AI-assisted, programmer-built desktop apps

twoplus11 offers several dozen utilities for macOS and Windows, free for personal use. According to the author, these are AI-assisted apps rather than hands-off exercises in vibe coding: "I used AI as a coding assistant while building Blockr. I am a full-time software developer, and working with AI is just a part of development now. The design decisions, the security review, and the testing are mine, and I'm the one who answers when it breaks."

Files, folders, and storage

Aliasr: Batch-renames files using stackable rules, with live previews, collision detection, and undo.
Copyr: Copies files with error recovery, pause and resume, and SHA-256 verification.
Diskr: Visualizes disk usage with an interactive sunburst chart and lets you trash unwanted files.
Mirrorr: Creates scheduled full-disk backups, preserving the previous good copy if interrupted.
Sweepr: Organizes cluttered folders with a preview of planned moves and undo support, without deleting files.

Documents and file conversion

AltFormr: Converts images, audio, video, and documents between formats, including conversion to and from Markdown.
Heftr: Analyzes what makes files large and creates smaller copies of images, PDFs, and Office documents.
Redlinr: Compares document versions with highlighted insertions and deletions, side-by-side views, and change summaries.
Stapl: Provides PDF tools for merging, splitting, compression, OCR, redaction, signing, and more.

Images, audio, and video

GifMintr: Records a selected screen area and saves it as an optimized GIF or MP4.
Liftr: Removes image backgrounds using an on-device model, with replacement backgrounds and manual touch-up tools.
Loopr: Turns still images into animated GIFs with adjustable frame timing, loop controls, and live previews.
Monthr: Turns twelve photos into a printable wall-calendar PDF with birthdays and optional US holidays.
Squishr: Compresses videos to fit a specified file-size limit without uploading them.
Trimr: Edits and converts audio with trimming, fades, normalization, silence removal, speed adjustment, and joining.
Viddlr: Downloads online videos and playlists, with parallel downloads, duplicate detection, and automatic sponsor-segment removal.

Privacy and network control

Blockr: Monitors outgoing internet connections and lets you allow or block individual apps and destinations.
Scrubbr: Inspects and removes metadata, including location, author, and device information, from photos, PDFs, Office documents, and audio.

Desktop workflow and system utilities

Anchr: Keeps your preferred audio output, microphone, and system-sound devices selected when other devices connect.
Blinkr: Reminds you to take eye breaks, automatically postponing interruptions during calls, presentations, and full-screen viewing.
Cheatr: Displays an app's keyboard shortcuts and suggests shortcuts for actions you perform with the mouse.
Pingr: Archives notifications in a searchable local history and provides copy buttons for one-time codes.
Pulsr: Monitors CPU, memory, network, disk, and battery activity, with plain-English summaries of resource-hungry apps.
Snapr: Arranges windows using keyboard shortcuts, edge snapping, and saved layouts that adapt to docking and undocking.
Wakr: Keeps your computer and display awake for a chosen duration or indefinitely.
WinKeyr: Brings familiar Windows keyboard shortcuts and navigation conventions to macOS.

❧ 2026-11-10

The culmination of spiritual life

"[W]hen the normal relationship between husband and wife, which is the strongest foundation of the worldly life, has been transcended and a man sees in his wife the divine presence, he then sees God everywhere in the universe. This is the culmination of the spiritual life."

—Swami Nikhilananda in Vivekananda: A Biography

❧ 2026-11-06

When was MacBook lid last opened to wake computer?

% pmset -g log | grep "Wake from.*lid" | awk '{print $1, $2}' | tail -n 1
2026-10-29 09:27:39

❧ 2026-10-29

Steve Jobs: touchscreen Macs "ergonomically terrible"

The much-anticipated MacBook Pro PROLED XDR Display arrived today with touch support, "a whole new way to interact with your work" according to Apple. Steve, Craig, and Phil were somewhat less enthusiastic:

‘Ergonomically Terrible’ — Steve Jobs Said Touchscreen MacBook Wouldn’t Work, but Apple Is Finally Doing It Anyway

"We've done tons of user testing on this, and it turns out it doesn't work," Jobs said at the company's "Back to the Mac" event in 2010. "Touch surfaces don't want to be vertical. It gives great demo but after a short period of time, you start to fatigue and after an extended period of time, your arm wants to fall off," Jobs continued. "It doesn't work, it's ergonomically terrible."
iOS app porting won't make Mac feel like iPhone, Apple's Federighi says

After a very clear "no" delivered during the keynote, Federighi once again denied any plans to merge iOS and macOS, or develop a touchscreen-equipped Mac.

