Get the most recent info and news about Freddie Mac on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Federal Home Loan Mortgage Corporation operates in the secondary mortgage market in the United States. The company purchases single-family and multifamily residential mortgage loans originated by lenders, as well as invests in mortgage loans and mortgage-related securities. It operates through two segments, Single-family and Multifamily. The Single-family segment purchases, securitizes, and guarantees single-family loans; and manages single-family mortgage credit risk, as well as manages mortgage-related investments portfolio, single-family securitization activities, and treasury functions. This segment serves mortgage banking companies, commercial banks, regional banks, community banks, credit unions, housing finance agencies, savings institutions, and non-depository financial institutions. The Multifamily segment engages in the purchase, sale, securitization, and guarantee of multifamily loans and securities through the issuance of multifamily K and SB certificates; issuing and guarantying other securitization products; issuing other credit risk transfer products; and provision of other mortgage-related guarantees. It serves banks and other depository institutions, insurance companies, money managers, central banks, pension funds, state and local governments, real estate investment trusts, brokers and dealers, and a range of lenders. The company was founded in 1970 and is headquartered in McLean, Virginia.

Freddie Mac

freddiemac

Homebuyers are facing yet more misery after US mortgage rates rose to their highest level in more than two months.

Mortgage rates reach their highest level in 10 WEEKS - typical homebuyer must now pay $700 a MONTH more than if they bought two years ago | Daily Mail Online

Mortgage interest rates for 30-year mortgages rose to 6.77% this week, up from 6.64% last week.

Mortgage interest rates jumped up this week, along with prices: Freddie Mac | Fox Business

On Thursday, Freddie Mac reported 6.77% as the average on a 30-year mortgage, up 13 basis points from last week.

Mortgage Rates Continue To Head Up

With a combined net worth of $125 billion after earnings this week, the mortgage giants remain on a path that could lead to eventual release from government conservatorship.

Strength In Home Prices Helped Boost Fannie, Freddie 2023 Profits - Inman

The 30-year mortgage rate is averaging 6.77%, Freddie Mac said in its latest weekly survey, released on Thursday.

Mortgage rates rise to highest level in two months and may stay higher for longer, Freddie Mac says - MarketWatch

Any views expressed in the below are the personal views of the author and should not form the basis for making investment decisions, nor be construed as a recommendation or advice to engage in investment transactions. I have no words… JK! I actually have a few thousand for y’all. I asked a question on Twitter a few days ago wondering who would be the Lehman Brothers of this crypto credit cycle. Many said it would be FTX, and I honestly laughed it off. I never believed they would go down — and yet, here we are. The catalyst for their downfall is the same as it always is — a good business overextending themselves by gorging on cheap credit collateralized by high and rising valuations of assets on their balance sheet. And as money became more expensive and the cycle turned, their nakedness was exposed for all to see. This essay is a simple story. I shall narrate a play-by-play, built around tweets and public statements. I intend to respectfully read between the lines and offer my opinion on how this all came to be and what the future holds. To that end, I believe the final down candle in this crypto bear market is near, and that we shall emerge from the bear market with less deadweight and more opportunity for those who survived. Before we begin, I sincerely believe that Sam Bankman-Fried (SBF) is a once in a generation trading talent. Even though he just blew up, the empire he put together took a certain amount of panache that is hard to come by. And I must give even more kudos to Baron CZ — when the news broke that Binance might acquire FTX, I sent CZ the following message: “I fucking love you man. Boss move, much respect.” Let’s begin. It was all a dream, I used to read Coindesk magazine… It all started with a article that featured a breakdown of a leaked Alameda balance sheet. Coindesk The financials make concrete what industry-watchers already suspect: Alameda is big. As of June 30, the company’s assets amounted to $14.6 billion. Its single biggest asset: $3.66 billion of “unlocked FTT.” The third-largest entry on the assets side of the accounting ledger? A $2.16 billion pile of “FTT collateral.” There are more FTX tokens among its $8 billion of liabilities: $292 million of “locked FTT.” (The liabilities are dominated by $7.4 billion of loans.) It’s fascinating to see that the majority of the in the Alameda business is actually FTX’s own centrally controlled and printed-out-of-thin-air token,” net equity Said Cory Klippsten, CEO of investment platform Swan Bitcoin, who is known for his critical views of altcoins, which refer to cryptocurrencies other than bitcoin ( ). BTC Alameda CEO Caroline Ellison declined to comment. FTX didn’t respond to a request for comment. Other significant assets on the balance sheet include $3.37 billion of “crypto held” and large amounts of the Solana blockchain’s native token: $292 million of “unlocked SOL,” $863 million of “locked SOL” and $41 million of “SOL collateral.” Bankman-Fried was an early investor in Solana. Other tokens mentioned by name are SRM ( Bankman-Fried co-founded), MAPS, OXY and FIDA. There is also $134 million of cash and equivalents and a $2 billion “investment in equity securities.” the token from the Serum decentralized exchange The majority of the “assets” on their balance sheet are illiquid FTT tokens and a bunch of shitcoins. Let’s break down why FTT is so important to the FTX / Alameda empire. Per FTT’s stated , FTX has pledged to use one third of all exchange fees to buy back FTT. SBF owns the majority of Alameda’s equity, while he continuously dilutes his FTX ownership to ship in capital from VC muppets. These VC muppets probably didn’t realise — or maybe they did, which is even worse for their investors — that a good chunk of FTX’s revenue was syphoned from FTX to Alameda through Alameda’s large FTT stake. Presumably, Alameda received a large allocation of FTT for providing essential market making services to FTX, and its participation in the FTT initial coin offering. tokenomics Ok, so that’s not ideal for FTX investors, but it isn’t what struck the deathblow to FTX’s solvency. The real issue — which folks started to raise questions about in the wake of the Coindesk article — was whether FTX loaned Alameda money (most likely USD or fiat stablecoins) and used Alameda’s FTT stake as collateral. If FTX did loan the funds, that isn’t an inherently fatal issue either — FTX was free to loan out its retained earnings to whomever it pleased. But the most pressing related concern was whether FTX rehypothecated customer deposits to Alameda and took FTT as collateral. And, as a subset of that, whether the fall in the value of FTT or some of the shitcoins on Alameda’s balance sheet would thus render Alameda insolvent. And finally, if Alameda became insolvent, would FTX lend money from client funds to prop it up? Again, these were the questions that people began asking after this article came out. has an interesting theory on the connection between FTX, Alameda, and FTT. In short, he argues that Alameda blew up alongside Three Arrows Capital and others, but FTX lent Alameda money to survive in exchange for FTT tokens due to Alameda via its participation in the FTT ICO. Lucas Nuzzi Then, CZ struck. Presumably, CZ read the same article as everyone else and decided to dump Binance’s sizeable FTT stake in the market. There was also apparently some beef between the two crypto Barons. That’s not very nice, Sam. CZ doesn’t make love after divorce … seems reasonable. I guess CZ and SBF had a messy breakup. Once CZ started airing his concerns, though, alarm bells started ringing in the heads of FTX depositors. In crypto, when you have even an inkling that an exchange might be insolvent, you pull your funds immediately and ask questions later. Logic suggests that even if Alameda went bust, FTX should suffer no issues processing all withdrawals. They are two separate companies, and according to SBF’s claims, FTX’s customer deposits weren’t reinvested — not even in super duper safe US Treasuries. At this point, there was no reason to believe SBF was lying. But even still, you ain’t gonna wait around to find out with your money on the line. And therefore, capital started sprinting for the exits. As always, he who sells first, sells best. Those who immediately withdrew their funds got them back quite quickly. But as Monday bled into Tuesday, FTX’s withdrawal processing crawled to a halt — and all the while, FTT continued to plunge in price. The market was clearly trying to sniff out whether there was an FTT margin call trigger to punish Alameda for overextending themselves. I woke up on Tuesday to many messages from friends spreading rumors speculating that FTX may have been playin’ funny wit da customer monay. I prayed to Lord Satoshi that FTX had not not dipped their hands in the customer cookie jar. And then, this bombshell hit the electromagnetic sphere: This insane announcement provided probable answers for many questions. Did FTX rehypothecate customer funds, or euphemistically use said funds “effectively”? We don’t have a definitive answer to this question. But, Binance signed a non-binding letter of intent to purchase the entirety of FTX and pledged to attempt to make whole all customer deposits. That suggests that FTX experienced a run on its deposits and was unable to fill all the withdrawal requests of its customers. Otherwise, why would FTX need a bailout from Binance? FTX could have blown all their VC-muppet dry powder propping up Alameda, but that still wouldn’t have impaired their ability to meet withdrawals — unless they were dipping into the honeypot. “Two big lessons.” Hmm … I wonder what situation he could be referring to? Baron CZ clearly seems to be insinuating that FTX may have done some improper things with customer funds, and thus, when the bank run happened, they were caught with their hands in the cookie jar. Over the last week, $1 billion of deposits fled FTX. That is what precipitated this awful turn of events. These are only the withdrawals that were processed. We do not know how many poor souls are still waiting for their money back. Is Alameda Insolvent or Bankrupt? We don’t have a definitive answer to this question. However, if FTX required a bailout from Binance, and it was needed because of financial help that FTX supposedly provided Alameda, then we can assume Alameda is probably donezo as well. I’ll leave you with these words from Caroline Ellison, CEO of Alameda. I am sure she is dedicating 100% of her brain to salvaging whatever she can of Alameda. Will the deal actually close? A non-binding LOI is nice, but a firm commitment by Binance to purchase FTX and ringfence all its liabilities would be nicer. I imagine that the Binance’s corporate finance folks are busy digging through the financials of FTX and Alameda. If the hole is too big, then I expect Binance will walk away from the deal. And if Binance can’t or won’t do the deal, no one will be able to swallow the losses billowing from FTX. What happens if FTX can’t find a new home? Similar to Mt Gox, we will probably enter yet another high profile bankruptcy situation where depositors queue up to recover what they can. This could be a long or short process, but given how complex I imagine the holdings of FTX to be, recovery of any portion of deposits will likely take a very long time. That’s obviously a travesty in and of itself. But when looking at the broader market impact, the even bigger question is: Which companies that run crypto loan books had exposure to FTX and or Alameda? SBF was the most pristine credit in all of crypto. He was the IT crypto pioneer. His beautiful cherub visage adorned the most prestigious mainstream financial magazines. His skin glistened like someone who only ate carrots and cucumbers. He could do no wrong — investing alongside anything SBF touched was a sure path to riches. Look at that Solana boom! SBF even got Tom Brady to YOLO into FTX, aiyah! You can therefore imagine that if FTX potentially used customer funds to save Alameda, that both entities probably cashed in on their sterling reputation to borrow as much money as they could in order to stay alive in their dying months. And therefore, it’s very possible that this possible default has already infected the loan books of the remaining major centralized lenders. In light of this potentially significant exposure, the questions becomes — can the largest centralized lenders survive: Can’t stop, won’t stop Jay Powell, who is determined to break inflation by continuing to raise short term interest rates and reduce the size of the Fed’s balance sheet? The implosion of LUNA / TerraUSD? The bankruptcy of Three Arrows Capital? And now, the possible bankruptcy of FTX and Alameda? Issues 1) through 3) already deaded “legendary” centralized lenders such as Celsius, BlockFi, and Voyager. There are few possible zombies remaining — I will not name them here, but anyone who thinks critically can surmise who might come under pressure next. FTX and Alameda biting the dust is bad enough. That is our Lehman Brothers — but who is AIG? Who is CountryWide? FannieMae and FreddieMac anyone? Oh, they are out there — hiding in plain sight. The bottom of the S&P 500 during the 2008 Global Financial Crisis wasn’t reached until March 2009. That was many months after Lehman Brothers went bankrupt, and every single independent US investment bank received money from Warren Buffett or was gobbled up by a commercial bank who could legally access the Fed’s discount winder. It was after Treasury Secretary Paulson supposedly bent the knee to congresswoman Nancy Pelosi in order to secure the passage of the Troubled Asset Relief Program (aka let’s print some fucking money and hand it to the banksters). And finally, it was after Nobel Laureate Ben “Helicopter Money” Bernanke announced the Fed would engage in Quantitative Easing. While I am supremely bullish on the prospects of crypto in 2023 (I know the date keeps slipping, but I reserve the right to change my mind in the face of market conditions), I believe we are about to trade to new lows as everyone wonders which crypto household name will go bankrupt next for lending money to FTX / Alameda. Bitcoin wicked down to almost $17,500 on BitMEX’s XBTUSD perpetual swap. On various other spot exchanges, Bitcoin experienced similar falls to levels close to $17,000. Get ready for lower for longer, because it’s coming. But the good thing about crypto is that no central bank will be riding the rescue with freshly printed fiat shitcoins to bolster the balance sheets of reckless companies. The crypto industry will be forced to devour its humble pie quickly — leading to a speedy recovery that leaves it stronger than ever. Before I end this essay, let me be clear: centralized exchanges will always face these issues of mistrust on behalf of their customers. FTX was not the first high-profile exchange to fail and it won’t be the last. But throughout all this, blocks on the Bitcoin, Ethereum, and all other blockchains were still produced and verified. Decentralized money and finance have and will continue to survive and thrive in the face of the failures of centralized entities.

