5859
Why crypto is like the internet (Twitter thread)
Interesting thread on the parallels between the Internet and Blockchain. In particular, the ability for anyone to create a new market in anything. A bit hyped, but worth reading.
internet  cryptography  markets  distribution  economics 
4 days ago
Vengeance As Justice: Passages I Highlighted in My Copy of "Eye for an Eye"
Review of "Eye for an Eye" (authored by William Ian Miller).

This is the logic of lex talionis. This is why "an eye for an eye" did not in fact make the whole world go blind. The principle of an eye for an eye, as Miller sees it, is "the more ancient and deeper notion that justice is a matter of restoring balance, achieving equity, determining equivalence, making reparations... getting back to zero, to even." [3] Trading eyes for eyes is not so much about indiscriminate, unthinking violence as it is carefully calculated attempts to match punishment to crime. Talionic justice is a system built on deterrence--not only deterring criminals from committing crimes, but deterring vengeance seekers from exacting too heavy a price in retaliation for crimes committed against them. This is empathy enforced by blood. You think carefully about the pain you inflict on others knowing, that measure for measure, the pain you give others will be given back to you.
justice  vengeance  retaliation 
4 days ago
Assessing software engineering candidates
Good read on hiring and evaluating candidates.
Mostly, the idea is to use written material.
Evaluate for the following traits:

- Aptitude
- Education
- Motivation
- Values
- Integrity
interviews  hiring  evaluation 
6 days ago
How to Design Programs, Second Edition
Online textbook for teaching programming. A first course on programming. An alternative to SICP. Uses Racket (more specifically DrRacket).
sicp  htdp  teaching  programming  firstcourse  introduction  felleisen  racket 
11 days ago
Sequences
Alex Miller's take on Clojure sequences.
Always worth a read.
alexmiller  clojure  sequnces 
15 days ago
Inside Clojure's Collection Model
Alex Miller's take on Clojure collections.
Always worth a read.
alexmiller  clojure  collections 
15 days ago
Flowchart for choosing the right Clojure type definition form
Clojure offers a number of different forms that define types (and generate Java classes). Choosing between deftype, defrecord, reify, proxy, and gen-class can be a tripping point for those new to Clojure.
clojure  deftype  defrecord  reify  proxy  gen-class  interop  type 
15 days ago
The Beauty of Clojure
Clojure component versus mount.
Claims that mount leads to more idiomatic clojure code.
clojure  component  mount 
16 days ago
The False Choice Between VC and Bootstrapping
The benefits and challenges of going the VC route. And the same for the bootstrapping route. And the false choice between them. You can do both. Bootstrap until you need financing. And then, choose wisely and on good terms.
Good short article on the tradeoffs.
vcs  bootstrapping  entrepreneurship  startups  financing 
16 days ago
Killing the Coding Interview
Instead of asking candidates to code, try:

1. Dig Into Their Experience
2. Have Them Review Your Code
3. Design a System Together

Also, the cases where you should use a coding interview:
a. Determining if entry-level candidates can program at all.
b. If your organization mandates lots of pair programming, pairing in the interview is a good preview of their future work.
c. If a role might require a very specific programming skill and you won’t have time to teach it. This is especially true if you’re on the fence between two seniority levels or titles.
d. When you’re transitioning from a coding interview to a code-free interview.
interview  coding  pairprogramming  questions 
16 days ago
Hardening macOS
Some steps to take to harden MacOS.
Worth considering.
macos  hardening  security 
16 days ago
Leaked Andreessen Horowitz data reveals how much Silicon Valley startup execs really get paid, from CEOs to Sales VPs
CTO (Sep 2018) median (excluding cash bonuses)
Series A: 240lk
Series B: 250k
Series C: 263k
Series D: 450k

CTO Equity median (consumer)
Series A: 1%
Series B: 1.6%
Sereis C; 1.5%
Series D: 1%
compensation  startups  salary  bonuses  equity 
16 days ago
Hardware for Deep Learning. Part 1: Introduction
Semi-regularly updated review of the issues related to deep learning and hardware.

