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Microsoft bailed out Apple and see how that turned out.

Their competition isn't dashboards it's modelling.

Customers want dashboards and Palantir provides excellent dashboard tooling. Dashboards make people feel smart and feel like they are learning something. But dashboards are not as useful as people think and usually fail to return enough value to cover the cost. As Palantir is so expensive the bar is higher and often not met - hence the losing of customers.

Customers need data models but they don't know it yet. The charts used with models are usually not interesting if they exist at all. If you did show charts of the data models to the customer it often makes them feel dumb and out of control - few people like that. They're also dependent on you to interpret the models and customers don't like that either.

So given the choice of comforting lie or uncomfortable truth the vast majority will choose the lie. So if you're in the business of selling comforting lies don't be surprised when they fail to work.

It is an ontology company - see their website. This is how they derive their analytics and visualizations. So my points are relevant.

You should check out Ab Inito, an even more secretive ETL company that predates Palantir and is also milking the DoD (it's an age old racket). They're $90K per seat per year and it's basically a crappy version of Jenkins. Apparently they have staff that search the internet and sue anyone who mentions them. Not even Palantir does that. So let's see how long this posts lasts :)

You are being worse than Palantir.

I meant generally they are generally not useful. Sometimes they are. It depends on the purpose and what you want to build and who it's for.

Given that you're building a descriptive model it would depend if you're working with facts or with probabilities. If it's facts then Ontologies should work fine, for probabilities I'd recommend Bayesian techniques.

The input for these are usually small. From the sounds of it you're generating the input yourself so you should be safe.

This matches my experiences with them and other Peter Thiel funded startups.

There is usually something wrong with the ontologies approach as it rarely works. There is roughly two decades of evidence for this for anyone who cares to look. Five decades if you loosen the definition to include the family of logic and constraint programming - see AI Winter. There is nothing new about these ideas. It always looks and feels like it's going to work which is why humanity has persisted with it for so long and will likely continue to persist for some time to come.

There is a whole generation of better techniques that have come out of machine learning that totally eclipses ontologies and I know Palantir isn't using them. Their corporate culture isn't set up for fostering that kind of applied research.

No-one is advocating for a fully automated approaches. I don't know where that notion came from.

In my view is that Palantir is a consulting company that is pretending to be a tooling company. And their consultants are not worth the money they charge. Just one of many Silicon Valley based frauds.

Thanks. There is some interesting work being done with Deep Learning mixed with Bayesian Models and external databases of facts. The training apparently ungodly slow though. My work these days is visual so CNN works well enough. I'm not an authoritative expert in this field though so I'm taking 6 months off to study it intensely, after which I hope to be able to make some meaningful contributions.

That matches my understanding of their tech stack as well. I also understand why people don't do an open sourced version - it doesn't pay nearly as well as having a job.

What I find interesting about this is that in effect Palantir is acting as a consulting company while pretending to be a software tooling company. This allows them to claim a higher earnings multiple to inflate value and extract more money out of VCs and offer a lower percentage of equity to employees. This is a very old trick. The problem is that consulting companies are much harder to scale than software companies and the inevitable disappointment will lead to a loss of equity.

There is good money in consulting (I am one) but it's hard to build a large consulting company when the 'tooling' companies can poach your talent away with cheap VC money and fairy tales about future piles of cash. It spoils the market. VC powered tooling companies masquerading as consulting companies are a real problem right now.

Huh, so they are open about it now. They definitely were not when I talked to them. I guess enough time has passed that people have forgotten the hard won lessons of the past.

I worked on scaling and generalizing ontologies at university and had already switched to working with Big Data / ML at a big company when Palantir tried to recruit me. I talked to some of their senior engineers about their tech and made the point that their tech sounded just like ontologies. I tried to get them to admit what it was so I could be sure I was having an honest conversation with them. They flatly denied it and made it out like the whole thing was their great new idea. I was unimpressed.

I was still interested in working for them. Access to hard interesting problems can be hard to come by. In the end I couldn't take their legendary arrogance and insecurities - to me these are bright red flags of a toxic corporate culture. And they low balled me. I would have temporarily put up with the toxic culture for large piles of money.