Speechless

Sin Palabras

Muet

अवाक

無言

Mudo

Không nói nên lời

无语

On the heels of a major effort to address redlining announced by the Biden administration this fall, legislators and regulators across the country are calling for additional scrutiny, including some that represent areas with vast lending disparities between White applicants and applicants of color. “To counteract the gap in lending rates, we must put forth legislative solutions to combat these alarming disparities on the city, state, and federal level,” Rep. Ilhan Omar (D-MN) said in a statement. “This gap exists due to the decades of racism and discrimination embedded in policy decisions that fundamentally hurt people of color.” Her reaction came in response to The Markup’s , which found that people of color are significantly more likely to be denied a mortgage than White people with similar financial qualifications. recent investigation Nationally, lenders were 80 percent more likely to deny Black applicants than similarly qualified White applicants, 70 percent more likely to deny Native American applicants, 50 percent more likely to deny Asian/Pacific Islander applicants, and 40 percent more likely to deny Latino applicants. The Markup also identified 89 metro areas with stark lending disparities spanning nearly every region of the country, from Nashville, Tenn., and Waco, Texas, to New York City and Los Angeles. One of the worst was in Omar’s home state of Minnesota. Lenders were about 100 percent more likely to deny Black and Native American applicants in the Minneapolis metro area than White applicants there. Mortgage lenders were about 50 percent more likely to deny Latino and 70 percent more likely to deny Asian/Pacific Islander applicants there. It was the only metro area in The Markup’s analysis where all four racial and ethnic groups were more likely to be denied home loans. Minnesota attorney general Keith Ellison said that lenders need to examine their lending standards and “cannot rely on the algorithms and approval formulas they’ve been using.” “It is clear that their criteria and policies, intentionally or not, result in serious disparities in lending patterns,” he wrote in an email. “Lenders who do not do so should not be surprised if they are investigated for violating state and federal laws, like the Fair Housing Act.” The Richmond, Va., metro area had particularly high lending disparities for people of color. Black, Latino, and Asian/Pacific Islander applicants were all more than two-and-a-half times more likely than White applicants to be denied a mortgage. “It’s appalling—though not surprising—that aspiring Black and brown homebuyers are still haunted by the kind of discriminatory housing practices that prevailed in this nation for decades,” Sen. Mark Warner (D-VA) said in a statement. Rep. André Carson (D-IN) called The Markup’s findings “disheartening, but not surprising.” We need federal policies that not only hold discriminatory mortgage lenders accountable, but also reduce their incentive to engage in these practices in the first place. Rep. André Carson “For many years, there have been systemic, coordinated efforts to deny home ownership to minorities and deny them that most essential American dream,” he said. “Today, we need federal policies that not only hold discriminatory mortgage lenders accountable, but also reduce their incentive to engage in these practices in the first place.” Industry representatives at the time criticized The Markup’s investigation, saying that the public data couldn’t be used to draw conclusions because it did not include credit scores, which the government collects but are not public. The Mortgage Bankers Association and American Bankers Association also said the scope of The Markup’s analysis was too narrow and should have included government-insured loans rather than focus on conventional loans. More than 160 news outlets and publications carried The Markup’s original story, including The Associated Press, The Washington Post, and Bloomberg. Several, including , NBC News affiliate , and , used The Markup’s findings and analysis as a basis for their own stories. The Post and Courier 9News in Denver The Des Moines Register The U.S. Department of Justice, Consumer Financial Protection Bureau, and Office of the Comptroller of the Currency launched their “Combating Redlining Initiative” in October, in their announcement. citing The Markup’s findings This new initiative is in stark contrast to how the previous administration handled redlining cases. The number of these cases referred to the Department of Justice dropped precipitously since 2010, from about 25 cases that year to fewer than 10 cases in 2019. At a hearing ushering in CFPB director Rohit Chopra, Sen. Sherrod Brown (D-OH), who chairs the Senate’s banking committee, said algorithmic bias “risks reinstating Jim Crow in a new, high-tech form.” Biases in technology and algorithms are the newest manifestation of housing discrimination. Sen. Sherrod Brown “Biases in technology and algorithms are the newest manifestation of housing discrimination,” he said in a statement to The Markup. “Regulators and Congress must ensure that our fair housing and fair lending laws are fully enforced to root out discrimination in all its forms.” In Brown’s home state, The Markup found, Black, Latino, and Asian/Pacific Islander applicants in the Cincinnati metro area were all at least 100 percent more likely to be denied than White applicants. and , two quasi-governmental agencies that buy half of the mortgages in the country, announced changes to their underwriting software this year after we contacted them and asked about their algorithms. They both will start incorporating on-time rent payments into their algorithms to allow more people to qualify for homeownership. Fannie Mae Freddie Mac Michele Rayner, a Democratic state representative in Florida, said the issue is a “huge priority” for her. She tasked her staff with reaching out to several of the companies mentioned in The Markup’s investigation about how they conduct business in Florida. She sits on the state house’s banking and insurance legislative subcommittee. To me this goes to the heart of self-determination. Florida state representative Michele Rayner “To me this issue just goes to the heart of self-determination,” Rayner said. “You’re denying people who would be otherwise eligible for a home based on an algorithm somehow.” Florida had the highest number of metro areas—nine—with statistically significant differences in mortgages given to White applicants versus applicants of color. California had eight, the second-highest. California’s attorney general, Rob Bonta, recently said the California Department of Justice’s Housing Strike Force and the new Racial Justice Bureau would be taking on housing issues, including examining “mortgage servicing issues.” Nina Sheridan, press secretary for the California Attorney General’s Office, declined to comment on specific enforcement actions, saying, “In terms of any potential future actions, stay tuned!” Credits: & Malena Carollo Emmanuel Martinez Photo by on Breno Assis Unsplash Also published here

What Do U.S. Politicians Have to Say About Mortgage Discrimination Against Minorities? 

