pages with lots of links between the documents. The link structure is used to determine which pages are the best for various topics. Effectively links are like votes – when pages about a topic link to other pages about that topic, they are effectively voting for or endorsing those pages.

While PageRank may be ideal for figuring out what Web pages are best, it doesn’t help much for searching messages, because messages may have no links at all, or may be only very sparsely linked together. There isn’t enough data in individual messages to figure out much about them.

So how do you know if a given message is important? How do you figure out what messages in a stream actually matter?

When searching the stream, instead of finding everything, we need to NOT find the stuff we don’t want. We need to filter out the noise. And that requires new approaches to search. We’ve already discussed filtering above and the ability to filter streams is a per-requisite for searching them intelligently. Beyond that, you need to be able to measure what is going on within streams, in order to detect emerging trends and influence.

The approach we’re taking in Bottlenose to solve this is a set of algorithms we call “StreamRank.” In StreamRank we analyze the series of messages in a stream to figure out what topics, people, links and messages are trending over time.

We also analyze the reputations or influence of message authors, and the amount of response (such as retweets or replies or likes) that messages receive.

In addition, we also measure the relevance of messages and their authors to the user, based on what we know of the user’s interest graph and social graph.

This knowledge enables us to rank messages in a number of ways: by date, by popularity, by relevance, by influence, and by activity.

Another issue that comes up when searching the Stream is that many messages in streams are quite strange looking – they don’t look like properly formed sentences or paragraphs. They don’t look like English, for example. They contain all sorts of abbreviations, hashtags, @replies, and short URLs, and they often lack punctuation and are scrunched to fit in 140 character Twitter messages.

Search algorithms that use any kind of linguistics, disambiguation, natural language processing, or semantics, don’t work well out of the box on these messy messages.

To apply such techniques you need to rewrite them so that they work on short, messy, strange looking messages. This is also something we’ve built in Bottlenose — we’ve built a new natural language processing and topic detection engine in Javascript that is designed specifically to handle these types of streams and messages.

These are some of the new challenges and solutions we’re applying in Bottlenose to make working with streams more productive. They are components of what we call our “StreamOS,” a new high-level Javascript and HTML5 operating system for applications that need to do smart things with streams. We’ll be writing a lot more about this in future articles.


Drowning in the Stream — New Challenges for a New Web

This is Part II of a three-part series of articles on how the Stream is changing the Web.

In Part I of this series, The Message is the Medium, I wrote about some of the shifts that are taking place as the center of online attention shifts from documents to messages.

Here in Part II, we will explore some of the deeper problems that this shift is bringing about.

New Challenges in the Era of the Stream

Today the Stream has truly arrived. The Stream is becoming primary and the Web is becoming secondary. And with this shift, we face tremendous new challenges, particularly around overload. I wrote about some of these problems for Mashable in an article called, “Sharepocalypse Now.”

The Sharepocalypse is here. It’s just too easy to share, there is too much stuff being shared, there are more people sharing, and more redundant ways to share the same things. The result is that we are overloaded with messages coming at us from all sides.

For example, I receive around 13,000 messages/day via various channels, and I’m probably a pretty typical case. You can see a more detailed analysis here.

As the barrier to messaging has become lower and people have started sending more messages than ever before, messaging behavior has changed. What used to be considered spam is now considered to be quite acceptable.

Noise is Increasing

In the 1990’s emailing out a photo of the interesting taco you are having for lunch to everyone you know would have been considered highly spammy behavior. But today we call that “foodspotting” and we happily send out pictures of our latest culinary adventure on multiple different social networks at once.

Spam is the New Normal

It’s not just foodspotting – the same thing is happening with check-ins, and with the new behavior of “pinning” things (the new social bookmarking) that is taking place in Pinterest. Activities that used to be considered noise have somehow started to be thought of as signal. But in fact, for most people, they are still really noise.

The reason this is happening is that the barrier to sharing is much lower than it once was. Email messages took some thought to compose – they were at least a few paragraphs long. But today you can share things that are 140 characters or less, or just a photo without even any comments. It’s instant and requires no investment or thought.