In particular, see Part 3 about GPUs.
hardware  servers  deeplearning  dlcpus  gpus 
16 days ago
Why building your own Deep Learning Computer is 10x cheaper than AWS
First of a good series on building your own deep learning machine.
aws  deeplearning  dl  server  host  rig  diy  hardware 
17 days ago
How Are Structured Logs Different From Events?
The case for logging structured data. And tons of it (never know what you'll need).

See also the twitter thread
https://mobile.twitter.com/mipsytipsy/status/1042817542648082432
logs  logging  events  structuredata  structure  structuredlogs 
17 days ago
Machine Learning Confronts the Elephant in the Room
A visual prank (adding an elephant profile into a living room scene) exposes an Achilles’ heel of computer vision systems: Unlike humans, they can’t do a double take.

When human beings see something unexpected, we do a double take. It’s a common phrase with real cognitive implications — and it explains why neural networks fail when scenes get weird.

The human visual system says, ‘I don’t have right answer yet, so I have to go backwards to see where I might have made an error. Most neural networks lack this ability to go backward.
ml  machinelearning  imagerecognition  failure  adversarialattacks  feedforward  selectivetuning 
17 days ago
3 facts about time series forecasting that surprise experienced machine learning practitioners.
1. You need to retrain your model every time you want to generate a new prediction.
2. Sometimes, you have to do away with train/test splits.
3. The uncertainty of the forecast is just as important as, or even more so, than the forecast itself.
timeseries  forecasting  forecasts  ml  machinelearning  uncertainty  retraining 
17 days ago
Atlassian Team Health Monitors
Forms and processes to monitor the health of teams. Project teams, leadership teams and service teams. They also provide playbooks to address deficiencies. Essential reading. From Atlassian.
See also
https://www.atlassian.com/team-playbook
atlassian  teams  culture  evaluation  monitoring  monitors  teamhealth  deficiencies 
17 days ago
Glitch Employee Handbook
Radically transparent handbook for Glitch employees.
Good reading. Good ideas.
glitch  handbook  employeehandbook  culture  hr 
17 days ago
Notes to Myself on Software Engineering
A list of reminders when developing software.
On Development, API design and software careers.
Worth a read.
software  careers  design  api  philosophy 
19 days ago
Your Calendrical Fallacy Is...
A list of all the various false beliefs we have about calendars.
calendars  fallacy  fallacies  beliefs  false 
19 days ago
Interviewing is a noisy prediction problem
Good, in-depth article on the technical interviewing process. All the things that can go wrong. What some good/bad signals are. Recommends combining various types of signals. And warns against biases. Good read.
interviews  design  process  questions 
19 days ago
Measuring the Effectiveness of Error Messages Designed for Novice Programmers
Good error messages are critical for novice programmers. Recognizing this, the DrRacket programming environment provides a series of pedagogically-inspired language subsets with error messages customized to each subset. We apply human-factors research methods to explore the effectiveness of these messages. Unlike existing work in this area, we study messages at a finegrained level by analyzing the edits students make in response to various classes of errors. We present a rubric (which is not language specific) to evaluate student responses, apply it to a course-worth of student lab work, and describe what we have learned about using the rubric effectively. We also discuss some concrete observations on the effectiveness of these messages.

See also https://cs.brown.edu/~sk/Publications/Papers/Published/mfk-measur-effect-error-msg-novice/
errormesages  programminglanguages  novices 
25 days ago
From Private To Public: How To Read An S-1
Good article on how to read and understand S-1 filings.

Meet The S-1 Filing
Where To Find S-1 Filings
Financial Nuts and Bolts
Sizing The Offering
Company Details
Risks
Every S-1 Is Special
s1  goingpublic  ipo  sec 
25 days ago
The Error Model
A reasonably good exploration of the error models in a programming language and system. Common LISP's condition system is a conspicuous exception. Long.
errormodel  errors  programmingerrors  errorcodes  checkedexceptions  exceptions  preconditions 
27 days ago
The seven tools of causal inference with reflections on machine learning
The usual great synopsis by Adrian Colyer at A Morning Paper, of Judea Pearl's paper, on the differences between machine learning models and structural causal models.