Photo by on Chris Lawton Unsplash Citing a as evidence of a nationwide problem, the U.S. Department of Justice, Consumer Financial Protection Bureau, and other agencies announced a new initiative Friday to combat discriminatory mortgage lending practices. recent investigation by The Markup , Rohit Chopra, the director of the CFPB, said an all-hands approach is required to combat the “endemic” practice of redlining, the term for denying mortgages in neighborhoods with high populations of Black people and immigrants, once encouraged by the federal government. He said that includes fighting “digital redlining” by algorithms used to decide who gets a mortgage. At the press conference “These algorithms are black boxes behind brick walls,” Chopra said. “When families and regulators do not know how decisions are made by these algorithms, we are unable to participate in a fair and competitive market free from illegal bias.” The new multi-agency effort will increase government analyses of lending patterns with an eye toward fair lending, task state attorneys general with enforcing fair lending practices more stringently, and strengthen reporting pipelines between the Department of Justice and “financial regulatory agencies” to identify fair lending violations. The Department of Justice described the Combating Redlining Initiative as its “most aggressive” attempt to curb the practice. This push comes after The Markup’s August investigation, which held constant 17 variables, including factors that the lending industry previously said would explain away lending disparities. We found the disparities continued. Lenders nationwide were more likely to deny people of color than White applicants. They were 80 percent more likely to deny Black applicants than similarly qualified White applicants with similar incomes, debt-to-income, combined loan-to-value ratios, and many other key factors. Chopra cited that finding in his remarks—and criticized the mortgage industry’s response. At the time, the industry argued that The Markup’s analysis wasn’t complete because it didn’t include credit scores, a key component in their decision-making. “Their defense illuminates the problem,” he said. “Black-box underwriting decisions are not necessarily creating a more level playing field and actually may be exacerbating the biases that feed into them.” The Mortgage Bankers Association did not respond to requests for comment on Friday, and the American Bankers Association declined to comment. The Home Mortgage Disclosure Act requires financial institutions to report applicants’ credit scores, but the CFPB does not make them available in the public version of the dataset, citing consumer privacy. The government agency also withholds the decisions made by algorithms required by Fannie Mae and Freddie Mac, the two quasi-governmental agencies that buy about half of the mortgages made in the country. As part of the new effort, the Department of Justice announced a proposed settlement with a Mississippi-based lender, Trustmark National Bank. to “majority-white neighborhoods” in Memphis, Tenn., between 2014 and 2018, discriminating against Black and Latino prospective borrowers. The bank was accused of targeting its financial services , and a court still needs to approve the agreement for it to take effect. The bank did not admit fault as part of the settlement If approved, Trustmark would pay a $5 million civil penalty to the Consumer Financial Protection Bureau and Office of the Comptroller of the Currency, pay $3.85 million to create a fund to subsidize loans for residents of Black and Latino neighborhoods in the metro area, and pay for outreach and financial education efforts around the area. “We fully cooperated with the agencies and have entered into these settlements to avoid the distraction of protracted litigation,” Duane Dewey said in a statement, “and because we share the common goals of breaking down barriers to home financing and exploring innovative ways to help residents of underserved areas achieve the dream of homeownership.” Trustmark CEO As part of our investigation, The Markup also looked at the lending patterns of individual lenders and between people of color and their White counterparts in 2019. found seven financial institutions that produced the widest disparities Among them were three lenders associated with the largest homebuilders in the country, DHI Mortgage, Lennar Mortgage, and Pulte Mortgage. These three companies were more likely to deny Black and Latino applicants than similarly qualified White applicants. The seven lenders previously told The Markup that they don’t deny people based on their race or ethnicity, that they follow the law, and that The Markup’s findings did not accurately reflect their lending patterns. Two lenders said that the data used for the analysis can’t provide an accurate and complete picture of their lending patterns because of the data’s limitations. “Today, we are committing ourselves to address modern-day redlining by making far more robust use of our fair lending authorities,” U.S. attorney general Merrick Garland said at Friday’s multi-agency press conference. “We will spare no resource to ensure that federal fair lending laws are vigorously enforced and that financial institutions provide equal opportunity for every American to obtain credit.” Correction An earlier version of this story described Fannie Mae and Freddie Mac as nonprofit corporations. They are quasi-governmental agencies. Written by: and Emmanuel Martinez Malena Carollo Originally published . here

Will the U.S. Government do Anything About Mortgage Rate Discrimination?

A bit of background 2021 has been an eventful year for cryptocurrencies. Bitcoin’s price breached $60,000 in April from around $7,000 where it was in April 2020. , the largest cryptocurrency exchange in the United States listed on NASDAQ around the same time and for some time had a market cap higher than $100 Billion. A few months later, El Salvador, a country in Central America with a population of 6.8 million people, that had been using US Dollars as its official currency, passed legislation making Bitcoin a legal tender. Coinbase I bought my first Bitcoin few years back, more out of curiosity than anything else. Since then, I had followed cryptocurrencies passively from the sidelines. But as this multitude of events unfolded in 2021, I couldn’t resist taking a deeper look. While there is a lot of noise about the prices of cryptocurrencies in media and Twitterverse, the one statistic that stood out for me was the number of crypto users which had surpassed 100 Million. I was surprised. And what was more surprising is that it was growing almost 100% Year on Year. You could see the parallels between the growth of the internet and smartphones with that of cryptocurrency. How it began Back in 2008, the world was grappling with the Subprime Crisis. Bear Stearns, one of the largest securities firms was sold to JP Morgan in March 2008. A few months later in September, within a span of a week, the Federal takeover of Fannie Mae and Freddie Mac happened, Merril Lynch got sold to Bank of America, and Lehman Brothers collapsed. The IMF estimated that US and European banks lost over $1 Trillion on bad assets and loans. Regular people lost their retirement savings and millions were unemployed. It was amidst the global financial crisis, Bitcoin was born. On October 31, 2008, a pseudonymous person with the name shared a link to a paper on a cryptography mailing list. The paper was titled The first Bitcoin block was mined on January 3, 2009. Embedded in the first block was a message Satoshi Nakamoto Bitcoin: A Peer-to-Peer Electronic Cash System. The Times 03/Jan/2009 Chancellor on brink of second bailout for banks A world of coins Soon after Bitcoin new coins started emerging. Ethereum, which is presently the second-largest cryptocurrency in terms of market cap, launched in 2015. Where Bitcoin was built primarily as a decentralized currency, Etherium aimed to be a platform for decentralized applications. This ushered an era of innovation that was hard to imagine in the early days of Bitcoin. There are 5821 coins listed on Coinmarketcap as of now. To call all of them, cryptocurrencies will not be accurate as there are coins with specific utilities other than being a currency. The lines can be a bit blurry though at times. Currencies Having launched in 2009, Bitcoin is the first decentralized cryptocurrency. Hal Finney, a Computer Scientist, was the first receiver of a bitcoin transaction. In December 2020 the number of daily transactions reached 330,000. by its design can process about 4-5 transactions per second. The transaction throughput is decided by the block size and the block creation time. One of the earliest altcoins, was a source code fork that had lower block creation time. Bitcoin Litecoin The block size had been a point of debate for the Bitcoin developer community which saw multiple forks happening in the blockchain in 2017. , , and ( ) were born out of these forks. However, the forks could not displace Bitcoin, which continues to be the most well-known and adopted cryptocurrency. Lightning Network, a Layer 2 payment protocol has been gaining traction rapidly. It is expected to support millions of transactions per second. Bitcoin Cash Bitcoin Gold Bitcoin SV forked from Bitcoin Cash Cryptocurrencies are a new asset class and extremely volatile. For instance, Bitcoin which peaked in April 2021 at $63000 is presently (August 2021) at $42000. Price fluctuations of 10% or higher over a day are not rare. are cryptocurrencies that are typically pegged to a fiat currency. People use Stablecoins as a medium to buy cryptocurrencies but also as a bridge to access services and assets on blockchains that they couldn’t through fiat currencies. is the largest stablecoin with over $60 Billion of coins in circulation. It is followed by which has over $20 Billion in circulation. Both USDC and Tether are fiat-backed and claim to be holding reserves in fiat currencies to back the peg. Stablecoins Tether USDC Over last few years serious questions have been raised over the validity of these backings for Tether. is another stablecoin created by the Maker Dao Foundation. DAI uses automated smart contracts to give out loans in DAI against over-collateralized crypto assets and maintains its peg against the dollar by controlling the assets, the interest rates, and collateralization ratios. DAI There are a few other coins that attempt to improve cryptocurrencies on different dimensions. focused on faster transactions at lower cost and has demonstrated 1000-1200 transactions per second on its beta network. focused on enhanced privacy features and complete anonymity in transactions despite having a public ledger. Nano Monero Smart Contract Platforms In late 2013, a young Canadian programmer of Russian origin published a whitepaper about a general-purpose blockchain called Ethereum. His vision was to create a platform that enables secure decentralized applications to extend the utility of blockchains beyond money. Vitalik Buterin is the second-largest cryptocurrency after Bitcoin with a market cap of $287 Billion. But the more interesting stat is that owning to its richer ecosystem, over a million transactions happened per day in July 2021 on Ethereum. New currencies, new kinds of decentralized organizations, exchanges, and derivatives markets have been built over Ethereum. Ether Charles Hoskinson, one of the co-founders of Ethereum started . It launched in 2017 although the smart contract capability is expected to launch sometime later this year in 2021. is another smart contract platform developed by Gavin Wood, one of the co-founders of Ethereum. More than the technical differences, what stands out is how these three projects with shared history operate. Cardano has taken an approach akin to Academic Research where each proposal is peer-reviewed and goes through a lot of rigor before getting implemented. This translates into longer development cycles. Polkadot differs in terms of governance where all of its governance is on-chain. The proposals go through voting systems and the one with majority votes is accepted. Cardano Polkadot While Polkadot and Cardano are still getting started in terms of becoming a smart contract platform, , one of the largest crypto exchanges launched Binance Smart Chain (BSC) in 2020 and has grown rapidly in adoption. Binance surpassed Ethereum in the number of transactions on February 2021 and is presently doing almost five times the transaction volumes on Ethereum. This was largely driven by interoperability with Ethereum Applications and much lower gas fees. A fundamental way in which BSC differs from Ethereum is that it follows a semi-decentralized model. The top 21 holders of Binance Coins act as validators of the transactions. This is in contrast with Ethereum which has more than 6000 validators. Binance Governance Tokens Governance Tokens allow you to participate in the governance of different decentralized applications. You can use your tokens to cast vote on changes being proposed in these protocols. Otherwise, you can delegate your tokens to someone who can represent you in these discussions. For instance, Maker Dao, the foundation behind the stable coin DAI, issued a token on Ethereum ( ) called MKR that allows you governance rights. There are 1 Million MKR issued. These tokens don’t pay dividends. MKR token acts as an incentive mechanism for ensuring DAI is pegged at a dollar value. such tokens are called ERC-20 tokens ystem. - MakerDAO launched with 1,000,000 MKR tokens at its inception. The tokens are created and destroyed under different circumstances. MKR is destroyed when the Maker Protocol’s system surplus exceeds a minimum threshold, resulting in excess Dai being auctioned for MKR that is then destroyed. Inversely, when the Maker Protocol is running a deficit and the system debt exceeds a maximum threshold, MKR is created and auctioned for Dai in order to recapitalize the s Maker Dao Utility Coins Utility coins or tokens are primarily used as an incentive mechanism for a specific use case. For instance, is a distributed and decentralized storage service where the token is used as an incentive for the storage miners to store the data of clients. Coin acts as a backing for digital gaming assets created on its platform. The process is reversible and the assets can be melted back into the coin. is a Layer 2 Scaling Solution for Ethereum. It supports faster and cheaper transactions while still maintaining Ethereum as the final settlement layer. MATIC is the native token for Polygon and is used as a medium for payment of services on Polygon Network. If there is a decentralized app on Ethereum you are interested in trying out, you will find it cheaper to give it a go on Polygon than do it directly on Ethereum. filecoin Enjin Polygon Meme Coins No conversation on cryptocurrency is complete without mentioning Meme Coins. Meme Coins emerged as a phenomenon in the last few years with being the most prominent one. Dogecoin Dogecoin started as a joke with the source code forked from Bitcoin. For a while, it didn’t have any active development happening. The distinct thing about Dogecoin was the face of a Shiba Inu dog on its logo. For whatever reason, sometime in early 2021 Elon Musk started tweeting about Dogecoin which rose a frenzy. Its prices grew from a mere 5 cents to almost 68 cents during a Saturday Night Live show hosted by Elon Musk where he gifted a Dogecoin to his mom. While its prices have since crashed, Dogecoin played its role in history in a strange way by growing the awareness of cryptocurrencies. Acquiring and storing cryptocurrencies There are multiple ways of acquiring cryptocurrencies. The simplest being buying them on exchanges such as or . There are over 300 exchanges listed on and you should do your due diligence to identify which one works best for you. A few aspects you should look into while selecting an exchange are the reputation of the exchange, the liquidity in the exchange, and the number of assets listed on it. Coinbase Binance coinmarketcap The other way you can earn cryptocurrencies is by using programs such as , , and that will pay you in cryptocurrencies for finishing learning assignments on their platforms. If you are really want to get into the weeds, you can build a mining setup to earn. Miners act as validators of the transactions on the blockchain and in exchange for this service they get paid in the cryptocurrency native to the blockchain. Coinbase Earn 1729 Coinmarketcap Earn If you are buying cryptocurrency from an exchange, one way to store the currency is on the exchange itself. But in some way, it defeats the purpose of cryptocurrencies which are supposed to be decentralized. When you hold the currency on an exchange, it is the exchange that has the custody of your holdings. Your account can get frozen. Or worse the exchange can get hacked and you can lose your money. The most infamous being that of Mt Gox which at one time was handling 70% of bitcoin trades. Not your keys, not your coins. A popular crypto saying. Today’s exchanges are probably more secure. Still, if you have a sizeable amount of crypto assets built up, you should set yourself a wallet and transfer it there. A wallet essentially stores the public key and the private key. The private key is used to access the fund and transfer them. In Bitcoin, the private key is 256 bits number i.e. a number with 78 digits. The public key is 256 bits too and the wallet address is a hash of the public key. Since the private key is not human readable, most wallets use a set of 12 or 24 words called seed phrase to generate the key. In case your device crashes, you can use the seed phrase to access your funds. Sample Bitcoin Private Key Hexadecimal Representation E9873D79C6D87DC0FB6A5778633389F4453213303DA61F20BD67FC233AA33262 Decimal Representation 105627842363267744400190144423808258002852957479547731009248450467191 077417570 Sample Bitcoin Address 1BoatSLRHtKNngkdXEeobR76b53LETtpyT Please don’t use the above key and address for any transactions. Here is an interesting detail. When you create an address for yourself, the address details remain on your device or software unless you share it. There is no registration event involved. The blockchain only maintains the transactions getting debited and credited between different addresses. Technically, you can create an address completely offline. This does present the error possibility where people send crypto to an incorrect address and unlike email, here all addresses even the ones that were never seen before are valid. Cryptocurrency Wallets come in different flavors. You have Hardware Wallets that store the public key and private key in a secure device. Hardware Wallets are the most secure of storing your cryptocurrencies. However, they are expensive and might be an overkill unless you are holding a large amount of crypto. Trezor and Ledger are some of the well known hardware wallets. Then you have Software wallets that are mobile apps, desktop app and even chrome apps. Metamask, Exodus, Trust, and Coinbase some of the well known crypto wallets. While choosing a wallet, a few things that you should be looking at are its reputation, security, and the cryptocurrencies that it supports. Coinbase Wallet is a different application than the exchange. Most cryptocurrency owners still hold their assets on the exchanges. This is reflected in the fact that popular cryptocurrency wallets have anywhere between 10 to 20 Million Installed userbase as of now. However, a vibrant ecosystem of decentralised applications is getting built that provides interesting ways of using your cryptocurrencies. As the adoption of such application increases, wallets will play a critical role as they will be the gateway to access and utilise them. These are still early days of cryptocurrencies and decentralised applications. It is a highly debated field and opinions range from that cryptocurrencies will change how we live our lives to that it is an anarchic phenomenon. But what started as a niche technology used by geeks is slowly signs of going mainstream. And what no one can dispute is how interesting the technology is. Irrespective of where you stand, you should explore the space and know on your own. There is a high chance, the world will look very different from how it is in next ten years. This was meant to be a single post but as I kept writing, I realised there is too much content to cover. The next post will cover interesting applications that are getting built in this space. If you found this post interesting, please do checkout . We’re building something that simplifies access to Decentralized Finance and earn higher yield on your money. Brew References The Bitcoin Whitepaper - https://bitcoin.org/bitcoin.pdf BBC Article on El Salvador declaring Bitcoin as legal tender https://www.bbc.com/news/world-latin-america-57398274#:\~:text=El Salvador has become the, possible votes on Tuesday night.&text=Bitcoin will become legal tender, US dollar%2C in 90 days. research on crypto users Crypto.com https://assets.ctfassets.net/hfgyig42jimx/5u8QqK4lqjEgL506mOx4m3/d44d8e204aecfc75a839e2a9d505f5d1/Crypto.com_Data_Report_-_On-chain_Market_Sizing.pdf Statista Global Cryptocurrency Users https://www.statista.com/statistics/1202503/global-cryptocurrency-user-base/ Subprime Crisis Bank Losses https://www.reuters.com/article/banks-writedowns-losses-idCNL554155620091105 Satoshi’s mail introducing Bitcoin - https://www.metzdowd.com/pipermail/cryptography/2008-October/014810.html Questions on Tether Dollar Backing https://finance.yahoo.com/news/tether-controversies-seem-never-ending-084512532.html#:\~:text=In 2018%2C Tether failed to,record high of roughly %2420%2C000. Crypto School #tech https://a16z.com/crypto-startup-school/?video=12 Daily Bitcoin Transactions https://www.statista.com/statistics/730806/daily-number-of-bitcoin-transactions/ Daily Ether Transactions https://www.statista.com/statistics/730806/daily-number-of-bitcoin-transactions/ A Deep Dive of Filecoin https://coinmarketcap.com/alexandria/article/what-is-decentralized-storage-a-deep-dive-by-filecoin How to mine bitcoin - https://www.youtube.com/watch?v=mBStK_vj6j4