Likewise, in the days of email you had to at least think, “is it appropriate to send this or will it be viewed as spam?” Today people don’t even have that thought anymore. Send everything to everyone all the time. Spam is the new normal.

Sharing is a good thing, but like any good thing, too much of it becomes a problem.

The solution is not to get people to think before sharing, or to share less, or to unfollow people, or to join social networks where you can only follow a few people (like Path or Pair), it’s to find a smarter way to deal with the overload that is being created.

Notifications Overload

Sharing is not the only problem we’re facing. There are many other activities that generate messages as well. For example, we’re getting increasing numbers of notifications messages from apps. These notifications are not the result of a person sharing something, they are the result of an app wanting to get our attention.

We’re getting many types of notifications, for example:

  • When people follow us
  • When we’re tagged in photos
  • When people want to be friends with us
  • When there are news articles that match our interests
  • When friends check-in to various places
  • When people are near us
  • When our flights are delayed
  • When our credit scores change
  • When things we ordered are shipped
  • When there are new features in apps we use
  • When issue tickets are filed or changed
  • When files are shared with us
  • When people mention or reply to us
  • When we have meeting invites, acceptances, cancellations, or meetings are about to start
  • When we have unread messages waiting for us in a social network

The last bullet bears an extra mention. I have noticed that LinkedIn for example, sends me these notifications about notifications. Yes, we are even getting notifications about notifications!

When you get messages telling you that you have messages, that’s when you really know the problem is getting out of hand.

Fragmented Attention

Another major problem that the Stream is bringing about is the fragmentation of attention.

Today email is not enough. If it wasn’t enough work that we each have several email inboxes to manage, we are now also getting increasing volumes of messages outside of email in entirely different inboxes for specialized apps. We have too many inboxes.

It used to be that to keep up with your messages all you needed was an email client.

Then the pendulum swung to the Web and it started to become a challenge to keep up with all the Web sites we needed to track every day.

So RSS was invented and for a brief period it seemed that the RSS reader would be adopted widely and solve the problem of keeping up with the Web.

But then social networks came out and they circumvented RSS, forcing users to keep up in social-network specific apps and inboxes.

So a new class of “social dashboard” apps (like Tweetdeck) were created to keep up with social networks, but they didn’t include email or RSS, or all the other Web apps and silos.

This trend towards fragmentation has continued – an increasing array of social apps and web apps can really only be adequately monitored in those same apps. You can’t really effectively keep up with them in email, in RSS, or via social networks. You have to login to those apps to get high-fidelity information about what is going on.

We’re juggling many different inboxes. These include email, SMS, voicemail, Twitter, Facebook, LinkedIn, Pinterest, Tumblr, Google+, YouTube, Yammer, Dropbox, Chatter, Google Reader, Flipboard, Pulse, Zite, as well as inboxes in specialized tools like Github, Uservoice, Salesforce, and many other apps and services.

Alan Lepofsky, at Constellation Research, created a somewhat sarcastic graph to illustrate this problem, in his article, “Are We Really Better Off Without Email?” The graph is qualitative – it’s not based on direct numbers – but in my opinion it is probably very close to the truth.

What this graph shows is that email usage peaked around 2005/2006, after which several new forms of messaging began to get traction. As these new apps grew, they displaced email for some kinds of messaging activities, but more importantly, they fragmented our messaging and thus our attention.

The takeaway from this graph is that we will all soon be wishing for the good old days of email overload. Email overload was nothing compared to what we’re facing now.

The Message Volume Explosion

As well as increasing noise and the fragmentation of the inbox, we’re also seeing huge increases in message volume.

Message volume per day, in all messaging channels, is growing. In some of these channels, such as social messaging, it is growing exponentially. For example, look at this graph of Twitter’s growth in message volume per day since 2009, from the Bottlenose blog:

Twitter now transmits 340 Million messages per day, which is more than double the number of messages per day in March of 2011.