"We really feel that the ergonomics of using a Mac are that your hands are rested on a surface, and that lifting your arm up to poke a screen is a pretty fatiguing thing to do," he said. "I don't think we've looked at any of the other guys to date and said, how fast can we get there?"
The Inside Story of Apple’s New iMacs

There’s one thing, however, that is conspicuously not in the new iMacs — a touch screen. While Microsoft and others now believe that multi-touch should extend to the desktop display, Apple believes this is dead wrong. “From the ergonomic standpoint we have studied this pretty extensively and we believe that on a desktop scenario where you have a fixed keyboard, having to reach up to do touch interfaces is uncomfortable,” says Schiller. “iOS from its start has been designed as a multi-touch experience — you don’t have the things you have in a mouse-driven interface, like a cursor to move around, or teeny little ‘close’ boxes that you can’t hit with your finger. The Mac OS has been designed from day one for an indirect pointing mechanism. These two worlds are different on purpose, and that’s a good thing — we can optimize around the best experience for each and not try to mesh them together into a least-common-denominator experience.”
Apple Goes Part Time on Touch

[M]aking the Macintosh screen—even just on the laptop—touch-centric is a crime against nature, as it violates the functionality intrinsic to the form of a notebook or desktop computer, at least as Apple sees it. Indeed, last week, Schiller was even more emphatic when that subject came up.

“We think of the whole platform,” he says. “If we were to do Multi-Touch on the screen of the notebook, that wouldn’t be enough — then the desktop wouldn’t work that way.” And touch on the desktop, he says, would be a disaster. “Can you imagine a 27-inch iMac where you have to reach over the air to try to touch and do things? That becomes absurd.” He also explains that such a move would mean totally redesigning the menu bar for fingers, in a way that would ruin the experience for those using pointer devices like the touch or mouse. “You can’t optimize for both,” he says. “It’s the lowest common denominator thinking.”

Apple came to this conclusion by testing if touch screens made sense on the Mac. “Our instincts were that it didn't, but, what the heck, we could be wrong—so our teams worked on that for a number of times over the years,” says Schiller. “We’ve absolutely come away with the belief that it isn't the right thing to do. Our instincts were correct.”

[Emphases added.]

❧ 2026-10-28

Insanely great OCR

Datalab's open-source Marker and Chandra convert images and PDFs into Markdown, HTML, JSON, and other structured formats with astonishing accuracy, even on degraded text.

Their hosted service runs an improved version of Chandra that promises higher accuracy than the open weights. It includes $20 in free monthly credits, zero data retention by default, SOC 2 Type 2 compliance, and custom BAAs. Opting into data retention earns a 25% discount (sensitive files are best kept off cloud services in any case). Pricing per 1,000 pages: $4 for Fast or Balanced mode; $10 for Accurate.

Tested with 1,525 pages of poorly typed and copied material through Balanced mode, with astounding results. A few sample pages: Original PDF | Markdown. The whole job took just a few minutes and used only $4.57 of the $20 in free monthly credits. Kick the tires in the Playground.

❧ 2026-10-16

Defrag files or entire FAT/exFAT volumes in macOS or Linux

🤖 tinydefrag 1.0.0 [155k] {S} Dependency-free C utility for macOS and Linux that defragments individual files or entire FAT12/16/32 and exFAT volumes. Inspired by Eric Helgeson's account of Claude helping get fatdefrag over the finish line; independently implemented via Claude Opus 5.5 (Ultracode effort). 📺

❧ 2026-10-08

Random harvest

RemoveMacAI:
- An open-source tool lets you delete 12GB of Apple Intelligence data on macOS
- Turn off Apple Intelligence on macOS 27 and get its disk space back
"defaults write -g ComputerUseAllowForbiddenTargets -bool YES unlocks full YOLO mode for ChatGPT/Codex app in macOS"
- "Another useful one: BROWSER_USE_SECURITY_MODE=disabled-for-local-testing The codex built in browser has a load of features but its permissions gated, even with yolo mode. OpenAI seems to want to pretend you use the built in browser as your main normal browser. So enforces required permissions checks. But if you just use it for development these are just annoying and get in your way. This setting disabled it."
  - "Browser Use calls https://chatgpt.com/backend-api/aura/site_status and fails closed with 'not permitted' on hosts like Naver Blog. The bundled plugin already has a local-testing flag that skips that check: BROWSER_USE_SECURITY_MODE=disabled-for-local-testing. I wrapped that into a small third-party helper (macOS): wrap node_repl, optional origin allowlists, re-apply after ChatGPT updates. Repo: https://github.com/leixyou/bypass-codex-site-safety"
Live subtitles with translation for macOS:
- v2s: "Live bilingual subtitles for any app on macOS. Captures audio, transcribes speech, and translates offline — all from your menu bar." | GH repo
- Subtitles: "Live captions for anything your Mac plays. Calls, videos, podcasts, and lectures, transcribed as they play and drawn in an overlay that stays above every window. Translated live if you want it. Nothing leaves the Mac." Licensing: "The download is a free seven-day trial, and a license key from Gumroad keeps it going. Everything needed to build your own copy is in this repository, and build.sh below does exactly that." GH repo
Talon: "Write, code, and control your computer with Talon. Voice, eye tracking, noise recognition, and Python scripting for macOS, Windows, and Linux."
Remove or disable Windows Defender:
- Defender Remover / Defender Disabler: "A tool which is uses to remove Windows Defender in Windows 8.x, Windows 10 (every version) and Windows 11."
- Defender Control: "Open source Windows Defender disabler. Works on Windows 10 and 11."
Logo Design Skill for Claude & AI Agents
Offlined v2.0.0: offline Wikipedia from Kiwix ZIMs + a local AI tutor + maps.: "Free for personal use (not open source), Windows 10/11."
TypingMind: "The best frontend for LLMs. Advanced chat UI for AI models. ChatGPT, Gemini, Claude, and more. All in one place." | HN (2023)
OpenMontage: "World's first open-source, agentic video production system. 12 production pipelines, 100+ tools, 700+ agent skill and production-knowledge files. Turn your AI coding assistant into a full video production studio."
Your LG ‘Smart TV’ is Spying on You - Here’s How to Stop It Permanently
REA (Reverse Engineer Anything): "See a feature in an app that you want in your own product? Ask your agent to investigate it with REA. It can inspect the app without its source code, explain how the feature works, show the evidence, and build a version for your project."

