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Kanata FTW!

Problem: Alt. keyboard layout issue differences between my exp. and laptop internal keeb.

I’m building my new custom low-profile 38-key (3x6) -2 +3 thumbs - but I need to solve a problem I’ll hit.

My Piantor is configured with Vial / QMK with

  • Colemak-DH
  • Home row mods
  • symbol layers
  • combo keys

The mac internal keyboard has no layers and minimal special functions.

  • Colemak-DH ANSI installed as input source
  • CapsLock as LeftCtrl per ModifierKeys
  • HyperKey allows Super ROpt / RMet
  • Aerospace keyboard shortcuts dont work when i switch, it needs stable input source.

The workaround so far: switch input source to Colemak-DH when on laptop with internal keyboard and to Australia when using the Piantor split keeb. Lots of down-sides:

  • no layers or combos on mac internal keyboard
  • Aerospace shortcuts I had to use the qwerty location always.

Action / Research

I’ve researched most of this before, I’ve tried Karabiner-Elements but I didn’t want the complexity of the kernal extension. I’d come across kanata, saw it needed that as a dependency and wrote it off.

A chance Gemini / Google AI mode question put me onto the path that let me rediscover kanata and how it can depend on the karabiner device-kit architecture.

Result

The result is I have a working base kanata configuration with qwerty (for others/troubleshooting) and colemak-dh. Keeps my Aerospace shortcuts same for either layout. I’ll continue to add the additional layers and combo keys I have from vial.

This enables me to try Sturdy / Magic Sturdy regularly till proficient - when working

It’s not 100% smooth, I’ll keep testing out and find any other issues. E.g My Arc shortcuts aren’t working.

Learnings

Continual probing of AI helps us explore the problem space and solution space quickly to eliminate sub-par paths and save time. I re-discovered a major problem worth solving (the issue was back of mind) it can help find the path to follow before commiting much time.

R&D / AI: mini-rag evaluation of cross-encoder efficiency

R&D / AI: Mini-RAG Cross-Encoder Efficiency Evaluation

Plenty of strong RAG content exists on papers and evaluation (e.g., Miraflow Guide).

Forked florianbuetow/mini-rag for local deep dive: how much efficiency gain from the cross-encoder?

Added token test in bdargan/mini-rag + bdargan/mini-rag-runner container. (Output posted below showing savings.)

See rag_token_test.py.

[TOKEN BENCHMARK REPORT]
• Baseline Prompt Tokens (K=10): 308
• Reranked Prompt Tokens (K=3): 112
• Absolute Token Savings: 196
• Efficiency Gains: 63.64%
PASSED

Key takeaways

  • Code from Gemini impressed again.
  • Follow-up: evaluate with Ragas

Priorities

Do you also struggle with those “frog” tasks that are important, but somehow you procrastinate enough to always put them off, till I probably used more time avoiding than it actually takes to complete. :facepalm:

Well I was trying a few ideas, beyond Eisenhower matrix, and came across the low energy task model and the Friction-Sized framework.

Google AI suggested a method to combine, so if identify tasks in md with X.Y.Z, it offered variations depending on tools used and search/filter needs see query

X=Importance (1-2) Y=Urgency (1-2) Z=Friction (1-4)

Z is 1-4 quadrants even though my rule of thumb is to batch the little things for a low energy time. I may sometimes hit those < 2mins immediately and skip the overhead of writing it down, but how many things reallly are 2mins and don’t spin you off distracted elsewhere.

An interesting experiment to help me match my mental energy with different tasks.

The “Friction-Sized” Framework

Because these tasks have deadlines and evoke reluctance, traditional advice like “Eat the Frog” actually backfires. When your mental energy is depleted, staring at a giant “frog” causes Executive Dysfunction—making you freeze and do nothing.To solve this, we must mathematically re-engineer how we measure task weight. Instead of measuring by Time (Duration), we must measure by Friction (Resistance to Start).

Total Effort = Friction (Energy to Start) + Drag (Energy to Complete)

1. The Friction Matrix

Use this 4-quadrant system to categorize both your work and personal life tasks. The goal is to isolate the “Sticky Notes”—tasks that take very little time to complete but have massive mental resistance.

Low Drag (Takes <15 Mins)High Drag (Takes >15 Mins)
High Friction(Reluctant to Start)🛑 The Sticky Notes
• Paying a bill / Renewing a license
• Sending a “difficult” but short email
• Booking a dentist appointment
🐸 The True Frogs
• Tax returns
• Writing a project proposal
• Planning a major trip
Low Friction(Easy to Start)⚡ Quick Wins
• Deleting spam / Sorting mail
• Putting away dishes
• Approving a simple request
🏃‍♂️ The Momentum Builders
• Organizing a closet / Desk
• Exercising / Walking
• Data entry while listening to music2.

2. The 3 Rules of Engagement

To defeat the “Sticky Notes” and “True Frogs” before their deadlines hit, apply these rules:

Rule 1: The “Micro-Leap” (For High Friction, Low Drag)

Never write the actual task on your to-do list. Write the absolute first physical action.

  • Instead of: “Pay car registration” (High Friction)
  • Write: “Open laptop and pull up registration email” (Zero Friction)
  • The Logic: Once the laptop is open and the email is in front of you, the friction drops to zero, and the 5-minute task gets done.

Rule 2: The 10-Minute “Micro-Block” (For True Frogs)

Do not try to “Eat the Frog” all at once. Set a timer for exactly 10 minutes. Tell yourself: “I am allowed to completely stop and quit after 10 minutes.”

  • The Logic: The hardest part of finite mental energy is the anticipation of the effort. A 10-minute boundary tricks your brain into starting. Usually, momentum takes over.

Rule 3: The Work-Life Hybrid Sweep

Never separate your personal and work to-do lists when energy is low. Your brain draws from the exact same central tank of mental energy for both. Batch your “Sticky Notes” (both work and personal) into a single 30-minute window right before lunch or at the very end of the day.