After the BuzzFeed article and Unicorn Flyers they bumped pay and bonuses by ~20%. This was around 6-8 months ago. From what you're saying it sounds like it wasn't enough.

Sure, I'll give a few examples.

A guy from BP told me in 2013 that they forked iPython, replaced all iPython references with Palantir and tried to sell it to them for $500K p.a.

For me; back in the day (2010) they were less secretive about their technology which was essentially an ontological reasoner. This was pre the Big Data hype boom - and AFAIK Palantir has never been about Big Data. Ontological reasoners have problems that prevent them from scaling or generalizing so they generally fail. Due to a long long history of failing ontological systems have a very bad name. But they look good for guided demos and has a ton of academic backing so it's easy to sell - as long as you call it something else - which is what they did. So if you want to use ontologies a better open source alternative software is Protege. But for the problems Palantir targets I'd recommend using standard machine learning technology where all the good stuff is open sourced.

As an aside, Peter Thiel also helped found Quid. A start-up that ripped off the Gephi layout engine and charges people $20K p.a. a seat. They've since rebuilt it but like Palantir it's still not solving people's problem and they've evolved into a consulting firm.

I cannot say enough bad things about Palantir. Their technology is worse than the free alternatives and their consultants are not worth the money. They are losing their big commercial customers because of this and now need 'Hail Mary' contracts from the US govt to remain in business.

There is already a lot of information out how bad Palantir is. I hear from friends who work there that the BuzzFeed article on them is accurate. In short, like much in the valley, Palantir financially set up as an unmaintainable pyramid scheme.

One way for them to get out would be to pay Goldman Sachs to get Meg Wittman use HP shareholder money to buy Palantir at face value. This worked for Autonomy - a similar scam 'Big Data' company where the founders got away with it.

It's counter productive for big projects as well. The Too Big To Fail attitudes to process cover all manor of sins and corruptions to the point the projects is guaranteed to fail. See the Joint Strike Fighter for the worlds single most expensive example of this.

Samuel Brannon established his tool monopoly by diverting church money and using thugs to keep out the competition. Once he made his money he lost it all in real estate speculation and a divorce.

I'm in the same boat. I switched to data science / ml.

XMPP is a poor protocol. I can completely understand why they would want to break compatibility.

Giving people a very small stake in their work adds much greater inventive than cost. That alone is a good idea (See Starbucks). Emotions trump math - even for programmers. I expect the confusing terms are intenional to make the math harder.

But... this is the same company that was giving out work quotes in less than 10 minutes. I expect gigsters existence is subsidized by VC investment and it will be unable to succeed long term. I'm not expecting good things for anyone involved.

My first day in SF after coming from London for a tech job I accidentally walked into a protest. I asked one of the protestors who they were protesting and he told me it was foreign tech people. So for fun I pretended to be a banker. Apparently bankers are ok.

It's so when they kill it there are fewer people to complain

For big companies that benefit from being able to say they have lots of users there is a big incentive not to be good at finding fraudulent accounts. I worked in Big Data Analytics at one of these companies $10B+. We were separate from the fraud department. They'd filter out the fraud accounts and we'd have to re-filter because their behavior was so out of whack it would mess up our analytics. We tried to move our filters upstreams and teach the fraud dept how to identify these accounts but absolutely no-one was interested. Also it's common to have bonuses tied to user numbers.

I'm pretty sure it's by design.

except that one

You can be multicultural but not multiracial. I'm a white male but from a different culture.

As an aside; is it racist to assume all white men have the same culture?

A subcontractor can make way more money by internally making parts of substandard quality to ensure they get bought.

I agree. Someone making free OS software for others to use shouldn't bundle.

I made the assumption on the top post that they were in-house proprietary software given the reference to keeping everything in git.

It worked for me. After countless injections of errors after updating a large list dependencies we made the decision to stop updating and freeze the versions, patch remaining errors ourselves and leave it alone. Worked so well we now have time to rebuild the whole front end on a new stack as an experiment instead wasting time with endless bug hunts.

How many more years of losing $2B a year until we find out the Uber model doesn't work for Uber either?

I love ETL. I've worked with real Big Data (5PB+) and working on the data pipeline was my favorite part. The feeling you get when you rewrite a job to run 1000x faster so the company can make way more money.