The Lay of the Cryptoland - Part I: Coins, Wallets, and Exchanges

CIA Supervisor: Jesus F***ing Christ. What did we learn, Palmer? Palmer: I don't know sir. CIA Supervisor: I don't f***ing know either. I guess we learned not to do it again. I'm f***ed if I know what we did. Palmer: Yes sir, it's hard to say. Many people erroneously believe that the 2008 crisis was about people getting home mortgages and not paying them back. That is true, but it is only part of the truth. In reality, the consequences wouldn’t be so dramatic if it wasn’t for financers that thought it would be a great idea to repackage hundreds of billions of dollars in mortgages into something called collateralized debt obligations (CDOs). As Mark Baum said in The Big Short: “mortgage bonds are dog shit. CDOs are dog shit wrapped in cat shit.” Theoretically, CDOs were supposed to shift risk from banks. Unfortunately, banks are not charities and they exist to make money. Thus, it shouldn’t be a surprise that banks that issued home loans also bet on CDOs. Major rating agencies, on the other hand, were very slow in revising the rating of Collateralized Debt Obligations (CDOs). The reasoning was also explained in The Big Short: Two hedge fund managers visit a Standard & Poor’s executive in her office in Manhattan. One asks the exec to name a time in the past year when the company didn’t give a bank the AAA rating it was seeking. She demurs. “If we don’t work with them,” she says, “they will go to our competitors. It’s not our fault. It’s simply the way the world works.” As a result, when the bubble burst, those banks were doubly punched. Lehman Brothers went bankrupt, meanwhile, Merrill Lynch, AIG, Freddie Mac, Fannie Mae, HBOS, Royal Bank of Scotland, Bradford & Bingley, Fortis, Hypo, and Alliance & Leicester had to be rescued. The "funny" part of that is that these banks were supposed to be backed up by teams of math whizzes and computer geniuses, even though they managed to lose billions of dollars. As a result, the crisis of confidence has infected the world, driving the dollar to record lows - and helping send the prices of commodities, especially oil, soaring to historic highs. Curiously, we do observe a fall in the dollar now but the prices of commodities also suffered. It is worth mentioning that with the Subprime crisis, Wall Street banks couldn’t even calculate how much risky securities on their books were really worth. In conference calls and press releases, they have been changing their estimates of the value of these assets. Merrill Lynch, for , expected a $4.5 billion subprime loss for the third quarter, but three weeks later, its analysts announced that its real deficit was $7.9 billion. Probably, it wasn’t the best idea to become a huge investor in the funds it assisted. But, as the saying goes, big chances are never small stakes. example Howbeit, investors couldn’t trust management's estimates of future losses anymore. Today, we see that time cures everything, which is why was able to operate without being objected to by the auditors for such a long time… Wirecard AG This brings us to the idea that when Wall Street is bringing its attention to mysterious products and complex trades, the game is on and so is a risk. The only question is when it will hit the market. But did we learn something? It has been recently announced that the first ETF Tracking the $700 Billion CLO-Market begins trading on the New York Stock Exchange. According to co-founder Peter Copp, the fund will track the highest-rated CLOs. “These particular bonds have maintained their AAA rating through a variety of market selloffs, and we think that’s a testimony to it being a good product and reasonable for retail investors to have access,” Coppa said. It is important to highlight that ETF could make CLOs more accessible to retail investors and this can be dangerous in case something goes wrong, again. Alternative Access Funds Some are scared that if the economy continues , corporate loan defaults could rise, leading to a wave of CLO write-downs and default—as it happened with CDOs in 2008. It should be mentioned that the structure of CLOs to CDOs from the economic perspective. Each pools multiple loans to create synthetic, bond-like investments. Investors buy a slice (or tranche) of the underlying interest and principal cash flows of the portfolio. A defined order of which investors get repaid first and which bear the most losses allocates risk differentially. to suffer from COVID-19 According to , Japan's top CLO investor, Norinchukin Bank, told in May that it would refrain from investing in more CLOs. Despite the fact that the default risk of CLOs appears low for now, rising risk aversion among investors had led to an unrealized loss of about ¥400 billion of its CLO portfolio in the March-end quarter. SPGlobal On the positive side, CLOs have greater protection in comparison with CDOs, as repackage corporate loans, primarily leveraged loans, as well as consumer credit such as automobile loans. Besides that, higher levels of losses are required before they lose money. Even though they are still pretty risky. Probably, it would be rational for investors to check the credit quality of the leveraged loans, which underlie the bulk of CLOs before anything. The information set forth herein has been obtained or derived from sources believed by the author to be reliable. However, the author does not make any representation or warranty, express or implied, as to the information's accuracy or completeness. Nor does the author recommend that the attached information serve as the basis of any investment decision and it has been provided to you solely for informational purposes only and does not constitute an offer or solicitation of an offer, or any advice or recommendation, to purchase any securities or other financial instruments, and may not be construed as such. Disclosure:

Subprime 2.0 – Is 2008 is back?