❧ 2026-10-07

tinynudge: macOS mouse jiggler

Casting about for a software alternative to CRU's Mouse Jiggler MJ-3, stumbled upon WakeyWakey, move-mouse-macOS, and MoveMouse, but sought something simpler. Generated tinynudge with Opus 5.5 in one pass, then refined it with GPT-6 Astra, yielding 33 lines of Swift that compile to a 55k executable:

// Build: swiftc -O tinynudge.swift -o tinynudge && strip tinynudge
// Run: ./tinynudge [positive whole seconds] (default 60; Ctrl-C to quit)
import ApplicationServices
import IOKit.pwr_mgt

func fail(_ message: String) -> Never {
    fputs("tinynudge: \(message)\n", stderr)
    exit(1)
}
guard CommandLine.arguments.count <= 2,
      let interval = UInt32(CommandLine.arguments.dropFirst().first ?? "60"), interval > 0
else { fail("Usage: tinynudge [positive whole seconds]") }
guard AXIsProcessTrustedWithOptions([kAXTrustedCheckOptionPrompt.takeUnretainedValue(): true] as CFDictionary)
else { fail("Grant Device Control and Data Access (formerly Accessibility) access in System Settings → Privacy & Security, then rerun.") }

var assertion = IOPMAssertionID(0)
let result = IOPMAssertionCreateWithName(
    kIOPMAssertionTypePreventUserIdleDisplaySleep as CFString,
    IOPMAssertionLevel(kIOPMAssertionLevelOn), "tinynudge" as CFString, &assertion)
guard result == kIOReturnSuccess else { fail("Display-sleep assertion failed (\(result)).") }
setbuf(stdout, nil)
print("tinynudge: active (interval: \(interval)s, check: \(interval / 2)s). Ctrl-C to quit.")

let anyInput = CGEventType(rawValue: UInt32.max)!
while true {
    sleep(interval)
    guard CGEventSource.secondsSinceLastEventType(.hidSystemState, eventType: anyInput) >= Double(interval) / 2,
          let p = CGEvent(source: nil)?.location else { continue }
    for position in [CGPoint(x: p.x + 1, y: p.y), p] {
        CGEvent(mouseEventSource: nil, mouseType: .mouseMoved,
                mouseCursorPosition: position, mouseButton: .left)?.post(tap: .cghidEventTap)
    }
}

Checks input idle time every N seconds (default: 60). If idle for at least half that interval, moves the cursor 1px right and back. Requires Accessibility access ("Device Control and Data Access" in macOS 27+); prompts if your terminal lacks it.

Separately, holds a caffeinate -d-style power assertion, visible as tinynudge in pmset -g assertions; released on exit.

Tested in macOS 26 Tahoe and 27 Golden Gate.

❧ 2026-10-01

Run Splash on M1 & M2 Macs

Splash is making waves in LLM inference on Apple silicon:

"It delivers 2× the decode speed of the next-fastest engine we measured on Qwen3.8-27B and stays ahead at every context length we tested, to 32K tokens. At four parallel subagents, the speedup increases to almost 4×."

