Why would any body bet Yes on Jesus' return in 2025?

Polymarket peg the price of Yes and No share using the following mechanism. $1 can be minted into 1 Yes share and 1 No share. Conversely, 1 Yes share and 1 No share can combine and redeem back to $1. So if the prices of Yes and No shares don't add up to $1, arbitrageurs will come and profit from the difference.

Yes share holders expect the No share holders to sell their No shares at lower price later on, for example, when some more interesting future event happens that they need money to bet on. When that happens they will want to sell their No shares at lower price, or equivalently buy Yes shares to redeem their dollars back. The Yes share holders can therefore sell their tokens and profit from such price raise.

In conclusion, the Yes hoarders are betting not on Jesus but on whether there will be interesting events that people need money to bet on before the end of 2025.

An video explainer of Polymarket mechanism: https://www.youtube.com/watch?v=c6HWyACjE2A

Find vendor names from MAC addresses.

Observation: bitstream with low density of 1s (< p=0.32) can be encoded more efficiently by positions of the ones. We store those positions into a set implemented using bloom filter. We can calculate the theoretically optimal parameters for bloom filter to maximize compression. With a bloomfilter bitmap plus a data structure for recovering info in case of false positive we can achieve lossless compression. To decompress, we iterate through all possible positions, set the bit to one by querying set membership of the location.

To compress video, the author encodes the difference between video frames as a bitstream of mostly one, which then gets compressed with bloom filter.

Another innovation is rational bloom filter - the number of optimal hash functions is usually not integer. Typical implementation just round the number to an integer and use that. The author proposed to use ⌊k⌋ hash functions deterministically and with k-⌊k⌋ probability applying an additional hash. The probability is applied deterministically based on the value.

Stephen Diehl's summary of the most influential advancements on top of the Attention Mechanism. The article comes with detailed descriptions and from-scratch codes demonstrating how exactly these techniques work.

A very nice FAQ for Lojban.

Deliminted continuation is a control flow mechanism involving two primitives prompt and control. It resembles call/cc at a glance, but the scope is different.

For example, the following expression returns 7:

(prompt
  (+ 1 (control k 
    (let ([x (k 1)] [y (k 2)] [z (* x y)])
      (k z)))

k is a continuation reified as a function. To understand the code above, read k as a blackbox function. Its input replaces the (control ...) where the part of body of (prompt ...) starting from the (control ...) gets evaluated, then the output of (prompt ...) gets returned as the output of (k ...). I find visualizing (prompt ...) as a box and (control k ...) as a hole helpful for understanding.

Reservoir sampling is a technique for selecting a fair random sample when you don't know the size of the set you're sampling from.

An algorithm like this is applicable in cases like rate limiting sending a fixed number of logs per time interval to protect against bursting. A naive greedy approach will result in biases. We want to evenly sample the logs.

The trick is to use a fixed K slots of log entries (K corresponds to the per-bucket rate limit). For upcoming entries, first fill the slots (N<=K), for an upcoming entry after N>K, we decide to replace a slot with the new entry at the probability of K/N. The evicted slot is chosen randomly from the K slots. In the end of the time interval, flush the slots.

Clipper is a network debugging tool that intercepts TLS traffic to allow the traffic to be viewed on Chrome Dev Tools. What interests me the most is how it decrypt TLS traffic.

There are several ways to do that that I know of: The first method is with environment variable SSLKEYLOGFILE; tools that respect the environment can dump the keys to the specified file, which can picked up by tools like Wireshark. The problem is that many tools doesn't respect the variable out of box. The second is MITM the traffic with a self-signed certificate. This method doesn't work with TLS key pinning and does not truly reflect the traffic due to the proxy layer.

Clipper instead used the trick to LD_PRELOAD a library that uses Frida to hook library functions (e.g. OpenSSL) to extract the keys, and implement a universal SSLKEYLOGFILE support.

TIL the author of SQLite wrote a tool called sqlite3_rsync that can be used to copy one database across machines. It does so incrementally like rsync while preserving the integrity of the database so both the source and the replica databases are safe to use when copying.

An encyclopedia of consistency models for concurrent systems.