In 2017, cyber-attacks invaded the privacy of millions, grabbing headlines as established firms struggled to keep the noise down. Interestingly, these breaches had a common theme — they were reported weeks after their initial discovery. Here are three of the many security breaches of 2017 that impacted millions due to reasons including low security, weak passwords or avoidable negligence. Equifax The breach of the Atlanta-based credit reporting agency, Equifax is one of the worst data breaches recorded in the history of cybercrimes. The heist involved highly sensitive information of consumer data that included personal details of 143 million customers, their social security numbers, credit card accounts and US drivers’ licenses. 11 million Many aggrieved customers are facing the of the breach that include “multiple fraudulent charges” on credit cards and “unauthorized mortgage loans” on associated accounts. Additionally, driver licenses information is paving the way for impersonators to impact websites and services that accept driver’s license as a requirement. repercussions But it is Equifax’s negligence and delays in addressing the issue that deteriorated matters further for the firm. The hackers had retrieved the data between May and July 2017 but it took the company six weeks to report the breach. After uncovering the breach, Equifax’s attempt to address concerns through a data breach tool was also a failure and its support site resembled just another phishing site. The site directed the consumers to sign up for Equifax’s credit monitoring product TrustID. Considered to be the “ ”, the agency was eventually forced to retract clause on the site that prevented the affected customers from suing the company. mother of all hacks Yahoo — Verizon Reported in August 2016, Yahoo’s hack originally traces back to 2014. Initially, the breach impacted at least 500 million user accounts. But over a period of time, Yahoo’s breach took complicated turns, with deeper revelations. By the end of 2016, the company opened up on another hack which impacted one billion users. After four months of Yahoo-Verizon merger in mid-June, Yahoo’s bidding firm Verizon made a startling announcement. In a recent update in October 2017, it was revealed that each and every Yahoo account was impacted and this was close to . 3 billion The disclosure made Yahoo a home to the largest data breach in history, raising serious concerns on the stolen data. In its statement, Verizon , “The company recently obtained new intelligence and now believes, following an investigation with the assistance of outside forensic experts, that all Yahoo user accounts were affected by the August 2013 theft.” said But on July 13, 2017, Verizon too found itself amidst a hack that impacted 14 million subscribers. The consumers who had called customer services in the past six months of the July hack were exposed to the data breach. reported that the data of Verizon customers were available for download after an employee at an Israel-based firm, Nice Systems left them on an unsecured Amazon S3 storage server. The security lapse was discovered by the Director of cyber risk research at security firm UPGuard, Chris Vickery in June of 2017. ZDNet Such breach exposed records of customers’ information which included their names, cell phone number and account PIN. Uber When talking about breaches, Uber’s wait-watch-disclose policy provides a learning lesson for many startup hypes on why a business model needs to be an ethical one. On November 21, 2017, Uber announced that personal data of as many 50 million riders and 7 million Uber drivers in the US was stolen in a breach that dated back to October 2016. The stolen information (as reported by ) can allow hackers to find their homes and even their travel history. CNBC The company had not revealed any information about the hacker or how it paid him the money. To worsen matters, Uber paid the hacker to delete the data. According to , Uber’s newly appointed CEO Dara Khosrowshahi said that the disclosure to regulators should have been made at the time it was discovered. $100,000 Reuters After the disclosure of the security breach by Uber, two of the company’s top security officials were dismissed. The breach raised some serious concerns about the ethical and privacy policy of the ride-hailing app. Why are data breaches getting common? Poor cybersecurity practices can give way to cybercrimes. For established firms like Deloitte that pride themselves on Cyber Intelligence Centre, data breaches can be embarrassing. In March 2017, accounting giant Deloitte fell victim to a cyber-attack. reported that “a host of clients had material that was made vulnerable by the hack” which included The US departments of state, energy, homeland security and defense, the US Postal Service, the housing giants that fund and guarantee mortgages in the US, Fannie Mae and Freddie Mac. A Data Breach Investigations Report (DBIR) by Verizon showed that 70% of the breaches are financially motivated and 80% of the hacking-related breaches were either due to stolen passwords and/or weak passwords. The Guardian recent Any security breach stems from lapses in the privacy practices of the firm. At the time of the hack, Deloitte did not have multi-factor authentication, which allowed outside hackers to get into the system through the administrator’s account. Outdated Security Technology Outside hacking can be malicious and the cost of such attacks is costlier when compared to data breaches through system glitches and human errors. Extraction of personal data by hackers should be addressed immediately without delay before it spreads across the entire customer base and hackers cover their tracks. As cyber-attacks become common, it is important for CEOs of firms address the issue of cybersecurity diligently and create a protected work environment. In an interview with , McAfee CEO Chris Young said CEOs must enforce a “culture of security”. CNBC An report shows that “faster the breach can be identified and contained, the lower the costs”. But real-time detection remains distant for many companies but a grave concern is when reports of data breaches are kept under wraps for a long time. Delay in Report and Response IBM Reporting data breach years after it has been committed only worsens the situation for both the consumers and the firm’s goodwill. Late disclosures erode the trust in organizations. Uber and Yahoo are examples of how companies should not delay the disclosure. More than anything immediate efforts should be made to fix the issue since delaying it only aggravates the existing issue. Some of the biggest breaches like that of Yahoo took years to be revealed. Yahoo in its two consecutive hacks reported in 2016 revealed that they actually dated back to as early as 2013 and 2014. Yahoo’s recent update in 2017 worsened the issue when it was revealed that the breach actually impacted 3 billion accounts instead of 1 billion reported in 2016. According to latest findings by digital security provider , in the first half of 2017, almost 2 billion data records around the world were either lost or stolen through cyber-attacks. A cyber-attack is a serious crime and its impact can be devastating to the security of people of any country. In the coming times, protecting sensitive information from hacks is highly crucial and in the light of growing cyber-attacks, people should keep a close eye on fraudulent activities like bank account activities, revelations of credit card information and phishing scams. Gemalto Originally Published on Digital Disruption