See the original paper at
http://ftp.cs.ucla.edu/pub/stat_ser/r481.pdf
causality  inference  interventions  counterfactual  structuralcausalmodels 
28 days ago
What-If Tool
Building effective machine learning systems means asking a lot of questions. It's not enough to train a model and walk away. Instead, good practitioners act as detectives, probing to understand their model better.

But answering these kinds of questions isn't easy. Probing "what if" scenarios often means writing custom, one-off code to analyze a specific model. Not only is this process inefficient, it makes it hard for non-programmers to participate in the process of shaping and improving machine learning models. For us, making it easier for a broad set of people to examine, evaluate, and debug machine learning systems is a key concern.

That's why we built the What-If Tool. Built into the open-source TensorBoard web application - a standard part of the TensorFlow platform - the tool allows users to analyze an machine learning model without the need for writing any further code. Given pointers to a TensorFlow model and a dataset, the What-If Tool offers an interactive visual interface for exploring model results.
tensorflow  tf  tersorboard  whatif  ml  machinelearning  tools 
4 weeks ago
How To Be Wrong
Fantastic essay on how to modestly be wrong and why.
If you have a disagreement, run experiments based on the disagreement to see where the truth might be.
By admitting error graciously, you antagonize fewer people and so they are less likely to summarily reject your opinions in a subsequent disagreement.
Make more (preferably small) mistakes. One of the ways of being wrong is "early and often". So, you get used to mistakes, when/where you make them and the areas most likely to lead to mistakes.
If folks have changed opinion from A to B, but none from B to A, then B is more likely correct - even if more people believe in A over B.
Making mistakes often leads to surprising discoveries.
mathematics  mistakes  errors  wrong  heuristics 
4 weeks ago
Learning to Learn
The Art of Doing Science and Engineering: Learning to Learn" was the capstone course by Dr. Richard W. Hamming (1915-1998) for graduate students at the Naval Postgraduate School (NPS) in Monterey, California.
hamming  youtube  learning  science  engineering 
4 weeks ago
The Strange Numbers That Birthed Modern Algebra
The 19th-century discovery of numbers called “quaternions” gave mathematicians a way to describe rotations in space, forever changing physics and math.
quaternions  algebra  3drotations  rotations  numbersystems  methematics  physics  quantummechanics 
5 weeks ago
Forecasting: Principles and Practice
This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details.
ebook  forecasting  statistics 
5 weeks ago
Product Management Mental Models for Everyone
1. Return on Investment.
2. Time value of shipping.
3. Time Horizon.
4. Expected Value.
5. Working Backwards (Inversion).
6. Confidence determines Speed vs. Quality.
7. Solve the Whole Customer Experience.
8. Experiment, Feature, Platform.
9. Feedback Loops.
10. Flywheel (recursive feedback loop).
11. Diminishing Returns.
12. Local Maxima.
13. Version two is a lie.
14. Freeroll.
15. Most value is created after version one.
16. Key Failure Indicator (KFI).
productmanagement  mentalmodels 
6 weeks ago
Financial Modeling for Startups: An Introduction
A somewhat basic financial model for startups.
A good base to build on.
entrepreneurship  startups  finance  financialmodel  excel 
6 weeks ago
The Elephant in the Room
How to fool deep learning.

We showcase a family of common failures of state-of-the art object detectors. These are obtained by replacing image sub-regions by another sub-image that contains a trained object. We call this "object transplanting". Modifying an image in this manner is shown to have a non-local impact on object detection. Slight changes in object position can affect its identity according to an object detector as well as that of other objects in the image. We provide some analysis and suggest possible reasons for the reported phenomena.
deeplearning  computervision  objectrecognition  failures  recognitionfailures 
7 weeks ago
How Technology Grows (a restatement of definite optimism)
Technology should be understood in three distinct forms: as processes embedded into tools (like pots, pans, and stoves); explicit instructions (like recipes); and as process knowledge, or what we can also refer to as tacit knowledge, know-how, and technical experience. Process knowledge is the kind of knowledge that’s hard to write down as an instruction. You can give someone a well-equipped kitchen and an extraordinarily detailed recipe, but unless he already has some cooking experience, we shouldn’t expect him to prepare a great dish.