If this trend continues then in a year there will be between 500 million and 800 million messages per day flowing through Twitter.

And that’s just Twitter – Facebook, Pinterest, LinkedIn, Google+, Tumblr, and many other streams are also growing. And email messages are also increasing as well, thanks to all the notifications that are being sent to email by various apps.

Message volume is growing across all channels. This is going to have several repercussions for all of us.

Engagement is Threatened

First of all, the signal-to-noise ratio of social media, and other messaging channels, is going to become increasingly bad as volume increases. There’s going to be less signal and more noise. It is going to get harder to find the needles in the haystack that we want, because there is going to be so much more hay.

Today, on services like Twitter and Facebook, signal-to-noise is barely tolerable already. But as this situation gets worse in the next two years, we are going to become increasingly frustrated. And when this happens we are going to stop engaging.

When signal-to-noise in a channel gets too out of hand, it becomes unproductive and inefficient to use that channel. In the case of social media, we are right on cusp of this happening. And when this happens, people will simply stop engaging. And when engagement falls the entire premise of social media will start to fail.

This is already starting to happen. One recent article by George Colony, CEO of analyst firm, Forrester Research, cites a recent study that found that 56% of time spent on social media is wasted.

When you start hearing numbers like this, it means that consumers are not getting the signal they need most of the time, and this will inevitably result in a decrease in satisfaction and engagement.

What’s Next?

We have seen some of the issues that are coming about, or may soon come about, as the Stream continues to grow. But what’s going to happen next? How is the Stream, and our tools for interacting with it, going to adapt?

Click here to read Part III of this series, Keeping Up With the Stream, where we’ll explore various approaches do solving these problems.

The Message is the Medium – Attention is Shifting from the Web to the Stream

Shift Happens

A major shift has taken place on the Web. Web pages and Web search are no longer the center of online activity and attention. Instead, the new center of attention is messaging and streams. We have moved from the era of the Web to the era of the Stream. This changes everything.

Back in 2009, I wrote an article called “Welcome to the Stream – Next Phase of the Web” which discussed the early signs of this shift. Around the same time, Erick Schonfeld, at TechCrunch, also used the term in his article, “Jump Into the Stream.” Many others undoubtedly were thinking the same thing: The Stream would be the next evolution of the Web.

What we predicted has come to pass, and now we’re in this new landscape of the Stream, facing new challenges and opportunities that we’re only beginning to understand.

In this series of articles I’m going to explore some of the implications of this shift to the Stream, and where I think this trend is going. Along the way we’re going to dive deep into some major sea changes, emerging problems, and new solutions.

From Documents to Messages

The shift to the Stream is the latest step in a cycle that seems to repeat. Online attention appears to swing like a pendulum from documents to messages and back every few decades.

Before the advent of the Web, the pendulum was swinging towards messaging. The center of online attention was messaging via email, chat and threaded discussions. People spent most of their online time doing things with messages. Secondarily, they spent time in documents, for example doing word-processing.

Then the Web was born and the pendulum swung rapidly from messages to documents. All of a sudden Web pages – documents – became more important than messages. During this period the Web browser became more important than the email client.

But with the growth of social media, the pendulum is swinging back from documents to messaging again.

Today, the focus of our online attention is increasingly focused towards messages, not Web pages. We are getting more messages, and more types of messages, from more apps and relationships, than ever before.

We’re not only getting social messages, we’re getting notifications messages. And they are coming to us from more places – especially from social networks, content providers, and social apps of all kinds.

More importantly, messages are now our starting points for the Web — we are discovering things on the Web from messages. When we visit Web pages, it’s more often a result of us finding some link via a message that was sent to us, or shared with us. The messages are where we begin, they are primary, and Web pages are secondary.

From Search to Social

Another sign of the shift from the Web to the Stream is that consumers are spending more time in social sites like Facebook, Pinterest and Twitter than on search engines or content sites.

In December of 2011, Comscore reported that social networking ranked as the most popular content category in online engagement, accounting for 19% of all consumer time spent online.