The official release requires M3 or newer, but Erik Paperniuk has backported Splash for M1 and M2 Macs:

Steps

Install: curl -fsSL https://github.com/paperniuk/splash/releases/download/1.0.2-m1/install-m1.sh | sh
Serve: splash-m1 serve --model incoai/Qwen3.8-27B-Splash (First run downloads the 17.4GB model package and prepares it for the GPU; later starts are much faster.)
Chat at http://127.0.0.1:8000. To disable the chat page, add --no-webui to the command in step 2. More settings.
Set http://127.0.0.1:8000 as the OpenAI-compatible API base URL in apps like Goose. No key is needed unless you set one. Several coding agents are supported too, e.g., splash-m1 opencode.

Update

Shortly after posting, stumbled onto this tweet from @ashxhart earlier today:

M1-4 silicone, I got you🫡

TensorFold v0.3.4 is up! Enabling lanes on M1–M4 more than doubled Qwen3.8-27B code decode on M3 Ultra to 141–158 tok/s.

Lane batching is coming next, with concurrency already showing early gains.

Follow the progress at tensorfold.dev.

Installed TensorFold 0.3.4 on a 2021 MacBook Pro (M1 Pro, 32 GB) via uv tool install git+https://github.com/ashhart/TensorFold.git and ran it with TF_ROW_MATMUL=row_qmv tensorfold serve Vontra/Qwen3.8-27B-MLX-4bit. The TF_ROW_MATMUL=row_qmv setting switches to an older fallback path, because the new v0.3.4 kernel for M1-M4 Macs crashes at load on M1 and M2 models.

Benchmarked both projects with a script crafted by Opus 5.5 in Claude Code:

#!/usr/bin/env python3
"""Chat decode speed of any OpenAI-compatible server. No dependencies.

    python3 llm_bench.py http://127.0.0.1:8000

Sends one chat prompt with thinking off, 5 times sampled and 5 times greedy,
and prints the median tokens/second (first token to last, like most benchmarks).
"""
import json, statistics, sys, time, urllib.request

URL = (sys.argv[1] if len(sys.argv) > 1 else "http://127.0.0.1:8000").rstrip("/").removesuffix("/v1")
PROMPT = "Explain how matrix multiplication uses a GPU in plain English, then give a small numerical example."
TOKENS, RUNS = 64, 5


def post(body):
    req = urllib.request.Request(URL + "/v1/chat/completions", json.dumps(body).encode(),
                                 {"Content-Type": "application/json"})
    return urllib.request.urlopen(req, timeout=600)


def run(model, temperature, seed):
    body = {"model": model, "messages": [{"role": "user", "content": PROMPT}], "max_tokens": TOKENS,
            "temperature": temperature, "top_p": 0.95, "top_k": 20, "seed": seed, "stream": True,
            "stream_options": {"include_usage": True}, "ignore_eos": True,
            "chat_template_kwargs": {"enable_thinking": False}, "reasoning_effort": "none"}  # thinking off, two ways
    first = last = None
    chunks, tokens = 0, None
    for line in post(body):
        line = line.decode().strip()
        if not line.startswith("data:") or line == "data: [DONE]":
            continue
        data = json.loads(line[5:])
        tokens = (data.get("usage") or {}).get("completion_tokens", tokens)
        for choice in data.get("choices", []):
            delta = choice.get("delta") or {}
            if delta.get("content") or delta.get("reasoning_content") or delta.get("reasoning"):
                last = time.perf_counter()
                first = first or last
                chunks += 1
    return ((tokens or chunks) - 1) / (last - first)


model = json.load(urllib.request.urlopen(URL + "/v1/models"))["data"][0]["id"]
print(f"{model} at {URL}: chat, thinking off, {TOKENS} tokens, median of {RUNS}")
run(model, 0, 0)  # warm-up
for name, temperature in [("sampled", 1.0), ("greedy", 0.0)]:
    speeds = [run(model, temperature, 1234 + i) for i in range(RUNS)]
    print(f"  {name:8}{statistics.median(speeds):6.1f} tok/s   (runs: {', '.join(f'{s:.1f}' for s in speeds)})")

Results

Splash-M1 ran Qwen3.8-27B chat about 1.3-1.5x faster than TensorFold, though that may change once the M1/M2 bug is fixed:

Splash-M1

incoai/Qwen3.8-27B-Splash at http://127.0.0.1:8000: chat, thinking off, 64 tokens, median of 5
  sampled   13.0 tok/s   (runs: 10.1, 13.0, 14.4, 11.2, 16.4)
  greedy    14.5 tok/s   (runs: 11.6, 14.4, 14.5, 14.5, 14.5)

TensorFold

Qwen3.8-27B-MLX-4bit at http://127.0.0.1:8080: chat, thinking off, 64 tokens, median of 5
  sampled    9.7 tok/s   (runs: 9.8, 9.5, 9.7, 9.4, 9.7)
  greedy     9.7 tok/s   (runs: 9.6, 9.7, 9.7, 9.7, 9.7)

❧ 2026-09-26