Lessons from 3 Big Data Breaches of 2017

We all hate spending money on utilities. I highlight companies and other initiatives working to save renters money on energy — by providing portable, cheaper tools, new financing tools to afford expensive retrofits, or new sources of green energy cheaper than your utility. As renters, we should be aware and support these efforts. According to a survey by , more renters are worried about rising utility bills (70%) than rising rents (63%). Freddie Mac These concerns aren’t unfounded. Utility costs (electricity, gas, water, and sewer), from one analysis of public real estate records, add up to to renters’ costs. These costs hit low-income renters the hardest, eating up . The biggest utility culprit is — Of electric and gas costs, of it is due to heating and cooling. It’s clear, then, that more energy-efficient and energy- units mean more and financial empowerment for renters. 25% to homeowners’ costs and 27% 21% of their incomes entire energy electric and gas take up 49% of total utility costs, followed by internet and cell phones (29%) then water (7%). 58% producing money Most solutions are impractical or too expensive for renters and landlords Renters are to be energy efficient. Solar panels or insulation retrofits are (1) too annoying or impossible to move to your next apartment or (2) too with . Landlords — who own the building — otherwise do not care about saving you money since you pay for your own utilities. This gap between renters’ and landlords’ interests is called the “split incentives problem.” So you — the renter — use what’s there: Drafty windows and earth-killing appliances your landlord hasn’t updated since the 1980s. 30% — 40% less likely than homeowners expensive large up-front costs The split incentives problem leaves a lot of money on the table. of the floor, wall, and ceilings, , saved 63% off heating costs, resulting in $500 of savings a year. The most cost-effective retrofits, such as $5000 fabric insulation and double-glazed windows, the University of Nottingham found, have . Similar studies found that controlling the temperature in each room throughout the day — and shutting off air flow in empty rooms using smart vents, like or — can result in energy savings from to for a cost of . Closing your shades at night, for instance, can save up to of your heating costs. If the thought of closing all those blinds everyday overwhelms you, . Insulation retrofits the Salford Energy House study found payback periods of 3 to 6 years Ecovent Flair 25% 40% $490 per room 17% Luftron’s smart shades open and close at optimal times at $350 per window Unfortunately, these cost-saving solutions are too expensive or impractical for most renters, especially if they can’t take those improvements with them to their next apartment. Fortunately, startups and other initiatives are starting to address these problems. Meet the startups fighting energy waste Strategy #1: Affordable and portable tools for renters To address the fact that some tools are too cumbersome or impossible to move to your next apartment, a swath of portable energy-saving or energy-producing tools have come onto the market. . A $250 Nest can result in savings of of your heating and cooling costs, with a by saving $120/year. These are small and easy enough to install and reinstall as you move to different apartments. produces a smart thermometer that collects hourly temperature data to advocate for tenants who suffer from a lack of heat. Smart Thermometers 10–15% payback period, as one analyst estimates, of 1.5 years Heat Seek Example smart thermostat . The connects to your home’s electrical panel and provides insight into your energy usage. Neurio, which is a monitor for solar energy, claims to reduce your payback period for solar by . pays you money to cut back on electricity when there is high demand for it in your area. Residents, according to the FAQs, can make a year. Energy monitors Sense Home Energy Monitor 30% Ohm Connect $100-$300 Sense Home Energy Monitor tells you where you’re wasting the most energy . For $200-$400 or , local technicians measure airflow and detect sources of drafts in your house. Using this information, you can use targeted insulation to save money on heating and cooling costs. One person documents that he spent $400 on the audit and , resulting in a one-year payback. Search the ’s database to find an energy auditor near you, but confirm they’ve passed muster by searching their business in the Better Business Bureau database. Furthermore, check to see whether your electric or gas provider will give you a rebate to install insulation as a result of your audit. One person found for these items. Energy audits even for free $500 on insulation and labor costs to save $1000 a year residential energy services network his provider gave a 75% rebate Energy auditors come to your home and find sources of drafts and energy loss using data like the above. . The $200 — $3000 sets and offer allow you to avoid expensive labor and construction costs because these panels can be mounted on decks or placed on the ground. Additionally, they can be plugged straight into your home’s existing outlets and generate electricity. Enthusiasts have adapted these panels for use in their apartments, placing them near or balconies. One Michigan Technological University study found that , . In the future, could be commonplace, sidestepping the need for space or labor-intensive physical alterations to install solar. Plug and play or portable solar Pluggedsolar Inergy Solar window sills plug and play systems can create payback periods of under 5 years 2 years faster than normal solar systems energy generating solar windows ” using window sill and balcony panels. One author aspires to put his apartment “off the grid By wrapping a $250 Cozy over your radiator, you can control your room’s heat better. No longer do you have to open your windows in the dead of winter. Plus, a Cozy is digitally monitored. So when they’re placed throughout a building, data is fed back to boilers to redistribute heat. Increases in energy efficiency result in a . Smart Radiator Covers. payback of two years . ThinkEco’s $45 modifying outlet (or “modlet”) easily plugs into existing outlets and prevents appliances from consuming standby power. Also called “vampire energy,” this form of power use has accounted for up to nearly . . Smart outlets 7–8% of the UK’s household energy use Payback periods range from 6–9 months Strategy #2: New financing to overcome up-front costs To address the fact that some tools or retrofits too expensive to purchase, a swath of new financing opportunities have come onto the market to make $10k-$20k solar panels or insulation retrofits affordable for renters. Importantly, while landlords have to provide consent, these tools don’t require the same renter — who will, on average, move out in 1–2 years — to pay back the obligation. . Landlords have to disclose this obligation to new tenants, but this should not be a barrier as tenants generally face unchanging utility costs and (in Arkansas) are initiatives that pay for energy-efficiency or solar retrofits upfront (or help you find lenders). The energy savings over decades pays for the upfront costs plus interest, resulting in minimal or new increases in the renter’s utility bill. This financing tool is called “on-bill financing.” If a tenant moves out before the payback period, the , since the improvements stay with the physical structure. Similarly, targets homeowners and has plans to work with renters in the future. On-bill financing. Matter.solar Ouachita Energy Cooperative obligation can be transferred to the new tenant Sealed , where third parties pay for costs up front (the “solar loan payment”) and beneficiaries pay back the loan helped by electric bill savings Sealed’s visual illustration of on-bill financing Instead of relying on a single company, like Sealed, to provide financing, and scales this concept. Investors in the same state can lend money to fund energy-saving or energy-producing retrofit projects. or, in Mosaic’s case, even by selling extra power back to utilities. using BlocPower’s scheme. Crowdfunding. BlocPower Mosiac Tenants can pay their obligations back through on-bill financing One church saved $3000 a month People and other smaller investors — not banks or big investors — to finance your project. lend you capital . While this solution still requires you to get consent from your landlord, Grid Alternatives provide to low-income families in exchange for allowing community volunteers and job trainees to get hands-on experience (“ ”) to install those panels. Because this model is free for the renter and landlord, landlords can’t complain about the cost and there are no obligations to payback. Barnraising free or low-cost panels barnraising Grid Alternative supervisor and trainee at work Strategy #3: New sources to buy energy beyond your local utility This strategy minimizes both aspects of the split-incentives problem. Tenure isn’t an issue. Renters can buy cheaper, renewable energy sources regardless of where they live. Upfront costs aren’t an issue. Third parties build and maintain these farms. Lastly, landlord consent isn’t an issue. Local solar farms sidestep any need to install solar on your roof; a requirement that blocked up to of US residents from using solar energy. 92% , which is available for NYC residents, buys, trades, and sells energy from multiple suppliers using machine learning algorithms to optimize for price. They . “Smart” energy retailers. Drift estimate cost savings of 10%-20% . , Renovus, and build solar gardens into which renters can for little or no upfront cost using on-bill financing. Renovus in New York and Nexamp in Massachusetts, for instance, allows renters to purchase energy with no upfront cost that is — cheaper than market rate. Cost savings from solar-produced energy not only pay for the lease but also result in credits to your utility bill. you can use to find a community solar project near you. Solar farms Solstice Arcadia Power lease solar panels 10% 15% Energysage has a database No longer do you need a large, centralized solar farm, which would be expensive or impractical to house in space-scarce cities. Microgrids are networks of independent energy producers — your neighbors, for instance — who can sell their excess energy to you. You can buy cheaper energy and they can make extra money. up to 13% of costs. University of California San Diego, for instance, saves with its microgrid. Green Mountain Power’s . Efforts are underway by , ME SOLshare, and Sonnen, providing access to energy to rural and urban dwellers alike. Micro-grids. By using the newest technologies, reducing the distance of electricity transmissions, and removing overhead, microgrids can cut $8 million a year $2.7 million microgrid will pay itself off within 2–5 years Lo3 Energy With nightfall, solar panels can no longer generate electricity, forcing homes to rely on the grid. But with batteries, solar can store extra energy produced by microgrids and solar farms throughout the day. A $14,000 battery can take to pay back. While targeted towards homeowners, can benefit renters by increasing the efficiency of solar farms and microgrids who sell energy to renters. Batteries that store extra energy. 8–12 years Swell Illustration of Brooklyn’s Microgrid Project Ideally, we’d live in a society where policies would incentivize developers, landlords, or renters to build or retrofit green. See, for instance, Germany’s to help renter access solar on their apartments. But until then, to save money on your utility bill, it’s going to take some work. These products can be good investments in the long-term, paying itself off and saving you money over the decades. recent bill If you’ve used any of these products or services, I’d love to hear your experiences. ☞ Agree or disagree? Add me on LinkedIn with a note here .

3 types of startups helping you fight energy waste and save money

If I were to describe the perfect virtuous act, it would be to take an uncomfortable position, one penalized by the common discourse. — Nassim Nicholas Taleb, Skin in the Game, p. 188 Nassim Taleb’s latest book heaps praise on the trait that personality psychologists call low agreeableness. My essay is a short meditation on the trait of disagreeableness and how it relates to my own life. I am pretty far out on the disagreeable end of the spectrum myself, but Taleb makes me look like a goody two-shoes. (This is not a full review of . I plan to get around to that after I have read it carefully a second time.) Skin in the Game Agreeableness is one of the elements in what is known as the five-factor model of personality. A quick web search turned up a description from something called the . Positive Psychology Program People on the low end of the agreeableness spectrum . . . tend to be callous, blunt, rude, ill-tempered, antagonistic, and sarcastic. I doubt that anyone reading would question that Taleb displays these traits. His tone has its charms for those of us who share his disagreeableness to some degree. My guess is that it puts other people off. Skin in the Game We disagreeables tend to lean libertarian. Indeed, found that, when compared to liberals and conservatives, a large study Libertarians score the lowest of the three groups on empathizing, and highest of the three groups on systemizing. . . On this and other measures, libertarians consistently come out as the most cerebral, most rational, and least emotional. . . .They score lowest of the three groups on many traits related to sociability, including extroversion, agreeableness, and conscientiousness. Agreeables follow orthodoxy. . Eric Weinstein, another disagreeable, thinks that we would see more scientific progress if we to disagreeable scientists and left them alone. Taleb makes liberal use of the expression (expletive) money, but his idea for science is different. He thinks that professional scientists who use other people’s money have no skin in the game. He would prefer to see people from other professions do research in their spare time. But although their solutions may differ, Taleb and Weinstein share the disagreeable view that mainstream science is too risk-averse and conformist. We disagreeables do not gave (expletive) money Agreeables are good at climbing hierarchies, such as the corporate ladder. Disagreeables are more likely to become entrepreneurs. I hardly advanced in two hierarchical organizations, the Federal Reserve Board and Freddie Mac. If you count academia as another hierarchy where I hoped for achievement, then that makes three unsuccessful attempts. Then, in 1994, I launched my own business, and that worked out better. Once I had skin in the game, I took on tasks, like cultivating contacts and making sales pitches, that I never would have tried otherwise. Agreeables tend to take people’s words at face value. Disagreeables do not. One of Taleb’s themes is that people’s words are not trustworthy unless they are backed by skin-in-the-game action. He writes, The doer wins by doing, not convincing. Entire fields (say economics and other social sciences) become themselves charlatanic because of the absence of skin in the game connecting them back to earth Perhaps Taleb would agree with my idea to . I believe that my own experience, both in large organizations and in my own business, helped shape my views. require internships for economists A recent powerful statement of the disagreeable outlook on hypocrisy is , by Kevin Simler and Robin Hanson. In their book, they look at the strategic motivations for human behavior, especially how we present ourselves to others. Hanson is known for advocating prediction markets, which would turn forecasting into a field in which participants have skin in the game. The Elephant in the Brain Another recent book, , could only have been written by a disagreeable. Author Bryan Caplan is fond of challenging people to bet on their opinions. Implicitly, he believes in skin in the game. The Case Against Education Caplan thinks that people only get degrees in order to “signal” to employers that they have desirable personality characteristics. For his part, Taleb writes, Someone who has been employed for a while is giving you strong evidence of submission. . .going through years depriving himself of his personal freedom for nine hours every day. . .He is an obedient, housebroken dog. Combining Taleb and Caplan into a single disagreeable point of view, one might say that employers take the graduates of obedience school. Taleb writes, You can define a free person precisely as someone whose fate is not centrally or directly dependent on peer assessment. Agreeables play games where judges award points to determine the winner. Disagreeables play games where you win by scoring goals. Agreeables are sorry that Hillary Clinton lost. Disagreeables are not. Taleb refers to her as “Hillary Monsanto-Malmaison.” I am not sure what that means, but it is safe to assume that it is not a compliment. In any case, disagreeables take a negative view of politics as a vocation. Agreeables believe in the propriety of existing institutions and in their personal status within those institutions. Disagreeables expect to see existing institutions fail and to see the phoniness of the high-status individuals exposed. But my guess is that society needs to include a mix of both agreeables and disagreeables. It appears that we have had such a mix for a long time, and Taleb would be the first to say that whatever has survived a long time has demonstrated fitness.