The algorithm for de-pixelating a pixelated moving window with fixed content:

1. extract the frames and find the absolute positions of the window for each frame
2. assume the color of each pixelated cell is the color for the center pixel of each cell (think of pixelation as a magnifier filter focusing on the center pixel for each cell)
3. by tracking the locations of the centered pixels and its colors in each frame, we fill in more and more pixels with known colors.
4. for the remaining unfillled pixels, interpolate from nearest pixel of known colors (e.g. voronoi)

Analyze the bytes in a SQL database.

A small hash function reconstruct a ultra-blurred image from a 20-bit integer, used for image preview. The integer is bit-decomposed into nine values, six brightness values (0-3) at the 2x3 grid-points of the image, three other values to encode a color for the background (oklab). Then the blurred image is rendered by drawing six radial gradient at the points fading into transparency, then a solid background.

A non-interactive zero-knowledge proof scheme: The prover commits (shuffle+lock) the graph N times: s[0..N]. He then calculate r=hash(s[0] || s[1] || .. || s[N]). And then he use the random bits in r generate N pseduorandom numbers c[0..N]. For each i=0..N, use c[i] to select a fixed edge in s[i] and reveal the keys k[i][0] and k[i][1] for the two nodes of the edge. The final message consists of s[0..N] and k[0..N][0..1]. The verifier is able to use these information to replay the steps to verify the prover's claim.

Another thing I learned from the video is how to "lock" messages cryptographically and allow the verifier to verify any message without revealing the others. First, the prover calculate h[i] = hash(m[i] || nounce[i]) and send h[0..N] to the verifier. The verify choose any integer t between 0 and N and send to prover. The prover will then send back (m[t], nounce[t]) to the verifier that h[t] is indeed calculated from m[t]. Here h[t] is the locked message m[t] and nounce[t] is the key.

Various IP lists for download - useful for setting up firewall against attackers, malware, p2p, crawlers etc.

I'd like to note the AirTag privacy-preserving tracking scheme I learned:

1. the tag shares its key pair with its owning host as part of the pairing process
2. the tag broadcasts its public key when away from its host
3. nearby phones pick up the broadcast and send a location report to the apple server, which includes:
   • the phone's gps location encrypted using the tag's public key
   • the hashed public key of the tag
4. the apple server store the location reports for each hashed public key
5. the host can request the apple server to download location report for the tag using a public key. then it decrypts the gps location using the private key it got during pairing

TIL succinct data structures - data stored in a compact fashion as in a compression, but the compact form itself has useful properties.

The Rank/Select Bit Vector is a bit vector equipped with two fast operations - rank and select. The rank operator returns the number of set bits up to the n-th bit. The select operator returns the index of the n-th set bit, i.e. inverse of rank. The data structure has nice uses as in string lookup, etc.

The Wavelet Matrix is a generalization of rank/select bit vector on non-binary symbols. Another data structure of the class is the FM-index, whose operators extend to working on patterns (substrings) instead of symbols.

How can a calculator program perform an exact calculation involving arithmetic operations, roots, trigonometric functions, etc? It's a difficult task. Here is how it can be done.

• rational type: bignum for both nom and denom
• recursive real arithmetic (RRA):
  • represent a real number as a computation that maps from a precision value (a rational) to the rational result etc.
  • for composite expression, compute the precision value needed for each of its components (e.g. for 2pi to be shown with a precision of 0.01, pi needs to be calculated to 0.005 precision)
• determine the exact equality between RRA
  • undecidable but decidable for a finite set of ops (proven by Dan Richardson, 1994), but slow
• the optimization
  • represent every number as an exact rational times a RRA. (e.g. (3/2)pi)
  • hard code the common types of RRA (sin, log, ..) whose identity can be checked directly.
  • for uncommon types of RRA, just store RRA (e.g. pi * sqrt(2))

the result: the digits shown on screen are always 100% correct. 99% of calculation gets perfect UX.

A site dedicated to Geometric Algebra courses, libraries, discussion, etc.

My notes on Geometric Algebra: https://shaarli.lain.li/shaare/OGClBA

A blog dedicated in explaining obscure and seemingly-magical code snippets. Like the submissions to IOCCC contest.