- Process knowledge is represented by an experienced workforce.
- The US industrial base has been in decline.
- Knowledge should circulate throughout the supply chain, flowing both up and down the stack.
- Let’s try to preserve process knowledge.
- The future should be more than services.
- How are we going to do science fiction without an industrial base?
- The US should emulate a different country. (Germany).
- Development is the only hard truth (or, the social consequences of economic growth).
- Optimism as a propellant of growth (or, more industry and less Twitter).
- People with the right priorities. Bill Gates, Freeman Dyson, Andy Grove.
- Does better capital allocation always lead to technological acceleration?
- What are some challenges to these views?
- Definite optimism as human capital.
technology  tools  skills  processes  tacitknowledge  optimism 
7 weeks ago
The North Sense
The North Sense is an exo-sense intelligently designed for evolution, which means it sits outside the body but is permanently attached. It allows a person to sense the electromagnetic field of the planet. The North Sense is typically attached to the upper chest and gently vibrates when facing magnetic north.
senses  exosense  cyborg 
7 weeks ago
How to find stuff in Git
Good examples of commands in git to locate different files, lines of code, changes, authors, etc.
Useful.

Also at https://www.tygertec.com/find-stuff-git/
git  examples  find  locate 
7 weeks ago
Utilizing a Rubric to share expectations of the QA Engineer Role
Good example of using rubrics (method to evaluate performance) for different roles. In this case, for QA. Mostly good as an exemplar.
example  rubrics  roles 
7 weeks ago
Parameterize, execute, and analyze notebooks
What the title says.
Treats notebooks as immutable functions and schedules applying specified inputs to the notebooks to generate outputs.
notebooks  scheduling  paramterization 
7 weeks ago
Bumper-sticker Computer Science
Pithy CS phrases. From Jon Bentley of Programming Pearls fame.
Wisdom.
wisdom  computerscience  sayings  phrases 
8 weeks ago
Stayzilla will reboot its operations
Why Stayzilla failed. And lessons learned. What metrics might not be as useful.
marketplaces  stayzilla  metrics 
8 weeks ago
The Three Stages of Online Marketplaces
Phase 1: Connect buyers and sellers.
Phase 2: Own the delivery network.
Phase 2: Own the supply network.

How marketplaces transition from one phase to another. With some examples.
marketplaces  growth  stages  evolution 
8 weeks ago
How the 100 largest marketplaces solved the chicken and egg problem
Great article. Analyzes marketplace companies for the strategies for solving the chicken-and-egg problem. The so-called "Single Player Mode" was the winning strategy in terms of revenue and capital efficiency. In this strategy the supply-side (usually) is provide a product that works just for them. Over time, that aggregates suppliers. Then, you bring in the demand-side.
marketplaces  liquidity  chickenandegg  singleplayermode 
8 weeks ago
Paving The Way To Marketplace Liquidity
Podcast with the following Q/A

Q: The Chicken and Egg Problem.
A: Usually supply-side first. Alternatively create a product for the supply-side to use and then bring in the demand-side. Also called Single Player Mode.

Q: Horizontal vs. Vertical Marketplaces.
A: Either. Preference for vertical. Horizontals eventually become vertical.

Q: From Online to Offline.
A: There are synergies. Especially branding.

Q: Are Marketplaces Winner Take All?.
A: Yes. Mostly. First to scale.

Q: Can Craigslist Be Beat?
A: Maybe.
marketplaces  liquidity  evolution  scaling 
8 weeks ago
Liquidity hacking: How to build a two-sided marketplace
1. Provide value to one side.
1a. Offer portfolios.
1b. Build community.
1c. Offer tools

2. Find aggregators.
2a. Find physical aggregators (universities, workplaces).
2b. Find an enterprise client.
2c. Find supply aggregators.
2d. Scrape listings.