These trends have led some such as VC, Fred Wilson, to ask, “how long until social drives more traffic than search?”  Fred’s observation was that his own blog was getting more traffic from social media sites than from Google.

Ben Elowitz, the CEO of Wetpaint, followed up on this by pointing out that according to several sources of metrics, the shift to social supplanting search as the primary traffic driver on the Web was well underway.

According to Ben’s analysis, the top 50 sites were getting almost as much traffic from Facebook as from Google by December of 2011. Seven of these top 50 sites were already getting 12% more visits from Facebook than from Google, up from 5 of these top sites just a month earlier.

The shift from search to social is just one of many signs that the era of the Stream has arrived and we are now in a different landscape than before.

The Web has changed, the focus is now on messages, not documents. This leads to many new challenges and opportunities. It’s almost as if we are in a new Web, starting from scratch – it’s 1994 all over again.

Click here to continue on to Part II of this series, Drowning in the Stream, where we’ll dig more deeply into some of the unique challenges of the Stream.

StreamGlider Launches Today!

Today I’m happy to announce the launch of StreamGlider, a new tablet app (initially on iPad) that provides the first live streaming dashboard for keeping up with your interests.

TechCrunch just broke the story.

The inspiration for StreamGlider was a product that launched in the early 1990’s called Pointcast. Pointcast streamed news, entertainment, ads and other updates to screensavers. Pointcast was great, and we, (myself and my co-founders, Bill McDaniel and John Breslin) wondered whether we could evolve that concept and update it for the tablet and mobile era.

We designed StreamGlider to be the ultimate live streaming newsreader. It does what you have come to expect, plus a lot more. And it does it live – it streams live updates to your tablet.

It also offers a lot of new functionality that supports new ways of using a reader.

  • StreamGlider pulls live updates from content sources on the Web (RSS feeds, Google Reader, and Web API’s like Twitter, Facebook, Youtube, Flickr, etc.) onto mobile devices, and displays them in a variety of formats.
  • It can function as a live digital picture frame for the Web, showing news articles, photos from friends, videos, etc. as full-screen slides that scroll past.
  • It can also show streams as a live interactive filmstrips that function like tickers.
  • And it can show streams in an interactive magazine format that is similar to a newspaper layout.
  • You can also play and watch videos in StreamGlider.

Powerful Features

StreamGlider is fully gesture controlled – everything can be controlled by swiping, pinching, pointing, tapping, etc. You can easily customize the streams you want.

You can also create mashups of streams that pull from many different sources on a theme – for example you can pull from different news sources about sports, or different photo and video sources about a topic.

In addition to all this, you can make very personalized streams that pull from your social media accounts, and filtered streams that search for particular topics in content sources.

StreamGlider is also social. You can share individual items, or even entire streams of items, with your friends.

We designed StreamGlider to be brandable. Partners and customers can create their own private-labelled versions of StreamGlider, with their brand and their content, for their audiences. Brands can sell it or give it away free and run ads in it if they want. (Contact StreamGlider, if you’re interested in doing this for your brand).

This frees publishers, brands, and enterprises to create their own powerful readers for their audiences, with their brand, instead of having to live inside of other apps like FlipBoard or Pulse. They can have their own icon on the desktop and keep their direct relationship with their customers.

There are many use-cases for this – for example, you might want to distribute your own branded StreamGlider for your publication, or for a consumer product, or to your fans, or for a big event, or to your customers or employees. There are many reasons to do this – and you don’t have to be a software company to do it – you can almost instantly get your own branded StreamGlider.

We also designed StreamGlider to be open-source in the future. More news on that later. We hope we can become the Mozilla of newsreaders.

What’s next?

The team behind StreamGlider has a long history of making smart, semantic apps. You can expect that in future versions of StreamGlider, the app will begin to get smarter, more personalized, and even more social. This is just the beginning of our roadmap.

We will also be adding in support for more types of streams. Stay tuned!

Meanwhile, you should check it out. Download it to your iPad and see what I’m talking about.