sciyoshi

This vulnerability had to do with the multisig wallet library code, not with Parity itself. Using geth won't protect you from badly-written contracts.

Looks exciting - will need to take some time to read this in more detail. Off-hand, will this not be able to handle ASN.1 certificates due to their Turing completeness?

Newer versions of setuptools have builtin support for more fields in setup.cfg: http://setuptools.readthedocs.io/en/latest/setuptools.html#c...

That along with setuptools-scm and pip-tools have for me solved most of the issues that pbr addresses.

Under growing complexity, average understanding will decrease and tend to become centralized. How many people really understand the dynamics of Bitcoin's current scaling debate? In that scenario, you have a return to manipulation by special interests (in this case, "important" nodes like miners, exchanges, and wallets).

OP's argument is that this distributed consensus algorithm works only if there is already consensus about which algorithm to use in the first place! If you and I disagree about which block history is valid (for example, because my history includes blocks with a lower difficulty), then the assumptions are no longer valid and there's a hard fork. Also, note that a 51% attack can only exclude transactions or allow double spending - the ledger still can't be arbitrarily manipulated since transactions are signed by private keys.

This argument sums up my recent thoughts on blockchains. Proofs of their trustlessness and eventual consistency rely on explicit assumptions (no cartels with >50% computation power, bounds on network partitions), but the underlying unstated assumption is that there is consensus about the protocol and its rules to begin with. We've seen this assumption break down with the block size debate and the DAO fork.

However, I don't think that this completely rules out their usefulness. Blockchains are more like "consensus abstractions" - if you have the underlying consensus (achieved through a governance model that can engender it), then it allows for uses like smart contracts that can efficiently work on top of it. The "trustless" property is also maintained - for example, even if a cryptocurrency is designed, issued and governed by a central bank, the fact that the network is decentralized and its integrity can be independently verified (even if it is centrally secured) still has some social benefit.

Have you tried Dashlane? I've been using it for over a year and it's been great. Their mobile app is really handy as well.

There's a type of Gray code which is particularly useful for small-scale rotary encoders: a single-track Gray code. The idea is to find a code where the sequences for each position are cyclic shifts of each other. Then you can use a single "track" with arms at different angles, rather than multiple tracks with a single arm.

http://sciyoshi.com/2010/12/gray-codes/#single-track

Webpack has the very useful "chunking" system which is able to figure out common dependencies, and based on how often they are re-used across your app, determine whether they should be bundled separately. It's really good if you're trying to find an optimal balance between the number of files on a page and the cacheability of each one. I haven't seen that feature in Browserify or other module bundlers. Of course, with HTTP/2 server push hopefully a lot of these issues will go away.

Looks great! Any plans to document the synchronization protocol?

I've been using SyncThing but it lacks the main feature I need, which is to selectively synchronize only certain files to other devices.

This is really exciting stuff. Mathematica is by far my favorite environment for doing data exploration and mathematics, and although there is a learning curve, the features of the language itself (pattern matching, symbolic manipulation) are extremely powerful. The quality and depth of Mathematica's documentation are excellent, and with these notebook interface changes things are bound to become even easier. Jupyter + tensorflow/scikitlearn comes close nowadays for machine learning, but I love having access to consistent data sets from within the environment.

I actually really like the consistency of Mathematica's syntax - parentheses are reserved for grouping expressions. You can also create matrices using the cell inputs in notebooks, which uses standard mathematical notation.

I wonder if AlphaGo has some specialized knowledge to handle ladders, where stones can have an effect at a distance that might only come into play after 20 moves.