start new:
tmux
start new with session name:
tmux new -s myname
We are entering an era where AI agents can read, write, and modify configuration files on your behalf. In this new paradigm, the best tool is no longer the one with the prettiest GUI or the most menu options—it's the one with the most programmable surface area.
WezTerm is that tool for terminals.
_ (underscore) to move the cursor at the beginning of line (doesn't switch to insert mode)
0 (zero) moves the cursor to the zeroth position of the line (doesn't switch to insert mode)$ (dollar) to move the cursor at the end of line (doesn't switch to insert mode)d$ will delete from wherever your cursor is till the end of the linef<character> to move cursor to the first occurrence of <character>
f( to move cursor to first occurence of (t<character> to move cursor to upto but not on the first occurrence of <character>t( to move cursor to first occurence of (The instance resource is often at full capacity. Therefore, we should create a script to automatically retry launching the instance at regular intervals.
The script also includes basic logging and will stop launching once you have successfully launched the instance.
| superwhite() { swift - "$1" "${1%.*}-hdr.jpg" <<'SW' | |
| import Foundation; import CoreGraphics; import ImageIO; import UniformTypeIdentifiers | |
| let a = CommandLine.arguments | |
| guard a.count >= 3, let s = CGImageSourceCreateWithURL(URL(fileURLWithPath: a[1]) as CFURL, nil), | |
| let c = CGImageSourceCreateImageAtIndex(s, 0, nil) else { print("usage: superwhite image.png"); exit(1) } | |
| let w = c.width, h = c.height, r = w * 4 | |
| var b = [UInt8](repeating: 0, count: r * h) | |
| let x = CGContext(data: &b, width: w, height: h, bitsPerComponent: 8, bytesPerRow: r, | |
| space: CGColorSpace(name: CGColorSpace.sRGB)!, bitmapInfo: CGImageAlphaInfo.premultipliedLast.rawValue)! | |
| x.setFillColor(red: 1, green: 1, blue: 1, alpha: 1); x.fill(CGRect(x: 0, y: 0, width: w, height: h)) |
| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
I prototyped a v128-based lexer optimization using the JSON parser in moonbitlang/core as the target. This note distills the design into a SIMD lexer pipeline that can be applied to JSON, JavaScript, and other languages.
The short version is that bitmasks are indeed the key abstraction. However, the main opportunity is not matching CST node kinds after construction. It is classifying input bytes and extracting only the positions where lexical context can change.
A typical pipeline can be decomposed as follows:
| { | |
| "meta": { | |
| "theme": "consultant-polished", | |
| "lastModified": "2026-07-19T00:00:00.000Z" | |
| }, | |
| "basics": { | |
| "name": "Andrew Gentry", | |
| "label": "Salesforce Solution Consultant & Application Architect", | |
| "email": "agentry1226@gmail.com", | |
| "phone": "(270) 935-7345", |