3. Narrow the problem.
3a. Focus on geography..
3b. Focus on niche community.
3c. Focus on a vertical.

4. Curate one side.

5. Use "hamsters" (i.e., manual work) to get to scale. Then automate.
marketplaces  liquidity  supply  demand  bootstrap 
8 weeks ago
Who Left Open The Cookie Jar?
Complexity of the attack surface makes for exploitable bugs.
Good reading of what kinds of things can happen.
browser  security  tracking  cookies  xss 
8 weeks ago
Working with timezones
Nice way of visualizing timezones. Informative.
timezones  visualization 
8 weeks ago
Entrepreneurs Are The New Labor: Part I
Compares the current hackers and hustlers entrepreneurship model with the steel revolution. "entrepreneurs are the new labor" is the catch phrase. Required reading.
See also, parts 2 and 3.
entrepreneurship  vc  venturecapital  capital  mercenaries  guilds 
9 weeks ago
TEDxGoodenoughCollege - Ole Peters - Time and Chance
Why ensemble averages can be different from time averages.
Life lessons too! - at the end.
probability  ergodicity  averages  timeaverages  ensembleaverages  video  tedx  youtube 
9 weeks ago
An Elementary Introduction to Kalman Filtering
Kalman filtering is a classic state estimation technique used widely in engineering applications such as statistical signal processing and control of vehicles. It is now being used to solve problems in computer systems, such as controlling the voltage and frequency of processors to minimize energy while meeting throughput requirements.
Although there are many presentations of Kalman filtering in the literature, they are usually focused on particular problem domains such as linear systems with Gaussian noise or robot navigation, which makes it difficult to understand the general principles behind Kalman filtering. In this paper, we first present the general statistical ideas behind Kalman filtering at a level accessible to anyone with a basic knowledge of probability theory and calculus, and then show how these abstract concepts can be applied to state estimation problems in linear systems. This separation of abstract concepts from applications should make it easier to apply Kalman filtering to other problems in computer systems.
kalman  filters  estimation  controltheory  controlsystems  tutorial 
9 weeks ago
The Holloway Guide to Equity Compensation
In-depth discussion of various types of equity compensation and tax implications. Also has a section on agreements and negotiations. Helpful.
equity  compensation  equitycompensation 
9 weeks ago
A Visual, Intuitive Guide to Imaginary Numbers
Wonderful visual explanations for negative and imaginary numbers.
Skillfully shows that numbers are helpful fictions.
math  explanations  visual  imaginarynumbers 
9 weeks ago
Data Visualization Caveats
A collection of data visualization errors. With explanations as to why. And suggestions to change. Useful.
data  visualization  dataviz  datavisualization  errors  caveats 
9 weeks ago
Three Horizons Framework - a quick introduction
Short video describing the 3 Horizons framework. Of particular interest is the latter half where the presenter asks questions that are worth asking and thinking about. See also
http://www.iffpraxis.com/transformative-innovation
3horizons  innovation  disruption  youtube  video 
9 weeks ago
Repo Security Scanner
CLI tool that finds secrets accidentally committed to a git repo, eg passwords, private keys. Run it against your entire repo's history by piping the output from git log -p.
git  repository  security  scanner  audit 
10 weeks ago
Evolving Floorplans
Evolving Floor Plans is an experimental research project exploring speculative, optimized floor plan layouts. The rooms and expected flow of people are given to a genetic algorithm which attempts to optimize the layout to minimize walking time, the use of hallways, etc. The creative goal is to approach floor plan design solely from the perspective of optimization and without regard for convention, constructability, etc. The research goal is to see how a combination of explicit, implicit and emergent methods allow floor plans of high complexity to evolve. The floorplan is 'grown' from its genetic encoding using indirect methods such as graph contraction and emergent ones such as growing hallways using an ant-colony inspired algorithm.
floorplans  geneticalgorithms 
10 weeks ago
Machine Learning 101
Basic introduction and overview of Machine Learning. A presentation by a Google engineer.
presentation  machinelearning 
10 weeks ago
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