Nassim Taleb and the Disagreeables

The American military command thought this was like any other war: you searched out the enemy, fixed him, killed him, and went home. The only measure of the war the Americans were interested in was quantitative. . .That the body count might be a misleading indicator did not penetrate the command; large stacks of dead Vietcong were taken as signs of success. — David Halberstam, The Best and the Brightest Economists preach that incentives matter, and this is true. But when we preach that incentive systems should be based on quantitative measures, we encourage business leaders and government policy makers to go badly wrong. Misleading statistics cause mis-leadership. The debate over these practices too often gets channeled into a search for better measures of performance. But this side-steps the deeper problem, which is the . Executives in business, non-profits, and government sometimes have too much faith in their ability to direct processes that operate deep inside the organization or the larger society. The statisticians and efficiency experts fail to appreciate the autonomy of human beings. illusion of control During the Vietnam War, Secretary of Defense Robert McNamara relied on quantitative measures, including the infamous body count, that showed progress. It turned out that these were the wrong indicators, or perhaps it was the wrong war to be fighting in the first place. The war was a tragedy of mis-leadership Closer to home, years ago when we moved to a new neighborhood a friend excitedly told us that average test scores at the local elementary school were the highest in the region. It turned out that this was achieved by the school administrators telling weaker students to stay home on test day. The state Board of Education, believing that publicizing test scores would lead schools to undertake real improvements, was engaging in mis-leadership. For more examples of mis-leadership involving quantitative measures, I recommend a recent book, , by Jerry Z. Muller. Also, a recent article by Megan McArdle, , gives still more examples. The Tyranny of Metrics Metrics and Their Unintended Consequences Goal Diversion and Corruption The problems with quantitative measures are well known. One is what Muller calls goal diversion. In a complex situation, such as the Vietnam War, there are many intermediate objectives to be met. Measuring one objective, attrition of the enemy, diverted attention from other objectives, including winning the hearts and minds of the local population. Another problem is what Muller calls corruption, in which the numerical goal is met by means that subvert its intent. Corruption is illustrated by my example of the school that boosted its scores by telling weaker students to stay home on test days. Another example of corruption is the . Wells Fargo fake accounts scandal Both goal diversion and corruption played big roles in the financial crisis of 2008. Goal diversion resulted from regulatory “affordable housing goals,” which were intended to pressure Freddie Mac and Fannie Mae to expand mortgage credit to low-income borrowers. But this diverted attention from the goal of safety and soundness at the two mortgage giants. Their regulator should have required the two firms to augment their loss reserves and capital buffers to align with the additional risks that these loans represented. Corruption was involved in the way that banks used to adopt flimsy capital structures while apparently meeting or exceeding the imposed by regulators. The exotic financial instruments of the pre-crisis period, including “collateralized debt obligations” (CDOs) and “structured investment vehicles” (SIVs) were created to meet the letter of the regulations while totally violating their spirit. regulatory arbitrage risk-based capital requirements Judgment vs. Formulas Some of the worst problems of goal diversion and corruption can be reduced by following Amar Bhide’s advice to rather than relying solely on rigid formulas. For example, bank examiners can assess the purpose of a bank’s use of exotic financial instruments. They can assess the ability of bank executives to understand and control the risks that are embedded in their operations. incorporate judgment In fact, most businesses do not rely on formulas alone to set compensation. Instead, they give supervisors latitude to use judgment in determining bonuses and salary adjustments for the employees who work for them. This is because formulas cannot capture all of the performance goals that are embedded in today’s complex business environment. Supervisors who are closer to the employee are in a better position to weigh various factors, including contributions that are difficult to quantify. Even when businesses tie compensation partly to formulas, they tinker with the formulas from year to year. The longer they stick with any one formula, the more ways that employees will come up to “game the system.” Every formula is subject to becoming corrupted after it has been in place for a while. The Illusion of Control The worst cases of mis-leadership come about when metrics are used to feed the illusion of control. Sometimes, achieving a goal is not within the organization’s power. In that case, the intermediate measures produce arbitrary rewards and punishments, and they lead to behavior that makes things worse rather than better. For example, raising student test scores nationwide, using policies devised and implemented in Washington, D.C., is not an achievable goal. The overwhelming majority of rigorously-studied educational interventions fail to achieve a meaningful, lasting effect. Yet under the No Child Left Behind Act and the efforts that followed, schools are assumed to have solutions at hand for educating every pupil. They are measured and graded according to quantitative measures of performance. In short, Washington acts as if schools have exquisite control over student learning as reflected in (un-corrupted) test scores, even though research suggests otherwise. Politicians operate with the illusion of control. Instead of holding teachers accountable to a centralized statistical office, I believe that teacher evaluation should be undertaken by peers, principals, and parents. Test scores can be unreliable indicators for many reasons. Parents can readily assess whether a teacher is working conscientiously and effectively. I have only started reading Nassim Taleb’s latest book, . But I gather that he would make the point that when it comes to children’s education, parents have real skin in the game. If I allow my child to try to learn from a bad teacher, I suffer from that. If a bureaucratic system fails to remove a bad teacher, the designer of the system does not suffer consequences. Skin in the Game What is true in education is also true in other important realms. Statistical measures can help in certain well-defined activities, like running a baseball team. But in complex realms, including education, health care, and financial regulation, the best judges of performance are constituents who are close to the local situation (with skin in the game), not remote statisticians. Seeing Like a State Data collection and statistics have always been part of the process that James Scott calls . But no matter how intently the officials stare, their vision is blurry and out of focus. Seeing Like a State Regardless of what may take place in the minds of leaders, human beings are autonomous and difficult to control. Successful business executives understand this and learn to live with limitations on their ability to fine-tune organizational behavior. They know that they cannot measure performance with precision or design a perfect incentive system. Rather than stick rigidly to formulaic systems, successful executives encourage subordinates to exercise autonomy and judgment. In government, trying to rely on judgment raises the issue of favoritism. There is a bias toward using formulas to determine compensation, and there is a bias against flexibility in the design and especially in the implementation of those systems. In the public sector, there is an expectation of consistency and uniformity. This tends to run counter to giving autonomy to low-level managers. Government officials often do not work with realistic goals. Politicians, unfortunately, are rewarded for sounding confident about their ability to control the uncontrollable. The economists that appeal most to politicians are those who claim to offer solutions, and economists’ solutions typically involve metrics and incentives. But the statistical measures do not move organizations toward the desired objectives. Instead, they produce harmful unintended consequences. Because problems like education and financial regulation are complex, it probably would be best to approach them with flexibility than is typical in private business. But the nature of government works in the other direction. In this context, rewarding and punishing people based on metrics is mis-leadership. greater

Mis-Leadership and Metrics

Product Development Interview Disclosure: Manifold , the developer marketplace, has previously sponsored Hacker Noon. Use code HACKERNOON2018 to get $10 off any service. If you missed it, definitely check out the interview I did with them last quarter, . Today, we’re going to catch up with Manifold’s VP Product to find out what makes great products. We also discuss what makes boring people boring, product integrations, and what it’s like to live for over 100 years. Building the First Independent Marketplace for Developer Services Peter Cho David: On your LinkedIn profile, it says you worked at Heroku from 1900–2016. Do you think you held that job for too long? Peter: Those of us who work in tech love finding new challenges and learning new things. A byproduct of this culture is that many of us swap through roles, companies, or careers every 1–2 years rather than really, deeply learning something (I’m absolutely guilty of this). Many of us are in this loop where we go to and from each of the major tech companies or cycle between the top players in our particular niche, at times even “boomeranging” back for round two. (I’ve left jobs where on my last day they winked and said “If you decide to boomerang back we’ll be here..”). The great thing about this is that you get some awesome cross-pollination in the industry, spreading new ideas and processes in really short cycles. From a personal perspective, if done correctly you can also greatly accelerate your career growth. That said, it takes time to truly master something, and though I don’t believe we’ll revert back to being “lifers” at companies (e.g. IBM, GE, etc), I think we need to find a happy medium where people find career fulfillment for 5–10 years, even if it is at a fast-moving technology company. I think this will especially hold true as we start investing in deep tech, e.g. robotics, biotech, AI/ML. In all seriousness though, I actually put 116 years on LinkedIn to throw off recruiters. Ever since I revealed myself to be a vampire they’ve stopped sliding into my DMs. What makes a great product? A great product is one that fulfills the promise it makes to users. Every product promises , whether it’s an iMac, a stick of MAC lipstick, a Mack truck, or a Freddie Mac-backed mortgage. It promises to make your life easier, promises to make you feel a certain way, maybe it promises to provide nutrition. Bad products don’t fulfill their promise, they might be poorly designed laptops that run hot, have loud fans, and shut down a lot, making it hard to log onto Reddit. Bad products get your hopes up and shatter your dreams. something People like to say “oh, great products will change your life” and “they have to be world class” but I don’t think every product has to do that to be “world class”. You know, I don’t expect my living room rug to change my life. But when I bought it, I said as long as you provide me with some acoustic dampening, look nice and don’t shed, you are great in my book. I have that promise to set my mouth on fire, and they do the job admirably. Those are also great products. bottles of hot sauce With Manifold , you can add all kinds of services to your stack (Bonsai Elasticsearch, Mailgun, LogDNA, etc. ). How do you approach making the use of third party products within Manifold a seamless experience? The one thing that blows my mind about developers today is how patient they are. Every time they need a new service, they’ll check Google, Quora, Stack Overflow, StackShare, inevitably spending more time researching than they probably should. I am absolutely guilty of this. When I was deploying my first application to Heroku, I spent more time comparing Mandrill to Mailgun than I did on picking my general practitioner, the person responsible for my actual health. Once we do find a service that’s right for us, we create an account, set up a billing profile, find and read the relevant documentation, install the correct client libraries, and then go back to Stack Overflow to troubleshoot it. To be honest, that’s not a lot to ask for. You want a MySQL database? No worries y’all, I got this, be back in an hour. Oh wait, you want Email? ..and Monitoring? Redis? Elasticsearch? RabbitMQ? Memcache? Logging? Which credit card do each of these go on again? Frankie is leaving on Friday, which of these accounts were in his name again? Our credit card expired? Sorry, but I quit. The flip side of this is that you end up locked into one of the big box clouds, settling for a bunch of white-label services. Listen, we want to eat Cocoa Puffs, not CVS Chocolate-flavored Corn-Spheres. We want the convenience of a big box, with the freedom to choose best-in-class developer services. With Manifold, you use one account, a single credit card, and provision any of the aforementioned services within seconds. You want a Redis database that you can share with your team? Two clicks. Do you want to use that with Laravel, Terraform, or Kubernetes? We’ve got you. How and why did you end up becoming VP Product at Manifold? They assure me that it’s not because I’m the worst coder on the team, but I think they’re just being nice. Prior to , I was running Heroku’s Elements Marketplace. While I was there, the thing that struck me most was how everything still felt. As big as the industry feels right now, there are so many aspects of development that are unnecessarily painful, and many people think AWS has won the day. What I saw at Heroku was that there is still a huge opportunity to empower and nurture the next generation of Twilios, MongoDBs, and New Relics, hopefully preventing a future Parse or RethinkDB from happening again. The cloud today is the tip of a very large iceberg, and there are hundreds of entrepreneurs out there that are willing and able to create the next generation of developer tools and services, they just don’t know it yet. Manifold early What are your KPIs for product performance? How did you choose them? And what KPIs did you once think, but they weren’t? they may be important, Right now we’re really focused on onboarding, so funnel metrics are priority number one. What percentage of people are registering? Of those people, what portion verify, start to create a resource, finish creating a resource, and SSO into their dashboard? We’re dead-set on increasing these conversion metrics. Admittedly it’s not a glamorous answer, but we’re trying to be disciplined about the fundamentals before we get fancy. Before this, we had a really long list of vanity metrics like # of users, pageviews, etc, which felt good but didn’t help us understand our business. We’re a small, over-leveraged team, so prioritization is key. We decided that we need to do one thing, do it really well, and create a focused petri dish of strong engagement. (Focused Petri Dish of Strong Engagement was actually the name of my high school band). You could have a really successful Product Hunt launch and generate a ton of users, but if they’re just signing up and leaving, they’re not providing much value. I’m probably not going to invite these people to my wedding. Can you share some numbers about Manifold’s current usage? David, I am a huge fan of full transparency and would love to show you all our usage numbers, but unfortunately we’ve reached the max # of users on our Mode Analytics subscription. That said, my favorite number is 32, which is the percentage of users that come back for a second resource after provisioning their first. It’s a number we’re continuing to improve, and I love it because it shows the progress we are making on building a compelling, sticky product. What does your day to day work app usage look like? How do you maximize your time in these apps? 90% of my time is in Google Hangouts, the other 10% is a mix of Trello, GitHub, and Slack (coincidentally, these %s also accurately reflect my laptops CPU and RAM usage). Since we’re a distributed team with folks in San Francisco, Toronto, Hamilton, New York, Denver, Halifax, Fredericton, and Belgium, we rely a lot on tools that enable remote work. Most of our day to day happens in Slack, and we use Hangouts to coordinate, kickoff, and plan, the byproduct of which ends up in GitHub and Trello across our Engineering Waffle board and Product Roadmap boards. Although I’m happy with the current state of remote work apps, one of the things that we sorely lack is the ability to build camaraderie and create spontaneous watercooler moments. I’ve been pushing for us to incorporate apps like Overwatch and League of Legends to combat this, but apparently these aren’t considered “work apps”. fairly What changes to functionality of one of those products is an example of quality product development? My favorite new feature of recent memory is the Shared Channels feature on Slack. It’s not something that would be immediately obvious, but it’s been a big pain point for me, across my last few roles. When I was at Heroku, we partnered with Facebook on an integration with Parse. I had to create a new standalone Slack instance and invite everyone involved to it. And then at Manifold, with our first dozen integrations we had been creating one-off guest channels and inviting users, but it just adds a lot of overhead. (Can you invite this person? Can you create a new account for this one instance?) Being able to create a channel and share it using the same account details is amazing, and they made it really seamless. In building Manifold, what feature surprised you with high customer value? What feature surprised you with low customer value? I mentioned earlier we’ve been really focused on onboarding. One of the things that surprised me the most is that the things you think are really obvious, are typically not that obvious to your users. The things that provide the most value are the little things that push your users into better understanding your product, which we often take for granted. We’d spend days writing and rewriting all this amazing copy, then realize that users were getting hung up on what a “resource” was. A “resource” is a thing we sort of invented, it’s an instance of a service that you’ve provisioned. However without context, it was confusing users in our tests, which is when we realized we need to either stop inventing words or we needed to spend more time providing education and context. Ideally a little mix of both. In a similar vein, we’ll spend weeks or months building some “game-changing” feature, but take for granted that users might not understand or agree how revolutionary the feature actually is. Investing in the small things, in making sure your own biases and perspective don’t blindside you with users, is a “duh it makes sense in retrospect” lesson that happens more regularly than I’d like, but keeps us humble. According to your Twitter bio , you describe yourself as “a boring person.” Could you share something you did today that is so boring that it is interesting? My team’s reply to my first draft answer to this question was “you sound like a dad”. Which probably says enough in itself to explain my Twitter bio. I do want to mention that beyond KPIs, day to day tools, or the gift of immortality, the single most important factor that determines whether you will build a great product, is whether you have a great team. I would be nothing without our incredibly talented product management duo and , or the design powerhouse known as and . I would be a sad shell of a human being without them. Also, this interview couldn’t have happened without our marketing programs manager, . If we’re being honest I’m just Manifold’s product puppet figurehead. Thus, boring person. Ming Sitara Meg Nick Margaret MacAfee What’s the coolest new project built with Manifold? ! One of the most interesting projects we’ve got underway right now is figuring out how to build Manifold with Manifold. We’re going to be writing a blog post about how we made it happen, and how we use Kubernetes internally, so make sure to keep an eye out on ! Manifold our blog

Building Great Products with Manifold

Learnings from getting my feet wet in a Kaggle competition The dust settles on the first Kaggle competition I’ve put serious time into and though still quite far from the coveted top spots, I reached the goal I had set out of earning a first medal for , and even got a silver medal for placing in the top 2% in the competition, ending up as number 58 out of over 4000 competing teams. my profile page More than the end placement though, the competition gave some good learnings when it comes to competitive data science, and data science in general, that I’m noting down in hope it will help other people avoid making the same mistakes I’ve made. Learning from others mistakes is the best strategy after all, since you won’t have time to make them all yourself. 1. You are not special. You’re not a beautiful and unique snowflake Start simple, get inspiration from kernels available and read through the competitions discussion forum. The likelihood that you will have a genius break-through idea early on that no one else have thought of is very small. What not to do as first steps includes; Taking inspiration from the paper and try word2vec embeddings or various categoricals as the first thing after the first simple feature aggregations. Prod2Vec Seeing how that didn’t work, spend a lot of time on t-SNE dimensionality reduction of the same categoricals, not even if you are using an autoencoder trained on the non-outlier set. This 50/50 sample of outliers and non-outliers might look promising, but the extreme imbalance of the dataset adds too much noice for it to yield anything useful in the end . Definitely interesting to try out, and read through the paper, but it was a poor fit trying to try to shoe horn it into this problem. Field-aware Factorisation Machines Rolling window time series features. Trying to catch temporal aspects of customer behaviour changes over time. I still like the idea though it didn’t pan out on this data set and it was a rabbit hole I went way too deep into before I explored some much simpler feature engineering ideas. That said though, this amount of failed experiments have meant a lot of good learnings for the future. I might sum up the whole experience in Thomas Edisons immortal words: I have not failed. I’ve just found 10,000 ways that won’t work. Rather than what not to do, let’s move on to what is a better way to spend your time. 2. Get to know your data I know, I know. This is so elementary that it feels silly to bring up. Still, it’s so easy to proceed to model building after just a cursory data exploration, and in case the data contains some curveballs, you will end up spending a lot of time on a hopeless cause. Distribution of target variable. Is this a regression or classification task? Make sure to read through the data descriptions, write down the question marks you have and try to answer them. In hip ML circles, everyone these days wants to brand themselves a data scientist and not a data analyst. Still, I’d say a thorough understanding of your data in most cases is worth much more than any fancy algorithms you might throw at it. 3. The struggle (with data leakage) is real You have this brilliant idea, calculate outliers likelihood per merchant and use it as feature in your regression model. You see amazing results in your train and val sets, and horrifying atrocities on the test set. What happened? Stacking models is powerful, maybe not so often for production use cases but definitely to squeeze out the last third decimal loss reduction in a Kaggle competition. If you are not disciplined and careful though, you end up shooting yourself in the foot. Make sure you read up on and before you go wild. data leakage model stacking 4. Deep Learning won’t automagically solve all your problems Though deep learning is sometimes hyped as the end-all solution, there are still plenty of problems where other algorithms perform better in practice. With this datasets combination of regression and classification aspects, gradient boosted tree models turned out clearly ahead of neural networks that had a hard time converging on a stable minima due to the non-continuous distribution of target variables. Tree based models further have the great benefit of not being based on the same as many other models, meaning you don’t have to do as much data preparation. Starting with a Random Forest and/or Gradient Boosting Machine will give you a good benchmark to beat if you decide to get fancy with neural networks. assumptions of normality Seriously, it’s quite insane how good is. LightGBM 5. Speed of iteration over correctness With big datasets and number of features, everything slows down. The speed of a full training iteration is equal to how fast you can run an experiment too validate if you are on the right track or wasting your time, so this is a big problem. Thus you should always be on the look-out for how to eliminate redundant work. A first step is to store aggregated features so you don’t have to recreate them every training run if nothing has changed, and so you easily can mix and match different feature combinations during different tests. Further, for many intents and purposes, working on subsets of data is good enough and much faster. When possible, work with smaller samples during analysis to evaluate ideas and trust in the that the sample is somewhat representative. law of large numbers An apt metaphor for the efficiency of speed of iterations over absolute correctness would be how mini-batch gradient descent allows faster convergence than using the full data for each learning iteration. 6. …and all the pieces matter Going rogue in a Jupyter notebook only works for so long before the freedom given by the interactive programming paradigm ends up with your cell execution in the wrong order, or you realise you’ve accidentally changed the code used to generate the feature set that got you those splendid results and now you can’t figure out how to recreate them. It’s true what they say, with great power comes great responsibility, and the two things that has helped me get a better structure during work are: Make sure you store relevant hyper parameter settings and feature combinations used for each experiment together with the cross validation results. Use version control to be able to go back to earlier states when you encounter dead ends. My current workflow; import local package containing the stable code and use automatic reload in the notebook so that changes in the files are automatically detected and picked up. Jupyter magic; %load_ext autoreload%autoreload 2 Once this works, you can prototype new logic and functions in the notebook while calling all stable logic from your package. When you find a new piece of logic that is worth saving, directly move it over to your package, ensure nothing got lost in translation and then commit. If related to an experiment, you can store the current CrossVal in the commit message. Conclusion or: How I Learned to Stop Worrying and Love the Bomb After the competition ended, the public leaderboard vs. the private leaderboard became a great shake-up, and I was at first very positively surprised to see that I had overnight advanced quite a bit. When checking the submissions though, I fell like the ground disappeared underneath me when I realised that my final two candidate submissions that I was ranked on were in fact not my best submissions. Had I chosen the best final submission, I’d ended up still 20 spots higher or so in the final tally (conveniently ignore the fact here that that’s likely true for a whole bunch of people). Similarly, when browsing the forum explaining the top teams submissions, I realised all those simple things that I just didn’t think of that would have improved my model even more. Clinging to this type of what ifs won’t yield anything productive, but is all too easy. If I had only… A more constructive path is to practice mindfulness and try to let go of any regrets. Once it’s all said and done and there’s nothing you can change, you are better off instead focusing on learning as much as possible from all the great minds sharing their insights and strategies in the forum after the competition ends. What is next for me then? To keep it simple, I will end this post the way it started, with an inspirational quote since there is always someone who already have said it better than you ever would be able to. This time one of my all time favourite quotes from the old fox Samuel Becket; Try again. Fail again. Fail better.

Stories, News & Mentions about FREDDIEMAC

Freddiemac’s Stories on HackerNoon

Freddiemac's News & Mentions